Patent Application
20250095782
The instant application contains a Sequence Listing which has been submitted electronically in XML file format and is hereby incorporated by reference in its entirety. Said XML copy, created on Jul. 18, 2024, is named 63906-701_301_SL.xml and is 378,874 bytes in size.
All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and/or take precedence over any such contradictory material.
The presently disclosed subject matter relates to a high-throughput, high-resolution and low-cost method of next generation amplicon fragment sequencing of biological samples.
Liquid biopsy based on circulating cell-free DNA (cfDNA) provides a new prospect for the diagnosis, monitoring and risk assessment of a range of diseases. cfDNA molecules circulating in peripheral blood originate from dying human cells as well as from viruses, parasites, and colonizing or invasive microbes that release their nucleic acids into the blood as they die and break down (Jahr et al, 2001). Human-derived cfDNA has evolved into an indispensable biomarker in clinical practice for rapid and noninvasive diagnosis in prenatal screening, organ transplantation, and oncology (Decker and Sholl, 2020; Liang et al, 2019; Sun and Yiang, 2019; Wu et al, 2020).
Although early studies did not focus on cfDNA of microbial origin (hereinafter referred to as mcfDNA), the development of circulating mcfDNA-based tests for infectious diseases has recently been gaining traction in clinical practice. An increasing number of studies have demonstrated that mcfDNA detection offers the potential to reliably identify a wide variety of infections, such as invasive fungal infection, tuberculosis, sepsis, cystic fibrosis (Rassoulian Barrett et al, 2020) and chorioamnionitis (Witt et al, 2020; for review see Han et al, 2020).
In addition to their role in infectious diseases, several studies have shown the presence of distinct cultivable bacteria in a range of cancers, including lung (Jin et al, 2019), prostate (Gorelick et al, 1988; Cohen et al, 2005), pancreas (Geller et al, 2017; Riquelme et al, 2019), and colon cancers (Bullman et al, 2019; Castellarin et al, 2012). It was only recently suggested that cancer types outside of the aerodigestive tract, such as breast (Urbaniak et al, 2016) or brain cancer (Venkataramani et al. 2019; Zeng et al, 2019), may also harbor microbiota with distinctive compositions (for review, see Sepich-Poore et al, 2021), including fungi (Narunsky-Haziza et al, 2022). Both Nejman et al. (2020) and Poore et al. (2020) suggested the existence of distinct intratumoral microbiomes among >30 cancer types; these microbiomes also vary in composition at different developmental stages of the tumor, thus providing biomarkers for disease progression and prognosis for patient outcomes. As for other bacteria that are colonizing or infecting the body, the tumor associated bacteria will release distinct mcfDNA in the blood stream, and this let Poore et al (2020) propose the analysis of mcfDNA from the peripheral blood as a tool to gain valuable information regarding the progression of various types of cancers.
Conventional amplicon-based sequencing approaches are routinely used to determine microbial community composition in a wide range of biological samples. The most used approach is amplicon sequencing of the 16S rRNA gene based on its variable regions, such as the V1-V2 and V3-V4 regions (Gupta et al, 2019). Shahir et al (2020) applied 16S rRNA gene sequencing to identify region-specific composition and aerotolerance profiles of mucosally adherent bacteria in biopsy samples taken from the colon and ileum of Crohn's disease and non-IBD patients. As an alternative to 16S rRNA gene sequencing, single copy proteins encoding housekeeping genes including the genes for the DNA gyrase subunit B (gyrB) (Poirier et al, 2018), RNA polymerase subunit B (rpoB) (Vos at al, 2012; Ogier et al, 2019), the heat shock protein 60 (hsp60), the superoxide dismutase A (sodA), the TU elongation factor (tuf) (Ghebremedhin et al, 2008) and the 60 kDa chaperonin protein (cpn60) (Links et al, 2012) have been proposed as phylogenetic marker genes.
Liquid biopsy samples, especially peripheral blood, represent unique challenges for the analysis of microbial signatures. The majority of mcfDNA fragments in blood was found to be approximately 40-100 bp in size (Bumham et al, 2016), as was confirmed by Rassoulian Barrett et al (2020). Due to the small size of mcfDNA fragments conventional amplicon-based sequencing approaches that target DNA fragments of several hundred nucleotides (>400) are not suitable for determining the composition of colonizing or invasive microorganisms using mcfDNA from liquid biopsy samples. For example, the V1-V2 and the V3-V4 regions of the 16S rRNA gene have an average length of 437 and 443 nucleotides, respectively. Furthermore, the concentrations of plasma cfDNA in healthy individuals varies greatly, generally within the range of 0-100 ng per milliliter of plasma, sometimes exceeding 1500 ng per milliliter. Human cfDNA accounts for the vast majority (>90% or even >99%), while mcfDNA accounts for only a small fraction with 0.08%-4.85% from bacteria, 0.00%-0.010% from fungi, and 0.00%-0.16% from viruses/phages. However, it should be noted that elevated levels of mcfDNA can sometimes be observed in certain pathological conditions, including infection, sepsis, trauma, and autoimmune diseases (Han et al, 2020). Because the analysis of mcfDNA requires deep next generation sequencing (NGS) of plasma cfDNA to overcome the limitations of small mcfDNA fragment size and low concentration, this approach is unsuitable for the testing of large patient cohorts or routine health screening.
For example, although a lot of progress has been made in reducing the cost and increasing the throughput of NGS sequencing, it remains very expensive to analyze the mcfDNA on a routine basis for community health screening and disease prognosis/diagnostics, as is routinely performed for many other health related parameters (blood cell panels, metabolic panels, etc.) or non-invasive early detection of diseases in at risk populations, such as the screening for colorectal cancer. Thus, there remains an unmet need for improved methods to accurately determine in a high throughput and cost-efficient way to detect the presence of colonizing and invasive microbes that contribute to mcfDNA present in peripheral blood as part of clinical diagnostics and community health screening. The presently disclosed subject matter provides such improved method for high resolution, high-throughput and low-cost detection of microorganisms.
