CRISPR EFFECTOR SYSTEM BASED MULTIPLEX DIAGNOSTICS

Abstract
Systems and methods for rapid diagnostics related to the use of CRISPR effector systems and optimized guide sequences, including multiplex lateral flow diagnostic devices and methods of use, are provided.
Description
REFERENCE TO AN ELECTRONIC SEQUENCE LISTING

The contents of the electronic sequence listing (BROD-3980WP_ST25.txt”; Size is 709,752 bytes and it was created on Mar. 13, 2020) is herein incorporated by reference in its entirety.


TECHNICAL FIELD

The subject matter disclosed herein is generally directed to rapid diagnostics related to the use of CRISPR effector systems.


BACKGROUND

Nucleic acids are a universal signature of biological information. The ability to rapidly detect nucleic acids with high sensitivity and single-base specificity on a portable platform has the potential to revolutionize diagnosis and monitoring for many diseases, provide valuable epidemiological information, and serve as a generalizable scientific tool. Although many methods have been developed for detecting nucleic acids (Du et al., 2017; Green et al., 2014; Kumar et al., 2014; Pardee et al., 2014; Pardee et al., 2016; Urdea et al., 2006), they inevitably suffer from trade-offs among sensitivity, specificity, simplicity, and speed. For example, qPCR approaches are sensitive but are expensive and rely on complex instrumentation, limiting usability to highly trained operators in laboratory settings. Other approaches, such as new methods combining isothermal nucleic acid amplification with portable platforms (Du et al., 2017; Pardee et al., 2016), offer high detection specificity in a point-of-care (POC) setting, but have somewhat limited applications due to low sensitivity. As nucleic acid diagnostics become increasingly relevant for a variety of healthcare applications, detection technologies that provide high specificity and sensitivity at low cost would be of great utility in both clinical and basic research settings.


Sensitive and rapid detection of nucleic acids is important for clinical diagnostics and biotechnological applications. Previously, Applicants developed a platform for nucleic acid detection using CRISPR enzymes called SHERLOCK (Specific High Sensitivity Enzymatic Reporter unLOCKing)(Gootenberg, 2018; Gootenberg, 2017), which combines pre-amplification with the RNA-guided RNase CRISPR-Cas13 (Abudayyeh, 2016; East-Seletsky, 2016; Shmakov, 2015; Smargon, 201; Shmakov, 2017) and DNase CRISPR-Cas12 (Zetsche, 2015 599; Chen, 2018) for sensing of nucleic acids via fluorescence or portable lateral flow. Here, Applicants extend this platform by applying machine learning to predict strongly active crRNAs for rapid detection of nucleic acid targets in an optimized one-pot reaction with lateral flow readout. Applicants further develop novel lateral flow strips for multiplexed detection of two or three targets per strip. The combination of predictive guide design tools with a one-pot SHERLOCK format and multiplexed lateral flow detection allows for rapid deployment of robust and portable SHERLOCK assays in the laboratory, clinic, and field.


The SHERLOCK platform is a low-cost CRISPR-based diagnostic that enables single-molecule detection of DNA or RNA with single-nucleotide specificity (Gootenberg, 2018; Gootenberg, 2017; Myhrvold, 2018). Nucleic acid detection with SHERLOCK relies on the collateral activity of Cas13 and Cas12, which unleashes promiscuous cleavage of reporters upon target detection (Abudayyeh, 2016; East-Seletsky, 2016)(Smargon, 2017). SHERLOCK is capable of single-molecule detection in less than an hour and can be used for multiplexed target detection when using CRISPR enzymes with orthogonal cleavage preference, such as Cas13a from Leptotrichia wadei (LwaCas13a), Cas13b from Capnocytophaga canimorsus Cc5 (CcaCas13b), and Cas12a from Acidaminococcus sp. BV3L6 (AsCas12a)(Gootenberg, 2018; Myhrvold, 2018; Gootenberg, 2017; Chen, 2018; Li, 2018; Li, 2018). While these enzymes have been widely used for both in vivo and in vitro applications (Konermann, 2018; Gootenberg, 2018; Gootenberg, 2017; Abudayyeh, 2017; Cox, 2017; Myhrvold, 2018; Chen, 2018; Li, 2018; Li, 2018)(Zhao, 2018), a major limitation to widespread adoption is the lack of predictive Cas13 guide design tools to help users in designing experiments or assays.


The development of data-driven models for aiding experimental design has featured prominently during the maturation of molecular tools. Software for choosing optimal primer or probe sequences is vital for amplification and molecular detection technologies as well as CRISPR-based methods. Genome-informed thermodynamic models for primer selection (Ye, 2012), computational probe design for nucleic acid detection (Kim, 2015), and machine learning models for CRISPR off-target (Hsu, 2013) and on-target (Doench, 2014) prediction have all broadened use of corresponding technologies. An accurate model for activity-based Cas13 guide selection would facilitate design of optimal SHERLOCK assays, especially in applications requiring high-activity guides like lateral flow detection, and enable guide RNA design for in vivo RNA targeting applications with Cas13.


SUMMARY

In certain example embodiments, a lateral flow device is provided comprising a substrate comprising a first end and a second end, the first end comprising a sample loading portion, a first region comprising a detectable ligand, two or more CRISPR effector systems, two or more detection constructs, and one or more first capture regions, each comprising a first binding agent; and the substrate comprising two or more second capture regions between the first region of the first end and the second end, each second capture region comprising a different binding agent; wherein each of the two or more CRISPR effector systems comprises a CRISPR effector protein or polynucleotide encoding a CRISPR effector protein and one or more guide sequences, each guide sequence configured to bind one or more target molecules.


In embodiments, the first end comprises two detection constructs, wherein each of the two detection constructs comprises an RNA or DNA oligonucleotide, comprising a first molecule on a first end and a second molecule on a second end. In certain embodiments, the first molecule on the first end of the first detection construct is FAM and the second molecule on the second end of the first detection construct is biotin or vice versa; and the first molecule on the first end of the second detection construct is FAM and the second molecule on the second end of the second detection construct is Digoxigenin (DIG) or vice versa. In embodiments, the first end comprises three detection constructs, wherein each of the three detection constructs comprises an RNA or DNA oligonucleotide, comprising a first molecule on a first end and a second molecule on a second end. In certain embodiments, the first and second molecules on the detection constructs comprise Tye 665 and Alexa 488; Tye 665 and FAM; and Tye 665 and Digoxigenin (DIG).


In embodiments, the CRISPR effector protein is an RNA-targeting effector protein, in some instances, the RNA-targeting effector protein is C2c2, Cas13b, or Cas13a. In some embodiments, the system comprises a polynucleotide encoding a CRISPR effector protein and the one or more guide RNAS are provided as a multiplexing polynucleotide, the multiplexing polynucleotide configured to comprise two or more guide sequences.


Methods for detecting a target nucleic acid in a sample are provided, comprising contacting a sample with the first end of a lateral flow device disclosed herein. In embodiments, the lateral flow device comprises the sample loading portion, wherein the sample flows from the sample loading portion of the substrate towards the first and second capture regions and generates a detectable signal. In preferred embodiments, the lateral flow device is capable of detecting two different target nucleic acid sequences. In particular embodiments, when the target nucleic acid sequences are absent from the sample, a fluorescent signal is generated at each capture region. In embodiments, the detectable signal is a loss of fluorescence that appears at the first and second capture regions. In embodiments, the lateral flow device is capable of detecting three different target nucleic acid sequences. In embodiments, the lateral flow device comprises three capture regions wherein the fluorescent signal appears at the first, second, and third capture regions. In embodiments, when the sample contains one or more target nucleic acid sequences, a fluorescent signal is absent at the capture region for the corresponding target nucleic acid sequence.


Nucleic acid detection systems comprising two or more CRISPR systems are provided, each CRISPR system comprising an effector protein and a guide RNA designed to bind to a corresponding target molecule; a set of detection constructs, each detection construct comprising a cutting motif sequence that is preferentially cut by one of the activated CRISPR effector proteins; and reagents for helicase dependent nucleic acid amplification (HDA). In embodiments, the HDA reagents comprise a helicase super mutant, selected from WP_003870487.1 Thermoanaerobacter ethanolicus comprising mutations D403A/D404, WP_049660019.1 Bacillus sp. FJAT-27231 comprising mutations D407A/D408A, WP_034654680.1 Bacillus megaterium comprising mutations D415A/D416A, WP_095390358.1, Bacillus simplex comprising mutations D407A/D408A, and WP_055343022.1 Paeniclostridium sordellii comprising mutations D402A/D403A. In embodiments, the systems provide methods for quantifying target nucleic acids in samples comprising distributing a sample or set of samples into one or more individual discrete volumes comprising two or more CRISPR systems, amplifying one or more target molecules in the sample or set of samples by HDA; incubating the sample or set of samples under conditions sufficient to allow binding of the guide RNAs to one or more target molecules; activating the CRISPR effector protein via binding of the guide RNAs to the one or more target molecules, wherein activating the CRISPR effector protein results in modification of the detection construct such that a detectable positive signal is generated; detecting the one or more detectable positive signal, wherein detection of the one or more detectable positive signal indicates a presence of one or more target molecules in the sample; and comparing the intensity of the one or more signals to a control to quantify the nucleic acid in the sample; wherein the steps of amplifying, incubating, activating, and detecting are all performed in the same individual discrete volume. In embodiments, the detectable positive signal is a loss of fluorescent signal. In embodiments, the detectable positive signal is detected on a lateral flow device.


Methods for designing guide RNAs for use in the detection systems disclosed herein are provided, comprising the steps of designing putative guide RNAs tiled across a target molecule of interest; incubating putative guide RNAs with a Cas effector protein and the target molecule and measuring cleavage activity of the each putative guide RNA; creating a training model based on the cleavage activity results of incubating the putative guide RNAs with the Cas effector protein and the target molecule; predicting highly active guide RNAs for the target molecule, wherein the predicting comprises optimizing the nucleotide at each base position in the guide RNA based on the training model; and validating the predicted highly active guide RNAs by incubating the guide RNAs with the Cas effector protein and the target molecule. In embodiments, the training model comprises one or more input features selected from guide sequence, flanking target sequence, normalized positions of the guide in the target and guide GC content. In an aspect, the guide sequence and/or flanking sequence input comprises one hit encoding mono-nucleotide and/or dinucleotide based identities across a guide length and flanking sequence in the target. In an aspect, the training model comprises applying logistic regression model on the activity of the guides across the one or more input features.


In embodiments, the predicting highly active guides for the target molecule comprises selecting guides with an increase in activity of a guide relative to the median activity, or selecting guides with highest guide activity. The increase in activity can be measured by an increase in fluorescence. In one aspect, the guides are selected with a 1.5, 2, 2.5 or 3-fold activity relative to median, or are in the top quartile or quintile for each target tested. In embodiments, the Cas effector protein is a Cas12 or Cas13 protein. In certain embodiments, the Cas protein is a Cas13a or Cas13b protein, in embodiments, the Cas protein is LwaCas13a or CcaCas13b.


These and other aspects, objects, features, and advantages of the example embodiments will become apparent to those having ordinary skill in the art upon consideration of the following detailed description of illustrated example embodiments.





BRIEF DESCRIPTION OF THE DRAWINGS

An understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention may be utilized, and the accompanying drawings of which:



FIGS. 1A-1F—illustrate that one-pot HDA-SHERLOCK is capable of quantitative detection of different targets. (FIG. 1A) Schematic of helicase reporter for screening DNA unwinding activity (SEQ ID NOs: 1-7). (FIG. 1B) Temperature sensitivity screen of different helicase orthologs with and without super-helicase mutations using the high-throughput fluorescent reporter. (FIG. 1C) Schematic of one-pot SHERLOCK with RPA or Super-HDA. (FIG. 1D) Kinetic curves of one-pot RPA detection of a restriction endonuclease gene fragment (Ea175) from T. denticola. (FIG. 1E) Kinetic curves of one-pot HDA detection of Ea175. (FIG. 1F) Quantitative nature of HDA-SHERLOCK compared to one-pot RPA.



FIGS. 2A-2I—illustrate that one-pot RPA-SHERLOCK is capable of rapid detection of different targets. (FIG. 2A) Kinetic curves of one-pot RPA detection of a restriction endonuclease gene fragment (Ea175) from T. denticola. (FIG. 2B) One-pot RPA end-point detection of Ea175 gene fragment. (FIG. 2C) One-pot RPA lateral flow readout of the Ea175 fragment in 30 minutes. (FIG. 2D) Kinetic curves of one-pot RPA detection of a restriction endonuclease gene fragment (Ea81) from T. denticola. (FIG. 2E) One-pot RPA end-point detection of Ea81 gene fragment. (FIG. 2F) One-pot RPA lateral flow readout of the Ea81 fragment in 3 hours. (FIG. 2G) Kinetic curves of one-pot RPA detection of acyltransferase gene fragment (acyltransferase) from P. aeruginosa. (FIG. 2H) One-pot RPA end-point detection of acyltransferase gene fragment. (FIG. 2I) One-pot RPA lateral flow readout of the acyltransferase fragment in 3 hours.



FIGS. 3A-3F—Multiplexed lateral flow detection with two-pot SHERLOCK. FIG. 3A Schematic of multiplex lateral flow with RPA preamplification design for two probes. FIG. 3B Multiplexed lateral flow detection with RPA preamplification of two targets, ssDNA 1 and a gene fragment of lectin from soybeans. FIG. 3C Multiplexed lateral flow detection with RPA preamplification of two targets, ssDNA 1 and lectin gene fragment, at a range of concentrations down to 2 aM. FIG. 3D Schematic for custom-made lateral flow strips enabling detection of three targets simultaneously with SHERLOCK. FIG. 3E Images of multiplexed lateral flow strips detecting three targets, ssDNA 1, Zika ssRNA, and Dengue ssRNA, in various combinations using LwaCas13a, CcaCas13b, and AsCas12a. FIG. 3F Quantitation of Tye-665 fluorescent intensity of multiplexed lateral flow strips detecting three targets, ssDNA 1, Zika ssRNA, and Dengue ssRNA, in various combinations using LwaCas13a, CcaCas13b, and AsCas12a.



FIGS. 4A-4G—SHERLOCK guide design model is capable of predicting highly active crRNAs for SHERLOCK detection. (FIG. 4A) Schematic of computational workflow of the SHERLOCK guide design tool. (FIG. 4B) Collateral activity of LwaCas13a with crRNAs tiling 5 synthetic targets. (FIG. 4C) ROC and AUC results of the best performing logistic regression model trained using the data from part B. (FIG. 4D) Mono-nucleotide feature weights of the best performing logistic regression model. (FIG. 4E) Di-nucleotide feature weights of the best performing logistic regression model. (FIG. 4F) Kinetic data of predicted best and worst performing crRNAs on three targets. (FIG. 4G) Predicted scores of multiple novel guides on three targets compared to guide activity.



FIGS. 5A-5C—SHERLOCK guide design model is capable of predicting highly active crRNAs for SHERLOCK detection. FIG. 5A Collateral activity of LwaCas13a and CcaCas13b with crRNAs tiling Ebola and Zika synthetic ssRNA targets demonstrates wide variation in guide performance. FIG. 5B ROC and AUC results of the best performing logistic regression model for LwaCas13a and CcaCas13b trained using crRNAs tiled and five different synthetic RNA targets FIGS. 5B and 5C show trained models predict PFS. FIG. 5C Selected mono-nucleotide feature weights of the best performing logistic regression model for LwaCas13a (left) and CcaCas13b (right). Known PFS constraints are shown as letters above the appropriate flanking positions.



FIGS. 6A-6F SHERLOCK guide design model validates across many crRNAs and can predict crRNAs with high activity on lateral flow strips. FIG. 6A Validation of best performing model for LwaCas13a across multiple crRNA, showing the predicted score of each crRNA versus actual collateral activity upon target recognition of thermonuclease, APML long, or APML short synthetic targets. The best and worst crRNAs predicted by the model are highlighted, and indicates the models predict good guides on novel targets. FIG. 6B Validation of best performing model for CcaCas13b across multiple crRNAs, showing the predicted score of each crRNA versus actual collateral activity upon target recognition of thermonuclease, APML long, or APML short synthetic targets. The best and worst crRNAs predicted by the model are highlighted, respectively. FIG. 6C Kinetic data of predicted best and worst performing LwaCas13a crRNAs highlighted in FIG. 6A on thermonuclease, APML long, and APML short synthetic RNA targets. FIG. 6D Kinetic data of predicted best and worst performing CcaCas13b crRNAs highlighted in FIG. 6B on thermonuclease, APML long, and APML short synthetic RNA targets. FIG. 6E Lateral flow performance of the predicted best and worst LwaCas13a crRNAs from FIG. 6A on detecting thermonuclease, APML long, and APML short synthetic RNA targets. FIG. 6F Lateral flow performance of the predicted best and worst CcaCas13b crRNAs from FIG. 6B on detecting thermonuclease, APML long, and APML short synthetic RNA targets.



FIG. 7A-7L One-pot RPA-SHERLOCK is capable of rapid and portable detection of different targets FIG. 7A Schematic of one-pot LwaCas13a SHERLOCK detection of acyltransferase target from P. aeruginosa with the top and worst predicted crRNAs from the guide design model. FIG. 7B Kinetic curves of one-pot LwaCas13a SHERLOCK detection of acyltransferase target from P. aeruginosa with the top predicted crRNA. FIG. 7C Kinetic curves of one-pot LwaCas13a SHERLOCK detection of acyltransferase target from P. aeruginosa with the worst predicted crRNA. Together, FIGS. 7B and 7C show the models of the top predicted guide has improved kinetics. FIG. 7D One-pot LwaCas13a SHERLOCK end-point detection of acyltransferase target from P. aeruginosa for the top and worst crRNAs at 1 hour. FIG. 7E One-pot LwaCas13a SHERLOCK lateral flow detection of acyltransferase target from P. aeruginosa using the top and worst predicted crRNAs at 1 hour. FIG. 7F Quantitation of one-pot LwaCas13a SHERLOCK end-point lateral flow detection of acyltransferase target from P. aeruginosa using the top and worst predicted crRNAs at 1 hour. FIG. 7G Schematic CcaCas13b one-pot SHERLOCK detection of thermonuclease target from S. aureus with the top and worst predicted crRNAs from the guide design model. FIG. 7H Kinetic curves of one-pot CcaCas13b SHERLOCK detection of thermonuclease target from S. aureus with the top predicted crRNA. FIG. 7I Kinetic curves of one-pot CcaCas13b SHERLOCK detection of thermonuclease target from S. aureus with the worst predicted crRNA. FIG. 7J One-pot CcaCas13b SHERLOCK end-point detection of thermonuclease target from S. aureus for the top and worst crRNAs at 1 hour. FIG. 7K One-pot CcaCas13b SHERLOCK lateral flow detection of thermonuclease target from S. aureus using the top and worst predicted crRNAs at 1 hour, with top performing guides allowing sensitive detection. FIG. 7L Quantitation of one-pot CcaCas13b SHERLOCK end-point lateral flow detection of thermonuclease target from S. aureus using the top and worst predicted crRNAs at 1 hour.



FIG. 8A-8D Multiplexed lateral flow detection with SHERLOCK. FIG. 8A Schematic of multiplex detection with one-pot SHERLOCK, with either fluorescent readout or lateral flow format. FIG. 8B Multiplexed fluorescence detection with one-pot SHERLOCK detection of Ea175 and thermonuclease targets using LwaCas13a and CcaCas13b orthologs, respectively, and the top predicted cRNAs. FIG. 8C Schematic of multiplex lateral flow with SHERLOCK. FIG. 8D. Multiplexed lateral flow detection with one-pot SHERLOCK detection of Ea175 and thermonuclease targets using LwaCas13a and CcaCas13b orthologs, respectively, and the top predicted cRNAs.



FIG. 9A-9C Training data and features of the SHERLOCK guide design model. FIG. 9A Collateral activity of LwaCas13a (blue) and CcaCas13b (red) with crRNAs tiling Ebola and Zika synthetic ssRNA targets. FIG. 9B Mono-nucleotide feature weights of the best performing logistic regression model for LwaCas13a (top) and CcaCas13b (bottom). FIG. 9C Di-nucleotide feature weights of the best performing logistic regression model for LwaCas13a (left) and CcaCas13b (right).



FIG. 10A-10F Additional targets are easily detected via one-pot SHERLOCK with lateral flow. FIG. 10A Kinetic curves of one-pot LwaCas13a SHERLOCK detection of Ea175 target. FIG. 10B One-pot LwaCas13a SHERLOCK end-point detection of Ea175 target at 45 minutes. FIG. 10C Quantitation of one-pot LwaCas13a SHERLOCK end-point lateral flow detection of Ea175 target at 30 minutes. FIG. 10D Kinetic curves of one-pot LwaCas13a SHERLOCK detection of Ea81 target. FIG. 10E One-pot LwaCas13a SHERLOCK end-point detection of Ea81 target at 45 minutes. FIG. 10F Quantitation of one-pot LwaCas13a SHERLOCK end-point lateral flow detection of Ea81 target at 3 hours.



FIG. 11A-11D—SHERLOCK guide design model is capable of predicting highly active crRNAs for SHERLOCK detection. FIG. 11A Schematic of computational workflow of the SHERLOCK guide design tool, FIG. 11B Collateral activity of LwaCas13a and CcaCas13b with crRNAs tiling Ebola and Zika synthetic ssRNA targets, FIG. 11C ROC and AUC results of the best performing logistic regression model for LwaCas13a (gray) and CcaCas13b (darker gray) trained using crRNAs tiled and five different synthetic RNA targets, FIG. 11D Selected mono-nucleotide feature weights of the best performing logistic regression model for LwaCas13a (left) and CcaCas13b (right). Known PFS constraints are shown as letters above the appropriate flanking positions.



FIG. 12—LwaCas13a guide design model predicts highly active guides for in vivo knockdown. A panel of guides (plus symbols) predicted to be highly active or not active, as well as random guides, are tested for knockdown of the Gluc transcript in HEK293FT cells. Each plus symbol represents the mean of three biological replicates. The mean of the distributions are shown as red dotted lines while the quartiles are shown as blue dotted lines.



FIG. 13A-13E—SHERLOCK guide design machine learning model validates across many crRNAs, can predict crRNAs with high activity on lateral flow strips, and correlates with in vivo knockdown. FIG. 13A Validation of best performing model for LwaCas13a across multiple crRNAs, showing the predicted score of each crRNA versus actual collateral activity upon target recognition of thermonuclease, APML long, or APML short synthetic targets. The best and worst crRNAs predicted by the model are highlighted in blue and red, respectively. FIG. 13B Kinetic data of predicted best and worst performing LwaCas13a crRNAs highlighted in panel 13a on thermonuclease, APML long, and APML short synthetic RNA targets. FIG. 13C Lateral flow performance of the predicted best and worst LwaCas13a crRNAs from panel 13a on detecting thermonuclease, APML long, and APML short synthetic RNA targets. FIG. 13D Schematic for evaluating the predictive performance of the guide design model for in vivo knockdown activity. FIG. 13E Previously measured knockdown activity of LwaCas13a guides tiled across Gluc and KRAS targets14 was ranked according to the predicted activity of the guide based on the guide design model. The means of the distributions are shown as red dotted lines while the quartiles are shown as blue dotted lines. ***p<0.001; *p<0.05; two-tailed student's T-test.



FIG. 14A-14E Multiplexed lateral flow detection with SHERLOCK. FIG. 14A Schematic of multiplex detection with one-pot SHERLOCK, with either fluorescent readout or lateral flow format. FIG. 14B Multiplexed fluorescence detection with one-pot SHERLOCK detection of Ea175 and thermonuclease targets using LwaCas13a and CcaCas13b orthologs, respectively, and the best predicted cRNAs; FIG. 14C Schematic of multiplex lateral flow with SHERLOCK; FIG. 14D Representative images of multiplexed lateral flow detection with one-pot SHERLOCK of Ea175 and thermonuclease targets using LwaCas13a and CcaCas13b orthologs, with quantitation of lateral flow strip band intensities. Lateral flow strip band intensities are inverted such that loss of signal is shown as positive signal; FIG. 14E Multiplexed lateral flow detection with one-pot SHERLOCK detection of Ea175 and thermonuclease targets using LwaCas13a and CcaCas13b orthologs, respectively, and the best predicted cRNAs. Lateral flow strip band intensities are inverted such that loss of signal is shown as positive signal.



FIG. 15A-15F Detection of PML-RARa and BCR-ABL cancer fusion transcripts from clinical samples. FIG. 15A Diagram of guide design for PML-RARa and BCR-ABL fusion transcripts tested in this study using the guide design model. Diagram of fusion transcripts adapted from van Dongen et al28. FIG. 15B Workflow for SHERLOCK testing of clinical samples of patients exhibiting PML-RARa and BCR-ABL fusion transcripts. Patient blood or bone marrow is extracted, pelleted, and RNA is purified from patient cells. Extracted RNA is then used as input into an RT-RPA reaction, the products of which are used as input for Cas13 detection; FIG. 15C RT-PCR of APML and BCR-ABL cancer variants from purified RNA. Composite image is made up of bands cut out from several gels running PCR products for the different transcripts (full gel images shown in FIG. 14A-14E). PCR products for the different fusions should have the following sizes: PML-RARa Intron 6 (214 bp); PML-RARa Intron 3: 289 bp; BCR-ABL p210 e14a2 (360 bp); BCR-ABL p210 e13a2 (285 bp); BCR-ABL p190 e1a2 (381 bp); FIG. 15D Two-step SHERLOCK end-point fluorescence detection of PML-RARa and BCR-ABL fusion transcripts using best predicted crRNAs at 45 minutes. RNA from each patient was amplified using primer sets for the three fusion transcripts shown, and Cas13 detection was setup with corresponding crRNAs. Greyed out bars (sample 15) indicate that data was not collected; FIG. 15E Two-step SHERLOCK lateral flow detection of PML-RARa and BCR-ABL fusion transcripts using best predicted crRNAs at 3 hours. Sample bands were cropped out from the lateral flow strips; full lateral flow images, containing both sample and control bands, are shown in FIG. 15. Greyed out boxes (sample 15) indicate that data was not collected; FIG. 5F Quantitation of the lateral flow data shown in (e). Greyed out bars (sample 15) indicate that data was not collected.



FIG. 16A-16C Multiplexed detection of PML-RARa and BCR-ABL cancer fusion transcripts from clinical samples FIG. 16A Schematic of two-step SHERLOCK multiplexed detection from RNA input; FIG. 16B Images of multiplexed lateral flow detection with two-step SHERLOCK detection of PML-RARa Intron/Exon 6 and Intron 3 fusion transcripts using LwaCas13a and CcaCas13b orthologs, respectively, and the best predicted cRNAs; FIG. 16C Quantitation of lateral flow strip band intensities; data are inverted such that loss of signal is shown as positive signal.



FIG. 17A-17C: SHERLOCK guide design machine learning model validates across many crRNAs (CcaCas13b). FIG. 17A. Validation of best performing model for CcaCas13b across multiple crRNAs, showing the predicted score of each crRNA versus actual collateral activity upon target recognition of thermonuclease, APML long, or APML short synthetic targets. The best and worst crRNAs predicted by the model are highlighted in blue or red, respectively. FIG. 17B. Kinetic data of predicted best and worst performing CcaCas13b crRNAs highlighted in panel 17A on thermonuclease, APML long, and APML short synthetic RNA targets. FIG. 17C. Lateral flow performance of the predicted best and worst CcaCas13b crRNAs from panel 17A on detecting thermonuclease, APML long, and APML short synthetic RNA targets.



FIG. 18A-18D SHERLOCK guide design machine learning model validates for crRNAs targeting BCR-ABL p210 b3a2. FIG. 18A Validation of best performing model for CcaCas13b across crRNAs tiling the BCR-ABL p210 b3a2 fusion transcript, showing the predicted score of each crRNA versus actual collateral activity upon target recognition. The best and worst crRNAs predicted by the model, respectively. FIG. 18B Validation of best performing model for LwaCas13a across crRNAs tiling the BCR-ABL p210 b3a2 fusion transcript, showing the predicted score of each crRNA versus actual collateral activity upon target recognition. The best and worst crRNAs predicted by the model are highlighted, respectively. FIG. 18C Kinetic data of predicted best and worst performing LwaCas13a crRNAs highlighted in 18A on the BCR-ABL p210 b3a2 fusion transcript. FIG. 18D Kinetic data of predicted best and worst performing CcaCas13b crRNAs highlighted in 18B on the BCR-ABL p210 b3a2 fusion transcript.



FIG. 19A-19E Nested RT-PCR detection of PML-RARa and BCR-ABL cancer fusion transcripts from clinical samples. FIG. 19A Whole gel images of detection of PML-RARa Intron 6: 214 bp. For sample 6, because the breakpoint is in exon 6 of PML, the band size can be variable. FIG. 19B Whole gel images of detection of PML-RARa Intron 3: 289 bp. Some patients that have intron/exon 6 breakpoints, as in samples 4-6, can demonstrate several larger size bands (as seen), due to alternative splicing of PML. FIG. 19C Whole gel images of detection of BCR-ABL p210: e14a2 360 bp, e13a2 285 bp. FIG. 19D Whole gel images of detection of BCR-ABL p190: e1a2 381 bp. FIG. 19E Whole gel images of detection of GAPDH: 138 bp.



FIG. 20 Detection of PML-RARa and BCR-ABL cancer fusion transcripts from clinical samples. Two-step SHERLOCK lateral flow detection of PML-RARa and BCR-ABL fusion transcripts using best predicted crRNAs at 3 hours. Lateral flow strips are depicted with both the sample and control bands. Greyed out strips (sample 15) indicate that data was not collected.





The figures herein are for illustrative purposes only and are not necessarily drawn to scale.


DETAILED DESCRIPTION OF THE EXAMPLE EMBODIMENTS
General Definitions

Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains. Definitions of common terms and techniques in molecular biology may be found in Molecular Cloning: A Laboratory Manual, 2nd edition (1989) (Sambrook, Fritsch, and Maniatis); Molecular Cloning: A Laboratory Manual, 4th edition (2012) (Green and Sambrook); Current Protocols in Molecular Biology (1987) (F. M. Ausubel et al. eds.); the series Methods in Enzymology (Academic Press, Inc.): PCR 2: A Practical Approach (1995) (M. J. MacPherson, B. D. Hames, and G. R. Taylor eds.): Antibodies, A Laboratory Manual (1988) (Harlow and Lane, eds.): Antibodies A Laboratory Manual, 2nd edition 2013 (E. A. Greenfield ed.); Animal Cell Culture (1987) (R. I. Freshney, ed.); Benjamin Lewin, Genes IX, published by Jones and Bartlet, 2008 (ISBN 0763752223); Kendrew et al. (eds.), The Encyclopedia of Molecular Biology, published by Blackwell Science Ltd., 1994 (ISBN 0632021829); Robert A. Meyers (ed.), Molecular Biology and Biotechnology: a Comprehensive Desk Reference, published by VCH Publishers, Inc., 1995 (ISBN 9780471185710); Singleton et al., Dictionary of Microbiology and Molecular Biology 2nd ed., J. Wiley & Sons (New York, N.Y. 1994), March, Advanced Organic Chemistry Reactions, Mechanisms and Structure 4th ed., John Wiley & Sons (New York, N.Y. 1992); and Marten H. Hofker and Jan van Deursen, Transgenic Mouse Methods and Protocols, 2nd edition (2011).


As used herein, the singular forms “a” “an”, and “the” include both singular and plural referents unless the context clearly dictates otherwise.


The term “optional” or “optionally” means that the subsequent described event, circumstance or substituent may or may not occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.


The recitation of numerical ranges by endpoints includes all numbers and fractions subsumed within the respective ranges, as well as the recited endpoints.


The terms “about” or “approximately” as used herein when referring to a measurable value such as a parameter, an amount, a temporal duration, and the like, are meant to encompass variations of and from the specified value, such as variations of +/−10% or less, +/−5% or less, +/−1% or less, and +/−0.1% or less of and from the specified value, insofar such variations are appropriate to perform in the disclosed invention. It is to be understood that the value to which the modifier “about” or “approximately” refers is itself also specifically, and preferably, disclosed.


As used herein, a “biological sample” may contain whole cells and/or live cells and/or cell debris. The biological sample may contain (or be derived from) a “bodily fluid”. The present invention encompasses embodiments wherein the bodily fluid is selected from amniotic fluid, aqueous humour, vitreous humour, bile, blood serum, breast milk, cerebrospinal fluid, cerumen (earwax), chyle, chyme, endolymph, perilymph, exudates, feces, female ejaculate, gastric acid, gastric juice, lymph, mucus (including nasal drainage and phlegm), pericardial fluid, peritoneal fluid, pleural fluid, pus, rheum, saliva, sebum (skin oil), semen, sputum, synovial fluid, sweat, tears, urine, vaginal secretion, vomit and mixtures of one or more thereof. Biological samples include cell cultures, bodily fluids, cell cultures from bodily fluids. Bodily fluids may be obtained from a mammal organism, for example by puncture, or other collecting or sampling procedures.


The terms “subject,” “individual,” and “patient” are used interchangeably herein to refer to a vertebrate, preferably a mammal, more preferably a human. Mammals include, but are not limited to, murines, simians, humans, farm animals, sport animals, and pets. Tissues, cells and their progeny of a biological entity obtained in vivo or cultured in vitro are also encompassed.


Various embodiments are described hereinafter. It should be noted that the specific embodiments are not intended as an exhaustive description or as a limitation to the broader aspects discussed herein. One aspect described in conjunction with a particular embodiment is not necessarily limited to that embodiment and can be practiced with any other embodiment(s). Reference throughout this specification to “one embodiment”, “an embodiment,” “an example embodiment,” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, appearances of the phrases “in one embodiment,” “in an embodiment,” or “an example embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment, but may. Furthermore, the particular features, structures or characteristics may be combined in any suitable manner, as would be apparent to a person skilled in the art from this disclosure, in one or more embodiments. Furthermore, while some embodiments described herein include some but not other features included in other embodiments, combinations of features of different embodiments are meant to be within the scope of the invention. For example, in the appended claims, any of the claimed embodiments can be used in any combination.


All publications, published patent documents, and patent applications cited herein are hereby incorporated by reference to the same extent as though each individual publication, published patent document, or patent application was specifically and individually indicated as being incorporated by reference.


Overview

Embodiments disclosed herein provide multiplex lateral flow devices and methods of use. The embodiments disclosed herein are directed to lateral flow detection devices that comprise CRISPR Cas systems for target molecule detection.


Instead of relying on general capture of antibody that was not bound by intact reporter RNAs (Gootenberg, 2018), the presently disclosed system is more suitable for detecting two targets. Applicants adapted a lateral flow approach with two separate detection lines consisting of deposited materials that capture reporter RNA appended with a fluorophore and a molecule specific to the deposited material, allowing fluorescent visualization of signal loss at detection lines due to collateral activity and cleavage of corresponding reporter RNA. Further advances were made utilizing guide design that allows for design of highly active guide RNAs for use with the specific Cas protein of the systems as well as for the desired target molecule.


Lateral Flow Devices

In one embodiment, the invention provides a lateral flow device comprising a substrate comprising a first end and a second end. The first end may comprise a sample loading portion, a first region comprising a detectable ligand, two or more CRISPR effector systems, two or more detection constructs, and one or more first capture regions, each comprising a first binding agent. The substrate may also comprise two or more second capture regions between the first region of the first end and the second end, each second capture region comprising a different binding agent. Each of the two or more CRISPR effector systems may comprise a CRISPR effector protein and one or more guide sequences, each guide sequence configured to bind one or more target molecules.


The embodiments disclosed herein are directed to lateral flow detection devices that comprise SHERLOCK systems. SHERLOCK utilizes Cas13s non-specific RNase activity to cleave fluorescent reporters upon target recognition, providing sensitive and specific diagnostics using Cas13, including single nucleotide variants, detection based on rRNA sequences, screening for drug resistance, monitoring microbe outbreaks, genetic perturbations, and screening of environmental samples, as described, for example, in PCT/US18/054472 filed Oct. 22, 2018 at [0183]-[0327], incorporated herein by reference. Reference is made to WO 2017/219027, WO2018/107129, US20180298445, US 2018-0274017, US 2018-0305773, WO 2018/170340, U.S. application Ser. No. 15/922,837, filed Mar. 15, 2018 entitled “Devices for CRISPR Effector System Based Diagnostics”, PCT/US18/50091, filed Sep. 7, 2018 “Multi-Effector CRISPR Based Diagnostic Systems”, PCT/US18/66940 filed Dec. 20, 2018 entitled “CRISPR Effector System Based Multiplex Diagnostics”, PCT/US18/054472 filed Oct. 4, 2018 entitled “CRISPR Effector System Based Diagnostic”, U.S. Provisional 62/740,728 filed Oct. 3, 2018 entitled “CRISPR Effector System Based Diagnostics for Hemorrhagic Fever Detection”, U.S. Provisional 62/690,278 filed Jun. 26, 2018 and U.S. Provisional 62/767,059 filed Nov. 14, 2018 both entitled “CRISPR Double Nickase Based Amplification, Compositions, Systems and Methods”, U.S. Provisional 62/690,160 filed Jun. 26, 2018 and U.S. Pat. No. 62,767,077 filed Novemebr 14, 2018, both entitled “CRISPR/CAS and Transposase Based Amplification Compositions, Systems, And Methods”, U.S. Provisional 62/690,257 filed Jun. 26, 2018 and 62/767,052 filed Nov. 14, 2018 both entitled “CRISPR Effector System Based Amplification Methods, Systems, And Diagnostics”, U.S. Provisional 62/767,076 filed Nov. 14, 2018 entitled “Multiplexing Highly Evolving Viral Variants With SHERLOCK” and 62/767,070 filed Nov. 14, 2018 entitled “Droplet SHERLOCK.” Reference is further made to WO2017/127807, WO2017/184786, WO 2017/184768, WO 2017/189308, WO 2018/035388, WO 2018/170333, WO 2018/191388, WO 2018/213708, WO 2019/005866, PCT/US18/67328 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems”, PCT/US18/67225 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems” and PCT/US18/67307 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems”, U.S. 62/712,809 filed Jul. 31, 2018 entitled “Novel CRISPR Enzymes and Systems”, U.S. 62/744,080 filed Oct. 10, 2018 entitled “Novel Cas12b Enzymes and Systems” and U.S. 62/751,196 filed Oct. 26, 2018 entitled “Novel Cas12b Enzymes and Systems”, U.S. 715,640 filed August 7, 2-18 entitled “Novel CRISPR Enzymes and Systems”, WO 2016/205711, U.S. Pat. No. 9,790,490, WO 2016/205749, WO 2016/205764, WO 2017/070605, WO 2017/106657, and WO 2016/149661, WO2018/035387, WO2018/194963, Cox DBT, et al., RNA editing with CRISPR-Cas13, Science. 2017 Nov. 24; 358(6366):1019-1027; Gootenberg J S, et al., Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6., Science. 2018 Apr. 27; 360(6387):439-444; Gootenberg J S, et al., Nucleic acid detection with CRISPR-Cas13a/C2c2, Science. 2017 Apr. 28; 356(6336):438-442; Abudayyeh O O, et al., RNA targeting with CRISPR-Cas13, Nature. 2017 Oct. 12; 550(7675):280-284; Smargon A A, et al., Cas13b Is a Type VI-B CRISPR-Associated RNA-Guided RNase Differentially Regulated by Accessory Proteins Csx27 and Csx28. Mol Cell. 2017 Feb. 16; 65(4):618-630.e7; Abudayyeh 00, et al., C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector, Science. 2016 Aug. 5; 353(6299):aaf5573; Yang L, et al., Engineering and optimising deaminase fusions for genome editing. Nat Commun. 2016 Nov. 2; 7:13330, Myrvhold et al., Field deployable viral diagnostics using CRISPR-Cas13, Science 2018 360, 444-448, Shmakov et al. “Diversity and evolution of class 2 CRISPR-Cas systems,” Nat Rev Microbiol. 2017 15(3):169-182, each of which is incorporated herein by reference in its entirety.


The device may comprise a lateral flow substrate for detecting a SHERLOCK reaction. Substrates suitable for use in lateral flow assays are known in the art. These may include, but are not necessarily limited to membranes or pads made of cellulose and/or glass fiber, polyesters, nitrocellulose, or absorbent pads (J Saudi Chem Soc 19(6):689-705; 2015). The SHERLOCK system, i.e. one or more CRISPR systems and corresponding reporter constructs are added to the lateral flow substrate at a defined reagent portion of the lateral flow substrate, typically on one end of the lateral flow substrate. Reporting constructs used within the context of the present invention comprise a first molecule and a second molecule linked by an RNA or DNA linker. The lateral flow substrate further comprises a sample portion. The sample portion may be equivalent to, continuous with, or adjacent to the reagent portion.


Lateral Flow Substrate

In certain example embodiments, a lateral flow device comprises a lateral flow substrate on which detection can be performed. Substrates suitable for use in lateral flow assays are known in the art. These may include, but are not necessarily limited to, membranes or pads made of cellulose and/or glass fiber, polyesters, nitrocellulose, or absorbent pads (J Saudi Chem Soc 19(6):689-705; 2015).


Lateral support substrates comprise a first and second end, and one or more capture regions that each comprise binding agents. The first end may comprise a sample loading portion, a first region comprising a detectable ligand, two or more CRISPR effector systems, two or more detection constructs, and one or more first capture regions, each comprising a first binding agent. The substrate may also comprise two or more second capture regions between the first region of the first end and the second end, each second capture region comprising a different binding agent. Each of the two or more CRISPR effector systems may comprise a CRISPR effector protein and one or more guide sequences, each guide sequence configured to bind one or more target molecules. The lateral flow substrates may be configured to detect a SHERLOCK reaction. Reference is made to Gootenberg, et al., “Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6,” Science. 2018 Apr. 27; 360(6387):439-444. doi: 10.1126/science.aaq0179, and International Patent Publication No, WO 2019/071051, each specifically incorporated herein by reference. Lateral support substrates may be located within a housing (see for example, “Rapid Lateral Flow Test Strips” Merck Millipore 2013). The housing may comprise at least one opening for loading samples and a second single opening or separate openings that allow for reading of detectable signal generated at the first and second capture regions.


The embodiments disclosed herein can be prepared in freeze-dried format for convenient distribution and point-of-care (POC) applications. Such embodiments are useful in multiple scenarios in human health including, for example, viral detection, bacterial strain typing, sensitive genotyping, and detection of disease-associated cell free DNA. Accordingly, the lateral substrate comprising one or more of the elements of the system, including detectable ligands, CRISPR effector systems, detection constructs and binding agents may be freeze-dried to the lateral flow substrate and packaged as a ready to use device. Alternatively, all or a portion of the elements of the system may be added to the reagent portion of the lateral flow substrate at the time of using the device.


First End and Second End of the Substrate

The substrate of the lateral flow device comprises a first and second end. The SHERLOCK system, i.e. one or more CRISPR systems and corresponding reporter constructs are added to the lateral flow substrate at a defined reagent portion of the lateral flow substrate, typically on a first end of the lateral flow substrate. Reporting constructs used within the context of the present invention comprise a first molecule and a second molecule linked by an RNA or DNA linker. The lateral flow substrate further comprises a sample portion. The sample portion may be equivalent to, continuous with, or adjacent to the reagent portion. The first end of the substrate for application of a sample.


In certain example embodiments, the first end comprises a first region. The first region comprises a detectable ligand, two or more CRISPR effector systems, two or more detection constructs, and one or more first capture regions, each comprising a first binding agent.


Capture Regions

The lateral flow substrate can comprise one or more capture regions. In embodiments the first end of the lateral flow substrate comprises one or more first capture regions, with two or more second capture regions between the first region of the first end of the substrate and the second end of the substrate. The capture regions may be provided as a capture line, typically a horizontal line running across the device, but other configurations are possible. The first capture region is proximate to and on the same end of the lateral flow substrate as the sample loading portion.


Binding Agents

Specific binding-integrating molecules comprise any members of binding pairs that can be used in the present invention. Such binding pairs are known to those skilled in the art and include, but are not limited to, antibody-antigen pairs, enzyme-substrate pairs, receptor-ligand pairs, and streptavidin-biotin. In addition to such known binding pairs, novel binding pairs may be specifically designed. A characteristic of binding pairs is the binding between the two members of the binding pair.


A first binding agent that specifically binds the first molecule of the reporter construct is fixed or otherwise immobilized to the first capture region. The second capture region is located towards the opposite end of the lateral flow substrate from the first capture region. A second binding agent is fixed or otherwise immobilized at the second capture region. The second binding agent specifically binds the second molecule of the reporter construct, or the second binding agent may bind a detectable ligand. For example, the detectable ligand may be a particle, such as a colloidal particle, that when it aggregates can be detected visually, and generates a detectable positive signal. The particle may be modified with an antibody that specifically binds the second molecule on the reporter construct. If the reporter construct is not cleaved it will facilitate accumulation of the detectable ligand at the first binding region. If the reporter construct is cleaved the detectable ligand is released to flow to the second binding region. In such an embodiment, the second binding region comprises a second binding agent capable of specifically or non-specifically binding the detectable ligand on the antibody of the detectable ligand. Binding agents can be, for example, antibodies, that recognize a particular affinity tag. Such binding agents can further contain, for example, detectable labels, such as isotope labels and/or nucleic acid barcodes. A barcode is a short sequence of nucleotides (for example, DNA, RNA, or combinations thereof) that is used as an identifier. A nucleic acid barcode may have a length of 4-100 nucleotides and be either single or double-stranded. Methods for identifying cells with barcodes are known in the art. Accordingly, guide RNAs of the CRISPR effector systems described herein may be used to detect the barcode.


Detectable Ligands

The first region is loaded with a detectable ligand, such as those disclosed herein, for example a gold nanoparticle. The detectable ligand may be a particle, such as a colloidal particle, that when it aggregates can be detected visually. The particle may be modified with an antibody that specifically binds the second molecule on the reporter construct. If the reporter construct is not cleaved it will facilitate accumulation of the detectable ligand at the first binding region. If the reporter construct is cleaved the detectable ligand is released to flow to the second binding region. In such an embodiment, the second binding agent is an agent capable of specifically or non-specifically binding the detectable ligand on the antibody on the detectable ligand. Examples of suitable binding agents for such an embodiment include, but are not limited to, protein A and protein G. In some examples, the detectable ligand is a gold nanoparticle, which may be modified with a first antibody, such as an anti-FITC antibody.


Detection Constructs

The first region also comprises a detection construct. In one example embodiment, a RNA detection construct and a CRISPR effector system (a CRISPR effector protein and one or more guide sequences configured to bind to one or more target sequences) as disclosed herein. In one example embodiment, and for purposes of further illustration, the RNA construct may comprise a FAM molecule on a first end of the detection construction and a biotin on a second end of the detection construct. Upstream of the flow of solution from the first end of the lateral flow substrate is a first test band. The test band may comprise a biotin ligand. Accordingly, when the RNA detection construct is present it its initial state, i.e. in the absence of target, the FAM molecule on the first end will bind the anti-FITC antibody on the gold nanoparticle, and the biotin on the second end of the RNA construct will bind the biotin ligand allowing for the detectable ligand to accumulate at the first test, generating a detectable signal. Generation of a detectable signal at the first band indicates the absence of the target ligand. In the presence of target, the CRISPR effector complex forms and the CRISPR effector protein is activated resulting in cleavage of the RND detection construct. In the absence of intact RNA detection construct the colloidal gold will flow past the second strip. The lateral flow device may comprise a second band, upstream of the first band. The second band may comprise a molecule capable of binding the antibody-labeled colloidal gold molecule, for example an anti-rabbit antibody capable of binding a rabbit anti-FITC antibody on the colloidal gold. Therefore, in the presence of one or more targets, the detectable ligand will accumulate at the second band, indicating the presence of the one or more targets in the sample.


In some embodiments, the first end of the lateral flow device comprises two detection constructs and each of the two detection constructs comprises an RNA or DNA oligonucleotide, comprising a first molecule on a first end and a second molecule on a second end. The first molecule and the second molecule may be linked by an RNA or DNA linker.


In some embodiments, the first molecule on the first end of the first detection construct may be FAM and the second molecule on the second end of the first detection construct may be biotin, or vice versa. In some embodiments, the first molecule on the first end of the second detection construct may be FAM and the second molecule on the second end of the second detection construct may be Digoxigenin (DIG), or vice versa.


In some embodiments, the first end may comprise three detection constructs, wherein each of the three detection constructs comprises an RNA or DNA oligonucleotide, comprising a first molecule on a first end and a second molecule on a second end. In specific embodiments, the first and second molecules on the detection constructs comprise Tye 665 and Alexa 488; Tye 665 and FAM, and Tye 665 and Digoxigenin (DIG), respectively.


As used herein, a “detection construct” refers to a molecule that can be cleaved or otherwise deactivated by an activated CRISPR system effector protein described herein. The term “detection construct” may also be referred to in the alternative as a “masking construct.” Depending on the nuclease activity of the CRISPR effector protein, the masking construct may be a RNA-based masking construct or a DNA-based masking construct. The Nucleic Acid-based masking constructs comprises a nucleic acid element that is cleavable by a CRISPR effector protein. Cleavage of the nucleic acid element releases agents or produces conformational changes that allow a detectable signal to be produced. Example constructs demonstrating how the nucleic acid element may be used to prevent or mask generation of detectable signal are described below and embodiments of the invention comprise variants of the same. Prior to cleavage, or when the masking construct is in an ‘active’ state, the masking construct blocks the generation or detection of a positive detectable signal. It will be understood that in certain example embodiments a minimal background signal may be produced in the presence of an active masking construct. A positive detectable signal may be any signal that can be detected using optical, fluorescent, chemiluminescent, electrochemical or other detection methods known in the art. The term “positive detectable signal” is used to differentiate from other detectable signals that may be detectable in the presence of the masking construct. For example, in certain embodiments a first signal may be detected when the masking agent is present or when a CRISPR system has not been activated (i.e. a negative detectable signal), which then converts to a second signal (e.g. the positive detectable signal) upon detection of the target molecules and cleavage or deactivation of the masking agent, or upon activation of the CRISPR effector protein. The positive detectable signal, then, is a signal detected upon activation of the CRISPR effector protein, and may be, in a colorimetric or fluorescent assay, a decrease in fluorescence or color relative to a control or an increase in fluorescence or color relative to a control, depending on the configuration of the lateral flow substrate, and as described further herein.


In certain example embodiments, the masking construct may comprise a HCR initiator sequence and a cutting motif, or a cleavable structural element, such as a loop or hairpin, that prevents the initiator from initiating the HCR reaction. The cutting motif may be preferentially cut by one of the activated CRISPR effector proteins. Upon cleavage of the cutting motif or structure element by an activated CRISPR effector protein, the initiator is then released to trigger the HCR reaction, detection thereof indicating the presence of one or more targets in the sample. In certain example embodiments, the masking construct comprises a hairpin with a RNA loop. When an activated CRISPR effector protein cuts the RNA loop, the initiator can be released to trigger the HCR reaction.


In certain example embodiments, the masking construct may suppress generation of a gene product. The gene product may be encoded by a reporter construct that is added to the sample. The masking construct may be an interfering RNA involved in a RNA interference pathway, such as a short hairpin RNA (shRNA) or small interfering RNA (siRNA). The masking construct may also comprise microRNA (miRNA). While present, the masking construct suppresses expression of the gene product. The gene product may be a fluorescent protein or other RNA transcript or proteins that would otherwise be detectable by a labeled probe, aptamer, or antibody but for the presence of the masking construct. Upon activation of the effector protein the masking construct is cleaved or otherwise silenced allowing for expression and detection of the gene product as the positive detectable signal.


In specific embodiments, the masking construct comprises a silencing RNA that suppresses generation of a gene product encoded by a reporting construct, wherein the gene product generates the detectable positive signal when expressed.


In certain example embodiments, the masking construct may sequester one or more reagents needed to generate a detectable positive signal such that release of the one or more reagents from the masking construct results in generation of the detectable positive signal. The one or more reagents may combine to produce a colorimetric signal, a chemiluminescent signal, a fluorescent signal, or any other detectable signal and may comprise any reagents known to be suitable for such purposes. In certain example embodiments, the one or more reagents are sequestered by RNA aptamers that bind the one or more reagents. The one or more reagents are released when the effector protein is activated upon detection of a target molecule and the RNA or DNA aptamers are degraded.


In certain example embodiments, the masking construct may be immobilized on a solid substrate in an individual discrete volume (defined further below) and sequesters a single reagent. For example, the reagent may be a bead comprising a dye. When sequestered by the immobilized reagent, the individual beads are too diffuse to generate a detectable signal, but upon release from the masking construct are able to generate a detectable signal, for example by aggregation or simple increase in solution concentration. In certain example embodiments, the immobilized masking agent is a RNA- or DNA-based aptamer that can be cleaved by the activated effector protein upon detection of a target molecule.


In certain other example embodiments, the masking construct binds to an immobilized reagent in solution thereby blocking the ability of the reagent to bind to a separate labeled binding partner that is free in solution. Thus, upon application of a washing step to a sample, the labeled binding partner can be washed out of the sample in the absence of a target molecule. However, if the effector protein is activated, the masking construct is cleaved to a degree sufficient to interfere with the ability of the masking construct to bind the reagent thereby allowing the labeled binding partner to bind to the immobilized reagent. Thus, the labeled binding partner remains after the wash step indicating the presence of the target molecule in the sample. In certain aspects, the masking construct that binds the immobilized reagent is a DNA or RNA aptamer. The immobilized reagent may be a protein and the labeled binding partner may be a labeled antibody. Alternatively, the immobilized reagent may be streptavidin and the labeled binding partner may be labeled biotin. The label on the binding partner used in the above embodiments may be any detectable label known in the art. In addition, other known binding partners may be used in accordance with the overall design described herein.


In certain example embodiments, the masking construct may comprise a ribozyme. Ribozymes are RNA molecules having catalytic properties. Ribozymes, both naturally and engineered, comprise or consist of RNA that may be targeted by the effector proteins disclosed herein. The ribozyme may be selected or engineered to catalyze a reaction that either generates a negative detectable signal or prevents generation of a positive control signal. Upon deactivation of the ribozyme by the activated effector protein the reaction generating a negative control signal, or preventing generation of a positive detectable signal, is removed thereby allowing a positive detectable signal to be generated. In one example embodiment, the ribozyme may catalyze a colorimetric reaction causing a solution to appear as a first color. When the ribozyme is deactivated the solution then turns to a second color, the second color being the detectable positive signal. An example of how ribozymes can be used to catalyze a colorimetric reaction are described in Zhao et al. “Signal amplification of glucosamine-6-phosphate based on ribozyme glmS,” Biosens Bioelectron. 2014; 16:337-42, and provide an example of how such a system could be modified to work in the context of the embodiments disclosed herein. Alternatively, ribozymes, when present can generate cleavage products of, for example, RNA transcripts. Thus, detection of a positive detectable signal may comprise detection of non-cleaved RNA transcripts that are only generated in the absence of the ribozyme.


In some embodiments, the masking construct may be a ribozyme that generates a negative detectable signal, and wherein a positive detectable signal is generated when the ribozyme is deactivated.


In certain example embodiments, the one or more reagents is a protein, such as an enzyme, capable of facilitating generation of a detectable signal, such as a colorimetric, chemiluminescent, or fluorescent signal, that is inhibited or sequestered such that the protein cannot generate the detectable signal by the binding of one or more DNA or RNA aptamers to the protein. Upon activation of the effector proteins disclosed herein, the DNA or RNA aptamers are cleaved or degraded to an extent that they no longer inhibit the protein's ability to generate the detectable signal. In certain example embodiments, the aptamer is a thrombin inhibitor aptamer. In certain example embodiments the thrombin inhibitor aptamer has a sequence of GGGAACAAAGCUGAAGUACUUACCC (SEQ ID NO: 8). When this aptamer is cleaved, thrombin will become active and will cleave a peptide colorimetric or fluorescent substrate. In certain example embodiments, the colorimetric substrate is para-nitroanilide (pNA) covalently linked to the peptide substrate for thrombin. Upon cleavage by thrombin, pNA is released and becomes yellow in color and easily visible to the eye. In certain example embodiments, the fluorescent substrate is 7-amino-4-methylcoumarin a blue fluorophore that can be detected using a fluorescence detector. Inhibitory aptamers may also be used for horseradish peroxidase (HRP), beta-galactosidase, or calf alkaline phosphatase (CAP) and within the general principals laid out above.


In certain embodiments, RNAse or DNAse activity is detected colorimetrically via cleavage of enzyme-inhibiting aptamers. One potential mode of converting DNAse or RNAse activity into a colorimetric signal is to couple the cleavage of a DNA or RNA aptamer with the re-activation of an enzyme that is capable of producing a colorimetric output. In the absence of RNA or DNA cleavage, the intact aptamer will bind to the enzyme target and inhibit its activity. The advantage of this readout system is that the enzyme provides an additional amplification step: once liberated from an aptamer via collateral activity (e.g. Cpf1 collateral activity), the colorimetric enzyme will continue to produce colorimetric product, leading to a multiplication of signal.


In certain embodiments, an existing aptamer that inhibits an enzyme with a colorimetric readout is used. Several aptamer/enzyme pairs with colorimetric readouts exist, such as thrombin, protein C, neutrophil elastase, and subtilisin. These proteases have colorimetric substrates based upon pNA and are commercially available. In certain embodiments, a novel aptamer targeting a common colorimetric enzyme is used. Common and robust enzymes, such as beta-galactosidase, horseradish peroxidase, or calf intestinal alkaline phosphatase, could be targeted by engineered aptamers designed by selection strategies such as SELEX. Such strategies allow for quick selection of aptamers with nanomolar binding efficiencies and could be used for the development of additional enzyme/aptamer pairs for colorimetric readout.


In certain embodiments, the masking construct may be a DNA or RNA aptamer and/or may comprise a DNA or RNA-tethered inhibitor.


In certain embodiments, the masking construct may comprise a DNA or RNA oligonucleotide to which a detectable ligand and a masking component are attached.


In certain embodiments, RNAse or DNase activity is detected colorimetrically via cleavage of RNA-tethered inhibitors. Many common colorimetric enzymes have competitive, reversible inhibitors: for example, beta-galactosidase can be inhibited by galactose. Many of these inhibitors are weak, but their effect can be increased by increases in local concentration. By linking local concentration of inhibitors to DNase RNAse activity, colorimetric enzyme and inhibitor pairs can be engineered into DNase and RNAse sensors. The colorimetric DNase or RNAse sensor based upon small-molecule inhibitors involves three components: the colorimetric enzyme, the inhibitor, and a bridging RNA or DNA that is covalently linked to both the inhibitor and enzyme, tethering the inhibitor to the enzyme. In the uncleaved configuration, the enzyme is inhibited by the increased local concentration of the small molecule; when the DNA or RNA is cleaved (e.g. by Cas13 or Cas12 collateral cleavage), the inhibitor will be released and the colorimetric enzyme will be activated.


In certain embodiments, the aptamer or DNA- or RNA-tethered inhibitor may sequester an enzyme, wherein the enzyme generates a detectable signal upon release from the aptamer or DNA or RNA tethered inhibitor by acting upon a substrate. In some embodiments, the aptamer may be an inhibitor aptamer that inhibits an enzyme and prevents the enzyme from catalyzing generation of a detectable signal from a substance. In some embodiments, the DNA- or RNA-tethered inhibitor may inhibit an enzyme and may prevent the enzyme from catalyzing generation of a detectable signal from a substrate.


In certain embodiments, RNAse activity is detected colorimetrically via formation and/or activation of G-quadruplexes. G quadruplexes in DNA can complex with heme (iron (III)-protoporphyrin IX) to form a DNAzyme with peroxidase activity. When supplied with a peroxidase substrate (e.g. ABTS: (2,2′-Azinobis [3-ethylbenzothiazoline-6-sulfonic acid]-diammonium salt)), the G-quadruplex-heme complex in the presence of hydrogen peroxide causes oxidation of the substrate, which then forms a green color in solution. An example G-quadruplex forming DNA sequence is: GGGTAGGGCGGGTTGGGA (SEQ ID NO: 9). By hybridizing an additional DNA or RNA sequence, referred to herein as a “staple,” to this DNA aptamer, formation of the G-quadraplex structure will be limited. Upon collateral activation, the staple will be cleaved allowing the G quadraplex to form and heme to bind. This strategy is particularly appealing because color formation is enzymatic, meaning there is additional amplification beyond collateral activation.


In certain embodiments, the masking construct may comprise an RNA oligonucleotide designed to bind a G-quadruplex forming sequence, wherein a G-quadruplex structure is formed by the G-quadruplex forming sequence upon cleavage of the masking construct, and wherein the G-quadruplex structure generates a detectable positive signal.


In certain example embodiments, the masking construct may be immobilized on a solid substrate in an individual discrete volume (defined further below) and sequesters a single reagent. For example, the reagent may be a bead comprising a dye. When sequestered by the immobilized reagent, the individual beads are too diffuse to generate a detectable signal, but upon release from the masking construct are able to generate a detectable signal, for example by aggregation or simple increase in solution concentration. In certain example embodiments, the immobilized masking agent is a DNA- or RNA-based aptamer that can be cleaved by the activated effector protein upon detection of a target molecule.


In one example embodiment, the masking construct comprises a detection agent that changes color depending on whether the detection agent is aggregated or dispersed in solution. For example, certain nanoparticles, such as colloidal gold, undergo a visible purple to red color shift as they move from aggregates to dispersed particles. Accordingly, in certain example embodiments, such detection agents may be held in aggregate by one or more bridge molecules. At least a portion of the bridge molecule comprises RNA or DNA. Upon activation of the effector proteins disclosed herein, the RNA or DNA portion of the bridge molecule is cleaved allowing the detection agent to disperse and resulting in the corresponding change in color. In certain example embodiments, the detection agent is a colloidal metal. The colloidal metal material may include water-insoluble metal particles or metallic compounds dispersed in a liquid, a hydrosol, or a metal sol. The colloidal metal may be selected from the metals in groups IA, IB, IIB and IIIB of the periodic table, as well as the transition metals, especially those of group VIII. Preferred metals include gold, silver, aluminum, ruthenium, zinc, iron, nickel and calcium. Other suitable metals also include the following in all of their various oxidation states: lithium, sodium, magnesium, potassium, scandium, titanium, vanadium, chromium, manganese, cobalt, copper, gallium, strontium, niobium, molybdenum, palladium, indium, tin, tungsten, rhenium, platinum, and gadolinium. The metals are preferably provided in ionic form, derived from an appropriate metal compound, for example the A13+, Ru3+, Zn2+, Fe3+, Ni2+ and Ca2+ ions.


When the RNA or DNA bridge is cut by the activated CRISPR effector, the aforementioned color shift is observed. In certain example embodiments the particles are colloidal metals. In certain other example embodiments, the colloidal metal is a colloidal gold. In certain example embodiments, the colloidal nanoparticles are 15 nm gold nanoparticles (AuNPs). Due to the unique surface properties of colloidal gold nanoparticles, maximal absorbance is observed at 520 nm when fully dispersed in solution and appear red in color to the naked eye. Upon aggregation of AuNPs, they exhibit a red-shift in maximal absorbance and appear darker in color, eventually precipitating from solution as a dark purple aggregate. In certain example embodiments the nanoparticles are modified to include DNA linkers extending from the surface of the nanoparticle. Individual particles are linked together by single-stranded RNA (ssRNA) or single-stranded DNA bridges that hybridize on each end to at least a portion of the DNA linkers. Thus, the nanoparticles will form a web of linked particles and aggregate, appearing as a dark precipitate. Upon activation of the CRISPR effectors disclosed herein, the ssRNA or ssDNA bridge will be cleaved, releasing the AU NPS from the linked mesh and producing a visible red color. Example DNA linkers and bridge sequences are listed below. Thiol linkers on the end of the DNA linkers may be used for surface conjugation to the AuNPS. Other forms of conjugation may be used. In certain example embodiments, two populations of AuNPs may be generated, one for each DNA linker. This will help facilitate proper binding of the ssRNA bridge with proper orientation. In certain example embodiments, a first DNA linker is conjugated by the 3′ end while a second DNA linker is conjugated by the 5′ end.


In certain other example embodiments, the masking construct may comprise an RNA or DNA oligonucleotide to which are attached a detectable label and a masking agent of that detectable label. An example of such a detectable label/masking agent pair is a fluorophore and a quencher of the fluorophore. Quenching of the fluorophore can occur as a result of the formation of a non-fluorescent complex between the fluorophore and another fluorophore or non-fluorescent molecule. This mechanism is known as ground-state complex formation, static quenching, or contact quenching. Accordingly, the RNA or DNA oligonucleotide may be designed so that the fluorophore and quencher are in sufficient proximity for contact quenching to occur. Fluorophores and their cognate quenchers are known in the art and can be selected for this purpose by one having ordinary skill in the art. The particular fluorophore/quencher pair is not critical in the context of this invention, only that selection of the fluorophore/quencher pairs ensures masking of the fluorophore. Upon activation of the effector proteins disclosed herein, the RNA or DNA oligonucleotide is cleaved thereby severing the proximity between the fluorophore and quencher needed to maintain the contact quenching effect. Accordingly, detection of the fluorophore may be used to determine the presence of a target molecule in a sample.


In certain other example embodiments, the masking construct may comprise one or more RNA oligonucleotides to which are attached one or more metal nanoparticles, such as gold nanoparticles. In some embodiments, the masking construct comprises a plurality of metal nanoparticles crosslinked by a plurality of RNA or DNA oligonucleotides forming a closed loop. In one embodiment, the masking construct comprises three gold nanoparticles crosslinked by three RNA or DNA oligonucleotides forming a closed loop. In some embodiments, the cleavage of the RNA or DNA oligonucleotides by the CRISPR effector protein leads to a detectable signal produced by the metal nanoparticles.


In certain other example embodiments, the masking construct may comprise one or more RNA or DNA oligonucleotides to which are attached one or more quantum dots. In some embodiments, the cleavage of the RNA or DNA oligonucleotides by the CRISPR effector protein leads to a detectable signal produced by the quantum dots.


In one example embodiment, the masking construct may comprise a quantum dot. The quantum dot may have multiple linker molecules attached to the surface. At least a portion of the linker molecule comprises RNA or DNA. The linker molecule is attached to the quantum dot at one end and to one or more quenchers along the length or at terminal ends of the linker such that the quenchers are maintained in sufficient proximity for quenching of the quantum dot to occur. The linker may be branched. As above, the quantum dot/quencher pair is not critical, only that selection of the quantum dot/quencher pair ensures masking of the fluorophore. Quantum dots and their cognate quenchers are known in the art and can be selected for this purpose by one having ordinary skill in the art. Upon activation of the effector proteins disclosed herein, the RNA or DNA portion of the linker molecule is cleaved thereby eliminating the proximity between the quantum dot and one or more quenchers needed to maintain the quenching effect. In certain example embodiments the quantum dot is streptavidin conjugated. RNA or DNA are attached via biotin linkers and recruit quenching molecules with the sequences /5Biosg/UCUCGUACGUUC/3IAbRQSp/ (SEQ ID NO: 10) or /5Biosg/UCUCGUACGUUCUCUCGUACGUUC/3IAbRQSp/ (SEQ ID NO. 11) where /5Biosg/ is a biotin tag and /31AbRQSp/ is an Iowa black quencher (Iowa Black FQ). Upon cleavage, by the activated effectors disclosed herein the quantum dot will fluoresce visibly.


In specific embodiments, the detectable ligand may be a fluorophore and the masking component may be a quencher molecule.


In a similar fashion, fluorescence energy transfer (FRET) may be used to generate a detectable positive signal. FRET is a non-radiative process by which a photon from an energetically excited fluorophore (i.e. “donor fluorophore”) raises the energy state of an electron in another molecule (i.e. “the acceptor”) to higher vibrational levels of the excited singlet state. The donor fluorophore returns to the ground state without emitting a fluoresce characteristic of that fluorophore. The acceptor can be another fluorophore or non-fluorescent molecule. If the acceptor is a fluorophore, the transferred energy is emitted as fluorescence characteristic of that fluorophore. If the acceptor is a non-fluorescent molecule the absorbed energy is loss as heat. Thus, in the context of the embodiments disclosed herein, the fluorophore/quencher pair is replaced with a donor fluorophore/acceptor pair attached to the oligonucleotide molecule. When intact, the masking construct generates a first signal (negative detectable signal) as detected by the fluorescence or heat emitted from the acceptor. Upon activation of the effector proteins disclosed herein the RNA oligonucleotide is cleaved and FRET is disrupted such that fluorescence of the donor fluorophore is now detected (positive detectable signal).


In certain example embodiments, the masking construct comprises the use of intercalating dyes which change their absorbance in response to cleavage of long RNAs or DNAs to short nucleotides. Several such dyes exist. For example, pyronine-Y will complex with RNA and form a complex that has an absorbance at 572 nm. Cleavage of the RNA results in loss of absorbance and a color change. Methylene blue may be used in a similar fashion, with changes in absorbance at 688 nm upon RNA cleavage. Accordingly, in certain example embodiments the masking construct comprises a RNA and intercalating dye complex that changes absorbance upon the cleavage of RNA by the effector proteins disclosed herein.


In certain example embodiments, the masking construct may comprise an initiator for an HCR reaction. See e.g. Dirks and Pierce. PNAS 101, 15275-15728 (2004). HCR reactions utilize the potential energy in two hairpin species. When a single-stranded initiator having a portion of complementary to a corresponding region on one of the hairpins is released into the previously stable mixture, it opens a hairpin of one species. This process, in turn, exposes a single-stranded region that opens a hairpin of the other species. This process, in turn, exposes a single stranded region identical to the original initiator. The resulting chain reaction may lead to the formation of a nicked double helix that grows until the hairpin supply is exhausted. Detection of the resulting products may be done on a gel or colorimetrically. Example colorimetric detection methods include, for example, those disclosed in Lu et al. “Ultra-sensitive colorimetric assay system based on the hybridization chain reaction-triggered enzyme cascade amplification ACS Appl Mater Interfaces, 2017, 9(1):167-175, Wang et al. “An enzyme-free colorimetric assay using hybridization chain reaction amplification and split aptamers” Analyst 2015, 150, 7657-7662, and Song et al. “Non covalent fluorescent labeling of hairpin DNA probe coupled with hybridization chain reaction for sensitive DNA detection.” Applied Spectroscopy, 70(4): 686-694 (2016).


In certain example embodiments, the masking construct may comprise a HCR initiator sequence and a cleavable structural element, such as a loop or hairpin, that prevents the initiator from initiating the HCR reaction. Upon cleavage of the structure element by an activated CRISPR effector protein, the initiator is then released to trigger the HCR reaction, detection thereof indicating the presence of one or more targets in the sample. In certain example embodiments, the masking construct comprises a hairpin with a RNA loop. When an activated CRISRP effector protein cuts the RNA loop, the initiator can be released to trigger the HCR reaction.