In one embodiment, a method is provided for amplifying microbial cell free DNA (mcfDNA). The method includes performing, on a sample comprising microbial cell-free DNA (mcfDNA), an amplification reaction using (i) one or more degenerate primers comprising complementarity to one or more conserved regions, wherein the one or more conserved regions span at least 18 nucleotides of one or more phylogenetic marker genes designated for a set of reference microbes and (ii) a second primer comprising complementarity to a repaired version of an adaptor ligated to ends of the mcfDNA, wherein at least 25 adjacent nucleotides upstream or downstream of an end of the one or more conserved regions comprise a hypervariable region, and the one or more degenerate primers are oriented to prime polymerase extension of the hypervariable region to generate amplified mcfDNA fragments.
In another embodiment, a method is provided for amplifying microbial cell free DNA (mcfDNA), that includes performing an amplification reaction on a sample comprising microbial cell-free DNA (mcfDNA) to generate amplified mcfDNA fragments using: (i) one or more degenerate primers comprising complementarity to one or more conserved regions, wherein the one or more conserved regions span at least 18 nucleotides of one or more phylogenetic marker genes designated for a set of reference microbes, and (ii) a second amplification primer comprising complementarity to an end of the mcfDNA. In some cases, at least 25 adjacent nucleotides upstream or downstream of an end of the one or more conserved regions comprise a hypervariable region, and the one or more degenerate primers are oriented to prime polymerase extension of the hypervariable region. In some embodiments, the end of the mcfDNA can include an adaptor and the primer can include complementarity to a repaired version of the adaptor.
In some instances, the method described herein can further include sequencing the amplified mcfDNA fragments.
In some embodiments, the method can further include, using a computer: (a) aligning the mcfDNA fragment sequences on a sequence of the one or more degenerate primers and assigning matching sequences from the hypervariable region as representative of the same microbial species; (b) for each microbial species in part (a), searching a database of the one or more phylogenetic marker genes against the mcfDNA fragment sequences and assigning the microbial species based on the closest match; and; and (c) for the one or more phylogenetic marker genes, calculating a microbial community composition based on the relative abundance of the mcfDNA fragment sequences assigned to each microbial species. In the case of multicopy phylogenetic marker genes, such as the 16S rRNA gene, the method can further include correcting for copy number variation between each species. In the case where there are two or more phylogenetic marker genes, the method can further include determining a consolidated microbial community composition by calculating a mathematical mean of the relative abundance of each species for each of the two or more phylogenetic marker genes.
The methods described herein can be used to determine the presence of one or more microbial species and/or to determine a microbial community composition. In some cases, the microbial community composition comprises one or more members of Eukaryotes, bacteria, or fungi.
In other instances, a kit is provided that includes: (a) an adaptor for ligating to the ends of cfDNA; (b) one or more degenerate primers having complementarity to one or more conserved regions, and the one or more conserved regions span at least 18 nucleotides of one or more phylogenetic marker genes designated for a set of reference microbes, wherein at least 25 adjacent nucleotides upstream or downstream of an end of the one or more conserveds region comprise a hypervariable region on the one or more phylogenetic marker genes, and the degenerate primer is oriented to prime polymerase extension of the hypervariable region; (c) a primer complementary to a repaired version of the adaptor; and (d) instructions for performing an amplification reaction on mcfDNA having the adaptor-ligated ends with the one or more degenerate primers and the primer complementary to the repaired adaptor to generate amplified mcfDNA fragments. Like the methods described above, the amplified mcfDNA fragments generated in the amplification reaction using the kit can be sequenced. In addition, the mcfDNA fragments generated using the kit can be used to determine the presence of one or more microbial species and/or to determine the microbial community composition according to the methods provided herein.
In the cases where the microbial community composition is calculated as described above, the method can be utilized as a screening for: tuberculosis and other diseases caused by Mycobacterium species; pulmonary infection risks and causes in cystic fibrosis patients; the risk and onset of sepsis in patients with compromised immune systems; detection of opportunistic bacterial pathogens originating from the oral cavity that have been linked to Alzheimer's disease, pancreatic cancer and other conditions such as endocarditis; women's health issues including Chlamydia linked to mucopurulent cervicitis, pelvic inflammatory disease, tubal factor infertility, ectopic pregnancy and cervical cancer; detection and monitoring of progression in cancer; monitoring of minimal residual disease after oncology treatments; detection and monitoring of progression and minimal residual disease of breast cancer including triple negative breast cancer; detection of esophageal cancer, precancerous colonic polyps and early stage colorectal cancer, and detection and monitoring of progression and minimal residual disease of gastrointestinal cancers in general; detection and monitoring of progression and minimal residual disease in lung cancer; non-invasive analysis of the microbiome in pancreatic cancer patients to propose treatment protocols and prognostics for long-term survival; detection of Clostridium difficile infections; post-transplant bloodstream infections and Graft versus Host Disease (GvHD); detection of hospital acquired infections by emerging pathogens of clinical concern; detection of an infection in an immune compromised person; or detection of infection or inflammation of the gastrointestinal track in Irritable Bowel Disease (Crohn's disease, Ulcerative colitis); and combinations thereof.
In the methods and kits provided herein, the conserved region can have an average sequence variance score of greater than 0.175. In some cases, the hypervariable region can have an average sequence variance score of less than 0.075. In other instances, the hypervariable region can have an average sequence variance score of less than 0.15. In yet other cases, the hypervariable region can have an average sequence variance score of less than 0.1.
In the methods and kits, the one or more conserved regions can span 18 to 40 nucleotides, 20 to 30 nucleotides, or 22 to 28 nucleotides of the phylogenetic marker gene.