In certain example embodiments, the masking construct may comprise a HCR initiator sequence and a cutting motif, or a cleavable structural element, such as a loop or hairpin, that prevents the initiator from initiating the HCR reaction. The cutting motif may be preferentially cut by one of the activated CRISPR effector proteins. Upon cleavage of the cutting motif or structure element by an activated CRISPR effector protein, the initiator is then released to trigger the HCR reaction, detection thereof indicating the presence of one or more targets in the sample. In certain example embodiments, the masking construct comprises a hairpin with a RNA loop. When an activated CRISPR effector protein cuts the RNA loop, the initiator can be released to trigger the HCR reaction.


In embodiments, different orthologs with different sequence specificities may be used. Cutting motifs may be used to take advantage of the sequence specificities of different orthologs. The masking construct can comprise a cutting motif preferentially cut by a Cas protein. A cutting motif sequence can be a particular nucleotide base, a repeat nucleotide base in a homopolymer, or a heteropolymer of bases. The cutting motif can be a dinucleotide sequence, a trinucleotide sequence or more complex motifs comprising 4, 5, 6, 7, 8, 9, or 10 nucleotide motifs. For example, one orthologue may preferentially cut A, while others preferentially cut C, G, U/T. Reference is made to Gootenberg, et al., “Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6,” Science. 2018 Apr. 27; 360(6387):439-444. doi: 10.1126/science.aaq0179, and WO 2019/126577, incorporated by reference in their entirety. Accordingly, masking constructs completely comprising, or comprised of a substantial portion, of a single nucleotide may be generated, each with a different fluorophore that can be detected at differing wavelengths. In this way up to four different targets may be screened in a single individual discrete volume. In certain example embodiments, different orthologues from a same class of CRISPR effector protein may be used, such as two Cas13a orthologues, two Cas13b orthologues, or two Cas13c orthologues. In certain other example embodiments, different orthologues with different nucleotide editing preferences may be used such as a Cas13a and Cas13b orthologs, or a Cas13a and a Cas13c orthologs, or a Cas13b orthologs and a Cas13c orthologs etc. In certain example embodiments, a Cas13 protein with a polyU preference and a Cas13 protein with a polyA preference are used. In certain example embodiments, the Cas13 protein with a polyU preference is a Prevotella intermedia Cas13b, and the Cas13 protein with a polyA preference is a Prevotella sp. MA2106 Cas13b protein (PsmCas13b). In certain example embodiments, the Cas13 protein with a polyU preference is a Leptotrichia wadei Cas13a (LwaCas13a) protein and the Cas13 protein with a poly A preference is a Prevotella sp. MA2106 Cas13b protein. In certain example embodiments, the Cas13 protein with a polyU preference is Capnocytophaga canimorsus Cas13b protein (CcaCas13b).


In certain example embodiments, the masking construct suppresses generation of a detectable positive signal until cleaved, or modified by an activated CRISPR effector protein. In some embodiments, the masking construct may suppress generation of a detectable positive signal by masking the detectable positive signal, or generating a detectable negative signal instead.


CRISPR Systems

In some embodiments, the first end of the lateral flow device comprises two or more CRISPR effector systems, also referred to as a CRISPR-Cas or CRISPR system. In some embodiments, such a CRISPR effector system may include a CRISPR effector protein and one or more guide sequences configured to bind to one or more target sequences.


The two or more CRISPR effector systems may be RNA-targeting effector proteins, DNA-targeting effector proteins, or a combination thereof. The RNA-targeting effector proteins may be a Cas13 protein, such as Cas13a, Cas13b, or Cas13c. The DNA-targeting effector protein may be a Cas12 protein such as Cpf1 and C2c1.


In general, a CRISPR-Cas or CRISPR system as used herein and in documents, such as WO 2014/093622 (PCT/US2013/074667), refers collectively to transcripts and other elements involved in the expression of or directing the activity of CRISPR-associated (“Cas”) genes, including sequences encoding a Cas gene, a tracr (trans-activating CRISPR) sequence (e.g. tracrRNA or an active partial tracrRNA), a tracr-mate sequence (encompassing a “direct repeat” and a tracrRNA-processed partial direct repeat in the context of an endogenous CRISPR system), a guide sequence (also referred to as a “spacer” in the context of an endogenous CRISPR system), or “RNA(s)” as that term is herein used (e.g., RNA(s) to guide Cas, such as Cas9, e.g. CRISPR RNA and transactivating (tracr) RNA or a single guide RNA (sgRNA) (chimeric RNA)) or other sequences and transcripts from a CRISPR locus. In general, a CRISPR system is characterized by elements that promote the formation of a CRISPR complex at the site of a target sequence (also referred to as a protospacer in the context of an endogenous CRISPR system). When the CRISPR protein is a C2c2 protein, a tracrRNA is not required. C2c2 has been described in Abudayyeh et al. (2016) “C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector”; Science; DOI: 10.1126/science.aaf5573; and Shmakov et al. (2015) “Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems”, Molecular Cell, DOI: dx.doi.org/10.1016/j.molcel.2015.10.008; which are incorporated herein in their entirety by reference. Cas13b has been described in Smargon et al. (2017) “Cas13b Is a Type VI-B CRISPR-Associated RNA-Guided RNases Differentially Regulated by Accessory Proteins Csx27 and Csx28,” Molecular Cell. 65, 1-13; dx.doi.org/10.1016/j.molcel.2016.12.023., which is incorporated herein in its entirety by reference.


In certain embodiments, a protospacer adjacent motif (PAM) or PAM-like motif directs binding of the effector protein complex as disclosed herein to the target locus of interest. In some embodiments, the PAM may be a 5′ PAM (i.e., located upstream of the 5′ end of the protospacer). In other embodiments, the PAM may be a 3′ PAM (i.e., located downstream of the 5′ end of the protospacer). The term “PAM” may be used interchangeably with the term “PFS” or “protospacer flanking site” or “protospacer flanking sequence”.


In a preferred embodiment, the CRISPR effector protein may recognize a 3′ PAM. In certain embodiments, the CRISPR effector protein may recognize a 3′ PAM which is 5′H, wherein H is A, C or U. In certain embodiments, the effector protein may be Leptotrichia shahii C2c2p, more preferably Leptotrichia shahii DSM 19757 C2c2, and the 3′ PAM is a 5′ H.


In the context of formation of a CRISPR complex, “target molecule” or “target sequence” or “target nucleic acid” refers to a molecule harboring a sequence, or a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. A target sequence may comprise RNA polynucleotides. The term “target RNA” refers to a RNA polynucleotide being or comprising the target sequence. In other words, the target RNA may be a RNA polynucleotide or a part of a RNA polynucleotide to which a part of the gRNA, i.e. the guide sequence, is designed to have complementarity and to which the effector function mediated by the complex comprising CRISPR effector protein and a gRNA is to be directed. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell. A target sequence may comprise DNA polynucleotides.


As such, a CRISPR system may comprise RNA-targeting effector proteins. A CRISPR system may comprise DNA-targeting effector proteins. In some embodiments, a CRISPR system may comprise a combination of RNA- and DNA-targeting effector proteins, or effector proteins that target both RNA and DNA.


The nucleic acid molecule encoding a CRISPR effector protein, in particular C2c2, is advantageously codon optimized CRISPR effector protein. An example of a codon optimized sequence, is in this instance a sequence optimized for expression in eukaryotes, e.g., humans (i.e. being optimized for expression in humans), or for another eukaryote, animal or mammal as herein discussed; see, e.g., SaCas9 human codon optimized sequence in WO 2014/093622 (PCT/US2013/074667). Whilst this is preferred, it will be appreciated that other examples are possible and codon optimization for a host species other than human, or for codon optimization for specific organs is known. In some embodiments, an enzyme coding sequence encoding a CRISPR effector protein is a codon optimized for expression in particular cells, such as eukaryotic cells. The eukaryotic cells may be those of or derived from a particular organism, such as a plant or a mammal, including but not limited to human, or non-human eukaryote or animal or mammal as herein discussed, e.g., mouse, rat, rabbit, dog, livestock, or non-human mammal or primate. In some embodiments, processes for modifying the germ line genetic identity of human beings and/or processes for modifying the genetic identity of animals which are likely to cause them suffering without any substantial medical benefit to man or animal, and also animals resulting from such processes, may be excluded. In general, codon optimization refers to a process of modifying a nucleic acid sequence for enhanced expression in the host cells of interest by replacing at least one codon (e.g. about or more than about 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more codons) of the native sequence with codons that are more frequently or most frequently used in the genes of that host cell while maintaining the native amino acid sequence. Various species exhibit particular bias for certain codons of a particular amino acid. Codon bias (differences in codon usage between organisms) often correlates with the efficiency of translation of messenger RNA (mRNA), which is in turn believed to be dependent on, among other things, the properties of the codons being translated and the availability of particular transfer RNA (tRNA) molecules. The predominance of selected tRNAs in a cell is generally a reflection of the codons used most frequently in peptide synthesis. Accordingly, genes can be tailored for optimal gene expression in a given organism based on codon optimization. Codon usage tables are readily available, for example, at the “Codon Usage Database” available at kazusa.orjp/codon/and these tables can be adapted in a number of ways. See Nakamura, Y., et al. “Codon usage tabulated from the international DNA sequence databases: status for the year 2000” Nucl. Acids Res. 28:292 (2000). Computer algorithms for codon optimizing a particular sequence for expression in a particular host cell are also available, such as Gene Forge (Aptagen; Jacobus, Pa.), are also available. In some embodiments, one or more codons (e.g. 1, 2, 3, 4, 5, 10, 15, 20, 25, 50, or more, or all codons) in a sequence encoding a Cas correspond to the most frequently used codon for a particular amino acid.


In certain embodiments, the methods as described herein may comprise providing a Cas transgenic cell, in particular a C2c2 transgenic cell, in which one or more nucleic acids encoding one or more guide RNAs are provided or introduced operably connected in the cell with a regulatory element comprising a promoter of one or more gene of interest. As used herein, the term “Cas transgenic cell” refers to a cell, such as a eukaryotic cell, in which a Cas gene has been genomically integrated. The nature, type, or origin of the cell are not particularly limiting according to the present invention. Also the way the Cas transgene is introduced in the cell may vary and can be any method as is known in the art. In certain embodiments, the Cas transgenic cell is obtained by introducing the Cas transgene in an isolated cell. In certain other embodiments, the Cas transgenic cell is obtained by isolating cells from a Cas transgenic organism. By means of example, and without limitation, the Cas transgenic cell as referred to herein may be derived from a Cas transgenic eukaryote, such as a Cas knock-in eukaryote. Reference is made to WO 2014/093622 (PCT/US13/74667), incorporated herein by reference. Methods of US Patent Publication Nos. 20120017290 and 20110265198 assigned to Sangamo BioSciences, Inc. directed to targeting the Rosa locus may be modified to utilize the CRISPR Cas system of the present invention. Methods of US Patent Publication No. 20130236946 assigned to Cellectis directed to targeting the Rosa locus may also be modified to utilize the CRISPR Cas system of the present invention. By means of further example reference is made to Platt et. al. (Cell; 159(2):440-455 (2014)), describing a Cas9 knock-in mouse, which is incorporated herein by reference. The Cas transgene can further comprise a Lox-Stop-polyA-Lox(LSL) cassette thereby rendering Cas expression inducible by Cre recombinase. Alternatively, the Cas transgenic cell may be obtained by introducing the Cas transgene in an isolated cell. Delivery systems for transgenes are well known in the art. By means of example, the Cas transgene may be delivered in for instance eukaryotic cell by means of vector (e.g., AAV, adenovirus, lentivirus) and/or particle and/or nanoparticle delivery, as also described herein elsewhere.


It will be understood by the skilled person that the cell, such as the Cas transgenic cell, as referred to herein may comprise further genomic alterations besides having an integrated Cas gene or the mutations arising from the sequence specific action of Cas when complexed with RNA capable of guiding Cas to a target locus.


In certain aspects the invention involves vectors, e.g. for delivering or introducing in a cell Cas and/or RNA capable of guiding Cas to a target locus (i.e. guide RNA), but also for propagating these components (e.g. in prokaryotic cells). A used herein, a “vector” is a tool that allows or facilitates the transfer of an entity from one environment to another. It is a replicon, such as a plasmid, phage, or cosmid, into which another DNA segment may be inserted so as to bring about the replication of the inserted segment. Generally, a vector is capable of replication when associated with the proper control elements. In general, the term “vector” refers to a nucleic acid molecule capable of transporting another nucleic acid to which it has been linked. Vectors include, but are not limited to, nucleic acid molecules that are single-stranded, double-stranded, or partially double-stranded; nucleic acid molecules that comprise one or more free ends, no free ends (e.g. circular); nucleic acid molecules that comprise DNA, RNA, or both; and other varieties of polynucleotides known in the art. One type of vector is a “plasmid,” which refers to a circular double stranded DNA loop into which additional DNA segments can be inserted, such as by standard molecular cloning techniques. Another type of vector is a viral vector, wherein virally-derived DNA or RNA sequences are present in the vector for packaging into a virus (e.g. retroviruses, replication defective retroviruses, adenoviruses, replication defective adenoviruses, and adeno-associated viruses (AAVs)). Viral vectors also include polynucleotides carried by a virus for transfection into a host cell. Certain vectors are capable of autonomous replication in a host cell into which they are introduced (e.g. bacterial vectors having a bacterial origin of replication and episomal mammalian vectors). Other vectors (e.g., non-episomal mammalian vectors) are integrated into the genome of a host cell upon introduction into the host cell, and thereby are replicated along with the host genome. Moreover, certain vectors are capable of directing the expression of genes to which they are operatively-linked. Such vectors are referred to herein as “expression vectors.” Common expression vectors of utility in recombinant DNA techniques are often in the form of plasmids.


Recombinant expression vectors can comprise a nucleic acid of the invention in a form suitable for expression of the nucleic acid in a host cell, which means that the recombinant expression vectors include one or more regulatory elements, which may be selected on the basis of the host cells to be used for expression, that is operatively-linked to the nucleic acid sequence to be expressed. Within a recombinant expression vector, “operably linked” is intended to mean that the nucleotide sequence of interest is linked to the regulatory element(s) in a manner that allows for expression of the nucleotide sequence (e.g. in an in vitro transcription/translation system or in a host cell when the vector is introduced into the host cell). With regards to recombination and cloning methods, mention is made of U.S. patent application Ser. No. 10/815,730, published Sep. 2, 2004 as US 2004-0171156 A1, the contents of which are herein incorporated by reference in their entirety. Thus, the embodiments disclosed herein may also comprise transgenic cells comprising the CRISPR effector system. In certain example embodiments, the transgenic cell may function as an individual discrete volume. In other words samples comprising a masking construct may be delivered to a cell, for example in a suitable delivery vesicle and if the target is present in the delivery vesicle the CRISPR effector is activated and a detectable signal generated.


The vector(s) can include the regulatory element(s), e.g., promoter(s). The vector(s) can comprise Cas encoding sequences, and/or a single, but possibly also can comprise at least 3 or 8 or 16 or 32 or 48 or 50 guide RNA(s) (e.g., sgRNAs) encoding sequences, such as 1-2, 1-3, 1-4 1-5, 3-6, 3-7, 3-8, 3-9, 3-10, 3-8, 3-16, 3-30, 3-32, 3-48, 3-50 RNA(s) (e.g., sgRNAs). In a single vector there can be a promoter for each RNA (e.g., sgRNA), advantageously when there are up to about 16 RNA(s); and, when a single vector provides for more than 16 RNA(s), one or more promoter(s) can drive expression of more than one of the RNA(s), e.g., when there are 32 RNA(s), each promoter can drive expression of two RNA(s), and when there are 48 RNA(s), each promoter can drive expression of three RNA(s). By simple arithmetic and well established cloning protocols and the teachings in this disclosure one skilled in the art can readily practice the invention as to the RNA(s) for a suitable exemplary vector such as AAV, and a suitable promoter such as the U6 promoter. For example, the packaging limit of AAV is ˜4.7 kb. The length of a single U6-gRNA (plus restriction sites for cloning) is 361 bp. Therefore, the skilled person can readily fit about 12-16, e.g., 13 U6-gRNA cassettes in a single vector. This can be assembled by any suitable means, such as a golden gate strategy used for TALE assembly (genome-engineering.org/taleffectors/). The skilled person can also use a tandem guide strategy to increase the number of U6-gRNAs by approximately 1.5 times, e.g., to increase from 12-16, e.g., 13 to approximately 18-24, e.g., about 19 U6-gRNAs. Therefore, one skilled in the art can readily reach approximately 18-24, e.g., about 19 promoter-RNAs, e.g., U6-gRNAs in a single vector, e.g., an AAV vector. A further means for increasing the number of promoters and RNAs in a vector is to use a single promoter (e.g., U6) to express an array of RNAs separated by cleavable sequences. And an even further means for increasing the number of promoter-RNAs in a vector, is to express an array of promoter-RNAs separated by cleavable sequences in the intron of a coding sequence or gene; and, in this instance it is advantageous to use a polymerase II promoter, which can have increased expression and enable the transcription of long RNA in a tissue specific manner. (see, e.g., nar.oxfordjoumals.org/content/34/7/e53.short and nature.com/mt/journal/v16/n9/abs/mt2008144a.html). In an advantageous embodiment, AAV may package U6 tandem gRNA targeting up to about 50 genes. Accordingly, from the knowledge in the art and the teachings in this disclosure the skilled person can readily make and use vector(s), e.g., a single vector, expressing multiple RNAs or guides under the control or operatively or functionally linked to one or more promoters—especially as to the numbers of RNAs or guides discussed herein, without any undue experimentation.


The guide RNA(s) encoding sequences and/or Cas encoding sequences, can be functionally or operatively linked to regulatory element(s) and hence the regulatory element(s) drive expression. The promoter(s) can be constitutive promoter(s) and/or conditional promoter(s) and/or inducible promoter(s) and/or tissue specific promoter(s). The promoter can be selected from the group consisting of RNA polymerases, pol I, pol II, pol III, T7, U6, H1, retroviral Rous sarcoma virus (RSV) LTR promoter, the cytomegalovirus (CMV) promoter, the SV40 promoter, the dihydrofolate reductase promoter, the 3-actin promoter, the phosphoglycerol kinase (PGK) promoter, and the EF1α promoter. An advantageous promoter is the promoter is U6.


In some embodiments, one or more elements of a nucleic acid-targeting system is derived from a particular organism comprising an endogenous CRISPR RNA-targeting system. In certain example embodiments, the effector protein CRISPR RNA-targeting system comprises at least one HEPN domain, including but not limited to the HEPN domains described herein, HEPN domains known in the art, and domains recognized to be HEPN domains by comparison to consensus sequence motifs. Several such domains are provided herein. In one non-limiting example, a consensus sequence can be derived from the sequences of C2c2 or Cas13b orthologs provided herein. In certain example embodiments, the effector protein comprises a single HEPN domain. In certain other example embodiments, the effector protein comprises two HEPN domains.


In one example embodiment, the effector protein comprises one or more HEPN domains comprising a RxxxxH motif sequence. The RxxxxH motif sequence can be, without limitation, from a HEPN domain described herein or a HEPN domain known in the art. RxxxxH motif sequences further include motif sequences created by combining portions of two or more HEPN domains. As noted, consensus sequences can be derived from the sequences of the orthologs disclosed in U.S. Provisional Patent Application 62/432,240 entitled “Novel CRISPR Enzymes and Systems,” U.S. Provisional Patent Application 62/471,710 entitled “Novel Type VI CRISPR Orthologs and Systems” filed on Mar. 15, 2017, and U.S. Provisional Patent Application entitled “Novel Type VI CRISPR Orthologs and Systems,” labeled as attorney docket number 47627-05-2133 and filed on Apr. 12, 2017.


In an embodiment of the invention, a HEPN domain comprises at least one RxxxxH motif comprising the sequence of R(N/H/K)X1X2X3H In an embodiment of the invention, a HEPN domain comprises a RxxxxH motif comprising the sequence of R(N/H)X1X2X3H In an embodiment of the invention, a HEPN domain comprises the sequence of R(N/K)X1X2X3H In certain embodiments, X1 is R, S, D, E, Q, N, G, Y, or H. In certain embodiments, X2 is I, S, T, V, or L. In certain embodiments, X3 is L, F, N, Y, V, I, S, D, E, or A.


CRISPR-Cas Systems


Embodiments disclosed herein utilize Cas proteins possessing non-specific nuclease collateral activity to cleave detectable reporters upon target recognition, providing sensitive and specific diagnostics, including single nucleotide variants, detection based on rRNA sequences, screening for drug resistance, monitoring microbe outbreaks, genetic perturbations, and screening of environmental samples, as described, for example, in PCT/US18/054472 filed Oct. 22, 2018 at [0183]-[0327], incorporated herein by reference. Reference is made to WO 2017/219027, WO2018/107129, US20180298445, US 2018-0274017, US 2018-0305773, WO 2018/170340, U.S. application Ser. No. 15/922,837, filed Mar. 15, 2018 entitled “Devices for CRISPR Effector System Based Diagnostics”, PCT/US18/50091, filed Sep. 7, 2018 “Multi-Effector CRISPR Based Diagnostic Systems”, PCT/US18/66940 filed Dec. 20, 2018 entitled “CRISPR Effector System Based Multiplex Diagnostics”, PCT/US18/054472 filed Oct. 4, 2018 entitled “CRISPR Effector System Based Diagnostic”, U.S. Provisional 62/740,728 filed Oct. 3, 2018 entitled “CRISPR Effector System Based Diagnostics for Hemorrhagic Fever Detection”, U.S. Provisional 62/690,278 filed Jun. 26, 2018 and U.S. Provisional 62/767,059 filed Nov. 14, 2018 both entitled “CRISPR Double Nickase Based Amplification, Compositions, Systems and Methods”, U.S. Provisional 62/690,160 filed Jun. 26, 2018 and U.S. Pat. No. 62,767,077 filed Nov. 14, 2018, both entitled “CRISPR/CAS and Transposase Based Amplification Compositions, Systems, And Methods”, U.S. Provisional 62/690,257 filed Jun. 26, 2018 and 62/767,052 filed Nov. 14, 2018 both entitled “CRISPR Effector System Based Amplification Methods, Systems, And Diagnostics”, U.S. Provisional 62/767,076 filed Nov. 14, 2018 entitled “Multiplexing Highly Evolving Viral Variants With SHERLOCK” and 62/767,070 filed Nov. 14, 2018 entitled “Droplet SHERLOCK.” Reference is further made to WO2017/127807, WO2017/184786, WO 2017/184768, WO 2017/189308, WO 2018/035388, WO 2018/170333, WO 2018/191388, WO 2018/213708, WO 2019/005866, PCT/US18/67328 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems”, PCT/US18/67225 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems” and PCT/US18/67307 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems”, U.S. 62/712,809 filed Jul. 31, 2018 entitled “Novel CRISPR Enzymes and Systems”, U.S. 62/744,080 filed Oct. 10, 2018 entitled “Novel Cas12b Enzymes and Systems” and U.S. 62/751,196 filed Oct. 26, 2018 entitled “Novel Cas12b Enzymes and Systems”, U.S. 715,640 filed August 7, 2-18 entitled “Novel CRISPR Enzymes and Systems”, WO 2016/205711, U.S. Pat. No. 9,790,490, WO 2016/205749, WO 2016/205764, WO 2017/070605, WO 2017/106657, and WO 2016/149661, WO2018/035387, WO2018/194963, Cox DBT, et al., RNA editing with CRISPR-Cas13, Science. 2017 Nov. 24; 358(6366):1019-1027; Gootenberg J S, et al., Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6, Science. 2018 Apr. 27; 360(6387):439-444; Gootenberg J S, et al., Nucleic acid detection with CRISPR-Cas13a/C2c2, Science. 2017 Apr. 28; 356(6336):438-442; Abudayyeh 00, et al., RNA targeting with CRISPR-Cas13, Nature. 2017 Oct. 12; 550(7675):280-284; Smargon A A, et al., Cas13b Is a Type VI-B CRISPR-Associated RNA-Guided RNase Differentially Regulated by Accessory Proteins Csx27 and Csx28. Mol Cell. 2017 Feb. 16; 65(4):618-630.e7; Abudayyeh 00, et al., C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector, Science. 2016 Aug. 5; 353(6299):aaf5573; Yang L, et al., Engineering and optimising deaminase fusions for genome editing. Nat Commun. 2016 Nov. 2; 7:13330, Myhrvold et al., Field deployable viral diagnostics using CRISPR-Cas13, Science 2018 360, 444-448, Shmakov et al. “Diversity and evolution of class 2 CRISPR-Cas systems,” Nat Rev Microbiol. 2017 15(3):169-182, each of which is incorporated herein by reference in its entirety.


When using two or more CRISPR effector systems, the CRISPR effector systems may be RNA-targeting effector proteins, DNA-targeting effector proteins, or a combination thereof. The RNA-targeting effector proteins may be a Type VI Cas protein, such as Cas13 protein, including Cas13b, Cas13c, or Cas13d. The DNA-targeting effector protein may be a Type V Cas protein, such as Cas12a (Cpf1), Cas12b (C2c2), Cas12c (C2c3), Cas X, Cas Y, or Cas14.


In general, a CRISPR-Cas or CRISPR system as used in herein and in documents, such as WO 2014/093622 (PCT/US2013/074667), refers collectively to transcripts and other elements involved in the expression of or directing the activity of CRISPR-associated (“Cas”) genes, including sequences encoding a Cas gene, a tracr (trans-activating CRISPR) sequence (e.g. tracrRNA or an active partial tracrRNA), a tracr-mate sequence (encompassing a “direct repeat” and a tracrRNA-processed partial direct repeat in the context of an endogenous CRISPR system), a guide sequence (also referred to as a “spacer” in the context of an endogenous CRISPR system), or “RNA(s)” as that term is herein used (e.g., RNA(s) to guide Cas, such as Cas9, e.g. CRISPR RNA and transactivating (tracr) RNA or a single guide RNA (sgRNA) (chimeric RNA)) or other sequences and transcripts from a CRISPR locus. In general, a CRISPR system is characterized by elements that promote the formation of a CRISPR complex at the site of a target sequence (also referred to as a protospacer in the context of an endogenous CRISPR system). See, e.g, Shmakov et al. (2015) “Discovery and Functional Characterization of Diverse Class 2 CRISPR-Cas Systems”, Molecular Cell, DOI: dx.doi.org/10.1016/j.molcel.2015.10.008.


RNA Targeting Cas Protein

In an aspect, the invention utilizes an RNA targeting Cas protein. In certain embodiments, protospacer flanking site, or protospacer flanking sequence (PFS) directs binding of the effector proteins (e.g. Type VI) as disclosed herein to the target locus of interest. A PFS is a region that can affect the efficacy of Cas13a mediated targeting, and may be adjacent to the protospacer target in certain Cas13a proteins, while other orthologs do not require a specific PFS. In a preferred embodiment, the CRISPR effector protein may recognize a 3′ PFS. In certain embodiments, the CRISPR effector protein may recognize a 3′ PFS which is 5′H, wherein H is A, C or U. See, e.g. Abudayyeh, 2016. In certain embodiments, the effector protein may be Leptotrichia shahii Cas13p, more preferably Leptotrichia shahii DSM 19757 Cas13, and the 3′ PFS is a 5′ H.


In the context of formation of a CRISPR complex, “target molecule” or “target sequence” or “target nucleic acid” refers to a molecule harboring a sequence, or a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. A target sequence may comprise RNA polynucleotides. The term “target RNA” refers to a RNA polynucleotide being or comprising the target sequence. In other words, the target RNA may be a RNA polynucleotide or a part of a RNA polynucleotide to which a part of the gRNA, i.e. the guide sequence, is designed to have complementarity and to which the effector function mediated by the complex comprising CRISPR effector protein and a gRNA is to be directed. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell. A target sequence may comprise DNA polynucleotides.


As such, a CRISPR system may comprise RNA-targeting effector proteins. A CRISPR system may comprise DNA-targeting effector proteins. In some embodiments, a CRISPR system may comprise a combination of RNA- and DNA-targeting effector proteins, or effector proteins that target both RNA and DNA.


In some embodiments, one or more elements of a nucleic acid-targeting system is derived from a particular organism comprising an endogenous CRISPR RNA-targeting system. In certain example embodiments, the effector protein CRISPR RNA-targeting system comprises at least one HEPN domain, including but not limited to the HEPN domains described herein, HEPN domains known in the art, and domains recognized to be HEPN domains by comparison to consensus sequence motifs. Several such domains are provided herein. In one non-limiting example, a consensus sequence can be derived from the sequences of Cas13a or Cas13b orthologs provided herein. In certain example embodiments, the effector protein comprises a single HEPN domain. In certain other example embodiments, the effector protein comprises two HEPN domains.


In one example embodiment, the effector protein comprises one or more HEPN domains comprising a RxxxxH motif sequence. The RxxxxH motif sequence can be, without limitation, from a HEPN domain described herein or a HEPN domain known in the art. RxxxxH motif sequences further include motif sequences created by combining portions of two or more HEPN domains. As noted, consensus sequences can be derived from the sequences of the orthologs disclosed in U.S. Provisional Patent Application 62/432,240 entitled “Novel CRISPR Enzymes and Systems,” U.S. Provisional Patent Application 62/471,710 entitled “Novel Type VI CRISPR Orthologs and Systems” filed on Mar. 15, 2017, and U.S. Provisional Patent Application entitled “Novel Type VI CRISPR Orthologs and Systems,” labeled as attorney docket number 47627-05-2133 and filed on Apr. 12, 2017.


In an embodiment of the invention, a HEPN domain comprises at least one RxxxxH motif comprising the sequence of R(N/H/K)X1X2X3H (SEQ ID NO:XX). In an embodiment of the invention, a HEPN domain comprises a RxxxxH motif comprising the sequence of R(N/H)X1X2X3H (SEQ ID NO:XX). In an embodiment of the invention, a HEPN domain comprises the sequence of R(N/K)X1X2X3H (SEQ ID NO:XX). In certain embodiments, X1 is R, S, D, E, Q, N, G, Y, or H. In certain embodiments, X2 is I, S, T, V, or L. In certain embodiments, X3 is L, F, N, Y, V, I, S, D, E, or A.


In particular embodiments, the Type VI RNA-targeting Cas enzyme is Cas13a. In other example embodiments, the Type VI RNA-targeting Cas enzyme is Cas13b. In certain embodiments, the Cas13b protein is from an organism of a genus selected from the group consisting of: Bergeyella, Prevotella, Porphyromonas, Bacterioides, Alistipes, Riemerella, Myroides, Capnocytophaga, Porphyromonas, Flavobacterium, Porphyromonas, Chryseobacterium, Paludibacter, Psychroflexus, Riemerella, Phaeodactylibacter, Sinomicrobium, Reichenbachiella.


In particular embodiments, the homologue or orthologue of a Type VI protein such as Cas13a as referred to herein has a sequence homology or identity of at least 30%, or at least 40%, or at least 50%, or at least 60%, or at least 70%, or at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with a Type VI protein such as Cas13a (e.g., based on the wild-type sequence of any of Leptotrichia shahii Cas13a, Lachnospiraceae bacterium MA2020 Cas13a, Lachnospiraceae bacterium NK4A179 Cas13a, Clostridium aminophilum (DSM 10710) Cas13a, Carnobacterium gallinarum (DSM 4847) Cas13, Paludibacter propionicigenes (WB4) Cas13, Listeria weihenstephanensis (FSL R9-0317) Cas13, Listeriaceae bacterium (FSL M6-0635) Cas13, Listeria newyorkensis (FSL M6-0635) Cas13, Leptotrichia wadei (F0279) Cas13, Rhodobacter capsulatus (SB 1003) Cas13, Rhodobacter capsulatus (R121) Cas13, Rhodobacter capsulatus (DE442) Cas13, Leptotrichia wadei (Lw2) Cas13, or Listeria seeligeri Cas13). In further embodiments, the homologue or orthologue of a Type VI protein such as Cas13 as referred to herein has a sequence identity of at least 30%, or at least 40%, or at least 50%, or at least 60%, or at least 70%, or at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the wild type Cas13 (e.g., based on the wild-type sequence of any of Leptotrichia shahii Cas13, Lachnospiraceae bacterium MA2020 Cas13, Lachnospiraceae bacterium NK4A179 Cas13, Clostridium aminophilum (DSM 10710) Cas13, Carnobacterium gallinarum (DSM 4847) Cas13, Paludibacter propionicigenes (WB4) Cas13, Listeria weihenstephanensis (FSL R9-0317) Cas13, Listeriaceae bacterium (FSL M6-0635) Cas13, Listeria newyorkensis (FSL M6-0635) Cas13, Leptotrichia wadei (F0279) Cas13, Rhodobacter capsulatus (SB 1003) Cas13, Rhodobacter capsulatus (R121) Cas13, Rhodobacter capsulatus (DE442) Cas13, Leptotrichia wadei (Lw2) Cas13, or Listeria seeligeri Cas13).


In certain other example embodiments, the CRISPR system the effector protein is a Cas13 nuclease. The activity of Cas13 may depend on the presence of two HEPN domains. These have been shown to be RNase domains, i.e. nuclease (in particular an endonuclease) cutting RNA. Cas13a HEPN may also target DNA, or potentially DNA and/or RNA. On the basis that the HEPN domains of Cas13a are at least capable of binding to and, in their wild-type form, cutting RNA, then it is preferred that the Cas13a effector protein has RNase function. Regarding Cas13a CRISPR systems, reference is made to U.S. Provisional 62/351,662 filed on Jun. 17, 2016 and U.S. Provisional 62/376,377 filed on Aug. 17, 2016. Reference is also made to U.S. Provisional 62/351,803 filed on Jun. 17, 2016. Reference is also made to U.S. Provisional entitled “Novel Crispr Enzymes and Systems” filed Dec. 8, 2016 bearing Broad Institute No. 10035.PA4 and Attorney Docket No. 47627.03.2133. Reference is further made to East-Seletsky et al. “Two distinct RNase activities of CRISPR-C2c2 enable guide-RNA processing and RNA detection” Nature doi:10/1038/nature19802 and Abudayyeh et al. “C2c2 is a single-component programmable RNA-guided RNA targeting CRISPR effector” bioRxiv doi:10.1101/054742.


RNase function in CRISPR systems is known, for example mRNA targeting has been reported for certain type III CRISPR-Cas systems (Hale et al., 2014, Genes Dev, vol. 28, 2432-2443; Hale et al., 2009, Cell, vol. 139, 945-956; Peng et al., 2015, Nucleic acids research, vol. 43, 406-417) and provides significant advantages. In the Staphylococcus epidermis type III-A system, transcription across targets results in cleavage of the target DNA and its transcripts, mediated by independent active sites within the Cas10-Csm ribonucleoprotein effector protein complex (see, Samai et al., 2015, Cell, vol. 151, 1164-1174). A CRISPR-Cas system, composition or method targeting RNA via the present effector proteins is thus provided.


In an embodiment, the Cas protein may be a Cas13a ortholog of an organism of a genus which includes but is not limited to Leptotrichia, Listeria, Corynebacter, Sutterella, Legionella, Treponema, Filifactor, Eubacterium, Streptococcus, Lactobacillus, Mycoplasma, Bacteroides, Flaviivola, Flavobacterium, Sphaerochaeta, Azospirillum, Gluconacetobacter, Neisseria, Roseburia, Parvibaculum, Staphylococcus, Nitratifractor, Mycoplasma and Campylobacter. Species of organism of such a genus can be as otherwise herein discussed.


It will be appreciated that any of the functionalities described herein may be engineered into CRISPR enzymes from other orthologs, including chimeric enzymes comprising fragments from multiple orthologs. Examples of such orthologs are described elsewhere herein. Thus, chimeric enzymes may comprise fragments of CRISPR enzyme orthologs of an organism which includes but is not limited to Leptotrichia, Listeria, Corynebacter, Sutterella, Legionella, Treponema, Filifactor, Eubacterium, Streptococcus, Lactobacillus, Mycoplasma, Bacteroides, Flaviivola, Flavobacterium, Sphaerochaeta, Azospirillum, Gluconacetobacter, Neisseria, Roseburia, Parvibaculum, Staphylococcus, Nitratifractor, Mycoplasma and Campylobacter. A chimeric enzyme can comprise a first fragment and a second fragment, and the fragments can be of CRISPR enzyme orthologs of organisms of genera herein mentioned or of species herein mentioned; advantageously the fragments are from CRISPR enzyme orthologs of different species.


In embodiments, the Cas13a protein as referred to herein also encompasses a functional variant of Cas13a or a homologue or an orthologue thereof. A “functional variant” of a protein as used herein refers to a variant of such protein which retains at least partially the activity of that protein. Functional variants may include mutants (which may be insertion, deletion, or replacement mutants), including polymorphs, etc. Also included within functional variants are fusion products of such protein with another, usually unrelated, nucleic acid, protein, polypeptide or peptide. Functional variants may be naturally occurring or may be man-made. Advantageous embodiments can involve engineered or non-naturally occurring Type VI RNA-targeting effector protein.


In an embodiment, nucleic acid molecule(s) encoding the Cas13 or an ortholog or homolog thereof, may be codon-optimized for expression in a eukaryotic cell. A eukaryote can be as herein discussed. Nucleic acid molecule(s) can be engineered or non-naturally occurring.


In an embodiment, the Cas13a or an ortholog or homolog thereof, may comprise one or more mutations (and hence nucleic acid molecule(s) coding for same may have mutation(s). The mutations may be artificially introduced mutations and may include but are not limited to one or more mutations in a catalytic domain. Examples of catalytic domains with reference to a Cas9 enzyme may include but are not limited to RuvC I, RuvC II, RuvC III and HNH domains.


In an embodiment, the Cas13a or an ortholog or homolog thereof, may comprise one or more mutations. The mutations may be artificially introduced mutations and may include but are not limited to one or more mutations in a catalytic domain. Examples of catalytic domains with reference to a Cas enzyme may include but are not limited to HEPN domains.


In an embodiment, the Cas13a or an ortholog or homolog thereof, may be used as a generic nucleic acid binding protein with fusion to or being operably linked to a functional domain. Exemplary functional domains may include but are not limited to translational initiator, translational activator, translational repressor, nucleases, in particular ribonucleases, a spliceosome, beads, a light inducible/controllable domain or a chemically inducible/controllable domain.


In certain example embodiments, the Cas13a effector protein may be from an organism selected from the group consisting of, Leptotrichia, Listeria, Corynebacter, Sutterella, Legionella, Treponema, Filifactor, Eubacterium, Streptococcus, Lactobacillus, Mycoplasma, Bacteroides, Flaviivola, Flavobacterium, Sphaerochaeta, Azospirillum, Gluconacetobacter, Neisseria, Roseburia, Parvibaculum, Staphylococcus, Nitratifractor, Mycoplasma, and Campylobacter.


In certain embodiments, the effector protein may be a Listeria sp. Cas13p, preferably Listeria seeligeria Cas13p, more preferably Listeria seeligeria serovar 1/2b str. SLCC3954 Cas13p and the crRNA sequence may be 44 to 47 nucleotides in length, with a 5′ 29-nt direct repeat (DR) and a 15-nt to 18-nt spacer.


In certain embodiments, the effector protein may be a Leptotrichia sp. Cas13p, preferably Leptotrichia shahii Cas13p, more preferably Leptotrichia shahii DSM 19757 Cas13p and the crRNA sequence may be 42 to 58 nucleotides in length, with a 5′ direct repeat of at least 24 nt, such as a 5′ 24-28-nt direct repeat (DR) and a spacer of at least 14 nt, such as a 14-nt to 28-nt spacer, or a spacer of at least 18 nt, such as 19, 20, 21, 22, or more nt, such as 18-28, 19-28, 20-28, 21-28, or 22-28 nt.


In certain example embodiments, the effector protein may be a Leptotrichia sp., Leptotrichia wadei F0279, or a Listeria sp., preferably Listeria newyorkensis FSL M6-0635.


In certain example embodiments, the Cas13 effector proteins of the invention include, without limitation, the following 21 ortholog species (including multiple CRISPR loci: Leptotrichia shahii; Leptotrichia wadei (Lw2); Listeria seeligeri; Lachnospiraceae bacterium MA2020; Lachnospiraceae bacterium NK4A179; [Clostridium] aminophilum DSM 10710; Carnobacterium gallinarum DSM 4847; Carnobacterium gallinarum DSM 4847 (second CRISPR Loci); Paludibacter propionicigenes WB4; Listeria weihenstephanensis FSL R9-0317; Listeriaceae bacterium FSL M6-0635; Leptotrichia wadei F0279; Rhodobacter capsulatus SB 1003; Rhodobacter capsulatus R121; Rhodobacter capsulatus DE442; Leptotrichia buccalis C-1013-b; Herbinix hemicellulosilytica; [Eubacterium] rectale; Eubacteriaceae bacterium CHKCI004; Blautia sp. Marseille-P2398; and Leptotrichia sp. oral taxon 879 str. F0557. Twelve (12) further non-limiting examples are: Lachnospiraceae bacterium NK4A144; Chloroflexus aggregans; Demequina aurantiaca; Thalassospira sp. TSL5-1; Pseudobutyrivibrio sp. OR37; Butyrivibrio sp. YAB3001; Blautia sp. Marseille-P2398; Leptotrichia sp. Marseille-P3007; Bacteroides ihuae; Porphyromonadaceae bacterium KH3CP3RA; Listeria riparia; and Insolitispirillum peregrinum.


In certain embodiments, the Cas13 protein according to the invention is or is derived from one of the orthologues as described herein, or is a chimeric protein of two or more of the orthologues as described herein, or is a mutant or variant of one of the orthologues as described in the table below (or a chimeric mutant or variant), including dead Cas13, split Cas13, destabilized Cas13, etc. as defined herein elsewhere, with or without fusion with a heterologous/functional domain.


In certain example embodiments, the Cas13a effector protein is from an organism of a genus selected from the group consisting of: Leptotrichia, Listeria, Corynebacter, Sutterella, Legionella, Treponema, Filifactor, Eubacterium, Streptococcus, Lactobacillus, Mycoplasma, Bacteroides, Flaviivola, Flavobacterium, Sphaerochaeta, Azospirillum, Gluconacetobacter, Neisseria, Roseburia, Parvibaculum, Staphylococcus, Nitratifractor, Mycoplasma, Campylobacter, and Lachnospira.


In an embodiment of the invention, there is provided an effector protein which comprises an amino acid sequence having at least 80% sequence homology to the wild-type sequence of any of Leptotrichia shahii Cas13, Lachnospiraceae bacterium MA2020 Cas13, Lachnospiraceae bacterium NK4A179 Cas13, Clostridium aminophilum (DSM 10710) Cas13, Carnobacterium gallinarum (DSM 4847) Cas13, Paludibacter propionicigenes (WB4) Cas13, Listeria weihenstephanensis (FSL R9-0317) Cas13, Listeriaceae bacterium (FSL M6-0635) Cas13, Listeria newyorkensis (FSL M6-0635) Cas13, Leptotrichia wadei (F0279) Cas13, Rhodobacter capsulatus (SB 1003) Cas13, Rhodobacter capsulatus (R121) Cas13, Rhodobacter capsulatus (DE442) Cas13, Leptotrichia wadei (Lw2) Cas13, or Listeria seeligeri Cas13. According to the invention, a consensus sequence can be generated from multiple Cas13 orthologs, which can assist in locating conserved amino acid residues, and motifs, including but not limited to catalytic residues and HEPN motifs in Cas13 orthologs that mediate Cas13 function. One such consensus sequence, generated from selected orthologs.


In an embodiment of the invention, the effector protein comprises an amino acid sequence having at least 80% sequence homology to a Type VI effector protein consensus sequence including but not limited to a consensus sequence described herein.


In another non-limiting example, a sequence alignment tool to assist generation of a consensus sequence and identification of conserved residues is the MUSCLE alignment tool (www.ebi.ac.uk/Tools/msa/muscle/). For example, using MUSCLE, the following amino acid locations conserved among Cas13a orthologs can be identified in Leptotrichia wadei Cas13a:K2; K5; V6; E301; L331; I335; N341; G351; K352; E375; L392; L396; D403; F446; I466; I470; R474 (HEPN); H475; H479 (HEPN), E508; P556; L561; I595; Y596; F600; Y669; I673; F681; L685; Y761; L676; L779; Y782; L836; D847; Y863; L869; I872; K879; I933; L954; I958; R961; Y965; E970; R971; D972; R1046 (HEPN), H1051 (HEPN), Y1075; D1076; K1078; K1080; 11083; 11090.


In certain example embodiments, the RNA-targeting effector protein is a Type VI-B effector protein, such as Cas13b and Group 29 or Group 30 proteins. In certain example embodiments, the RNA-targeting effector protein comprises one or more HEPN domains. In certain example embodiments, the RNA-targeting effector protein comprises a C-terminal HEPN domain, a N-terminal HEPN domain, or both. Regarding example Type VI-B effector proteins that may be used in the context of this invention, reference is made to U.S. application Ser. No. 15/331,792 entitled “Novel CRISPR Enzymes and Systems” and filed Oct. 21, 2016, International Patent Application No. PCT/US2016/058302 entitled “Novel CRISPR Enzymes and Systems”, and filed Oct. 21, 2016, and Smargon et al. “Cas13b is a Type VI-B CRISPR-associated RNA-Guided RNase differentially regulated by accessory proteins Csx27 and Csx28” Molecular Cell, 65, 1-13 (2017); dx.doi.org/10.1016/j.molcel.2016.12.023. In certain example embodiments, the Cas13b effector protein is, or comprises an amino acid sequence having at least 80% sequence homology to any of the sequences of Table 1 of International Patent Application No. PCT/US2016/058302. Further reference is made to example Type VI-B effector proteins of U.S. Provisional Application Nos. 62/471,710, 62/566,829 and International Patent Publication No. WO2018/1703333, entitled “Novel Cas13b Orthologues CRISPR Enzymes and System”. In particular embodiments, the Cas13b enzyme is derived from Bergeyella zoohelcum. In certain other example embodiments, the effector protein is, or comprises an amino acid sequence having at least 80% sequence homology to any of the sequences listed in Tables 1A or 1B of International Patent Publication No. WO2018/1703333, specifically incorporated herein by reference. In certain embodiments, the Cas13b effector protein is, or comprises an amino acid sequence having at least 80% sequence homology to any of the polypeptides in U.S. Provisional Applications 62/484,791, 62/561,662, 62/568,129 or International Patent Publication WO2018/191388, all entitled “Novel Type VI CRISPR Orthologs and Systems,” incorporated herein by reference. In certain embodiments, the Cas13b effector protein is, or comprises an amin acid sequence having at least 80% sequence homology to a polypeptide as set forth in FIG. 1 of International Patent Publication WO2018/191388, specifically incorporated herein by reference. In an aspect, the Cas13b protein is selected from the group consisting of Porphyromonas gulae Cas13b (accession number WP 039434803), Prevotella sp. P5-125 Cas13b (accession number WP 044065294), Porphyromonas gingivalis Cas13b (accession number WP 053444417), Porphyromonas sp. COT-052 OH4946 Cas13b (accession number WP 039428968), Bacteroides pyogenes Cas13b (accession number WP 034542281), Riemerella anatipestifer Cas13b (accession number WP 004919755).


In certain example embodiments, the RNA-targeting effector protein is a Cas13c effector protein as disclosed in U.S. Provisional Patent Application No. 62/525,165 filed Jun. 26, 2017, and International Patent Publication No. WO2018/035250 filed Aug. 16, 2017. In certain example embodiments, the Cas13c protein may be from an organism of a genus such as Fusobacterium or Anaerosalibacter. Example wildtype orthologue sequences of Cas13c are: EH019081, WP_094899336, WP_040490876, WP_047396607, WP_035935671, WP_035906563, WP_042678931, WP_062627846, WP_005959231, WP_027128616, WP_062624740, WP_096402050.


In certain example embodiments, the Cas13 protein may be selected from any of the following: Cas13a: Leptotrichia shahii, Leptotrichia wadei (Lw2), Listeria seeligeri, Lachnospiraceae bacterium MA2020, Lachnospiraceae bacterium NK4A179, [Clostridium]aminophilum DSM 10710, Carnobacterium gallinarum DSM 4847, Carnobacterium gallinarum DSM 4847, Paludibacter propionicigenes WB4, Listeria weihenstephanensis FSL R9-0317, Listeriaceae bacterium FSL M6-0635, Leptotrichia wadei F0279, Rhodobacter capsulatus SB 1003, Rhodobacter capsulatus R121, Rhodobacter capsulatus DE442, Leptotrichia buccalis C-1013-b, Herbinix hemicellulosilytica, [Eubacterium] rectale, Eubacteriaceae bacterium CHKCI004, Blautia sp. Marseille-P2398, Leptotrichia sp. oral taxon 879 str. F0557; Cas13b: Bergeyella zoohelcum, Prevotella intermedia, Prevotella buccae, Alistipes sp. ZOR0009, Prevotella sp. MA2016, Riemerella anatipestifer, Prevotella aurantiaca, Prevotella saccharolytica, Prevotella intermedia, Capnocytophaga canimorsus, Porphyromonas gulae, Prevotella sp. P5-125, Flavobacterium branchiophilum, Porphyromonas gingivalis, Prevotella intermedia; Cas13c: Fusobacterium necrophorum subsp. funduliforme ATCC 51357 contig00003, Fusobacterium necrophorum DJ-2 contig0065, whole genome shotgun sequence, Fusobacterium necrophorum BFTR-1 contig0068, Fusobacterium necrophorum subsp. funduliforme 1_1_36S cont1.14, Fusobacterium perfoetens ATCC 29250 T364DRAFT_scaffold00009.9_C, Fusobacterium ulcerans ATCC 49185 cont2.38, Anaerosalibacter sp. ND1 genome assembly Anaerosalibacter massiliensis ND1.Cas13s non-specific RNase activity can be leveraged to cleave reporters upon target recognition, allowing for the design of sensitive and specific diagnostics using Cas13, including single nucleotide variants, detection based on rRNA sequences, screening for drug resistance, monitoring microbe outbreaks, genetic perturbations, and screening of environmental samples, as described, for example, in PCT/US18/054472 filed Oct. 22, 2018 at [0183]-[0327], incorporated herein by reference. Reference is made to WO 2017/219027, WO2018/107129, US20180298445, US 2018-0274017, US 2018-0305773, WO 2018/170340, U.S. application Ser. No. 15/922,837, filed Mar. 15, 2018 entitled “Devices for CRISPR Effector System Based Diagnostics”, PCT/US18/50091, filed Sep. 7, 2018 “Multi-Effector CRISPR Based Diagnostic Systems”, PCT/US18/66940 filed Dec. 20, 2018 entitled “CRISPR Effector System Based Multiplex Diagnostics”, PCT/US18/054472 filed Oct. 4, 2018 entitled “CRISPR Effector System Based Diagnostic”, U.S. Provisional 62/740,728 filed Oct. 3, 2018 entitled “CRISPR Effector System Based Diagnostics for Hemorrhagic Fever Detection”, U.S. Provisional 62/690,278 filed Jun. 26, 2018 and U.S. Provisional 62/767,059 filed Nov. 14, 2018 both entitled “CRISPR Double Nickase Based Amplification, Compositions, Systems and Methods”, U.S. Provisional 62/690,160 filed Jun. 26, 2018 and U.S. Pat. No. 62,767,077 filed Nov. 14, 2018, both entitled “CRISPR/CAS and Transposase Based Amplification Compositions, Systems, And Methods”, U.S. Provisional 62/690,257 filed Jun. 26, 2018 and 62/767,052 filed Nov. 14, 2018 both entitled “CRISPR Effector System Based Amplification Methods, Systems, And Diagnostics”, U.S. Provisional 62/767,076 filed Nov. 14, 2018 entitled “Multiplexing Highly Evolving Viral Variants With SHERLOCK” and 62/767,070 filed Nov. 14, 2018 entitled “Droplet SHERLOCK.” Reference is further made to WO2017/127807, WO2017/184786, WO 2017/184768, WO 2017/189308, WO 2018/035388, WO 2018/170333, WO 2018/191388, WO 2018/213708, WO 2019/005866, PCT/US18/67328 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems”, PCT/US18/67225 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems” and PCT/US18/67307 filed Dec. 21, 2018 entitled “Novel CRISPR Enzymes and Systems”, U.S. 62/712,809 filed Jul. 31, 2018 entitled “Novel CRISPR Enzymes and Systems”, U.S. 62/744,080 filed Oct. 10, 2018 entitled “Novel Cas12b Enzymes and Systems” and U.S. 62/751,196 filed Oct. 26, 2018 entitled “Novel Cas12b Enzymes and Systems”, U.S. 715,640 filed August 7, 2-18 entitled “Novel CRISPR Enzymes and Systems”, WO 2016/205711, U.S. Pat. No. 9,790,490, WO 2016/205749, WO 2016/205764, WO 2017/070605, WO 2017/106657, and WO 2016/149661, WO2018/035387, WO2018/194963, Cox DBT, et al., RNA editing with CRISPR-Cas13, Science. 2017 Nov. 24; 358(6366):1019-1027; Gootenberg J S, et al., Multiplexed and portable nucleic acid detection platform with Cas13, Cas12a, and Csm6, Science. 2018 Apr. 27; 360(6387):439-444; Gootenberg J S, et al., Nucleic acid detection with CRISPR-Cas13a/C2c2, Science. 2017 Apr. 28; 356(6336):438-442; Abudayyeh O O, et al., RNA targeting with CRISPR-Cas13, Nature. 2017 Oct. 12; 550(7675):280-284; Smargon A A, et al., Cas13b Is a Type VI-B CRISPR-Associated RNA-Guided RNase Differentially Regulated by Accessory Proteins Csx27 and Csx28. Mol Cell. 2017 Feb. 16; 65(4):618-630.e7; Abudayyeh 00, et al., C2c2 is a single-component programmable RNA-guided RNA-targeting CRISPR effector, Science. 2016 Aug. 5; 353(6299):aaf5573; Yang L, et al., Engineering and optimising deaminase fusions for genome editing. Nat Commun. 2016 Nov. 2; 7:13330, Myrvhold et al., Field deployable viral diagnostics using CRISPR-Cas13, Science 2018 360, 444-448, Shmakov et al. “Diversity and evolution of class 2 CRISPR-Cas systems,” Nat Rev Microbiol. 2017 15(3):169-182, each of which is incorporated herein by reference in its entirety.


DNA-Targeting Effector Proteins

In certain example embodiments, the assays may comprise a DNA-targeting effector protein. In certain example embodiments, the assays may comprise multiple DNA-targeting effectors or one or more orthologs in combination with one or more RNA-targeting effectors. In certain example embodiments, the DNA targeting are Type V Cas proteins, such as Cas12 proteins. In certain other example embodiments, the Cas12 proteins are Cas12a, Cas12b, Cas12c, or a combination thereof.


Cas12a Orthologs


The present invention encompasses the use of a Cpf1 effector protein, derived from a Cpf1 locus denoted as subtype V-A. Herein such effector proteins are also referred to as “Cpf1p”, e.g., a Cpf1 protein (and such effector protein or Cpf1 protein or protein derived from a Cpf1 locus is also called “CRISPR enzyme”). Presently, the subtype V-A loci encompasses cas1, cas2, a distinct gene denoted cpf1 and a CRISPR array. Cpf1 (CRISPR-associated protein Cpf1, subtype PREFRAN) is a large protein (about 1300 amino acids) that contains a RuvC-like nuclease domain homologous to the corresponding domain of Cas9 along with a counterpart to the characteristic arginine-rich cluster of Cas9. However, Cpf1 lacks the HNH nuclease domain that is present in all Cas9 proteins, and the RuvC-like domain is contiguous in the Cpf1 sequence, in contrast to Cas9 where it contains long inserts including the HNH domain. Accordingly, in particular embodiments, the CRISPR-Cas enzyme comprises only a RuvC-like nuclease domain.


The programmability, specificity, and collateral activity of the RNA-guided Cpf1 also make it an ideal switchable nuclease for non-specific cleavage of nucleic acids. In one embodiment, a Cpf1 system is engineered to provide and take advantage of collateral non-specific cleavage of RNA. In another embodiment, a Cpf1 system is engineered to provide and take advantage of collateral non-specific cleavage of ssDNA. Accordingly, engineered Cpf1 systems provide platforms for nucleic acid detection and transcriptome manipulation. Cpf1 is developed for use as a mammalian transcript knockdown and binding tool. Cpf1 is capable of robust collateral cleavage of RNA and ssDNA when activated by sequence-specific targeted DNA binding.


Homologs and orthologs may be identified by homology modelling (see, e.g., Greer, Science vol. 228 (1985) 1055, and Blundell et al. Eur J Biochem vol 172 (1988), 513) or “structural BLAST” (Dey F, Cliff Zhang Q, Petrey D, Honig B. Toward a “structural BLAST”: using structural relationships to infer function. Protein Sci. 2013 April; 22(4):359-66. doi: 10.1002/pro.2225.). See also Shmakov et al. (2015) for application in the field of CRISPR-Cas loci. Homologous proteins may but need not be structurally related, or are only partially structurally related. The Cpf1 gene is found in several diverse bacterial genomes, typically in the same locus with cas1, cas2, and cas4 genes and a CRISPR cassette (for example, FNFX1_1431-FNFX1_1428 of Francisella cf. novicida Fxl). In particular embodiments, the effector protein is a Cpf1 effector protein from an organism from a genus comprising Streptococcus, Campylobacter, Nitratifractor, Staphylococcus, Parvibaculum, Roseburia, Neisseria, Gluconacetobacter, Azospirillum, Sphaerochaeta, Lactobacillus, Eubacterium, Corynebacter, Carnobacterium, Rhodobacter, Listeria, Paludibacter, Clostridium, Lachnospiraceae, Clostridiaridium, Leptotrichia, Francisella, Legionella, Alicyclobacillus, Methanomethyophilus, Porphyromonas, Prevotella, Bacteroidetes, Helcococcus, Letospira, Desulfovibrio, Desulfonatronum, Opitutaceae, Tuberibacillus, Bacillus, Brevibacilus, Methylobacterium or Acidaminococcus.


In further particular embodiments, the Cpf1 effector protein is from an organism selected from S. mutans, S. agalactiae, S. equisimilis, S. sanguinis, S. pneumonia; C. jejuni, C. coli; N. salsuginis, N. tergarcus; S. auricularis, S. carnosus; N. meningitides, N. gonorrhoeae; L. monocytogenes, L. ivanovii; C. botulinum, C. difficile, C. tetani, C. sordellii.


The effector protein may comprise a chimeric effector protein comprising a first fragment from a first effector protein (e.g., a Cpf1) ortholog and a second fragment from a second effector (e.g., a Cpf1) protein ortholog, and wherein the first and second effector protein orthologs are different. At least one of the first and second effector protein (e.g., a Cpf1) orthologs may comprise an effector protein (e.g., a Cpf1) from an organism comprising Streptococcus, Campylobacter, Nitratifractor, Staphylococcus, Parvibaculum, Roseburia, Neisseria, Gluconacetobacter, Azospirillum, Sphaerochaeta, Lactobacillus, Eubacterium, Corynebacter, Carnobacterium, Rhodobacter, Listeria, Paludibacter, Clostridium, Lachnospiraceae, Clostridiaridium, Leptotrichia, Francisella, Legionella, Alicyclobacillus, Methanomethyophilus, Porphyromonas, Prevotella, Bacteroidetes, Helcococcus, Letospira, Desulfovibrio, Desulfonatronum, Opitutaceae, Tuberibacillus, Bacillus, Brevibacilus, Methylobacterium or Acidaminococcus; e.g., a chimeric effector protein comprising a first fragment and a second fragment wherein each of the first and second fragments is selected from a Cpf1 of an organism comprising Streptococcus, Campylobacter, Nitratifractor, Staphylococcus, Parvibaculum, Roseburia, Neisseria, Gluconacetobacter, Azospirillum, Sphaerochaeta, Lactobacillus, Eubacterium, Corynebacter, Carnobacterium, Rhodobacter, Listeria, Paludibacter, Clostridium, Lachnospiraceae, Clostridiaridium, Leptotrichia, Francisella, Legionella, Alicyclobacillus, Methanomethyophilus, Porphyromonas, Prevotella, Bacteroidetes, Helcococcus, Letospira, Desulfovibrio, Desulfonatronum, Opitutaceae, Tuberibacillus, Bacillus, Brevibacilus, Methylobacterium or Acidaminococcus wherein the first and second fragments are not from the same bacteria; for instance a chimeric effector protein comprising a first fragment and a second fragment wherein each of the first and second fragments is selected from a Cpf1 of S. mutans, S. agalactiae, S. equisimilis, S. sanguinis, S. pneumonia; C. jejuni, C. coli; N. salsuginis, N. tergarcus; S. auricularis, S. carnosus; N. meningitides, N. gonorrhoeae; L. monocytogenes, L. ivanovii; C. botulinum, C. difficile, C. tetani, C. sordellii; Francisella tularensis 1, Prevotella albensis, Lachnospiraceae bacterium MC2017 1, Butyrivibrio proteoclasticus, Peregrinibacteria bacterium GW2011_GWA2_33_10, Parcubacteria bacterium GW2011_GWC2_44_17, Smithella sp. SCADC, Acidaminococcus sp. BV3L6, Lachnospiraceae bacterium MA2020, Candidatus Methanoplasma termitum, Eubacterium eligens, Moraxella bovoculi 237, Leptospira inadai, Lachnospiraceae bacterium ND2006, Porphyromonas crevioricanis 3, Prevotella disiens and Porphyromonas macacae, wherein the first and second fragments are not from the same bacteria. In a more preferred embodiment, the Cpf1p is derived from a bacterial species selected from Francisella tularensis 1, Prevotella albensis, Lachnospiraceae bacterium MC2017 1, Butyrivibrio proteoclasticus, Peregrinibacteria bacterium GW2011_GWA2_33_10, Parcubacteria bacterium GW2011_GWC2_44_17, Smithella sp. SCADC, Acidaminococcus sp. BV3L6, Lachnospiraceae bacterium MA2020, Candidatus Methanoplasma termitum, Eubacterium eligens, Moraxella bovoculi 237, Leptospira inadai, Lachnospiraceae bacterium ND2006, Porphyromonas crevioricanis 3, Prevotella disiens and Porphyromonas macacae. In certain embodiments, the Cpf1p is derived from a bacterial species selected from Acidaminococcus sp. BV3L6, Lachnospiraceae bacterium MA2020. In certain embodiments, the effector protein is derived from a subspecies of Francisella tularensis 1, including but not limited to Francisella tularensis subsp. Novicida.


In some embodiments, the Cpf1p is derived from an organism from the genus of Eubacterium. In some embodiments, the CRISPR effector protein is a Cpf1 protein derived from an organism from the bacterial species of Eubacterium rectale. In some embodiments, the amino acid sequence of the Cpf1 effector protein corresponds to NCBI Reference Sequence WP_055225123.1, NCBI Reference Sequence WP_055237260.1, NCBI Reference Sequence WP_055272206.1, or GenBank ID OLA16049.1. In some embodiments, the Cpf1 effector protein has a sequence homology or sequence identity of at least 60%, more particularly at least 70, such as at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95%, with NCBI Reference Sequence WP_055225123.1, NCBI Reference Sequence WP_055237260.1, NCBI Reference Sequence WP_055272206.1, or GenBank ID OLA16049.1. The skilled person will understand that this includes truncated forms of the Cpf1 protein whereby the sequence identity is determined over the length of the truncated form. In some embodiments, the Cpf1 effector recognizes the PAM sequence of TTTN or CTTN.


In particular embodiments, the homologue or orthologue of Cpf1 as referred to herein has a sequence homology or identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with Cpf1. In further embodiments, the homologue or orthologue of Cpf1 as referred to herein has a sequence identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the wild type Cpf1. Where the Cpf1 has one or more mutations (mutated), the homologue or orthologue of said Cpf1 as referred to herein has a sequence identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the mutated Cpf1.


In an embodiment, the Cpf1 protein may be an ortholog of an organism of a genus which includes, but is not limited to Acidaminococcus sp, Lachnospiraceae bacterium or Moraxella bovoculi; in particular embodiments, the type V Cas protein may be an ortholog of an organism of a species which includes, but is not limited to Acidaminococcus sp. BV3L6; Lachnospiraceae bacterium ND2006 (LbCpf1) or Moraxella bovoculi 237. In particular embodiments, the homologue or orthologue of Cpf1 as referred to herein has a sequence homology or identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with one or more of the Cpf1 sequences disclosed herein. In further embodiments, the homologue or orthologue of Cpf as referred to herein has a sequence identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the wild type FnCpf1, AsCpf1 or LbCpf1. The skilled person will understand that this includes truncated forms of the Cpf1 protein whereby the sequence identity is determined over the length of the truncated form. In certain of the following, Cpf1 amino acids are followed by nuclear localization signals (NLS) (italics), a glycine-serine (GS) linker, and 3×HA tag. Further Cpf1 orthologs include NCBI WP_055225123.1, NCBI WP_055237260.1, NCBI WP_055272206.1, and GenBank OLA16049.1.


Cas12b Orthologs


The present invention encompasses the use of a Cas12b (C2c1) effector proteins, derived from a C2c1 locus denoted as subtype V-B. Herein such effector proteins are also referred to as “C2c1p”, e.g., a C2c1 protein (and such effector protein or C2c1 protein or protein derived from a C2c1 locus is also called “CRISPR enzyme”). Presently, the subtype V-B loci encompasses cas1-Cas4 fusion, cas2, a distinct gene denoted C2c1 and a CRISPR array. C2c1 (CRISPR-associated protein C2c1) is a large protein (about 1100-1300 amino acids) that contains a RuvC-like nuclease domain homologous to the corresponding domain of Cas9 along with a counterpart to the characteristic arginine-rich cluster of Cas9. However, C2c1 lacks the HNH nuclease domain that is present in all Cas9 proteins, and the RuvC-like domain is contiguous in the C2c1 sequence, in contrast to Cas9 where it contains long inserts including the HNH domain. Accordingly, in particular embodiments, the CRISPR-Cas enzyme comprises only a RuvC-like nuclease domain.


The programmability, specificity, and collateral activity of the RNA-guided C2c1 also make it an ideal switchable nuclease for non-specific cleavage of nucleic acids. In one embodiment, a C2c1 system is engineered to provide and take advantage of collateral non-specific cleavage of RNA. In another embodiment, a C2c1 system is engineered to provide and take advantage of collateral non-specific cleavage of ssDNA. Accordingly, engineered C2c1 systems provide platforms for nucleic acid detection and transcriptome manipulation, and inducing cell death. C2c1 is developed for use as a mammalian transcript knockdown and binding tool. C2c1 is capable of robust collateral cleavage of RNA and ssDNA when activated by sequence-specific targeted DNA binding.


In certain embodiments, C2c1 is provided or expressed in an in vitro system or in a cell, transiently or stably, and targeted or triggered to non-specifically cleave cellular nucleic acids. In one embodiment, C2c1 is engineered to knock down ssDNA, for example viral ssDNA. In another embodiment, C2c1 is engineered to knock down RNA. The system can be devised such that the knockdown is dependent on a target DNA present in the cell or in vitro system, or triggered by the addition of a target nucleic acid to the system or cell.


C2c1 (also known as Cas12b) proteins are RNA guided nucleases. In certain embodiments, the Cas protein may comprise at least 80% sequence identity to a polypeptide as described in International Patent Publication WO 2016/205749 at FIG. 17-21, FIG. 41A-41M, 44A-44E, incorporated herein by reference. Its cleavage relies on a tracr RNA to recruit a guide RNA comprising a guide sequence and a direct repeat, where the guide sequence hybridizes with the target nucleotide sequence to form a DNA/RNA heteroduplex. Based on current studies, C2c1 nuclease activity also requires relies on recognition of PAM sequence. C2c1 PAM sequences are T-rich sequences. In some embodiments, the PAM sequence is 5′ TTN 3′ or 5′ ATTN 3′, wherein N is any nucleotide. In a particular embodiment, the PAM sequence is 5′ TTC 3′. In a particular embodiment, the PAM is in the sequence of Plasmodium falciparum.


In particular embodiments, the effector protein is a C2c1 effector protein from an organism from a genus comprising Alicyclobacillus, Desulfovibrio, Desulfonatronum, Opitutaceae, Tuberibacillus, Bacillus, Brevibacillus, Candidatus, Desulfatirhabdium, Citrobacter, Elusimicrobia, Methylobacterium, Omnitrophica, Phycisphaerae, Planctomycetes, Spirochaetes, and Verrucomicrobiaceae.


In further particular embodiments, the C2c1 effector protein is from a species selected from Alicyclobacillus acidoterrestris (e.g., ATCC 49025), Alicyclobacillus contaminans (e.g., DSM 17975), Alicyclobacillus macrosporangiidus (e.g. DSM 17980), Bacillus hisashii strain C4, Candidatus Lindowbacteria bacterium RIFCSPLOWO2, Desulfovibrio inopinatus (e.g., DSM 10711), Desulfonatronum thiodismutans (e.g., strain MLF-1), Elusimicrobia bacterium RIFOXYA12, Omnitrophica WOR_2 bacterium RIFCSPHIGHO2, Opitutaceae bacterium TAV5, Phycisphaerae bacterium ST-NAGAB-D1, Planctomycetes bacterium RBG_13_46_10, Spirochaetes bacterium GWB1_27_13, Verrucomicrobiaceae bacterium UBA2429, Tuberibacillus calidus (e.g., DSM 17572), Bacillus thermoamylovorans (e.g., strain B4166), Brevibacillus sp. CF112, Bacillus sp. NSP2.1, Desulfatirhabdium butyrativorans (e.g., DSM 18734), Alicyclobacillus herbarius (e.g., DSM 13609), Citrobacter freundii (e.g., ATCC 8090), Brevibacillus agri (e.g., BAB-2500), Methylobacterium nodulans (e.g., ORS 2060).


The effector protein may comprise a chimeric effector protein comprising a first fragment from a first effector protein (e.g., a C2c1) ortholog and a second fragment from a second effector (e.g., a C2c1) protein ortholog, and wherein the first and second effector protein orthologs are different. At least one of the first and second effector protein (e.g., a C2c1) orthologs may comprise an effector protein (e.g., a C2c1) from an organism comprising Alicyclobacillus, Desulfovibrio, Desulfonatronum, Opitutaceae, Tuberibacillus, Bacillus, Brevibacillus, Candidatus, Desulfatirhabdium, Elusimicrobia, Citrobacter, Methylobacterium, Omnitrophicai, Phycisphaerae, Planctomycetes, Spirochaetes, and Verrucomicrobiaceae; e.g., a chimeric effector protein comprising a first fragment and a second fragment wherein each of the first and second fragments is selected from a C2c1 of an organism comprising Alicyclobacillus, Desulfovibrio, Desulfonatronum, Opitutaceae, Tuberibacillus, Bacillus, Brevibacillus, Candidatus, Desulfatirhabdium, Elusimicrobia, Citrobacter, Methylobacterium, Omnitrophicai, Phycisphaerae, Planctomycetes, Spirochaetes, and Verrucomicrobiaceae wherein the first and second fragments are not from the same bacteria; for instance a chimeric effector protein comprising a first fragment and a second fragment wherein each of the first and second fragments is selected from a C2c1 of Alicyclobacillus acidoterrestris (e.g., ATCC 49025), Alicyclobacillus contaminans (e.g., DSM 17975), Alicyclobacillus macrosporangiidus (e.g. DSM 17980), Bacillus hisashii strain C4, Candidatus Lindowbacteria bacterium RIFCSPLOWO2, Desulfovibrio inopinatus (e.g., DSM 10711), Desulfonatronum thiodismutans (e.g., strain MLF-1), Elusimicrobia bacterium RIFOXYA12, Omnitrophica WOR_2 bacterium RIFCSPHIGHO2, Opitutaceae bacterium TAV5, Phycisphaerae bacterium ST-NAGAB-D1, Planctomycetes bacterium RBG_13_46_10, Spirochaetes bacterium GWB1_27_13, Verrucomicrobiaceae bacterium UBA2429, Tuberibacillus calidus (e.g., DSM 17572), Bacillus thermoamylovorans (e.g., strain B4166), Brevibacillus sp. CF112, Bacillus sp. NSP2.1, Desulfatirhabdium butyrativorans (e.g., DSM 18734), Alicyclobacillus herbarius (e.g., DSM 13609), Citrobacter freundii (e.g., ATCC 8090), Brevibacillus agri (e.g., BAB-2500), Methylobacterium nodulans (e.g., ORS 2060), wherein the first and second fragments are not from the same bacteria.


In a more preferred embodiment, the C2c1p is derived from a bacterial species selected from Alicyclobacillus acidoterrestris (e.g., ATCC 49025), Alicyclobacillus contaminans (e.g., DSM 17975), Alicyclobacillus macrosporangiidus (e.g. DSM 17980), Bacillus hisashii strain C4, Candidatus Lindowbacteria bacterium RIFCSPLOWO2, Desulfovibrio inopinatus (e.g., DSM 10711), Desulfonatronum thiodismutans (e.g., strain MLF-1), Elusimicrobia bacterium RIFOXYA12, Omnitrophica WOR_2 bacterium RIFCSPHIGHO2, Opitutaceae bacterium TAV5, Phycisphaerae bacterium ST-NAGAB-D1, Planctomycetes bacterium RBG_13_46_10, Spirochaetes bacterium GWB1_27_13, Verrucomicrobiaceae bacterium UBA2429, Tuberibacillus calidus (e.g., DSM 17572), Bacillus thermoamylovorans (e.g., strain B4166), Brevibacillus sp. CF112, Bacillus sp. NSP2.1, Desulfatirhabdium butyrativorans (e.g., DSM 18734), Alicyclobacillus herbarius (e.g., DSM 13609), Citrobacter freundii (e.g., ATCC 8090), Brevibacillus agri (e.g., BAB-2500), Methylobacterium nodulans (e.g., ORS 2060). In certain embodiments, the C2c1p is derived from a bacterial species selected from Alicyclobacillus acidoterrestris (e.g., ATCC 49025), Alicyclobacillus contaminans (e.g., DSM 17975).


In particular embodiments, the homologue or orthologue of C2c1 as referred to herein has a sequence homology or identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with C2c1. In further embodiments, the homologue or orthologue of C2c1 as referred to herein has a sequence identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the wild type C2c1. Where the C2c1 has one or more mutations (mutated), the homologue or orthologue of said C2c1 as referred to herein has a sequence identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the mutated C2c1.


In an embodiment, the C2c1 protein may be an ortholog of an organism of a genus which includes, but is not limited to Alicyclobacillus, Desulfovibrio, Desulfonatronum, Opitutaceae, Tuberibacillus, Bacillus, Brevibacillus, Candidatus, Desulfatirhabdium, Elusimicrobia, Citrobacter, Methylobacterium, Omnitrophicai, Phycisphaerae, Planctomycetes, Spirochaetes, and Verrucomicrobiaceae; in particular embodiments, the type V Cas protein may be an ortholog of an organism of a species which includes, but is not limited to Alicyclobacillus acidoterrestris (e.g., ATCC 49025), Alicyclobacillus contaminans (e.g., DSM 17975), Alicyclobacillus macrosporangiidus (e.g. DSM 17980), Bacillus hisashii strain C4, Candidatus Lindowbacteria bacterium RIFCSPLOWO2, Desulfovibrio inopinatus (e.g., DSM 10711), Desulfonatronum thiodismutans (e.g., strain MLF-1), Elusimicrobia bacterium RIFOXYA12, Omnitrophica WOR_2 bacterium RIFCSPHIGHO2, Opitutaceae bacterium TAV5, Phycisphaerae bacterium ST-NAGAB-D1, Planctomycetes bacterium RBG_13_46_10, Spirochaetes bacterium GWB1_27_13, Verrucomicrobiaceae bacterium UBA2429, Tuberibacillus calidus (e.g., DSM 17572), Bacillus thermoamylovorans (e.g., strain B4166), Brevibacillus sp. CF112, Bacillus sp. NSP2.1, Desulfatirhabdium butyrativorans (e.g., DSM 18734), Alicyclobacillus herbarius (e.g., DSM 13609), Citrobacter freundii (e.g., ATCC 8090), Brevibacillus agri (e.g., BAB-2500), Methylobacterium nodulans (e.g., ORS 2060). In particular embodiments, the homologue or orthologue of C2c1 as referred to herein has a sequence homology or identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with one or more of the C2c1 sequences disclosed herein. In further embodiments, the homologue or orthologue of C2c1 as referred to herein has a sequence identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the wild type AacC2c1 or BthC2c1.


In particular embodiments, the C2c1 protein of the invention has a sequence homology or identity of at least 60%, more particularly at least 70, such as at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with AacC2c1 or BthC2c1. In further embodiments, the C2c1 protein as referred to herein has a sequence identity of at least 60%, such as at least 70%, more particularly at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the wild type AacC2c1. In particular embodiments, the C2c1 protein of the present invention has less than 60% sequence identity with AacC2c1. The skilled person will understand that this includes truncated forms of the C2c1 protein whereby the sequence identity is determined over the length of the truncated form.


In certain methods according to the present invention, the CRISPR-Cas protein is preferably mutated with respect to a corresponding wild-type enzyme such that the mutated CRISPR-Cas protein lacks the ability to cleave one or both DNA strands of a target locus containing a target sequence. In particular embodiments, one or more catalytic domains of the C2c1 protein are mutated to produce a mutated Cas protein which cleaves only one DNA strand of a target sequence.


In particular embodiments, the CRISPR-Cas protein may be mutated with respect to a corresponding wild-type enzyme such that the mutated CRISPR-Cas protein lacks substantially all DNA cleavage activity. In some embodiments, a CRISPR-Cas protein may be considered to substantially lack all DNA and/or RNA cleavage activity when the cleavage activity of the mutated enzyme is about no more than 25%, 10%, 5%, 1%, 0.1%, 0.01%, or less of the nucleic acid cleavage activity of the non-mutated form of the enzyme; an example can be when the nucleic acid cleavage activity of the mutated form is nil or negligible as compared with the non-mutated form.


In certain embodiments of the methods provided herein the CRISPR-Cas protein is a mutated CRISPR-Cas protein which cleaves only one DNA strand, i.e. a nickase. More particularly, in the context of the present invention, the nickase ensures cleavage within the non-target sequence, i.e. the sequence which is on the opposite DNA strand of the target sequence and which is 3′ of the PAM sequence. By means of further guidance, and without limitation, an arginine-to-alanine substitution (R911A) in the Nuc domain of C2c1 from Alicyclobacillus acidoterrestris converts C2c1 from a nuclease that cleaves both strands to a nickase (cleaves a single strand). It will be understood by the skilled person that where the enzyme is not AacC2c1, a mutation may be made at a residue in a corresponding position.


Cas12c Orthologs

In certain embodiments, the effector protein, particularly a Type V loci effector protein, more particularly a Type V-C loci effector protein, a Cas12c protein, even more particularly a C2c3p, may originate, may be isolated or may be derived from a bacterial metagenome selected from the group consisting of the bacterial metagenomes listed in the Table in FIG. 43A-43B of PCT/US2016/038238, specifically incorporated by reference, which presents analysis of the Type-V-C Cas12c loci.


In certain embodiments, the effector protein, particularly a Type V loci effector protein, more particularly a Type V-C loci effector protein, even more particularly a C2c3p, may comprise, consist essentially of or consist of an amino acid sequence selected from the group consisting of amino acid sequences shown in the multiple sequence alignment in FIG. 13I of PCT/US2016/038238, specifically incorporated by reference.


In certain embodiments, a Type V-C locus as intended herein may encode Cas1 and the C2c3p effector protein. See FIG. 14 of PCT/US2016/038238, specifically incorporated by reference, depicting the genomic architecture of the Cas12c CRISPR-Cas loci. In certain embodiments, a Cas1 protein encoded by a Type V-C locus as intended herein may cluster with Type I-B system. See FIGS. 10A and 10B and FIG. 10C-V of PCT/US2016/038238, specifically incorporated by reference, illustrating a Cas1 tree including Cas1 encoded by representative Type V-C loci.


In certain embodiments, the effector protein, particularly a Type V loci effector protein, more particularly a Type V-C loci effector protein, even more particularly a C2c3p, such as a native C2c3p, may be about 1100 to about 1500 amino acids long, e.g., about 1100 to about 1200 amino acids long, or about 1200 to about 1300 amino acids long, or about 1300 to about 1400 amino acids long, or about 1400 to about 1500 amino acids long, e.g., about 1100, about 1200, about 1300, about 1400 or about 1500 amino acids long, or at least about 1100, at least about 1200, at least about 1300, at least about 1400 or at least about 1500 amino acids long.


In certain embodiments, the effector protein, particularly a Type V loci effector protein, more particularly a Type V-C loci effector protein, even more particularly a C2c3p, and preferably the C-terminal portion of said effector protein, comprises the three catalytic motifs of the RuvC-like nuclease (i.e., RuvCI, RuvCII and RuvCIII). In certain embodiments, said effector protein, and preferably the C-terminal portion of said effector protein, may further comprise a region corresponding to the bridge helix (also known as arginine-rich cluster) that in Cas9 protein is involved in crRNA-binding. In certain embodiments, said effector protein, and preferably the C-terminal portion of said effector protein, may further comprise a Zn finger region. Preferably, the Zn-binding cysteine residue(s) may be conserved in C2c3p. In certain embodiments, said effector protein, and preferably the C-terminal portion of said effector protein, may comprise the three catalytic motifs of the RuvC-like nuclease (i.e., RuvCI, RuvCII and RuvCIII), the region corresponding to the bridge helix, and the Zn finger region, preferably in the following order, from N to C terminus: RuvCI-bridge helix-RuvCII-Zinc finger-RuvCIII. See FIGS. 13A and 13C of PCT/US2016/038238, specifically incorporated by reference, for illustration of representative Type V-C effector proteins domain architecture.


In certain embodiments, Type V-C loci as intended herein may comprise CRISPR repeats between 20 and 30 bp long, more typically between 22 and 27 bp long, yet more typically 25 bp long, e.g., 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 bp long.


Orthologous proteins may but need not be structurally related, or are only partially structurally related. In particular embodiments, the homologue or orthologue of a Type V protein such as Cas12c as referred to herein has a sequence homology or identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with a Cas12c. In further embodiments, the homologue or orthologue of a Type V Cas12c as referred to herein has a sequence identity of at least 80%, more preferably at least 85%, even more preferably at least 90%, such as for instance at least 95% with the wild type Cas12c.


In an embodiment, the Type V RNA-targeting Cas protein may be a Cas12c ortholog of an organism of a genus which includes but is not limited to Corynebacter, Sutterella, Legionella, Treponema, Filifactor, Eubacterium, Streptococcus, Lactobacillus, Mycoplasma, Bacteroides, Flaviivola, Flavobacterium, Sphaerochaeta, Azospirillum, Gluconacetobacter, Neisseria, Roseburia, Parvibaculum, Staphylococcus, Nitratifractor, Mycoplasma and Campylobacter.


In an embodiment, the Cas12c or an ortholog or homolog thereof, may comprise one or more mutations (and hence nucleic acid molecule(s) coding for same may have mutation(s). The mutations may be artificially introduced mutations and may include but are not limited to one or more mutations in a catalytic domain. Examples of catalytic domains with reference to a Cas enzyme may include but are not limited to RuvC I, RuvC II, RuvC III, HNH domains, and HEPN domains, as described herein. In an embodiment, the Cas12c or an ortholog or homolog thereof, may comprise one or more mutations. The mutations may be artificially introduced mutations and may include but are not limited to one or more mutations in a catalytic domain. Guide Sequences


As used herein, the term “guide sequence” and “guide molecule” in the context of a CRISPR-Cas system, comprises any polynucleotide sequence having sufficient complementarity with a target nucleic acid sequence to hybridize with the target nucleic acid sequence and direct sequence-specific binding of a nucleic acid-targeting complex to the target nucleic acid sequence. The guide sequences made using the methods disclosed herein may be a full-length guide sequence, a truncated guide sequence, a full-length sgRNA sequence, a truncated sgRNA sequence, or an E+F sgRNA sequence. In some embodiments, the degree of complementarity of the guide sequence to a given target sequence, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. In certain example embodiments, the guide molecule comprises a guide sequence that may be designed to have at least one mismatch with the target sequence, such that a RNA duplex formed between the guide sequence and the target sequence. Accordingly, the degree of complementarity is preferably less than 99%. For instance, where the guide sequence consists of 24 nucleotides, the degree of complementarity is more particularly about 96% or less. In particular embodiments, the guide sequence is designed to have a stretch of two or more adjacent mismatching nucleotides, such that the degree of complementarity over the entire guide sequence is further reduced. For instance, where the guide sequence consists of 24 nucleotides, the degree of complementarity is more particularly about 96% or less, more particularly, about 92% or less, more particularly about 88% or less, more particularly about 84% or less, more particularly about 80% or less, more particularly about 76% or less, more particularly about 72% or less, depending on whether the stretch of two or more mismatching nucleotides encompasses 2, 3, 4, 5, 6 or 7 nucleotides, etc. In some embodiments, aside from the stretch of one or more mismatching nucleotides, the degree of complementarity, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined with the use of any suitable algorithm for aligning sequences, non-limiting example of which include the Smith-Waterman algorithm, the Needleman-Wunsch algorithm, algorithms based on the Burrows-Wheeler Transform (e.g., the Burrows Wheeler Aligner), ClustalW, Clustal X, BLAT, Novoalign (Novocraft Technologies; available at www.novocraft.com), ELAND (Illumina, San Diego, Calif.), SOAP (available at soap.genomics.org.cn), and Maq (available at maq.sourceforge.net). The ability of a guide sequence (within a nucleic acid-targeting guide RNA) to direct sequence-specific binding of a nucleic acid-targeting complex to a target nucleic acid sequence may be assessed by any suitable assay. For example, the components of a nucleic acid-targeting CRISPR system sufficient to form a nucleic acid-targeting complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target nucleic acid sequence, such as by transfection with vectors encoding the components of the nucleic acid-targeting complex, followed by an assessment of preferential targeting (e.g., cleavage) within the target nucleic acid sequence, such as by Surveyor assay as described herein. Similarly, cleavage of a target nucleic acid sequence (or a sequence in the vicinity thereof) may be evaluated in a test tube by providing the target nucleic acid sequence, components of a nucleic acid-targeting complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at or in the vicinity of the target sequence between the test and control guide sequence reactions. Other assays are possible, and will occur to those skilled in the art. A guide sequence, and hence a nucleic acid-targeting guide RNA may be selected to target any target nucleic acid sequence.


As used herein, the term “guide sequence,” “crRNA,” “guide RNA,” or “single guide RNA,” or “gRNA” refers to a polynucleotide comprising any polynucleotide sequence having sufficient complementarity with a target nucleic acid sequence to hybridize with the target nucleic acid sequence and to direct sequence-specific binding of a RNA-targeting complex comprising the guide sequence and a CRISPR effector protein to the target nucleic acid sequence. In some example embodiments, the degree of complementarity, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined with the use of any suitable algorithm for aligning sequences, non-limiting example of which include the Smith-Waterman algorithm, the Needleman-Wunsch algorithm, algorithms based on the Burrows-Wheeler Transform (e.g., the Burrows Wheeler Aligner), ClustalW, Clustal X, BLAT, Novoalign (Novocraft Technologies; available at www.novocraft.com), ELAND (Illumina, San Diego, Calif.), SOAP (available at soap.genomics.org.cn), and Maq (available at maq.sourceforge.net). The ability of a guide sequence (within a nucleic acid-targeting guide RNA) to direct sequence-specific binding of a nucleic acid-targeting complex to a target nucleic acid sequence may be assessed by any suitable assay. For example, the components of a nucleic acid-targeting CRISPR system sufficient to form a nucleic acid-targeting complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target nucleic acid sequence, such as by transfection with vectors encoding the components of the nucleic acid-targeting complex, followed by an assessment of preferential targeting (e.g., cleavage) within the target nucleic acid sequence, such as by Surveyor assay as described herein. Similarly, cleavage of a target nucleic acid sequence may be evaluated in a test tube by providing the target nucleic acid sequence, components of a nucleic acid-targeting complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at the target sequence between the test and control guide sequence reactions. Other assays are possible, and will occur to those skilled in the art. A guide sequence, and hence a nucleic acid-targeting guide may be selected to target any target nucleic acid sequence. The target sequence may be DNA. The target sequence may be any RNA sequence. In some embodiments, the target sequence may be a sequence within a RNA molecule selected from the group consisting of messenger RNA (mRNA), pre-mRNA, ribosomal RNA (rRNA), transfer RNA (tRNA), micro-RNA (miRNA), small interfering RNA (siRNA), small nuclear RNA (snRNA), small nucleolar RNA (snoRNA), double stranded RNA (dsRNA), non-coding RNA (ncRNA), long non-coding RNA (lncRNA), and small cytoplasmatic RNA (scRNA). In some preferred embodiments, the target sequence may be a sequence within a RNA molecule selected from the group consisting of mRNA, pre-mRNA, and rRNA. In some preferred embodiments, the target sequence may be a sequence within a RNA molecule selected from the group consisting of ncRNA, and lncRNA. In some more preferred embodiments, the target sequence may be a sequence within an mRNA molecule or a pre-mRNA molecule.


In certain embodiments, the guide sequence or spacer length of the guide molecules is from 15 to 50 nt. In certain embodiments, the spacer length of the guide RNA is at least 15 nucleotides. In certain embodiments, the spacer length is from 15 to 17 nt, e.g., 15, 16, or 17 nt, from 17 to 20 nt, e.g., 17, 18, 19, or 20 nt, from 20 to 24 nt, e.g., 20, 21, 22, 23, or 24 nt, from 23 to 25 nt, e.g., 23, 24, or 25 nt, from 24 to 27 nt, e.g., 24, 25, 26, or 27 nt, from 27-30 nt, e.g., 27, 28, 29, or 30 nt, from 30-35 nt, e.g., 30, 31, 32, 33, 34, or 35 nt, or 35 nt or longer. In certain example embodiment, the guide sequence is 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39 40, 41, 42, 43, 44, 45, 46, 47 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100 nt.


In some embodiments, the sequence of the guide molecule (direct repeat and/or spacer) is selected to reduce the degree secondary structure within the guide molecule. In some embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%, 15%, 10%, 5%, 1%, or fewer of the nucleotides of the nucleic acid-targeting guide RNA participate in self-complementary base pairing when optimally folded. Optimal folding may be determined by any suitable polynucleotide folding algorithm. Some programs are based on calculating the minimal Gibbs free energy. An example of one such algorithm is mFold, as described by Zuker and Stiegler (Nucleic Acids Res. 9 (1981), 133-148). Another example folding algorithm is the online webserver RNAfold, developed at Institute for Theoretical Chemistry at the University of Vienna, using the centroid structure prediction algorithm (see e.g., A. R. Gruber et al., 2008, Cell 106(1): 23-24; and PA Carr and GM Church, 2009, Nature Biotechnology 27(12): 1151-62).


In some embodiments, it is of interest to reduce the susceptibility of the guide molecule to RNA cleavage, such as to cleavage by Cas13. Accordingly, in particular embodiments, the guide molecule is adjusted to avoide cleavage by Cas13 or other RNA-cleaving enzymes.


In certain embodiments, the guide molecule comprises non-naturally occurring nucleic acids and/or non-naturally occurring nucleotides and/or nucleotide analogs, and/or chemically modifications. Preferably, these non-naturally occurring nucleic acids and non-naturally occurring nucleotides are located outside the guide sequence. Non-naturally occurring nucleic acids can include, for example, mixtures of naturally and non-naturally occurring nucleotides. Non-naturally occurring nucleotides and/or nucleotide analogs may be modified at the ribose, phosphate, and/or base moiety. In an embodiment of the invention, a guide nucleic acid comprises ribonucleotides and non-ribonucleotides. In one such embodiment, a guide comprises one or more ribonucleotides and one or more deoxyribonucleotides. In an embodiment of the invention, the guide comprises one or more non-naturally occurring nucleotide or nucleotide analog such as a nucleotide with phosphorothioate linkage, a locked nucleic acid (LNA) nucleotides comprising a methylene bridge between the 2′ and 4′ carbons of the ribose ring, or bridged nucleic acids (BNA). Other examples of modified nucleotides include 2′-O-methyl analogs, 2′-deoxy analogs, or 2′-fluoro analogs. Further examples of modified bases include, but are not limited to, 2-aminopurine, 5-bromo-uridine, pseudouridine, inosine, 7-methylguanosine. Examples of guide RNA chemical modifications include, without limitation, incorporation of 2′-O-methyl (M), 2′-O-methyl 3′phosphorothioate (MS), S-constrained ethyl(cEt), or 2′-O-methyl 3′thioPACE (MSP) at one or more terminal nucleotides. Such chemically modified guides can comprise increased stability and increased activity as compared to unmodified guides, though on-target vs. off-target specificity is not predictable. (See, Hendel, 2015, Nat Biotechnol. 33(9):985-9, doi: 10.1038/nbt.3290, published online 29 Jun. 2015 Ragdarm et al., 0215, PNAS, E7110-E7111; Allerson et al., J. Med. Chem. 2005, 48:901-904; Bramsen et al., Front. Genet., 2012, 3:154; Deng et al., PNAS, 2015, 112:11870-11875; Sharma et al., MedChemComm., 2014, 5:1454-1471; Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989; Li et al., Nature Biomedical Engineering, 2017, 1, 0066 DOI:10.1038/s41551-017-0066). In some embodiments, the 5′ and/or 3′ end of a guide RNA is modified by a variety of functional moieties including fluorescent dyes, polyethylene glycol, cholesterol, proteins, or detection tags. (See Kelly et al., 2016, J. Biotech. 233:74-83). In certain embodiments, a guide comprises ribonucleotides in a region that binds to a target RNA and one or more deoxyribonucletides and/or nucleotide analogs in a region that binds to Cas13. In an embodiment of the invention, deoxyribonucleotides and/or nucleotide analogs are incorporated in engineered guide structures, such as, without limitation, stem-loop regions, and the seed region. For Cas13 guide, in certain embodiments, the modification is not in the 5′-handle of the stem-loop regions. Chemical modification in the 5′-handle of the stem-loop region of a guide may abolish its function (see Li, et al., Nature Biomedical Engineering, 2017, 1:0066). In certain embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides of a guide is chemically modified. In some embodiments, 3-5 nucleotides at either the 3′ or the 5′ end of a guide is chemically modified. In some embodiments, only minor modifications are introduced in the seed region, such as 2′-F modifications. In some embodiments, 2′-F modification is introduced at the 3′ end of a guide. In certain embodiments, three to five nucleotides at the 5′ and/or the 3′ end of the guide are chemically modified with 2′-O-methyl (M), 2′-O-methyl 3′ phosphorothioate (MS), S-constrained ethyl(cEt), or 2′-O-methyl 3′ thioPACE (MSP). Such modification can enhance genome editing efficiency (see Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989). In certain embodiments, all of the phosphodiester bonds of a guide are substituted with phosphorothioates (PS) for enhancing levels of gene disruption. In certain embodiments, more than five nucleotides at the 5′ and/or the 3′ end of the guide are chemicially modified with 2′-O-Me, 2′-F or S-constrained ethyl(cEt). Such chemically modified guide can mediate enhanced levels of gene disruption (see Ragdarm et al., 0215, PNAS, E7110-E7111). In an embodiment of the invention, a guide is modified to comprise a chemical moiety at its 3′ and/or 5′ end. Such moieties include, but are not limited to amine, azide, alkyne, thio, dibenzocyclooctyne (DBCO), or Rhodamine. In certain embodiment, the chemical moiety is conjugated to the guide by a linker, such as an alkyl chain. In certain embodiments, the chemical moiety of the modified guide can be used to attach the guide to another molecule, such as DNA, RNA, protein, or nanoparticles. Such chemically modified guide can be used to identify or enrich cells generically edited by a CRISPR system (see Lee et al., eLife, 2017, 6:e25312, DOI:10.7554).


In some embodiments, a nucleic acid-targeting guide is selected to reduce the degree secondary structure within the nucleic acid-targeting guide. In some embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%, 15%, 10%, 5%, 1%, or fewer of the nucleotides of the nucleic acid-targeting guide participate in self-complementary base pairing when optimally folded. Optimal folding may be determined by any suitable polynucleotide folding algorithm. Some programs are based on calculating the minimal Gibbs free energy. An example of one such algorithm is mFold, as described by Zuker and Stiegler (Nucleic Acids Res. 9 (1981), 133-148). Another example folding algorithm is the online webserver RNAfold, developed at Institute for Theoretical Chemistry at the University of Vienna, using the centroid structure prediction algorithm (see e.g., A. R. Gruber et al., 2008, Cell 106(1): 23-24; and PA Carr and GM Church, 2009, Nature Biotechnology 27(12): 1151-62).


In some embodiments, a nucleic acid-targeting guide is designed or selected to modulate intermolecular interactions among guide molecules, such as among stem-loop regions of different guide molecules. It will be appreciated that nucleotides within a guide that base-pair to form a stem-loop are also capable of base-pairing to form an intermolecular duplex with a second guide and that such an intermolecular duplex would not have a secondary structure compatible with CRISPR complex formation. Accordingly, is useful to select or design DR sequences in order to modulate stem-loop formation and CRISPR complex formation. In some embodiments, about or less than about 75%, 50%, 40%, 30%, 25%, 20%, 15%, 10%, 5%, 1%, or fewer of nucleic acid-targeting guides are in intermolecular duplexes. It will be appreciated that stem-loop variation will often be within limits imposed by DR-CRISPR effector interactions. One way to modulate stem-loop formation or change the equilibrium between stem-loop and intermolecular duplex is to vary nucleotide pairs in the stem of the stem-loop of a DR. For example, in one embodiment, a G-C pair is replaced by an A-U or U-A pair. In another embodiment, an A-U pair is substituted for a G-C or a C-G pair. In another embodiment, a naturally occurring nucleotide is replaced by a nucleotide analog. Another way to modulate stem-loop formation or change the equilibrium between stem-loop and intermolecular duplex is to modify the loop of the stem-loop of a DR. Without be bound by theory, the loop can be viewed as an intervening sequence flanked by two sequences that are complementary to each other. When that intervening sequence is not self-complementary, its effect will be to destabilize intermolecular duplex formation. The same principle applies when guides are multiplexed: while the targeting sequences may differ, it may be advantageous to modify the stem-loop region in the DRs of the different guides. Moreover, when guides are multiplexed, the relative activities of the different guides can be modulated by balancing the activity of each individual guide. In certain embodiments, the equilibrium between intermolecular stem-loops vs. intermolecular duplexes is determined. The determination may be made by physical or biochemical means and can be in the presence or absence of a CRISPR effector.


In certain embodiments, a guide RNA or crRNA may comprise, consist essentially of, or consist of a direct repeat (DR) sequence and a guide sequence or spacer sequence. In certain embodiments, the guide RNA or crRNA may comprise, consist essentially of, or consist of a direct repeat sequence fused or linked to a guide sequence or spacer sequence. In certain embodiments, the direct repeat sequence may be located upstream (i.e., 5′) from the guide sequence or spacer sequence. In other embodiments, the direct repeat sequence may be located downstream (i.e., 3′) from the guide sequence or spacer sequence.


In certain embodiments, the crRNA comprises a stem loop, preferably a single stem loop. In certain embodiments, the direct repeat sequence forms a stem loop, preferably a single stem loop.


In certain embodiments, the spacer length of the guide RNA is from 15 to 35 nt. In certain embodiments, the spacer length of the guide RNA is at least 15 nucleotides. In certain embodiments, the spacer length is from 15 to 17 nt, e.g., 15, 16, or 17 nt, from 17 to 20 nt, e.g., 17, 18, 19, or 20 nt, from 20 to 24 nt, e.g., 20, 21, 22, 23, or 24 nt, from 23 to 25 nt, e.g., 23, 24, or 25 nt, from 24 to 27 nt, e.g., 24, 25, 26, or 27 nt, from 27-30 nt, e.g., 27, 28, 29, or 30 nt, from 30-35 nt, e.g., 30, 31, 32, 33, 34, or 35 nt, or 35 nt or longer.


In general, the CRISPR-Cas, CRISPR-Cas9 or CRISPR system may be as used in the foregoing documents, such as WO 2014/093622 (PCT/US2013/074667) and refers collectively to transcripts and other elements involved in the expression of or directing the activity of CRISPR-associated (“Cas”) genes, including sequences encoding a Cas gene, in particular a Cas9 gene in the case of CRISPR-Cas9, a tracr (trans-activating CRISPR) sequence (e.g. tracrRNA or an active partial tracrRNA), a tracr-mate sequence (encompassing a “direct repeat” and a tracrRNA-processed partial direct repeat in the context of an endogenous CRISPR system), a guide sequence (also referred to as a “spacer” in the context of an endogenous CRISPR system), or “RNA(s)” as that term is herein used (e.g., RNA(s) to guide Cas9, e.g. CRISPR RNA and transactivating (tracr) RNA or a single guide RNA (sgRNA) (chimeric RNA)) or other sequences and transcripts from a CRISPR locus. In general, a CRISPR system is characterized by elements that promote the formation of a CRISPR complex at the site of a target sequence (also referred to as a protospacer in the context of an endogenous CRISPR system). In the context of formation of a CRISPR complex, “target sequence” refers to a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. The section of the guide sequence through which complementarity to the target sequence is important for cleavage activity is referred to herein as the seed sequence. A target sequence may comprise any polynucleotide, such as DNA or RNA polynucleotides. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell, and may include nucleic acids in or from mitochondrial, organelles, vesicles, liposomes or particles present within the cell. In some embodiments, especially for non-nuclear uses, NLSs are not preferred. In some embodiments, a CRISPR system comprises one or more nuclear exports signals (NESs). In some embodiments, a CRISPR system comprises one or more NLSs and one or more NESs. In some embodiments, direct repeats may be identified in silico by searching for repetitive motifs that fulfill any or all of the following criteria: 1. found in a 2 Kb window of genomic sequence flanking the type II CRISPR locus; 2. span from 20 to 50 bp; and 3. interspaced by 20 to 50 bp. In some embodiments, 2 of these criteria may be used, for instance 1 and 2, 2 and 3, or 1 and 3. In some embodiments, all 3 criteria may be used.


In embodiments of the invention the terms guide sequence and guide RNA, i.e. RNA capable of guiding Cas to a target genomic locus, are used interchangeably as in foregoing cited documents such as WO 2014/093622 (PCT/US2013/074667). In general, a guide sequence is any polynucleotide sequence having sufficient complementarity with a target polynucleotide sequence to hybridize with the target sequence and direct sequence-specific binding of a CRISPR complex to the target sequence. In some embodiments, the degree of complementarity between a guide sequence and its corresponding target sequence, when optimally aligned using a suitable alignment algorithm, is about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or more. Optimal alignment may be determined with the use of any suitable algorithm for aligning sequences, non-limiting example of which include the Smith-Waterman algorithm, the Needleman-Wunsch algorithm, algorithms based on the Burrows-Wheeler Transform (e.g. the Burrows Wheeler Aligner), ClustalW, Clustal X, BLAT, Novoalign (Novocraft Technologies; available at www.novocraft.com), ELAND (Illumina, San Diego, Calif.), SOAP (available at soap.genomics.org.cn), and Maq (available at maq.sourceforge.net). In some embodiments, a guide sequence is about or more than about 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 75, or more nucleotides in length. In some embodiments, a guide sequence is less than about 75, 50, 45, 40, 35, 30, 25, 20, 15, 12, or fewer nucleotides in length. Preferably the guide sequence is 10 30 nucleotides long. The ability of a guide sequence to direct sequence-specific binding of a CRISPR complex to a target sequence may be assessed by any suitable assay. For example, the components of a CRISPR system sufficient to form a CRISPR complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target sequence, such as by transfection with vectors encoding the components of the CRISPR sequence, followed by an assessment of preferential cleavage within the target sequence, such as by Surveyor assay as described herein. Similarly, cleavage of a target polynucleotide sequence may be evaluated in a test tube by providing the target sequence, components of a CRISPR complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at the target sequence between the test and control guide sequence reactions. Other assays are possible, and will occur to those skilled in the art.


In some embodiments of CRISPR-Cas systems, the degree of complementarity between a guide sequence and its corresponding target sequence can be about or more than about 50%, 60%, 75%, 80%, 85%, 90%, 95%, 97.5%, 99%, or 100%; a guide or RNA or sgRNA can be about or more than about 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 75, or more nucleotides in length; or guide or RNA or sgRNA can be less than about 75, 50, 45, 40, 35, 30, 25, 20, 15, 12, or fewer nucleotides in length; and advantageously tracr RNA is 30 or 50 nucleotides in length. However, an aspect of the invention is to reduce off-target interactions, e.g., reduce the guide interacting with a target sequence having low complementarity. Indeed, in the examples, it is shown that the invention involves mutations that result in the CRISPR-Cas system being able to distinguish between target and off-target sequences that have greater than 80% to about 95% complementarity, e.g., 83%-84% or 88-89% or 94-95% complementarity (for instance, distinguishing between a target having 18 nucleotides from an off-target of 18 nucleotides having 1, 2 or 3 mismatches). Accordingly, in the context of the present invention the degree of complementarity between a guide sequence and its corresponding target sequence is greater than 94.5% or 95% or 95.5% or 96% or 96.5% or 97% or 97.5% or 98% or 98.5% or 99% or 99.5% or 99.9%, or 100%. Off target is less than 100% or 99.9% or 99.5% or 99% or 99% or 98.5% or 98% or 97.5% or 97% or 96.5% or 96% or 95.5% or 95% or 94.5% or 94% or 93% or 92% or 91% or 90% or 89% or 88% or 87% or 86% or 85% or 84% or 83% or 82% or 81% or 80% complementarity between the sequence and the guide, with it advantageous that off target is 100% or 99.9% or 99.5% or 99% or 99% or 98.5% or 98% or 97.5% or 97% or 96.5% or 96% or 95.5% or 95% or 94.5% complementarity between the sequence and the guide.


Multiplexing Polynucleotides

Provided herein are engineered polynucleotide sequences that can direct the activity of a CRISPR protein to multiple targets using a single crRNA. The engineered polynucleotide sequences, also referred to as a multiplexing polynucleotides, can include two or more direct repeats interspersed with two or more guide sequences. More specifically, the engineered polynucleotide sequences can include a direct repeat sequence having one or more mutations relative to the corresponding wild type direct repeat sequence. The engineered polynucleotide can be configured, for example, as: 5′ DR1-G1-DR2-G2 3′. In some embodiments, the engineered polynucleotide can be configured to include three, four, five, or more additional direct repeat and guide sequences, for example: 5′ DR1-G1-DR2-G2-DR3-G3 3′, 5″ DR1-G1-DR2-G2-DR3-G3-DR4-G4 3′, or 5′ DR1-G1-DR2-G2-DR3-G3-DR4-G4-DR5-G5 3′.


Regardless of the number of direct repeat sequences, the direct repeat sequences differ from one another. Thus, DR1 can be a wild type sequence and DR2 can include one or more mutations relative to the wild type sequence in accordance with the disclosure provided herein regarding direct repeats for Cas orthologs. The guide sequences can also be the same or different. In some embodiments, the guide sequences can bind to different nucleic acid targets, for example, nucleic acids encoding different polypeptides. The multiplexing polynucleotides can be as described, for example, at [0039]-[0072] in U.S. Application 62/780,748 entitled “CRISPR Cpf1 Directe Repeat Variants” and filed Dec. 17, 2018, incorporated herein in its entirety by reference.


Guide Modifications

In certain embodiments, guides of the invention comprise non-naturally occurring nucleic acids and/or non-naturally occurring nucleotides and/or nucleotide analogs, and/or chemical modifications. Non-naturally occurring nucleic acids can include, for example, mixtures of naturally and non-naturally occurring nucleotides. Non-naturally occurring nucleotides and/or nucleotide analogs may be modified at the ribose, phosphate, and/or base moiety. In an embodiment of the invention, a guide nucleic acid comprises ribonucleotides and non-ribonucleotides. In one such embodiment, a guide comprises one or more ribonucleotides and one or more deoxyribonucleotides. In an embodiment of the invention, the guide comprises one or more non-naturally occurring nucleotide or nucleotide analog such as a nucleotide with phosphorothioate linkage, boranophosphate linkage, a locked nucleic acid (LNA) nucleotides comprising a methylene bridge between the 2′ and 4′ carbons of the ribose ring, or bridged nucleic acids (BNA). Other examples of modified nucleotides include 2′-O-methyl analogs, 2′-deoxy analogs, 2-thiouridine analogs, N6-methyladenosine analogs, or 2′-fluoro analogs. Further examples of modified bases include, but are not limited to, 2-aminopurine, 5-bromo-uridine, pseudouridine (Ψ), N1-methylpseudouridine (me1Ψ), 5-methoxyuridine(5moU), inosine, 7-methylguanosine. Examples of guide RNA chemical modifications include, without limitation, incorporation of 2′-O-methyl (M), 2′-O-methyl-3′-phosphorothioate (MS), phosphorothioate (PS), S-constrained ethyl(cEt), or 2′-O-methyl-3′-thioPACE (MSP) at one or more terminal nucleotides. Such chemically modified guides can comprise increased stability and increased activity as compared to unmodified guides, though on-target vs. off-target specificity is not predictable. (See, Hendel, 2015, Nat Biotechnol. 33(9):985-9, doi: 10.1038/nbt.3290, published online 29 Jun. 2015; Ragdarm et al., 0215, PNAS, E7110-E7111; Allerson et al., J. Med. Chem. 2005, 48:901-904; Bramsen et al., Front. Genet., 2012, 3:154; Deng et al., PNAS, 2015, 112:11870-11875; Sharma et al., MedChemComm., 2014, 5:1454-1471; Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989; Li et al., Nature Biomedical Engineering, 2017, 1, 0066 DOI:10.1038/s41551-017-0066). In some embodiments, the 5′ and/or 3′ end of a guide RNA is modified by a variety of functional moieties including fluorescent dyes, polyethylene glycol, cholesterol, proteins, or detection tags. (See Kelly et al., 2016, J. Biotech. 233:74-83). In certain embodiments, a guide comprises ribonucleotides in a region that binds to a target DNA and one or more deoxyribonucleotides and/or nucleotide analogs in a region that binds to Cas9, Cpf1, or C2c1. In an embodiment of the invention, deoxyribonucleotides and/or nucleotide analogs are incorporated in engineered guide structures, such as, without limitation, 5′ and/or 3′ end, stem-loop regions, and the seed region. In certain embodiments, the modification is not in the 5′-handle of the stem-loop regions. Chemical modification in the 5′-handle of the stem-loop region of a guide may abolish its function (see Li, et al., Nature Biomedical Engineering, 2017, 1:0066). In certain embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides of a guide is chemically modified. In some embodiments, 3-5 nucleotides at either the 3′ or the 5′ end of a guide is chemically modified. In some embodiments, only minor modifications are introduced in the seed region, such as 2′-F modifications. In some embodiments, 2′-F modification is introduced at the 3′ end of a guide. In certain embodiments, three to five nucleotides at the 5′ and/or the 3′ end of the guide are chemically modified with 2′-O-methyl (M), 2′-O-methyl-3′-phosphorothioate (MS), S-constrained ethyl(cEt), or 2′-O-methyl-3′-thioPACE (MSP). Such modification can enhance genome editing efficiency (see Hendel et al., Nat. Biotechnol. (2015) 33(9): 985-989). In certain embodiments, all of the phosphodiester bonds of a guide are substituted with phosphorothioates (PS) for enhancing levels of gene disruption. In certain embodiments, more than five nucleotides at the 5′ and/or the 3′ end of the guide are chemically modified with 2′-O-Me, 2′-F or S-constrained ethyl(cEt). Such chemically modified guide can mediate enhanced levels of gene disruption (see Ragdarm et al., 0215, PNAS, E7110-E7111). In an embodiment of the invention, a guide is modified to comprise a chemical moiety at its 3′ and/or 5′ end. Such moieties include, but are not limited to amine, azide, alkyne, thio, dibenzocyclooctyne (DBCO), or Rhodamine. In certain embodiment, the chemical moiety is conjugated to the guide by a linker, such as an alkyl chain. In certain embodiments, the chemical moiety of the modified guide can be used to attach the guide to another molecule, such as DNA, RNA, protein, or nanoparticles. Such chemically modified guide can be used to identify or enrich cells generically edited by a CRISPR system (see Lee et al., eLife, 2017, 6:e25312, DOI:10.7554).


In certain embodiments, the CRISPR system as provided herein can make use of a crRNA or analogous polynucleotide comprising a guide sequence, wherein the polynucleotide is an RNA, a DNA or a mixture of RNA and DNA, and/or wherein the polynucleotide comprises one or more nucleotide analogs. The sequence can comprise any structure, including but not limited to a structure of a native crRNA, such as a bulge, a hairpin or a stem loop structure. In certain embodiments, the polynucleotide comprising the guide sequence forms a duplex with a second polynucleotide sequence which can be an RNA or a DNA sequence.


In certain embodiments, use is made of chemically modified guide RNAs. Examples of guide RNA chemical modifications include, without limitation, incorporation of 2′-O-methyl (M), 2′-O-methyl 3′phosphorothioate (MS), or 2′-O-methyl 3′thioPACE (MSP) at one or more terminal nucleotides. Such chemically modified guide RNAs can comprise increased stability and increased activity as compared to unmodified guide RNAs, though on-target vs. off-target specificity is not predictable. (See, Hendel, 2015, Nat Biotechnol. 33(9):985-9, doi: 10.1038/nbt.3290, published online 29 Jun. 2015). Chemically modified guide RNAs further include, without limitation, RNAs with phosphorothioate linkages and locked nucleic acid (LNA) nucleotides comprising a methylene bridge between the 2′ and 4′ carbons of the ribose ring.


In some embodiments, a guide sequence is about or more than about 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 75, or more nucleotides in length. In some embodiments, a guide sequence is less than about 75, 50, 45, 40, 35, 30, 25, 20, 15, 12, or fewer nucleotides in length. Preferably the guide sequence is 10 to 30 nucleotides long. The ability of a guide sequence to direct sequence-specific binding of a CRISPR complex to a target sequence may be assessed by any suitable assay. For example, the components of a CRISPR system sufficient to form a CRISPR complex, including the guide sequence to be tested, may be provided to a host cell having the corresponding target sequence, such as by transfection with vectors encoding the components of the CRISPR sequence, followed by an assessment of preferential cleavage within the target sequence, such as by Surveyor assay. Similarly, cleavage of a target RNA may be evaluated in a test tube by providing the target sequence, components of a CRISPR complex, including the guide sequence to be tested and a control guide sequence different from the test guide sequence, and comparing binding or rate of cleavage at the target sequence between the test and control guide sequence reactions. Other assays are possible, and will occur to those skilled in the art.


In some embodiments, the modification to the guide is a chemical modification, an insertion, a deletion or a split. In some embodiments, the chemical modification includes, but is not limited to, incorporation of 2′-O-methyl (M) analogs, 2′-deoxy analogs, 2-thiouridine analogs, N6-methyladenosine analogs, 2′-fluoro analogs, 2-aminopurine, 5-bromo-uridine, pseudouridine (Ψ), N1-methylpseudouridine (me1Ψ), 5-methoxyuridine(5moU), inosine, 7-methylguanosine, 2′-O-methyl-3′-phosphorothioate (MS), S-constrained ethyl(cEt), phosphorothioate (PS), or 2′-O-methyl-3′-thioPACE (MSP). In some embodiments, the guide comprises one or more of phosphorothioate modifications. In certain embodiments, at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or 25 nucleotides of the guide are chemically modified. In certain embodiments, one or more nucleotides in the seed region are chemically modified. In certain embodiments, one or more nucleotides in the 3′-terminus are chemically modified. In certain embodiments, none of the nucleotides in the 5′-handle is chemically modified. In some embodiments, the chemical modification in the seed region is a minor modification, such as incorporation of a 2′-fluoro analog. In a specific embodiment, one nucleotide of the seed region is replaced with a 2′-fluoro analog. In some embodiments, 5 or 10 nucleotides in the 3′-terminus are chemically modified. Such chemical modifications at the 3′-terminus of the Cpf1 CrRNA improve gene cutting efficiency (see Li, et al., Nature Biomedical Engineering, 2017, 1:0066). In a specific embodiment, 5 nucleotides in the 3′-terminus are replaced with 2′-fluoro analogues. In a specific embodiment, 10 nucleotides in the 3′-terminus are replaced with 2′-fluoro analogues. In a specific embodiment, 5 nucleotides in the 3′-terminus are replaced with 2′-O-methyl (M) analogs.


In some embodiments, the loop of the 5′-handle of the guide is modified. In some embodiments, the loop of the 5′-handle of the guide is modified to have a deletion, an insertion, a split, or chemical modifications. In certain embodiments, the loop comprises 3, 4, or 5 nucleotides. In certain embodiments, the loop comprises the sequence of UCUU, UUUU, UAUU, or UGUU.


A guide sequence, and hence a nucleic acid-targeting guide RNA may be selected to target any target nucleic acid sequence. In the context of formation of a CRISPR complex, “target sequence” refers to a sequence to which a guide sequence is designed to have complementarity, where hybridization between a target sequence and a guide sequence promotes the formation of a CRISPR complex. A target sequence may comprise RNA polynucleotides. The term “target RNA” refers to a RNA polynucleotide being or comprising the target sequence. In other words, the target RNA may be a RNA polynucleotide or a part of a RNA polynucleotide to which a part of the gRNA, i.e. the guide sequence, is designed to have complementarity and to which the effector function mediated by the complex comprising CRISPR effector protein and a gRNA is to be directed. In some embodiments, a target sequence is located in the nucleus or cytoplasm of a cell. The target sequence may be DNA. The target sequence may be any RNA sequence. In some embodiments, the target sequence may be a sequence within a RNA molecule selected from the group consisting of messenger RNA (mRNA), pre-mRNA, ribosomal RNA (rRNA), transfer RNA (tRNA), micro-RNA (miRNA), small interfering RNA (siRNA), small nuclear RNA (snRNA), small nuclear RNA (snoRNA), double stranded RNA (dsRNA), non coding RNA (ncRNA), long non-coding RNA (lncRNA), and small cytoplasmic RNA (scRNA). In some preferred embodiments, the target sequence may be a sequence within a RNA molecule selected from the group consisting of mRNA, pre-mRNA, and rRNA. In some preferred embodiments, the target sequence may be a sequence within a RNA molecule selected from the group consisting of ncRNA, and lncRNA. In some more preferred embodiments, the target sequence may be a sequence within an mRNA molecule or a pre-mRNA molecule.


In certain embodiments, the spacer length of the guide RNA is less than 28 nucleotides. In certain embodiments, the spacer length of the guide RNA is at least 18 nucleotides and less than 28 nucleotides. In certain embodiments, the spacer length of the guide RNA is between 19 and 28 nucleotides. In certain embodiments, the spacer length of the guide RNA is between 19 and 25 nucleotides. In certain embodiments, the spacer length of the guide RNA is 20 nucleotides. In certain embodiments, the spacer length of the guide RNA is 23 nucleotides. In certain embodiments, the spacer length of the guide RNA is 25 nucleotides.


In certain embodiments, modulations of cleavage efficiency can be exploited by introduction of mismatches, e.g. 1 or more mismatches, such as 1 or 2 mismatches between spacer sequence and target sequence, including the position of the mismatch along the spacer/target. The more central (i.e. not 3′ or 5′) for instance a double mismatch is, the more cleavage efficiency is affected. Accordingly, by choosing mismatch position along the spacer, cleavage efficiency can be modulated. By means of example, if less than 100% cleavage of targets is desired (e.g. in a cell population), 1 or more, such as preferably 2 mismatches between spacer and target sequence may be introduced in the spacer sequences. The more central along the spacer of the mismatch position, the lower the cleavage percentage.


In certain example embodiments, the cleavage efficiency may be exploited to design single guides that can distinguish two or more targets that vary by a single nucleotide, such as a single nucleotide polymorphism (SNP), variation, or (point) mutation. The CRISPR effector may have reduced sensitivity to SNPs (or other single nucleotide variations) and continue to cleave SNP targets with a certain level of efficiency. Thus, for two targets, or a set of targets, a guide RNA may be designed with a nucleotide sequence that is complementary to one of the targets i.e. the on-target SNP. The guide RNA is further designed to have a synthetic mismatch. As used herein a “synthetic mismatch” refers to a non-naturally occurring mismatch that is introduced upstream or downstream of the naturally occurring SNP, such as at most 5 nucleotides upstream or downstream, for instance 4, 3, 2, or 1 nucleotide upstream or downstream, preferably at most 3 nucleotides upstream or downstream, more preferably at most 2 nucleotides upstream or downstream, most preferably 1 nucleotide upstream or downstream (i.e. adjacent the SNP). When the CRISPR effector binds to the on-target SNP, only a single mismatch will be formed with the synthetic mismatch and the CRISPR effector will continue to be activated and a detectable signal produced. When the guide RNA hybridizes to an off-target SNP, two mismatches will be formed, the mismatch from the SNP and the synthetic mismatch, and no detectable signal generated. Thus, the systems disclosed herein may be designed to distinguish SNPs within a population. For, example the systems may be used to distinguish pathogenic strains that differ by a single SNP or detect certain disease specific SNPs, such as but not limited to, disease associated SNPs, such as without limitation cancer associated SNPs.


In certain embodiments, the guide RNA is designed such that the SNP is located on position 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the spacer sequence (starting at the 5′ end). In certain embodiments, the guide RNA is designed such that the SNP is located on position 1, 2, 3, 4, 5, 6, 7, 8, or 9 of the spacer sequence (starting at the 5′ end). In certain embodiments, the guide RNA is designed such that the SNP is located on position 2, 3, 4, 5, 6, or 7 of the spacer sequence (starting at the 5′ end). In certain embodiments, the guide RNA is designed such that the SNP is located on position 3, 4, 5, or 6 of the spacer sequence (starting at the 5′ end). In certain embodiments, the guide RNA is designed such that the SNP is located on position 3 of the spacer sequence (starting at the 5′ end).


In certain embodiments, the guide RNA is designed such that the mismatch (e.g. the synthetic mismatch, i.e. an additional mutation besides a SNP) is located on position 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 of the spacer sequence (starting at the 5′ end). In certain embodiments, the guide RNA is designed such that the mismatch is located on position 1, 2, 3, 4, 5, 6, 7, 8, or 9 of the spacer sequence (starting at the 5′ end). In certain embodiments, the guide RNA is designed such that the mismatch is located on position 4, 5, 6, or 7 of the spacer sequence (starting at the 5′ end. In certain embodiments, the guide RNA is designed such that the mismatch is located at position 3, 4, 5, or 6 of the spacer, preferably position 3. In certain embodiments, the guide RNA is designed such that the mismatch is located on position 5 of the spacer sequence (starting at the 5′ end).


In certain embodiments, said mismatch is 1, 2, 3, 4, or 5 nucleotides upstream or downstream, preferably 2 nucleotides, preferably downstream of said SNP or other single nucleotide variation in said guide RNA.


In certain embodiments, the guide RNA is designed such that the mismatch is located 2 nucleotides upstream of the SNP (i.e. one intervening nucleotide).


In certain embodiments, the guide RNA is designed such that the mismatch is located 2 nucleotides downstream of the SNP (i.e. one intervening nucleotide).


In certain embodiments, the guide RNA is designed such that the mismatch is located on position 5 of the spacer sequence (starting at the 5′ end) and the SNP is located on position 3 of the spacer sequence (starting at the 5′ end).


In certain embodiments, the guide RNA comprises a spacer which is truncated relative to a wild type spacer. In certain embodiments, the guide RNA comprises a spacer which comprises less than 28 nucleotides, preferably between and including 20 to 27 nucleotides.


In certain embodiments, the guide RNA comprises a spacer which consists of 20-25 nucleotides or 20-23 nucleotides, such as preferably 20 or 23 nucleotides.


In certain embodiments, the one or more guide RNAs are designed to detect a single nucleotide polymorphism in a target RNA or DNA, or a splice variant of an RNA transcript.


In certain embodiments, the one or more guide RNAs may be designed to bind to one or more target molecules that are diagnostic for a disease state. In some embodiments, the disease may be cancer. In some embodiments, the disease state may be an autoimmune disease. In some embodiments, the disease state may be an infection. In some embodiments, the infection may be caused by a virus, a bacterium, a fungus, a protozoa, or a parasite. In specific embodiments, the infection is a viral infection. In specific embodiments, the viral infection is caused by a DNA virus.


The embodiments described herein comprehend inducing one or more nucleotide modifications in a eukaryotic cell (in vitro, i.e. in an isolated eukaryotic cell) as herein discussed comprising delivering to cell a vector as herein discussed. The mutation(s) can include the introduction, deletion, or substitution of one or more nucleotides at each target sequence of cell(s) via the guide(s) RNA(s). The mutations can include the introduction, deletion, or substitution of 1-75 nucleotides at each target sequence of said cell(s) via the guide(s) RNA(s). The mutations can include the introduction, deletion, or substitution of 1, 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides at each target sequence of said cell(s) via the guide(s) RNA(s). The mutations can include the introduction, deletion, or substitution of 5, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides at each target sequence of said cell(s) via the guide(s) RNA(s). The mutations include the introduction, deletion, or substitution of 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides at each target sequence of said cell(s) via the guide(s) RNA(s). The mutations can include the introduction, deletion, or substitution of 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, or 75 nucleotides at each target sequence of said cell(s) via the guide(s) RNA(s). The mutations can include the introduction, deletion, or substitution of 40, 45, 50, 75, 100, 200, 300, 400 or 500 nucleotides at each target sequence of said cell(s) via the guide(s) RNA(s).


Typically, in the context of an endogenous CRISPR system, formation of a CRISPR complex (comprising a guide sequence hybridized to a target sequence and complexed with one or more Cas proteins) results in cleavage in or near (e.g. within 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 50, or more base pairs from) the target sequence, but may depend on for instance secondary structure, in particular in the case of RNA targets.


Example orthologs include Alicyclobacillus macrosporangiidus strain DSM 17980, Bacillus hisashii strain C4, Candidatus Lindowbacteria bacterium RIFCSPLOWO2, Elusimicrobia bacterium RIFOXYA12, Omnitrophica WOR_2 bacterium RIFCSPHIGHO2, Phycisphaerae bacterium ST-NAGAB-D1, Planctomycetes bacterium RBG_13_46_10, Spirochaetes bacterium GWB1_27_13, Verrucomicrobiaceae bacterium UBA2429.


Samples

Samples to be screened are loaded at the sample loading portion of the lateral flow substrate. The samples must be liquid samples or samples dissolved in an appropriate solvent, usually aqueous. The liquid sample reconstitutes the SHERLOCK reagents such that a SHERLOCK reaction can occur. The liquid sample begins to flow from the sample portion of the substrate towards the first and second capture regions.


A sample for use with the invention may be a biological or environmental sample, such as a surface sample, a fluid sample, or a food sample (fresh fruits or vegetables, meats). Food samples may include a beverage sample, a paper surface, a fabric surface, a metal surface, a wood surface, a plastic surface, a soil sample, a freshwater sample, a wastewater sample, a saline water sample, exposure to atmospheric air or other gas sample, or a combination thereof. For example, household/commercial/industrial surfaces made of any materials including, but not limited to, metal, wood, plastic, rubber, or the like, may be swabbed and tested for contaminants. Soil samples may be tested for the presence of pathogenic bacteria or parasites, or other microbes, both for environmental purposes and/or for human, animal, or plant disease testing. Water samples such as freshwater samples, wastewater samples, or saline water samples can be evaluated for cleanliness and safety, and/or potability, to detect the presence of, for example, Cryptosporidium parvum, Giardia lamblia, or other microbial contamination. In further embodiments, a biological sample may be obtained from a source including, but not limited to, a tissue sample, saliva, blood, plasma, sera, stool, urine, sputum, mucous, lymph, synovial fluid, spinal fluid, cerebrospinal fluid, ascites, pleural effusion, seroma, pus, bile, aqueous or vitreous humor, transudate, exudate, or swab of skin or a mucosal membrane surface. In some particular embodiments, an environmental sample or biological samples may be crude samples and/or the one or more target molecules may not be purified or amplified from the sample prior to application of the method. Identification of microbes may be useful and/or needed for any number of applications, and thus any type of sample from any source deemed appropriate by one of skill in the art may be used in accordance with the invention.


Methods for Detecting and/or Quantifying Target Nucleic Acids


In some embodiments, the invention provides methods for detecting target nucleic acids in a sample. Such methods may comprise contacting a sample with the first end of a lateral flow device as described herein. The first end of the lateral flow device may comprise a sample loading portion, wherein the sample flows from the sample loading portion of the substrate towards the first and second capture regions and generates a detectable signal.


A positive detectable signal may be any signal that can be detected using optical, fluorescent, chemiluminescent, electrochemical or other detection methods known in the art, as described elsewhere herein.


In some embodiments, the lateral flow device may be capable of detecting two different target nucleic acid sequences. In some embodiments, this detection of two different target nucleic acid sequences may occur simultaneously.


In some embodiments, the absence of target nucleic acid sequences in a sample elicits a detectable fluorescent signal at each capture region. In such instances, the absence of any target nucleic acid sequences in a sample may cause a detectable signal to appear at the first and second capture regions.


In some embodiments, the lateral flow device as described herein is capable of detecting three different target nucleic acid sequences. In specific embodiments, when the target nucleic acid sequences are absent from the sample, a fluorescent signal may be generated at each of the three capture regions. In such exemplary embodiments, a fluorescent signal may be absent at the capture region for the corresponding target nucleic acid sequence when the sample contains one or more target nucleic acid sequences.


Samples to be screened are loaded at the sample loading portion of the lateral flow substrate. The samples must be liquid samples or samples dissolved in an appropriate solvent, usually aqueous. The liquid sample reconstitutes the system reagents such that a SHERLOCK reaction can occur. Intact reporter construct is bound at the first capture region by binding between the first binding agent and the first molecule. Likewise, the detection agent will begin to collect at the first binding region by binding to the second molecule on the intact reporter construct. If target molecule(s) are present in the sample, the CRISPR effector protein collateral effect is activated. As activated CRISPR effector protein comes into contact with the bound reporter construct, the reporter constructs are cleaved, releasing the second molecule to flow further down the lateral flow substrate towards the second binding region. The released second molecule is then captured at the second capture region by binding to the second binding agent, where additional detection agent may also accumulate by binding to the second molecule. Accordingly, if the target molecule(s) is not present in the sample, a detectable signal will appear at the first capture region, and if the target molecule(s) is present in the sample, a detectable signal will appear at the location of the second capture region.


In some embodiments, the invention provides a method for quantifying target nucleic acids in samples comprising distributing a sample or set of samples into one or more individual discrete volumes comprising two or more CRISPR systems as described herein. The method may comprise using HDA to amplify one or more target molecules in the sample or set of samples, as described herein. The method may further comprise incubating the sample or set of samples under conditions sufficient to allow binding of the guide RNAs to one or more target molecules. The method may further comprise activating the CRISPR effector protein via binding of the guide RNAs to the one or more target molecules. Activating the CRISPR effector protein may result in modification of the detection construct such that a detectable positive signal is generated. The method may further comprise detecting the one or more detectable positive signals, wherein detection indicates the presence of one or more target molecules in the sample. The method may further comprise comparing the intensity of the one or more signals to a control to quantify the nucleic acid in the sample. The steps of amplifying, incubating, activating, and detecting may all be performed in the same individual discrete volume.


Amplifying Target Molecules

The step of amplifying one or more target molecules can comprise amplification systems known in the art. In some embodiments, amplification is isothermal. In certain example embodiments, target RNAs and/or DNAs may be amplified prior to activating the CRISPR effector protein. Any suitable RNA or DNA amplification technique may be used. In certain example embodiments, the RNA or DNA amplification is an isothermal amplification. In certain example embodiments, the isothermal amplification may be nucleic-acid sequenced-based amplification (NASBA), recombinase polymerase amplification (RPA), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), helicase-dependent amplification (HDA), or nicking enzyme amplification reaction (NEAR). In certain example embodiments, non-isothermal amplification methods may be used which include, but are not limited to, PCR, multiple displacement amplification (MDA), rolling circle amplification (RCA), ligase chain reaction (LCR), or ramification amplification method (RAM).


In certain example embodiments, the RNA or DNA amplification is NASBA, which is initiated with reverse transcription of target RNA by a sequence-specific reverse primer to create a RNA/DNA duplex. RNase H is then used to degrade the RNA template, allowing a forward primer containing a promoter, such as the T7 promoter, to bind and initiate elongation of the complementary strand, generating a double-stranded DNA product. The RNA polymerase promoter-mediated transcription of the DNA template then creates copies of the target RNA sequence. Importantly, each of the new target RNAs can be detected by the guide RNAs thus further enhancing the sensitivity of the assay. Binding of the target RNAs by the guide RNAs then leads to activation of the CRISPR effector protein and the methods proceed as outlined above. The NASBA reaction has the additional advantage of being able to proceed under moderate isothermal conditions, for example at approximately 41° C., making it suitable for systems and devices deployed for early and direct detection in the field and far from clinical laboratories.


In certain other example embodiments, a recombinase polymerase amplification (RPA) reaction may be used to amplify the target nucleic acids. RPA reactions employ recombinases which are capable of pairing sequence-specific primers with homologous sequence in duplex DNA. If target DNA is present, DNA amplification is initiated and no other sample manipulation such as thermal cycling or chemical melting is required. The entire RPA amplification system is stable as a dried formulation and can be transported safely without refrigeration. RPA reactions may also be carried out at isothermal temperatures with an optimum reaction temperature of 37-42° C. The sequence specific primers are designed to amplify a sequence comprising the target nucleic acid sequence to be detected. In certain example embodiments, a RNA polymerase promoter, such as a T7 promoter, is added to one of the primers. This results in an amplified double-stranded DNA product comprising the target sequence and a RNA polymerase promoter. After, or during, the RPA reaction, a RNA polymerase is added that will produce RNA from the double-stranded DNA templates. The amplified target RNA can then in turn be detected by the CRISPR effector system. In this way target DNA can be detected using the embodiments disclosed herein. RPA reactions can also be used to amplify target RNA. The target RNA is first converted to cDNA using a reverse transcriptase, followed by second strand DNA synthesis, at which point the RPA reaction proceeds as outlined above.


In an embodiment of the invention may comprise nickase-based amplification. The nicking enzyme may be a CRISPR protein. Accordingly, the introduction of nicks into dsDNA can be programmable and sequence-specific. FIG. 115 depicts an embodiment of the invention, which starts with two guides designed to target opposite strands of a dsDNA target. According to the invention, the nickase can be Cpf1, C2c1, Cas9 or any ortholog or CRISPR protein that cleaves or is engineered to cleave a single strand of a DNA duplex. The nicked strands may then be extended by a polymerase. In an embodiment, the locations of the nicks are selected such that extension of the strands by a polymerase is towards the central portion of the target duplex DNA between the nick sites. In certain embodiments, primers are included in the reaction capable of hybridizing to the extended strands followed by further polymerase extension of the primers to regenerate two dsDNA pieces: a first dsDNA that includes the first strand Cpf1 guide site or both the first and second strand Cpf1 guide sites, and a second dsDNA that includes the second strand Cpf1 guide site or both the first and second strand Cprf guide sites. These pieces continue to be nicked and extended in a cyclic reaction that exponentially amplifies the region of the target between nicking sites.


The amplification can be isothermal and selected for temperature. In one embodiment, the amplification proceeds rapidly at 37 degrees. In other embodiments, the temperature of the isothermal amplification may be chosen by selecting a polymerase (e.g. Bsu, Bst, Phi29, klenow fragment etc.) operable at a different temperature.


Thus, whereas nicking isothermal amplification techniques use nicking enyzmes with fixed sequence preference (e.g. in nicking enzyme amplification reaction or NEAR), which requires denaturing of the original dsDNA target to allow annealing and extension of primers that add the nicking substrate to the ends of the target, use of a CRISPR nickase wherein the nicking sites can be programed via guide RNAs means that no denaturing step is necessary, enabling the entire reaction to be truly isothermal. This also simplifies the reaction because these primers that add the nicking substrate are different than the primers that are used later in the reaction, meaning that NEAR requires two primer sets (i.e. 4 primers) while Cpf1 nicking amplification only requires one primer set (i.e. two primers). This makes nicking Cpf1 amplification much simpler and easier to operate without complicated instrumentation to perform the denaturation and then cooling to the isothermal temperature.


Accordingly, in certain example embodiments the systems disclosed herein may include amplification reagents. Different components or reagents useful for amplification of nucleic acids are described herein. For example, an amplification reagent as described herein may include a buffer, such as a Tris buffer. A Tris buffer may be used at any concentration appropriate for the desired application or use, for example including, but not limited to, a concentration of 1 mM, 2 mM, 3 mM, 4 mM, 5 mM, 6 mM, 7 mM, 8 mM, 9 mM, 10 mM, 11 mM, 12 mM, 13 mM, 14 mM, 15 mM, 25 mM, 50 mM, 75 mM, 1 M, or the like. One of skill in the art will be able to determine an appropriate concentration of a buffer such as Tris for use with the present invention.


A salt, such as magnesium chloride (MgCl2), potassium chloride (KCl), or sodium chloride (NaCl), may be included in an amplification reaction, such as PCR, in order to improve the amplification of nucleic acid fragments. Although the salt concentration will depend on the particular reaction and application, in some embodiments, nucleic acid fragments of a particular size may produce optimum results at particular salt concentrations. Larger products may require altered salt concentrations, typically lower salt, in order to produce desired results, while amplification of smaller products may produce better results at higher salt concentrations. One of skill in the art will understand that the presence and/or concentration of a salt, along with alteration of salt concentrations, may alter the stringency of a biological or chemical reaction, and therefore any salt may be used that provides the appropriate conditions for a reaction of the present invention and as described herein.


Other components of a biological or chemical reaction may include a cell lysis component in order to break open or lyse a cell for analysis of the materials therein. A cell lysis component may include, but is not limited to, a detergent, a salt as described above, such as NaCl, KCl, ammonium sulfate [(NH4)2SO4], or others. Detergents that may be appropriate for the invention may include Triton X-100, sodium dodecyl sulfate (SDS), CHAPS (3-[(3-cholamidopropyl)dimethylammonio]-1-propanesulfonate), ethyl trimethyl ammonium bromide, nonyl phenoxypolyethoxylethanol (NP-40). Concentrations of detergents may depend on the particular application, and may be specific to the reaction in some cases. Amplification reactions may include dNTPs and nucleic acid primers used at any concentration appropriate for the invention, such as including, but not limited to, a concentration of 100 nM, 150 nM, 200 nM, 250 nM, 300 nM, 350 nM, 400 nM, 450 nM, 500 nM, 550 nM, 600 nM, 650 nM, 700 nM, 750 nM, 800 nM, 850 nM, 900 nM, 950 nM, 1 mM, 2 mM, 3 mM, 4 mM, 5 mM, 6 mM, 7 mM, 8 mM, 9 mM, 10 mM, 20 mM, 30 mM, 40 mM, 50 mM, 60 mM, 70 mM, 80 mM, 90 mM, 100 mM, 150 mM, 200 mM, 250 mM, 300 mM, 350 mM, 400 mM, 450 mM, 500 mM, or the like. Likewise, a polymerase useful in accordance with the invention may be any specific or general polymerase known in the art and useful or the invention, including Taq polymerase, Q5 polymerase, or the like.


In some embodiments, amplification reagents as described herein may be appropriate for use in hot-start amplification. Hot start amplification may be beneficial in some embodiments to reduce or eliminate dimerization of adaptor molecules or oligos, or to otherwise prevent unwanted amplification products or artifacts and obtain optimum amplification of the desired product. Many components described herein for use in amplification may also be used in hot-start amplification. In some embodiments, reagents or components appropriate for use with hot-start amplification may be used in place of one or more of the composition components as appropriate. For example, a polymerase or other reagent may be used that exhibits a desired activity at a particular temperature or other reaction condition. In some embodiments, reagents may be used that are designed or optimized for use in hot-start amplification, for example, a polymerase may be activated after transposition or after reaching a particular temperature. Such polymerases may be antibody-based or aptamer-based. Polymerases as described herein are known in the art. Examples of such reagents may include, but are not limited to, hot-start polymerases, hot-start dNTPs, and photo-caged dNTPs. Such reagents are known and available in the art. One of skill in the art will be able to determine the optimum temperatures as appropriate for individual reagents.


Amplification of nucleic acids may be performed using specific thermal cycle machinery or equipment, and may be performed in single reactions or in bulk, such that any desired number of reactions may be performed simultaneously. In some embodiments, amplification may be performed using microfluidic or robotic devices, or may be performed using manual alteration in temperatures to achieve the desired amplification. In some embodiments, optimization may be performed to obtain the optimum reactions conditions for the particular application or materials. One of skill in the art will understand and be able to optimize reaction conditions to obtain sufficient amplification.


In certain embodiments, detection of DNA with the methods or systems of the invention requires transcription of the (amplified) DNA into RNA prior to detection.


It will be evident that detection methods of the invention can involve nucleic acid amplification and detection procedures in various combinations. The nucleic acid to be detected can be any naturally occurring or synthetic nucleic acid, including but not limited to DNA and RNA, which may be amplified by any suitable method to provide an intermediate product that can be detected. Detection of the intermediate product can be by any suitable method including but not limited to binding and activation of a CRISPR protein which produces a detectable signal moiety by direct or collateral activity.


Helicase-Dependent Amplification


In helicase-dependent amplification, a helicase enzyme is used to unwind a double stranded nucleic acid to generate templates for primer hybridization and subsequent primer-extension. This process utilizes two oligonucleotide primers, each hybridizing to the 3′-end of either the sense strand containing the target sequence or the anti-sense strand containing the reverse-complementary target sequence. The HDA reaction is a general method for helicase-dependent nucleic acid amplification.


In combining this method with a CRISPR-SHERLOCK system, the target nucleic acid may be amplified by opening R-loops of the target nucleic acid using first and second CRISPR/Cas complexes. The first and second strand of the target nucleic acid may thus be unwound using a helicase, allowing primers and polymerase to bind and extend the DNA under isothermal conditions.