In some embodiments of the methods and kits, the at least 25 adjacent nucleotides upstream or downstream of an end of the conserved region that includes the hypervariable region is less than 150 adjacent nucleotides. The at least 25 adjacent nucleotides upstream or downstream of an end of the conserved region that includes the hypervariable region can be less than 75 adjacent nucleotides. In other embodiments, the at least 25 adjacent nucleotides upstream or downstream of an end of the conserved region that includes the hypervariable region is less than 50 adjacent nucleotides.
In the method and kit, the adaptor can be a double stranded asymmetric linker cassette comprising a 5′ asymmetrical end and a 3′ end where the two strands are complementary. The asymmetric linker cassette can be, for example, a Y-shaped linker cassette or a single arm linker cassette. In the case of the asymmetric linker cassette, the primer complementary to the adaptor is complementary to a repaired 5′ end of the asymmetric linker cassette and, in the PCR reaction, polymerase extension from the first degenerate primer results in repair of the asymmetric linker cassette.
The method can further include performing one or more reactions to repair the ends of the mcfDNA.
In the method, each of the primers in the amplification reaction can include one or more sequencing adapter sequences. In another embodiment, the method can further include adding one or more sequencing adapter sequences to the amplified mcfDNA fragments in a second PCR or amplification reaction.
In the methods and kits provided herein, the set of reference microbes can be eukaryotic, fungal, or bacterial, and combinations thereof. In one embodiment, the set of reference microbes are eubacterial microbes.
In the method and kit, the phylogenetic marker gene can include rpoB, cpn60, 16S rRNA, or combinations thereof.
In some embodiments, the one or more degenerate primers includes primers targeting the rpoB gene, the cpn60 gene, the 16S rRNA gene, or combinations thereof.
In the method and kit, the phylogenetic marker gene can include 16S rRNA and the conserved region can include a V3, V4, or V6 region of the 16S rRNA phylogenetic marker gene.
In the methods and kits provided herein, the phylogenetic marker gene can include rpoB and the conserved region can include nucleotide positions 1327-1355 based on the Escherichia coli rpoB gene sequence. Alternatively, the phylogenetic marker gene can include rpoB and the conserved region includes nucleotide positions 1627-1652 based on the Escherichia coli rpoB gene sequence. In another embodiment, the phylogenetic marker gene includes cpn60 and the conserved region includes nucleotide positions 571-596 based on the Escherichia coli cpn60 gene sequence. In other instances, the phylogenetic marker gene includes the 16S rRNA gene and the conserved region includes nucleotide positions 785-805 based on the Escherichia coli 16S rRNA gene sequence.
In some embodiments of the method and kit, the one or more degenerate primers includes RpoB1-R1327, RpoB6-R1630, RpoB-F1652, RpoB7-R2039, Cpn60-R571, 16S-V4-R, or combinations thereof.
In other instances, the one or more degenerate primers includes RpoB1-R1327, Cpn60-R571, or both RpoB1R1327 and Cpn60R571 degenerate primers.
In some embodiments of the method and kit, the set of reference microbes includes reference fungal microbes. In these instances, the method can be used to determine the presence of one or more fungi and/or to determine the fungal community composition. In this embodiment, the one or more phylogenetic marker genes comprise a human fungal phylogenetic marker gene designated for the set of reference fungal microbes, and the one or more degenerate primers comprises complementarity to a conserved region of a the human fungal phylogenetic marker gene. In some instances, the fungal phylogenetic marker gene can be nuclear ribosomal internal transcribed spacer region 1 (ITS1) or nuclear ribosomal internal transcribed spacer region 2 (ITS2). The microbial community composition that can be calculated based on the percent of the sequences assigned to each species is a fungal community composition. The amplified mcfDNA fragments can include mcfDNA from one or more members of the Ascomycota, Basidiomycota and Mucoromycota, including Alternaria species, Aspergillus species, Blastomyces species, Candida species, Capnodiales species, Cladosporium species, Malassezia species, Phaeosphaeria species, Pseudozyma species, Saccharomyces species, Sporobolomyces species, Vishniacozyma species, and Yarrowia species.
In the methods and kits, the one or more phylogenetic marker genes can be rpoB, chaperonin protein 60 (cpn60), 165 rRNA gene, ITS1, ITS2, DNA gyrase subunit B (gyrB), heat shock protein 60 (hsp60), superoxide dismutase A protein (sodA), TU elongation factor (tuf), DNA recombinase proteins (including recA, recE), trr1 gene that encodes for thioredoxin reductase; rim8 gene that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH; kre2 gene that encodes for α-1,2-mannosyltransferase; or erg6 gene that encodes for Δ(24)-sterol C-methyltransferase, and combinations thereof.
In one embodiment, the method or kit can further include adding in the amplification reaction a primer to determine the presence of a functional gene designated for the set of reference microbes. The functional gene primer has complementarity to a conserved region of the functional gene. In some cases, polymerase extension from the functional gene primer results in amplification of the mcfDNA only when the adaptor is ligated to a mcfDNA fragment of the mcfDNA that has the functional gene conserved region. The functional gene can be, for example, a pathogenicity factor, a PKS gene cluster essential for colibactin synthesis, or a choline trimethylaminelyase gene.
In another embodiment of the method and kit, a primer for a conserved viral gene is included in the amplification reaction, wherein the viral gene primer comprises complementarity to a conserved region of the viral gene to determine the presence of the virus. The viral gene can be a human DNA- or RNA-based oncovirus gene. The oncovirus can be one or a combination of Epstein-Barr Virus (EBV), Human Papillomavirus (HPV), Hepatitis B virus (HBV), Human Herpesvirus-8 (HHV-8), or Merkel Cell Polyomavirus (MCPyV). In other instances, the virus is SARS-CoV-2 and the conserved viral gene is SARS-CoV-2spike protein.
In the kit, the mcfDNA can be included in a sample. In the method and kit, the sample can be a bodily fluid, a tissue, or an extracellular bodily substance. The sample can be whole blood, a blood fraction, serum, plasma, or combinations thereof. In some instances, the sample is a biopsy sample from a solid tumor, a skin graft, a liquid biopsy samples other than blood, or combinations thereof. In one embodiment, the sample is a stool sample.