The term “helicase” refers here to any enzyme capable of unwinding a double stranded nucleic acid enzymatically. For example, helicases are enzymes that are found in all organisms and in all processes that involve nucleic acid such as replication, recombination, repair, transcription, translation and RNA splicing. (Kornberg and Baker, DNA Replication, W. H. Freeman and Company (2nd ed. (1992)), especially chapter 11). Any helicase that translocates along DNA or RNA in a 5′ to 3′ direction or in the opposite 3′ to 5′ direction may be used in present embodiments of the invention. This includes helicases obtained from prokaryotes, viruses, archaea, and eukaryotes or recombinant forms of naturally occurring enzymes as well as analogues or derivatives having the specified activity. Examples of naturally occurring DNA helicases, described by Kornberg and Baker in chapter 11 of their book, DNA Replication, W. H. Freeman and Company (2nd ed. (1992)), include E. coli helicase I, II, III, & IV, Rep, DnaB, PriA, PcrA, T4 Gp41 helicase, T4 Dda helicase, T7 Gp4 helicases, SV40 Large T antigen, yeast RAD. Additional helicases that may be useful in HDA include RecQ helicase (Harmon and Kowalczykowski, J. Biol. Chem. 276:232-243 (2001)), thermostable UvrD helicases from T. tengcongensis (disclosed in this invention, Example XII) and T. thermophilus (Collins and McCarthy, Extremophiles. 7:35-41. (2003)), thermostable DnaB helicase from T. aquaticus (Kaplan and Steitz, J. Biol. Chem. 274:6889-6897 (1999)), and MCM helicase from archaeal and eukaryotic organisms ((Grainge et al., Nucleic Acids Res. 31:4888-4898 (2003)).


A traditional definition of a helicase is an enzyme that catalyzes the reaction of separating/unzipping/unwinding the helical structure of nucleic acid duplexes (DNA, RNA or hybrids) into single-stranded components, using nucleoside triphosphate (NTP) hydrolysis as the energy source (such as ATP). However, it should be noted that not all helicases fit this definition anymore. A more general definition is that they are motor proteins that move along the single-stranded or double stranded nucleic acids (usually in a certain direction, 3′ to 5′ or 5 to 3, or both), i.e. translocases, that can or cannot unwind the duplexed nucleic acid encountered. In addition, some helicases simply bind and “melt” the duplexed nucleic acid structure without an apparent translocase activity.


Helicases exist in all living organisms and function in all aspects of nucleic acid metabolism. Helicases are classified based on the amino acid sequences, directionality, oligomerization state and nucleic-acid type and structure preferences. The most common classification method was developed based on the presence of certain amino acid sequences, called motifs. According to this classification helicases are divided into 6 super families: SF1, SF2, SF3, SF4, SF5 and SF6. SF1 and SF2 helicases do not form a ring structure around the nucleic acid, whereas SF3 to SF6 do. Superfamily classification is not dependent on the classical taxonomy.


DNA helicases are responsible for catalyzing the unwinding of double-stranded DNA (dsDNA) molecules to their respective single-stranded nucleic acid (ssDNA) forms. Although structural and biochemical studies have shown how various helicases can translocate on ssDNA directionally, consuming one ATP per nucleotide, the mechanism of nucleic acid unwinding and how the unwinding activity is regulated remains unclear and controversial (T. M. Lohman, E. J. Tomko, C. G. Wu, “Non-hexameric DNA helicases and translocases: mechanisms and regulation,” Nat Rev Mol Cell Biol 9:391-401 (2008)). Since helicases can potentially unwind all nucleic acids encountered, understanding how their unwinding activities are regulated can lead to harnessing helicase functions for biotechnology applications.


The term “HDA” refers to Helicase Dependent Amplification, which is an in vitro method for amplifying nucleic acids by using a helicase preparation for unwinding a double stranded nucleic acid to generate templates for primer hybridization and subsequent primer-extension. This process utilizes two oligonucleotide primers, each hybridizing to the 3′-end of either the sense strand containing the target sequence or the anti-sense strand containing the reverse-complementary target sequence. The HDA reaction is a general method for helicase-dependent nucleic acid amplification.


The invention comprises use of any suitable helicase known in the art. These include, but are not necessarily limited to, UvrD helicase, CRISPR-Cas3 helicase, E. coli helicase I, E. coli helicase II, E. coli helicase III, E. coli helicase IV, Rep helicase, DnaB helicase, PriA helicase, PcrA helicase, T4 Gp41 helicase, T4 Dda helicase, SV40 Large T antigen, yeast RAD helicase, RecD helicase, RecQ helicase, thermostable T. tengcongensis UvrD helicase, thermostable T. thermophilus UvrD helicase, thermostable T. aquaticus DnaB helicase, Dda helicase, papilloma virus E1 helicase, archaeal MCM helicase, eukaryotic MCM helicase, and T7 Gp4 helicase.


In particularly preferred embodiments, the helicase comprises a super mutation. In particular embodiments, Although the E. coli mutation has been described, the mutations were generated by sequence alignment (e.g. D409A/D410A for TteUvrd) and result in thermophilic enzymes working at lower temperatures like 37 C, which is advantageous for amplification methods and systems described herein. In some embodiments, the super mutant is an aspartate to alanine mutation, with position based on sequence alignment. In some embodiments, the super mutant helicase is selected from WP_003870487.1 Thermoanaerobacter ethanolicus 403/404, WP_049660019.1 Bacillus sp. FJAT-27231 407/408, WP_034654680.1 Bacillus megaterium 415/416, WP_095390358.1 Bacillus simplex 407/408, and WP_055343022.1 Paeniclostridium sordellii 402/403.


An “individual discrete volume” is a discrete volume or discrete space, such as a container, receptacle, or other defined volume or space that can be defined by properties that prevent and/or inhibit migration of nucleic acids and reagents necessary to carry out the methods disclosed herein, for example a volume or space defined by physical properties such as walls, for example the walls of a well, tube, or a surface of a droplet, which may be impermeable or semipermeable, or as defined by other means such as chemical, diffusion rate limited, electro-magnetic, or light illumination, or any combination thereof. By “diffusion rate limited” (for example diffusion defined volumes) is meant spaces that are only accessible to certain molecules or reactions because diffusion constraints effectively defining a space or volume as would be the case for two parallel laminar streams where diffusion will limit the migration of a target molecule from one stream to the other. By “chemical” defined volume or space is meant spaces where only certain target molecules can exist because of their chemical or molecular properties, such as size, where for example gel beads may exclude certain species from entering the beads but not others, such as by surface charge, matrix size or other physical property of the bead that can allow selection of species that may enter the interior of the bead. By “electro-magnetically” defined volume or space is meant spaces where the electro-magnetic properties of the target molecules or their supports such as charge or magnetic properties can be used to define certain regions in a space such as capturing magnetic particles within a magnetic field or directly on magnets. By “optically” defined volume is meant any region of space that may be defined by illuminating it with visible, ultraviolet, infrared, or other wavelengths of light such that only target molecules within the defined space or volume may be labeled. One advantage to the used of non-walled, or semipermeable is that some reagents, such as buffers, chemical activators, or other agents maybe passed in Applicants' through the discrete volume, while other material, such as target molecules, maybe maintained in the discrete volume or space. Typically, a discrete volume will include a fluid medium, (for example, an aqueous solution, an oil, a buffer, and/or a media capable of supporting cell growth) suitable for labeling of the target molecule with the indexable nucleic acid identifier under conditions that permit labeling. Exemplary discrete volumes or spaces useful in the disclosed methods include droplets (for example, microfluidic droplets and/or emulsion droplets), hydrogel beads or other polymer structures (for example poly-ethylene glycol di-acrylate beads or agarose beads), tissue slides (for example, fixed formalin paraffin embedded tissue slides with particular regions, volumes, or spaces defined by chemical, optical, or physical means), microscope slides with regions defined by depositing reagents in ordered arrays or random patterns, tubes (such as, centrifuge tubes, microcentrifuge tubes, test tubes, cuvettes, conical tubes, and the like), bottles (such as glass bottles, plastic bottles, ceramic bottles, Erlenmeyer flasks, scintillation vials and the like), wells (such as wells in a plate), plates, pipettes, or pipette tips among others. In certain example embodiments, the individual discrete volumes are the wells of a microplate. In certain example embodiments, the microplate is a 96 well, a 384 well, or a 1536 well microplate.


Incubating

Methods of detection and or quantifying using the systems disclosed herein can comprise incubating the sample or set of samples under conditions sufficient to allow binding of the guide RNAs to one or more target molecules. In certain example embodiments, the incubation time of the present invention may be shortened. The assay may be performed in a period of time required for an enzymatic reaction to occur. One skilled in the art can perform biochemical reactions in 5 minutes (e.g., 5 minute ligation). Incubating may occur at one or more temperatures over timeframes between about 10 minutes and 3 hours, preferably less than 200 minutes, 150 minutes, 100 minutes, 75 minutes, 60 minutes, 45 minutes, 30 minutes, or 20 minutes, depending on sample, reagents and components of the system. In some embodiments, incubating is performed at one or more temperatures between about 20° C. and 80° C., in some embodiments, about 37° C.


Activating

Activating of the CRISPR effector protein occurs via binding of the guide RNAs to the one or more target molecules, wherein activating the CRISPR effector protein results in modification of the detection construct such that a detectable positive signal is generated.


Detecting a Signal

Detecting may comprise visual observance of a positive signal relative to a control. Detecting may comprise a loss of signal or presence of signal at one or more capture regions, for example colorimetric detection, or fluorescent detection. In certain example embodiments, further modifications may be introduced that further amplify the detectable positive signal. For example, activated CRISPR effector protein collateral activation may be used to generate a secondary target or additional guide sequence, or both. In one example embodiment, the reaction solution would contain a secondary target that is spiked in at high concentration. The secondary target may be distinct from the primary target (i.e. the target for which the assay is designed to detect) and in certain instances may be common across all reaction volumes. A secondary guide sequence for the secondary target may be protected, e.g. by a secondary structural feature such as a hairpin with an RNA loop, and unable to bind the second target or the CRISPR effector protein. Cleavage of the protecting group by an activated CRISPR effector protein (i.e. after activation by formation of complex with the primary target(s) in solution) and formation of a complex with free CRISPR effector protein in solution and activation from the spiked in secondary target. In certain other example embodiments, a similar concept is used with free guide sequence to a secondary target and protected secondary target. Cleavage of a protecting group off the secondary target would allow additional CRISPR effector protein, guide sequence, secondary target sequence to form. In yet another example embodiment, activation of CRISPR effector protein by the primary target(s) may be used to cleave a protected or circularized primer, which would then be released to perform an isothermal amplification reaction, such as those disclosed herein, on a template for either secondary guide sequence, secondary target, or both. Subsequent transcription of this amplified template would produce more secondary guide sequence and/or secondary target sequence, followed by additional CRISPR effector protein collateral activation.


Quantifying

In particular methods, comparing the intensity of the one or more signals to a control is performed to quantify the nucleic acid in the sample. The term “control” refers to any reference standard suitable to provide a comparison to the expression products in the test sample. In one embodiment, the control comprises obtaining a “control sample” from which expression product levels are detected and compared to the expression product levels from the test sample. Such a control sample may comprise any suitable sample, including but not limited to a sample from a control patient (can be stored sample or previous sample measurement) with a known outcome; normal tissue, fluid, or cells isolated from a subject, such as a normal patient or the patient having a condition of interest.


The intensity of a signal is “significantly” higher or lower than the normal intensity if the signal is greater or less, respectively, than the normal or control level by an amount greater than the standard error of the assay employed to assess amount, and preferably at least 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%, 150%, 200%, 300%, 350%, 400%, 500%, 600%, 700%, 800%, 900%, 1000% or than that amount. Alternatively, the signal can be considered “significantly” higher or lower than the normal and/or control signal if the amount is at least about two, and preferably at least about 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, 105%, 110%, 115%, 120%, 125%, 130%, 135%, 140%, 145%, 150%, 155%, 160%, 165%, 170%, 175%, 180%, 185%, 190%, 195%, two times, three times, four times, five times, or more, or any range in between, such as 5%-100%, higher or lower, respectively, than the normal and/or control signal. Such significant modulation values can be applied to any metric described herein, such as altered level of expression, altered activity, changes in biomarker inhibition, changes in test agent binding, and the like.


In some embodiments, the detectable positive signal may be a loss of fluorescent signal relative to a control, as described herein. In some embodiments, the detectable positive signal may be detected on a lateral flow device, as described herein.


Applications of Detection Methods

In certain example embodiments, the systems, devices, and methods, disclosed herein are directed to detecting the presence of one or more microbial agents in a sample, such as a biological sample obtained from a subject. In certain example embodiments, the microbe may be a bacterium, a fungus, a yeast, a protozoan, a parasite, or a virus. Accordingly, the methods disclosed herein can be adapted for use in other methods (or in combination) with other methods that require quick identification of microbe species, monitoring the presence of microbial proteins (antigens), antibodies, antibody genes, detection of certain phenotypes (e.g. bacterial resistance), monitoring of disease progression and/or outbreak, and antibiotic screening. Because of the rapid and sensitive diagnostic capabilities of the embodiments disclosed here, detection of microbe species type, down to a single nucleotide difference, and the ability to be deployed as a POC device, the embodiments disclosed herein may be used as guide therapeutic regimens, such as a selection of the appropriate antibiotic or antiviral. The embodiments disclosed herein may also be used to screen environmental samples (air, water, surfaces, food etc.) for the presence of microbial contamination.


Disclosed is a method to identify microbial species, such as bacterial, viral, fungal, yeast, or parasitic species, or the like. Particular embodiments disclosed herein describe methods and systems that will identify and distinguish microbial species within a single sample, or across multiple samples, allowing for recognition of many different microbes. The present methods allow the detection of pathogens and distinguishing between two or more species of one or more organisms, e.g., bacteria, viruses, yeast, protozoa, and fungi or a combination thereof, in a biological or environmental sample, by detecting the presence of a target nucleic acid sequence in the sample. A positive signal obtained from the sample indicates the presence of the microbe. Multiple microbes can be identified simultaneously using the methods and systems of the invention, by employing the use of more than one effector protein, wherein each effector protein targets a specific microbial target sequence. In this way, a multi-level analysis can be performed for a particular subject in which any number of microbes can be detected at once. In some embodiments, simultaneous detection of multiple microbes may be performed using a set of probes that can identify one or more microbial species.


The systems and methods of detection can be used to identify single nucleotide variants, detection based on rRNA sequences, screening for drug resistance, monitoring microbe outbreaks, genetic perturbations, and screening of environmental samples, as described in PCT/US2018/054472 filed Oct. 22, 2018 at [0183]-[0327], incorporated herein by reference.


In certain example embodiments, the systems, devices, and methods disclosed herein may be used for biomarker detection. For example, the systems, devices and method disclosed herein may be used for SNP detection and/or genotyping. The systems, devices and methods disclosed herein may be also used for the detection of any disease state or disorder characterized by aberrant gene expression. Aberrant gene expression includes aberration in the gene expressed, location of expression and level of expression. Multiple transcripts or protein markers related to cardiovascular, immune disorders, and cancer among other diseases may be detected. In certain example embodiments, the embodiments disclosed herein may be used for cell free DNA detection of diseases that involve lysis, such as liver fibrosis and restrictive/obstructive lung disease. In certain example embodiments, the embodiments could be utilized for faster and more portable detection for pre-natal testing of cell-free DNA. The embodiments disclosed herein may be used for screening panels of different SNPs associated with, among others, cardiovascular health, lipid/metabolic signatures, ethnicity identification, paternity matching, human ID (e.g. matching suspect to a criminal database of SNP signatures). The embodiments disclosed herein may also be used for cell free DNA detection of mutations related to and released from cancer tumors. The embodiments disclosed herein may also be used for detection of meat quality, for example, by providing rapid detection of different animal sources in a given meat product. Embodiments disclosed herein may also be used for the detection of GMOs or gene editing related to DNA. As described herein elsewhere, closely related genotypes/alleles or biomarkers (e.g. having only a single nucleotide difference in a given target sequence) may be distinguished by introduction of a synthetic mismatch in the gRNA.


In an aspect, the invention relates to a method for detecting target nucleic acids in samples, comprising:


distributing a sample or set of samples into one or more individual discrete volumes, the individual discrete volumes comprising a CRISPR system according to the invention as described herein;


incubating the sample or set of samples under conditions sufficient to allow binding of the one or more guide RNAs to one or more target molecules;


activating the CRISPR effector protein via binding of the one or more guide RNAs to the one or more target molecules, wherein activating the CRISPR effector protein results in modification of the RNA-based masking construct such that a detectable positive signal is generated; and


detecting the detectable positive signal, wherein detection of the detectable positive signal indicates a presence of one or more target molecules in the sample.


The sensitivity of the assays described herein are well suited for detection of target nucleic acids in a wide variety of biological sample types, including sample types in which the target nucleic acid is dilute or for which sample material is limited. Biomarker screening may be carried out on a number of sample types including, but not limited to, saliva, urine, blood, feces, sputum, and cerebrospinal fluid. The embodiments disclosed herein may also be used to detect up- and/or down-regulation of genes. For example, as sample may be serially diluted such that only over-expressed genes remain above the detection limit threshold of the assay.


In certain embodiments, the present invention provides steps of obtaining a sample of biological fluid (e.g., urine, blood plasma or serum, sputum, cerebral spinal fluid), and extracting the DNA. The mutant nucleotide sequence to be detected, may be a fraction of a larger molecule or can be present initially as a discrete molecule.


In certain embodiments, DNA is isolated from plasma/serum of a cancer patient. For comparison, DNA samples isolated from neoplastic tissue and a second sample may be isolated from non-neoplastic tissue from the same patient (control), for example, lymphocytes. The non-neoplastic tissue can be of the same type as the neoplastic tissue or from a different organ source. In certain embodiments, blood samples are collected and plasma immediately separated from the blood cells by centrifugation. Serum may be filtered and stored frozen until DNA extraction.


In certain example embodiments, target nucleic acids are detected directly from a crude or unprocessed sample sample, such as blood, serum, saliva, cebrospinal fluid, sputum, or urine. In certain example embodiments, the target nucleic acid is cell free DNA.


Methods for Designing Guides

A method for designing guide RNAs for use in the detection systems of the preceding claims, the method comprising the steps of designing putative guide RNAs tiled across a target molecule of interest; creating a training model based on results of incubating guide RNAs with a Cas13 protein and the target molecule; predicting highly active guide RNAs for the target molecule, wherein the predicting comprises optimizing the nucleotide at each base position in the guide RNA based on the training model; and validating the predicted highly active guide RNAs by incubating the guide RNAs with the Cas13 protein and the target molecule.


In some embodiments, the invention provides a method for designing guide RNAs for use in the detection systems described herein. The method may comprise designing putative guide RNAs tiled across a target molecule of interest. The method may further comprise creating a training model based on results of incubating guide RNAs with a Cas13 protein and the target molecule. The method may further comprise predicting highly active guide RNAs for the target molecule. Predicting may comprise optimizing the nucleotide at each base position in the guide RNA based on the training model. The method may further comprise validating the predicted highly active guide RNAs by incubating the guide RNAs with the Cas13 protein and the target molecule.


In certain instances, the optimized guide for the target molecule is generated by pooling a set of guides, the guides produced by tiling guides across the target molecule; incubating the set of guides with a Cas polypeptide and the target molecule and measuring cleavage activity of each guide in the set; creating a training model based on the cleavage activity of the set of guides in the incubating step. Steps of predicting highly active guides for the target molecule and identifying the optimized guides by incubating the predicted highly active guides with the Cas polypeptide and the target molecule and selecting optimized guides may also be utilized in generating optimized guides. In embodiments, the training model comprises one or more input features selected from guide sequence, flanking target sequence, normalized positions of the guide in the target and guide GC content. In certain instances, the guide sequence and/or flanking sequence input comprises one hit encoding mono-nucleotide and/or dinucleotide In an embodiments, the training model comprises applying logistic regression model on the activity of the guides across the one or more input features.


In an aspect, the predicting highly active guides for the target molecule comprises selecting guides with an increase in activity of a guide relative to the median activity, or selecting guides with highest guide activity. In certain instances, the increase in activity is measured by an increase in fluorescence. Guides may be selected based on a particular cutoff, in certain instances based on activity relative to a median or above a particular cutoff-, for instance, are selected with a 1.5, 2, 2.5 or 3-fold activity relative to median, or are in the top quartile or quintile for each target tested.


The optimized guides may be generated for a Cas13 ortholog, in some instances, the optimized guide is generated for an LwaCas13a or a Cca13b ortholog.


In some embodiments, the invention provides a method for designing guide RNAs for use in the detection systems described herein. The method may comprise designing putative guide RNAs tiled across a target molecule of interest. The method may further comprise creating a training model based on results of incubating guide RNAs with a Cas13 protein and the target molecule. The method may further comprise predicting highly active guide RNAs for the target molecule. Predicting may comprise optimizing the nucleotide at each base position in the guide RNA based on the training model. The method may further comprise validating the predicted highly active guide RNAs by incubating the guide RNAs with the Cas13 protein and the target molecule.


The design of putative guide RNAs for target molecules of interest is described elsewhere herein.


The creation of training models is known in the art. Machine learning can be generalized as the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. Machine learning may include the following concepts and methods. Supervised learning concepts may include AODE; Artificial neural network, such as Backpropagation, Autoencoders, Hopfield networks, Boltzmann machines, Restricted Boltzmann Machines, and Spiking neural networks; Bayesian statistics, such as Bayesian network and Bayesian knowledge base; Case-based reasoning; Gaussian process regression; Gene expression programming; Group method of data handling (GMDH); Inductive logic programming; Instance-based learning; Lazy learning; Learning Automata; Learning Vector Quantization; Logistic Model Tree; Minimum message length (decision trees, decision graphs, etc.), such as Nearest Neighbor Algorithm and Analogical modeling; Probably approximately correct learning (PAC) learning; Ripple down rules, a knowledge acquisition methodology; Symbolic machine learning algorithms; Support vector machines; Random Forests; Ensembles of classifiers, such as Bootstrap aggregating (bagging) and Boosting (meta-algorithm); Ordinal classification; Information fuzzy networks (IFN); Conditional Random Field; ANOVA; Linear classifiers, such as Fisher's linear discriminant, Linear regression, Logistic regression, Multinomial logistic regression, Naive Bayes classifier, Perceptron, Support vector machines; Quadratic classifiers; k-nearest neighbor; Boosting; Decision trees, such as C4.5, Random forests, ID3, CART, SLIQ, SPRINT; Bayesian networks, such as Naive Bayes; and Hidden Markov models. Unsupervised learning concepts may include; Expectation-maximization algorithm; Vector Quantization; Generative topographic map; Information bottleneck method; Artificial neural network, such as Self-organizing map; Association rule learning, such as, Apriori algorithm, Eclat algorithm, and FP-growth algorithm; Hierarchical clustering, such as Single-linkage clustering and Conceptual clustering; Cluster analysis, such as, K-means algorithm, Fuzzy clustering, DBSCAN, and OPTICS algorithm; and Outlier Detection, such as Local Outlier Factor. Semi-supervised learning concepts may include; Generative models; Low-density separation; Graph-based methods; and Co-training. Reinforcement learning concepts may include; Temporal difference learning; Q-learning; Learning Automata; and SARSA. Deep learning concepts may include; Deep belief networks; Deep Boltzmann machines; Deep Convolutional neural networks; Deep Recurrent neural networks; and Hierarchical temporal memory.


The methods as disclosed herein designing putative guide RNAs may comprise design based on one or more variables, including guide sequence, flanking target sequence, guide position and guide GC content as input features. In certain embodiments, the length of the flanking target region can be considered a freeparameter and can be further selected during cross-validation. Additionally, mono-nucleotide and/or dinucleotide based identities across a guide length and flanking sequence in the target, varying one or more of flanking sequence length, normalized positions of the guide in the target, and GC content of the guide, or a combination thereof.


In embodiments, the training model for the guide design is Cas protein specific. In embodiments, the Cas protein is a Cas13a, Cas13b or Cas12 a protein. In certain embodiments, the protein is LwaCas13a or CcaCas13b. Selection for the best guides can be dependent on each enzyme. In particular embodiments, where majority of guides have activity above background on a per-target basis, selection of guides may be based on 1.5 fold, 2, 2.5, 3 or more fold activity over the median activity. In other instances, the best performing guides may be at or near background fluorescence. In this instance, the guide selection may be based on a top percentile, e.g. quartile or quintile, of performing guides.


Codon optimization is described elsewhere herein. In specific embodiments, the nucleotide at each base position in the guide RNA may be optimized based on the training model, thus allowing for prediction of highly active guide RNAs for the target molecule.


The predicted highly active guide RNAs may then be validated or verified by incubating the guide RNAs with a Cas effector protein, such as Cas13 protein and the target molecule, as described in the examples.


In certain embodiments, optimization comprises validation of best performing models for a particular Cas polypeptide across multiple guides may comprise comparing the predicted score of each guide versus actual collateral activity upon target recognition. In embodiments, kinetic data of the best and worst predicted guides are evaluated. In embodiments, lateral flow performance of the predicted guides is evaluated for a target sequence.


The following table 1 is comprised of sequences contained in the accompanying Sequence Listing. Sequences referenced in Column 4 “Complete crRNA sequence” are represented in the Sequence Listing by SEQ ID NOs: 12-1100; Sequences referenced in Column 5 “Spacer” are represented in the Sequence Listing by SEQ ID Nos: 1101-2189; and Sequences referenced in Column 6 “Direct Repeat” are represented in the Sequence Listing by SEQ ID NOs: 2190-3278, all in the order in which they appear.









TABLE 1







Guide RNA sequences used in this study

















Complete crRNA



1st


Fig
Name
Ortholog
sequence
Spacer
Direct repeat
Target
Fig.





9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
ttgagaggtt
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACtt
ggcccctga
CCCAAAAACGAA
ssRNA






gagaggttggcccctgaat
atatgtact
GGGGACTAAAAC







atgtact









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
gttgagaggt
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACgt
tggcccctg
CCCAAAAACGAA
ssRNA






tgagaggttggcccctgaa
aatatgtac
GGGGACTAAAAC







tatgtac









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
tgttgagagg
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACtg
ttggcccctg
CCCAAAAACGAA
ssRNA






ttgagaggttggcccctga
aatatgta
GGGGACTAAAAC







atatgta









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
ttgttgagag
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACtt
gttggcccct
CCCAAAAACGAA
ssRNA






gttgagaggttggcccctg
gaatatgt
GGGGACTAAAAC







aatatgt









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
attgttgaga
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACat
ggttggccc
CCCAAAAACGAA
ssRNA






tgttgagaggttggcccct
ctgaatatg
GGGGACTAAAAC







gaatatg









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
cattgttgag
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACca
aggttggcc
CCCAAAAACGAA
ssRNA






ttgttgagaggttggcccct
cctgaatat
GGGGACTAAAAC







gaatat









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
tcattgttgag
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACtc
aggttggcc
CCCAAAAACGAA
ssRNA






attgttgagaggttggccc
cctgaata
GGGGACTAAAAC







ctgaata









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
gtcattgttga
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACgt
gaggttggc
CCCAAAAACGAA
ssRNA






cattgttgagaggttggcc
ccctgaat
GGGGACTAAAAC







cctgaat









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
cgtcattgttg
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACcg
agaggttgg
CCCAAAAACGAA
ssRNA






tcattgttgagaggttggc
cccctgaa
GGGGACTAAAAC







ccctgaa









9a
dengue_0
LwaCas13a
GATTTAGACTACCCCAAAA
tcgtcattgtt
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACtc
gagaggttg
CCCAAAAACGAA
ssRNA






gtcattgttgagaggttgg
gcccctga
GGGGACTAAAAC







cccctga









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
ttcgtcattgt
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACtt
tgagaggttg
CCCAAAAACGAA
ssRNA






cgtcattgttgagaggttg
gcccctg
GGGGACTAAAAC







gcccctg









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
cttcgtcattg
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACct
ttgagaggtt
CCCAAAAACGAA
ssRNA






tcgtcattgttgagaggtt
ggcccct
GGGGACTAAAAC







ggcccct









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
tcttcgtcatt
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACtc
gttgagaggt
CCCAAAAACGAA
ssRNA






ttcgtcattgttgagaggt
tggcccc
GGGGACTAAAAC







tggcccc









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
gtcttcgtcat
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACgt
tgttgagagg
CCCAAAAACGAA
ssRNA






cttcgtcattgttgagagg
ttggccc
GGGGACTAAAAC







ttggccc









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
ggtcttcgtc
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACgg
attgttgaga
CCCAAAAACGAA
ssRNA






tcttcgtcattgttgagag
ggttggcc
GGGGACTAAAAC







gttggcc









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
tggtcttcgtc
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACtg
attgttgaga
CCCAAAAACGAA
ssRNA






gtcttcgtcattgttgaga
ggttggc
GGGGACTAAAAC







ggttggc









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
atggtcttcgt
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACat
cattgttgag
CCCAAAAACGAA
ssRNA






ggtcttcgtcattgttgag
aggttgg
GGGGACTAAAAC







aggttgg









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
catggtcttc
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACca
gtcattgttga
CCCAAAAACGAA
ssRNA






tggtcttcgtcattgttga
gaggttg
GGGGACTAAAAC







gaggttg









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
gcatggtctt
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACgc
cgtcattgttg
CCCAAAAACGAA
ssRNA






atggtcttcgtcattgttg
agaggtt
GGGGACTAAAAC







agaggtt









9a
dengue_1
LwaCas13a
GATTTAGACTACCCCAAAA
agcatggtct
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACag
tcgtcattgtt
CCCAAAAACGAA
ssRNA






catggtcttcgtcattgtt
gagaggt
GGGGACTAAAAC







gagaggt









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
tgagcatggt
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACtg
cttcgtcattg
CCCAAAAACGAA
ssRNA






agcatggtcttcgtcattg
ttgagag
GGGGACTAAAAC







ttgagag









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
agtgagcat
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACag
ggtcttcgtc
CCCAAAAACGAA
ssRNA






tgagcatggtcttcgtcat
attgttgag
GGGGACTAAAAC







tgttgag









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
ccagtgagc
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACcc
atggtcttcgt
CCCAAAAACGAA
ssRNA






agtgagcatggtcttcgtc
cattgttg
GGGGACTAAAAC







attgttg









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
gtccagtga
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACgt
gcatggtctt
CCCAAAAACGAA
ssRNA






ccagtgagcatggtcttcg
cgtcattgt
GGGGACTAAAAC







tcattgt









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
ctgtccagtg
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACct
agcatggtct
CCCAAAAACGAA
ssRNA






gtccagtgagcatggtctt
tcgtcatt
GGGGACTAAAAC







cgtcatt









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
ttctgtccagt
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACtt
gagcatggt
CCCAAAAACGAA
ssRNA






ctgtccagtgagcatggtc
cttcgtca
GGGGACTAAAAC







ttcgtca









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
gcttctgtcc
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACgc
agtgagcat
CCCAAAAACGAA
ssRNA






ttctgtccagtgagcatgg
ggtcttcgt
GGGGACTAAAAC







tcttcgt









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
ttgcttctgtc
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACtt
cagtgagca
CCCAAAAACGAA
ssRNA






gcttctgtccagtgagcat
tggtcttc
GGGGACTAAAAC







ggtcttc









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
ttttgcttctgt
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACtt
ccagtgagc
CCCAAAAACGAA
ssRNA






ttgcttctgtccagtgagc
atggtct
GGGGACTAAAAC







atggtct









9a
dengue_2
LwaCas13a
GATTTAGACTACCCCAAAA
atttttgcttct
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACat
gtccagtga
CCCAAAAACGAA
ssRNA






ttttgcttctgtccagtga
gcatggt
GGGGACTAAAAC







gcatggt









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
gcatttttgct
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACgc
tctgtccagt
CCCAAAAACGAA
ssRNA






atttttgcttctgtccagt
gagcatg
GGGGACTAAAAC







gagcatg









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
cagcatttttg
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACca
cttctgtcca
CCCAAAAACGAA
ssRNA






gcatttttgcttctgtcca
gtgagca
GGGGACTAAAAC







gtgagca









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
agcagcattt
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACag
ttgcttctgtc
CCCAAAAACGAA
ssRNA






cagcatttttgcttctgtc
cagtgag
GGGGACTAAAAC







cagtgag









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
ccagcagca
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACcc
tttttgcttctg
CCCAAAAACGAA
ssRNA






agcagcatttttgcttctg
tccagtg
GGGGACTAAAAC







tccagtg









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
gtccagcag
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACgt
catttttgcttc
CCCAAAAACGAA
ssRNA






ccagcagcatttttgcttc
tgtccag
GGGGACTAAAAC







tgtccag









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
ttgtccagca
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACtt
gcatttttgct
CCCAAAAACGAA
ssRNA






gtccagcagcatttttgct
tctgtcc
GGGGACTAAAAC







tctgtcc









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
tgttgtccag
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACtg
cagcatttttg
CCCAAAAACGAA
ssRNA






ttgtccagcagcatttttg
cttctgt
GGGGACTAAAAC







cttctgt









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
gatgttgtcc
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACga
agcagcattt
CCCAAAAACGAA
ssRNA






tgttgtccagcagcatttt
ttgcttct
GGGGACTAAAAC







tgcttct









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
ttgatgttgtc
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACtt
cagcagcatt
CCCAAAAACGAA
ssRNA






gatgttgtccagcagcatt
tttgctt
GGGGACTAAAAC







tttgctt









9a
dengue_3
LwaCas13a
GATTTAGACTACCCCAAAA
tgttgatgttg
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACtg
tccagcagc
CCCAAAAACGAA
ssRNA






ttgatgttgtccagcagca
atttttgc
GGGGACTAAAAC







tttttgc









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
tgtgttgatgt
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACtg
tgtccagca
CCCAAAAACGAA
ssRNA






tgttgatgttgtccagcag
gcattttt
GGGGACTAAAAC







cattttt









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
ggtgtgttga
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACgg
tgttgtccag
CCCAAAAACGAA
ssRNA






tgtgttgatgttgtccagc
cagcattt
GGGGACTAAAAC







agcattt









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
ctggtgtgtt
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACct
gatgttgtcc
CCCAAAAACGAA
ssRNA






ggtgtgttgatgttgtcca
agcagcat
GGGGACTAAAAC







gcagcat









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
ttctggtgtgt
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACtt
tgatgttgtcc
CCCAAAAACGAA
ssRNA






ctggtgtgttgatgttgtc
agcagc
GGGGACTAAAAC







cagcagc









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
ccttctggtgt
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACcc
gttgatgttgt
CCCAAAAACGAA
ssRNA






ttctggtgtgttgatgttg
ccagca
GGGGACTAAAAC







tccagca









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
tcccttctggt
GATTTAGACTAC
Dengue
9a



5 

ACGAAGGGGACTAAAACtc
gtgttgatgtt
CCCAAAAACGAA
ssRNA






ccttctggtgtgttgatgt
gtccag
GGGGACTAAAAC







tgtccag









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
aatcccttct
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACaa
ggtgtgttga
CCCAAAAACGAA
ssRNA






tcccttctggtgtgttgat
tgttgtcc
GGGGACTAAAAC







gttgtcc









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
ataatcccttc
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACat
tggtgtgttg
CCCAAAAACGAA
ssRNA






aatcccttctggtgtgttg
atgttgt
GGGGACTAAAAC







atgttgt









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
gtataatccc
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACgta
ttctggtgtgt
CCCAAAAACGAA
ssRNA






taatcccttctggtgtgtt
tgatgtt
GGGGACTAAAAC







gatgtt









9a
dengue_4
LwaCas13a
GATTTAGACTACCCCAAAA
tggtataatc
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACt
ccttctggtgt
CCCAAAAACGAA
ssRNA






ggtataatcccttctggtg
gttgatg
GGGGACTAAAAC







tgttgatg









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
gctggtataa
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACgc
tcccttctggt
CCCAAAAACGAA
ssRNA






tggtataatcccttctggt
gtgttga
GGGGACTAAAAC







gtgttga









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
gagctggtat
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACga
aatcccttct
CCCAAAAACGAA
ssRNA






gctggtataatcccttctg
ggtgtgtt
GGGGACTAAAAC







gtgtgtt









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
gagagctgg
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACga
tataatccctt
CCCAAAAACGAA
ssRNA






gagctggtataatcccttc
ctggtgtg
GGGGACTAAAAC







tggtgtg









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
aagagagct
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACaa
ggtataatcc
CCCAAAAACGAA
ssRNA






gagagctggtataatccct
cttctggtg
GGGGACTAAAAC







tctggtg









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
caaagagag
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACca
ctggtataat
CCCAAAAACGAA
ssRNA






aagagagctggtataatcc
cccttctgg
GGGGACTAAAAC







cttctgg









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
ttcaaagaga
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACtt
gctggtataa
CCCAAAAACGAA
ssRNA






caaagagagctggtataat
tcccttct
GGGGACTAAAAC







cccttct









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
ggttcaaag
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACgg
agagctggt
CCCAAAAACGAA
ssRNA






ttcaaagagagctggtata
ataatccctt
GGGGACTAAAAC







atccctt









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
ctggttcaaa
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACct
gagagctgg
CCCAAAAACGAA
ssRNA






ggttcaaagagagctggta
tataatccc
GGGGACTAAAAC







taatccc









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
ttctggttcaa
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACtt
agagagctg
CCCAAAAACGAA
ssRNA






ctggttcaaagagagctgg
gtataatc
GGGGACTAAAAC







tataatc









9a
dengue_5
LwaCas13a
GATTTAGACTACCCCAAAA
ctttctggttc
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACct
aaagagagc
CCCAAAAACGAA
ssRNA






ttctggttcaaagagagct
tggtataa
GGGGACTAAAAC







ggtataa









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
ccctttctggt
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACcc
tcaaagaga
CCCAAAAACGAA
ssRNA






ctttctggttcaaagagag
gctggtat
GGGGACTAAAAC







ctggtat









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
ctccctttctg
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACct
gttcaaaga
CCCAAAAACGAA
ssRNA






ccctttctggttcaaagag
gagctggt
GGGGACTAAAAC







agctggt









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
ttctccctttct
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACtt
ggttcaaag
CCCAAAAACGAA
ssRNA






ctccctttctggttcaaag
agagctg
GGGGACTAAAAC







agagctg









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
acttctccctt
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACac
tctggttcaa
CCCAAAAACGAA
ssRNA






ttctccctttctggttcaa
agagagc
GGGGACTAAAAC







agagagc









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
tgacttctcc
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACtg
ctttctggttc
CCCAAAAACGAA
ssRNA






acttctccctttctggttc
aaagaga
GGGGACTAAAAC







aaagaga









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
ctgacttctc
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACct
cctttctggtt
CCCAAAAACGAA
ssRNA






gacttctccctttctggtt
caaagag
GGGGACTAAAAC







caaagag









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
gctgacttct
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACgc
ccctttctggt
CCCAAAAACGAA
ssRNA






tgacttctccctttctggt
tcaaaga
GGGGACTAAAAC







tcaaaga









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
ggctgacttc
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACgg
tccctttctgg
CCCAAAAACGAA
ssRNA






ctgacttctccctttctgg
ttcaaag
GGGGACTAAAAC







ttcaaag









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
cggctgactt
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACcg
ctccctttctg
CCCAAAAACGAA
ssRNA






gctgacttctccctttctg
gttcaaa
GGGGACTAAAAC







gttcaaa









9a
dengue_6
LwaCas13a
GATTTAGACTACCCCAAAA
gcggctgac
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACgc
ttctccctttct
CCCAAAAACGAA
ssRNA






ggctgacttctccctttct
ggttcaa
GGGGACTAAAAC







ggttcaa









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
ggcggctga
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACgg
cttctcccttt
CCCAAAAACGAA
ssRNA






cggctgacttctccctttc
ctggttca
GGGGACTAAAAC







tggttca









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
tggcggctg
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACtg
acttctccctt
CCCAAAAACGAA
ssRNA






gcggctgacttctcccttt
tctggttc
GGGGACTAAAAC







ctggttc









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
atggcggct
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACat
gacttctccc
CCCAAAAACGAA
ssRNA






ggcggctgacttctccctt
tttctggtt
GGGGACTAAAAC







tctggtt









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
tatggcggct
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACta
gacttctccc
CCCAAAAACGAA
ssRNA






tggcggctgacttctccct
tttctggt
GGGGACTAAAAC







ttctggt









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
ctatggcgg
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACct
ctgacttctc
CCCAAAAACGAA
ssRNA






atggcggctgacttctccc
cctttctgg
GGGGACTAAAAC







tttctgg









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
tctatggcgg
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACtc
ctgacttctc
CCCAAAAACGAA
ssRNA






tatggcggctgacttctcc
cctttctg
GGGGACTAAAAC







ctttctg









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
gtctatggcg
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACgt
gctgacttct
CCCAAAAACGAA
ssRNA






ctatggcggctgacttctc
ccctttct
GGGGACTAAAAC







cctttct









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
cgtctatggc
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACcg
ggctgacttc
CCCAAAAACGAA
ssRNA






tctatggcggctgacttct
tccctttc
GGGGACTAAAAC







ccctttc









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
ccgtctatgg
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACcc
cggctgactt
CCCAAAAACGAA
ssRNA






gtctatggcggctgacttc
ctcccttt
GGGGACTAAAAC







tcccttt









9a
dengue_7
LwaCas13a
GATTTAGACTACCCCAAAA
accgtctatg
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACac
gcggctgac
CCCAAAAACGAA
ssRNA






cgtctatggcggctgactt
ttctccctt
GGGGACTAAAAC







ctccctt









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
caccgtctat
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACca
ggcggctga
CCCAAAAACGAA
ssRNA






ccgtctatggcggctgact
cttctccct
GGGGACTAAAAC







tctccct









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
tcaccgtcta
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACtc
tggcggctg
CCCAAAAACGAA
ssRNA






accgtctatggcggctgac
acttctccc
GGGGACTAAAAC







ttctccc









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
ttcaccgtct
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACtt
atggcggct
CCCAAAAACGAA
ssRNA






caccgtctatggcggctga
gacttctcc
GGGGACTAAAAC







cttctcc









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
attcaccgtc
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACat
tatggcggct
CCCAAAAACGAA
ssRNA






tcaccgtctatggcggctg
gacttctc
GGGGACTAAAAC







acttctc









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
tattcaccgt
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACta
ctatggcgg
CCCAAAAACGAA
ssRNA






ttcaccgtctatggcggct
ctgacttct
GGGGACTAAAAC







gacttct









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
gtattcaccg
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACgt
tctatggcgg
CCCAAAAACGAA
ssRNA






attcaccgtctatggcggc
ctgacttc
GGGGACTAAAAC







tgacttc









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
ggtattcacc
GATTTAGACTAC
Dengue
9a



6

ACGAAGGGGACTAAAACgg
gtctatggcg
CCCAAAAACGAA
ssRNA






tattcaccgtctatggcgg
gctgactt
GGGGACTAAAAC







ctgactt









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
cggtattcac
GATTTAGACTAC
Dengue
9a



7

ACGAAGGGGACTAAAACcg
cgtctatggc
CCCAAAAACGAA
ssRNA






gtattcaccgtctatggcg
ggctgact
GGGGACTAAAAC







gctgact









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
gcggtattca
GATTTAGACTAC
Dengue
9a



8

ACGAAGGGGACTAAAACgc
ccgtctatgg
CCCAAAAACGAA
ssRNA






ggtattcaccgtctatggc
cggctgac
GGGGACTAAAAC







ggctgac









9a
dengue_8
LwaCas13a
GATTTAGACTACCCCAAAA
ggcggtattc
GATTTAGACTAC
Dengue
9a



9

ACGAAGGGGACTAAAACgg
accgtctatg
CCCAAAAACGAA
ssRNA






cggtattcaccgtctatgg
gcggctga
GGGGACTAAAAC







cggctga









9a
dengue_9
LwaCas13a
GATTTAGACTACCCCAAAA
aggcggtatt
GATTTAGACTAC
Dengue
9a



0

ACGAAGGGGACTAAAACag
caccgtctat
CCCAAAAACGAA
ssRNA






gcggtattcaccgtctatg
ggcggctg
GGGGACTAAAAC







gcggctg









9a
dengue_9
LwaCas13a
GATTTAGACTACCCCAAAA
caggcggta
GATTTAGACTAC
Dengue
9a



1

ACGAAGGGGACTAAAACca
ttcaccgtct
CCCAAAAACGAA
ssRNA






ggcggtattcaccgtctat
atggcggct
GGGGACTAAAAC







ggcggct









9a
dengue_9
LwaCas13a
GATTTAGACTACCCCAAAA
tcaggcggt
GATTTAGACTAC
Dengue
9a



2

ACGAAGGGGACTAAAACtc
attcaccgtc
CCCAAAAACGAA
ssRNA






aggcggtattcaccgtcta
tatggcggc
GGGGACTAAAAC







tggcggc









9a
dengue_9
LwaCas13a
GATTTAGACTACCCCAAAA
ttcaggcggt
GATTTAGACTAC
Dengue
9a



3

ACGAAGGGGACTAAAACtt
attcaccgtc
CCCAAAAACGAA
ssRNA






caggcggtattcaccgtct
tatggcgg
GGGGACTAAAAC







atggcgg









9a
dengue_9
LwaCas13a
GATTTAGACTACCCCAAAA
cttcaggcg
GATTTAGACTAC
Dengue
9a



4

ACGAAGGGGACTAAAACct
gtattcaccg
CCCAAAAACGAA
ssRNA






tcaggcggtattcaccgtc
tctatggcg
GGGGACTAAAAC







tatggcg









9a
dengue_9
LwaCas13a
GATTTAGACTACCCCAAAA
ccttcaggc
GATTTAGACTAC
Dengue
9a



5

ACGAAGGGGACTAAAACcc
ggtattcacc
CCCAAAAACGAA
ssRNA






ttcaggcggtattcaccgt
gtctatggc
GGGGACTAAAAC







ctatggc









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ctgtgaaag
GATTTAGACTAC
Ebola
1b



_guide_01

ACGAAGGGGACTAAAACct
acaactcttc
CCCAAAAACGAA
ssRNA






gtgaaagacaactcttcac
actgcgaat
GGGGACTAAAAC







tgcgaat









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
gatacaactg
GATTTAGACTAC
Ebola
1b



_guide_06

ACGAAGGGGACTAAAACga
tgaaagaca
CCCAAAAACGAA
ssRNA






tacaactgtgaaagacaac
actcttcac
GGGGACTAAAAC







tcttcac









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
tgatacaact
GATTTAGACTAC
Ebola
1b



_guide_07

ACGAAGGGGACTAAAACtg
gtgaaagac
CCCAAAAACGAA
ssRNA






atacaactgtgaaagacaa
aactcttca
GGGGACTAAAAC







ctcttca









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
tttgatacaa
GATTTAGACTAC
Ebola
1b



_guide_08

ACGAAGGGGACTAAAACtt
ctgtgaaag
CCCAAAAACGAA
ssRNA






tgatacaactgtgaaagac
acaactctt
GGGGACTAAAAC







aactctt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
tccgtttgata
GATTTAGACTAC
Ebola
1b



_guide_11

ACGAAGGGGACTAAAACtc
caactgtgaa
CCCAAAAACGAA
ssRNA






cgtttgatacaactgtgaa
agacaac
GGGGACTAAAAC







agacaac









l1b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ggctccgttt
GATTTAGACTAC
Ebola
1b



_guide_13

ACGAAGGGGACTAAAACgg
gatacaactg
CCCAAAAACGAA
ssRNA






ctccgtttgatacaactgt
tgaaagac
GGGGACTAAAAC







gaaagac









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ccactgatgt
GATTTAGACTAC
Ebola
1b



_guide_20

ACGAAGGGGACTAAAACcc
ttttggctccg
CCCAAAAACGAA
ssRNA






actgatgtttttggctccg
tttgata
GGGGACTAAAAC







tttgata









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
accactgatg
GATTTAGACTAC
Ebola
1b



_guide_21

ACGAAGGGGACTAAAACac
tttttggctcc
CCCAAAAACGAA
ssRNA






cactgatgtttttggctcc
gtttgat
GGGGACTAAAAC







gtttgat









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
gaccactgat
GATTTAGACTAC
Ebola
1b



_guide_22

ACGAAGGGGACTAAAACga
gtttttggctc
CCCAAAAACGAA
ssRNA






ccactgatgtttttggctc
cgtttga
GGGGACTAAAAC







cgtttga









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ctgaccactg
GATTTAGACTAC
Ebola
1b



_guide_23

ACGAAGGGGACTAAAACct
atgtttttggc
CCCAAAAACGAA
ssRNA






gaccactgatgtttttggc
tccgttt
GGGGACTAAAAC







tccgttt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ctctgaccac
GATTTAGACTAC
Ebola
1b



_guide_25

ACGAAGGGGACTAAAACct
tgatgtttttg
CCCAAAAACGAA
ssRNA






ctgaccactgatgtttttg
gctccgt
GGGGACTAAAAC







gctccgt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
cgccggact
GATTTAGACTAC
Ebola
1b



_guide_30

ACGAAGGGGACTAAAACcg
ctgaccactg
CCCAAAAACGAA
ssRNA






ccggactctgaccactgat
gatgtttttg
GGGGACTAAAAC







tttttg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
cgcgccgga
GATTTAGACTAC
Ebola
1b



_guide_31

ACGAAGGGGACTAAAACcg
ctctgaccac
CCCAAAAACGAA
ssRNA






cgccggactctgaccactg
tgatgtttt
GGGGACTAAAAC







atgtttt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ttcgcgccg
GATTTAGACTAC
Ebola
1b



_guide_32

ACGAAGGGGACTAAAACtt
gactctgacc
CCCAAAAACGAA
ssRNA






cgcgccggactctgaccac
actgatgtt
GGGGACTAAAAC







tgatgtt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
gttcgcgcc
GATTTAGACTAC
Ebola
1b



_guide_33

ACGAAGGGGACTAAAACgt
ggactctga
CCCAAAAACGAA
ssRNA






tcgcgccggactctgacca
ccactgatgt
GGGGACTAAAAC







ctgatgt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
agttcgcgc
GATTTAGACTAC
Ebola
1b



_guide_34

ACGAAGGGGACTAAAACag
cggactctg
CCCAAAAACGAA
ssRNA






ttcgcgccggactctgacc
accactgatg
GGGGACTAAAAC







actgatg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
aagttcgcg
GATTTAGACTAC
Ebola
1b



_guide_35

ACGAAGGGGACTAAAACaa
ccggactct
CCCAAAAACGAA
ssRNA






gttcgcgccggactctgac
gaccactgat
GGGGACTAAAAC







cactgat









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
agaagttcg
GATTTAGACTAC
Ebola
1b



_guide_36

ACGAAGGGGACTAAAACag
cgccggact
CCCAAAAACGAA
ssRNA






aagttcgcgccggactctg
ctgaccactg
GGGGACTAAAAC







accactg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ggtcggaag
GATTTAGACTAC
Ebola
1b



_guide_42

ACGAAGGGGACTAAAACgg
aagttcgcg
CCCAAAAACGAA
ssRNA






tcggaagaagttcgcgccg
ccggactct
GGGGACTAAAAC







gactctg
g








11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ctgggtcgg
GATTTAGACTAC
Ebola
1b



_guide_43

ACGAAGGGGACTAAAACct
aagaagttc
CCCAAAAACGAA
ssRNA






gggtcggaagaagttcgcg
gcgccggac
GGGGACTAAAAC







ccggact
t








11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
cctgggtcg
GATTTAGACTAC
Ebola
1b



_guide_44

ACGAAGGGGACTAAAACcc
gaagaagtt
CCCAAAAACGAA
ssRNA






tgggtcggaagaagttcgc
cgcgccgga
GGGGACTAAAAC







gccggac
c








11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ccctgggtc
GATTTAGACTAC
Ebola
1b



_guide_45

ACGAAGGGGACTAAAACcc
ggaagaagt
CCCAAAAACGAA
ssRNA






ctgggtcggaagaagttcg
tcgcgccgg
GGGGACTAAAAC







cgccgga
a








11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
tccctgggtc
GATTTAGACTAC
Ebola
1b



_guide_46

ACGAAGGGGACTAAAACtcc
ggaagaagt
CCCAAAAACGAA
ssRNA






ctgggtcggaagaagttcg
tcgcgccgg
GGGGACTAAAAC







cgccgg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ggtccctgg
GATTTAGACTAC
Ebola
1b



_guide_48

ACGAAGGGGACTAAAACgg
gtcggaaga
CCCAAAAACGAA
ssRNA






tccctgggtcggaagaagt
agttcgcgc
GGGGACTAAAAC







tcgcgcc
c








11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
gttggtccct
GATTTAGACTAC
Ebola
1b



_guide_49

ACGAAGGGGACTAAAACgt
gggtcggaa
CCCAAAAACGAA
ssRNA






tggtccctgggtcggaaga
gaagttcgc
GGGGACTAAAAC







agttcgc









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
agttgttgtgt
GATTTAGACTAC
Ebola
1b



_guide_55

ACGAAGGGGACTAAAACag
tggtccctgg
CCCAAAAACGAA
ssRNA






ttgttgtgttggtccctgg
gtcggaa
GGGGACTAAAAC







gtcggaa









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
tcagttgttgt
GATTTAGACTAC
Ebola
1b



_guide_57

ACGAAGGGGACTAAAACtc
gttggtccct
CCCAAAAACGAA
ssRNA






agttgttgtgttggtccct
gggtcgg
GGGGACTAAAAC







gggtcgg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ttcagttgttg
GATTTAGACTAC
Ebola
1b



_guide_58

ACGAAGGGGACTAAAACtt
tgttggtccct
CCCAAAAACGAA
ssRNA






cagttgttgtgttggtccc
gggtcg
GGGGACTAAAAC







tgggtcg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
cttcagttgtt
GATTTAGACTAC
Ebola
1b



_guide_59

ACGAAGGGGACTAAAACct
gtgttggtcc
CCCAAAAACGAA
ssRNA






tcagttgttgtgttggtcc
ctgggtc
GGGGACTAAAAC







ctgggtc









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
tcttcagttgt
GATTTAGACTAC
Ebola
1b



_guide_60

ACGAAGGGGACTAAAACtc
tgtgttggtc
CCCAAAAACGAA
ssRNA






ttcagttgttgtgttggtc
cctgggt
GGGGACTAAAAC







cctgggt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
tggtcttcagt
GATTTAGACTAC
Ebola
1b



_guide_61

ACGAAGGGGACTAAAACtg
tgttgtgttgg
CCCAAAAACGAA
ssRNA






gtcttcagttgttgtgttg
tccctg
GGGGACTAAAAC







gtccctg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
attttgtggtc
GATTTAGACTAC
Ebola
1b



_guide_66

ACGAAGGGGACTAAAACat
ttcagttgttg
CCCAAAAACGAA
ssRNA






tttgtggtcttcagttgtt
tgttgg
GGGGACTAAAAC







gtgttgg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
gccatgatttt
GATTTAGACTAC
Ebola
1b



_guide_70

ACGAAGGGGACTAAAACgc
gtggtcttca
CCCAAAAACGAA
ssRNA






catgattttgtggtcttca
gttgttg
GGGGACTAAAAC







gttgttg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
aagccatgat
GATTTAGACTAC
Ebola
1b



_guide_72

ACGAAGGGGACTAAAACaa
tttgtggtctt
CCCAAAAACGAA
ssRNA






gccatgattttgtggtctt
cagttgt
GGGGACTAAAAC







cagttgt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
ctgaagccat
GATTTAGACTAC
Ebola
1b



_guide_73

ACGAAGGGGACTAAAACct
gattttgtggt
CCCAAAAACGAA
ssRNA






gaagccatgattttgtggt
cttcagt
GGGGACTAAAAC







cttcagt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
gaattttctga
GATTTAGACTAC
Ebola
1b



_guide_78

ACGAAGGGGACTAAAACga
agccatgatt
CCCAAAAACGAA
ssRNA






attttctgaagccatgatt
ttgtggt
GGGGACTAAAAC







ttgtggt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
agaggaattt
GATTTAGACTAC
Ebola
1b



_guide_81

ACGAAGGGGACTAAAACag
tctgaagcca
CCCAAAAACGAA
ssRNA






aggaattttctgaagccat
tgattttg
GGGGACTAAAAC







gattttg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
cagaggaat
GATTTAGACTAC
Ebola
1b



_guide_82

ACGAAGGGGACTAAAACca
tttctgaagc
CCCAAAAACGAA
ssRNA






gaggaattttctgaagcca
catgatttt
GGGGACTAAAAC







tgatttt









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
cattgcaga
GATTTAGACTAC
Ebola
1b



_guide_85

ACGAAGGGGACTAAAACca
ggaattttctg
CCCAAAAACGAA
ssRNA






ttgcagaggaattttctga
aagccatg
GGGGACTAAAAC







agccatg









11b
Ebola_GP
LwaCas13a
GATTTAGACTACCCCAAAA
cacttgaacc
GATTTAGACTAC
Ebola
1b



_guide_90

ACGAAGGGGACTAAAACca
attgcagag
CCCAAAAACGAA
ssRNA






cttgaaccattgcagagga
gaattttct
GGGGACTAAAAC







attttct









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ccccgggta
GATTTAGACTAC
ssRNA1
9a



guide_01

ACGAAGGGGACTAAAACcc
ccgagctcg
CCCAAAAACGAA







ccgggtaccgagctcgaat
aattcactgg
GGGGACTAAAAC







tcactgg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tccccgggt
GATTTAGACTAC
ssRNA1
9a



guide_02

ACGAAGGGGACTAAAACtc
accgagctc
CCCAAAAACGAA







cccgggtaccgagctcgaa
gaattcactg
GGGGACTAAAAC







ttcactg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
atccccggg
GATTTAGACTAC
ssRNA1
9a



guide_03

ACGAAGGGGACTAAAACat
taccgagctc
CCCAAAAACGAA







ccccgggtaccgagctcga
gaattcact
GGGGACTAAAAC







attcact









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
aggatcccc
GATTTAGACTAC
ssRNA1
9a



guide_04

ACGAAGGGGACTAAAACag
gggtaccga
CCCAAAAACGAA







gatccccgggtaccgagct
gctcgaattc
GGGGACTAAAAC







cgaattc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
agaggatcc
GATTTAGACTAC
ssRNA1
9a



guide_05

ACGAAGGGGACTAAAACag
ccgggtacc
CCCAAAAACGAA







aggatccccgggtaccgag
gagctcgaa
GGGGACTAAAAC







ctcgaat
t








9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tctagaggat
GATTTAGACTAC
ssRNA1
9a



guide_06

ACGAAGGGGACTAAAACtc
ccccgggta
CCCAAAAACGAA







tagaggatccccgggtacc
ccgagctcg
GGGGACTAAAAC







gagctcg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ttctagagga
GATTTAGACTAC
ssRNA1
9a



guide_07

ACGAAGGGGACTAAAACtt
tccccgggt
CCCAAAAACGAA







ctagaggatccccgggtac
accgagctc
GGGGACTAAAAC







cgagctc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
atttctagag
GATTTAGACTAC
ssRNA1
9a



guide_08

ACGAAGGGGACTAAAACat
gatccccgg
CCCAAAAACGAA







ttctagaggatccccgggt
gtaccgagc
GGGGACTAAAAC







accgagc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tatttctagag
GATTTAGACTAC
ssRNA1
9a



guide_09

ACGAAGGGGACTAAAACta
gatccccgg
CCCAAAAACGAA







tttctagaggatccccggg
gtaccgag
GGGGACTAAAAC







taccgag









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ccatatttcta
GATTTAGACTAC
ssRNA1
9a



guide_10

ACGAAGGGGACTAAAACcc
gaggatccc
CCCAAAAACGAA







atatttctagaggatcccc
cgggtacc
GGGGACTAAAAC







gggtacc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tccatatttct
GATTTAGACTAC
ssRNA1
9a



guide_11

ACGAAGGGGACTAAAACtc
agaggatcc
CCCAAAAACGAA







catatttctagaggatccc
ccgggtac
GGGGACTAAAAC







cgggtac









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
atccatatttc
GATTTAGACTAC
ssRNA1
9a



guide_12

ACGAAGGGGACTAAAACat
tagaggatc
CCCAAAAACGAA







ccatatttctagaggatcc
cccgggta
GGGGACTAAAAC







ccgggta









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
taatccatatt
GATTTAGACTAC
ssRNA1
9a



guide_13

ACGAAGGGGACTAAAACta
tctagaggat
CCCAAAAACGAA







atccatatttctagaggat
ccccggg
GGGGACTAAAAC







ccccggg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gtaatccata
GATTTAGACTAC
ssRNA1
9a



guide_14

ACGAAGGGGACTAAAACgt
tttctagagg
CCCAAAAACGAA







aatccatatttctagagga
atccccgg
GGGGACTAAAAC







tccccgg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
taccaagtaa
GATTTAGACTAC
ssRNA1
9a



guide_15

ACGAAGGGGACTAAAACta
tccatatttct
CCCAAAAACGAA







ccaagtaatccatatttct
agaggat
GGGGACTAAAAC







agaggat









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tctaccaagt
GATTTAGACTAC
ssRNA1
9a



guide_16

ACGAAGGGGACTAAAACtc
aatccatattt
CCCAAAAACGAA







taccaagtaatccatattt
ctagagg
GGGGACTAAAAC







ctagagg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gttctaccaa
GATTTAGACTAC
ssRNA1
9a



guide_17

ACGAAGGGGACTAAAACgt
gtaatccata
CCCAAAAACGAA







tctaccaagtaatccatat
tttctaga
GGGGACTAAAAC







ttctaga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gctgttctac
GATTTAGACTAC
ssRNA1
9a



guide_18

ACGAAGGGGACTAAAACgc
caagtaatcc
CCCAAAAACGAA







tgttctaccaagtaatcca
atatttct
GGGGACTAAAAC







tatttct









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
attgctgttct
GATTTAGACTAC
ssRNA1
9a



guide_20

ACGAAGGGGACTAAAACat
accaagtaat
CCCAAAAACGAA







tgctgttctaccaagtaat
ccatatt
GGGGACTAAAAC







ccatatt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tagattgctgt
GATTTAGACTAC
ssRNA1
9a



guide_21

ACGAAGGGGACTAAAACta
tctaccaagt
CCCAAAAACGAA







gattgctgttctaccaagt
aatccat
GGGGACTAAAAC







aatccat









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gtagattgct
GATTTAGACTAC
ssRNA1
9a



guide_22

ACGAAGGGGACTAAAACgt
gttctaccaa
CCCAAAAACGAA







agattgctgttctaccaag
gtaatcca
GGGGACTAAAAC







taatcca









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
agtagattgc
GATTTAGACTAC
ssRNA1
9a



guide_23

ACGAAGGGGACTAAAACag
tgttctacca
CCCAAAAACGAA







tagattgctgttctaccaa
agtaatcc
GGGGACTAAAAC







gtaatcc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gagtagattg
GATTTAGACTAC
ssRNA1
9a



guide_24

ACGAAGGGGACTAAAACga
ctgttctacc
CCCAAAAACGAA







gtagattgctgttctacca
aagtaatc
GGGGACTAAAAC







agtaatc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tcgagtagat
GATTTAGACTAC
ssRNA1
9a



guide_25

ACGAAGGGGACTAAAACtc
tgctgttctac
CCCAAAAACGAA







gagtagattgctgttctac
caagtaa
GGGGACTAAAAC







caagtaa









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gtcgagtag
GATTTAGACTAC
ssRNA1
9a



guide_26

ACGAAGGGGACTAAAACgt
attgctgttct
CCCAAAAACGAA







cgagtagattgctgttcta
accaagta
GGGGACTAAAAC







ccaagta









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
caggtcgag
GATTTAGACTAC
ssRNA1
9a



guide_28

ACGAAGGGGACTAAAACca
tagattgctgt
CCCAAAAACGAA







ggtcgagtagattgctgtt
tctaccaa
GGGGACTAAAAC







ctaccaa









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gcaggtcga
GATTTAGACTAC
ssRNA1
9a



guide_29

ACGAAGGGGACTAAAACgc
gtagattgct
CCCAAAAACGAA







aggtcgagtagattgctgt
gttctacca
GGGGACTAAAAC







tctacca









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tgcaggtcg
GATTTAGACTAC
ssRNA1
9a



guide_30

ACGAAGGGGACTAAAACtg
agtagattgc
CCCAAAAACGAA







caggtcgagtagattgctg
tgttctacc
GGGGACTAAAAC







ttctacc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ctgcaggtc
GATTTAGACTAC
ssRNA1
9a



guide_31

ACGAAGGGGACTAAAACct
gagtagattg
CCCAAAAACGAA







caggtcgagtagattgctg
ctgttctac
GGGGACTAAAAC







ttctac









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
cctgcaggt
GATTTAGACTAC
ssRNA1
9a



guide_32

ACGAAGGGGACTAAAACcc
cgagtagatt
CCCAAAAACGAA







tgcaggtcgagtagattgc
gctgttcta
GGGGACTAAAAC







tgttcta









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tgcctgcag
GATTTAGACTAC
ssRNA1
9a



guide_33

ACGAAGGGGACTAAAACtg
gtcgagtag
CCCAAAAACGAA







cctgcaggtcgagtagatt
attgctgttc
GGGGACTAAAAC







gctgttc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
atgcctgca
GATTTAGACTAC
ssRNA1
9a



guide_34

ACGAAGGGGACTAAAACat
ggtcgagta
CCCAAAAACGAA







gcctgcaggtcgagtagat
gattgctgtt
GGGGACTAAAAC







tgctgtt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
catgcctgca
GATTTAGACTAC
ssRNA1
9a



guide_35

ACGAAGGGGACTAAAACca
ggtcgagta
CCCAAAAACGAA







tgcctgcaggtcgagtaga
gattgctgt
GGGGACTAAAAC







ttgctgt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tgcatgcctg
GATTTAGACTAC
ssRNA1
9a



guide_36

ACGAAGGGGACTAAAACtg
caggtcgag
CCCAAAAACGAA







catgcctgcaggtcgagta
tagattgct
GGGGACTAAAAC







gattgct









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
cttgcatgcc
GATTTAGACTAC
ssRNA1
9a



guide_38

ACGAAGGGGACTAAAACct
tgcaggtcg
CCCAAAAACGAA







tgcatgcctgcaggtcgag
agtagattg
GGGGACTAAAAC







tagattg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
agcttgcatg
GATTTAGACTAC
ssRNA1
9a



guide_40

ACGAAGGGGACTAAAACag
cctgcaggt
CCCAAAAACGAA







cttgcatgcctgcaggtcg
cgagtagat
GGGGACTAAAAC







agtagat









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
caagcttgca
GATTTAGACTAC
ssRNA1
9a



guide_42

ACGAAGGGGACTAAAACca
tgcctgcag
CCCAAAAACGAA







agcttgcatgcctgcaggt
gtcgagtag
GGGGACTAAAAC







cgagtag









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ccaagcttgc
GATTTAGACTAC
ssRNA1
9a



guide_43

ACGAAGGGGACTAAAACcc
atgcctgca
CCCAAAAACGAA







aagcttgcatgcctgcagg
ggtcgagta
GGGGACTAAAAC







tcgagta









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
cgccaagctt
GATTTAGACTAC
ssRNA1
9a



guide_44

ACGAAGGGGACTAAAACcg
gcatgcctg
CCCAAAAACGAA







ccaagcttgcatgcctgca
caggtcgag
GGGGACTAAAAC







ggtcgag









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
acgccaagc
GATTTAGACTAC
ssRNA1
9a



guide_45

ACGAAGGGGACTAAAACac
ttgcatgcct
CCCAAAAACGAA







gccaagcttgcatgcctgc
gcaggtcga
GGGGACTAAAAC







aggtcga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tacgccaag
GATTTAGACTAC
ssRNA1
9a



guide_46

ACGAAGGGGACTAAAACta
cttgcatgcc
CCCAAAAACGAA







cgccaagcttgcatgcctg
tgcaggtcg
GGGGACTAAAAC







caggtcg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ttacgccaag
GATTTAGACTAC
ssRNA1
9a



guide_47

ACGAAGGGGACTAAAACtt
cttgcatgcc
CCCAAAAACGAA







acgccaagcttgcatgcct
tgcaggtc
GGGGACTAAAAC







gcaggtc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
attacgccaa
GATTTAGACTAC
ssRNA1
9a



guide_48

ACGAAGGGGACTAAAACat
gcttgcatgc
CCCAAAAACGAA







tacgccaagcttgcatgcc
ctgcaggt
GGGGACTAAAAC







tgcaggt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gattacgcca
GATTTAGACTAC
ssRNA1
9a



guide_49

ACGAAGGGGACTAAAACga
agcttgcatg
CCCAAAAACGAA







ttacgccaagcttgcatgc
cctgcagg
GGGGACTAAAAC







ctgcagg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ccatgattac
GATTTAGACTAC
ssRNA1
9a



guide_52

ACGAAGGGGACTAAAACcc
gccaagctt
CCCAAAAACGAA







atgattacgccaagcttgc
gcatgcctg
GGGGACTAAAAC







atgcctg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
accatgatta
GATTTAGACTAC
ssRNA1
9a



guide_53

ACGAAGGGGACTAAAACac
cgccaagctt
CCCAAAAACGAA







catgattacgccaagcttg
gcatgcct
GGGGACTAAAAC







catgcct









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gaccatgatt
GATTTAGACTAC
ssRNA1
9a



guide_54

ACGAAGGGGACTAAAACga
acgccaagc
CCCAAAAACGAA







ccatgattacgccaagctt
ttgcatgcc
GGGGACTAAAAC







gcatgcc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
atgaccatga
GATTTAGACTAC
ssRNA1
9a



guide_55

ACGAAGGGGACTAAAACat
ttacgccaag
CCCAAAAACGAA







gaccatgattacgccaagc
cttgcatg
GGGGACTAAAAC







ttgcatg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tatgaccatg
GATTTAGACTAC
ssRNA1
9a



guide_56

ACGAAGGGGACTAAAACta
attacgccaa
CCCAAAAACGAA







tgaccatgattacgccaag
gcttgcat
GGGGACTAAAAC







cttgcat









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
agctatgacc
GATTTAGACTAC
ssRNA1
9a



guide_57

ACGAAGGGGACTAAAACag
atgattacgc
CCCAAAAACGAA







ctatgaccatgattacgcc
caagcttg
GGGGACTAAAAC







aagcttg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
cagctatgac
GATTTAGACTAC
ssRNA1
9a



guide_58

ACGAAGGGGACTAAAACca
catgattacg
CCCAAAAACGAA







gctatgaccatgattacgc
ccaagctt
GGGGACTAAAAC







caagctt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
acagctatga
GATTTAGACTAC
ssRNA1
9a



guide_59

ACGAAGGGGACTAAAACac
ccatgattac
CCCAAAAACGAA







agctatgaccatgattacg
gccaagct
GGGGACTAAAAC







ccaagct









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
aacagctatg
GATTTAGACTAC
ssRNA1
9a