The mcfDNA can have an average fragment length of less than about 100 bp.
The percentage of the mcfDNA in the sample can be less than about 0.05%, less than about 0.1%, less than about 1%, less than about 5%, or less than about 15%.
In the cases of the method and kit where the microbial community composition is calculated, the community composition can include one or more members of Eukaryotes, bacteria, or fungi.
The amplified mcfDNA that is generated in the methods provided herein can include mcfDNA from one or more bacterial members of: Flavobacterium sp., Staphylococcus auricularis, Pseudomonas toyotomiensis, Rheinheimera sediminis, Finegoldia magna, Parvularcula sp., Pseudomonas stutzeri, Pseudomonas soyae, Pseudomonas saponiphila, Pseudomonas sp., Peptoniphilus harei, Quisquilii bacterium sp., Azoarcus sp., Sphingopyxis terrae, uncultured Clostridiales bacterium strain UMGS460, Staphylococcus schweitzeri, Flavobacterium erciyesense, Rhodococcus yananensis, Dietzia massiliensis, Cutibacterium acnes subsp. elongatum, Angustibacter aerolatus, Aerococcus urinae, Klebsiella quasivariicola, Comamonas fluminis, Mycobacterium tuberculosis, Mycobacterium abscessus, Mycobacterium avium, Mycobacterium chimaera, Mycobacterium leprae, Mycobacterium xenopi, Mycobacterium (para)intracellulare, Mycobacterium kansasii, Mycobacterium gilvum, Mycolicibacterium gen. nov. (“fortuitum-vaccae” clade), Mycobacterium gen. (“tuberculosis-simiae” clade), Staphylococcus aureus, Staphylococcus argenteus, Staphylococcus schweitzeri, Pseudomonas aeruginosa, Burkholderia cepacia complex, Burkholderia ubonensis, Burkholderia species Nov., Burkholderia multivorans, Burkholderia pseudomultivorans, Burkholderia pseudomallei, Burkholderia mallei, Trinickia species, Burkholderia thailandensis, Haemophilus influenzae, Haemophilus parainfluenzae, Streptococcus species at the various group and species levels, Streptococcus dysgalactiae, Streptococcus pyogenes, Streptococcus mutans, Streptococcus suis, Streptococcus mitis, Streptococcus pneumoniae, Streptococcus agalactiae, Streptococcus anginosus, Streptococcus intermedius, Streptococcus constellatus, Streptococcus equi subsp. zooepidemicus, Streptococcus oralis, Streptococcus gordonii, Streptococcus uberis, Streptococcus parasanguinis, Streptococcus sanguinis Streptococcus parauberis, Streptococcus infantarius, Streptococcus iniae, Streptococcus salivarius, Streptococcus thermophilus, Streptococcus vestibularis, Streptococcus bovis, Streptococcus gallolyticus subsp. gallolyticus, Streptococcus gallolyticus subsp. macedonicus, Streptococcus gallolyticus subsp. pasteurianus, Streptococcus equinus, Enterococcus faecalis, Enterococcus faecium, Porphyromonas gingivalis, Porphyromonas cangingivalis, Porphyromonas uenonis, Porphyromonas endodontalis, Propionibacterium acidifaciens, Porphyromonas asaccharolytica, Porphyromonas macacae, Prevotella pallens, Prevotella histicola, Prevotella melaninogenica, Prevotella copri, Prevotella intermedia, Prevotella oral, Prevotella nanceiensis, Prevotella salivae, Prevotella nigrescens, Prevotella denticola, Prevotella buccae, Prevotella stercorea, Prevotella oris, Prevotella disiens, Prevotella bryantii, Prevotella shahii, Tannerellaforsythia, Bacteroides fragilis, Helicobacter pylori, Chlamydia trachomatis, Neisseria meningitidis, Neisseria gonorrhoeae, Neisseria subflava, Neisseria perflava, Neisseria flavescens, Neisseria cinerea, Neisseria lactamica, Neisseria weaver, Neisseria zoodegmatis, Neisseria brasiliensis, Neisseria mucosa, Neisseria animaloris, Aggregatibacter actinomycetemcomitans, Aggregatibacter aphrophilus, Aggregatibacter segnis, Saccharopolyspora species, Bacillus clausii, members of the genera Pseudoxanthomonas and Streptomyces, Fusobacterium nucleatum subsp. polymorphum, Fusobacterium hwasookii, Fusobacterium canifelinum, Fusobacterium nucleatum subsp. animalis, Fusobacterium periodonticum, Fusobacterium necrophorum subsp. funduliforme, Fusobacterium mortiferum, Fusobacterium varium, Fusobacterium nucleatum subsp. nucleatum, Fusobacterium ulcerans, Fusobacterium nucleatum subsp. vincentii, Fusobacterium equinum, Fusobacterium gonidiaformans, Fusobacterium necrogenes, Fusobacterium naviforme, Peptostreptococcus stomatis, Pseudonocardia asaccharolytica, Parvimonas species including Parvimonas oral and Parvimonas micra, Gemella species including Gemella morbillorum, Gemella haemolysans, Gemella palaticanis and Gemella sanguinis, Clostridium difficile, Acinetobacter baumannii, Acinetobacter lactucae, Acinetobacter pittii, Acinetobacter calcoaceticus, Acinetobacter oleivorans, Acinetobacter nosocomialis, Acinetobacter radioresistens, Acinetobacter variabilis, Acinetobacter courvalinii, Acinetobacter ursingii, Enterobacteriaceae, Escherichia, or Klebsiella species.
In another embodiment, a system is provided for amplifying microbial cell free DNA (mcfDNA). The system includes a reaction vessel, a reagent dispensing module, and software to execute any of the methods for amplifying microbial mcfDNA described herein, where the method is executed robotically.
In one instance, a computer implemented method is provided for identifying a degenerate primer. The method includes using a computer and a database comprising more than one thousand DNA sequences of a phylogenetic marker gene from a set of microbes to perform the following steps: (i) identifying a highly conserved region within the DNA sequences of the phylogenetic marker gene, wherein the highly conserved region spans at least 18 nucleotides in length and has an average sequence variance score of greater than 0.175; (ii) calculating an average sequence variance score of 25-75 nucleotides upstream of the beginning of the highly conserved region and downstream of the end of the highly conserved region, wherein an average variance score of less than 0.15 is used to identify a hypervariable region; and (iii) designing a degenerate primer sequence complementary to the highly conserved DNA region based on the relative abundance of each nucleotide in the aligned phylogenetic marker gene sequences, wherein the degenerate primer sequence is oriented to prime polymerase extension of the hypervariable region. In the computer implemented method for identifying a degenerate primer, the conserved region can span 18 to 40 nucleotides, 20 to 30 nucleotides, or 22 to 28 nucleotides of the phylogenetic marker gene.
In the computer implemented method, the set of microbes can include one or more members of Proteobacteria (including representative α-, β-, γ-, δ- and ε-Proteobacteria), Firmicutes (including representatives for the classes Bacilli, Clostridia, Erysipelotrichia and Negativicutes), Acinetobacteria, and Fusobacteria. In another embodiment, the set of microbes can include one or more members of Ascomycota, Basidiomycota and Mucoromycota, including Alternaria species, Aspergillus species, Blastomyces species, Candida species, Capnodiales species, Cladosporium species, Malassezia species, Phaeosphaeria species, Pseudozyma species, Saccharomyces species, Sporobolomyces species, Vishniacozyma species, and Yarrowia species.
In one embodiment, a degenerate oligonucleotide primer RpoB1-R1327 is provided consisting of a mixture of oligonucleotides having the sequences 5′ to 3′: CGRTTDCCNARRTGRTCRATRTCRTC (SEQ ID NO: 1), wherein A=adenine, G=guanidine, C=cytosine, T=thymine, R=purine (A or G), D=not C (A, T or G), and N=any nucleotide (A, G, C or T).
In another embodiment, a degenerate oligonucleotide primer RpoB6-R1630 is provided consisting of a mixture of oligonucleotides having the sequences 5′ to 3′: TGHACRTCDCGNACYTCRWADCC (SEQ ID NO: 2), wherein A=adenine, G=guanidine, C=cytosine, T=thymine, R=purine (A or G), Y=pyrimidine (T or C), W=weak (A or T), H=not G (A, T or C), D=not C (A, T or G), and N=any nucleotide (A, G, C or T).
In another instance, a degenerate oligonucleotide primer Cpn60-R571 is provided consisting of a mixture of oligonucleotides having the sequences 5′ to 3′: CCNYKRTCRAABYGCATNCCYTC (SEQ ID NO: 3), wherein A=adenine, G=guanidine, C=cytosine, T=thymine, R=purine (A or G), Y=pyrimidine (T or C), K=amino (T or G), B=not A (T, G or C), and N=any nucleotide (A, G, C or T).
In other embodiments, degenerate oligonucleotide primers RpoB1-R1327, RpoB6-R1630, and Cpn60-R571 are provided in which one or more of the nucleotides at primer positions represented by B, D, or N are replaced by inosine.
The presently disclosed subject matter now will be described more fully hereinafter. The presently disclosed subject matter may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will satisfy applicable legal requirements. Indeed, many modifications and other embodiments of the presently disclosed subject matter set forth herein will come to mind to one skilled in the art to which the presently disclosed subject matter pertains having the benefit of the teachings presented in the descriptions provided herein. Therefore, it is to be understood that the presently disclosed subject matter is not to be limited to the specific embodiments disclosed and that modifications and other embodiments are intended to be included within the scope of the appended claims.
Following long-standing patent law convention, the terms “a,” “an,” and “the” refer to “one or more” when used in this application, including the claims. Thus, for example, reference to “a sample” includes a plurality of samples, unless the context clearly is to the contrary, and so forth.
Throughout this specification and the claims, the terms “comprise,” “comprises,” and “comprising” are used in a non-exclusive sense, except where the context requires otherwise. Likewise, the terms “having” and “including” and their grammatical variants are intended to be non-limiting, such that recitation of items in a list is not to the exclusion of other like items that can be substituted or added to the listed items.
For the purposes of this specification and claims, the term “about” when used in connection with one or more numbers or numerical ranges, should be understood to refer to all such numbers, including all numbers in a range and modifies that range by extending the boundaries above and below the numerical values set forth. The recitation of numerical ranges by endpoints includes all numbers, e.g., whole integers, including fractions thereof, subsumed within that range (for example, the recitation of 1 to 5 includes 1, 2, 3, 4, and 5, as well as fractions thereof, e.g., 1.5, 2.25, 3.75, 4.1, and the like) and any range within that range. In addition, as used herein, the term “about”, when referring to a value can encompass variations of, in some embodiments +/−20%, in some embodiments +/−10%, in some embodiments +/−5%, in some embodiments +/−1%, in some embodiments +/−0.5%, and in some embodiments +/−0.100, from the specified amount, as such variations are appropriate in the disclosed compositions and methods. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude, preferably within 5-fold, and more preferably within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated the term “about” meaning within an acceptable error range for the particular value should be assumed.
Throughout this specification and the claims, the term “subject” includes humans and animals and can be used interchangeably with the term “human” and the term “patient”.
The terms “SPA fragment” and “SPA fragment sequence” are herein used interchangeably.
The terms “PCR reaction” and “amplification reaction” are herein used interchangeably.
The term “phylogenetic marker gene” as used herein means any conserved gene from any organism, including but not limited to bacteria, fungi, parasites, and viruses, that is suitable for phylogenetic identification.