guide_60

ACGAAGGGGACTAAAACaa
accatgatta
CCCAAAAACGAA







cagctatgaccatgattac
cgccaagc
GGGGACTAAAAC







gccaagc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
aacacagga
GATTTAGACTAC
ssRNA1
9a



guide_64

ACGAAGGGGACTAAAACaa
aacagctatg
CCCAAAAACGAA







cacaggaaacagctatgac
accatgatt
GGGGACTAAAAC







catgatt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
taaacacag
GATTTAGACTAC
ssRNA1
9a



guide_65

ACGAAGGGGACTAAAACta
gaaacagct
CCCAAAAACGAA







aacacaggaaacagctatg
atgaccatga
GGGGACTAAAAC







accatga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ataaacaca
GATTTAGACTAC
ssRNA1
9a



guide_66

ACGAAGGGGACTAAAACat
ggaaacagc
CCCAAAAACGAA







aaacacaggaaacagctat
tatgaccatg
GGGGACTAAAAC







gaccatg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gataaacac
GATTTAGACTAC
ssRNA1
9a



guide_67

ACGAAGGGGACTAAAACga
aggaaacag
CCCAAAAACGAA







taaacacaggaaacagcta
ctatgaccat
GGGGACTAAAAC







tgaccat









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ggataaaca
GATTTAGACTAC
ssRNA1
9a



guide_68

ACGAAGGGGACTAAAACgg
caggaaaca
CCCAAAAACGAA







ataaacacaggaaacagct
gctatgacca
GGGGACTAAAAC







atgacca









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
cggataaac
GATTTAGACTAC
ssRNA1
9a



guide_69

ACGAAGGGGACTAAAACcg
acaggaaac
CCCAAAAACGAA







gataaacacaggaaacagc
agctatgacc
GGGGACTAAAAC







tatgacc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gcggataaa
GATTTAGACTAC
ssRNA1
9a



guide_70

ACGAAGGGGACTAAAACgc
cacaggaaa
CCCAAAAACGAA







ggataaacacaggaaacag
cagctatgac
GGGGACTAAAAC







ctatgac









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
agcggataa
GATTTAGACTAC
ssRNA1
9a



guide_71

ACGAAGGGGACTAAAACag
acacaggaa
CCCAAAAACGAA







cggataaacacaggaaaca
acagctatga
GGGGACTAAAAC







gctatga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tgagcggat
GATTTAGACTAC
ssRNA1
9a



guide_72

ACGAAGGGGACTAAAACtg
aaacacagg
CCCAAAAACGAA







agcggataaacacaggaaa
aaacagctat
GGGGACTAAAAC







cagctat









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tgtgagcgg
GATTTAGACTAC
ssRNA1
9a



guide_73

ACGAAGGGGACTAAAACtg
ataaacaca
CCCAAAAACGAA







tgagcggataaacacagga
ggaaacagc
GGGGACTAAAAC







aacagct
t








9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ttgtgagcgg
GATTTAGACTAC
ssRNA1
9a



guide_74

ACGAAGGGGACTAAAACtt
ataaacaca
CCCAAAAACGAA







gtgagcggataaacacagg
ggaaacagc
GGGGACTAAAAC







aaacagc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ggaattgtga
GATTTAGACTAC
ssRNA1
9a



guide_76

ACGAAGGGGACTAAAACgg
gcggataaa
CCCAAAAACGAA







aattgtgagcggataaaca
cacaggaaa
GGGGACTAAAAC







caggaaa









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tggaattgtg
GATTTAGACTAC
ssRNA1
9a



guide_77

ACGAAGGGGACTAAAACtg
agcggataa
CCCAAAAACGAA







gaattgtgagcggataaac
acacaggaa
GGGGACTAAAAC







acaggaa









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gtggaattgt
GATTTAGACTAC
ssRNA1
9a



guide_78

ACGAAGGGGACTAAAACgt
gagcggata
CCCAAAAACGAA







ggaattgtgagcggataaa
aacacagga
GGGGACTAAAAC







cacagga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tgtggaattg
GATTTAGACTAC
ssRNA1
9a



guide_79

ACGAAGGGGACTAAAACtg
tgagcggat
CCCAAAAACGAA







tggaattgtgagcggataa
aaacacagg
GGGGACTAAAAC







acacagg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tgtgtggaat
GATTTAGACTAC
ssRNA1
9a



guide_80

ACGAAGGGGACTAAAACtg
tgtgagcgg
CCCAAAAACGAA







tgtggaattgtgagcggat
ataaacaca
GGGGACTAAAAC







aaacaca









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ttgtgtggaa
GATTTAGACTAC
ssRNA1
9a



guide_81

ACGAAGGGGACTAAAACtt
ttgtgagcgg
CCCAAAAACGAA







gtgtggaattgtgagcgga
ataaacac
GGGGACTAAAAC







taaacac









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gttgtgtgga
GATTTAGACTAC
ssRNA1
9a



guide_82

ACGAAGGGGACTAAAACgt
attgtgagcg
CCCAAAAACGAA







tgtgtggaattgtgagcgg
gataaaca
GGGGACTAAAAC







ataaaca









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
atgttgtgtg
GATTTAGACTAC
ssRNA1
9a



guide_83

ACGAAGGGGACTAAAACat
gaattgtgag
CCCAAAAACGAA







gttgtgtggaattgtgagc
cggataaa
GGGGACTAAAAC







ggataaa









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tatgttgtgtg
GATTTAGACTAC
ssRNA1
9a



guide_84

ACGAAGGGGACTAAAACta
gaattgtgag
CCCAAAAACGAA







tgttgtgtggaattgtgag
cggataa
GGGGACTAAAAC







cggataa









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tcgtatgttgt
GATTTAGACTAC
ssRNA1
9a



guide_86

ACGAAGGGGACTAAAACtc
gtggaattgt
CCCAAAAACGAA







gtatgttgtgtggaattgt
gagcgga
GGGGACTAAAAC







gagcgga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ggctcgtatg
GATTTAGACTAC
ssRNA1
9a



guide_88

ACGAAGGGGACTAAAACgg
ttgtgtggaa
CCCAAAAACGAA







ctcgtatgttgtgtggaat
ttgtgagc
GGGGACTAAAAC







tgtgagc









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
cggctcgtat
GATTTAGACTAC
ssRNA1
9a



guide_89

ACGAAGGGGACTAAAACcg
gttgtgtgga
CCCAAAAACGAA







gctcgtatgttgtgtggaa
attgtgag
GGGGACTAAAAC







ttgtgag









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ccggctcgt
GATTTAGACTAC
ssRNA1
9a



guide_90

ACGAAGGGGACTAAAACcc
atgttgtgtg
CCCAAAAACGAA







ggctcgtatgttgtgtgga
gaattgtga
GGGGACTAAAAC







attgtga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ttccggctcg
GATTTAGACTAC
ssRNA1
9a



guide_91

ACGAAGGGGACTAAAACtt
tatgttgtgtg
CCCAAAAACGAA







ccggctcgtatgttgtgtg
gaattgt
GGGGACTAAAAC







gaattgt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
cttccggctc
GATTTAGACTAC
ssRNA1
9a



guide_92

ACGAAGGGGACTAAAACct
gtatgttgtgt
CCCAAAAACGAA







tccggctcgtatgttgtgt
ggaattg
GGGGACTAAAAC







ggaattg









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
gcttccggct
GATTTAGACTAC
ssRNA1
9a



guide_93

ACGAAGGGGACTAAAACgc
cgtatgttgt
CCCAAAAACGAA







ttccggctcgtatgttgtg
gtggaatt
GGGGACTAAAAC







tggaatt









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
atgcttccgg
GATTTAGACTAC
ssRNA1
9a



guide_94

ACGAAGGGGACTAAAACat
ctcgtatgttg
CCCAAAAACGAA







gcttccggctcgtatgttg
tgtggaa
GGGGACTAAAAC







tgtggaa









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
tatgcttccg
GATTTAGACTAC
ssRNA1
9a



guide_95

ACGAAGGGGACTAAAACta
gctcgtatgtt
CCCAAAAACGAA







tgcttccggctcgtatgtt
gtgtgga
GGGGACTAAAAC







gtgtgga









9a
ssRNA1_
LwaCas13a
GATTTAGACTACCCCAAAA
ttatgcttccg
GATTTAGACTAC
ssRNA1
9a



guide_96

ACGAAGGGGACTAAAACtt
gctcgtatgtt
CCCAAAAACGAA







atgcttccggctcgtatgt
gtgtgg
GGGGACTAAAAC







tgtgtgg









9a
therm_00
LwaCas13a
GATTTAGACTACCCCAAAA
taatttaaca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACta
gtatcaccat
CCCAAAAACGAA
nuclease






atttaacagtatcaccatc
caatcgct
GGGGACTAAAAC







aatcgct









9a
therm_01
LwaCas13a
GATTTAGACTACCCCAAAA
ttaatttaaca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
gtatcaccat
CCCAAAAACGAA
nuclease






aatttaacagtatcaccat
caatcgc
GGGGACTAAAAC







caatcgc









9a
therm_02
LwaCas13a
GATTTAGACTACCCCAAAA
attaatttaac
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
agtatcacca
CCCAAAAACGAA
nuclease






taatttaacagtatcacca
tcaatcg
GGGGACTAAAAC







tcaatcg









9a
therm_03
LwaCas13a
GATTTAGACTACCCCAAAA
cattaatttaa
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACca
cagtatcacc
CCCAAAAACGAA
nuclease






ttaatttaacagtatcacc
atcaatc
GGGGACTAAAAC







atcaatc









9a
therm_04
LwaCas13a
GATTTAGACTACCCCAAAA
acattaattta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACac
acagtatcac
CCCAAAAACGAA
nuclease






attaatttaacagtatcac
catcaat
GGGGACTAAAAC







catcaat









9a
therm_05
LwaCas13a
GATTTAGACTACCCCAAAA
tacattaattt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACta
aacagtatca
CCCAAAAACGAA
nuclease






cattaatttaacagtatca
ccatcaa
GGGGACTAAAAC







ccatcaa









9a
therm_06
LwaCas13a
GATTTAGACTACCCCAAAA
gtacattaatt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgt
taacagtatc
CCCAAAAACGAA
nuclease






acattaatttaacagtatc
accatca
GGGGACTAAAAC







accatca









9a
therm_07
LwaCas13a
GATTTAGACTACCCCAAAA
tgtacattaat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtg
ttaacagtat
CCCAAAAACGAA
nuclease






tacattaatttaacagtat
caccatc
GGGGACTAAAAC







caccatc









9a
therm_08
LwaCas13a
GATTTAGACTACCCCAAAA
ttgtacattaa
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
tttaacagtat
CCCAAAAACGAA
nuclease






gtacattaatttaacagta
caccat
GGGGACTAAAAC







tcaccat









9a
therm_09
LwaCas13a
GATTTAGACTACCCCAAAA
tttgtacatta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
atttaacagt
CCCAAAAACGAA
nuclease






tgtacattaatttaacagt
atcacca
GGGGACTAAAAC







atcacca









9a
therm_10
LwaCas13a
GATTTAGACTACCCCAAAA
ctttgtacatt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
aatttaacag
CCCAAAAACGAA
nuclease






ttgtacattaatttaacag
tatcacc
GGGGACTAAAAC







tatcacc









9a
therm_11
LwaCas13a
GATTTAGACTACCCCAAAA
cctttgtacat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACcc
taatttaaca
CCCAAAAACGAA
nuclease






tttgtacattaatttaaca
gtatcac
GGGGACTAAAAC







gtatcac









9a
therm_12
LwaCas13a
GATTTAGACTACCCCAAAA
acctttgtac
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACac
attaatttaac
CCCAAAAACGAA
nuclease






ctttgtacattaatttaac
agtatca
GGGGACTAAAAC







agtatca









9a
therm_13
LwaCas13a
GATTTAGACTACCCCAAAA
gacctttgta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACga
cattaatttaa
CCCAAAAACGAA
nuclease






cctttgtacattaatttaa
cagtatc
GGGGACTAAAAC







cagtatc









9a
therm_14
LwaCas13a
GATTTAGACTACCCCAAAA
tgacctttgta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtg
cattaatttaa
CCCAAAAACGAA
nuclease






acctttgtacattaattta
cagtat
GGGGACTAAAAC







acagtat









9a
therm_15
LwaCas13a
GATTTAGACTACCCCAAAA
ttgacctttgt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
acattaattta
CCCAAAAACGAA
nuclease






gacctttgtacattaattt
acagta
GGGGACTAAAAC







aacagta









9a
therm_16
LwaCas13a
GATTTAGACTACCCCAAAA
gttgacctttg
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgt
tacattaattt
CCCAAAAACGAA
nuclease






tgacctttgtacattaatt
aacagt
GGGGACTAAAAC







taacagt









9a
therm_17
LwaCas13a
GATTTAGACTACCCCAAAA
ggttgaccttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgg
gtacattaatt
CCCAAAAACGAA
nuclease






ttgacctttgtacattaat
taacag
GGGGACTAAAAC







ttaacag









9a
therm_18
LwaCas13a
GATTTAGACTACCCCAAAA
tggttgacctt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtg
tgtacattaat
CCCAAAAACGAA
nuclease






gttgacctttgtacattaa
ttaaca
GGGGACTAAAAC







tttaaca









9a
therm_19
LwaCas13a
GATTTAGACTACCCCAAAA
ttggttgacct
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
ttgtacattaa
CCCAAAAACGAA
nuclease






ggttgacctttgtacatta
tttaac
GGGGACTAAAAC







atttaac









9a
therm_20
LwaCas13a
GATTTAGACTACCCCAAAA
cattggttga
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACca
cctttgtacat
CCCAAAAACGAA
nuclease






ttggttgacctttgtacat
taattta
GGGGACTAAAAC







taattta









9a
therm_21
LwaCas13a
GATTTAGACTACCCCAAAA
gtcattggtt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgt
gacctttgta
CCCAAAAACGAA
nuclease






cattggttgacctttgtac
cattaatt
GGGGACTAAAAC







attaatt









9a
therm_22
LwaCas13a
GATTTAGACTACCCCAAAA
atgtcattggt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
tgacctttgta
CCCAAAAACGAA
nuclease






gtcattggttgacctttgt
cattaa
GGGGACTAAAAC







acattaa









9a
therm_23
LwaCas13a
GATTTAGACTACCCCAAAA
gaatgtcatt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACga
ggttgaccttt
CCCAAAAACGAA
nuclease






atgtcattggttgaccttt
gtacatt
GGGGACTAAAAC







gtacatt









9a
therm_24
LwaCas13a
GATTTAGACTACCCCAAAA
ctgaatgtca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
ttggttgacct
CCCAAAAACGAA
nuclease






gaatgtcattggttgacct
ttgtaca
GGGGACTAAAAC







ttgtaca









9a
therm_25
LwaCas13a
GATTTAGACTACCCCAAAA
gtctgaatgt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgt
cattggttga
CCCAAAAACGAA
nuclease






ctgaatgtcattggttgac
cctttgta
GGGGACTAAAAC







ctttgta









9a
therm_26
LwaCas13a
GATTTAGACTACCCCAAAA
tagtctgaat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACta
gtcattggtt
CCCAAAAACGAA
nuclease






gtctgaatgtcattggttg
gacctttg
GGGGACTAAAAC







acctttg









9a
therm_27
LwaCas13a
GATTTAGACTACCCCAAAA
aatagtctga
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACaa
atgtcattggt
CCCAAAAACGAA
nuclease






tagtctgaatgtcattggt
tgacctt
GGGGACTAAAAC







tgacctt









9a
therm_28
LwaCas13a
GATTTAGACTACCCCAAAA
ataatagtct
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
gaatgtcatt
CCCAAAAACGAA
nuclease






aatagtctgaatgtcattg
ggttgacc
GGGGACTAAAAC







gttgacc









9a
therm_29
LwaCas13a
GATTTAGACTACCCCAAAA
caataatagt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACca
ctgaatgtca
CCCAAAAACGAA
nuclease






ataatagtctgaatgtcat
ttggttga
GGGGACTAAAAC







tggttga









9a
therm_30
LwaCas13a
GATTTAGACTACCCCAAAA
accaataata
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACac
gtctgaatgt
CCCAAAAACGAA
nuclease






caataatagtctgaatgtc
cattggtt
GGGGACTAAAAC







attggtt









9a
therm_31
LwaCas13a
GATTTAGACTACCCCAAAA
caaccaata
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACca
atagtctgaa
CCCAAAAACGAA
nuclease






accaataatagtctgaatg
tgtcattgg
GGGGACTAAAAC







tcattgg









9a
therm_32
LwaCas13a
GATTTAGACTACCCCAAAA
atcaaccaat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
aatagtctga
CCCAAAAACGAA
nuclease






caaccaataatagtctgaa
atgtcatt
GGGGACTAAAAC







tgtcatt









9a
therm_33
LwaCas13a
GATTTAGACTACCCCAAAA
gtatcaacca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgt
ataatagtct
CCCAAAAACGAA
nuclease






atcaaccaataatagtctg
gaatgtca
GGGGACTAAAAC







aatgtca









9a
therm_34
LwaCas13a
GATTTAGACTACCCCAAAA
gtgtatcaac
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgt
caataatagt
CCCAAAAACGAA
nuclease






gtatcaaccaataatagtc
ctgaatgt
GGGGACTAAAAC







tgaatgt









9a
therm_35
LwaCas13a
GATTTAGACTACCCCAAAA
aggtgtatca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACag
accaataata
CCCAAAAACGAA
nuclease






gtgtatcaaccaataatag
gtctgaat
GGGGACTAAAAC







tctgaat









9a
therm_36
LwaCas13a
GATTTAGACTACCCCAAAA
tcaggtgtat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtc
caaccaata
CCCAAAAACGAA
nuclease






aggtgtatcaaccaataat
atagtctga
GGGGACTAAAAC







agtctga









9a
therm_37
LwaCas13a
GATTTAGACTACCCCAAAA
tttcaggtgta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
tcaaccaata
CCCAAAAACGAA
nuclease






tcaggtgtatcaaccaata
atagtct
GGGGACTAAAAC







atagtct









9a
therm_38
LwaCas13a
GATTTAGACTACCCCAAAA
tgtttcaggt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtg
gtatcaacca
CCCAAAAACGAA
nuclease






tttcaggtgtatcaaccaa
ataatagt
GGGGACTAAAAC







taatagt









9a
therm_39
LwaCas13a
GATTTAGACTACCCCAAAA
tttgtttcagg
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
tgtatcaacc
CCCAAAAACGAA
nuclease






tgtttcaggtgtatcaacc
aataata
GGGGACTAAAAC







aataata









9a
therm_40
LwaCas13a
GATTTAGACTACCCCAAAA
gctttgtttca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgc
ggtgtatcaa
CCCAAAAACGAA
nuclease






tttgtttcaggtgtatcaa
ccaataa
GGGGACTAAAAC







ccaataa









9a
therm_41
LwaCas13a
GATTTAGACTACCCCAAAA
atgctttgttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
caggtgtatc
CCCAAAAACGAA
nuclease






gctttgtttcaggtgtatc
aaccaat
GGGGACTAAAAC







aaccaat









9a
therm_42
LwaCas13a
GATTTAGACTACCCCAAAA
ggatgctttg
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgg
tttcaggtgta
CCCAAAAACGAA
nuclease






atgctttgtttcaggtgta
tcaacca
GGGGACTAAAAC







tcaacca









9a
therm_43
LwaCas13a
GATTTAGACTACCCCAAAA
taggatgcttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACta
gtttcaggtg
CCCAAAAACGAA
nuclease






ggatgctttgtttcaggtg
tatcaac
GGGGACTAAAAC







tatcaac









9a
therm_44
LwaCas13a
GATTTAGACTACCCCAAAA
GATTTAGACTAC
tttaggatgct
thermo-
9a





ACGAAGGGGACTAAAACtt
ttgtttcaggt
CCCAAAAACGAA
nuclease






taggatgctttgtttcagg
gtatca
GGGGACTAAAAC







tgtatca









9a
therm_45
LwaCas13a
GATTTAGACTACCCCAAAA
tttttaggatg
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
ctttgtttcag
CCCAAAAACGAA
nuclease






tttaggatgctttgtttca
gtgtat
GGGGACTAAAAC







ggtgtat









9a
therm_46
LwaCas13a
GATTTAGACTACCCCAAAA
cttttttagga
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
tgctttgtttc
CCCAAAAACGAA
nuclease






tttttaggatgctttgttt
aggtgt
GGGGACTAAAAC







caggtgt









9a
therm_47
LwaCas13a
GATTTAGACTACCCCAAAA
accttttttag
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACac
gatgctttgtt
CCCAAAAACGAA
nuclease






cttttttaggatgctttgt
tcaggt
GGGGACTAAAAC







ttcaggt









9a
therm_48
LwaCas13a
GATTTAGACTACCCCAAAA
acacctttttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACac
aggatgcttt
CCCAAAAACGAA
nuclease






accttttttaggatgcttt
gtttcag
GGGGACTAAAAC







gtttcag









9a
therm_49
LwaCas13a
GATTTAGACTACCCCAAAA
ctacacctttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
ttaggatgctt
CCCAAAAACGAA
nuclease






acaccttttttaggatgct
tgtttc
GGGGACTAAAAC







ttgtttc









9a
therm_50
LwaCas13a
GATTTAGACTACCCCAAAA
ctctacacctt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
ttttaggatgc
CCCAAAAACGAA
nuclease






ctacaccttttttaggatg
tttgtt
GGGGACTAAAAC







ctttgtt









9a
therm_51
LwaCas13a
GATTTAGACTACCCCAAAA
ttctctacacc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
ttttttaggat
CCCAAAAACGAA
nuclease






ctctacaccttttttagga
gctttg
GGGGACTAAAAC







tgctttg









9a
therm_52
LwaCas13a
GATTTAGACTACCCCAAAA
atttctctaca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
ccttttttagg
CCCAAAAACGAA
nuclease






ttctctacaccttttttag
atgctt
GGGGACTAAAAC







gatgctt









9a
therm_53
LwaCas13a
GATTTAGACTACCCCAAAA
atatttctcta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
cacctttttta
CCCAAAAACGAA
nuclease






atttctctacacctttttt
ggatgc
GGGGACTAAAAC







aggatgc









9a
therm_54
LwaCas13a
GATTTAGACTACCCCAAAA
ccatatttctc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACcc
tacacctttttt
CCCAAAAACGAA
nuclease






atatttctctacacctttt
aggat
GGGGACTAAAAC







ttaggat









9a
therm_55
LwaCas13a
GATTTAGACTACCCCAAAA
gaccatattt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACga
ctctacacctt
CCCAAAAACGAA
nuclease






ccatatttctctacacctt
ttttagg
GGGGACTAAAAC







ttttagg









9a
therm_56
LwaCas13a
GATTTAGACTACCCCAAAA
aggaccatat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACag
ttctctacacc
CCCAAAAACGAA
nuclease






gaccatatttctctacacc
tttttta
GGGGACTAAAAC







tttttta









9a
therm_57
LwaCas13a
GATTTAGACTACCCCAAAA
tcaggaccat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtc
atttctctaca
CCCAAAAACGAA
nuclease






aggaccatatttctctaca
ccttttt
GGGGACTAAAAC







ccttttt









9a
therm_58
LwaCas13a
GATTTAGACTACCCCAAAA
cttcaggacc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
atatttctcta
CCCAAAAACGAA
nuclease






tcaggaccatatttctcta
caccttt
GGGGACTAAAAC







caccttt









9a
therm_59
LwaCas13a
GATTTAGACTACCCCAAAA
tgcttcagga
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtg
ccatatttctc
CCCAAAAACGAA
nuclease






cttcaggaccatatttctc
tacacct
GGGGACTAAAAC







tacacct









9a
therm_60
LwaCas13a
GATTTAGACTACCCCAAAA
cttgcttcag
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
gaccatattt
CCCAAAAACGAA
nuclease






tgcttcaggaccatatttc
ctctacac
GGGGACTAAAAC







tctacac









9a
therm_61
LwaCas13a
GATTTAGACTACCCCAAAA
cacttgcttc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACca
aggaccatat
CCCAAAAACGAA
nuclease






cttgcttcaggaccatatt
ttctctac
GGGGACTAAAAC







tctctac









9a
therm_62
LwaCas13a
GATTTAGACTACCCCAAAA
tgcacttgctt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtg
caggaccat
CCCAAAAACGAA
nuclease






cacttgcttcaggaccata
atttctct
GGGGACTAAAAC







tttctct









9a
therm_63
LwaCas13a
GATTTAGACTACCCCAAAA
aatgcacttg
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACaa
cttcaggacc
CCCAAAAACGAA
nuclease






tgcacttgcttcaggacca
atatttct
GGGGACTAAAAC







tatttct









9a
therm_64
LwaCas13a
GATTTAGACTACCCCAAAA
taaatgcact
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACta
tgcttcagga
CCCAAAAACGAA
nuclease






aatgcacttgcttcaggac
ccatattt
GGGGACTAAAAC







catattt









9a
therm_65
LwaCas13a
GATTTAGACTACCCCAAAA
gtaaatgcac
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgt
ttgcttcagg
CCCAAAAACGAA
nuclease






aaatgcacttgcttcagga
accatatt
GGGGACTAAAAC







ccatatt









9a
therm_66
LwaCas13a
GATTTAGACTACCCCAAAA
cgtaaatgca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACcg
cttgcttcag
CCCAAAAACGAA
nuclease






taaatgcacttgcttcagg
gaccatat
GGGGACTAAAAC







accatat









9a
therm_67
LwaCas13a
GATTTAGACTACCCCAAAA
tcgtaaatgc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtc
acttgcttca
CCCAAAAACGAA
nuclease






gtaaatgcacttgcttcag
ggaccata
GGGGACTAAAAC







gaccata









9a
therm_68
LwaCas13a
GATTTAGACTACCCCAAAA
ttcgtaaatg
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
cacttgcttc
CCCAAAAACGAA
nuclease






cgtaaatgcacttgcttca
aggaccat
GGGGACTAAAAC







ggaccat









9a
therm_69
LwaCas13a
GATTTAGACTACCCCAAAA
tttcgtaaatg
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
cacttgcttc
CCCAAAAACGAA
nuclease






tcgtaaatgcacttgcttc
aggacca
GGGGACTAAAAC







aggacca









9a
therm_70
LwaCas13a
GATTTAGACTACCCCAAAA
GATTTAGACTAC
ttttcgtaaat
thermo-
9a





ACGAAGGGGACTAAAACtt
gcacttgctt
CCCAAAAACGAA
nuclease






ttcgtaaatgcacttgctt
caggacc
GGGGACTAAAAC







caggacc









9a
therm_71
LwaCas13a
GATTTAGACTACCCCAAAA
tttttcgtaaat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
gcacttgctt
CCCAAAAACGAA
nuclease






tttcgtaaatgcacttgct
caggac
GGGGACTAAAAC







tcaggac









9a
therm_72
LwaCas13a
GATTTAGACTACCCCAAAA
ctttttcgtaa
GATTTAGACTAC
thermo-
9a





ACGAAGGGACTAAAACct
atgcacttgc
CCCAAAAACGAA
nuclease






ttttcgtaaatgcacttgc
ttcagga
GGGGACTAAAAC







ttcagga









9a
therm_73
LwaCas13a
GATTTAGACTACCCCAAAA
tctttttcgtaa
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtc
atgcacttgc
CCCAAAAACGAA
nuclease






tttttcgtaaatgcacttg
ttcagg
GGGGACTAAAAC







cttcagg









9a
therm_74
LwaCas13a
GATTTAGACTACCCCAAAA
atctttttcgta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
aatgcacttg
CCCAAAAACGAA
nuclease






ctttttcgtaaatgcactt
cttcag
GGGGACTAAAAC







gcttcag









9a
therm_75
LwaCas13a
GATTTAGACTACCCCAAAA
catctttttcgt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACca
aaatgcactt
CCCAAAAACGAA
nuclease






tctttttcgtaaatgcact
gcttca
GGGGACTAAAAC







tgcttca









9a
therm_76
LwaCas13a
GATTTAGACTACCCCAAAA
ccatctttttc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACcc
gtaaatgcac
CCCAAAAACGAA
nuclease






atctttttcgtaaatgcac
ttgcttc
GGGGACTAAAAC







ttgcttc









9a
therm_77
LwaCas13a
GATTTAGACTACCCCAAAA
accatcttttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACac
cgtaaatgca
CCCAAAAACGAA
nuclease






catctttttcgtaaatgca
cttgctt
GGGGACTAAAAC







cttgctt









9a
therm_78
LwaCas13a
GATTTAGACTACCCCAAAA
taccatcttttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACta
cgtaaatgca
CCCAAAAACGAA
nuclease






ccatctttttcgtaaatgc
cttgct
GGGGACTAAAAC







acttgct









9a
therm_79
LwaCas13a
GATTTAGACTACCCCAAAA
ctaccatcttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
ttcgtaaatg
CCCAAAAACGAA
nuclease






accatctttttcgtaaatg
cacttgc
GGGGACTAAAAC







cacttgc









9a
therm_80
LwaCas13a
GATTTAGACTACCCCAAAA
tctaccatctt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtc
tttcgtaaatg
CCCAAAAACGAA
nuclease






taccatctttttcgtaaat
cacttg
GGGGACTAAAAC







gcacttg









9a
therm_81
LwaCas13a
GATTTAGACTACCCCAAAA
ttctaccatct
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
ttttcgtaaat
CCCAAAAACGAA
nuclease






ctaccatctttttcgtaaa
gcactt
GGGGACTAAAAC







tgcactt









9a
therm_82
LwaCas13a
GATTTAGACTACCCCAAAA
tttctaccatc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
tttttcgtaaat
CCCAAAAACGAA
nuclease






tctaccatctttttcgtaa
gcact
GGGGACTAAAAC







atgcact









9a
therm_83
LwaCas13a
GATTTAGACTACCCCAAAA
ttttctaccat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
ctttttcgtaa
CCCAAAAACGAA
nuclease






ttctaccatctttttcgta
atgcac
GGGGACTAAAAC







aatgcac









9a
therm_84
LwaCas13a
GATTTAGACTACCCCAAAA
attttctacca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
tctttttcgtaa
CCCAAAAACGAA
nuclease






tttctaccatctttttcgt
atgca
GGGGACTAAAAC







aaatgca









9a
therm_85
LwaCas13a
GATTTAGACTACCCCAAAA
cattttctacc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACca
atctttttcgta
CCCAAAAACGAA
nuclease






ttttctaccatctttttcg
aatgc
GGGGACTAAAAC







taaatgc









9a
therm_86
LwaCas13a
GATTTAGACTACCCCAAAA
gcattttctac
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACgc
catctttttcgt
CCCAAAAACGAA
nuclease






attttctaccatctttttc
aaatg
GGGGACTAAAAC







gtaaatg









9a
therm_87
LwaCas13a
GATTTAGACTACCCCAAAA
tgcattttcta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtg
ccatctttttc
CCCAAAAACGAA
nuclease






cattttctaccatcttttt
gtaaat
GGGGACTAAAAC







cgtaaat









9a
therm_88
LwaCas13a
GATTTAGACTACCCCAAAA
ttgcattttcta
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
ccatctttttc
CCCAAAAACGAA
nuclease






gcattttctaccatctttt
gtaaa
GGGGACTAAAAC







tcgtaaa









9a
therm_89
LwaCas13a
GATTTAGACTACCCCAAAA
tttgcattttct
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
accatcttttt
CCCAAAAACGAA
nuclease






tgcattttctaccatcttt
cgtaa
GGGGACTAAAAC







ttcgtaa









9a
therm_90
LwaCas13a
GATTTAGACTACCCCAAAA
ctttgcattttc
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACct
CCCAAAAACGAA
taccatcttttt
nuclease






ttgcattttctaccatctt
cgta
GGGGACTAAAAC







tttcgta









9a
therm_91
LwaCas13a
GATTTAGACTACCCCAAAA
tctttgcatttt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtc
ctaccatcttt
CCCAAAAACGAA
nuclease






tttgcattttctaccatct
ttcgt
GGGGACTAAAAC







ttttcgt









9a
therm_92
LwaCas13a
GATTTAGACTACCCCAAAA
ttctttgcattt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
tctaccatctt
CCCAAAAACGAA
nuclease






ctttgcattttctaccatc
tttcg
GGGGACTAAAAC







tttttcg









9a
therm_93
LwaCas13a
GATTTAGACTACCCCAAAA
tttctttgcatt
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
ttctaccatct
CCCAAAAACGAA
nuclease






tctttgcattttctaccat
ttttc
GGGGACTAAAAC







ctttttc









9a
therm_94
LwaCas13a
GATTTAGACTACCCCAAAA
ttttctttgcat
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACtt
tttctaccatc
CCCAAAAACGAA
nuclease






ttctttgcattttctacca
ttttt
GGGGACTAAAAC







tcttttt









9a
therm_95
LwaCas13a
GATTTAGACTACCCCAAAA
attttctttgca
GATTTAGACTAC
thermo-
9a





ACGAAGGGGACTAAAACat
ttttctaccat
CCCAAAAACGAA
nuclease






tttctttgcattttctacc
ctttt
GGGGACTAAAAC







atctttt









11b
zika_00
LwaCas13a
GATTTAGACTACCCCAAAA
tgttgttccag
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
tgtggagttc
CCCAAAAACGAA
ssRNA






ttgttccagtgtggagttc
cggtgtc
GGGGACTAAAAC







cggtgtc









11b
zika_01
LwaCas13a
GATTTAGACTACCCCAAAA
ttgttgttcca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
gtgtggagtt
CCCAAAAACGAA
ssRNA






gttgttccagtgtggagtt
ccggtgt
GGGGACTAAAAC







ccggtgt









11b
zika_02
LwaCas13a
GATTTAGACTACCCCAAAA
tttgttgttcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
agtgtggagt
CCCAAAAACGAA
ssRNA






tgttgttccagtgtggagt
tccggtg
GGGGACTAAAAC







tccggtg









11b
zika_03
LwaCas13a
GATTTAGACTACCCCAAAA
ctttgttgttc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
cagtgtgga
CCCAAAAACGAA
ssRNA






ttgttgttccagtgtggag
gttccggt
GGGGACTAAAAC







ttccggt









11b
zika_04
LwaCas13a
GATTTAGACTACCCCAAAA
tctttgttgttc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtc
cagtgtgga
CCCAAAAACGAA
ssRNA






tttgttgttccagtgtgga
gttccgg
GGGGACTAAAAC







gttccgg









11b
zika_05
LwaCas13a
GATTTAGACTACCCCAAAA
ttctttgttgtt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
ccagtgtgg
CCCAAAAACGAA
ssRNA






ctttgttgttccagtgtgg
agttccg
GGGGACTAAAAC







agttccg









11b
zika_06
LwaCas13a
GATTTAGACTACCCCAAAA
cttctttgagt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
tccagtgtgg
CCCAAAAACGAA
ssRNA






tctttgttgttccagtgtg
agttcc
GGGGACTAAAAC







gagttcc









11b
zika_07
LwaCas13a
GATTTAGACTACCCCAAAA
gcttctttgtt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgc
gttccagtgt
CCCAAAAACGAA
ssRNA






ttctttgttgttccagtgt
ggagttc
GGGGACTAAAAC







ggagttc









11b
zika_08
LwaCas13a
GATTTAGACTACCCCAAAA
tgcttctttgtt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
gttccagtgt
CCCAAAAACGAA
ssRNA






cttctttgttgttccagtg
ggagtt
GGGGACTAAAAC







tggagtt









11b
zika_09
LwaCas13a
GATTTAGACTACCCCAAAA
gtgcttctttg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgt
ttgttccagtg
CCCAAAAACGAA
ssRNA






gcttctttgttgttccagt
tggagt
GGGGACTAAAAC







gtggagt









11b
zika_10
LwaCas13a
GATTTAGACTACCCCAAAA
agtgcttcttt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
gagttccagt
CCCAAAAACGAA
ssRNA






tgcttctttgttgttccag
gtggag
GGGGACTAAAAC







tgtggag









11b
zika_11
LwaCas13a
GATTTAGACTACCCCAAAA
cagtgcttctt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
tgttgttccag
CCCAAAAACGAA
ssRNA






gtgcttctttgttgttcca
tgtgga
GGGGACTAAAAC







gtgtgga









11b
zika_12
LwaCas13a
GATTTAGACTACCCCAAAA
ccagtgcttc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
tttgagacc
CCCAAAAACGAA
ssRNA






agtgcttctttgttgttcc
agtgtgg
GGGGACTAAAAC







agtgtgg









11b
zika_13
LwaCas13a
GATTTAGACTACCCCAAAA
accagtgctt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
ctttgagttc
CCCAAAAACGAA
ssRNA






cagtgcttctttgttgttc
cagtgtg
GGGGACTAAAAC







cagtgtg









11b
zika_14
LwaCas13a
GATTTAGACTACCCCAAAA
taccagtgct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACta
tctttgttgttc
CCCAAAAACGAA
ssRNA






ccagtgcttctttgttgtt
cagtgt
GGGGACTAAAAC







ccagtgt









11b
zika_15
LwaCas13a
GATTTAGACTACCCCAAAA
ctaccagtgc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
ttctttgttgtt
CCCAAAAACGAA
ssRNA






accagtgcttctttgttgt
ccagtg
GGGGACTAAAAC







tccagtg









11b
zika_16
LwaCas13a
GATTTAGACTACCCCAAAA
tctaccagtg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtc
cttctttgagt
CCCAAAAACGAA
ssRNA






taccagtgcttctttgttg
tccagt
GGGGACTAAAAC







ttccagt









11b
zika_17
LwaCas13a
GATTTAGACTACCCCAAAA
ctctaccagt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
gcttctttgtt
CCCAAAAACGAA
ssRNA






ctaccagtgcttctttgtt
gttccag
GGGGACTAAAAC







gttccag









11b
zika_18
LwaCas13a
GATTTAGACTACCCCAAAA
actctaccag
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
tgcttctttgtt
CCCAAAAACGAA
ssRNA






tctaccagtgcttctttgt
gttcca
GGGGACTAAAAC







tgttcca









11b
zika_19
LwaCas13a
GATTTAGACTACCCCAAAA
aactctacca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACaa
gtgcttctttg
CCCAAAAACGAA
ssRNA






ctctaccagtgcttctttg
ttgttcc
GGGGACTAAAAC







ttgttcc









11b
zika_20
LwaCas13a
GATTTAGACTACCCCAAAA
tgaactctac
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
cagtgcttctt
CCCAAAAACGAA
ssRNA






aactctaccagtgcttctt
tgttgtt
GGGGACTAAAAC







tgttgtt









11b
zika_21
LwaCas13a
GATTTAGACTACCCCAAAA
cttgaactct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
accagtgctt
CCCAAAAACGAA
ssRNA






tgaactctaccagtgcttc
ctttgttg
GGGGACTAAAAC







tttgttg









11b
zika_22
LwaCas13a
GATTTAGACTACCCCAAAA
tccttgaact
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtc
ctaccagtgc
CCCAAAAACGAA
ssRNA






cttgaactctaccagtgct
ttctttgt
GGGGACTAAAAC







tctttgt









11b
zika_23
LwaCas13a
GATTTAGACTACCCCAAAA
cgtccttgaa
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcg
ctctaccagt
CCCAAAAACGAA
ssRNA






tccttgaactctaccagtg
gcttcttt
GGGGACTAAAAC







cttcttt









11b
zika_24
LwaCas13a
GATTTAGACTACCCCAAAA
tgcgtccttg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
aactctacca
CCCAAAAACGAA
ssRNA






cgtccttgaactctaccag
gtgcttct
GGGGACTAAAAC







tgcttct









11b
zika_25
LwaCas13a
GATTTAGACTACCCCAAAA
tgtgcgtcctt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
gaactctacc
CCCAAAAACGAA
ssRNA






tgcgtccttgaactctacc
agtgctt
GGGGACTAAAAC







agtgctt









11b
zika_26
LwaCas13a
GATTTAGACTACCCCAAAA
catgtgcgtc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
cttgaactct
CCCAAAAACGAA
ssRNA






tgtgcgtccttgaactcta
accagtgc
GGGGACTAAAAC







ccagtgc









11b
zika_27
LwaCas13a
GATTTAGACTACCCCAAAA
ggcatgtgc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgg
gtccttgaac
CCCAAAAACGAA
ssRNA






catgtgcgtccttgaactc
tctaccagt
GGGGACTAAAAC







taccagt









11b
zika_28
LwaCas13a
GATTTAGACTACCCCAAAA
ttggcatgtg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
cgtccttgaa
CCCAAAAACGAA
ssRNA






ggcatgtgcgtccttgaac
ctctacca
GGGGACTAAAAC







tctacca









11b
zika_29
LwaCas13a
GATTTAGACTACCCCAAAA
ttttggcatgt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
gcgtccttga
CCCAAAAACGAA
ssRNA






ttggcatgtgcgtccttga
actctac
GGGGACTAAAAC







actctac









11b
zika_30
LwaCas13a
GATTTAGACTACCCCAAAA
ccttttggcat
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
gtgcgtcctt
CCCAAAAACGAA
ssRNA






ttttggcatgtgcgtcctt
gaactct
GGGGACTAAAAC







gaactct









11b
zika_31
LwaCas13a
GATTTAGACTACCCCAAAA
tgccttttggc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
atgtgcgtcc
CCCAAAAACGAA
ssRNA






ccttttggcatgtgcgtcc
ttgaact
GGGGACTAAAAC







ttgaact









11b
zika_32
LwaCas13a
GATTTAGACTACCCCAAAA
tttgccttttg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
gcatgtgcgt
CCCAAAAACGAA
ssRNA






tgccttttggcatgtgcgt
ccttgaa
GGGGACTAAAAC







ccttgaa









11b
zika_33
LwaCas13a
GATTTAGACTACCCCAAAA
agtttgccttt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
tggcatgtgc
CCCAAAAACGAA
ssRNA






tttgccttttggcatgtgc
gtccttg
GGGGACTAAAAC







gtccttg









11b
zika_34
LwaCas13a
GATTTAGACTACCCCAAAA
acagtttgcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
ttttggcatgt
CCCAAAAACGAA
ssRNA






agtttgccttttggcatgt
gcgtcct
GGGGACTAAAAC







gcgtcct









11b
zika_35
LwaCas13a
GATTTAGACTACCCCAAAA
cgacagtttg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcg
ccttttggcat
CCCAAAAACGAA
ssRNA






acagtttgccttttggcat
gtgcgtc
GGGGACTAAAAC







gtgcgtc









11b
zika_36
LwaCas13a
GATTTAGACTACCCCAAAA
cacgacagtt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
tgccttttggc
CCCAAAAACGAA
ssRNA






cgacagtttgccttttggc
atgtgcg
GGGGACTAAAAC







atgtgcg









11b
zika_37
LwaCas13a
GATTTAGACTACCCCAAAA
accacgaca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
gtttgcctttt
CCCAAAAACGAA
ssRNA






cacgacagtttgccttttg
ggcatgtg
GGGGACTAAAAC







gcatgtg









11b
zika_38
LwaCas13a
GATTTAGACTACCCCAAAA
gaaccacga
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACga
cagtttgcctt
CCCAAAAACGAA
ssRNA






accacgacagtttgccttt
ttggcatg
GGGGACTAAAAC







tggcatg









11b
zika_39
LwaCas13a
GATTTAGACTACCCCAAAA
tagaaccac
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACta
gacagtttgc
CCCAAAAACGAA
ssRNA






gaaccacgacagtttgcct
cttttggca
GGGGACTAAAAC







tttggca









11b
zika_40
LwaCas13a
GATTTAGACTACCCCAAAA
cctagaacc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
acgacagttt
CCCAAAAACGAA
ssRNA






tagaaccacgacagtttgc
gccttttgg
GGGGACTAAAAC







cttttgg









11b
zika_41
LwaCas13a
GATTTAGACTACCCCAAAA
tccctagaac
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtc
cacgacagtt
CCCAAAAACGAA
ssRNA






cctagaaccacgacagttt
tgcctttt
GGGGACTAAAAC







gcctttt









11b
zika_42
LwaCas13a
GATTTAGACTACCCCAAAA
actccctaga
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
accacgaca
CCCAAAAACGAA
ssRNA






tccctagaaccacgacagt
gtttgcctt
GGGGACTAAAAC







ttgcctt









11b
zika_43
LwaCas 13a
GATTTAGACTACCCCAAAA
tgactcccta
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
gaaccacga
CCCAAAAACGAA
ssRNA






actccctagaaccacgaca
cagtttgcc
GGGGACTAAAAC







gtttgcc









11b
zika_44
LwaCas13a
GATTTAGACTACCCCAAAA
cttgactccc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
tagaaccac
CCCAAAAACGAA
ssRNA






tgactccctagaaccacga
gacagtttg
GGGGACTAAAAC







cagtttg









11b
zika_45
LwaCas13a
GATTTAGACTACCCCAAAA
ttcttgactcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
ctagaacca
CCCAAAAACGAA
ssRNA






cttgactccctagaaccac
cgacagtt
GGGGACTAAAAC







gacagtt









11b
zika_46
LwaCas13a
GATTTAGACTACCCCAAAA
ccttcttgact
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
ccctagaac
CCCAAAAACGAA
ssRNA






ttcttgactccctagaacc
cacgacag
GGGGACTAAAAC







acgacag









11b
zika_47
LwaCas13a
GATTTAGACTACCCCAAAA
ctccttcttga
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
ctccctagaa
CCCAAAAACGAA
ssRNA






ccttcttgactccctagaa
ccacgac
GGGGACTAAAAC







ccacgac









11b
zika_48
LwaCas13a
GATTTAGACTACCCCAAAA
tgctccttctt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
gactccctag
CCCAAAAACGAA
ssRNA






ctccttcttgactccctag
aaccacg
GGGGACTAAAAC







aaccacg









11b
zika_49
LwaCas13a
GATTTAGACTACCCCAAAA
actgctcctt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
cttgactccc
CCCAAAAACGAA
ssRNA






tgctccttcttgactccct
tagaacca
GGGGACTAAAAC







agaacca









11b
zika_50
LwaCas13a
GATTTAGACTACCCCAAAA
gaactgctcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACga
ttcttgactcc
CCCAAAAACGAA
ssRNA






actgctccttcttgactcc
ctagaac
GGGGACTAAAAC







ctagaac









11b
zika_51
LwaCas13a
GATTTAGACTACCCCAAAA
gtgaactgct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgt
ccttcttgact
CCCAAAAACGAA
ssRNA






gaactgctccttcttgact
ccctaga
GGGGACTAAAAC







ccctaga









11b
zika_52
LwaCas13a
GATTTAGACTACCCCAAAA
gtgtgaactg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgt
ctccttcttga
CCCAAAAACGAA
ssRNA






gtgaactgctccttcttga
ctcccta
GGGGACTAAAAC







ctcccta









11b
zika_53
LwaCas13a
GATTTAGACTACCCCAAAA
ccgtgtgaa
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
ctgctccttct
CCCAAAAACGAA
ssRNA






gtgtgaactgctccttctt
tgactccc
GGGGACTAAAAC







gactccc









11b
zika_54
LwaCas13a
GATTTAGACTACCCCAAAA
ggccgtgtg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgg
aactgctcct
CCCAAAAACGAA
ssRNA






ccgtgtgaactgctccttc
tcttgactc
GGGGACTAAAAC







ttgactc









11b
zika_55
LwaCas13a
GATTTAGACTACCCCAAAA
agggccgtg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
tgaactgctc
CCCAAAAACGAA
ssRNA






ggccgtgtgaactgctcct
cttcttgac
GGGGACTAAAAC







tcttgac









11b
zika_56
LwaCas13a
GATTTAGACTACCCCAAAA
caagggccg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
tgtgaactgc
CCCAAAAACGAA
ssRNA






agggccgtgtgaactgctc
tccttcttg
GGGGACTAAAAC







cttcttg









11b
zika_57
LwaCas13a
GATTTAGACTACCCCAAAA
agcaagggc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
cgtgtgaact
CCCAAAAACGAA
ssRNA






caagggccgtgtgaactgc
gctccttct
GGGGACTAAAAC







tccttct









11b
zika_58
LwaCas13a
GATTTAGACTACCCCAAAA
ccagcaagg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
gccgtgtga
CCCAAAAACGAA
ssRNA






agcaagggccgtgtgaact
actgctcctt
GGGGACTAAAAC







gctcctt









11b
zika_59
LwaCas13a
GATTTAGACTACCCCAAAA
ctccagcaa
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
gggccgtgt
CCCAAAAACGAA
ssRNA






ccagcaagggccgtgtgaa
gaactgctcc
GGGGACTAAAAC







ctgctcc









11b
zika_60
LwaCas13a
GATTTAGACTACCCCAAAA
agctccagc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
aagggccgt
CCCAAAAACGAA
ssRNA






ctccagcaagggccgtgtg
gtgaactgct
GGGGACTAAAAC







aactgct









11b
zika_61
LwaCas13a
GATTTAGACTACCCCAAAA
agagctcca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
gcaagggcc
CCCAAAAACGAA
ssRNA






agctccagcaagggccgtg
gtgtgaactg
GGGGACTAAAAC







tgaactg









11b
zika_62
LwaCas13a
GATTTAGACTACCCCAAAA
ccagagctc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
cagcaaggg
CCCAAAAACGAA
ssRNA






agagctccagcaagggccg
ccgtgtgaa
GGGGACTAAAAC







tgtgaac
c








11b
zika_63
LwaCas13a
GATTTAGACTACCCCAAAA
ctccagagct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
ccagcaagg
CCCAAAAACGAA
ssRNA






ccagagctccagcaagggc
gccgtgtga
GGGGACTAAAAC







cgtgtga









11b
zika_64
LwaCas13a
GATTTAGACTACCCCAAAA
gcctccaga
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgc
gctccagca
CCCAAAAACGAA
ssRNA






ctccagagctccagcaagg
agggccgtg
GGGGACTAAAAC







gccgtgt
t








11b
zika_65
LwaCas13a
GATTTAGACTACCCCAAAA
cagcctcca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
gagctccag
CCCAAAAACGAA
ssRNA






gcctccagagctccagcaa
caagggccg
GGGGACTAAAAC







gggccgt
t








11b
zika_66
LwaCas13a
GATTTAGACTACCCCAAAA
ctcagcctcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
agagctcca
CCCAAAAACGAA
ssRNA






cagcctccagagctccagc
gcaagggcc
GGGGACTAAAAC







aagggcc









11b
zika_67
LwaCas13a
GATTTAGACTACCCCAAAA
atctcagcct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACat
ccagagctc
CCCAAAAACGAA
ssRNA






ctcagcctccagagctcca
cagcaaggg
GGGGACTAAAAC







gcaaggg









11b
zika_68
LwaCas13a
GATTTAGACTACCCCAAAA
catctcagcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
tccagagctc
CCCAAAAACGAA
ssRNA






tctcagcctccagagctcc
cagcaagg
GGGGACTAAAAC







agcaagg









11b
zika_69
LwaCas13a
GATTTAGACTACCCCAAAA
ccatctcagc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
ctccagagct
CCCAAAAACGAA
ssRNA






atctcagcctccagagctc
ccagcaag
GGGGACTAAAAC







cagcaag









11b
zika_70
LwaCas13a
GATTTAGACTACCCCAAAA
tccatctcag
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtc
cctccagag
CCCAAAAACGAA
ssRNA






catctcagcctccagagct
ctccagcaa
GGGGACTAAAAC







ccagcaa









11b
zika_71
LwaCas13a
GATTTAGACTACCCCAAAA
atccatctca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACat
gcctccaga
CCCAAAAACGAA
ssRNA






ccatctcagcctccagagc
gctccagca
GGGGACTAAAAC







tccagca









11b
zika_72
LwaCas13a
GATTTAGACTACCCCAAAA
catccatctc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
agcctccag
CCCAAAAACGAA
ssRNA






tccatctcagcctccagag
agctccagc
GGGGACTAAAAC







ctccagc









11b
zika_73
LwaCas13a
GATTTAGACTACCCCAAAA
ccatccatct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
cagcctcca
CCCAAAAACGAA
ssRNA






atccatctcagcctccaga
gagctccag
GGGGACTAAAAC







gctccag









11b
zika_74
LwaCas13a
GATTTAGACTACCCCAAAA
accatccatc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
tcagcctcca
CCCAAAAACGAA
ssRNA






catccatctcagcctccag
gagctcca
GGGGACTAAAAC







agctcca









11b
zika_75
LwaCas13a
GATTTAGACTACCCCAAAA
caccatccat
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
ctcagcctcc
CCCAAAAACGAA
ssRNA






ccatccatctcagcctcca
agagctcc
GGGGACTAAAAC







gagctcc









11b
zika_76
LwaCas13a
GATTTAGACTACCCCAAAA
gcaccatcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgc
atctcagcct
CCCAAAAACGAA
ssRNA






accatccatctcagcctcc
ccagagctc
GGGGACTAAAAC







agagctc









11b
zika_77
LwaCas13a
GATTTAGACTACCCCAAAA
tgcaccatcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtg
atctcagcct
CCCAAAAACGAA
ssRNA






caccatccatctcagcctc
ccagagct
GGGGACTAAAAC







cagagct









11b
zika_78
LwaCas13a
GATTTAGACTACCCCAAAA
ttgcaccatc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
catctcagcc
CCCAAAAACGAA
ssRNA






gcaccatccatctcagcct
tccagagc
GGGGACTAAAAC







ccagagc









11b
zika_79
LwaCas13a
GATTTAGACTACCCCAAAA
tttgcaccat
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
ccatctcagc
CCCAAAAACGAA
ssRNA






tgcaccatccatctcagcc
ctccagag
GGGGACTAAAAC







tccagag









11b
zika_80
LwaCas13a
GATTTAGACTACCCCAAAA
ctttgcacca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
tccatctcag
CCCAAAAACGAA
ssRNA






ttgcaccatccatctcagc
cctccaga
GGGGACTAAAAC







ctccaga









11b
zika_81
LwaCas13a
GATTTAGACTACCCCAAAA
cctttgcacc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
atccatctca
CCCAAAAACGAA
ssRNA






tttgcaccatccatctcag
gcctccag
GGGGACTAAAAC







cctccag









11b
zika_82
LwaCas13a
GATTTAGACTACCCCAAAA
ccctttgcac
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
catccatctc
CCCAAAAACGAA
ssRNA






ctttgcaccatccatctca
agcctcca
GGGGACTAAAAC







gcctcca









11b
zika_83
LwaCas13a
GATTTAGACTACCCCAAAA
tccctttgca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtc
ccatccatct
CCCAAAAACGAA
ssRNA






cctttgcaccatccatctc
cagcctcc
GGGGACTAAAAC







agcctcc









11b
zika_84
LwaCas13a
GATTTAGACTACCCCAAAA
ttccctttgca
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACtt
ccatccatct
CCCAAAAACGAA
ssRNA






ccctttgcaccatccatct
cagcctc
GGGGACTAAAAC







cagcctc









11b
zika_85
LwaCas13a
GATTTAGACTACCCCAAAA
cttccctttgc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACct
accatccatc
CCCAAAAACGAA
ssRNA






tccctttgcaccatccatc
tcagcct
GGGGACTAAAAC







tcagcct









11b
zika_86
LwaCas13a
GATTTAGACTACCCCAAAA
ccttccctttg
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACcc
caccatccat
CCCAAAAACGAA
ssRNA






ttccctttgcaccatccat
ctcagcc
GGGGACTAAAAC







ctcagcc









11b
zika_87
LwaCas13a
GATTTAGACTACCCCAAAA
gccttcccttt
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgc
gcaccatcc
CCCAAAAACGAA
ssRNA






cttccctttgcaccatcca
atctcagc
GGGGACTAAAAC







tctcagc









11b
zika_88
LwaCas13a
GATTTAGACTACCCCAAAA
agccttccct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
ttgcaccatc
CCCAAAAACGAA
ssRNA






ccttccctttgcaccatcc
catctcag
GGGGACTAAAAC







atctcag









11b
zika_89
LwaCas13a
GATTTAGACTACCCCAAAA
cagccttccc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACca
tttgcaccat
CCCAAAAACGAA
ssRNA






gccttccctttgcaccatc
ccatctca
GGGGACTAAAAC







catctca









11b
zika_90
LwaCas13a
GATTTAGACTACCCCAAAA
acagccttcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACac
ctttgcacca
CCCAAAAACGAA
ssRNA






agccttccctttgcaccat
tccatctc
GGGGACTAAAAC







ccatctc









11b
zika_91
LwaCas13a
GATTTAGACTACCCCAAAA
gacagccttc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACga
cctttgcacc
CCCAAAAACGAA
ssRNA






cagccttccctttgcacca
atccatct
GGGGACTAAAAC







tccatct









11b
zika_92
LwaCas13a
GATTTAGACTACCCCAAAA
ggacagcct
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACgg
tccctttgca
CCCAAAAACGAA
ssRNA






acagccttccctttgcacc
ccatccatc
GGGGACTAAAAC







atccatc









11b
zika_93
LwaCas13a
GATTTAGACTACCCCAAAA
aggacagcc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACag
ttccctttgca
CCCAAAAACGAA
ssRNA






gacagccttccctttgcac
ccatccat
GGGGACTAAAAC







catccat









11b
zika_94
LwaCas13a
GATTTAGACTACCCCAAAA
gaggacagc
GATTTAGACTAC
Zika
1b





ACGAAGGGGACTAAAACga
cttccctttgc
CCCAAAAACGAA
ssRNA






ggacagccttccctttgca
accatcca
GGGGACTAAAAC







ccatcca









11b
zika_0
CcaCas13b
tttgttgttccagtgtgga
tttgttgttcc
GTTGGAACTGCT
Zika
1b





gttccggtgtcGT
agtgtggagt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tccggtgtc
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_1
CcaCas13b
ctttgttgttccagtgtgg
ctttgttgttc
GTTGGAACTGCT
Zika
1b





agttccggtgtGT
cagtgtgga
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gttccggtgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_2
CcaCas13b
tctttgttgttccagtgtg
tctttgttgttc
GTTGGAACTGCT
Zika
1b





gagttccggtgGT
cagtgtgga
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gttccggtg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_3
CcaCas13b
ttctttgttgttccagtgt
ttctttgttgtt
GTTGGAACTGCT
Zika
1b





ggagttccggtGT
ccagtgtgg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agttccggt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_4
CcaCas13b
cttctttgttgttccagtg
cttctttgttgt
GTTGGAACTGCT
Zika
1b





tggagttccggGT
tccagtgtgg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agttccgg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_5
CcaCas13b
gcttctttgttgttccagt
gcttctttgtt
GTTGGAACTGCT
Zika
1b





gtggagttccgGT
gttccagtgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ggagttccg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_6
CcaCas13b
tgcttctttgttgttccag
tgcttctttgtt
GTTGGAACTGCT
Zika
1b





tgtggagttccGT
gttccagtgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ggagttcc
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_7
CcaCas13b
gtgcttctttgttgttcca
gtgcttctttg
GTTGGAACTGCT
Zika
1b





gtgtggagttcGT
ttgttccagtg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tggagttc
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_8
CcaCas13b
agtgcttctttgttgttcc
agtgcttcttt
GTTGGAACTGCT
Zika
1b





agtgtggagttGT
gttgttccagt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtggagtt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_9
CcaCas13b
cagtgcttctttgttgttc
cagtgcttctt
GTTGGAACTGCT
Zika
1b





cagtgtggagtGT
tgttgttccag
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tgtggagt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_10
CcaCas13b
ccagtgcttctttgttgtt
ccagtgcttc
GTTGGAACTGCT
Zika
1b





ccagtgtggagGT
tttgttgttcc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agtgtggag
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_11
CcaCas13b
accagtgcttctttgttgt
accagtgctt
GTTGGAACTGCT
Zika
1b





tccagtgtggaGT
ctttgttgttc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cagtgtgga
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_12
CcaCas13b
taccagtgcttctttgttg
taccagtgct
GTTGGAACTGCT
Zika
1b





ttccagtgtggGT
tctttgttgttc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cagtgtgg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_13
CcaCas13b
ctaccagtgcttctttgtt
ctaccagtgc
GTTGGAACTGCT
Zika
1b





gttccagtgtgGT
ttctttgttgtt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ccagtgtg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_14
CcaCas13b
tctaccagtgcttctttgt
tctaccagtg
GTTGGAACTGCT
Zika
1b





tgttccagtgtGT
cttctttgttgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tccagtgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_15
CcaCas13b
ctctaccagtgcttctttg
ctctaccagt
GTTGGAACTGCT
Zika
1b





ttgttccagtgGT
gcttctttgtt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gttccagtg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_16
CcaCas13b
actctaccagtgcttcttt
actctaccag
GTTGGAACTGCT
Zika
1b





gttgttccagtGT
tgcttctttgtt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gttccagt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_17
CcaCas13b
aactctaccagtgcttctt
aactctacca
GTTGGAACTGCT
Zika
1b





tgttgttccagGT
gtgcttctttg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ttgttccag
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_18
CcaCas13b
gaactctaccagtgcttct
gaactctacc
GTTGGAACTGCT
Zika
1b





ttgttgttccaGT
agtgcttcttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gttgttcca
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_19
CcaCas13b
tgaactctaccagtgcttc
tgaactctac
GTTGGAACTGCT
Zika
1b





tttgttgttccGT
cagtgcttctt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tgttgttcc
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_20
CcaCas13b
cttgaactctaccagtgct
cttgaactct
GTTGGAACTGCT
Zika
1b





tctttgttgttGTT
accagtgctt
CTCATTTTGGAGG
ssRNA






GGAACTGCTCTCATTTTGG
ctttgttgtt
GTAATCACAAC







AGGGTAATCACAAC









11b
zika_21
CcaCas13b
tccttgaactctaccagtg
tccttgaact
GTTGGAACTGCT
Zika
1b





cttctttgttgGT
ctaccagtgc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ttctttgttg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_22
CcaCas13b
cgtccttgaactctaccag
cgtccttgaa
GTTGGAACTGCT
Zika
1b





tgcttctttgtGT
ctctaccagt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gcttctttgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_23
CcaCas13b
tgcgtccttgaactctacc
tgcgtccttg
GTTGGAACTGCT
Zika
1b





agtgcttctttGT
aactctacca
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtgcttcttt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_24
CcaCas13b
tgtgcgtccttgaactcta
tgtgcgtcctt
GTTGGAACTGCT
Zika
1b





ccagtgcttctGT
gaactctacc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agtgcttct
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_25
CcaCas13b
catgtgcgtccttgaactc
catgtgcgtc
GTTGGAACTGCT
Zika
1b





taccagtgcttGT
cttgaactct
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
accagtgctt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_26
CcaCas13b
ggcatgtgcgtccttgaac
ggcatgtgc
GTTGGAACTGCT
Zika
1b





tctaccagtgcGT
gtccttgaac
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tctaccagtg
GTAATCACAAC







GAGGGTAATCACAAC
c








11b
zika_27
CcaCas13b
ttggcatgtgcgtccttga
ttggcatgtg
GTTGGAACTGCT
Zika
1b





actctaccagtGT
cgtccttgaa
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ctctaccagt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_28
CcaCas13b
ttttggcatgtgcgtcctt
ttttggcatgt
GTTGGAACTGCT
Zika
1b





gaactctaccaGT
gcgtccttga
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
actctacca
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_29
CcaCas13b
ccttttggcatgtgcgtcc
ccttttggcat
GTTGGAACTGCT
Zika
1b





ttgaactctacGT
gtgcgtcctt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gaactctac
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_30
CcaCas13b
tgccttttggcatgtgcgt
tgccttttggc
GTTGGAACTGCT
Zika
1b





ccttgaactctGT
atgtgcgtcc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ttgaactct
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_31
CcaCas13b
tttgccttttggcatgtgc
tttgccttttg
GTTGGAACTGCT
Zika
1b





gtccttgaactGT
gcatgtgcgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ccttgaact
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_32
CcaCas13b
agtttgccttttggcatgt
agtttgccttt
GTTGGAACTGCT
Zika
1b





gcgtccttgaaGT
tggcatgtgc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtccttgaa
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_33
CcaCas13b
acagtttgccttttggcat
acagtttgcc
GTTGGAACTGCT
Zika
1b





gtgcgtccttgGT
ttttggcatgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gcgtccttg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_34
CcaCas13b
cgacagtttgccttttggc
cgacagtttg
GTTGGAACTGCT
Zika
1b





atgtgcgtcctGT
ccttttggcat
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtgcgtcct
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_35
CcaCas13b
cacgacagtttgccttttg
cacgacagtt
GTTGGAACTGCT
Zika
1b





gcatgtgcgtcGT
tgccttttggc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
atgtgcgtc
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_36
CcaCas13b
accacgacagtttgccttt
accacgaca
GTTGGAACTGCT
Zika
1b





tggcatgtgcgGT
gtttgcctttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ggcatgtgc
GTAATCACAAC







GAGGGTAATCACAAC
g








11b
zika_37
CcaCas13b
gaaccacgacagtttgcct
gaaccacga
GTTGGAACTGCT
Zika
1b





tttggcatgtgGT
cagtttgcctt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ttggcatgtg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_38
CcaCas13b
tagaaccacgacagtttgc
tagaaccac
GTTGGAACTGCT
Zika
1b





cttttggcatgGT
gacagtttgc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cttttggcatg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_39
CcaCas13b
cctagaaccacgacagttt
cctagaacc
GTTGGAACTGCT
Zika
1b





gccttttggcaGT
acgacagttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gccttttggc
GTAATCACAAC







GAGGGTAATCACAAC
a








11b
zika_40
CcaCas13b
tccctagaaccacgacagt
tccctagaac
GTTGGAACTGCT
Zika
1b





ttgccttttggGT
cacgacagtt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tgccttttgg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_41
CcaCas13b
actccctagaaccacgaca
actccctaga
GTTGGAACTGCT
Zika
1b





gtttgccttttGT
accacgaca
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtttgcctttt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_42
CcaCas13b
tgactccctagaaccacga
tgactcccta
GTTGGAACTGCT
Zika
1b





cagtttgccttGT
gaaccacga
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cagtttgcctt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_43
CcaCas13b
cttgactccctagaaccac
cttgactccc
GTTGGAACTGCT
Zika
1b





gacagtttgccGT
tagaaccac
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gacagtttgc
GTAATCACAAC







GAGGGTAATCACAAC
c








11b
zika_44
CcaCas13b
ttcttgactccctagaacc
ttcttgactcc
GTTGGAACTGCT
Zika
1b





acgacagtttgGT
ctagaacca
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cgacagtttg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_45
CcaCas13b
ccttcttgactccctagaa
ccttcttgact
GTTGGAACTGCT
Zika
1b





ccacgacagttGT
ccctagaac
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cacgacagtt
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_46
CcaCas13b
ctccttcttgactccctag
ctccttcttga
GTTGGAACTGCT
Zika
1b





aaccacgacagGT
ctccctagaa
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ccacgacag
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_47
CcaCas13b
tgctccttcttgactccct
tgctccttctt
GTTGGAACTGCT
Zika
1b





agaaccacgacGT
gactccctag
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
aaccacgac
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_48
CcaCas13b
actgctccttcttgactcc
actgctcctt
GTTGGAACTGCT
Zika
1b





ctagaaccacgGT
cttgactccc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tagaaccac
GTAATCACAAC







GAGGGTAATCACAAC
g








11b
zika_49
CcaCas13b
gaactgctccttcttgact
gaactgctcc
GTTGGAACTGCT
Zika
1b





ccctagaaccaGT
ttcttgactcc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ctagaacca
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_50
CcaCas13b
gtgaactgctccttcttga
gtgaactgct
GTTGGAACTGCT
Zika
1b





ctccctagaacGT
ccttcttgact
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ccctagaac
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_51
CcaCas13b
gtgtgaactgctccttctt
gtgtgaactg
GTTGGAACTGCT
Zika
1b





gactccctagaGT
ctccttcttga
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ctccctaga
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_52
CcaCas13b
ccgtgtgaactgctccttc
ccgtgtgaa
GTTGGAACTGCT
Zika
1b





ttgactccctaGT
ctgctccttct
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tgactcccta
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_53
CcaCas13b
ggccgtgtgaactgctcct
ggccgtgtg
GTTGGAACTGCT
Zika
1b





tcttgactcccGT
aactgctcct
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tcttgactcc
GTAATCACAAC







GAGGGTAATCACAAC
c








11b
zika_54
CcaCas13b
agggccgtgtgaactgctc
agggccgtg
GTTGGAACTGCT
Zika
1b





cttcttgactcGT
tgaactgctc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cttcttgactc
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_55
CcaCas13b
caagggccgtgtgaactgc
caagggccg
GTTGGAACTGCT
Zika
1b





tccttcttgacGT
tgtgaactgc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tccttcttgac
GAGGGTAATCACAAC







GTAATCACAAC









11b
zika_56
CcaCas13b
agcaagggccgtgtgaact
agcaagggc
GTTGGAACTGCT
Zika
1b





gctccttcttgGT
cgtgtgaact
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gctccttcttg
GTAATCACAAC







GAGGGTAATCACAAC









11b
zika_57
CcaCas13b
ccagcaagggccgtgtgaa
ccagcaagg
GTTGGAACTGCT
Zika
1b





ctgctccttctG
gccgtgtga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
actgctcctt
GTAATCACAAC







GGAGGGTAATCACAAC
ct








11b
zika_58
CcaCas13b
ctccagcaagggccgtgtg
ctccagcaa
GTTGGAACTGCT
Zika
1b





aactgctccttG
gggccgtgt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
gaactgctcc
GTAATCACAAC







GGAGGGTAATCACAAC
tt








11b
zika_59
CcaCas13b
agctccagcaagggccgtg
agctccagc
GTTGGAACTGCT
Zika
1b





tgaactgctccG
aagggccgt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
gtgaactgct
GTAATCACAAC







GGAGGGTAATCACAAC
cc








11b
zika_60
CcaCas13b
agagctccagcaagggccg
agagctcca
GTTGGAACTGCT
Zika
1b





tgtgaactgctG
gcaagggcc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
gtgtgaactg
GTAATCACAAC







GGAGGGTAATCACAAC
ct








11b
zika_61
CcaCas13b
ccagagctccagcaagggc
ccagagctc
GTTGGAACTGCT
Zika
1b





cgtgtgaactgG
cagcaaggg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
ccgtgtgaa
GTAATCACAAC







GGAGGGTAATCACAAC
ctg








11b
zika_62
CcaCas13b
ctccagagctccagcaagg
ctccagagct
GTTGGAACTGCT
Zika
1b





gccgtgtgaacG
ccagcaagg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
gccgtgtga
GTAATCACAAC







GGAGGGTAATCACAAC
ac








11b
zika_63
CcaCas13b
gcctccagagctccagcaa
gcctccaga
GTTGGAACTGCT
Zika
1b





gggccgtgtgaG
gctccagca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
agggccgtg
GTAATCACAAC