There are a wide range of diseases where microbial community analysis, especially of the gut microbiome, provides important information regarding the disease or its treatment options. This includes conditions such as IBD (Ananthakrishnan et al, 2017), metabolic diseases (Boulange et al, 2016), diseases of the central nervous system (Bhattacharjee and Lukiw, 2013) and cancer where the interaction of the gut microbiome can provide clues regarding the response to specific treatments including immune checkpoint inhibitors (Gopalakrishnan et al, 2020; Sepich-Poore et al, 2021). Deep microbial metagenome sequencing is the most informative approach when it comes to microbial community analysis, as it will provide detailed information regarding community composition as well as the key functions encoded by the community members. Unfortunately, despite major breakthroughs in metagenome sequencing technologies to reduce its costs, it is currently still too expensive for routine screening purposes of human associated microbial communities in large population screenings. Another disadvantage of deep microbial metagenome sequencing is the need for relatively large amounts of high-quality microbial DNA. This has hindered its application to study the microbial communities associated with liquid and solid biopsy samples, where only a small fraction of the total DNA is of microbial origin.
The amplification and subsequent sequencing of phylogenetic marker genes provides an alternative, cheaper high throughput method for microbial community analysis. For example, in tissue biopsy samples where there is sufficient concentration of DNA having average fragment length of about 5,000 bp or more, amplification-based sequencing approaches have been successfully applied to identify differences in microbial communities between healthy individuals and patients suffering from a wide range of diseases. Advantages of the amplification and subsequent sequencing method include that it requires significantly less DNA than metagenome sequencing, and because specific DNA primers are used to amplify phylogenetic target genes, there is little contamination with host DNA, making this method suitable to analyze the microbial communities associated with tissue biopsy samples, from which small amounts of high molecular weight DNA can be obtained. However, analysis of microbial signatures in liquid biopsy samples, especially peripheral blood samples, results in additional challenges as compared to tissue biopsy samples, due to the low concentration of mcfDNA having small fragment sizes.
For example, in plasma, human cfDNA accounts for the vast majority of cfDNA (>90% or even >99%), while mcfDNA accounts for only a small fraction with 0.08%-4.85% from bacteria, 0.00%-0.01% from fungi, and 0.00%-0.16% from viruses/phages (Han et al, 2020). However, the percentage of mcfDNA compared to cfDNA should be placed in the context of the human genome size and the size of an average microbial genome, with sizes of 6.4 billion and approximately 6 million nucleotides, respectively, therefore providing similar coverage. Thus, mcfDNA represents an important signal that is largely being ignored in liquid biopsy testing.
The intrinsic properties of cfDNA and mcfDNA, especially its small fragment sizes, make its analysis for disease detection and monitoring challenging. More than 70% of plasma cfDNA is smaller than 300 bp, with an average size of 170 bp (Fernández-Carballo et al, 2019). However, the size of mcfDNA fragments was found to be significantly smaller, approximately 40-100 bp (Burnham et al, 2016), as was confirmed by Rassoulian Barrett et al (2020). As a result of this size limitation, conventional amplicon-based sequencing approaches including 16S rRNA gene and rpoB gene amplicon sequencing that target DNA fragments of several hundred nucleotides, are not suitable for determining the composition of colonizing or invasive microorganisms using mcfDNA from peripheral blood and other liquid biopsy samples. The small size of mcfDNA makes it nearly impossible to use mcfDNA in amplicon-based sequencing protocols, such as 16S rRNA gene sequencing, leaving no other option than high-cost and low-throughput NGS sequencing.
To overcome the above-mentioned limitations, the present inventors developed a single point amplification sequencing approach that exploits the combination of a degenerate primer for a conserved region of a marker gene located adjacent to a phylogenetic hypervariable region of the gene for a wide range of microbes. The method is based on the targeted amplification of high-resolution phylogenetic identifier fragments from mcfDNA, which comprises a fraction of the total cfDNA isolated from, for example, biopsy samples. To generate the phylogenetic identifier fragments, a hypervariable DNA region with high phylogenetic resolution is targeted. The hypervariable region located next to the highly conserved region that functions as a primer annealing site as is illustrated in
In various embodiments, methods and kits are provided herein for generating the SPA fragments. The methods and kits provided herein can be used to determine the presence of one or more microbial species and/or to determine one or more microbial community compositions. In the methods and kits provided herein, the set of reference microbes can be eukaryotic, fungal, or bacterial, and combinations thereof. In one embodiment, the set of reference microbes are eubacterial microbes.
In the methods of the invention, the length of the SPA fragment is determined by the distance between the end of the mcfDNA fragment and the 3′-end of the primer annealing site. Only mcfDNA fragments that contain the primer annealing site will give SPA fragments, which can be subsequently sequenced and used for high resolution phylogenetic identification and analysis of community composition.
In one aspect of the invention, the degenerate primer is used in combination with an adaptor, such as, for example, an asymmetric linker cassette which is attached to the 3′ ends of all the cfDNA fragments in the sample. A PCR amplification reaction is performed using the degenerate primer and a primer complementary to the 5′ asymmetrical end of the linker cassette. The degenerate primer is designed to allow for DNA synthesis into the hypervariable region. However, successful PCR amplification of the hypervariable region occurs only when the asymmetric linker cassette is repaired. In a PCR reaction, the asymmetric linker cassette will be repaired only when located downstream from the degenerate primer annealing site, i.e, when the asymmetric linker cassette has been ligated to a mcfDNA fragment that contains the conserved region of the phylogenetic marker gene. In this manner, microbial DNA fragments that originate from the hypervariable region are selectively amplified.