GGAGGGTAATCACAAC
tga








11b
zika_64
CcaCas13b
cagcctccagagctccagc
cagcctcca
GTTGGAACTGCT
Zika
1b





aagggccgtgt
gagctccag
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
caagggccg
GTAATCACAAC







TGGAGGGTAATCACAAC
tgt








11b
zika_65
CcaCas13b
ctcagcctccagagctcca
ctcagcctcc
GTTGGAACTGCT
Zika
1b





gcaagggccgt
agagctcca
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
gcaagggcc
GTAATCACAAC







TGGAGGGTAATCACAAC
gt








11b
zika_66
CcaCas13b
atctcagcctccagagctc
atctcagcct
GTTGGAACTGCT
Zika
1b





cagcaagggcc
ccagagctc
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
cagcaaggg
GTAATCACAAC







TGGAGGGTAATCACAAC
cc








11b
zika_67
CcaCas13b
ccatctcagcctccagagc
ccatctcagc
GTTGGAACTGCT
Zika
1b





tccagcaaggg
ctccagagct
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
ccagcaagg
GTAATCACAAC







TGGAGGGTAATCACAAC
g








11b
zika_68
CcaCas13b
tccatctcagcctccagag
tccatctcag
GTTGGAACTGCT
Zika
1b





ctccagcaagg
cctccagag
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
ctccagcaa
GTAATCACAAC







TGGAGGGTAATCACAAC
gg








11b
zika_69
CcaCas13b
atccatctcagcctccaga
atccatctca
GTTGGAACTGCT
Zika
1b





gctccagcaag
gcctccaga
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
gctccagca
GTAATCACAAC







TGGAGGGTAATCACAAC
ag








11b
zika_70
CcaCas13b
catccatctcagcctccag
catccatctc
GTTGGAACTGCT
Zika
1b





agctccagcaa
agcctccag
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
agctccagc
GTAATCACAAC







TGGAGGGTAATCACAAC
aa








11b
zika_71
CcaCas13b
ccatccatctcagcctcca
ccatccatct
GTTGGAACTGCT
Zika
1b





gagctccagca
cagcctcca
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
gagctccag
GTAATCACAAC







TGGAGGGTAATCACAAC
ca








11b
zika_72
CcaCas13b
accatccatctcagcctcc
accatccatc
GTTGGAACTGCT
Zika
1b





agagctccagc
tcagcctcca
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
gagctccag
GTAATCACAAC







TGGAGGGTAATCACAAC
c








11b
zika_73
CcaCas13b
caccatccatctcagcctc
caccatccat
GTTGGAACTGCT
Zika
1b





cagagctccag
ctcagcctcc
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
agagctcca
GTAATCACAAC







TGGAGGGTAATCACAAC
g








11b
zika_74
CcaCas13b
gcaccatccatctcagcct
gcaccatcc
GTTGGAACTGCT
Zika
1b





ccagagctcca
atctcagcct
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
ccagagctc
GTAATCACAAC







TGGAGGGTAATCACAAC
ca








11b
zika_75
CcaCas13b
tgcaccatccatctcagcc
tgcaccatcc
GTTGGAACTGCT
Zika
1b





tccagagctcc
atctcagcct
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
ccagagctc
GTAATCACAAC







TGGAGGGTAATCACAAC
c








11b
zika_76
CcaCas13b
ttgcaccatccatctcagc
ttgcaccatc
GTTGGAACTGCT
Zika
1b





ctccagagctcG
catctcagcc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
tccagagctc
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_77
CcaCas13b
tttgcaccatccatctcag
tttgcaccat
GTTGGAACTGCT
Zika
1b





cctccagagctG
ccatctcagc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
ctccagagct
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_78
CcaCas13b
ctttgcaccatccatctca
ctttgcacca
GTTGGAACTGCT
Zika
1b





gcctccagagcG
tccatctcag
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
cctccagag
GTAATCACAAC







GGAGGGTAATCACAAC
c








11b
zika_79
CcaCas13b
cctttgcaccatccatctc
cctttgcacc
GTTGGAACTGCT
Zika
1b





agcctccagagG
atccatctca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
gcctccaga
GTAATCACAAC







GGAGGGTAATCACAAC
g








11b
zika_80
CcaCas13b
ccctttgcaccatccatct
ccctttgcac
GTTGGAACTGCT
Zika
1b





cagcctccagaG
catccatctc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
agcctccag
GTAATCACAAC







GGAGGGTAATCACAAC
a








11b
zika_81
CcaCas13b
tccctttgcaccatccatc
tccctttgca
GTTGGAACTGCT
Zika
1b





tcagcctccagG
ccatccatct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
cagcctcca
GTAATCACAAC







GGAGGGTAATCACAAC
g








11b
zika_82
CcaCas13b
ttccctttgcaccatccat
ttccctttgca
GTTGGAACTGCT
Zika
1b





ctcagcctccaG
ccatccatct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
cagcctcca
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_83
CcaCas13b
cttccctttgcaccatcca
cttccctttgc
GTTGGAACTGCT
Zika
1b





tctcagcctccG
accatccatc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
tcagcctcc
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_84
CcaCas13b
ccttccctttgcaccatcc
ccttccctttg
GTTGGAACTGCT
Zika
1b





atctcagcctcG
caccatccat
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
ctcagcctc
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_85
CcaCas13b
gccttccctttgcaccatc
gccttcccttt
GTTGGAACTGCT
Zika
1b





catctcagcctG
gcaccatcc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
atctcagcct
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_86
CcaCas13b
agccttccctttgcaccat
agccttccct
GTTGGAACTGCT
Zika
1b





ccatctcagccG
ttgcaccatc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
catctcagcc
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_87
CcaCas13b
cagccttccctttgcacca
cagccttccc
GTTGGAACTGCT
Zika
1b





tccatctcagcG
tttgcaccat
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
ccatctcagc
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_88
CcaCas13b
acagccttccctttgcacc
acagccttcc
GTTGGAACTGCT
Zika
1b





atccatctcagG
ctttgcacca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
tccatctcag
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_89
CcaCas13b
gacagccttccctttgcac
gacagccttc
GTTGGAACTGCT
Zika
1b





catccatctcaG
cctttgcacc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
atccatctca
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_90
CcaCas13b
ggacagccttccctttgca
ggacagcct
GTTGGAACTGCT
Zika
1b





ccatccatctcG
tccctttgca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
ccatccatct
GTAATCACAAC







GGAGGGTAATCACAAC
c








11b
zika_91
CcaCas13b
aggacagccttccctttgc
aggacagcc
GTTGGAACTGCT
Zika
1b





accatccatctG
ttccctttgca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
ccatccatct
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_92
CcaCas13b
gaggacagccttccctttg
gaggacagc
GTTGGAACTGCT
Zika
1b





caccatccatcG
cttccctttgc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
accatccatc
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_93
CcaCas13b
agaggacagccttcccttt
agaggacag
GTTGGAACTGCT
Zika
1b





gcaccatccatG
ccttccctttg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
caccatccat
GTAATCACAAC







GGAGGGTAATCACAAC









11b
zika_94
CcaCas13b
cagaggacagccttccctt
cagaggaca
GTTGGAACTGCT
Zika
1b





tgcaccatcca
gccttcccttt
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTT
gcaccatcc
GTAATCACAAC







TGGAGGGTAATCACAAC
a








9a
dengue_0
CcaCas13b
tgttgagaggttggcccct
tgttgagagg
GTTGGAACTGCT
Dengue
9a





gaatatgtactG
ttggcccctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
aatatgtact
GTAATCACAAC







GGAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
ttgttgagaggttggcccc
ttgttgagag
GTTGGAACTGCT
Dengue
9a





tgaatatgtacG
gttggcccct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
gaatatgtac
GTAATCACAAC







GGAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
attgttgagaggttggccc
attgttgaga
GTTGGAACTGCT
Dengue
9a





ctgaatatgtaG
ggttggccc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
ctgaatatgt
GTAATCACAAC







GGAGGGTAATCACAAC
a








9a
dengue_3
CcaCas13b
cattgttgagaggttggcc
cattgttgag
GTTGGAACTGCT
Dengue
9a





cctgaatatgtG
aggttggcc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTT
cctgaatatg
GTAATCACAAC







GGAGGGTAATCACAAC
t








9a
dengue_4
CcaCas13b
tcattgttgagaggttggc
tcattgttgag
GTTGGAACTGCT
Dengue
9a





ccctgaatatgG
aggttggcc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cctgaatatg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
gtcattgttgagaggttgg
gtcattgttga
GTTGGAACTGCT
Dengue
9a





cccctgaatatG
gaggttggc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ccctgaatat
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
cgtcattgttgagaggttg
cgtcattgttg
GTTGGAACTGCT
Dengue
9a





gcccctgaataG
agaggttgg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cccctgaata
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
tcgtcattgttgagaggtt
tcgtcattgtt
GTTGGAACTGCT
Dengue
9a





ggcccctgaatG
gagaggttg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gcccctgaat
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_8
CcaCas13b
ttcgtcattgttgagaggt
ttcgtcattgt
GTTGGAACTGCT
Dengue
9a





tggcccctgaaG
tgagaggttg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gcccctgaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_9
CcaCas13b
cttcgtcattgttgagagg
cttcgtcattg
GTTGGAACTGCT
Dengue
9a





ttggcccctgaG
ttgagaggtt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggcccctga
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
tcttcgtcattgttgagag
tcttcgtcatt
GTTGGAACTGCT
Dengue
9a



0

gttggcccctgG
gttgagaggt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tggcccctg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
gtcttcgtcattgttgaga
gtcttcgtcat
GTTGGAACTGCT
Dengue
9a



1

ggttggcccctG
tgttgagagg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ttggcccct
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
ggtcttcgtcattgttgag
ggtcttcgtc
GTTGGAACTGCT
Dengue
9a



2

aggttggccccG
attgttgaga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggttggccc
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
dengue_1
CcaCas13b
tggtcttcgtcattgttga
tggtcttcgtc
GTTGGAACTGCT
Dengue
9a



3

gaggttggcccG
attgttgaga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggttggccc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
atggtcttcgtcattgttg
atggtcttcgt
GTTGGAACTGCT
Dengue
9a



4

agaggttggccG
cattgttgag
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
aggttggcc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
catggtcttcgtcattgtt
catggtcttc
GTTGGAACTGCT
Dengue
9a



5

gagaggttggcG
gtcattgttga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gaggttggc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
gcatggtcttcgtcattgt
gcatggtctt
GTTGGAACTGCT
Dengue
9a



6

tgagaggttggG
cgtcattgttg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
agaggttgg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
agcatggtcttcgtcattg
agcatggtct
GTTGGAACTGCT
Dengue
9a



7

ttgagaggttgG
tcgtcattgtt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gagaggttg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
gagcatggtcttcgtcatt
gagcatggt
GTTGGAACTGCT
Dengue
9a



8

gttgagaggttG
cttcgtcattg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ttgagaggtt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_1
CcaCas13b
tgagcatggtcttcgtcat
tgagcatggt
GTTGGAACTGCT
Dengue
9a



9

tgttgagaggtG
cttcgtcattg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ttgagaggt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
agtgagcatggtcttcgtc
agtgagcat
GTTGGAACTGCT
Dengue
9a



0

attgttgagagG
ggtcttcgtc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
attgttgaga
GTAATCACAAC







GAGGGTAATCACAAC
g








9a
dengue_2
CcaCas13b
ccagtgagcatggtcttcg
ccagtgagc
GTTGGAACTGCT
Dengue
9a



1

tcattgttgagG
atggtcttcgt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cattgttgag
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
gtccagtgagcatggtctt
gtccagtga
GTTGGAACTGCT
Dengue
9a



2

cgtcattgttgG
gcatggtctt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cgtcattgttg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
ctgtccagtgagcatggtc
ctgtccagtg
GTTGGAACTGCT
Dengue
9a



3

ttcgtcattgtG
agcatggtct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tcgtcattgt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
ttctgtccagtgagcatgg
ttctgtccagt
GTTGGAACTGCT
Dengue
9a



4

tcttcgtcattGT
gagcatggt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cttcgtcatt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
gcttctgtccagtgagcat
gcttctgtcc
GTTGGAACTGCT
Dengue
9a



5

ggtcttcgtcaG
agtgagcat
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggtcttcgtc
GTAATCACAAC







GAGGGTAATCACAAC
a








9a
dengue_2
CcaCas13b
ttgcttctgtccagtgagc
ttgcttctgtc
GTTGGAACTGCT
Dengue
9a



6

atggtcttcgtGT
cagtgagca
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tggtcttcgt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
ttttgcttctgtccagtga
ttttgcttctgt
GTTGGAACTGCT
Dengue
9a



7

gcatggtcttcGT
ccagtgagc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
atggtcttc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
atttttgcttctgtccagt
atttttgcttct
GTTGGAACTGCT
Dengue
9a



8

gagcatggtctGT
gtccagtga
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gcatggtct
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_2
CcaCas13b
gcatttttgcttctgtcca
gcatttttgct
GTTGGAACTGCT
Dengue
9a



9

gtgagcatggtGT
tctgtccagt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gagcatggt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
cagcatttttgcttctgtc
cagcatttttg
GTTGGAACTGCT
Dengue
9a



0

cagtgagcatgGT
cttctgtcca
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtgagcatg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
agcagcatttttgcttctg
agcagcattt
GTTGGAACTGCT
Dengue
9a



1

tccagtgagcaG
ttgcttctgtc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cagtgagca
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
ccagcagcatttttgcttc
ccagcagca
GTTGGAACTGCT
Dengue
9a



2

tgtccagtgagG
tttttgcttctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tccagtgag
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
gtccagcagcatttttgct
gtccagcag
GTTGGAACTGCT
Dengue
9a



3

tctgtccagtgGT
catttttgcttc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tgtccagtg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
ttgtccagcagcatttttg
ttgtccagca
GTTGGAACTGCT
Dengue
9a



4

cttctgtccagGT
gcatttttgct
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tctgtccag
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
tgttgtccagcagcatttt
tgttgtccag
GTTGGAACTGCT
Dengue
9a



5

tgcttctgtccGT
cagcatttttg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cttctgtcc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
gatgttgtccagcagcatt
gatgttgtcc
GTTGGAACTGCT
Dengue
9a



6

tttgcttctgtGT
agcagcattt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ttgcttctgt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
ttgatgttgtccagcagca
ttgatgttgtc
GTTGGAACTGCT
Dengue
9a



7

tttttgcttctGT
cagcagcatt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tttgcttct
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
tgttgatgttgtccagcag
tgttgatgttg
GTTGGAACTGCT
Dengue
9a



8

catttttgcttGT
tccagcagc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
atttttgctt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_3
CcaCas13b
tgtgttgatgttgtccagc
tgtgttgatgt
GTTGGAACTGCT
Dengue
9a



9

agcatttttgcGT
tgtccagca
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gcatttttgc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
ggtgtgttgatgttgtcca
ggtgtgttga
GTTGGAACTGCT
Dengue
9a



0

gcagcatttttGT
tgttgtccag
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cagcattttt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
ctggtgtgttgatgttgtc
ctggtgtgtt
GTTGGAACTGCT
Dengue
9a



1

cagcagcatttGT
gatgttgtcc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agcagcattt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
ttctggtgtgttgatgttg
ttctggtgtgt
GTTGGAACTGCT
Dengue
9a



2

tccagcagcatGT
tgatgttgtcc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agcagcat
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
ccttctggtgtgttgatgt
ccttctggtgt
GTTGGAACTGCT
Dengue
9a



3

tgtccagcagcG
gttgatgttgt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ccagcagc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
tcccttctggtgtgttgat
tcccttctggt
GTTGGAACTGCT
Dengue
9a



4

gttgtccagcaGT
gtgttgatgtt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtccagca
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
aatcccttctggtgtgttg
aatcccttct
GTTGGAACTGCT
Dengue
9a



5

atgttgtccagGT
ggtgtgttga
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tgttgtccag
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
ataatcccttctggtgtgt
ataatcccttc
GTTGGAACTGCT
Dengue
9a



6

tgatgttgtccGT
tggtgtgttg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
atgttgtcc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
gtataatcccttctggtgt
gtataatccc
GTTGGAACTGCT
Dengue
9a



7

gttgatgttgtGT
CTCATTTTGGAGG
ssRNA







TGGAACTGCTCTCATTTTG
ttctggtgtgt
GTAATCACAAC







GAGGGTAATCACAAC
tgatgttgt








9a
dengue_4
CcaCas13b
tggtataatcccttctggt
tggtataatc
GTTGGAACTGCT
Dengue
9a



8

gtgttgatgttGT
ccttctggtgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gttgatgtt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_4
CcaCas13b
gctggtataatcccttctg
gctggtataa
GTTGGAACTGCT
Dengue
9a



9

gtgtgttgatgGT
tcccttctggt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtgttgatg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
gagctggtataatcccttc
gagctggtat
GTTGGAACTGCT
Dengue
9a



0

tggtgtgttgaG
aatcccttct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggtgtgttga
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
gagagctggtataatccct
gagagctgg
GTTGGAACTGCT
Dengue
9a



1

tctggtgtgttG
tataatccctt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ctggtgtgtt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
aagagagctggtataatcc
aagagagct
GTTGGAACTGCT
Dengue
9a



2

cttctggtgtgG
ggtataatcc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cttctggtgt
GTAATCACAAC







GAGGGTAATCACAAC
g








9a
dengue_5
CcaCas13b
caaagagagctggtataat
caaagagag
GTTGGAACTGCT
Dengue
9a



3

cccttctggtgG
ctggtataat
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cccttctggt
GTAATCACAAC







GAGGGTAATCACAAC
g








9a
dengue_5
CcaCas13b
ttcaaagagagctggtata
ttcaaagaga
GTTGGAACTGCT
Dengue
9a



4

atcccttctggG
gctggtataa
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tcccttctgg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
ggttcaaagagagctggta
ggttcaaag
GTTGGAACTGCT
Dengue
9a



5

taatcccttctG
agagctggt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ataatcccttc
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
dengue_5
CcaCas13b
ctggttcaaagagagctgg
ctggttcaaa
GTTGGAACTGCT
Dengue
9a



6

tataatcccttG
gagagctgg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tataatccctt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
ttctggttcaaagagagct
ttctggttcaa
GTTGGAACTGCT
Dengue
9a



7

ggtataatcccG
agagagctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gtataatccc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
ctttctggttcaaagagag
ctttctggttc
GTTGGAACTGCT
Dengue
9a



8

ctggtataatcG
aaagagagc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tggtataatc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_5
CcaCas13b
ccctttctggttcaaagag
ccctttctggt
GTTGGAACTGCT
Dengue
9a



9

agctggtataaG
tcaaagaga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gctggtataa
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
ctccctttctggttcaaag
ctccctttctg
GTTGGAACTGCT
Dengue
9a



0

agagctggtatG
gttcaaaga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gagctggtat
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
ttctccctttctggttcaa
ttctccctttct
GTTGGAACTGCT
Dengue
9a



1

agagagctggtGT
ggttcaaag
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agagctggt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
acttctccctttctggttca
acttctccctt
GTTGGAACTGCT
Dengue
9a



2

aagagagctgG
tctggttcaa
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
agagagctg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
tgacttctccctttctggtt
tgacttctcc
GTTGGAACTGCT
Dengue
9a



3

caaagagagcG
ctttctggttc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
aaagagagc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
gctgacttctccctttctgg
gctgacttct
GTTGGAACTGCT
Dengue
9a



4

ttcaaagagaG
ccctttctggt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tcaaagaga
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
ggctgacttctccctttctg
ggctgacttc
GTTGGAACTGCT
Dengue
9a



5

gttcaaagagG
tccctttctgg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ttcaaagag
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
cggctgacttctccctttct
cggctgactt
GTTGGAACTGCT
Dengue
9a



6

ggttcaaagaG
ctccctttctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gttcaaaga
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
gcggctgacttctccctttc
gcggctgac
GTTGGAACTGCT
Dengue
9a



7

tggttcaaagG
ttctccctttct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggttcaaag
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
ggcggctgacttctcccttt
ggcggctga
GTTGGAACTGCT
Dengue
9a



8

ctggttcaaaG
cttctcccttt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ctggttcaaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_6
CcaCas13b
tggcggctgacttctccctt
t tggcggctg
GTTGGAACTGCT
Dengue
9a



9

ctggttcaaGT
acttctccctt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tctggttcaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
atggcggctgacttctccct
atggcggct
GTTGGAACTGCT
Dengue
9a



0

ttctggttcaGT
gacttctccc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tttctggttca
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
tatggcggctgacttctccc
tatggcggct
GTTGGAACTGCT
Dengue
9a



1

tttctggttcGT
gacttctccc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tttctggttc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
ctatggcggctgacttctcc
ctatggcgg
GTTGGAACTGCT
Dengue
9a



2

ctttctggttGT
ctgacttctc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cctttctggtt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
tctatggcggctgacttctc
tctatggcgg
GTTGGAACTGCT
Dengue
9a



3

cctttctggtGT
ctgacttctc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cctttctggt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
gtctatggcggctgacttct
gtctatggcg
GTTGGAACTGCT
Dengue
9a



4

ccctttctggG
gctgacttct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ccctttctgg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
cgtctatggcggctgacttc
cgtctatggc
GTTGGAACTGCT
Dengue
9a



5

tccctttctgGT
ggctgacttc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tccctttctg
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
ccgtctatggcggctgactt
ccgtctatgg
GTTGGAACTGCT
Dengue
9a



6

ctccctttctGT
cggctgactt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ctccctttct
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
accgtctatggcggctgact
accgtctatg
GTTGGAACTGCT
Dengue
9a



7

tctccctttcG
gcggctgac
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ttctccctttc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas13b
caccgtctatggcggctgac
caccgtctat
GTTGGAACTGCT
Dengue
9a



8

ttctccctttG
ggcggctga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cttctcccttt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_7
CcaCas 13b
tcaccgtctatggcggctga
tcaccgtcta
GTTGGAACTGCT
Dengue
9a



9

cttctcccttG
tggcggctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
acttctccctt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_8
CcaCas13b
ttcaccgtctatggcggctg
ttcaccgtct
GTTGGAACTGCT
Dengue
9a



0

acttctccctG
atggcggct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gacttctccc
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
dengue_8
CcaCas13b
attcaccgtctatggcggct
attcaccgtc
GTTGGAACTGCT
Dengue
9a



1

gacttctcccG
tatggcggct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gacttctccc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_8
CcaCas13b
tattcaccgtctatggcggc
tattcaccgt
GTTGGAACTGCT
Dengue
9a



2

tgacttctccG
ctatggcgg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ctgacttctc
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
dengue_8
CcaCas13b
gtattcaccgtctatggcgg
gtattcaccg
GTTGGAACTGCT
Dengue
9a



3

ctgacttctcG
tctatggcgg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ctgacttctc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_8
CcaCas13b
ggtattcaccgtctatggcg
ggtattcacc
GTTGGAACTGCT
Dengue
9a



4

gctgacttctG
gtctatggcg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gctgacttct
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_8
CcaCas13b
cggtattcaccgtctatggc
cggtattcac
GTTGGAACTGCT
Dengue
9a



5

ggctgacttcG
cgtctatggc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggctgacttc
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_8
CcaCas13b
gcggtattcaccgtctatgg
gcggtattca
GTTGGAACTGCT
Dengue
9a



6

cggctgacttG
ccgtctatgg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cggctgactt
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_8
CcaCas13b
ggcggtattcaccgtctatg
ggcggtattc
GTTGGAACTGCT
Dengue
9a



7

gcggctgact
accgtctatg
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
gcggctgac
GTAATCACAAC







GGAGGGTAATCACAAC
t








9a
dengue_8
CcaCas13b
aggcggtattcaccgtctat
aggcggtatt
GTTGGAACTGCT
Dengue
9a



8

ggcggctgac
caccgtctat
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
ggcggctga
GTAATCACAAC







GGAGGGTAATCACAAC
c








9a
dengue_8
CcaCas13b
caggcggtattcaccgtcta
caggcggta
GTTGGAACTGCT
Dengue
9a



9

tggcggctga
ttcaccgtct
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
atggcggct
GTAATCACAAC







GGAGGGTAATCACAAC
ga








9a
dengue_9
CcaCas13b
tcaggcggtattcaccgtct
tcaggcggt
GTTGGAACTGCT
Dengue
9a



0

atggcggctg
attcaccgtc
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
tatggcggct
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
dengue_9
CcaCas13b
ttcaggcggtattcaccgtc
ttcaggcggt
GTTGGAACTGCT
Dengue
9a



1

tatggcggctG
attcaccgtc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tatggcggct
GTAATCACAAC







GAGGGTAATCACAAC









9a
dengue_9
CcaCas13b
cttcaggcggtattcaccgt
cttcaggcg
GTTGGAACTGCT
Dengue
9a



2

ctatggcggcG
gtattcaccg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tctatggcgg
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
dengue_9
CcaCas13b
ccttcaggcggtattcaccg
ccttcaggc
GTTGGAACTGCT
Dengue
9a



3

tctatggcggG
ggtattcacc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gtctatggcg
GTAATCACAAC







GAGGGTAATCACAAC
g








9a
dengue_9
CcaCas13b
cccttcaggcggtattcacc
cccttcaggc
GTTGGAACTGCT
Dengue
9a



4

gtctatggcgG
ggtattcacc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gtctatggcg
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_0
CcaCas13b
attaatttaacagtatcacc
attaatttaac
GTTGGAACTGCT
Thermo-
9a





atcaatcgctGT
agtatcacca
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
tcaatcgct
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
cattaatttaacagtatcac
cattaatttaa
GTTGGAACTGCT
Thermo-
9a





catcaatcgcG
cagtatcacc
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
atcaatcgc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
acattaatttaacagtatca
acattaattta
GTTGGAACTGCT
Thermo-
9a





ccatcaatcgG
acagtatcac
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
catcaatcg
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
tacattaatttaacagtatc
tacattaattt
GTTGGAACTGCT
Thermo-
9a





accatcaatcGT
aacagtatca
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ccatcaatc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_4
CcaCas13b
gtacattaatttaacagtat
gtacattaatt
GTTGGAACTGCT
Thermo-
9a





caccatcaatGT
taacagtatc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
accatcaat
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_5
CcaCas13b
tgtacattaatttaacagta
tgtacattaat
GTTGGAACTGCT
Thermo-
9a





tcaccatcaaGT
ttaacagtat
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
caccatcaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
ttgtacattaatttaacagt
ttgtacattaa
GTTGGAACTGCT
Thermo-
9a





atcaccatcaGT
tttaacagtat
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
caccatca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
tttgtacattaatttaacag
tttgtacatta
GTTGGAACTGCT
Thermo-
9a





tatcaccatcGT
atttaacagt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
atcaccatc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_8
CcaCas13b
ctttgtacattaatttaaca
ctttgtacatt
GTTGGAACTGCT
Thermo-
9a





gtatcaccatGT
aatttaacag
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
tatcaccat
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_9
CcaCas13b
cctttgtacattaatttaac
cctttgtacat
GTTGGAACTGCT
Thermo-
9a





agtatcaccaGT
taatttaaca
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gtatcacca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
acctttgtacattaatttaa
acctttgtac
GTTGGAACTGCT
Thermo-
9a



0

cagtatcaccGT
attaatttaac
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
agtatcacc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
gacctttgtacattaattta
gacctttgta
GTTGGAACTGCT
Thermo-
9a



1

acagtatcacGT
cattaatttaa
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cagtatcac
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
tgacctttgtacattaattt
tgacctttgta
GTTGGAACTGCT
Thermo-
9a



2

aacagtatcaGT
cattaatttaa
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cagtatca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
ttgacctttgtacattaatt
ttgacctttgt
GTTGGAACTGCT
Thermo-
9a



3

taacagtatcGT
acattaattta
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
acagtatc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
gttgacctttgtacattaat
gttgacctttg
GTTGGAACTGCT
Thermo-
9a



4

ttaacagtatGT
tacattaattt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
aacagtat
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
ggttgacctttgtacattaa
ggttgaccttt
GTTGGAACTGCT
Thermo-
9a



5

tttaacagtaGT
CTCATTTTGGAGG
gtacattaatt
nuclease






TGGAACTGCTCTCATTTTG
taacagta
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
tggttgacctttgtacatta
tggttgacctt
GTTGGAACTGCT
Thermo-
9a



6

atttaacagtGT
tgtacattaat
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttaacagt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
ttggttgacctttgtacatt
ttggttgacct
GTTGGAACTGCT
Thermo-
9a



7

aatttaacagGT
ttgtacattaa
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
tttaacag
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
attggttgacctttgtacat
attggttgac
GTTGGAACTGCT
Thermo-
9a



8

taatttaacaGT
ctttgtacatt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
aatttaaca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_1
CcaCas13b
cattggttgacctttgtaca
cattggttga
GTTGGAACTGCT
Thermo-
9a



9

ttaatttaacGT
cctttgtacat
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
taatttaac
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
gtcattggttgacctttgta
gtcattggtt
GTTGGAACTGCT
Thermo-
9a



0

cattaatttaGTT
gacctttgta
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
cattaattta
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_2
CcaCas13b
atgtcattggttgacctttg
atgtcattggt
GTTGGAACTGCT
Thermo-
9a



1

tacattaattGTT
tgacctttgta
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
cattaatt
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_2
CcaCas13b
gaatgtcattggttgacctt
gaatgtcatt
GTTGGAACTGCT
Thermo-
9a



2

tgtacattaaGT
ggttgaccttt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gtacattaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
ctgaatgtcattggttgacc
ctgaatgtca
GTTGGAACTGCT
Thermo-
9a



3

tttgtacattGT
ttggttgacct
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttgtacatt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
gtctgaatgtcattggttga
gtctgaatgt
GTTGGAACTGCT
Thermo-
9a



4

cctttgtacaGT
cattggttga
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cctttgtaca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
tagtctgaatgtcattggtt
tagtctgaat
GTTGGAACTGCT
Thermo-
9a



5

gacctttgtaGT
gtcattggtt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gacctttgta
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
aatagtctgaatgtcattgg
aatagtctga
GTTGGAACTGCT
Thermo-
9a



6

ttgacctttgGT
atgtcattggt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
tgacctttg
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
ataatagtctgaatgtcatt
ataatagtct
GTTGGAACTGCT
Thermo-
9a



7

ggttgaccttGT
gaatgtcatt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ggttgacctt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
caataatagtctgaatgtca
caataatagt
GTTGGAACTGCT
Thermo-
9a



8

ttggttgaccG
ctgaatgtca
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
ttggttgacc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_2
CcaCas13b
accaataatagtctgaatgt
accaataata
GTTGGAACTGCT
Thermo-
9a



9

cattggttgaG
gtctgaatgt
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
cattggttga
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
caaccaataatagtctgaat
caaccaata
GTTGGAACTGCT
Thermo-
9a



0

gtcattggttG
atagtctgaa
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
tgtcattggtt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
atcaaccaataatagtctga
atcaaccaat
GTTGGAACTGCT
Thermo-
9a



1

atgtcattggG
aatagtctga
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
atgtcattgg
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
gtatcaaccaataatagtct
gtatcaacca
GTTGGAACTGCT
Thermo-
9a



2

gaatgtcattGT
ataatagtct
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gaatgtcatt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
gtgtatcaaccaataatagt
gtgtatcaac
GTTGGAACTGCT
Thermo-
9a



3

ctgaatgtcaG
caataatagt
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
ctgaatgtca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
aggtgtatcaaccaataata
aggtgtatca
GTTGGAACTGCT
Thermo-
9a



4

gtctgaatgtG
accaataata
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
gtctgaatgt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
tcaggtgtatcaaccaataa
tcaggtgtat
GTTGGAACTGCT
Thermo-
9a



5

tagtctgaatG
caaccaata
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
atagtctgaa
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
thermo_3
CcaCas13b
tttcaggtgtatcaaccaat
tttcaggtgta
GTTGGAACTGCT
Thermo-
9a



6

aatagtctgaG
tcaaccaata
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
atagtctga
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
tgtttcaggtgtatcaacca
tgtttcaggt
GTTGGAACTGCT
Thermo-
9a



7

ataatagtctGT
gtatcaacca
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ataatagtct
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
tttgtttcaggtgtatcaac
tttgtttcagg
GTTGGAACTGCT
Thermo-
9a



8

caataatagtGT
tgtatcaacc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
aataatagt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_3
CcaCas13b
gctttgtttcaggtgtatca
gctttgtttca
GTTGGAACTGCT
Thermo-
9a



9

accaataataGT
ggtgtatcaa
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ccaataata
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_4
CcaCas13b
atgctttgtttcaggtgtat
atgctttgttt
GTTGGAACTGCT
Thermo-
9a



0

caaccaataaGT
caggtgtatc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
aaccaataa
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_4
CcaCas13b
ggatgctttgtttcaggtgt
ggatgctttg
GTTGGAACTGCT
Thermo-
9a



1

atcaaccaatGT
tttcaggtgta
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
tcaaccaat
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_4
CcaCas13b
taggatgctttgtttcaggt
taggatgcttt
GTTGGAACTGCT
Thermo-
9a



2

gtatcaaccaGT
gtttcaggtg
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
tatcaacca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_4
CcaCas13b
tttaggatgctttgtttcag
tttaggatgct
GTTGGAACTGCT
Thermo-
9a



3

gtgtatcaacGT
ttgtttcaggt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gtatcaac
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_4
CcaCas13b
tttttaggatgctttgtttc
tttttaggatg
GTTGGAACTGCT
Thermo-
9a



4

aggtgtatcaGTT
ctttgtttcag
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
gtgtatca
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_4
CcaCas13b
cttttttaggatgctttgtt
cttttttagga
GTTGGAACTGCT
Thermo-
9a



5

tcaggtgtatGTT
tgctttgtttc
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
aggtgtat
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_4
CcaCas13b
accttttttaggatgctttg
accttttttag
GTTGGAACTGCT
Thermo-
9a



6

tttcaggtgtGTT
gatgctttgtt
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
tcaggtgt
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_4
CcaCas13b
acaccttttttaggatgctt
acacctttttt
GTTGGAACTGCT
Thermo-
9a



7

tgtttcaggtGT
aggatgcttt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gtttcaggt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_4
CcaCas13b
ctacaccttttttaggatgc
ctacacctttt
GTTGGAACTGCT
Thermo-
9a



8

tttgtttcagGTT
ttaggatgctt
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
tgtttcag
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_4
CcaCas13b
ctctacaccttttttaggat
ctctacacctt
GTTGGAACTGCT
Thermo-
9a



9

gctttgtttcGTT
ttttaggatgc
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
tttgtttc
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_5
CcaCas13b
ttctctacaccttttttagg
ttctctacacc
GTTGGAACTGCT
Thermo-
9a



0

atgctttgttGTT
ttttttaggat
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
gctttgtt
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_5
CcaCas13b
atttctctacacctttttta
atttctctaca
GTTGGAACTGCT
Thermo-
9a



1

ggatgctttgGTT
ccttttttagg
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
atgctttg
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_5
CcaCas13b
atatttctctacaccttttt
atatttctcta
GTTGGAACTGCT
Thermo-
9a



2

taggatgcttGTT
cacctttttta
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
ggatgctt
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_5
CcaCas13b
ccatatttctctacaccttt
ccatatttctc
GTTGGAACTGCT
Thermo-
9a



3

tttaggatgcGT
tacacctttttt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
aggatgc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_5
CcaCas13b
gaccatatttctctacacct
gaccatattt
GTTGGAACTGCT
Thermo-
9a



4

tttttaggatGT
ctctacacctt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttttaggat
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_5
CcaCas13b
aggaccatatttctctacac
aggaccatat
GTTGGAACTGCT
Thermo-
9a



5

cttttttaggGT
ttctctacacc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttttttagg
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_5
CcaCas13b
tcaggaccatatttctctac
tcaggaccat
GTTGGAACTGCT
Thermo-
9a



6

accttttttaGTT
atttctctaca
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
cctttttta
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_5
CcaCas13b
cttcaggaccatatttctct
cttcaggacc
GTTGGAACTGCT
Thermo-
9a



7

acacctttttGTT
atatttctcta
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
caccttttt
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_5
CcaCas13b
tgcttcaggaccatatttct
tgcttcagga
GTTGGAACTGCT
Thermo-
9a



8

ctacacctttGT
ccatatttctc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
tacaccttt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_5
CcaCas13b
cttgcttcaggaccatattt
cttgcttcag
GTTGGAACTGCT
Thermo-
9a



9

ctctacacctGT
gaccatattt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ctctacacct
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
cacttgcttcaggaccatat
cacttgcttc
GTTGGAACTGCT
Thermo-
9a



0

ttctctacacGT
aggaccatat
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttctctacac
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
tgcacttgcttcaggaccat
tgcacttgctt
GTTGGAACTGCT
Thermo-
9a



1

atttctctacGT
caggaccat
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
atttctctac
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
aatgcacttgcttcaggacc
aatgcacttg
GTTGGAACTGCT
Thermo-
9a



2

atatttctctGT
cttcaggacc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
atatttctct
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
taaatgcacttgcttcagga
taaatgcact
GTTGGAACTGCT
Thermo-
9a



3

ccatatttctGT
tgcttcagga
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG









GAGGGTAATCACAAC
ccatatttct
GTAATCACAAC







9a
thermo_6
CcaCas13b
cgtaaatgcacttgcttcag
cgtaaatgca
GTTGGAACTGCT
Thermo-
9a



4

gaccatatttG
cttgcttcag
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
gaccatattt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
tcgtaaatgcacttgcttca
tcgtaaatgc
GTTGGAACTGCT
Thermo-
9a



5

ggaccatattG
acttgcttca
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
ggaccatatt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
ttcgtaaatgcacttgcttc
ttcgtaaatg
GTTGGAACTGCT
Thermo-
9a



6

aggaccatatG
cacttgcttc
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
aggaccatat
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
tttcgtaaatgcacttgctt
tttcgtaaatg
GTTGGAACTGCT
Thermo-
9a



7

caggaccataG
cacttgcttc
CTCATTTTGGAGG
nuclease






TTGGAACTGCTCTCATTTTG
aggaccata
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
ttttcgtaaatgcacttgct
ttttcgtaaat
GTTGGAACTGCT
Thermo-
9a



8

tcaggaccatGT
gcacttgctt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
caggaccat
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_6
CcaCas13b
tttttcgtaaatgcacttgc
tttttcgtaaat
GTTGGAACTGCT
Thermo-
9a



9

ttcaggaccaGT
gcacttgctt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
caggacca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
ctttttcgtaaatgcacttg
ctttttcgtaa
GTTGGAACTGCT
Thermo-
9a



0

cttcaggaccGT
atgcacttgc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttcaggacc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
tctttttcgtaaatgcactt
tctttttcgtaa
GTTGGAACTGCT
Thermo-
9a



1

gcttcaggacGT
atgcacttgc
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttcaggac
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
atctttttcgtaaatgcact
atctttttcgta
GTTGGAACTGCT
Thermo-
9a



2

tgcttcaggaGT
aatgcacttg
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cttcagga
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
catctttttcgtaaatgcac
catctttttcgt
GTTGGAACTGCT
Thermo-
9a



3

ttgcttcaggGT
aaatgcactt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gcttcagg
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
ccatctttttcgtaaatgca
ccatctttttc
GTTGGAACTGCT
Thermo-
9a



4

cttgcttcagGT
gtaaatgcac
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
ttgcttcag
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
accatctttttcgtaaatgc
accatcttttt
GTTGGAACTGCT
Thermo-
9a



5

acttgcttcaGT
cgtaaatgca
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cttgcttca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
taccatctttttcgtaaatg
taccatcttttt
GTTGGAACTGCT
Thermo-
9a



6

cacttgcttcGT
cgtaaatgca
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cttgcttc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
ctaccatctttttcgtaaat
ctaccatcttt
GTTGGAACTGCT
Thermo-
9a



7

gcacttgcttGT
ttcgtaaatg
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cacttgctt
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
tctaccatctttttcgtaaa
tctaccatctt
GTTGGAACTGCT
Thermo-
9a



8

tgcacttgctGT
tttcgtaaatg
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
cacttgct
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_7
CcaCas13b
ttctaccatctttttcgtaa
ttctaccatct
GTTGGAACTGCT
Thermo-
9a



9

atgcacttgcGT
ttttcgtaaat
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
gcacttgc
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_8
CcaCas13b
tttctaccatctttttcgta
tttctaccatc
GTTGGAACTGCT
Thermo-
9a



0

aatgcacttgGTT
tttttcgtaaat
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
gcacttg
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
ttttctaccatctttttcgt
ttttctaccat
GTTGGAACTGCT
Thermo-
9a



1

aaatgcacttGTT
ctttttcgtaa
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
atgcactt
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
attttctaccatctttttcg
attttctacca
GTTGGAACTGCT
Thermo-
9a



2

taaatgcactGTT
tctttttcgtaa
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
atgcact
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
cattttctaccatctttttc
cattttctacc
GTTGGAACTGCT
Thermo-
9a



3

gtaaatgcacGTT
atctttttcgta
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
aatgcac
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
gcattttctaccatcttttt
gcattttctac
GTTGGAACTGCT
Thermo-
9a



4

cgtaaatgcaGT
catctttttcgt
CTCATTTTGGAGG
nuclease






TGGAACTGCTCTCATTTTG
aaatgca
GTAATCACAAC







GAGGGTAATCACAAC









9a
thermo_8
CcaCas13b
tgcattttctaccatctttt
tgcattttcta
GTTGGAACTGCT
Thermo-
9a



5

tcgtaaatgcGTT
ccatctttttc
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
gtaaatgc
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
ttgcattttctaccatcttt
ttgcattttcta
GTTGGAACTGCT
Thermo-
9a



6

ttcgtaaatgGTT
ccatctttttc
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
gtaaatg
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
tttgcattttctaccatctt
tttgcattttct
GTTGGAACTGCT
Thermo-
9a



7

tttcgtaaatGTT
accatcttttt
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
cgtaaat
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
ctttgcattttctaccatct
ctttgcattttc
GTTGGAACTGCT
Thermo-
9a



8

ttttcgtaaaGTT
taccatcttttt
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
cgtaaa
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_8
CcaCas13b
tctttgcattttctaccatc
tctttgcatttt
GTTGGAACTGCT
Thermo-
9a



9

tttttcgtaaGTT
ctaccatcttt
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
ttcgtaa
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_9
CcaCas13b
ttctttgcattttctaccat
ttctttgcattt
GTTGGAACTGCT
Thermo-
9a



0

ctttttcgtaGTT
tctaccatctt
CTCATTTTGGAGG
nuclease






GGAACTGCTCTCATTTTGG
tttcgta
GTAATCACAAC







AGGGTAATCACAAC









9a
thermo_9
CcaCas13b
tttctttgcattttctacca
tttctttgcatt
GTTGGAACTGCT
Thermo-
9a



1

tctttttcgtGTTG
ttctaccatct
CTCATTTTGGAGG
nuclease






GAACTGCTCTCATTTTGGA
ttttcgt
GTAATCACAAC







GGGTAATCACAAC









9a
thermo_9
CcaCas13b
ttttctttgcattttctacc
ttttctttgcat
GTTGGAACTGCT
Thermo-
9a



2

atctttttcgGTTG
tttctaccatc
CTCATTTTGGAGG
nuclease






GAACTGCTCTCATTTTGGA
tttttcg
GTAATCACAAC







GGGTAATCACAAC









9a
thermo_9
CcaCas13b
attttctttgcattttctac
attttctttgca
GTTGGAACTGCT
Thermo-
9a



3

catctttttcGTTG
ttttctaccat
CTCATTTTGGAGG
nuclease






GAACTGCTCTCATTTTGGA
ctttttc
GTAATCACAAC







GGGTAATCACAAC









9a
thermo_9
CcaCas13b
aattttctttgcattttcta
aattttctttgc
GTTGGAACTGCT
Thermo-
9a



4

ccatctttttGTTG
attttctacca
CTCATTTTGGAGG
nuclease






GAACTGCTCTCATTTTGGA
tcttttt
GTAATCACAAC







GGGTAATCACAAC









9a
thermo_9
CcaCas13b
caattttctttgcattttct
caattttctttg
GTTGGAACTGCT
Thermo-
9a



5

accatcttttGTTG
cattttctacc
CTCATTTTGGAGG
nuclease






GAACTGCTCTCATTTTGGA
atctttt
GTAATCACAAC







GGGTAATCACAAC









9a
ssrna1_0
CcaCas13b
atccccgggtaccgagctcg
atccccggg
GTTGGAACTGCT
ssRNA1
9a





aattcactgg
taccgagctc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gaattcactg
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_1
CcaCas13b
gatccccgggtaccgagctc
gatccccgg
GTTGGAACTGCT
ssRNA1
9a





gaattcactg
gtaccgagc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
tcgaattcac
GTAATCACAAC







GGAGGGTAATCACAAC
tg








9a
ssrna1_2
CcaCas13b
ggatccccgggtaccgagct
ggatccccg
GTTGGAACTGCT
ssRNA1
9a





cgaattcact
ggtaccgag
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ctcgaattca
GTAATCACAAC







GGAGGGTAATCACAAC
ct








9a
ssrna1_3
CcaCas13b
agaggatccccgggtaccga
agaggatcc
GTTGGAACTGCT
ssRNA1
9a





gctcgaattc
ccgggtacc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gagctcgaa
GTAATCACAAC







GGAGGGTAATCACAAC
ttc








9a
ssrna1_4
CcaCas13b
ctagaggatccccgggtacc
ctagaggat
GTTGGAACTGCT
ssRNA1
9a





gagctcgaat
ccccgggta
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ccgagctcg
GTAATCACAAC







GGAGGGTAATCACAAC
aat








9a
ssrna1_5
CcaCas13b
tttctagaggatccccgggt
tttctagagg
GTTGGAACTGCT
ssRNA1
9a





accgagctcg
atccccggg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
taccgagctc
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_6
CcaCas13b
atttctagaggatccccggg
atttctagag
GTTGGAACTGCT
ssRNA1
9a





taccgagctc
gatccccgg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gtaccgagc
GTAATCACAAC







GGAGGGTAATCACAAC
tc








9a
ssrna1_7
CcaCas13b
atatttctagaggatccccg
atatttctaga
GTTGGAACTGCT
ssRNA1
9a





ggtaccgagc
ggatccccg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ggtaccgag
GTAATCACAAC







GGAGGGTAATCACAAC
c








9a
ssrna1_8
CcaCas13b
catatttctagaggatcccc
catatttctag
GTTGGAACTGCT
ssRNA1
9a





gggtaccgag
aggatcccc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gggtaccga
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_9
CcaCas13b
atccatatttctagaggatc
atccatatttc
GTTGGAACTGCT
ssRNA1
9a





cccgggtaccG
tagaggatc
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
cccgggtac
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
ssrna1_10
CcaCas13b
aatccatatttctagaggat
aatccatattt
GTTGGAACTGCT
ssRNA1
9a





ccccgggtacG
ctagaggat
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
ccccgggta
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
ssrna1_11
CcaCas13b
taatccatatttctagagga
taatccatatt
GTTGGAACTGCT
ssRNA1
9a





tccccgggtaG
tctagaggat
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
ccccgggta
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_12
CcaCas13b
agtaatccatatttctagag
agtaatccat
GTTGGAACTGCT
ssRNA1
9a





gatccccgggG
atttctagag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gatccccgg
GTAATCACAAC







GAGGGTAATCACAAC
g








9a
ssrna1_13
CcaCas13b
aagtaatccatatttctaga
aagtaatcca
GTTGGAACTGCT
ssRNA1
9a





ggatccccggG
tatttctagag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gatccccgg
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_14
CcaCas13b
tctaccaagtaatccatatt
tctaccaagt
GTTGGAACTGCT
ssRNA1
9a





tctagaggatGT
aatccatattt
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
ctagaggat
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_15
CcaCas13b
gttctaccaagtaatccata
gttctaccaa
GTTGGAACTGCT
ssRNA1
9a





tttctagaggG
gtaatccata
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
tttctagagg
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_16
CcaCas13b
ctgttctaccaagtaatcca
ctgttctacc
GTTGGAACTGCT
ssRNA1
9a





tatttctagaGT
aagtaatcca
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
tatttctaga
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_17
CcaCas13b
ttgctgttctaccaagtaat
ttgctgttcta
GTTGGAACTGCT
ssRNA1
9a





ccatatttctGT
ccaagtaatc
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
catatttct
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_18
CcaCas13b
gattgctgttctaccaagta
gattgctgttc
GTTGGAACTGCT
ssRNA1
9a





atccatatttGT
taccaagtaa
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
tccatattt
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_19
CcaCas13b
agattgctgttctaccaagt
agattgctgtt
GTTGGAACTGCT
ssRNA1
9a





aatccatattGT
ctaccaagta
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
atccatatt
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_20
CcaCas13b
agtagattgctgttctacca
agtagattgc
GTTGGAACTGCT
ssRNA1
9a





agtaatccatG
tgttctacca
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
agtaatccat
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_21
CcaCas13b
gagtagattgctgttctacc
gagtagattg
GTTGGAACTGCT
ssRNA1
9a





aagtaatccaG
ctgttctacc
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
aagtaatcca
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_22
CcaCas13b
cgagtagattgctgttctac
cgagtagatt
GTTGGAACTGCT
ssRNA1
9a





caagtaatccG
gctgttctac
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
caagtaatcc
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_23
CcaCas13b
tcgagtagattgctgttcta
tcgagtagat
GTTGGAACTGCT
ssRNA1
9a





ccaagtaatcG
tgctgttctac
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
caagtaatc
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_24
CcaCas13b
ggtcgagtagattgctgttc
ggtcgagta
GTTGGAACTGCT
ssRNA1
9a





taccaagtaaG
gattgctgttc
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
taccaagtaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_25
CcaCas13b
aggtcgagtagattgctgtt
aggtcgagt
GTTGGAACTGCT
ssRNA1
9a





ctaccaagtaG
agattgctgtt
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
ctaccaagta
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_26
CcaCas13b
gcaggtcgagtagattgctg
gcaggtcga
GTTGGAACTGCT
ssRNA1
9a





ttctaccaagG
gtagattgct
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gttctaccaa
GTAATCACAAC







GAGGGTAATCACAAC
g








9a
ssrna1_27
CcaCas13b
tgcaggtcgagtagattgct
tgcaggtcg
GTTGGAACTGCT
ssRNA1
9a





gttctaccaaG
agtagattgc
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
tgttctacca
GTAATCACAAC







GAGGGTAATCACAAC
a








9a
ssrna1_28
CcaCas13b
ctgcaggtcgagtagattgc
ctgcaggtc
GTTGGAACTGCT
ssRNA1
9a





tgttctaccaG
gagtagattg
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
ctgttctacc
GTAATCACAAC







GAGGGTAATCACAAC
a








9a
ssrna1_29
CcaCas13b
cctgcaggtcgagtagattg
cctgcaggt
GTTGGAACTGCT
ssRNA1
9a





ctgttctaccG
cgagtagatt
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gctgttctac
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
ssrna1_30
CcaCas13b
gcctgcaggtcgagtagatt
gcctgcagg
GTTGGAACTGCT
ssRNA1
9a





gctgttctacG
tcgagtagat
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
tgctgttctac
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_31
CcaCas13b
tgcctgcaggtcgagtagat
tgcctgcag
GTTGGAACTGCT
ssRNA1
9a





tgctgttctaG
gtcgagtag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
attgctgttct
GTAATCACAAC







GAGGGTAATCACAAC
a








9a
ssrna1_32
CcaCas13b
catgcctgcaggtcgagtag
catgcctgca
GTTGGAACTGCT
ssRNA1
9a





attgctgttcG
ggtcgagta
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gattgctgttc
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_33
CcaCas13b
gcatgcctgcaggtcgagta
gcatgcctg
GTTGGAACTGCT
ssRNA1
9a





gattgctgttG
caggtcgag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
tagattgctgt
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
ssrna1_34
CcaCas13b
tgcatgcctgcaggtcgagt
tgcatgcctg
GTTGGAACTGCT
ssRNA1
9a





agattgctgtG
caggtcgag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
tagattgctgt
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_35
CcaCas13b
cttgcatgcctgcaggtcga
cttgcatgcc
GTTGGAACTGCT
ssRNA1
9a





gtagattgctG
tgcaggtcg
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
agtagattgc
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
ssrna1_36
CcaCas13b
gcttgcatgcctgcaggtcg
gcttgcatgc
GTTGGAACTGCT
ssRNA1
9a





agtagattgc
ctgcaggtc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gagtagattg
GTAATCACAAC







GGAGGGTAATCACAAC
c








9a
ssrna1_37
CcaCas13b
agcttgcatgcctgcaggtc
agcttgcatg
GTTGGAACTGCT
ssRNA1
9a





gagtagattg
cctgcaggt
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
cgagtagatt
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_38
CcaCas13b
aagcttgcatgcctgcaggt
aagcttgcat
GTTGGAACTGCT
ssRNA1
9a





cgagtagattG
gcctgcagg
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
tcgagtagat
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
ssrna1_39
CcaCas13b
caagcttgcatgcctgcagg
caagcttgca
GTTGGAACTGCT
ssRNA1
9a





tcgagtagat
tgcctgcag
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gtcgagtag
GTAATCACAAC







GGAGGGTAATCACAAC
at








9a
ssrna1_40
CcaCas13b
ccaagcttgcatgcctgcag
ccaagcttgc
GTTGGAACTGCT
ssRNA1
9a





gtcgagtaga
atgcctgca
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ggtcgagta
GTAATCACAAC







GGAGGGTAATCACAAC
ga








9a
ssrna1_41
CcaCas13b
gccaagcttgcatgcctgca
gccaagctt
GTTGGAACTGCT
ssRNA1
9a





ggtcgagtag
gcatgcctg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
caggtcgag
GTAATCACAAC







GGAGGGTAATCACAAC
tag








9a
ssrna1_42
CcaCas13b
cgccaagcttgcatgcctgc
cgccaagctt
GTTGGAACTGCT
ssRNA1
9a





aggtcgagta
gcatgcctg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
caggtcgag
GTAATCACAAC







GGAGGGTAATCACAAC
ta








9a
ssrna1_43
CcaCas13b
tacgccaagcttgcatgcct
tacgccaag
GTTGGAACTGCT
ssRNA1
9a





gcaggtcgag
cttgcatgcc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
tgcaggtcg
GTAATCACAAC







GGAGGGTAATCACAAC
ag








9a
ssrna1_44
CcaCas13b
ttacgccaagcttgcatgcc
ttacgccaag
GTTGGAACTGCT
ssRNA1
9a





tgcaggtcga
cttgcatgcc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
tgcaggtcg
GTAATCACAAC







GGAGGGTAATCACAAC
a








9a
ssrna1_45
CcaCas13b
attacgccaagcttgcatgc
attacgccaa
GTTGGAACTGCT
ssRNA1
9a





ctgcaggtcg
gcttgcatgc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ctgcaggtc
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_46
CcaCas13b
gattacgccaagcttgcatg
gattacgcca
GTTGGAACTGCT
ssRNA1
9a





cctgcaggtc
agcttgcatg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
cctgcaggt
GTAATCACAAC







GGAGGGTAATCACAAC
c








9a
ssrna1_47
CcaCas13b
tgattacgccaagcttgcat
tgattacgcc
GTTGGAACTGCT
ssRNA1
9a





gcctgcaggtG
aagcttgcat
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gcctgcagg
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
ssrna1_48
CcaCas13b
atgattacgccaagcttgca
atgattacgc
GTTGGAACTGCT
ssRNA1
9a





tgcctgcagg
caagcttgca
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
tgcctgcag
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_49
CcaCas13b
catgattacgccaagcttgc
catgattacg
GTTGGAACTGCT
ssRNA1
9a





atgcctgcag
ccaagcttgc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
atgcctgca
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_50
CcaCas13b
accatgattacgccaagctt
accatgatta
GTTGGAACTGCT
ssRNA1
9a





gcatgcctgcG
cgccaagctt
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gcatgcctg
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
ssrna1_51
CcaCas13b
gaccatgattacgccaagct
gaccatgatt
GTTGGAACTGCT
ssRNA1
9a





tgcatgcctg
acgccaagc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ttgcatgcct
GTAATCACAAC







GGAGGGTAATCACAAC
g








9a
ssrna1_52
CcaCas13b
tgaccatgattacgccaagc
tgaccatgat
GTTGGAACTGCT
ssRNA1
9a





ttgcatgcctG
tacgccaag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
cttgcatgcc
GTAATCACAAC







GAGGGTAATCACAAC
t








9a
ssrna1_53
CcaCas13b
atgaccatgattacgccaag
atgaccatga
GTTGGAACTGCT
ssRNA1
9a





cttgcatgccG
ttacgccaag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
cttgcatgcc
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_54
CcaCas13b
ctatgaccatgattacgcca
ctatgaccat
GTTGGAACTGCT
ssRNA1
9a





agcttgcatgG
gattacgcca
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
agcttgcatg
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_55
CcaCas13b
gctatgaccatgattacgcc
gctatgacca
GTTGGAACTGCT
ssRNA1
9a





aagcttgcatG
tgattacgcc
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
aagcttgcat
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_56
CcaCas13b
acagctatgaccatgattac
acagctatga
GTTGGAACTGCT
ssRNA1
9a





gccaagcttgG
ccatgattac
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gccaagctt
GTAATCACAAC







GAGGGTAATCACAAC
g








9a
ssrna1_57
CcaCas13b
aacagctatgaccatgatta
aacagctatg
GTTGGAACTGCT
ssRNA1
9a





cgccaagcttG
accatgatta
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
cgccaagctt
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_58
CcaCas13b
aaacagctatgaccatgatt
aaacagctat
GTTGGAACTGCT
ssRNA1
9a





acgccaagct
gaccatgatt
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
acgccaagc
GTAATCACAAC







GGAGGGTAATCACAAC
t








9a
ssrna1_59
CcaCas13b
gaaacagctatgaccatgat
gaaacagct
GTTGGAACTGCT
ssRNA1
9a





tacgccaagc
atgaccatga
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ttacgccaag
GTAATCACAAC







GGAGGGTAATCACAAC
c








9a
ssrna1_60
CcaCas13b
caggaaacagctatgaccat
caggaaaca
GTTGGAACTGCT
ssRNA1
9a





gattacgcca
gctatgacca
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
tgattacgcc
GTAATCACAAC







GGAGGGTAATCACAAC
a








9a
ssrna1_61
CcaCas13b
acaggaaacagctatgacca
acaggaaac
GTTGGAACTGCT
ssRNA1
9a





tgattacgcc
agctatgacc
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
atgattacgc
GTAATCACAAC







GGAGGGTAATCACAAC
c








9a
ssrna1_62
CcaCas13b
cacaggaaacagctatgacc
cacaggaaa
GTTGGAACTGCT
ssRNA1
9a





atgattacgc
cagctatgac
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
catgattacg
GTAATCACAAC







GGAGGGTAATCACAAC
c








9a
ssrna1_63
CcaCas13b
taaacacaggaaacagctat
taaacacag
GTTGGAACTGCT
ssRNA1
9a





gaccatgatt
gaaacagct
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
atgaccatga
GTAATCACAAC







GGAGGGTAATCACAAC
tt








9a
ssrna1_64
CcaCas13b
gataaacacaggaaacagct
gataaacac
GTTGGAACTGCT
ssRNA1
9a





atgaccatga
aggaaacag
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ctatgaccat
GTAATCACAAC







GGAGGGTAATCACAAC
ga








9a
ssrna1_65
CcaCas13b
ggataaacacaggaaacagc
ggataaaca
GTTGGAACTGCT
ssRNA1
9a





tatgaccatg
caggaaaca
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gctatgacca
GTAATCACAAC







GGAGGGTAATCACAAC
tg








9a
ssrna1_66
CcaCas13b
cggataaacacaggaaacag
cggataaac
GTTGGAACTGCT
ssRNA1
9a





ctatgaccat
acaggaaac
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
agctatgacc
GTAATCACAAC







GGAGGGTAATCACAAC
at








9a
ssrna1_67
CcaCas13b
gcggataaacacaggaaaca
gcggataaa
GTTGGAACTGCT
ssRNA1
9a





gctatgacca
cacaggaaa
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
cagctatgac
GTAATCACAAC







GGAGGGTAATCACAAC
ca








9a
ssrna1_68
CcaCas13b
agcggataaacacaggaaac
agcggataa
GTTGGAACTGCT
ssRNA1
9a





agctatgacc
acacaggaa
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
acagctatga
GTAATCACAAC







GGAGGGTAATCACAAC
cc








9a
ssrna1_69
CcaCas13b
gagcggataaacacaggaaa
gagcggata
GTTGGAACTGCT
ssRNA1
9a





cagctatga
aacacagga
CTCATTTTGGAGG







cGTTGGAACTGCTCTCATTT
aacagctatg
GTAATCACAAC







TGGAGGGTAATCACAAC
ac








9a
ssrna1_70
CcaCas13b
tgagcggataaacacaggaa
tgagcggat
GTTGGAACTGCT
ssRNA1
9a





acagctatga
aaacacagg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
aaacagctat
GTAATCACAAC







GGAGGGTAATCACAAC
ga








9a
ssrna1_71
CcaCas13b
tgtgagcggataaacacagg
tgtgagcgg
GTTGGAACTGCT
ssRNA1
9a





aaacagctat
ataaacaca
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ggaaacagc
GTAATCACAAC







GGAGGGTAATCACAAC
tat








9a
ssrna1_72
CcaCas13b
attgtgagcggataaacaca
attgtgagcg
GTTGGAACTGCT
ssRNA1
9a





ggaaacagct
gataaacac
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
aggaaacag
GTAATCACAAC







GGAGGGTAATCACAAC
ct








9a
ssrna1_73
CcaCas13b
aattgtgagcggataaacac
aattgtgagc
GTTGGAACTGCT
ssRNA1
9a





aggaaacagc
ggataaaca
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
caggaaaca
GTAATCACAAC







GGAGGGTAATCACAAC
gc








9a
ssrna1_74
CcaCas13b
gaattgtgagcggataaaca
gaattgtgag
GTTGGAACTGCT
ssRNA1
9a





caggaaacag
cggataaac
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
acaggaaac
GTAATCACAAC







GGAGGGTAATCACAAC
ag








9a
ssrna1_75
CcaCas13b
gtggaattgtgagcggataa
gtggaattgt
GTTGGAACTGCT
ssRNA1
9a





acacaggaaa
gagcggata
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
aacacagga
GTAATCACAAC







GGAGGGTAATCACAAC
aa








9a
ssrna1_76
CcaCas13b
tgtggaattgtgagcggata
tgtggaattg
GTTGGAACTGCT
ssRNA1
9a





aacacaggaa
tgagcggat
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
aaacacagg
GTAATCACAAC







GGAGGGTAATCACAAC
aa








9a
ssrna1_77
CcaCas13b
gtgtggaattgtgagcggat
gtgtggaatt
GTTGGAACTGCT
ssRNA1
9a





aaacacagga
gtgagcgga
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
taaacacag
GTAATCACAAC







GGAGGGTAATCACAAC
ga








9a
ssrna1_78
CcaCas13b
tgtgtggaattgtgagcgga
tgtgtggaat
GTTGGAACTGCT
ssRNA1
9a





taaacacagg
tgtgagcgg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
ataaacaca
GTAATCACAAC







GGAGGGTAATCACAAC
gg








9a
ssrna1_79
CcaCas13b
gttgtgtggaattgtgagcg
gttgtgtgga
GTTGGAACTGCT
ssRNA1
9a





gataaacaca
attgtgagcg
CTCATTTTGGAGG







GTTGGAACTGCTCTCATTTT
gataaacac
GTAATCACAAC







GGAGGGTAATCACAAC
a








9a
ssrna1_80
CcaCas13b
tgttgtgtggaattgtgagc
tgttgtgtgg
GTTGGAACTGCT
ssRNA1
9a





ggataaacacG
aattgtgagc
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
ggataaaca
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
ssrna1_81
CcaCas13b
atgttgtgtggaattgtgag
atgttgtgtg
GTTGGAACTGCT
ssRNA1
9a





cggataaacaG
gaattgtgag
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
cggataaac
GTAATCACAAC







GAGGGTAATCACAAC
a








9a
ssrna1_82
CcaCas13b
gtatgttgtgtggaattgtg
gtatgttgtgt
GTTGGAACTGCT
ssRNA1
9a





agcggataaaG
ggaattgtga
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gcggataaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_83
CcaCas13b
cgtatgttgtgtggaattgt
cgtatgttgt
GTTGGAACTGCT
ssRNA1
9a





gagcggataaG
gtggaattgt
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gagcggata
GTAATCACAAC







GAGGGTAATCACAAC
a








9a
ssrna1_84
CcaCas13b
tcgtatgttgtgtggaattg
tcgtatgttgt
GTTGGAACTGCT
ssRNA1
9a





tgagcggataG
gtggaattgt
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gagcggata
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_85
CcaCas13b
gctcgtatgttgtgtggaat
gctcgtatgtt
GTTGGAACTGCT
ssRNA1
9a





tgtgagcggaG
gtgtggaatt
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gtgagcgga
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_86
CcaCas13b
ggctcgtatgttgtgtggaa
ggctcgtatg
GTTGGAACTGCT
ssRNA1
9a





ttgtgagcggG
ttgtgtggaa
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
ttgtgagcgg
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_87
CcaCas13b
ccggctcgtatgttgtgtgg
ccggctcgt
GTTGGAACTGCT
ssRNA1
9a





aattgtgagcG
atgttgtgtg
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gaattgtgag
GTAATCACAAC







GAGGGTAATCACAAC
c








9a
ssrna1_88
CcaCas13b
tccggctcgtatgttgtgtg
tccggctcgt
GTTGGAACTGCT
ssRNA1
9a





gaattgtgagG
atgttgtgtg
CTCATTTTGGAGG







TTGGAACTGCTCTCATTTTG
gaattgtgag
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_89
CcaCas13b
ttccggctcgtatgttgtgt
ttccggctcg
GTTGGAACTGCT
ssRNA1
9a





ggaattgtgaGT
tatgttgtgtg
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
gaattgtga
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_90
CcaCas13b
gcttccggctcgtatgttgt
gcttccggct
GTTGGAACTGCT
ssRNA1
9a





gtggaattgtGT
cgtatgttgt
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
gtggaattgt
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_91
CcaCas13b
tgcttccggctcgtatgttg
tgcttccggc
GTTGGAACTGCT
ssRNA1
9a





tgtggaattgGT
tcgtatgttgt
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
gtggaattg
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_92
CcaCas13b
atgcttccggctcgtatgtt
atgcttccgg
GTTGGAACTGCT
ssRNA1
9a





gtgtggaattGT
ctcgtatgttg
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
tgtggaatt
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_93
CcaCas13b
ttatgcttccggctcgtatg
ttatgcttccg
GTTGGAACTGCT
ssRNA1
9a





ttgtgtggaaGT
gctcgtatgtt
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
gtgtggaa
GTAATCACAAC







GAGGGTAATCACAAC









9a
ssrna1_94
CcaCas13b
tttatgcttccggctcgtat
tttatgcttcc
GTTGGAACTGCT
ssRNA1
9a





gttgtgtggaGT
ggctcgtatg
CTCATTTTGGAGG







TGGAACTGCTCTCATTTTG
ttgtgtgga
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_0
CcaCas13b
aactgtgaaagacaactctt
aactgtgaaa
GTTGGAACTGCT
Ebola
11b





cactgcgaatG
gacaactctt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cactgcgaat
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_1
CcaCas13b
caactgtgaaagacaactct
caactgtgaa
GTTGGAACTGCT
Ebola
11b





tcactgcgaa
agacaactct
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
tcactgcgaa
GTAATCACAAC







GGAGGGTAATCACAAC









11b
ebola_2
CcaCas13b
acaactgtgaaagacaactc
acaactgtga
GTTGGAACTGCT
Ebola
11b





ttcactgcga
aagacaact
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
cttcactgcg
GTAATCACAAC







GGAGGGTAATCACAAC
a








11b
ebola_3
CcaCas13b
atacaactgtgaaagacaac
atacaactgt
GTTGGAACTGCT
Ebola
11b





tcttcactgcG
gaaagacaa
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ctcttcactg
GTAATCACAAC







GAGGGTAATCACAAC
c








11b
ebola_4
CcaCas13b
gatacaactgtgaaagacaa
gatacaactg
GTTGGAACTGCT
Ebola
11b





ctcttcactgG
tgaaagaca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
actcttcact
GTAATCACAAC







GAGGGTAATCACAAC
g








11b
ebola_5
CcaCas13b
ttgatacaactgtgaaagac
ttgatacaac
GTTGGAACTGCT
Ebola
11b





aactcttcacG
tgtgaaaga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
caactcttca
GTAATCACAAC







GAGGGTAATCACAAC
c








11b
ebola_6
CcaCas13b
tttgatacaactgtgaaaga
tttgatacaa
GTTGGAACTGCT
Ebola
11b





caactcttcaG
ctgtgaaag
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
acaactcttc
GTAATCACAAC







GAGGGTAATCACAAC
a








11b
ebola_7
CcaCas13b
cgtttgatacaactgtgaaa
cgtttgatac
GTTGGAACTGCT
Ebola
11b





gacaactcttG
aactgtgaaa
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gacaactctt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_8
CcaCas13b
ccgtttgatacaactgtgaa
ccgtttgata
GTTGGAACTGCT
Ebola
11b





agacaactctG
caactgtgaa
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
agacaactct
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_9
CcaCas13b
ctccgtttgatacaactgtg
ctccgtttgat
GTTGGAACTGCT
Ebola
11b





aaagacaactG
acaactgtga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
aagacaact
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_10
CcaCas13b
gctccgtttgatacaactgt
gctccgtttg
GTTGGAACTGCT
Ebola
11b





gaaagacaacG
atacaactgt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gaaagacaa
GTAATCACAAC