In one example of the invention, to overcome the above-mentioned limitations for determining microbial profiles from mcfDNA, such as, for example, mcfDNA in liquid biopsy samples, the present inventors developed a unique approach that exploits the phylogenetic resolution of a hypervariable region of the rpoB gene. In another example of the invention, the present inventors developed a unique approach that exploits the phylogenetic resolution of V3-V4 hypervariable region of the 16S rRNA gene. In contrast to commonly used amplicon sequencing, in which regions between two conserved DNA sequences are targeted for PCR amplification, the methods provided herein use a single conserved DNA sequence as the primer annealing site to initiate PCR amplification. The amplification initiated from this single conserved DNA sequence allows for targeted amplification of the hypervariable region located adjacent to the primer annealing site, independent of the size of the fragment, followed by sequencing of the amplified fragment. In another example of the invention, the phylogenetic resolution of a hypervariable region of the chaperonin cpn60 gene is used in the presently disclosed methods. This method may be referred to herein as Single Point Amplification (SPA) fragment sequencing.
Alternative embodiments of the invention include use of a conserved DNA sequence as the primer annealing site for more than one site on a phylogenetic marker gene or for a site on two or more different phylogenetic marker genes in a single amplification reaction. In one instance, two degenerate primers targeting different regions of the rpoB gene are included in the presently disclosed methods. In another instance, a degenerate primer for both the cpn60 and the rpoB gene are included in the presently disclosed methods. The use of two or more degenerate primers for annealing to two or more conserved regions on a single or two different phylogenetic marker genes may be referred to herein as “multi-loci SPA fragment sequencing”.
In the specific examples provided herein the RNA polymerase subunit B (rpoB) gene and the chaperonin 60 (cpn60) gene were used, but it should be noted that the SPA fragment sequencing method is very broadly applicable to conserved housekeeping genes, including, but not limited to, the prokaryotic genes coding for the DNA gyrase subunit B (gyrB), the heat shock protein 60 (hsp60), the superoxide dismutase A protein (sodA), the TU elongation factor (tuf), and the DNA recombinase proteins (including recA, recE). The SPA fragment sequencing method can also be applied on the Prokaryotic 16S rRNA gene, for instance to amplify (part of) the V1-V2 or V3-V4 hypervariable region. The SPA fragment sequencing method can also be applied on the Eukaryotic internal transcribes spacer (ITS) regions ITS1, which is located between the 18S and 5.8S rRNA genes, and ITS2, which is located between the 5.8S and 28S rRNA genes. The SPA fragment sequencing method can also be applied to genes that are unique to pathogenic fungi including the trr1 gene that encodes for thioredoxin reductase; the rim8 gene that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH; the kre2 gene that encodes for α-1,2-mannosyltransferase; and the erg6 gene that encodes for Δ(24)-sterol C-methyltransferase (Abadio et al, 2011); or any conserved gene from any organism, including bacteria, fungi, parasites, and viruses that is suitable for phylogenetic identification. This includes conserved genes from the human DNA-based oncoviruses, more specifically the Epstein-Barr Virus (EBV), Human Papillomavirus (HPV), Hepatitis B virus (HBV), Human Herpesvirus-8 (HHV-8), and Merkel Cell Polyomavirus (MCPyV) (Mui et al, 2017). One or a combination of any conserved housekeeping gene can be used in the presently disclosed methods.
Advantages of the disclosed SPA fragment sequencing method include an increase in the diversity of hypervariable regions that can be targeted for amplicon analysis as the method only requires one neighboring conserved region to bind the primer (compared with the two required by dual primer approaches). As such, the SPA fragment sequencing method is more adaptable, flexible, and offers greatly improved resolution over current methods. In addition, the multi-loci SPA sequencing methods include the advantage of improving phylogenetic resolution for the identification of the community members on the species and subspecies level, as is highlighted in EXAMPLE 13. Further, the multi-loci SPA sequencing methods provide an internal control for improved error correction in the SPA fragment amplification and sequencing process, as similar results for community species abundances are expected independent of the phylogenetic identifier gene.
In addition to the degenerate primer for the conserved region, an adaptor such as, for example, an asymmetric linker cassette, can be used to introduce a DNA sequence that is targeted by a second primer in the PCR amplification reaction. In one embodiment, to avoid amplification of any DNA fragment flanked by two adaptors, the adaptors are “defective” or in other words “asymmetric”. This can be accomplished by designing an adaptor as an asymmetric linker cassette where the strand that serves as the template for primer annealing is missing. Typical asymmetric linker cassette configurations include, but are not limited to:
Ideally, the single strands of the asymmetric linker cassette are complementary over a stretch of about at least 16 nucleotides with an annealing temperature of approximately 50° C. or higher, allowing for a linker cassette that is stable at room temperature. The single strand of the asymmetric linker can also contain 6 random nucleotides that constitute a Unique Molecular Identifier (UMI) to correct PCR induced errors and improve sequencing accuracy. To avoid self-ligation, in one example, the asymmetric linker cassette includes a 3′sticky end. The 3′sticky end can be formed by a single nucleotide, such as, for example, thymine. To avoid undesirable repair of the asymmetric linker cassette initiated from the shorter single stranded DNA fragment, the terminal 3′ nucleotide can be a dideoxy nucleotide that functions as a chain-elongating inhibitor of DNA polymerase.
In a PCR reaction, the asymmetric linker cassette will only be repaired when located downstream from the degenerate primer annealing site. For purposed of the specification and claims, the term “repaired” when used in the context of the asymmetric linker cassette, means that a new DNA strand is created in the PCR reaction that is complementary at the 5′ end of the asymmetric linker cassette. DNA synthesis initiated from the degenerate primer into the asymmetric linker cassette will restore the defective DNA strand complementary to the 5′-end of the linker and in this manner the asymmetric linker cassette is repaired. In subsequent PCR cycles this strand is used for primer annealing, allowing for the amplification of the hypervariable region. To allow for sample multiplexing and sequencing, the resulting amplicons can be further amplified in a second PCR reaction to introduce two Unique Dual Indexes (UDI), one at each end of the amplicons, and, for example, the Illumina sequencing anchors P5 and P7.