GAGGGTAATCACAAC
c








11b
ebola_11
CcaCas13b
tggctccgtttgatacaact
tggctccgttt
GTTGGAACTGCT
Ebola
11b





gtgaaagacaG
gatacaactg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tgaaagaca
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_12
CcaCas13b
ttggctccgtttgatacaac
ttggctccgtt
GTTGGAACTGCT
Ebola
11b





tgtgaaagacG
tgatacaact
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gtgaaagac
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_13
CcaCas13b
tttggctccgtttgatacaa
tttggctccgt
GTTGGAACTGCT
Ebola
11b





ctgtgaaagaG
ttgatacaac
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tgtgaaaga
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_14
CcaCas13b
tttttggctccgtttgatac
tttttggctcc
GTTGGAACTGCT
Ebola
11b





aactgtgaaaGT
gtttgataca
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
actgtgaaa
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_15
CcaCas13b
gatgtttttggctccgtttg
gatgtttttgg
GTTGGAACTGCT
Ebola
11b





atacaactgtGT
ctccgtttgat
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
acaactgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_16
CcaCas13b
tgatgtttttggctccgttt
tgatgtttttg
GTTGGAACTGCT
Ebola
11b





gatacaactgGT
gctccgtttg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
atacaactg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_17
CcaCas13b
ctgatgtttttggctccgtt
ctgatgttttt
GTTGGAACTGCT
Ebola
11b





tgatacaactGT
ggctccgttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gatacaact
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_18
CcaCas13b
actgatgtttttggctccgt
actgatgtttt
GTTGGAACTGCT
Ebola
11b





ttgatacaacGT
tggctccgttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gatacaac
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_19
CcaCas13b
gaccactgatgtttttggct
gaccactgat
GTTGGAACTGCT
Ebola
11b





ccgtttgataGT
gtttttggctc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cgtttgata
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_20
CcaCas13b
tgaccactgatgtttttggc
tgaccactga
GTTGGAACTGCT
Ebola
11b





tccgtttgatGT
tgtttttggct
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ccgtttgat
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_21
CcaCas13b
ctgaccactgatgtttttgg
ctgaccactg
GTTGGAACTGCT
Ebola
11b





ctccgtttgaGT
atgtttttggc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tccgtttga
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_22
CcaCas13b
ctctgaccactgatgttttt
ctctgaccac
GTTGGAACTGCT
Ebola
11b





ggctccgtttGT
tgatgtttttg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gctccgttt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_23
CcaCas13b
actctgaccactgatgtt
actctgacca
GTTGGAACTGCT
Ebola
11b





tttggctccgttGT
ctgatgttttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ggctccgtt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_24
CcaCas13b
gactctgaccactgatgttt
gactctgacc
GTTGGAACTGCT
Ebola
11b





ttggctccgtGT
actgatgtttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tggctccgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_25
CcaCas13b
cggactctgaccactgatgt
cggactctg
GTTGGAACTGCT
Ebola
11b





ttttggctccG
accactgatg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tttttggctcc
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_26
CcaCas13b
gccggactctgaccactgat
gccggactc
GTTGGAACTGCT
Ebola
11b





gtttttggctG
tgaccactga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tgtttttggct
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_27
CcaCas13b
cgccggactctgaccactga
cgccggact
GTTGGAACTGCT
Ebola
11b





tgtttttggcG
ctgaccactg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
atgtttttggc
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_28
CcaCas13b
gcgccggactctgaccactg
gcgccggac
GTTGGAACTGCT
Ebola
11b





atgtttttggG
tctgaccact
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gatgtttttgg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_29
CcaCas13b
cgcgccggactctgaccact
cgcgccgga
GTTGGAACTGCT
Ebola
11b





gatgtttttgG
ctctgaccac
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tgatgtttttg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_30
CcaCas13b
ttcgcgccggactctgacca
ttcgcgccg
GTTGGAACTGCT
Ebola
11b





ctgatgttttG
gactctgacc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
actgatgtttt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_31
CcaCas13b
agttcgcgccggactctgac
agttcgcgc
GTTGGAACTGCT
Ebola
11b





cactgatgttG
cggactctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
accactgatg
GTAATCACAAC







GAGGGTAATCACAAC
tt








11b
ebola_32
CcaCas13b
aagttcgcgccggactctga
aagttcgcg
GTTGGAACTGCT
Ebola
11b





ccactgatgt
ccggactct
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
gaccactgat
GTAATCACAAC







GGAGGGTAATCACAAC
gt








11b
ebola_33
CcaCas13b
gaagttcgcgccggactctg
gaagttcgc
GTTGGAACTGCT
Ebola
11b





accactgatg
gccggactc
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
tgaccactga
GTAATCACAAC







GGAGGGTAATCACAAC
tg








11b
ebola_34
CcaCas13b
agaagttcgcgccggactct
agaagttcg
GTTGGAACTGCT
Ebola
11b





gaccactgat
cgccggact
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
ctgaccactg
GTAATCACAAC







GGAGGGTAATCACAAC
at








11b
ebola_35
CcaCas13b
gaagaagttcgcgccggact
gaagaagtt
GTTGGAACTGCT
Ebola
11b





ctgaccactg
cgcgccgga
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
ctctgaccac
GTAATCACAAC







GGAGGGTAATCACAAC
tg








11b
ebola_36
CcaCas13b
ggaagaagttcgcgccggac
ggaagaagt
GTTGGAACTGCT
Ebola
11b





tctgaccact
tcgcgccgg
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
actctgacca
GTAATCACAAC







GGAGGGTAATCACAAC
ct








11b
ebola_37
CcaCas13b
tcggaagaagttcgcgccgg
tcggaagaa
GTTGGAACTGCT
Ebola
11b





actctgacca
gttcgcgcc
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
ggactctga
GTAATCACAAC







GGAGGGTAATCACAAC
cca








11b
ebola_38
CcaCas13b
gtcggaagaagttcgcgccg
gtcggaaga
GTTGGAACTGCT
Ebola
11b





gactctgacc
agttcgcgc
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
cggactctg
GTAATCACAAC







GGAGGGTAATCACAAC
acc








11b
ebola_39
CcaCas13b
ggtcggaagaagttcgcgcc
ggtcggaag
GTTGGAACTGCT
Ebola
11b





ggactctgac
aagttcgcg
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
ccggactct
GTAATCACAAC







GGAGGGTAATCACAAC
gac








11b
ebola_40
CcaCas13b
gggtcggaagaagttcgcgc
gggtcggaa
GTTGGAACTGCT
Ebola
11b





cggactctga
gaagttcgc
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
gccggactc
GTAATCACAAC







GGAGGGTAATCACAAC
tga








11b
ebola_41
CcaCas13b
tgggtcggaagaagttcgcg
tgggtcgga
GTTGGAACTGCT
Ebola
11b





ccggactctg
agaagttcg
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
cgccggact
GTAATCACAAC







GGAGGGTAATCACAAC
ctg








11b
ebola_42
CcaCas13b
ccctgggtcggaagaagttc
ccctgggtc
GTTGGAACTGCT
Ebola
11b





gcgccggact
ggaagaagt
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
tcgcgccgg
GTAATCACAAC







GGAGGGTAATCACAAC
act








11b
ebola_43
CcaCas13b
tccctgggtcggaagaagtt
tccctgggtc
GTTGGAACTGCT
Ebola
11b





cgcgccggac
ggaagaagt
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
tcgcgccgg
GTAATCACAAC







GGAGGGTAATCACAAC
ac








11b
ebola_44
CcaCas13b
gtccctgggtcggaagaagt
gtccctgggt
GTTGGAACTGCT
Ebola
11b





tcgcgccgga
cggaagaag
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
ttcgcgccg
GTAATCACAAC







GGAGGGTAATCACAAC
ga








11b
ebola_45
CcaCas13b
ggtccctgggtcggaagaag
ggtccctgg
GTTGGAACTGCT
Ebola
11b





ttcgcgccgg
gtcggaaga
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
agttcgcgc
GTAATCACAAC







GGAGGGTAATCACAAC
cgg








11b
ebola_46
CcaCas13b
tggtccctgggtcggaagaa
tggtccctgg
GTTGGAACTGCT
Ebola
11b





gttcgcgccg
gtcggaaga
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
agttcgcgc
GTAATCACAAC







GGAGGGTAATCACAAC
cg








11b
ebola_47
CcaCas13b
ttggtccctgggtcggaaga
ttggtccctg
GTTGGAACTGCT
Ebola
11b





agttcgcgcc
ggtcggaag
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
aagttcgcg
GTAATCACAAC







GGAGGGTAATCACAAC
cc








11b
ebola_48
CcaCas13b
gtgttggtccctgggtcgga
gtgttggtcc
GTTGGAACTGCT
Ebola
11b





agaagttcgc
ctgggtcgg
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
aagaagttc
GTAATCACAAC







GGAGGGTAATCACAAC
gc








11b
ebola_49
CcaCas13b
tgtgttggtccctgggtcgg
tgtgttggtc
GTTGGAACTGCT
Ebola
11b





aagaagttcgG
cctgggtcg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gaagaagtt
GTAATCACAAC







GAGGGTAATCACAAC
cg








11b
ebola_50
CcaCas13b
ttgtgttggtccctgggtcg
ttgtgttggtc
GTTGGAACTGCT
Ebola
11b





gaagaagttcG
cctgggtcg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gaagaagtt
GTAATCACAAC







GAGGGTAATCACAAC
c








11b
ebola_51
CcaCas13b
tgttgtgttggtccctgggt
tgttgtgttgg
GTTGGAACTGCT
Ebola
11b





cggaagaagtG
tccctgggtc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggaagaagt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_52
CcaCas13b
ttgttgtgttggtccctggg
ttgttgtgttg
GTTGGAACTGCT
Ebola
11b





tcggaagaagG
gtccctgggt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cggaagaag
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_53
CcaCas13b
gttgttgtgttggtccctgg
gttgttgtgtt
GTTGGAACTGCT
Ebola
11b





gtcggaagaaG
ggtccctgg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gtcggaaga
GTAATCACAAC







GAGGGTAATCACAAC
a








11b
ebola_54
CcaCas13b
tcagttgttgtgttggtccc
tcagttgttgt
GTTGGAACTGCT
Ebola
11b





tgggtcggaaG
gttggtccct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gggtcggaa
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_55
CcaCas13b
ttcagttgttgtgttggtcc
ttcagttgttg
GTTGGAACTGCT
Ebola
11b





ctgggtcggaG
tgttggtccct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gggtcgga
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_56
CcaCas13b
cttcagttgttgtgttggtc
cttcagttgtt
GTTGGAACTGCT
Ebola
11b





cctgggtcggG
gtgttggtcc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ctgggtcgg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_57
CcaCas13b
tcttcagttgttgtgttggt
tcttcagttgt
GTTGGAACTGCT
Ebola
11b





ccctgggtcgGT
tgtgttggtc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cctgggtcg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_58
CcaCas13b
gtcttcagttgttgtgttgg
gtcttcagttg
GTTGGAACTGCT
Ebola
11b





tccctgggtcGT
ttgtgttggtc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cctgggtc
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_59
CcaCas13b
ggtcttcagttgttgtgttg
ggtcttcagtt
GTTGGAACTGCT
Ebola
11b





gtccctgggtGT
gttgtgttggt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ccctgggt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_60
CcaCas13b
tgtggtcttcagttgttgtg
tgtggtcttca
GTTGGAACTGCT
Ebola
11b





ttggtccctgGT
gttgttgtgtt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ggtccctg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_61
CcaCas13b
ttgtggtcttcagttgttgt
ttgtggtcttc
GTTGGAACTGCT
Ebola
11b





gttggtccctGT
agttgttgtgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tggtccct
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_62
CcaCas13b
tttgtggtcttcagttgttg
tttgtggtctt
GTTGGAACTGCT
Ebola
11b





tgttggtcccGT
cagttgttgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gttggtccc
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_63
CcaCas13b
ttttgtggtcttcagttgtt
ttttgtggtctt
GTTGGAACTGCT
Ebola
11b





gtgttggtccGTT
cagttgttgt
CTCATTTTGGAGG
ssRNA






GGAACTGCTCTCATTTTGG
gttggtcc
GTAATCACAAC







AGGGTAATCACAAC









11b
ebola_64
CcaCas13b
gattttgtggtcttcagttg
gattttgtggt
GTTGGAACTGCT
Ebola
11b





ttgtgttggtGTT
cttcagttgtt
CTCATTTTGGAGG
ssRNA






GGAACTGCTCTCATTTTGG
gtgttggt
GTAATCACAAC







AGGGTAATCACAAC









11b
ebola_65
CcaCas13b
tgattttgtggtcttcagtt
tgattttgtgg
GTTGGAACTGCT
Ebola
11b





gttgtgttggGTT
tcttcagttgt
CTCATTTTGGAGG
ssRNA






GGAACTGCTCTCATTTTGG
tgtgttgg
GTAATCACAAC







AGGGTAATCACAAC









11b
ebola_66
CcaCas13b
atgattttgtggtcttcagt
atgattttgtg
GTTGGAACTGCT
Ebola
11b





tgttgtgttgGTT
gtcttcagttg
CTCATTTTGGAGG
ssRNA






GGAACTGCTCTCATTTTGG
ttgtgttg
GTAATCACAAC







AGGGTAATCACAAC









11b
ebola_67
CcaCas13b
ccatgattttgtggtcttca
ccatgattttg
GTTGGAACTGCT
Ebola
11b





gttgttgtgtGTT
tggtcttcagt
CTCATTTTGGAGG
ssRNA






GGAACTGCTCTCATTTTGG
tgttgtgt
GTAATCACAAC







AGGGTAATCACAAC









11b
ebola_68
CcaCas13b
agccatgattttgtggtctt
agccatgatt
GTTGGAACTGCT
Ebola
11b





cagttgttgtGT
ttgtggtcttc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
agttgttgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_69
CcaCas13b
aagccatgattttgtggtct
aagccatgat
GTTGGAACTGCT
Ebola
11b





tcagttgttgGT
tttgtggtctt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cagttgttg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_70
CcaCas13b
gaagccatgattttgtggtc
gaagccatg
GTTGGAACTGCT
Ebola
11b





ttcagttgttGT
attttgtggtc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ttcagttgtt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_71
CcaCas13b
tgaagccatgattttgtggt
tgaagccat
GTTGGAACTGCT
Ebola
11b





cttcagttgtGT
gattttgtggt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
cttcagttgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_72
CcaCas13b
ttctgaagccatgattttgt
ttctgaagcc
GTTGGAACTGCT
Ebola
11b





ggtcttcagtGT
atgattttgtg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtcttcagt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_73
CcaCas13b
tttctgaagccatgattttg
tttctgaagc
GTTGGAACTGCT
Ebola
11b





tggtcttcagGT
catgattttgt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ggtcttcag
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_74
CcaCas13b
attttctgaagccatgattt
attttctgaag
GTTGGAACTGCT
Ebola
11b





tgtggtcttcGT
ccatgattttg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
tggtcttc
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_75
CcaCas13b
aattttctgaagccatgatt
aattttctgaa
GTTGGAACTGCT
Ebola
11b





ttgtggtcttGT
gccatgatttt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
gtggtctt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_76
CcaCas13b
gaattttctgaagccatgat
gaattttctga
GTTGGAACTGCT
Ebola
11b





tttgtggtctGT
agccatgatt
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ttgtggtct
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_77
CcaCas13b
aggaattttctgaagccatg
aggaattttct
GTTGGAACTGCT
Ebola
11b





attttgtggtGT
gaagccatg
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
attttgtggt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_78
CcaCas13b
agaggaattttctgaagcca
agaggaattt
GTTGGAACTGCT
Ebola
11b





tgattttgtgG
tctgaagcca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
tgattttgtg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_79
CcaCas13b
cagaggaattttctgaagcc
cagaggaat
GTTGGAACTGCT
Ebola
11b





atgattttgtGT
tttctgaagc
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
catgattttgt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_80
CcaCas13b
gcagaggaattttctgaagc
gcagaggaa
GTTGGAACTGCT
Ebola
11b





catgattttgG
ttttctgaagc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
catgattttg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_81
CcaCas13b
tgcagaggaattttctgaag
tgcagagga
GTTGGAACTGCT
Ebola
11b





ccatgattttGT
attttctgaag
CTCATTTTGGAGG
ssRNA






TGGAACTGCTCTCATTTTG
ccatgatttt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_82
CcaCas13b
cattgcagaggaattttctg
cattgcaga
GTTGGAACTGCT
Ebola
11b





aagccatgatG
ggaattttctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
aagccatgat
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_83
CcaCas13b
ccattgcagaggaattttct
ccattgcaga
GTTGGAACTGCT
Ebola
11b





gaagccatgaG
ggaattttctg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
aagccatga
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_84
CcaCas13b
accattgcagaggaattttc
accattgcag
GTTGGAACTGCT
Ebola
11b





tgaagccatgG
aggaattttct
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gaagccatg
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_85
CcaCas13b
aaccattgcagaggaatttt
aaccattgca
GTTGGAACTGCT
Ebola
11b





ctgaagccatG
gaggaatttt
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ctgaagccat
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_86
CcaCas13b
ttgaaccattgcagaggaat
ttgaaccatt
GTTGGAACTGCT
Ebola
11b





tttctgaagcG
gcagaggaa
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ttttctgaagc
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_87
CcaCas13b
acttgaaccattgcagagga
acttgaacca
GTTGGAACTGCT
Ebola
11b





attttctgaaG
ttgcagagg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
aattttctgaa
GTAATCACAAC







GAGGGTAATCACAAC









1b
ebola_88
CcaCas13b
cacttgaaccattgcagagg
cacttgaacc
GTTGGAACTGCT
Ebola
11b





aattttctgaG
attgcagag
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
gaattttctga
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_89
CcaCas13b
tgcacttgaaccattgcaga
tgcacttgaa
GTTGGAACTGCT
Ebola
11b





ggaattttctG
ccattgcaga
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ggaattttct
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_90
CcaCas13b
gtgcacttgaaccattgcag
gtgcacttga
GTTGGAACTGCT
Ebola
11b





aggaattttcG
accattgcag
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
aggaattttc
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_91
CcaCas13b
ctgtgcacttgaaccattgc
ctgtgcactt
GTTGGAACTGCT
Ebola
11b





agaggaatttG
gaaccattgc
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
agaggaattt
GTAATCACAAC







GAGGGTAATCACAAC









11b
ebola_92
CcaCas13b
actgtgcacttgaaccattg
actgtgcact
GTTGGAACTGCT
Ebola
11b





cagaggaattG
tgaaccattg
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
cagaggaat
GTAATCACAAC







GAGGGTAATCACAAC
t








11b
ebola_93
CcaCas13b
tgactgtgcacttgaaccat
tgactgtgca
GTTGGAACTGCT
Ebola
11b





tgcagaggaa
cttgaaccat
CTCATTTTGGAGG
ssRNA






GTTGGAACTGCTCTCATTTT
tgcagagga
GTAATCACAAC







GGAGGGTAATCACAAC
a








11b
ebola_94
CcaCas13b
ttgactgtgcacttgaacca
ttgactgtgc
GTTGGAACTGCT
Ebola
11b





ttgcagaggaG
acttgaacca
CTCATTTTGGAGG
ssRNA






TTGGAACTGCTCTCATTTTG
ttgcagagg
GTAATCACAAC







GAGGGTAATCACAAC
a








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TATCAA
GATTTAGACTAC
thermonu-
6a



nuclease

ACGAAGGGGACTAAAACT
CCAATA
CCCAAAAACGAA
clease




validation

ATCAACCAATAATAGTCTG
ATAGTC
GGGGACTAAAAC





LwaCas13

AATGTCAT
TGAATG






a 1


TCAT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
ATGTCA
GATTTAGACTAC
thermonu-
6a



nuclease

ACGAAGGGGACTAAAACA
TTGGTT
CCCAAAAACGAA
clease




validation

TGTCATTGGTTGACCTTTGT
GACCTT
GGGGACTAAAAC





LwaCas13

ACATTAA
TGTACA






a 2


TTAA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTAGGA
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
TGCTTT
CCCAAAAACGAA
nuclease




validation

AGGATGCTTTGTTTCAGGT
GTTTCA
GGGGACTAAAAC





LwaCas13

GTATCAA
GGTGTA






a 3


TCAA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTTCTC
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
TACACC
CCCAAAAACGAA
nuclease




validation

TCTCTACACCTTTTTTAGGA
TTTTTT
GGGGACTAAAAC





LwaCas13

TGCTTT
AGGATG






a 4


CTTT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TGTCAT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
TGGTTG
CCCAAAAACGAA
nuclease




validation

GTCATTGGTTGACCTTTGT
ACCTTT
GGGGACTAAAAC





LwaCas13

ACATTAAT
GTACAT






a 5


TAAT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
ATAGTC
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACA
TGAATG
CCCAAAAACGAA
nuclease




validation

TAGTCTGAATGTCATTGGT
TCATTG
GGGGACTAAAAC





LwaCas13

TGACCTTT
GTTGAC






a 6


CTTT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
AGTCTG
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACA
AATGTC
CCCAAAAACGAA
nuclease




validation

GTCTGAATGTCATTGGTTG
ATTGGT
GGGGACTAAAAC





LwaCas13

ACCTTTGT
TGACCT






a 7


TTGT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TACATT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
AATTTA
CCCAAAAACGAA
nuclease




validation

ACATTAATTTAACAGTATC
ACAGTA
GGGGACTAAAAC





LwaCas13

ACCATCAA
TCACCA






a 8


TCAA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
ATGCTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACA
TGTTTC
CCCAAAAACGAA
nuclease




validation

TGCTTTGTTTCAGGTGTATC
AGGTGT
GGGGACTAAAAC





LwaCas13

AACCAAT
ATCAAC






a 9


CAAT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
AGGATG
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACA
CTTTGT
CCCAAAAACGAA
nuclease




validation

GGATGCTTTGTTTCAGGTG
TTCAGG
GGGGACTAAAAC





LwaCas13

TATCAACC
TGTATC






a 10


AACC








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
CATATT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACC
TCTCTA
CCCAAAAACGAA
nuclease




validation

ATATTTCTCTACACCTTTTT
CACCTT
GGGGACTAAAAC





LwaCas13

TAGGATG
TTTTAG






a 11


GATG








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
ACCATA
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACA
TTTCTC
CCCAAAAACGAA
nuclease




validation

CCATATTTCTCTACACCTTT
TACACC
GGGGACTAAAAC





LwaCas13

TTTAGGA
TTTTTT






a 12


AGGA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
CTTTTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACC
TAGGAT
CCCAAAAACGAA
nuclease




validation

TTTTTTAGGATGCTTTGTTT
GCTTTG
GGGGACTAAAAC





LwaCas13

CAGGTGT
TTTCAG






a 13


GTGT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TACACC
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
TTTTTT
CCCAAAAACGAA
nuclease




validation

ACACCTTTTTTAGGATGCTT
AGGATG
GGGGACTAAAAC





LwaCas13

TGTTTCA
CTTTGT






a 14


TTCA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TCTTTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
TCGTAA
CCCAAAAACGAA
nuclease




validation

CTTTTTCGTAAATGCACTTG
ATGCAC
GGGGACTAAAAC





LwaCas13

CTTCAGG
TTGCTT






a 15


CAGG








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTTTCT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
TTGCAT
CCCAAAAACGAA
nuclease




validation

TTCTTTGCATTTTCTACCAT
TTTCTA
GGGGACTAAAAC





LwaCas13

CTTTTT
CCATCT






a 16


TTTT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TGAATG
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
TCATTG
CCCAAAAACGAA
nuclease




validation

GAATGTCATTGGTTGACCT
GTTGAC
GGGGACTAAAAC





LwaCas13

TTGTACAT
CTTTGT






a 17


ACAT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTTTTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
AGGATG
CCCAAAAACGAA
nuclease




validation

TTTTAGGATGCTTTGTTTCA
CTTTGT
GGGGACTAAAAC





LwaCas13

GGTGTA
TTCAGG






a 18


TGTA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTTGTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
TCAGGT
CCCAAAAACGAA
nuclease




validation

TGTTTCAGGTGTATCAACC
GTATCA
GGGGACTAAAAC





LwaCas13

AATAATA
ACCAAT






a 19


AATA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTGCTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
CAGGA
CCCAAAAACGAA
nuclease




validation

GCTTCAGGACCATATTTCT
CCATAT
GGGGACTAAAAC





LwaCas13

CTACACC
TTCTCT






a 20


ACACC








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TCAGGT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
GTATCA
CCCAAAAACGAA
nuclease




validation

CAGGTGTATCAACCAATAA
ACCAAT
GGGGACTAAAAC





LwaCas13

TAGTCTGA
AATAGT






a 21


CTGA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
ACTTGC
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACA
TTCAGG
CCCAAAAACGAA
nuclease




validation

CTTGCTTCAGGACCATATT
ACCATA
GGGGACTAAAAC





LwaCas13

TCTCTACA
TTTCTC






a 22


TACA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTTGTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
TCAGGT
CCCAAAAACGAA
nuclease




validation

TGTTTCAGGTGTATCAACC
GTATCA
GGGGACTAAAAC





LwaCas13

AATAATA
ACCAAT






a 23


AATA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TCTACA
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
CCTTTT
CCCAAAAACGAA
nuclease




validation

CTACACCTTTTTTAGGATG
TTAGGA
GGGGACTAAAAC





LwaCas13

CTTTGTTT
TGCTTT






a 24


GTTT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
CTTCAG
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACC
GACCAT
CCCAAAAACGAA
nuclease




validation

TTCAGGACCATATTTCTCT
ATTTCT
GGGGACTAAAAC





LwaCas13

ACACCTTT
CTACAC






a 25


CTTT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TGACCT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
TTGTAC
CCCAAAAACGAA
nuclease




validation

GACCTTTGTACATTAATTT
ATTAAT
GGGGACTAAAAC





LwaCas13

AACAGTAT
TTAACA






a 26


GTAT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
ATTGGT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACA
TGACCT
CCCAAAAACGAA
nuclease




validation

TTGGTTGACCTTTGTACATT
TTGTAC
GGGGACTAAAAC





LwaCas13

AATTTAA
ATTAAT






a 27


TTAA








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
GTCATT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACG
GGTTGA
CCCAAAAACGAA
nuclease




validation

TCATTGGTTGACCTTTGTAC
CCTTTG
GGGGACTAAAAC





LwaCas13

ATTAATT
TACATT






a 28


AATT








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTCTCT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
ACACCT
CCCAAAAACGAA
nuclease




validation

CTCTACACCTTTTTTAGGAT
TTTTTA
GGGGACTAAAAC





LwaCas13

GCTTTG
GGATGC






a 29


TTTG








6a
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
GCATTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACG
TCTACC
CCCAAAAACGAA
nuclease




validation

CATTTTCTACCATCTTTTTC
ATCTTT
GGGGACTAAAAC





LwaCas13

GTAAATG
TTCGTA






a 30


AATG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GCGCCA
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACG
CTGGCC
CCCAAAAACGAA
long




validation

CGCCACTGGCCACGTGGTT
ACGTGG
GGGGACTAAAAC





LwaCas13

GCTGTTGG
TTGCTG






a 1


TTGG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TGGCTG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACT
CCTCCC
CCCAAAAACGAA
long




validation

GGCTGCCTCCCCGGCGCCA
CGGCGC
GGGGACTAAAAC





LwaCas13

CTGGCCAC
CACTGG






a 2


CCAC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CTGCCT
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
CCCCGG
CCCAAAAACGAA
long




validation

TGCCTCCCCGGCGCCACTG
CGCCAC
GGGGACTAAAAC





LwaCas13

GCCACGTG
TGGCCA






a 3


CGTG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GGCTGC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACG
CTCCCC
CCCAAAAACGAA
long




validation

GCTGCCTCCCCGGCGCCAC
GGCGCC
GGGGACTAAAAC





LwaCas13

TGGCCACG
ACTGGC






a 4


CACG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CCCCGG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
CGCCAC
CCCAAAAACGAA
long




validation

CCCGGCGCCACTGGCCACG
TGGCCA
GGGGACTAAAAC





LwaCas13

TGGTTGCT
CGTGGT






a 5


TGCT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GCTGCC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACG
TCCCCG
CCCAAAAACGAA
long




validation

CTGCCTCCCCGGCGCCACT
GCGCCA
GGGGACTAAAAC





LwaCas13

GGCCACGT
CTGGCC






a 6


ACGT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CGCCAC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
TGGCCA
CCCAAAAACGAA
long




validation

GCCACTGGCCACGTGGTTG
CGTGGT
GGGGACTAAAAC





LwaCas13

CTGTTGGG
TGCTGT






a 7


TGGG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CGGCGC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
CACTGG
CCCAAAAACGAA
long




validation

GGCGCCACTGGCCACGTGG
CCACGT
GGGGACTAAAAC





LwaCas13

TTGCTGTT
GGTTGC






a 8


TGTT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
ATGGCT
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACA
GCCTCC
CCCAAAAACGAA
long




validation

TGGCTGCCTCCCCGGCGCC
CCGGCG
GGGGACTAAAAC





LwaCas13

ACTGGCCA
CCACTG






a 9


GCCA








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CCCGGC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
GCCACT
CCCAAAAACGAA
long




validation

CCGGCGCCACTGGCCACGT
GGCCAC
GGGGACTAAAAC





LwaCas13

GGTTGCTG
GTGGTT






a 10


GCTG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
AATGGC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACA
TGCCTC
CCCAAAAACGAA
long




validation

ATGGCTGCCTCCCCGGCGC
CCCGGC
GGGGACTAAAAC





LwaCas13

CACTGGCC
GCCACT






a 11


GGCC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CTCCCC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
GGCGCC
CCCAAAAACGAA
long




validation

TCCCCGGCGCCACTGGCCA
ACTGGC
GGGGACTAAAAC





LwaCas13

CGTGGTTG
CACGTG






a 12


GTTG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CCTCCC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
CGGCGC
CCCAAAAACGAA
long




validation

CTCCCCGGCGCCACTGGCC
CACTGG
GGGGACTAAAAC





LwaCas13

ACGTGGTT
CCACGT






a 13


GGTT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCAATG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACT
GCTGCC
CCCAAAAACGAA
long




validation

CAATGGCTGCCTCCCCGGC
TCCCCG
GGGGACTAAAAC





LwaCas13

GCCACTGG
GCGCCA






a 14


CTGG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CCGGCG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
CCACTG
CCCAAAAACGAA
long




validation

CGGCGCCACTGGCCACGTG
GCCACG
GGGGACTAAAAC





LwaCas13

GTTGCTGT
TGGTTG






a 15


CTGT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CAATGG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACC
CTGCCT
CCCAAAAACGAA
long




validation

AATGGCTGCCTCCCCGGCG
CCCCGG
GGGGACTAAAAC





LwaCas13

CCACTGGC
CGCCAC






a 16


TGGC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TGCCTC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACT
CCCGGC
CCCAAAAACGAA
long




validation

GCCTCCCCGGCGCCACTGG
GCCACT
GGGGACTAAAAC





LwaCas13

CCACGTGG
GGCCAC






a 17


GTGG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCCCCG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACT
GCGCCA
CCCAAAAACGAA
long




validation

CCCCGGCGCCACTGGCCAC
CTGGCC
GGGGACTAAAAC





LwaCas13

GTGGTTGC
ACGTGG






a 18


TTGC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GGCGCC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACG
ACTGGC
CCCAAAAACGAA
long




validation

GCGCCACTGGCCACGTGGT
CACGTG
GGGGACTAAAAC





LwaCas13

TGCTGTTG
GTTGCT






a 19


GTTG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GCCTCC
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACG
CCGGCG
CCCAAAAACGAA
long




validation

CCTCCCCGGCGCCACTGGC
CCACTG
GGGGACTAAAAC





LwaCas13

CACGTGGT
GCCACG






a 20


TGGT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCTCAA
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
TGGCTT
CCCAAAAACGAA
short




validation

CTCAATGGCTTTCCCCTGG
TCCCCT
GGGGACTAAAAC





LwaCas13

GTGATGCA
GGGTGA






a 1


TGCA








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
ATGGCT
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACA
TTCCCC
CCCAAAAACGAA
short




validation

TGGCTTTCCCCTGGGTGAT
TGGGTG
GGGGACTAAAAC





LwaCas13

GCAAGAGC
ATGCAA






a 2


GAGC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
AATGGC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACA
TTTCCC
CCCAAAAACGAA
short




validation

ATGGCTTTCCCCTGGGTGA
CTGGGT
GGGGACTAAAAC





LwaCas13

TGCAAGAG
GATGCA






a 3


AGAG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GGGTGA
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACG
TGCAAG
CCCAAAAACGAA
short




validation

GGTGATGCAAGAGCTGAG
AGCTGA
GGGGACTAAAAC





LwaCas13

GTCCTGCAG
GGTCCT






a 4


GCAG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TGGCTT
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
TCCCCT
CCCAAAAACGAA
short




validation

GGCTTTCCCCTGGGTGATG
GGGTGA
GGGGACTAAAAC





LwaCas13

CAAGAGCT
TGCAAG






a 5


AGCT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CTCAAT
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACC
GGCTTT
CCCAAAAACGAA
short




validation

TCAATGGCTTTCCCCTGGG
CCCCTG
GGGGACTAAAAC





LwaCas13

TGATGCAA
GGTGAT






a 6


GCAA








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TTCCCC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACTT
TGGGTG
CCCAAAAACGAA
short




validation

CCCCTGGGTGATGCAAGAG
ATGCAA
GGGGACTAAAAC





LwaCas13

CTGAGGT
GAGCTG






a 7


AGGT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GCTTTC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACG
CCCTGG
CCCAAAAACGAA
short




validation

CTTTCCCCTGGGTGATGCA
GTGATG
GGGGACTAAAAC





LwaCas13

AGAGCTGA
CAAGA






a 8


GCTGA








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCCCCT
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
GGGTGA
CCCAAAAACGAA
short




validation

CCCCTGGGTGATGCAAGAG
TGCAAG
GGGGACTAAAAC





LwaCas13

CTGAGGTC
AGCTGA






a 9


GGTC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CTTTCC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACC
CCTGGG
CCCAAAAACGAA
short




validation

TTTCCCCTGGGTGATGCAA
TGATGC
GGGGACTAAAAC





LwaCas13

GAGCTGAG
AAGAG






a 10


CTGAG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CAATGG
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACC
CTTTCC
CCCAAAAACGAA
short




validation

AATGGCTTTCCCCTGGGTG
CCTGGG
GGGGACTAAAAC





LwaCas13

ATGCAAGA
TGATGC






a 11


AAGA








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CCTGGG
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACC
TGATGC
CCCAAAAACGAA
short




validation

CTGGGTGATGCAAGAGCTG
AAGAG
GGGGACTAAAAC





LwaCas13

AGGTCCTG
CTGAGG






a 12


TCCTG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GGTCTC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACG
AATGGC
CCCAAAAACGAA
short




validation

GTCTCAATGGCTTTCCCCT
TTTCCC
GGGGACTAAAAC





LwaCas13

GGGTGATG
CTGGGT






a 13


GATG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GGGTCT
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACG
CAATGG
CCCAAAAACGAA
short




validation

GGTCTCAATGGCTTTCCCC
CTTTCC
GGGGACTAAAAC





LwaCas13

TGGGTGAT
CCTGGG






a 14


TGAT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GGCTTT
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACG
CCCCTG
CCCAAAAACGAA
short




validation

GCTTTCCCCTGGGTGATGC
GGTGAT
GGGGACTAAAAC





LwaCas13

AAGAGCTG
GCAAG






a 15


AGCTG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TTTCCC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACTT
CTGGGT
CCCAAAAACGAA
short




validation

TCCCCTGGGTGATGCAAGA
GATGCA
GGGGACTAAAAC





LwaCas13

GCTGAGG
AGAGCT






a 16


GAGG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CCCCTG
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACC
GGTGAT
CCCAAAAACGAA
short




validation

CCCTGGGTGATGCAAGAGC
GCAAG
GGGGACTAAAAC





LwaCas13

TGAGGTCC
AGCTGA






a 17


GGTCC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TGGGTG
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
ATGCAA
CCCAAAAACGAA
short




validation

GGGTGATGCAAGAGCTGA
GAGCTG
GGGGACTAAAAC





LwaCas13

GGTCCTGCA
AGGTCC






a 18


TGCA








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GTCTCA
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACG
ATGGCT
CCCAAAAACGAA
short




validation

TCTCAATGGCTTTCCCCTG
TTCCCC
GGGGACTAAAAC





LwaCas13

GGTGATGC
TGGGTG






a 19


ATGC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CTGGGT
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACC
GATGCA
CCCAAAAACGAA
short




validation

TGGGTGATGCAAGAGCTGA
AGAGCT
GGGGACTAAAAC





LwaCas13

GGTCCTGC
GAGGTC






a 20


CTGC








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
CCCTGG
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACC
GTGATG
CCCAAAAACGAA
short




validation

CCTGGGTGATGCAAGAGCT
CAAGA
GGGGACTAAAAC





LwaCas13

GAGGTCCT
GCTGAG






a 21


GTCCT








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCAATG
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
GCTTTC
CCCAAAAACGAA
short




validation

CAATGGCTTTCCCCTGGGT
CCCTGG
GGGGACTAAAAC





LwaCas13

GATGCAAG
GTGATG






a 22


CAAG








6a
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TGGGTC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
TCAATG
CCCAAAAACGAA
short




validation

GGGTCTCAATGGCTTTCCC
GCTTTC
GGGGACTAAAAC





LwaCas13

CTGGGTGA
CCCTGG






a 23


GTGA








6b
APML
CcaCas13b
cggcgccactggccacgtg
cggcgccac
GTTGGAACTGCT
APML
6b



long

gttgctgttgg
tggccacgt
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
ggttgctgtt
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gg






b 1











6b
APML
CcaCas13b
ccggcgccactggccacgt
ccggcgcca
GTTGGAACTGCT
APML
6b



long

ggttgctgttg
ctggccacg
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
tggttgctgtt
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
g






b 2











6b
APML
CcaCas13b
cccggcgccactggccacg
GTTGGAACTGCT
cccggcgcc
APML
6b



long

tggttgctgtt
actggccac
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
gtggttgctg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
tt






b 3











6b
APML
CcaCas13b
ccccggcgccactggccac
ccccggcgc
GTTGGAACTGCT
APML
6b



long

gtggttgctgt
cactggcca
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
cgtggttgct
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gt






b 4











6b
APML
CcaCas13b
tccccggcgccactggcca
tccccggcg
GTTGGAACTGCT
APML
6b



long

cgtggttgctg
ccactggcc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
acgtggttgc
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
tg






b 5











6b
APML
CcaCas13b
ctccccggcgccactggcc
ctccccggc
GTTGGAACTGCT
APML
6b



long

acgtggttgct
gccactggc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
cacgtggttg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ct






b 6











6b
APML
CcaCas13b
cctccccggcgccactggc
cctccccgg
GTTGGAACTGCT
APML
6b



long

cacgtggttgc
cgccactgg
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
ccacgtggtt
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gc






b 7











6b
APML
CcaCas13b
gcctccccggcgccactgg
gcctccccg
GTTGGAACTGCT
APML
6b



long

ccacgtggttg
gcgccactg
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
gccacgtgg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ttg






b 8











6b
APML
CcaCas13b
tgcctccccggcgccactg
tgcctccccg
GTTGGAACTGCT
APML
6b



long

gccacgtggtt
gcgccactg
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
gccacgtgg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
tt






b 9











6b
APML
CcaCas13b
ctgcctccccggcgccact
ctgcctcccc
GTTGGAACTGCT
APML
6b



long

ggccacgtggt
ggcgccact
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
ggccacgtg
GTAATCACAAC





CcaCas 13

GGAGGGTAATCACAAC
gt






b 10











6b
APML
CcaCas13b
gctgcctccccggcgccac
gctgcctccc
GTTGGAACTGCT
APML
6b



long

tggccacgtgg
cggcgccac
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
tggccacgt
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gg






b 11











6b
APML
CcaCas13b
ggctgcctccccggcgcca
ggctgcctc
GTTGGAACTGCT
APML
6b



long

ctggccacgtg
cccggcgcc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
actggccac
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gtg






b 12











6b
APML
CcaCas13b
tggctgcctccccggcgcc
tggctgcctc
GTTGGAACTGCT
APML
6b



long

actggccacgt
cccggcgcc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
actggccac
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gt






b 13











6b
APML
CcaCas13b
atggctgcctccccggcgc
atggctgcct
GTTGGAACTGCT
APML
6b



long

cactggccacg
ccccggcgc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
cactggcca
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
cg






b 14











6b
APML
CcaCas13b
aatggctgcctccccggcg
aatggctgc
GTTGGAACTGCT
APML
6b



long

ccactggccac
ctccccggc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
gccactggc
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
cac






b 15











6b
APML
CcaCas13b
caatggctgcctccccggc
caatggctg
GTTGGAACTGCT
APML
6b



long

gccactggcca
cctccccgg
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
cgccactgg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
cca






b 16











6b
APML
CcaCas13b
ctgggtgatgcaagagctg
ctgggtgatg
GTTGGAACTGCT
APML
6b



short

aggtcctgcag
caagagctg
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
aggtcctgc
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ag






b 1











6b
APML
CcaCas13b
cctgggtgatgcaagagct
cctgggtgat
GTTGGAACTGCT
APML
6b



short

gaggtcctgca
gcaagagct
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gaggtcctg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ca






b 2











6b
APML
CcaCas13b
ccctgggtgatgcaagagc
ccctgggtg
GTTGGAACTGCT
APML
6b



short

tgaggtcctgc
atgcaagag
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
ctgaggtcct
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gc






b 3











6b
APML
CcaCas13b
cccctgggtgatgcaagag
cccctgggt
GTTGGAACTGCT
APML
6b



short

ctgaggtcctg
gatgcaaga
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gctgaggtc
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ctg






b 4











6b
APML
CcaCas13b
tcccctgggtgatgcaaga
tcccctgggt
GTTGGAACTGCT
APML
6b



short

gctgaggtcct
gatgcaaga
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gctgaggtc
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ct






b 5











6b
APML
CcaCas13b
ttcccctgggtgatgcaag
ttcccctggg
GTTGGAACTGCT
APML
6b



short

agctgaggtcc
tgatgcaag
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
agctgaggt
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
cc






b 6











6b
APML
CcaCas13b
tttcccctgggtgatgcaa
gagctgaggtc
GTTGGAACTGCT
APML
6b



short

tttcccctgg
gtgatgcaa
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gagctgagg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
tc






b 7











6b
APML
CcaCas13b
ctttcccctgggtgatgca
ctttcccctg
GTTGGAACTGCT
APML
6b



short

agagctgaggt
ggtgatgca
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
agagctgag
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gt






b 8











6b
APML
CcaCas13b
gctttcccctgggtgatgc
gctttcccct
GTTGGAACTGCT
APML
6b



short

aagagctgagg
gggtgatgc
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
aagagctga
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gg






b 9











6b
APML
CcaCas13b
ggctttcccctgggtgatg
ggctttcccc
GTTGGAACTGCT
APML
6b



short

caagagctgag
tgggtgatgc
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
aagagctga
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
g






b 10











6b
APML
CcaCas13b
tggctttcccctgggtgat
tggctttccc
GTTGGAACTGCT
APML
6b



short

gcaagagctga
ctgggtgatg
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
caagagctg
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
a






b 11











6b
APML
CcaCas13b
atggctttcccctgggtga
atggctttcc
GTTGGAACTGCT
APML
6b



short

tgcaagagctg
cctgggtgat
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gcaagagct
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
g






b 12











6b
APML
CcaCas13b
aatggctttcccctgggtg
aatggctttc
GTTGGAACTGCT
APML
6b



short

atgcaagagctG
ccctgggtg
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
atgcaagag
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
ct






b 13











6b
APML
CcaCas13b
caatggctttcccctgggt
caatggcttt
GTTGGAACTGCT
APML
6b



short

gatgcaagagc
cccctgggt
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gatgcaaga
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gc






b 14











6b
APML
CcaCas13b
tcaatggctttcccctggg
tcaatggcttt
GTTGGAACTGCT
APML
6b



short

tgatgcaagagG
cccctgggt
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
gatgcaaga
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
g






b 15











6b
APML
CcaCas13b
ctcaatggctttcccctgg
ctcaatggct
GTTGGAACTGCT
APML
6b



short

gtgatgcaagaG
ttcccctggg
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
tgatgcaag
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
a






b 16











6b
APML
CcaCas13b
tctcaatggctttcccctg
tctcaatggc
GTTGGAACTGCT
APML
6b



short

ggtgatgcaagG
tttcccctgg
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
gtgatgcaa
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
g






b 17











6b
APML
CcaCas13b
gtctcaatggctttcccct
gtctcaatgg
GTTGGAACTGCT
APML
6b



short

gggtgatgcaaG
ctttcccctg
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
ggtgatgca
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
a






b 18











6b
APML
CcaCas13b
ggtctcaatggctttcccc
ggtctcaatg
GTTGGAACTGCT
APML
6b



short

tgggtgatgcaG
gctttcccct
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
gggtgatgc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
a






b 19











6b
APML
CcaCas13b
gggtctcaatggctttccc
gggtctcaat
GTTGGAACTGCT
APML
6b



short

ctgggtgatgcG
ggctttcccc
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
tgggtgatgc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 20











6b
APML
CcaCas13b
tgggtctcaatggctttcc
tgggtctcaa
GTTGGAACTGCT
APML
6b



short

cctgggtgatgG
tggctttccc
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
ctgggtgatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 21











6b
APML
CcaCas13b
ctgggtctcaatggctttc
ctgggtctca
GTTGGAACTGCT
APML
6b



short

ccctgggtgatG
atggctttcc
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
cctgggtgat
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 22











6b
Thermo-
CcaCas13b
tcattggttgacctttgta
tcattggttga
GTTGGAACTGCT
Thermo-
6b



nuclease

cattaatttaaGTT
cctttgtacat
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
taatttaa
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 1











6b
Thermo-
CcaCas13b
tgtcattggttgacctttg
tgtcattggtt
GTTGGAACTGCT
Thermo-
6b



nuclease

tacattaatttGTT
gacctttgta
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
cattaattt
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 2











6b
Thermo-
CcaCas13b
aatgtcattggttgacctt
aatgtcattg
GTTGGAACTGCT
Thermo-
6b



nuclease

tgtacattaatGT
gttgacctttg
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tacattaat
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 3











6b
Thermo-
CcaCas13b
tgaatgtcattggttgacc
tgaatgtcatt
GTTGGAACTGCT
Thermo-
6b



nuclease

tttgtacattaGT
ggttgaccttt
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
gtacatta
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 4











6b
Thermo-
CcaCas13b
tctgaatgtcattggttga
tctgaatgtc
GTTGGAACTGCT
Thermo-
6b



nuclease

cctttgtacatGT
attggttgac
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
ctttgtacat
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 5











6b
Thermo-
CcaCas13b
agtctgaatgtcattggtt
agtctgaatg
GTTGGAACTGCT
Thermo-
6b



nuclease

gacctttgtacGT
tcattggttga
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
cctttgtac
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 6











6b
Thermo-
CcaCas13b
atagtctgaatgtcattgg
atagtctgaa
GTTGGAACTGCT
Thermo-
6b



nuclease

ttgacctttgtGT
tgtcattggtt
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
gacctttgt
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 7











6b
Thermo-
CcaCas13b
taatagtctgaatgtcatt
taatagtctg
GTTGGAACTGCT
Thermo-
6b



nuclease

ggttgacctttGT
aatgtcattg
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
gttgaccttt
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 8











6b
Thermo-
CcaCas13b
aataatagtctgaatgtca
aataatagtc
GTTGGAACTGCT
Thermo-
6b



nuclease

ttggttgacctGT
tgaatgtcatt
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
ggttgacct
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 9











6b
Thermo-
CcaCas13b
ccaataatagtctgaatgt
ccaataatag
GTTGGAACTGCT
Thermo-
6b



nuclease

cattggttgacG
tctgaatgtc
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
attggttgac
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 10











6b
Thermo-
CcaCas13b
aaccaataatagtctgaat
aaccaataat
GTTGGAACTGCT
Thermo-
6b



nuclease

gtcattggttgG
agtctgaatg
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
tcattggttg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 11











6b
Thermo-
CcaCas13b
tcaaccaataatagtctga
tcaaccaata
GTTGGAACTGCT
Thermo-
6b



nuclease

atgtcattggtG
atagtctgaa
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
tgtcattggt
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 12











6b
Thermo-
CcaCas13b
tatcaaccaataatagtct
tatcaaccaa
GTTGGAACTGCT
Thermo-
6b



nuclease

gaatgtcattgGT
taatagtctg
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
aatgtcattg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 13











6b
Thermo-
CcaCas13b
tgtatcaaccaataatagt
tgtatcaacc
GTTGGAACTGCT
Thermo-
6b



nuclease

ctgaatgtcatGT
aataatagtc
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tgaatgtcat
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 14











6b
Thermo-
CcaCas13b
ggtgtatcaaccaataata
ggtgtatcaa
GTTGGAACTGCT
Thermo-
6b



nuclease

gtctgaatgtcG
ccaataatag
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
tctgaatgtc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 15











6b
Thermo-
CcaCas13b
caggtgtatcaaccaataa
caggtgtatc
GTTGGAACTGCT
Thermo-
6b



nuclease

tagtctgaatgG
aaccaataat
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
agtctgaatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 16











6b
Thermo-
CcaCas13b
ttcaggtgtatcaaccaat
ttcaggtgtat
GTTGGAACTGCT
Thermo-
6b



nuclease

aatagtctgaaG
caaccaata
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
atagtctgaa
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 17











6b
Thermo-
CcaCas13b
gtttcaggtgtatcaacca
gtttcaggtg
GTTGGAACTGCT
Thermo-
6b



nuclease

ataatagtctgG
tatcaaccaa
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
taatagtctg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 18











6b
Thermo-
CcaCas13b
ttgtttcaggtgtatcaac
ttgtttcaggt
GTTGGAACTGCT
Thermo-
6b



nuclease

caataatagtcGT
gtatcaacca
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
ataatagtc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 19











6b
Thermo-
CcaCas13b
ctttgtttcaggtgtatca
ctttgtttcag
GTTGGAACTGCT
Thermo-
6b



nuclease

accaataatagGT
gtgtatcaac
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
caataatag
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 20











6b
Thermo-
CcaCas13b
tgctttgtttcaggtgtat
tgctttgtttc
GTTGGAACTGCT
Thermo-
6b



nuclease

caaccaataatGT
aggtgtatca
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
accaataat
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 21











6b
Thermo-
CcaCas13b
gatgctttgtttcaggtgt
gatgctttgtt
GTTGGAACTGCT
Thermo-
6b



nuclease

atcaaccaataGT
tcaggtgtat
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
caaccaata
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 22











6b
Thermo-
CcaCas13b
aggatgctttgtttcaggt
aggatgcttt
GTTGGAACTGCT
Thermo-
6b



nuclease

gtatcaaccaaG
gtttcaggtg
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
tatcaaccaa
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 23











6b
Thermo-
CcaCas13b
ttaggatgctttgtttcag
ttaggatgctt
GTTGGAACTGCT
Thermo-
6b



nuclease

gtgtatcaaccGT
tgtttcaggt
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
gtatcaacc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 24











6b
Thermo-
CcaCas13b
ttttaggatgctttgtttc
ttttaggatgc
GTTGGAACTGCT
Thermo-
6b



nuclease

aggtgtatcaaGT
tttgtttcagg
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tgtatcaa
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 25











6b
Thermo-
CcaCas13b
ttttttaggatgctttgtt
ttttttaggat
GTTGGAACTGCT
Thermo-
6b



nuclease

tcaggtgtatcGTT
gctttgtttca
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ggtgtatc
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 26











6b
Thermo-
CcaCas13b
ccttttttaggatgctttg
ccttttttagg
GTTGGAACTGCT
Thermo-
6b



nuclease

tttcaggtgtaGTT
atgctttgttt
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
caggtgta
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 27











6b
Thermo-
CcaCas13b
caccttttttaggatgctt
cacctttttta
GTTGGAACTGCT
Thermo-
6b



nuclease

tgtttcaggtgGT
ggatgctttg
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tttcaggtg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 28











6b
Thermo-
CcaCas13b
tacaccttttttaggatgc
tacacctttttt
GTTGGAACTGCT
Thermo-
6b



nuclease

tttgtttcaggGT
aggatgcttt
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
gtttcagg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 29











6b
Thermo-
CcaCas13b
tctacaccttttttaggat
tctacaccttt
GTTGGAACTGCT
Thermo-
6b



nuclease

gctttgtttcaGTT
tttaggatgct
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ttgtttca
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 30











6b
Thermo-
CcaCas13b
tctctacaccttttttagg
tctctacacct
GTTGGAACTGCT
Thermo-
6b



nuclease

atgctttgtttGTT
tttttaggatg
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ctttgttt
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 31











6b
Thermo-
CcaCas13b
tttctctacacctttttta
tttctctacac
GTTGGAACTGCT
Thermo-
6b



nuclease

ggatgctttgtGTT
cttttttagga
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
tgctttgt
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 32











6b
Thermo-
CcaCas13b
tatttctctacaccttttt
tatttctctac
GTTGGAACTGCT
Thermo-
6b



nuclease

taggatgctttGTT
accttttttag
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
gatgcttt
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 33











6b
Thermo-
CcaCas13b
catatttctctacaccttt
catatttctct
GTTGGAACTGCT
Thermo-
6b



nuclease

tttaggatgctGTT
acacctttttt
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
aggatgct
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 34











6b
Thermo-
CcaCas13b
accatatttctctacacct
accatatttct
GTTGGAACTGCT
Thermo-
6b



nuclease

tttttaggatgGT
ctacacctttt
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
ttaggatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 35











6b
Thermo-
CcaCas13b
ggaccatatttctctacac
ggaccatatt
GTTGGAACTGCT
Thermo-
6b



nuclease

cttttttaggaGT
tctctacacct
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tttttagga
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 36











6b
Thermo-
CcaCas13b
caggaccatatttctctac
caggaccat
GTTGGAACTGCT
Thermo-
6b



nuclease

accttttttagGT
atttctctaca
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
ccttttttag
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 37











6b
Thermo-
CcaCas13b
ttcaggaccatatttctct
ttcaggacca
GTTGGAACTGCT
Thermo-
6b



nuclease

acaccttttttGTT
tatttctctac
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
acctttttt
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 38











6b
Thermo-
CcaCas13b
gcttcaggaccatatttct
gcttcagga
GTTGGAACTGCT
Thermo-
6b



nuclease

ctacaccttttGT
ccatatttctc
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tacacctttt
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 39











6b
Thermo-
CcaCas13b
ttgcttcaggaccatattt
ttgcttcagg
GTTGGAACTGCT
Thermo-
6b



nuclease

ctctacaccttGT
accatatttct
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
ctacacctt
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 40











6b
Thermo-
CcaCas13b
acttgcttcaggaccatat
acttgcttca
GTTGGAACTGCT
Thermo-
6b



nuclease

ttctctacaccGT
ggaccatatt
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tctctacacc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 41











6b
Thermo-
CcaCas13b
gcacttgcttcaggaccat
gcacttgctt
GTTGGAACTGCT
Thermo-
6b



nuclease

atttctctacaGT
caggaccat
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
atttctctaca
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 42











6b
Thermo-
CcaCas13b
atgcacttgcttcaggacc
atgcacttgc
GTTGGAACTGCT
Thermo-
6b



nuclease

atatttctctaGT
ttcaggacca
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
tatttctcta
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 43











6b
Thermo-
CcaCas13b
aaatgcacttgcttcagga
aaatgcactt
GTTGGAACTGCT
Thermo-
6b



nuclease

ccatatttctcGT
gcttcagga
CTCATTTTGGAGG
nuclease




validation

TGGAACTGCTCTCATTTTG
ccatatttctc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 44











6b
Thermo-
CcaCas13b
gtaaatgcacttgcttcag
gtaaatgcac
GTTGGAACTGCT
Thermo-
6b



nuclease

gaccatatttcG
ttgcttcagg
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
accatatttc
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 45











6b
Thermo-
CcaCas13b
tcaattttctttgcatttt
tcaattttcttt
GTTGGAACTGCT
Thermo-
6b



nuclease

ctaccatctttGTTG
gcattttctac
CTCATTTTGGAGG
nuclease




validation

GAACTGCTCTCATTTTGGA
catcttt
GTAATCACAAC





CcaCas13

GGGTAATCACAAC







b 46











6b
Thermo-
CcaCas13b
ttcaattttctttgcattt
ttcaattttctt
GTTGGAACTGCT
Thermo-
6b



nuclease

tctaccatcttGTTG
tgcattttcta
CTCATTTTGGAGG
nuclease




validation

GAACTGCTCTCATTTTGGA
ccatctt
GTAATCACAAC





CcaCas13

GGGTAATCACAAC







b 47











6b
Thermo-
CcaCas13b
cttcaattttctttgcatt
cttcaattttct
GTTGGAACTGCT
Thermo-
6b



nuclease

ttctaccatctGTT
ttgcattttcta
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ccatct
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 48











6c
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TATCAA
GATTTAGACTAC
thermo-
6a



nulease

ACGAAGGGGACTAAAACT
CCAATA
CCCAAAAACGAA
nuclease




validation

ATCAACCAATAATAGTCTG
ATAGTC
GGGGACTAAAAC





LwaCas13

AATGTCAT
TGAATG






a 1 (top


TCAT






predicted)











6c
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTGCTT
GATTTAGACTAC
thermo-
6a



nulease

ACGAAGGGGACTAAAACTT
CAGGA
CCCAAAAACGAA
nuclease




validation

GCTTCAGGACCATATTTCT
CCATAT
GGGGACTAAAAC





LwaCas13

CTACACC
TTCTCT






a 20


ACACC






(bottom









predicted)











6c
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GCGCCA
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACG
CTGGCC
CCCAAAAACGAA
long




validation

CGCCACTGGCCACGTGGTT
ACGTGG
GGGGACTAAAAC





LwaCas13

GCTGTTGG
TTGCTG






a 1 (top


TTGG






predicted)











6c
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCCCCG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACT
GCGCCA
CCCAAAAACGAA
long




validation

CCCCGGCGCCACTGGCCAC
CTGGCC
GGGGACTAAAAC





LwaCas13

GTGGTTGC
ACGTGG






a 18


TTGC






(bottom









predicted)











6c
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCTCAA
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
TGGCTT
CCCAAAAACGAA
short




validation

CTCAATGGCTTTCCCCTGG
TCCCCT
GGGGACTAAAAC





LwaCas13

GTGATGCA
GGGTGA






a 1 (top


TGCA






predicted)











6c
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TGGGTC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
TCAATG
CCCAAAAACGAA
short




validation

GGGTCTCAATGGCTTTCCC
GCTTTC
GGGGACTAAAAC





LwaCas13

CTGGGTGA
CCCTGG






a23


GTGA






(bottom









predicted)











6d
Thermo-
CcaCas13b
caggtgtatcaaccaataat
caggtgtatc
GTTGGAACTGCT
Thermo-
6b



nuclease

agtctgaatgG
aaccaataat
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
agtctgaatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 16 (top









predicted)











6d
Thermo-
CcaCas13b
cttcaattttctttgcattt
cttcaattttct
GTTGGAACTGCT
Thermo-
6b



nuclease

tctaccatctGTT
ttgcattttcta
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ccatct
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 48









(bottom









predicted)











6d
APML
CcaCas13b
atggctgcctccccggcgcc
atggctgcct
GTTGGAACTGCT
APML
6b



long

actggccacg
ccccggcgc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
cactggcca
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
cg






b 14 (top









predicted)











6d
APML
CcaCas13b
tggctgcctccccggcgcca
tggctgcctc
GTTGGAACTGCT
APML
6b



long

ctggccacgt
cccggcgcc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
actggccac
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gt






b 13









(bottom









predicted)











6d
APML
CcaCas13b
cccctgggtgatgcaagagc
cccctgggt
GTTGGAACTGCT
APML
6b



short

tgaggtcctg
gatgcaaga
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gctgaggtc
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ctg






b 4 (top









predicted)











6d
APML
CcaCas13b
ctcaatggctttcccctggg
ctcaatggct
GTTGGAACTGCT
APML
6b



short

tgatgcaagaG
ttcccctggg
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
tgatgcaag
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
a






b 16









(bottom









predicted)











6e
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TATCAA
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACT
CCAATA
CCCAAAAACGAA
nuclease




validation

ATCAACCAATAATAGTCTG
ATAGTC
GGGGACTAAAAC





LwaCas13

AATGTCAT
TGAATG






a 1 (top


TCAT






predicted)











6e
thermo-
LwaCas13a
GATTTAGACTACCCCAAAA
TTGCTT
GATTTAGACTAC
thermo-
6a



nuclease

ACGAAGGGGACTAAAACTT
CAGGA
CCCAAAAACGAA
nuclease




validation

GCTTCAGGACCATATTTCT
CCATAT
GGGGACTAAAAC





LwaCas13

CTACACC
TTCTCT






a20


ACACC






(bottom









predicted)











6e
APML
LwaCas13a
GATTTAGACTACCCCAAAA
GCGCCA
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACG
CTGGCC
CCCAAAAACGAA
long




validation

CGCCACTGGCCACGTGGTT
ACGTGG
GGGGACTAAAAC





LwaCas13

GCTGTTGG
TTGCTG






a 1 (top


TTGG






predicted)











6e
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCCCCG
GATTTAGACTAC
APML
6a



long

ACGAAGGGGACTAAAACT
GCGCCA
CCCAAAAACGAA
long




validation

CCCCGGCGCCACTGGCCAC
CTGGCC
GGGGACTAAAAC





LwaCas13

GTGGTTGC
ACGTGG






a 18


TTGC






(bottom









predicted)











6e
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TCTCAA
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
TGGCTT
CCCAAAAACGAA
short




validation

CTCAATGGCTTTCCCCTGG
TCCCCT
GGGGACTAAAAC





LwaCas13

GTGATGCA
GGGTGA






a 1 (top


TGCA






predicted)











6e
APML
LwaCas13a
GATTTAGACTACCCCAAAA
TGGGTC
GATTTAGACTAC
APML
6a



short

ACGAAGGGGACTAAAACT
TCAATG
CCCAAAAACGAA
short




validation

GGGTCTCAATGGCTTTCCC
GCTTTC
GGGGACTAAAAC





LwaCas13

CTGGGTGA
CCCTGG






a 23


GTGA






(bottom









predicted)











6f
Thermo-
CcaCas13b
caggtgtatcaaccaataat
caggtgtatc
GTTGGAACTGCT
Thermo-
6b



nuclease

agtctgaatgG
aaccaataat
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
agtctgaatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 16 (top









predicted)











6f
Thermo-
CcaCas13b
cttcaattttctttgcattt
cttcaattttct
GTTGGAACTGCT
Thermo-
6b



nuclease

tctaccatctGTT
ttgcattttcta
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ccatct
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 48









(bottom









predicted)











6f
APML
CcaCas13b
atggctgcctccccggcgcc
atggctgcct
GTTGGAACTGCT
APML
6b



long

actggccacg
ccccggcgc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
cactggcca
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
cg






b 14 (top









predicted)











6f
APML
CcaCas13b
tggctgcctccccggcgcca
tggctgcctc
GTTGGAACTGCT
APML
6b



long

ctggccacgt
cccggcgcc
CTCATTTTGGAGG
long




validation

GTTGGAACTGCTCTCATTTT
actggccac
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
gt






b 13









(bottom









predicted)











6f
APML
CcaCas13b
cccctgggtgatgcaagagc
cccctgggt
GTTGGAACTGCT
APML
6b



short

tgaggtcctg
gatgcaaga
CTCATTTTGGAGG
short




validation

GTTGGAACTGCTCTCATTTT
gctgaggtc
GTAATCACAAC





CcaCas13

GGAGGGTAATCACAAC
ctg






b 4 (top









predicted)











6f
APML
CcaCas13b
ctcaatggctttcccctggg
ctcaatggct
GTTGGAACTGCT
APML
6b



short

tgatgcaagaG
ttcccctggg
CTCATTTTGGAGG
short




validation

TTGGAACTGCTCTCATTTTG
tgatgcaag
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC
a






b 16









(bottom









predicted)











7b
Acyltrans-
LwaCas13a
GATTTAGACTACCCCAAAA
GCACGC
GATTTAGACTAC
Acyl- 
7b



ferase

ACGAAGGGGACTAAAACgc
TGGAGG
CCCAAAAACGAA
trans-




LwaCas13

acgctggaggggtcgagcac
GGTCGA
GGGGACTAAAAC
ferase




atop

gctcac
GCACGC






predicted


TCAC






crRNA











7c
Acyltrans-
LwaCas13a
GATTTAGACTACCCCAAAA
CATCGC
GATTTAGACTAC
Acyl-
7c



ferase

ACGAAGGGGACTAAAACcat
AGAGC
CCCAAAAACGAA
trans-




LwaCas13

cgcagagcacgctggagggg
ACGCTG
GGGGACTAAAAC
ferase




a bottom

tcgag
GAGGG






predicted


GTCGAG






crRNA











7d-
Acyltrans-
LwaCas13a
GATTTAGACTACCCCAAAA
GCACGC
GATTTAGACTAC
Acyl-
7b


f
ferase

ACGAAGGGGACTAAAACgc
TGGAGG
CCCAAAAACGAA
trans-




LwaCas13

acgctggaggggtcgagca
GGTCGA
GGGGACTAAAAC
ferase




a top

cgctcac
GCACGC






predicted


TCAC






crRNA











7d-
Acyltrans-
LwaCas13a
GATTTAGACTACCCCAAAA
CATCGC
GATTTAGACTAC
Acyl-
7c


f
ferase

ACGAAGGGGACTAAAACcat
AGAGC
CCCAAAAACGAA
trans-




LwaCas13

cgcagagcacgctggagggg
ACGCTG
GGGGACTAAAAC
ferase




a bottom

tcgag
GAGGG






predicted


GTCGAG






crRNA











7h
Thermo-
CcaCas13b
caggtgtatcaaccaataat
caggtgtatc
GTTGGAACTGCT
Thermo-
6b



nuclease

agtctgaatgG
aaccaataat
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
agtctgaatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 16 (top









predicted)











7i
Thermo-
CcaCas13b
cttcaattttctttgcattt
cttcaattttct
GTTGGAACTGCT
Thermo-
6b



nuclease

tctaccatctGTT
ttgcattttcta
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ccatct
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 48









(bottom









predicted)











7j-
Thermo-
CcaCas13b
caggtgtatcaaccaataat
caggtgtatc
GTTGGAACTGCT
Thermo-
6b


l
nuclease

agtctgaatgG
aaccaataat
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
agtctgaatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 16 (top









predicted)











7j-
Thermo-
CcaCas13b
cttcaattttctttgcattt
cttcaattttct
GTTGGAACTGCT
Thermo-
6b


l
nuclease

tctaccatctGTT
ttgcattttcta
CTCATTTTGGAGG
nuclease




validation

GGAACTGCTCTCATTTTGG
ccatct
GTAATCACAAC





CcaCas13

AGGGTAATCACAAC







b 48









(bottom









predicted)











8b
Ea175
LwaCas13a
GATTTAGACTACCCCAAAA
AAGATG
GATTTAGACTAC
Ea175
8b



LwaCas13

ACGAAGGGGACTAAAACA
TGGATT
CCCAAAAACGAA





a top

AGATGTGGATTTTTACATA
TTTACA
GGGGACTAAAAC





predicted

GTAAAAAT
TAGTAA









AAAT








8b
Thermo-
CcaCas13b
caggtgtatcaaccaataat
caggtgtatc
GTTGGAACTGCT
Thermo-
6b



nuclease

agtctgaatgG
aaccaataat
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
agtctgaatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 16 (top









predicted)











8d-e
Ea175
LwaCas13a
GATTTAGACTACCCCAAAA
AAGATG
GATTTAGACTAC
Ea175
8b



LwaCas13

ACGAAGGGGACTAAAACA
TGGATT
CCCAAAAACGAA





a top

AGATGTGGATTTTTACATA
TTTACA
GGGGACTAAAAC





predicted

GTAAAAAT
TAGTAA









AAAT








8d-e
Thermo-
CcaCas13b
caggtgtatcaaccaataat
caggtgtatc
GTTGGAACTGCT
Thermo-
6b



nuclease

agtctgaatgG
aaccaataat
CTCATTTTGGAGG
nuclease




validation

TTGGAACTGCTCTCATTTTG
agtctgaatg
GTAATCACAAC





CcaCas13

GAGGGTAATCACAAC







b 16 (top









predicted)











12a-
Ea175
LwaCas13a
GATTTAGACTACCCCAAAA
AAGATG
GATTTAGACTAC
Ea175
8b


c
LwaCas13

ACGAAGGGGACTAAAACA
TGGATT
CCCAAAAACGAA





a top

AGATGTGGATTTTTACATA
TTTACA
GGGGACTAAAAC





predicted

GTAAAAAT
TAGTAA









AAAT








12d-
Ea81
LwaCas13a
GATTTAGACTACCCCAAAA
ATTTCT
GATTTAGACTAC
Ea81
12d


f
LwaCas13

ACGAAGGGGACTAAAACA
AGAATT
CCCAAAAACGAA





a top

TTTCTAGAATTGAAGGAAT
GAAGG
GGGGACTAAAAC





predicted

TAAACCAA
AATTAA









ACCAA








10d-
Ea175
LwaCas13a
GATTTAGACTACCCCAAAA
AAGATG
GATTTAGACTAC
Ea175
8b


e
LwaCas13

ACGAAGGGGACTAAAACA
TGGATT
CCCAAAAACGAA





a top

AGATGTGGATTTTTACATA
TTTACA
GGGGACTAAAAC





predicted

GTAAAAAT
TAGTAA









AAAT








1b-c
Lectin
LwaCas13a
GATTTAGACTACCCCAAAA
ggggtggag
GATTTAGACTAC
Lectin
1b



LwaCas13

ACGAAGGGGACTAAAACgg
tagagggcg
CCCAAAAACGAA





a crRNA

ggtggagtagagggcgcga
cgaccaaga
GGGGACTAAAAC







ccaagag
g








1b-c
ssDN1
CcaCas13b
acgccaagcttgcatgcct
acgccaagc
GTTGGAACTGCT
ssDNA 1
1b



CcaCas13

gcaggtcgagt
ttgcatgcct
CTCATTTTGGAGG





b crRNA

GTTGGAACTGCTCTCATTTT
gcaggtcga
GTAATCACAAC







GGAGGGTAATCACAAC
gt








1e-f
Zika
LwaCas13a
GATTTAGACTACCCCAAAA
actccctaga
GATTTAGACTAC
Zika
1e



LwaCas13

ACGAAGGGGACTAAAACact
accacgaca
CCCAAAAACGAA





a crRNA

ccctagaaccacgacagttt
gtttgcctt
GGGGACTAAAAC







gcctt









1e-f
Dengue
CcaCas13b
tttgcttctgtccagtgag
tttgcttctgt
GTTGGAACTGCT
Dengue
1e



CcaCas13

catggtcttcgGT
ccagtgagc
CTCATTTTGGAGG





b crRNA

TGGAACTGCTCTCATTTTG
atggtcttcg
GTAATCACAAC







GAGGGTAATCACAAC









1e-f
ssDNA 1
AsCas12a
TAATTTCTACTCTTGTAGAT
ctgtgtttatc
TAATTTCTACTCT
ssDNA1
1e



AsCas12a

ctgtgtttatccgctcacaa
cgctcacaa
TGTAGAT





crRNA
















TABLE 2







Target sequences used in this study













DNA/


FIG.
Name
Target sequence
RNA





11b
Ebola
attcgcagtgaagagttgtctttcacagttgtatcaaacggagccaaaaacatcagt
RNA



(SEQ ID No: 3279)
ggtcagagtccggcgcgaacttcttccgacccagggaccaacacaacaactgaagac





cacaaaatcatggcttcagaaaattcctctgcaatggttcaagtgcacagtcaa






11b
Zika
gacaccggaactccacactggaacaacaaagaagcactggtagagttcaaggacgca
RNA



(SEQ ID No: 3280)
catgccaaaaggcaaactgtcgtggttctagggagtcaagaaggagcagttcacacg





gcccttgctggagctctggaggctgagatggatggtgcaaagggaaggctgtcctct





ggc






6a-f
Thermonuclease
agcgattgatggtgatactgttaaattaatgtacaaaggtcaaccaatgacattcag
RNA



(SEQ ID No: 3281)
actattattggttgatacacctgaaacaaagcatcctaaaaaaggtgtagagaaata





tggtcctgaagcaagtgcatttacgaaaaagatggtagaaaatgcaaagaaaattga





ag






6a-f
APML long
cacctggatggaccgcctagccccaggagccccgtcataggaagtgaggtcttcctg
RNA



(SEQ ID No: 3282)
cccaacagcaaccacgtggccagtggcgccggggaggcagccattgagacccagagc





agcagttctgaagagatagtgcccagccctccctcgccaccccctctaccccgcatc





taca









6a-f
APML short
ggaggagccccagagcctgcaagctgccgtgcgcaccgatggcttcgacgagttcaa
RNA



(SEQ ID No: 3283)
ggtgcgcctgcaggacctcagctcttgcatcacccaggggaaagccattgagaccca





gagcagcagttctgaagagatagtgcccagccctccctcgccaccccctctaccccg





catc






7b-f
Acyltransferase
gtcgggcgcgcacgttttcccttcgctgagcacgctgcgcgcgtcgcctacgtgaat
DNA



(SEQ ID No: 3284)
gcgctgttcgatgcgttggccgaaggcaacccgcgggtgagcgtgctcgacccctcc





agcgtgctctgcgatggcctggattgtttcgccgaacgtgatggctggtcgctgtac





atgg






7h-l
Thermonuclease
agcgattgatggtgatactgttaaattaatgtacaaaggtcaaccaatgacattcag
DNA



(SEQ ID No: 3285)
actattattggttgatacacctgaaacaaagcatcctaaaaaaggtgtagagaaata





tggtcctgaagcaagtgcatttacgaaaaagatggtagaaaatgcaaagaaaattga





ag






8b
Thermonuclease
agcgattgatggtgatactgttaaattaatgtacaaaggtcaaccaatgacattcag
DNA



(SEQ ID No: 3286)
actattattggttgatacacctgaaacaaagcatcctaaaaaaggtgtagagaaata





tggtcctgaagcaagtgcatttacgaaaaagatggtagaaaatgcaaagaaaattga





ag






8b
Ea175
GGCCAGTTTGAATAAGACAATGAATTATTTTTACTATGTAAAAATCCACATCTTTCA
DNA



(SEQ ID No: 3287)
CATTTCCATTCTTGTGTTTCA






8d-e
Thermonuclease
agcgattgatggtgatactgttaaattaatgtacaaaggtcaaccaatgacattcag
DNA



(SEQ ID No: 3288)
actattattggttgatacacctgaaacaaagcatcctaaaaaaggtgtagagaaata





tggtcctgaagcaagtgcatttacgaaaaagatggtagaaaatgcaaagaaaattga





ag






8d-e
Ea175
GGCCAGTTTGAATAAGACAATGAATTATTTTTACTATGTAAAAATCCACATCTTTCA
DNA



(SEQ ID No: 3289)
CATTTCCATTCTTGTGTTTCA






9a
ssRNA 1
GGCCAGTGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAAATATGGATTACTTGgt
RNA



(SEQ ID No: 3290)
AGAACAGCAATCTACTCGACCTGCAGGCATGCAAGCTTGGCGTAATCATGGTCATAG





CTGTTTCCTGTGTTTATCCGCTCACAATTCCACACAACATACGAGCCGGAAGCATAA





AG






9a
Thermonuclease
agcgattgatggtgatactgttaaattaatgtacaaaggtcaaccaatgacattcag
RNA



(SEQ ID No: 3291)
actattattggttgatacacctgaaacaaagcatcctaaaaaaggtgtagagaaata





tggtcctgaagcaagtgcatttacgaaaaagatggtagaaaatgcaaagaaaattga





ag






9a
Dengue
agtacatattcaggggccaacctctcaacaatgacgaagaccatgctcactggacag
RNA



(SEQ ID No: 3292)
aagcaaaaatgctgctggacaacatcaacacaccagaagggattataccagctctct





ttgaaccagaaagggagaagtcagccgccatagacggtgaataccgcctgaagggt






12a-c
Ea175
GGCCAGTTTGAATAAGACAATGAATTATTTTTACTATGTAAAAATCCACATCTTTCA
DNA



(SEQ ID No: 3293)
CATTTCCATTCTTGTGTTTCA









12d-f
Ea81
ATTGTTACATTGTACACATACATAAGCAACATAAGCATCATTTGGTTTAATTCCTTC
DNA



(SEQ ID No: 3294)
AATTCTAGAAATATTTGTTTGATTTTTTACTTCACGCCTACTCAT






10d-f
Ea175
GGCCAGTTTGAATAAGACAATGAATTATTTTTACTATGTAAAAATCCACATCTTTCA
DNA



(SEQ ID No: 3295)
CATTTCCATTCTTGTGTTTCA






1b-c
Lectin
aagttacaactcaataaggttgacgaaaacggcaccccaaaaccctcgtctcttggt
DNA



(SEQ ID No: 3296)
cgcgccctctactccacccccatccacatttgggacaaagaaaccggtagcgttgcc





agcttcgccgcttccttcaacttcaccttctatgcccctgacacaaaaaggcttgca





gatgggcttgccttctttctcgc






1b-c
ssDNA 1
GGCCAGTGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAAATATGGATTACTTGgt
DNA



(SEQ ID No: 3297)
AGAACAGCAATCTACTCGACCTGCAGGCATGCAAGCTTGGCGTAATCATGGTCATAG





CTGTTTCCTGTGTTTATCCGCTCACAATTCCACACAACATACGAGCCGGAAGCATAA





AG






1e-f
Zika
gacaccggaactccacactggaacaacaaagaagcactggtagagttcaaggacgca
RNA



(SEQ ID No: 3298)
catgccaaaaggcaaactgtcgtggttctagggagtcaagaaggagcagttcacacg





gcccttgctggagctctggaggctgagatggatggtgcaaagggaaggctgtcctct





ggc






1e-f
Dengue
agtacatattcaggggccaacctctcaacaatgacgaagaccatgctcactggacag
RNA



(SEQ ID No: 3299)
aagcaaaaatgctgctggacaacatcaacacaccagaagggattataccagctctct





ttgaaccagaaagggagaagtcagccgccatagacggtgaataccgcctgaagggt






1e-f
ssDNA 1
GGCCAGTGAATTCGAGCTCGGTACCCGGGGATCCTCTAGAAATATGGATTACTTGgt
DNA



(SEQ ID No: 3300)
AGAACAGCAATCTACTCGACCTGCAGGCATGCAAGCTTGGCGTAATCATGGTCATAG





CTGTTTCCTGTGTTTATCCGCTCACAATTCCACACAACATACGAGCCGGAAGCATAA





AG
















TABLE 3







RPA primers used in this study (SEQ ID Nos: 3301-3342)










FIG.
Name
Sequence
Target





7b
RPA Acyltransferase F
gaaatTAATACGACTCACTATAGGGCTACGTGAA
acyltransferase



with T7
TGCGCTGTTCGATG






7b
RPA Acyltransferase R
CATCACGTTCGGCGAAACAATCCAG
acyltransferase





7c
RPA Acyltransferase F
gaaatTAATACGACTCACTATAGGGCTACGTGAA
acyltransferase



with T7
TGCGCTGTTCGATG






7c
RPA Acyltransferase R
CATCACGTTCGGCGAAACAATCCAG
acyltransferase





7e
RPA Acyltransferase F
gaaatTAATACGACTCACTATAGGGCTACGTGAA
acyltransferase



with T7
TGCGCTGTTCGATG






7e
RPA Acyltransferase R
CATCACGTTCGGCGAAACAATCCAG
acyltransferase





7h
RPA Thermonuclease F
gaaatTAATACGACTCACTATAGGGTGTACAAAG
thermonuclease



with T7
GTCAACCAATGACATTCAG






7h
RPA Thermonuclease R
TGCACTTGCTTCAGGACCATATTTC
thermonuclease





7i
RPA Thermonuclease F
gaaatTAATACGACTCACTATAGGGTGTACAAAG
thermonuclease



with T7
GTCAACCAATGACATTCAG






7i
RPA Thermonuclease R
TGCACTTGCTTCAGGACCATATTTC
thermonuclease





7k
RPA Thermonuclease F
gaaatTAATACGACTCACTATAGGGTGTACAAAG
thermonuclease



with T7
GTCAACCAATGACATTCAG






7k
RPA Thermonuclease R
TGCACTTGCTTCAGGACCATATTTC
thermonuclease





8b
Multiplexing RPA
gaaatTAATACGACTCACTATAGGGAGCGATTGA
thermonuclease



Thermonuclease F
TGGTGATACTGTTAAA




with T7







8b
Multiplexing RPA
TCGTAAATGCACTTGCTTCAGGACC
thermonuclease



Thermonuclease R







8b
RPA Ea175 F with T7
gaaattaatacgactcactatagggGGCCAGTTT
Ea175




GAATAAGACAATG






8b
RPA Ea175 R
GGCCAGTTTGAATAAGACAATG
Ea175





8d
Multiplexing RPA
gaaatTAATACGACTCACTATAGGGAGCGATTGA
thermonuclease



Thermonuclease F
TGGTGATACTGTTAAA




with T7







8d
Multiplexing RPA
TCGTAAATGCACTTGCTTCAGGACC
thermonuclease



Thermonuclease R







8d
RPA Ea175 F with T7
gaaattaatacgactcactatagggGGCCAGTTT
Ea175




GAATAAGACAATG






8d
RPA Ea175 R
GGCCAGTTTGAATAAGACAATG
Ea175





8d
Multiplexing RPA
gaaatTAATACGACTCACTATAGGGAGCGATTGA
thermonuclease



Thermonuclease F
TGGTGATACTGTTAAA




with T7







8d
Multiplexing RPA
TCGTAAATGCACTTGCTTCAGGACC
thermonuclease



Thermonuclease R







8d
RPA Ea175 F with T7
gaaattaatacgactcactatagggGGCCAGTTT
Ea175




GAATAAGACAATG






8d
RPA Ea175 R
GGCCAGTTTGAATAAGACAATG
Ea175





12a
RPA Ea175 F with T7
gaaattaatacgactcactatagggGGCCAGTTT
Ea175




GAATAAGACAATG






12a
RPA Ea175 R
GGCCAGTTTGAATAAGACAATG
Ea175





12c
RPA Ea175 F with T7
gaaattaatacgactcactatagggGGCCAGTTT
Ea175




GAATAAGACAATG






12c
RPA Ea175 R
GGCCAGTTTGAATAAGACAATG
Ea175





12d
RPA Ea81 F with T7
gaaattaatacgactcactatagggATTGTTACA
Ea81




TTGTACACATACA






12d
RPA Ea81 R
ATTGTTACATTGTACACATACA
Ea81





12f
RPA Ea81 F with T7
gaaattaatacgactcactatagggATTGTTACA
Ea81




TTGTACACATACA






12f
RPA Ea81 R
ATTGTTACATTGTACACATACA
Ea81





10e
RPA Ea175 F with T7
gaaattaatacgactcactatagggGGCCAGTTT
Ea175




GAATAAGACAATG






10e
RPA Ea175 R
TGAAACACAAGAATGGAAATGT
Ea175





1b
RPA ssDNA1 F with T7
gaaattaatacgactcactatagggGATCCTCTA
ssDNA1




GAAATATGGATTACTTGGTAGAACAG






1b
RPA ssDNA1 R
GATAAACACAGGAAACAGCTATGACCATGATTAC
ssDNA1




G






1b
RPA lectin F with T7
gaaatTAATACGACTCACTATAGGGTCAATAAGG
Lectin




TTGACGAAAACGGCAC






1b
RPA lectin R
TAGAAGGTGAAGTTGAAGGAAGCGG
Lectin





1c
RPA ssDNA1 F with T7
gaaattaatacgactcactatagggCATCCTCTA
ssDNA1




GAAATATGGATTACTTGGTAGAACAG






1c
RPA ssDNA1 R
GATAAACACAGGAAACAGCTATGACCATGATTAC
ssDNA1




G






1c
RPA lectin F with T7
gaaatTAATACGACTCACTATAGGGTCAATAAGG
Lectin




TTGACGAAAACGGCAC






1c
RPA lectin R
TAGAAGGTGAAGTTGAAGGAAGCGG
Lectin
















TABLE 4







HDA primers used in this study


(SEQ ID No. 3343 and 3344)










FIG.
Name
Sequence
Target





10e
HDA Ea175 F
gaaattaatacgactcactatagg
Ea175



with T7
gGGCCAGTTTGAATAAGACAATG






10e
HDA Ea175 R
TGAAACACAAGAATGGAAATGT
Ea175
















TABLE 5







Reporter sequences used in this study
















Antigen/
Compatible


FIG.
Name
Sequence
Flurorphore
quencher
enzyme





11b
Rnase Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





6a
Rnase Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





6b
Rnase Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





6c
Rnase Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





6d
Rnase Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





6e
Single-plex lateral
/56-FAM/rUrUrUrUrUrU/3Bio/
FAM
Biotin
LwaCas13a/



flow reporter



CcaCas13b





6f
Single-plex lateral
/56-FAM/rUrUrUrUrUrU/3Bio/
FAM
Biotin
LwaCas13a/



flow reporter



CcaCas13b





7b
Ranse Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





7c
Ranse Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





7e
Single-plex lateral
/56-FAM/rUrUrUrUrUrU/3Bio/
FAM
Biotin
LwaCas13a/



flow reporter



CcaCas13b





7h
Poly-U reporter
/56-FAM/rUrUrUrUrU/3IABkFQ/
FAM
Iowa Black
LwaCas13a/






FQ
CcaCas13b





7i
Poly-U reporter
/56-FAM/rUrUrUrUrU/3IABkFQ/
FAM
Iowa Black
LwaCas13a/






FQ
CcaCas13b





7k
Single-plex lateral
/56-FAM/rUrUrUrUrUrU/3Bio/
FAM
Biotin
LwaCas13a/



flow reporter



CcaCas13b





8b
LwaCas13a
/56-FAM/TArArUGC/3IABkFQ/
FAM
Iowa Black
LwaCas13a



Fluorescence


FQ




reporter









8b
CcaCas13b
/5HEX/TArUrAGC/3IABkFQ/
HEX
Iowa Black
CcaCas13b



Fluorescence


FQ




reporter









8d
LwaCas13a
/5TYE665/T*A*rArUG*C*/3
TYE 665
AlexaFluor
LwaCas13a



Lateral Flow
AlexF488N/

488




reporter









8d
CcaCas13b
/5TYE665/T*A*rUrAG*C*/3
TYE 665
FAM
CcaCas13b



Lateral Flow
6-FAM






reporter









8d
LwaCas13a
/5TYE665/T*A*rArUG*C*/3
TYE 665
AlexaFluor
LwaCas13a



Lateral Flow
AlexF488N/
488





reporter









8d
CcaCas13b
/5TYE665/T*A*rUrAG*C*/3
TYE 665
FAM
CcaCas13b



Lateral Flow
6-FAM/






reporter









9a
Rnase Alert v2
N/A
N/A
N/A
LwaCas13a/







CcaCas13b





12a
Poly-U reporter
/56-FAM/rUrUrUrUrU/3IABkFQ/
FAM
Iowa Black
LwaCas13a/






FQ
CcaCas13b





12c
Single-plex lateral
/56-FAM/rUrUrUrUrU/3Bio/
FAM
Biotin
LwaCas13a/



flow reporter



CcaCas13b





12d
Poly-U reporter
/56-FAM/rUrUrUrUrU/3IABkFQ/
FAM
Iowa Black
LwaCas13a/






FQ
CcaCas13b





12f
Single-plex lateral
/56-FAM/rUrUrUrUrU/3Bio/
FAM
Biotin
LwaCas13a/



flow reporter



CcaCas13b





10b
Helicase reporter
/56-FAM/CAGAGGAACGTCTATCTA
FAM
N/A
UvrD



FAM
ACGGTTGGTATCTTGAATGCTCAGTC


helicases



(SEQ ID NO: 3345)
CCTTT








10b
Helicase reporter
AAAGGGACTGAGCATTCAAGATACCA
N/A
BHQ-1
UvrD



BHQ1
ACCGTTAGATAGACGTTCCTCTG/


helicases



(SEQ ID NO: 3346)
3BHQ_1/








10d
Poly-U reporter
/56-FAM/rUrUrUrUrU/3IABkFQ/
FAM
Iowa Black
LwaCas13a/






FQ
CcaCas13b





10e
Poly-U reporter
/56-FAM/rUrUrUrUrU/3IABkFQ/
FAM
Iowa Black
LwaCas13a/






FQ
CcaCas13b





1b
FAM LwaCas13a
/56-FAM/TArArUGC/3Bio/
FAM
Biotin
LwaCas13a



Lateral Flow







reporter









1b
FAM CcaCas13b
/56-FAM/TArUrAGC/3Dig_N/
FAM
DIG
CcaCas13b



Lateral Flow







reporter









1c
FAM LwaCas13a
/56-FAM/TArArUGC/3Bio/
FAM
Biotin
LwaCas13a



Lateral Flow







reporter









1c
FAM CcaCas13b
/56-FAM/TArUrAGC/3Dig_N/
FAM
DIG
CcaCas13b



Lateral Flow







reporter









1e
LwsCas13a
/5TYE665/T*A*rArUG*C*/3
TYE 665
AlexaFlour
LwaCas13a



Lateral Flow
AlexF488N/

488




reporter









1e
CcaCas13b
/5TYE665/T*A*rUrAG*C*/3
TYE 665
FAM
CcaCas13b



Lateral Flow
6-FAM






reporter









1e
AsCas12a
/5TYE665/CCCCC/3Dig_N/
TYE 665
DIG
AsCas12a



Lateral Flow







reporter









1f
LwaCas13a
/5TYE665/T*A*rArUG*C*/3
TYE 665
AlexaFlour
LwaCas13a



Lateral Flow
AlexF488N/

488




reporter









1f
CcaCas13b
/5TYE665/T*A*rUrAG*C*/3
TYE 665
FAM
CcaCas13b



Lateral Flow
6-FAM






reporter









1f
AsCas12a
/5TYE665/CCCCC/3Dig_N/
TYE 665
DIG
AsCas12a



Lateral Flow







reporter
















TABLE 6







Cas13 proteins used in this study












Protein


Accession


Abbreviation
name
Strain name
Benchling link
number





Lwa
LwaCas13a

Leptotrichia wadei

https://benchling.com/s/seq-
WP_021746774.1





66CfLwu7sLMQMbcXe7Ih



Cca
CcaCas13b

Capnocytophaga canimorsus

https://benchling.com/s/seq-
WP_013997271





BNVzFUQjqSnkYLARxLwE
















TABLE 7







Helicase proteins used in this study















Accession number



Protein
Strain
Superhelicase
(lacks superhelicase


Abbreviation
name
name
mutation
mutations)





Tte
Tte-UvrD

Thermoanaerobacter


AAM23874.1





tengcongensis





Super Tte
Super Tte-UvrD

Thermoanaerobacter

+
AAM23874.1





tengcongensis





Tet
Tet-UvrD

Thermoanaerobacter


WP_003870487.1





ethanolicus





Super Tet
Super Tet-UvrD

Thermoanaerobacter

+
WP_003870487.1





ethanolicus





Bsp
Bsp-UvrD

Bacillus sp. FJAT-27231


WP_049660019.1


Super Bsp
Super Bsp-UvrD

Bacillus sp. FJAT-27231

+
WP_049660019.1


Bme
Bme-UvrD

Bacillus megaterium

+
WP_034654680.1


Bsi
Bsi-UvrD

Bacillus simplex

+
WP_095390358.1


Pso
Pso-UvrD

Paeniclostridium sordellii

+
WP_055343022.1









EXAMPLES
Example 1—One-Pot HDA-SHERLOCK is Capable of Quantitative Detection of Different Targets

A schematic of helicase reporter for screening DNA unwinding activity is shown in FIG. 1A. Temperature sensitivity screening of different helicase orthologs with and without super-helicase mutations using the high-throughput fluorescent reporter was performed (FIG. 1B). A schematic of one-pot SHERLOCK with RPA or Super-HDA is shown in FIG. 1C. Kinetic curves were generated of one-pot HDA detection of a restriction endonuclease gene fragment (Ea81) from Treponema denticola (FIGS. 1D, 1E). FIG. 1F illustrates the quantitative nature of HDA-SHERLOCK compared to one-pot RPA.


Example 2—One-Pot RPA-SHERLOCK is Capable of Rapid Detection of Different Targets

Kinetic curves were also generated of one-pot RPA detection of a restriction endonuclease gene fragment (Ea175) from Treponema denticola (FIG. 2A). One-pot RPA end-point detection of Ea175 gene fragment and one-pot RPA lateral flow readout of the Ea175 fragment in 30 minutes are shown in FIGS. 2B and 2C, respectively. Kinetic curves were generated of one-pot RPA detection of a restriction endonuclease gene fragment (Ea81) from Treponema denticola (FIG. 2D). One-pot RPA end-point detection of Ea81 gene fragment and one-pot RPA lateral flow readout of the Ea81 fragment in 3 hours are shown in FIGS. 2E and 2F, respectively. Kinetic curves were generated of one-pot RPA detection of acyltransferase gene fragment (acyltransferase) from P. aeruginosa (FIG. 2G). One-pot RPA end-point detection of acyltransferase gene fragment and one-pot RPA lateral flow readout of the acyltransferase fragment in 3 hours are shown in FIGS. 2H and 2I, respectively.


Example 3—Multiplexed Lateral Flow Detection with SHERLOCK

A schematic of the proposed multiplex lateral flow design with RPA preamplification for two probes is shown in FIG. 3A. Multiplexed lateral flow detection of two targets (ssDNA 1 and a gene fragment of lectin from soybean) was carried out as described (FIG. 3B). In one experiment, pre-amplification by RPA was done prior to detection, allowing for detection down to 2 aM (FIG. 3C). A schematic for custom-made lateral flow strips enabling detection of three targets simultaneously with SHERLOCK is shown in FIG. 3D. Multiplexed lateral flow strips using LwaCas13a, CcaCas13b, and AsCas12a effector proteins were used to detect three targets in various combinations—ssDNA1, Zika ssRNA, and Dengue ssRNA. Results are shown in FIG. 3E. Tye-665 fluorescent intensity for these three targets was quantified as shown in FIG. 3F.


Example 4—SHERLOCK Guide Design Model is Capable of Predicting Highly Active crRNAs for SHERLOCK Detection

Previous tiling of SHERLOCK guides along targets has demonstrated significant variation of signal between guide RNAs with LwaCas13a and CcaCas13b (Gootenberg, 2018), which has an effect on the overall kinetics and sensitivity of the assay. While a sequence constraint known as a protospacer flanking site (PFS) exists for Cas13 targeting (Abudayyeh, 2016; Smargon, 2017), many guide RNAs without the correct PFS retain activity. Applicants therefore hypothesized that some combination of the PFS and other sequence and guide features might be driving the efficacy of Cas13 detection. A machine learning approach was applied to train a logistic regression model on the collateral activity of hundreds of guides, using a combination of guide sequence, flanking target sequence, guide position, and guide GC content as input features (FIG. 11a). Applicants designed a panel of 410 crRNAs for LwaCas13a and 476 crRNAs for CcaCas13b across 5 different ssRNA targets: Ebola, Zika, the thermonuclease transcript from S. aureus, Dengue, and a synthetic ssRNA target (ssRNA 1). Using in vitro transcription to express these guides, the resulting collateral activity of LwaCas13a and CcaCas13b was evaluated by fluorescent reporter assays and significant variation between the crRNAs was found (FIG. 11b and FIG. 9a).


A schematic of the computational workflow of the SHERLOCK guide design tool is shown in FIG. 4A. Collateral activities of LwaCas13 with crRNAs tiling five synthetic targets are shown in FIG. 4B. FIG. 4C shows ROC and AUC results of the best performing logistic regression model trained using the data from FIG. 4B. Mono-nucleotide and di-nucleotide feature weights of the best performing logistic regression model are shown in FIGS. 4D and 4E, respectively. Validation data of predicted best and worst performing crRNAs on three targets are shown in FIG. 4F. FIG. 4G shows predicted scores of multiple novel guides on three targets compared to guide activity.


Given the wide variance of guide efficiencies for both LwaCas13a and CcaCas13b, Applicants designed a model that would select for the “best” performing guides for each enzyme. As a majority of LwaCas13a guides had activity above background (FIG. 11b, FIG. 9a), Applicants selected, on a per-target basis, for guides with 2-fold activity over the median activity as “best” performing guides. In contrast, as a majority of CcaCas13b guides were near background, (FIG. 11b, FIG. 9a), “best” performing guides were classified as the top quintile for each target tested. For each ortholog, a logistic regression model was trained to distinguish best performing guides from all other guides, based on the input features. The length of the flanking target region was considered as a free parameter and selected during cross-validation by maximizing the area under the curve (AUC) of the receiver operator characteristic (ROC) for each model. The data was split into train/test/validation sets and used to train the logistic model with three-fold cross validation with a hyperparameter search. This training process resulted in models with AUC of 0.84 and 0.89 for LwaCas13a and CcaCas13b, respectively (FIG. 11c). Examination of the full feature set for the model (FIG. 9b, 9c) revealed strong weights for both orthologs in the flanking regions that recapitulated the known PFS preferences of the enzymes (3′ H for LwaCas13a and 5′-D/3′-NAA for CcaCas3b) (FIG. 11d) (Abudayyeh, 2016; Smargon, 2017), providing biological validation to the model weights. To make design tool easily accessible and usable by the community, Applicants provide simple tool (sherlock.genome-engineering.org) that allows for LwaCas13a and CcaCas13b guide design through an easy-to-use interface online.


To further validate the models beyond the cross-validation, a panel of new crRNAs was designed on the thermonuclease transcript, as well as two additional transcripts from the long and short isoforms of the PML/RARA fusion associated with acute promyelocytic leukaemia (APML). Applicants found that both the LwaCas13a and CcaCas13b models succeeded at predicting guide RNA activity with significance (FIG. 6a, 6b). Additionally, the top and bottom predicted crRNAs display drastically different kinetics and sensitivity, showcasing the importance of the predictive tool (FIG. 6c, 6d). While the improvement in kinetics for top predicted crRNAs is relevant for increasing the speed of all SHERLOCK assays, the signal increase is especially relevant for portable versions of the test as color generation on the lateral flow strips is very sensitive to the overall collateral activity levels. Applicants evaluated the top and worst predicted crRNAs for the thermonuclease, short APML, and long APML targets on lateral flow strips and found that only the top predicted crRNAs generated a functional test suitable for portable detection (FIG. 6e, 6f). Moreover, Applicants also validated the LwaCas13a prediction model for in vivo transcript knockdown by targeting the Gaussia luciferase (Gluc) transcript in HEK293FT cells. Applicants found that guides predicted to have strong activity were significantly more effective at knockdown than either guides with poor predicted performance or just a random selection of guides (FIG. 12).


Applicants next attempted to combine Applicants' top predicted crRNAs with recombinase polymerase amplification (RPA)(Piepenburg, 2006) in a SHERLOCK reaction to attain single-molecule sensitivity. As previous versions of the SHERLOCK assay have been primarily two-step protocols with an initial RPA pre-amplification followed by T7 transcription and Cas13 detection, Applicants focused on enhancing the combination of these steps in order to generate a simplified SHERLOCK assay. After optimizing the relative RPA pellet amount to the overall Cas13 detection buffer, Applicants designed a one-pot SHERLOCK assay for the acyltransferase transcript derived from P. aeruginosa. Applicants found that the top predicted LwaCas13a allowed for fast and highly-sensitive (20 aM) detection of acyltransferase in a one-pot reaction format compared to the worst predicted crRNA (FIG. 7a-d). Additionally, the top predicted crRNA enabled an acyltransferase lateral flow assay with sensitivity down to 20 aM (FIG. 7e, 7f). Similarly, for CcaCas13b, Applicants used the guide prediction model to generate a one-pot SHERLOCK assay for detection of the thermonuclease transcript (FIG. 7g). As with LwaCas13a, Applicants found that CcaCas13b could achieve fast and sensitive detection down to 3 aM by fluorescence (FIG. 7h-7j) and 20 aM by portable lateral flow (FIG. 7k, 7l). The optimized one-pot format was readily extendable to additional targets, including Ea175 and Ea81 transcripts from Treponema denticola, and could be adapted for sensitive lateral flow tests (FIG. 10A-10F).


Although SHERLOCK with RPA provided rapid detection of targets in the attomolar range with one-pot assays, Applicants hypothesized that alternative amplification strategies could provide less bias and result in improved quantitation. Helicase displacement amplification (HDA)(Vincent, 2004), relies on helicases to separate the DNA duplex and allow for primer invasion and amplification. To enable rapid HDA, Applicants profiled a set of UvrD helicase orthologs with a helicase reporter assay (FIG. 1a)(Ozes, 2014) based on the separation of two DNA strands, each labeled with either a fluorophore or quencher. To augment Applicants' selection of helicases, Applicants also introduced a catalytic pair of super mutations (D403A/D404A) found to improve the activity of E. coli helicase II (UvrD)(Meiners, 2014) into these orthologs at analogous sites through sequence alignment (FIG. 1b). Profiling of orthologs with and without the super mutations revealed several candidates with strong helicase activity at 37° C., including Super TteUvrD, which allowed for 37° C. isothermal amplification and compatibility with Cas13-based collateral detection. Applicants combined Super TteUvrD with polymerases, single-stranded binding proteins, and LwaCas13a to create a one-pot super HDA SHERLOCK reaction. This reaction was capable of single molecule detection of the Ea175 target at 100 minutes, compared to 20 minute detection with one-pot RPA (FIG. 1c, ld). However, despite the reduced speed of one-pot super HDA SHERLOCK, the kinetics of the reaction were more representative of the input concentration, with strong correlation between input concentration and the Vmax, in contrast to RPA SHERLOCK (FIG. 1e). Therefore, this one-pot super HDA SHERLOCK assay can provide a more quantitative alternative to single-pot RPA SHERLOCK.


Finally, the one-pot RPA SHERLOCK assay was expanded to allow for multiplexing of multiple targets (FIG. 8a). Applicants first tested whether one-pot SHERLOCK could allow for multiplexed detection of two targets, Ea175 and thermonuclease, using LwaCas13a and CcaCas13b, respectively. By detecting the collateral activity of each enzyme in separate fluorescent channels, FAM and HEX, Applicants were able to achieve 2 aM detection of each target (FIG. 8b). Next, Applicants adapted the lateral flow format to allow for detection of two targets. As the previous lateral flow design relied on general capture of antibody that was not bound by intact reporter RNAs (Gootenberg, 2018), it would not be suitable for detecting two targets. Instead, Applicants adapted a lateral flow approach with two separate detection lines consisting of either deposited streptavidin or anti-DIG antibodies. These lines capture reporter RNA decorated with a fluorophore and either Biotin or DIG, allowing fluorescent visualization of signal loss at detection lines due to collateral activity and cleavage of corresponding reporter RNA. Applicants evaluated this lateral flow design using a two-step SHERLOCK format for detection of lectin DNA and a synthetic DNA target (ssDNA 1) (FIG. 3a), and found that Applicants could detect down to 2 aM of each target (FIG. 3b, 3c). Applicants then applied the one-pot multiplexed SHERLOCK assay for thermonuclease and Ea175 using to the new lateral flow format (FIG. 8c) and found that Applicants could detect down to 20 aM of each target successfully (FIG. 8d). As this lateral flow design can be extended indefinitely by depositing any molecule that is part of an orthogonal hybridization pair, Applicants developed lateral flow strips capable of detection three targets simultaneously by striping the anti-Alexa 488 antibody to capture Alexa 488 on a reporter DNA (FIG. 3d). By augmenting the lateral flow assay with Cas12a from Acidaminococcus sp. BV3L6 (AsCas12a), Applicants were able to independently assay a third target in an additional cleavage channel sensing DNA collateral activity (Gootenberg, 2018). This design was capable of independently assaying for three targets, Zika ssRNA, Dengue ssRNA, and ssDNA1 simultaneously (FIG. 3e, 3f).


In this study, Applicants show that SHERLOCK assays can be reliably designed with high sensitivity and fast kinetics using a machine learning approach, accessible at sherlock.genome-engineering.org. This guide design tool has broad applicability for both in vitro and in vivo RNA targeting applications and can be readily extended to include other useful Cas13 and Cas12 orthologs with collateral activity, including Cas13d (Yan, 2018; Konermann, 2018), Cas12a (Zetsche, 2015; Chen, 2018; Li, 2018), Cas12b (Shmakov, 2015; Li, 2018), and many other Cas12/Cas13 family members (Yan, 2019; Shmakov, 2017). Using Applicants' design tool, Applicants generate highly sensitive assays suitable for portable lateral flow detection of one or two targets using LwaCas13a and CcaCas13b, which can be performed in a single step, reducing pipetting steps and eliminating potential contamination concerns from opening of post-amplification samples. Additionally, by augmenting with DNA collateral detection with AsCas12a, Applicants can perform multiplexing of three targets in a portable lateral flow format. Applicants also apply helicase engineering to develop a new CRISPR-detection compatible amplification method, super HDA, and demonstrate the quantitative nature of super HDA SHERLOCK. The advances here increase the accessibility of the SHERLOCK platform, bringing it closer to deployment as a simple, portable nucleic acid diagnostic.


Example 2

Applicants use the machine learning model predicts the efficiency of Cas13 transcript knockdown in mammalian cells. We apply our guide prediction model to design optimal guides for sensitive detection of chromosomal fusion rearrangements characteristic of acute promyelocytic leukemia (APML) and acute lymphoblastic leukemia (ALL) in a multiplexed lateral flow readout.


To further validate the machine learning models beyond the cross-validation, we designed a panel of new crRNAs using the machine learning model targeting either the thermonuclease transcript or two additional transcripts from the long and short isoforms of the PML/RARA fusion associated with acute promyelocytic leukemia (APML). We found that both the LwaCas13a and CcaCas13b models succeeded at predicting guide RNA activity (LwaCas13a model validation has R values of 0.79, 0.54, and 0.41; CcaCas13b model validation has R values of 0.44, 0.69, and 0.89) (FIG. 13a, Supplementary FIG. 17a). Additionally, the best and worst predicted crRNAs display drastically different kinetics and sensitivity (FIG. 13b, FIG. 17b). Although the improvement in kinetics for best predicted crRNAs is relevant for increasing the speed of all SHERLOCK assays, the signal increase is especially relevant for portable versions of the test, as color generation on the lateral flow strips is sensitive to the overall collateral activity levels. While the guide model was trained for maximizing overall signal generation, the increase in kinetics was an added benefit that was not explicitly trained for in the machine learning model development. We evaluated the best and worst predicted crRNAs for the thermonuclease, short APML, and long APML targets on lateral flow strips and found that only the best predicted crRNAs generated a functional test suitable for portable detection (FIG. 13c, FIG. 17c). Moreover, we also validated the LwaCas13a prediction model for in vivo transcript knockdown by targeting the Gaussia luciferase (Gluc) transcript in HEK293FT cells and evaluating previously published LwaCas13a mammalian RNA knockdown data of reporter and endogenous transcripts (FIG. 13d)14. We found that guides predicted to have strong activity were significantly more effective at knockdown of Gluc and KRAS (FIG. 13e) and that Gluc guides with predicted good performance outperformed guides either with poor predicted performance or selected randomly (FIG. 12).


Previous versions of the SHERLOCK assay have been a two-step format with an initial recombinase polymerase amplification (RPA)19 followed by T7 transcription and Cas13 detection. To simplify the SHERLOCK assay, we focused on optimizing a one-pot amplification and detection protocol by combining both steps into a single reaction with the best predicted crRNAs. We designed a one-pot SHERLOCK assay for a synthetic acyltransferase transcript derived from Pseudomonas aeruginosa, a significant human pathogen that requires rapid diagnosis. We found that the best predicted crRNA for LwaCas13a allowed for fast and highly-sensitive (20 aM) detection of acyltransferase in a one-pot reaction format compared to the worst predicted crRNA (FIG. 9a-9d). Additionally, the best predicted crRNA enabled an acyltransferase lateral flow assay with sensitivity down to 20 aM (FIG. 9e, 9f). Similarly, for CcaCas13b, we used the guide prediction machine learning model to generate a one-pot SHERLOCK assay for detection of the thermonuclease transcript (FIG. 9g). As with LwaCas13a, we found that CcaCas13b could achieve fast and sensitive detection down to 3 aM by fluorescence (FIG. 9h-9j) and 20 aM by portable lateral flow (FIG. 9k, 9l). The optimized one-pot format was readily extendable to additional targets, including the Ea175 and Ea81 transcripts from Treponema denticola, a gram-negative bacteria that can cause severe periodontal disease, and could be adapted for sensitive lateral flow tests (FIG. 10A-10F).


To achieve even higher sensitivity with one-pot assays, we explored alternative amplification strategies, which could provide less bias and result in a more quantitative assay. Helicase displacement amplification (HDA)20 relies on helicases to separate the DNA duplex and allow for primer invasion and amplification, usually at high temperatures like 65° C. To enable rapid HDA, we profiled a set of UvrD helicase orthologs with engineered mutations21 with a helicase reporter assay (FIG. 1a, 1b)22 and found several candidates with strong helicase activity at 37° C., including Super UvrD from Thermoanaerobacter tengcongensis (TteUvrD), which allowed for 37° C. isothermal amplification and compatibility with Cas13-based collateral detection. We combined Super TteUvrD with polymerases, single-stranded binding proteins, and LwaCas13a to create a one-pot super HDA SHERLOCK reaction, which was capable of single molecule detection of the Ea175 target at 100 minutes and was highly quantitative (FIG. 1c-1e).


We further expanded the one-pot RPA SHERLOCK assay to allow for multiplexing of multiple targets (FIG. 14a). We first tested whether one-pot SHERLOCK could simultaneously detect two targets, Ea175 and thermonuclease, using LwaCas13a and CcaCas13b, respectively. By detecting the collateral activity of each enzyme in separate fluorescent channels, FAM and HEX, we were able to achieve 2 aM detection of each target (FIG. 14b). Next, we adapted the lateral flow format to allow for detection of two targets. As the previous lateral flow design relied on general capture of antibody that was not bound by intact reporter RNAs1, it is not suitable for detecting two targets. Instead, we adapted a lateral flow approach with two separate detection lines consisting of either deposited streptavidin or anti-DIG antibodies. These lines capture reporter RNA decorated with a fluorophore and either Biotin or DIG, allowing fluorescent visualization of signal loss at detection lines due to collateral activity and cleavage of corresponding reporter RNA. We evaluated this lateral flow design using a two-step SHERLOCK format for detection of lectin DNA and a synthetic DNA target (ssDNA 1) (FIG. 3a), and found that we could detect down to 2 aM of each target (FIG. 3b, 3c). We then applied the one-pot multiplexed SHERLOCK assay for thermonuclease and Ea175 to the new lateral flow format (FIG. 14c) and found that we could detect down to 20 aM of each target successfully (FIG. 14d, 14e). As this lateral flow design can be extended further by depositing any molecule that is part of an orthogonal hybridization pair, we developed lateral flow strips capable of detecting three targets simultaneously by striping the anti-Alexa 488 antibody to capture Alexa 488 on a reporter DNA (FIG. 3d). By augmenting the lateral flow assay with Cas12a from Acidaminococcus sp. BV3L6 (AsCas12a), we were able to independently assay a third target in an additional cleavage channel sensing DNA collateral activity1. This design was capable of independently assaying three targets, Zika ssRNA, Dengue ssRNA, and ssDNA1 simultaneously (FIG. 3e, 3f).


Lastly, we sought to apply SHERLOCK detection to a clinical setting, where using the best crRNA for a given target is essential for fast and sensitive performance. Acute promyelocytic leukaemia (APML) and acute lymphocytic leukemia (ALL) cancers are caused by chromosomal fusions in the transcribed mRNA, and distinguishing these rapidly is critical for effective treatment and prognosis23. To design robust clinical-grade SHERLOCK assays, we employed the Cas13 guide design tool to predict top guides for three fusion transcripts characteristic of APML and ALL cancers: PML-RARa Intron/exon 6 fusion, PML-RARa Intron 3 fusion, and BCR-ABL p210 b3a2 fusion23 (FIG. 15a). The developed SHERLOCK assay for these three targets (FIG. 18A-18D) was used to predict APML or ALL presence across a blinded set of 17 patient bone marrow samples, as well as 2 known samples (samples 12 and 15 in FIG. 15A-15F). Cas13 detection using the best predicted guide achieved clear fluorescence detection in 45 minutes or less for all samples verified by RT-PCR (FIG. 15b, 15c, 15d, FIG. 19A-19E). Detection with a lateral flow readout also yielded clear identification of the RNA fusion present in every sample (FIG. 15e, FIG. 20). Lastly, we showed that our multiplexed lateral flow test could be deployed to simultaneously test for multiple fusion transcripts (FIG. 16A-16C), enabling a simple, rapid, and portable test that can detect several cancer fusion transcripts simultaneously.


Together, these results demonstrate that SHERLOCK assays can be reliably designed with high sensitivity and fast kinetics using a machine learning approach, accessible at sherlock.genome-engineering.org. This guide design tool has broad applicability for both in vitro and in vivo RNA targeting applications and can be readily extended to include other useful Cas13 and Cas12 orthologs with collateral activity, including Cas13d13,24, Cas12a8,9,11, Cas12b5,12, and many other Cas12/Cas13 family members7,25. Using our design tool, we generated highly sensitive assays suitable for portable lateral flow detection of one or two targets using LwaCas13a and CcaCas13b, which can be performed in a single step, reducing pipetting steps and eliminating potential contamination of post-amplification samples. Additionally, by utilizing DNA collateral detection with AsCas12a, we can perform multiplexing of three targets in a lateral flow format. With these improvements, SHERLOCK can now achieve multiplexing of up to four targets simultaneously by fluorescence1 and three targets by lateral flow. We also apply helicase engineering to develop a new CRISPR-detection compatible amplification method, super HDA, and demonstrate the quantitative nature of super HDA SHERLOCK. Finally, we demonstrate the facile applicability of the guide design model to develop a clinically relevant test for APML and ALL cancers with high sensitivity and performance in a portable lateral flow format. The advances here increase the accessibility of the SHERLOCK platform, deploying it as a simple, portable nucleic acid diagnostic with broad clinical utility and provide a user-friendly web tool for Cas13 guide design for both in vivo RNA targeting and SHERLOCK assays.


Methods
Protein Expression and Purification of Cas13

Expression and purification of LwaCas13a and CcaCas13b was performed as previously described1,2. In brief, we transformed bacterial expression vectors into Rosetta™ 2(DE3)pLysS Singles Competent Cells (Millipore) and scaled up bacterial growth in 4 L of Terrific Broth 4 growth media (TB). Cell pellets were lysed by high-pressure cell disruption using the LM20 Microfluidizer system at 27,000 PSI and freed protein was bound via StrepTactin Sepharose (GE) resin. After washing, protein was released from the resin via SUMO protease digestion overnight and protein was subsequently purified by cation exchange chromatography and then gel filtration purification using an AKTA PURE FPLC (GE Healthcare Life Sciences). Eluted protein was then concentrated into Storage Buffer (600 mM NaCl, 50 mM Tris-HCl pH 7.5, 5% glycerol, 2 mM DTT) and frozen at −80° C. for storage.


Nucleic Acid Target and crRNA Preparation


Nucleic acid targets and crRNAs were prepared as previously described1,2. Briefly, targets were either used as ssDNA or PCR amplified with NEBNext PCR master mix, gel extracted, and purified using MinElute gel extraction kits (Qiagen). For RNA detection reactions, RNA was prepared by using either ssDNA targets with double-stranded T7-promoter regions or fully double-stranded PCR products in T7 RNA synthesis reactions at 30° C. using the HiScribe T7 Quick High Yield RNA Synthesis Kit (New England Biolabs). RNA was then purified using MEGAclear Transcription Clean-up kit (Thermo Fisher).


crRNAs were synthesized by using ultramer ssDNA substrates (IDT) that were double stranded in the T7 promoter region through an annealed primer. Synthesized crRNAs were prepared using these templates in T7 expression assays at 37 C using the HiScribe T7 Quick High Yield RNA Synthesis kit (NEB). RNAs were then purified using RNAXP clean beads (Beckman Coulter) at 2× ratio of beads to reaction volume, with an additional 1.8× supplementation of isopropanol (Sigma).


_All crRNA and target sequences are listed in Tables 1 and 2, respectively.


Fluorescent Cleavage Assay

Cas13 detection assays were performed as previously described1,2 In brief, 45 nM Cas13 protein (either CcaCas13b or LwaCas13a), 20 nM crRNA, 1 nM target RNA, 125 nM RNAse Alert v2 (Invitrogen), and 1 unit/μL murine RNase inhibitor (NEB) were combined together in 20 μL of cleavage buffer (20 mM HEPES, 60 mM NaCl, 6 mM MgCl2, pH 6.8). Reactions were incubated at 37° C. on a Biotek plate reader for 3 hours with fluorescent kinetic measurements taken every 5 minutes.


SHERLOCK Nucleic Acid Detection with RPA


For RPA reactions, primers were designed using NCBI Primer-BLAST26 under default parameters except for (100-140 nt), primer melting temperatures (54° C.-67° C.), and primer size (30-35 nt). All primers were ordered as DNA (Integrated DNA Technologies).


One-pot SHERLOCK-RPA reactions were carried out as previously described1,2 with slight modifications. Reactions were prepared with the following reagents (added in order): 0.5×RPA rehydration and 0.5× resuspended RPA lyophilized pellet, 2 mM rNTPs, 1.1 units/μL RNAse inhibitor, 1 unit/μL T7 RNA polymerase (Lucigen), 0.96 μM total RPA primers (0.48 μM each of forward primer with T7 handle and reverse primer), 57.8 nM Cas13 protein (CcaCas13b or LwaCas13a), 23.3 nM crRNA, 136.5 nM fluorescent substrate reporter, 5 mM MgCl2, 14 mM MgAc, and varying amounts of DNA target input.


For detection with fluorescent readout, either a quenched polyU FAM reporter (TriLink) or RNAse Alert v2 (Invitrogen), were used as reporters. 20 μL reactions were incubated for 2-6 hours at 37° C. on a Biotek plate reader with kinetic measurements taken either every 2.5 or 5 minutes. All reporter sequences are listed in Table 5.


One-pot SHERLOCK-RPA reactions were modified for multiplexing by maintaining total primer concentration at 0.96 μM over all four input primers (0.24 μM each of both forward primers with T7 handle and reverse primers), maintaining crRNA concentrations at 23.3 nM (with 11.7 nM each crRNA), maintaining Cas13 total protein concentration at 57.8 nM, (28.9 nM CcaCas13b and 28.9 nM LwaCas13a), and doubling total reporter concentration (136.5 nM LwaCas13a AU-FAM reporter; 136.5 nM CcaCas13b UA-HEX reporter; see Table 5 for all reporters). 20 μL reactions were incubated for 2-6 hours at 37° C. on a Biotek plate reader with kinetic measurements in wavelengths for HEX and FAM taken every 2.5 or 5 minutes.


Protein Expression and Purification of UvrD Helicases

UvrD Helicases sequences were ordered as E. coli codon optimized gBlocks Gene Fragments (IDT) and cloned into TwinStrep-SUMO-expression plasmid via Gibson assembly. Alanine ‘Super-helicase’ mutants were generated using PIPE-site-directed mutagenesis cloning from the TwinStrep-SUMO-UvrD Helicase expression plasmids. In brief, primers with short overlapping sequences at their ends were designed to harbor the desired changes. After incomplete-extension PCR amplification (KAPA HiFi HotStart 2×PCR), reactions were treated with Dpn1 restriction endonuclease for 30 minutes at 37° C. to degrade parental plasmid. Two microliters of the reaction were directly transformed into Stble3 chemically competent E. coli cells. For expression, sequence verified plasmids were transformed into BL21(DE3)pLysE E. coli cells. For each UvrD Helicase variant, 2 L of Terrific Broth media (12 g/L tryptone, 24 g/L yeast extract, 9.4 g/L K2HPO, 2.2 g/L KH2PO4), supplemented with 100 μg/mL ampicillin, was inoculated with 20 mL of overnight starter culture and grown until OD600 0.4-0.6. Protein expression was induced with the addition of 0.5 mM IPTG and carried out for 16 hours at 21° C. with 250 RPM shaking speed. Cells were collected by centrifugation at 5,000 RPM for 10 minutes, and paste was directly used for protein purification (10-20 g total cell paste). For lysis, 10 g of bacterial paste was resuspended via stirring at 4° C. in 50 mL of lysis buffer (50 mM Tris-HCl pH 8, 500 mM NaCl, 1 mM BME (Beta-Mercapotethanol, Sigma) supplemented with 50 mg Lysozyme, 10 tablets of protease inhibitors (cOmplete, EDTA-free, Roche Diagnostics Corporation), and 500 U of Benzonase (Sigma). The suspension was passed through a LM20 microfluidizer at 25,000 psi, and lysate was cleared by centrifugation at 10,000 RPM, 4° C. for 1 hour. Lysate was incubated with 2 mL of StrepTactin superflow resin (Qiagen) for 2 hours at 4° C. on a rotary shaker. Resin bound with protein was washed three times with 10 mL of lysis buffer, followed by addition of 50 μL SUMO protease (in house) in 20 mL of IGEPAL lysis buffer (0.2% IGEPAL). Cleavage of the SUMO tag and release of native protein was carried out overnight at 4° C. in Econo-column chromatography column under gentle mixing on a table shaker. Cleaved protein was collected as flow-through, washed three times with 5 mL of lysis buffer, and checked on a SDS-PAGE gel.


Protein was diluted ion exchange buffer A containing no salt (50 mM Tris-HCl pH 8, 6 mM BME (Beta-Mercapotethanol, Sigma), 5% Glycerol, 0.1 mM EDTA) to get the starting NaCl concentration of 50 mM. Protein was then loaded onto a 5 mL Heparin HP column (GE Healthcare Life Sciences) and eluted over a NaCl gradient from 50 mM to 1 M. Fractions of eluted protein were analyzed by SDS-PAGE gel and Coomassie staining, pooled and concentrated to 1 mL using 10 MWCO centrifugal filters (Amicon). Concentrated protein was loaded in 0.5-3 mL 10 MWCO Slide-A-Lyzer Dialysis cassettes and dialyzed overnight at 4° C. against protein storage buffer (20 mM Tris-HCl, pH 7.5, 200 mM NaCl, 1 mM EDTA, 1 mM TCEP, 50% glycerol). Protein was quantified using Pierce reagent (Thermo) and stored at −20° C.


Lateral Flow Readout of Cas13 and SHERLOCK

For single-plex detection with lateral flow readout, a FAM-RNA-biotin reporter was substituted in Cas13 or SHERLOCK reactions for the fluorescent reporter at a final concentration of 1 μM (unless otherwise indicated). 20 μL reactions were incubated between 30 and 180 minutes, after which the entire reaction was resuspended in 100 μL of HybriDetect 1 assay buffer (Milenia). Visual readout was achieved with HybriDetect 1 lateral flow strips (Milenia), and strips were imaged in a light box with a α7 III with 35-mm full-frame image sensor camera (Sony) equipped with a FE2.8/90 Macro G OSS lens.


Two-pot SHERLOCK-RPA multiplexed lateral flow reactions were adapted from previously described multiplexed fluorescent reactions1,2. In brief, RPA reactions were performed with the TwistAmp® Basic (TwistDx) protocol with the exception that 280 mM MgAc was added prior to input DNA. Reactions were run with 1 μL of input for 1 hr at 37° C. Cas13 detection assays were performed with 45 nM purified Cas13, 22.5 nM crRNA, lateral flow RNA reporter (4 μM LwaCas13a multiplexed reporter; 2 μM CcaCas13b multiplexed reporter; see Table 5 for all reporters), 0.5 μL murine RNase inhibitor (New England Biolabs), and 1 μL of post-RPA input nucleic acid target in nuclease assay buffer (20 mM HEPES, 60 mM NaCl, 6 mM MgCl2, pH 6.8). 20 μL reactions were suspended in 100 μL of HybriDetect 1 assay buffer (Milenia) and run on custom multiplexed strips (DCN Diagnostics). The custom lateral flow strips were designed to have capture lines containing Anti-digoxigenin antibodies (ab64509, abcam), Streptavidin, Anti-FITC antibodies (ab19224, abcam), and Anti-Alexa 488 antibodies (A619224, Life Technologies). The strips consisted of a 25 mm CN95 Sartorius nitrocellulose membrane, an 18 mm 6614 Ahlstrom synthetic conjugate pad for sample application, and a 22 mm Ahlstrom grade 319 paper wick pad. Strips were imaged using an Azure c400 imaging system in the Cy5 channel.


One-pot multiplexed SHERLOCK-RPA was adapted for lateral flow by lowering the CcaCas13b multiplexed reporter concentration to a concentration of 78 nM and the LwaCas13a reporter concentration to 1 μM (see Table 5 for all reporters). This was to accommodate for different fluorescent intensities observed for the reporter when binding to the DCN strips. Lateral flow reactions were resuspended in buffer, run on DCN strips, and imaged as described above.


Fluorescent Helicase Activity Assay

Helicase substrate was generated by annealing 300 pmol of fluorescent 5′-FAM-top strand with 900 pmol of quencher 3′-BHQ1 bottom strand in 1× duplex buffer (30 mM HEPES, pH 7.5; 100 mM potassium acetate) for 5 minutes at 95° C., followed by slow cool down to 4° C. (1° C./5 seconds) in PCR thermocycler. After annealing, reactions were diluted 1:10 in Nuclease free water (Gibco). Helicase unwinding assays were carried out in 20 μL reactions containing 1× Thermopol buffer (NEB), 250 nM of annealed quenched helicase substrate, 3 mM ATP or 3 mM dATP (The-UvrD dATP), 200 nM UvrD Helicase and 500 nM of capture strand oligonucleotide. To determine temperature activity profiles, reactions and no helicase control were incubated at temperatures ranging from 37° C. to 62° C. with 5° C. intervals for 60 minutes in a PCR thermocycler. Reactions were immediately transferred to a 384-well plate (Corning®) and analysed on a fluorescent plate reader (BioTek) equipped with a FAM/HEX filter set.


SHERLOCK Nucleic Acid Detection with HDA


For detection with SHERLOCK-HDA, procedures for amplification were inspired by previously described isothermal helicase dependent amplification20,27 with significant modifications. Reactions were prepared with the following reagents: 1× Sau polymerase buffer (Intact Genomics), 2.5% PEG 30%, 1 mM rNTPs, 0.4 mM dNTPs, and 3 mM ATP, 1 units/μL RNAse inhibitor, 1.5 unit/μL T7 RNA polymerase (Lucigen), 0.4 μM total HDA primers (0.2 μM each of forward primer with T7 handle and reverse primer), 43.3 nM Cas13 protein (CcaCas13b or LwaCas13a), 19.8 nM crRNA, 125 nM fluorescent substrate reporter (quenched polyU FAM reporter, TriLink), 0.2 units/μL Sau polymerase, 25 ng/μL T4 gp32 protein (NEB), 6.25 ng UvrD helicase, and varying amounts of DNA target input. 20 μL reactions were incubated for 2-6 hours at 37° C. on a Biotek plate reader with kinetic measurements taken either every 2.5 or 5 minutes.


Digital Droplet PCR Quantification of Input DNA

DNA and RNA dilution series used as input target for one-pot SHERLOCK-RPA amplification reactions were quantified separately using Droplet Digital PCR (BioRad), as described before1,2. Briefly, ddPCR probes were ordered from IDT PrimeTime qPCR probes with a quenched FAM/ZEN reporter. Dilution series were mixed with either (for DNA) BioRad's Supermix for Probes (no dUTP) or with (for RNA) BioRad's One-Step RT-ddPCR Advanced Kit for Probes and the corresponding qPCR probe for the target sequence. The QX200 droplet generator (BioRad) was used to generate droplets; after transferring to a droplet digital PCR plate (BioRad), thermal cycling was carried out with conditions as described in the BioRad protocol (with the exception of the Ea175 target, for which the annealing temperature was lowered according to the lower melting temperature of the primer set). Concentrations were measured using a QX200 droplet reader (Rare Event Detection, RED).


Analysis of SHERLOCK Fluorescence Data

Fluorescent measurements were analyzed as described previously1,2. Background subtracted fluorescence was calculated by subtracting the initial measured fluorescence. All reactions were run with at least three technical replicates and a control condition containing no target input.


Analysis of Lateral Flow Results

Acquired images were converted to 8-bit grayscale using photoshop and then imported into ImageLab software (BioRad Image Lab Software 6.0.1). Images were inverted and lanes were manually adjusted to fit the lateral flow strips. Bands were picked automatically and the background was adjusted manually to allow band comparison. Width of bands and background adjustment was kept constant between all bands in the same image.


Predictive Model of Cas13 crRNA Activity


Guide activity values from the Cas13 detection tiling experiments were pre-processed by background subtracting the zero time-point fluorescence from the terminal fluorescence value. On a per-target basis, these values were further normalized to the max or median value or used as raw fluorescence values. Training was performed using a series of thresholds to classify guides into two classes (good or bad) and the best threshold was selected based on model performance. Separately, performance was also compared to separating guides into two classes based on being in the top quintile per target (good guides). For each protein (LwaCas13a or CcaCas13b), the best guide classification method was selected based on model performance.


To generate features for each guide, one-hot encoding was used to represent mono-nucleotide and di-nucleotide base identities across the guide and flanking sequence in the target. The flanking sequence length was an additional variable that was determined by measuring model performance across different flanking sequence lengths. Additional features used were normalized positions of the guide in the target and the GC content of the guide.


Logistic regressions were tested across the variable guide classification methods, flanking sequence lengths, logistic regulation tuning parameters, and regularization methods (L1 and L2). Training was performed by separating the training set into three smaller sets for training, testing, and validation. After performing three-fold cross validation on the train and test sets, a final validation of the best model was used to generate AUC curves and assay final model performance. The best performing models were then selected for the LwaCas13a and CcaCas13b datasets.


In Vivo Knockdown Experiments

To evaluate the in vivo predictive performance of the LwaCas13a guide design model, we tested guide knockdown in mammalian cell culture. Knockdown experiments were performed in HEK293FT cells (American Type Culture Collection (ATCC)), which were grown in Dulbecco's Modified Eagle Medium with high glucose, sodium pyruvate, and GlutaMAX (Thermo Fisher Scientific), additionally supplemented with 1× penicillin-streptomycin (Thermo Fisher Scientific) and 10% fetal bovine serum (VWR Seradigm). Twenty-four hours prior to transfection, cells were plated at 20,000 cells per well in 96-well poly-D-lysine plates (BD Biocoat). When cells reached ˜90% confluency, 150 ng of LwaCas13 plasmid, 300 ng of guide expression plasmid, and 40 ng of luciferase reporter plasmid were transfected using Lipofectamine 2000 (Thermo Fisher Scientific). Plasmids were combined in Opti-MEM I Reduced Serum Medium (Thermo Fisher) to a total of 25 μL and added to 25 μL of a 2% Lipofectamine 2000 mixture in Opti-MEM. After incubation for 10 minutes, the plasmid Lipofectamine solutions were added to cells. At 48 hours post transfection, supernatant was harvested to measure secreted Gaussia luciferase and Cypridina luciferase levels using assay kits (Targeting Systems) on a plate reader (Biotek Synergy Neo 2) with an injection protocol. All replicates performed are biological replicates.


Sample Collection and Acquisition from Patients with PML-RARa and BCR-ABL Fusions


Cryopreserved bone marrow samples were obtained from the Pasquerello Tissue Bank at the Dana-Farber Cancer Institute following database query for samples harboring the PML-RARa and BCR-ABL fusion transcripts. Fresh peripheral blood and bone marrow aspirate was also obtained from 3 newly diagnosed patients (samples 1, 12, 15). All patients from whom samples were obtained had consented to the institutional tissue banking IRB protocol.


Extraction of RNA from Patient Samples with PML-RARa and BCR-ABL Fusions


Cryopreserved samples were washed with PBS and pelleted. Fresh samples (samples 1, 12, 15) collected in EDTA tubes were first treated with RBC Lysis Buffer (BD Pharmlyse) followed by PBS washes and then pelleted. RNA was then extracted using the Qiagen RNeasy Kit.


RT-PCR Validation of PML-RARa and BCR-ABL Transcripts

cDNA was generated from 0.2-lug of RNA per sample using the Qiagen Quantitect Reverse Transcription kit. Nested PCR was performed using the previously validated, target specific primers and protocol described in van Dongen et al.28. PCR products were visualized on a 2.5% agarose gel, shown in FIG. 18A-18D. Expected Band Sizes with nested primer sets: PML-RARa Intron 6 (214 bp); PML-RARa Intron 3 (289 bp); BCR-ABL p210 e14a2 (360 bp); BCR-ABL p210 e13a2 (285 bp); BCR-ABL p190 (e1a2: 381 bp). Note that samples with exon 6 breakpoint will have variable size bands depending on the position of breakpoint: for example, multiple bands are present in samples 4-6 (FIG. 19A-19E). GAPDH was run as a control (FIG. 19A-19E) with an expected band size of 138 bp.


Design of crRNA Targeting APML and BCR-ABL Fusion Transcripts with SHERLOCK Guide Model


Best and worst guides were predicted using the guide design web tool (sherlock.genome-engineering.org) for LwaCas13a and CcaCas13b guide design published in this study. For validation of the guide design tool, crRNAs tiling along the fusion transcript were also synthesized and tested for collateral activity (data reported in FIGS. 13A-13E, 17A-17C, and 20). The best predicted guides were used in detection of PML-RARa and BCR-ABL fusion transcripts in SHERLOCK detection assays described below.


Detection of APML and BCR-ABL Clinical RNA Samples with SHERLOCK


Two-step SHERLOCK assays were performed as previously described with slight modifications to the RPA protocol1,2. In brief, basic RPA reactions were performed with the TwistAmp® Basic (TwistDx) protocol modified to perform RT-RPA with the following changes: 10 units/uL of AMV-RT was added after resuspension of pellet and addition of primers, following which 280 mM MgAc was added, all prior to input DNA. RT-RPA reactions at a total volume of 11 uL were run with 1 μL of input RNA for 45 minutes at 42° C. RT-RPA reactions for each fusion transcript were performed with all primer sets for all three transcripts detected in this study (PML-RARa Intron/Exon 6; PML-RARa Intron 3; BCR-ABL p210 b3a2).


Cas13 detection reactions were performed as described above with LwaCas13a and the best guide determined with the machine learning model, with the exception that reactions with a final volume of 20 uL contained 0.5 uL of input from RPA reactions. Reactions were supplemented with either RNAse Alert v2 (Invitrogen) for fluorescent readout, or a FAM-RNA-biotin reporter for lateral flow readout; reactions were incubated and quantified as described above respectively.


The initial set of samples (samples 1-11, 13-14, 16-19) were blinded for both steps of SHERLOCK detection; samples 12 and 15 were run as separate experiments as new patient samples became available. Data for both fluorescence and lateral flow were normalized to make the combined figures shown in FIG. 15A-15F by subtracting the readout of a control reaction (RPA reaction with water input) for each experiment to include both blinded and non-blinded samples.


Two-pot SHERLOCK-RPA multiplexed lateral flow reactions were carried out as described above, with the exception reporter concentrations were lowered to a final concentration of 1 uM LwaCas13a reporter and 250 nM CcaCas13b reporter (see Table 5 for all reporters). 20 μL reactions were suspended in 100 μL of HybriDetect 1 assay buffer (Milenia) and run on custom multiplexed strips (DCN Diagnostics), and were visualized and quantified as described above.


Various modifications and variations of the described methods, pharmaceutical compositions, and kits of the invention will be apparent to those skilled in the art without departing from the scope and spirit of the invention. Although the invention has been described in connection with specific embodiments, it will be understood that it is capable of further modifications and that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are obvious to those skilled in the art are intended to be within the scope of the invention. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure come within known customary practice within the art to which the invention pertains and may be applied to the essential features herein before set forth.

Claims
  • 1. A lateral flow device comprising a substrate comprising a first end and a second end, a. the first end comprising a sample loading portion, a first region comprising a detectable ligand, two or more CRISPR effector systems, two or more detection constructs, and one or more first capture regions, each comprising a first binding agent; andb. the substrate comprising two or more second capture regions between the first region of the first end and the second end, each second capture region comprising a different binding agent;wherein each of the two or more CRISPR effector systems comprises a CRISPR effector protein or polynucleotide encoding a CRISPR effector protein and one or more guide sequences, each guide sequence configured to bind one or more target molecules.
  • 2. The lateral flow device of claim 1, wherein the first end comprises two detection constructs, wherein each of the two detection constructs comprises an RNA or DNA oligonucleotide, comprising a first molecule on a first end and a second molecule on a second end.
  • 3. The lateral flow device of claim 2, wherein the first molecule on the first end of the first detection construct is FAM and the second molecule on the second end of the first detection construct is biotin or vice versa; and the first molecule on the first end of the second detection construct is FAM and the second molecule on the second end of the second detection construct is Digoxigenin (DIG) or vice versa.
  • 4. The lateral flow device of any of claims 1 to 3, wherein the CRISPR effector protein is an RNA-targeting effector protein, DNA-targeting effector, or both.
  • 5. The lateral flow device of claim 4, wherein the RNA-targeting effector protein is a Class 2 Type VI Cas protein and the DNA-targeting effector protein is Class 2, Type V Cas protein.
  • 6. The lateral flow device of claim 4, wherein the RNA-targeting effector protein is Cas13a, Cas13b, Cas13c, or Cas13d.
  • 7. The lateral flow device of claim 1, wherein the first end comprises three detection constructs, wherein each of the three detection constructs comprises an RNA or DNA oligonucleotide, comprising a first molecule on a first end and a second molecule on a second end.
  • 8. The lateral flow device of claim 7, wherein the first and second molecules on the detection constructs comprise Tye 665 and Alexa 488; Tye 665 and FAM; and Tye 665 and Digoxigenin (DIG).
  • 9. The lateral flow device of claim 1, wherein a polynucleotide encoding a CRISPR effector protein and the one or more guide RNAs are provided as a multiplexing polynucleotide, the multiplexing polynucleotide configured to comprise two or more guide sequences.
  • 10. A method for detecting a target nucleic acid in a sample, comprising contacting a sample with the first end of the lateral flow device of claim 1 comprising the sample loading portion, wherein the sample flows from the sample loading portion of the substrate towards the first and second capture regions and generates a detectable signal.
  • 11. The method of claim 10, wherein the lateral flow device is capable of detecting two different target nucleic acid sequences.
  • 12. The method of claim 10 or 11, wherein when the target nucleic acid sequences are absent from the sample, a fluorescent signal is generated at each capture region.
  • 13. The method of claim 11, wherein the detectable signal appears at the first and second capture regions.
  • 14. The method of claim 10, wherein the lateral flow device is capable of detecting three different target nucleic acid sequences.
  • 15. The method of claim 14, wherein when the target nucleic acid sequences are absent from the sample, a fluorescent signal is generated at each capture region.
  • 16. The method of claim 15, wherein the fluorescent signal appears at the first, second, and third capture regions.
  • 17. The method of claim 13, wherein when the sample contains one or more target nucleic acid sequences, a fluorescent signal is absent at the capture region for the corresponding target nucleic acid sequence.
  • 18. A nucleic acid detection system comprising two or more CRISPR systems, each CRISPR system comprising an effector protein and a guide RNA designed to bind to a corresponding target molecule; a set of detection constructs, each detection construct comprising a cutting motif sequence that is preferentially cut by one of the activated CRISPR effector proteins; and reagents for helicase dependent nucleic acid amplification (HDA).
  • 19. A method for quantifying target nucleic acids in samples comprising distributing a sample or set of samples into one or more individual discrete volumes comprising two or more CRISPR systems according to claim 18, amplifying one or more target molecules in the sample or set of samples by HDA;incubating the sample or set of samples under conditions sufficient to allow binding
  • 20. The method of claim 19, wherein the detectable positive signal is a loss of fluorescent signal.
  • 21. The method of claim 19, wherein the detectable positive signal is detected on a lateral flow device.
  • 22. The method of claim 19, wherein the HDA reagents comprise a helicase super mutant, selected from WP_003870487.1 Thermoanaerobacter ethanolicus comprising mutations D403A/D404, WP_049660019.1 Bacillus sp. FJAT-27231 comprising mutations D407A/D408A, WP_034654680.1 Bacillus megaterium comprising mutations D415A/D416A, WP_095390358.1, Bacillus simplex comprising mutations D407A/D408A, and WP_055343022.1 Paeniclostridium sordellii comprising mutations D402A/D403A.
  • 23. A method for designing guide RNAs for use in the detection systems of the preceding claims, the method comprising: a. designing putative guide RNAs tiled across a target molecule of interest;b. incubating putative guide RNAs with a Cas effector protein and the target molecule and measuring cleavage activity of the each putative guide RNAc. creating a training model based on the cleavage activity results of incubating the putative guide RNAs with the Cas effector protein and the target molecule;d. predicting highly active guide RNAs for the target molecule, wherein the predicting comprises optimizing the nucleotide at each base position in the guide RNA based on the training model; ande. validating the predicted highly active guide RNAs by incubating the guide RNAs with the Cas effector protein and the target molecule.
  • 24. The method of claim 23, wherein the Cas effector protein is a Cas12 or Cas13 protein.
  • 25. The method of claim 24, wherein the Cas protein is a Cas13a or Cas13b protein.
  • 26. The method of claim 25, wherein the Cas protein is LwaCas13a or CcaCas13b.
  • 27. The method of claim 23, wherein the training model comprises one or more input features selected from guide sequence, flanking target sequence, normalized positions of the guide in the target and guide GC content.
  • 28. The method of claim 26, wherein the guide sequence and/or flanking sequence input comprises one hit encoding mono-nucleotide and/or dinucleotide based identities across a guide length and flanking sequence in the target.
  • 29. The method of claim 27, wherein the training model comprises applying logistic regression model on the activity of the guides across the one or more input features.
  • 30. The method of claim 23, wherein the predicting highly active guides for the target molecule comprises selecting guides with an increase in activity of a guide relative to the median activity, or selecting guides with highest guide activity.
  • 31. The method of claim 30, wherein the increase in activity is measured by an increase in fluorescence.
  • 32. The method of claim 29, wherein the guides are selected with a 1.5, 2, 2.5 or 3-fold activity relative to median, or are in the top quartile or quintile for each target tested.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No. 62/818,702 filed Mar. 14, 2019 and U.S. Provisional Application 62/890,555 filed Aug. 22, 2019. The entire contents of the above-identified applications are fully incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH

This invention was made with government support under grant numbers MH110049 HL141201, HG009761 and CA210382 awarded by the National Institutes of Health. The government has certain rights in the invention.

PCT Information
Filing Document Filing Date Country Kind
PCT/US2020/022795 3/13/2020 WO 00
Provisional Applications (2)
Number Date Country
62818702 Mar 2019 US
62890555 Aug 2019 US