In one embodiment of the invention, the method includes one or more of the following steps as detailed in
GTCTCGTGGGCTCGGAGATGTGT
ATAAGAGACAG-
GTCTCGTGGGCTCGGAGATGTGT
ATAAGAGACAG-
GTCTCGTGGGCTCGGAGATGTGT
ATAAGAGACAG-
GTCTCGTGGGCTCGGAGATGTGT
ATAAGAGACAG-
GTCTCGTGGGCTCGGAGATGTGT
ATAAGAGACAG-
GTCTCGTGGGCTCGGAGATGTGT
ATAAGAGACAG-
TATAAGAGACAG-
TCGTCGGCAGCGTCAGATGTGTAT
AAGAGACAG-
CAAGCAGAAGACGGCATACGAGA
T/Index5 (10 nt)/
AATGATACGGCGACCACCGAGAT
CTACAC/Index7 (10 nt)/
In some embodiments of the invention, the processing and analysis of the SPA fragment sequences includes one or more of the following steps as shown in
Additional primers besides those derived from the RpoB6-F1652 and the 16S-V4-R primers can be used for SPA fragment sequencing. EXAMPLE 2 describes the design of alternative rpoB gene specific primers. A RpoB1-R1327 primer, which recognizes the rpoB gene sequence between positions 1327-1352 (positions based on the Escherichia coli rpoB gene sequence) and allows for generation of SPA fragments upstream of this region, was validated in silico for the phylogenetic resolution of the sequences of 50 nucleotide Single Point Amplification (SPA) fragments as described in EXAMPLES 3 to 9. In EXAMPLE 7 a RpoB6-R1630 primer, which recognizes the rpoB gene sequence between positions 1630-1652 and allows for generation of SPA fragments upstream of this region, was validated, and EXAMPLE 10 describes the combined use of the RpoB1-R1327 primer and RpoB6-R1630 primer for improved identification of members of the Enterobacteriaceae. EXAMPLE 13 describes the Cpn60-R571 primer, which recognizes the cpn60 gene sequence between position 571-593, (position numbers based on the Escherichia coli cpn60 gene sequence). In another embodiment of the invention, a method is provided for multi loci SPA fragment sequencing. Use of two or more different gene-specific SPA primers in the same amplification reaction such as, for example, the RpoB1-R1327 and Cpn60-R571 primers is detailed in EXAMPLE 14. One example of a protocol for the method of amplifying mcfDNA provided herein is generally illustrated in
In one instance, the processing and analysis of the SPA fragment sequences includes the following steps:
To reconcile the outcomes obtained for the SPA fragments obtained from different phylogenetic identifier genes, their results are compared and consolidated into a consensus community description (species and their relative abundances), as is schematically shown in Step 2 of
In one embodiment of the invention, the reconciliation process of Step 2 in
The utility of the methods of the invention is exemplified in EXAMPLES 1-14 of the present disclosure. For example, in EXAMPLE 1 of the present disclosure, the inventors demonstrate that the primers RpoB6-SPA-seq-F1652 and 16S-SPA-seq-V4-R can be used to generate unique SPA fragments from mcfDNA present in blood that allowed for bacterial identification on the species level based on homology to the rpoB gene and the 16S rRNA gene, respectively. In EXAMPLE 2 of the present disclosure, the inventors demonstrate that a 50 nucleotide length cutoff enabled in silico generation of 20,919 unique SPA fragments covering the rpoB gene region upstream of the RpoB1-R1327 primer annealing site. The generated SPA fragments provided sufficient phylogenetic resolution to enable identification of many bacteria at the species level. These 50 nucleotide SPA fragments were generated from 50,569 unique rpoB gene sequences present in the PATRIC database (Wattam et al, 2014). Increasing this length to 75 nucleotides had only a marginal effect on the phylogenetic resolution of this method (22,603 unique fragments). The 50 nucleotide fragment size was selected based on the average length (40-100 nucleotides) of mcfDNA fragments. It should be noted that larger fragments will also be generated for each species, further improving the resolution for the phylogenetic identification.
EXAMPLES 3 to 9 demonstrate that, despite their relatively short size, the sequences of the 50 nucleotide long SPA fragments covering the rpoB gene region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification at the bacterial species level of many clinically relevant bacterial isolates.
EXAMPLE 10 describes a simulation showing that mcfDNA fragments with an average length of 60 base pairs can be reliably used to identify strains present at 0.5% or above in a known gut microbial community at the species and subspecies level. The species and subspecies are detectable in liquid biopsy samples, including peripheral blood. On average, strain abundances measured based on SPA fragments were within 1.4% of the actual abundance. For strains with less than 1% abundance, the average error was 1.8%, ranging from 0.1% to 7.2%; for strains with an abundance of 1% or higher, the average error was 1.2%, ranging from <0.1% to 4.5%.
EXAMPLE 11 describes an experiment to determine the phylogenetic accuracy of the SPA fragments generated using the RpoB1-R1327 primer in EXAMPLE 10. The results shows that the SPA fragments have very high phylogenetic specificity to reliably classify bacteria at both the taxonomic genus and species level.
EXAMPLE 12 is an experiment designed to access how the sensitivity and specificity of the SPA fragment sequencing methods compare to the current method of deep metagenome sequencing of cfDNA fragments followed by taxonomic classification using read-based metagenome analysis methods. The simulations described in EXAMPLE 12 using deep metagenome sequencing of cfDNA fragments followed by taxonomic classification of mcfDNA using read-based metagenome analysis methods show that current read-based tools are unsuitable for taxonomic classification of the short sequencing reads obtained from mcfDNA. As such the current approach lacks the sensitivity and specificity to provide meaningful insights for disease detection and progression monitoring. Overcoming this limitation would require very deep sequencing and assembly of short reads into larger fragments. In addition to higher sequencing costs, limitations in the assembly of short sequencing reads render the current approach unsuitable for scalable application to the routine analysis of microbial patterns in biopsy samples.
EXAMPLE 13 describes identification of a degenerate primer comprising complementarity to a conserved region spanning position 571 to 593 of the cpn60 gene (position numbers based on the Escherichia coli cpn60 gene, “Cpn60-R571 primer”) for SPA fragment sequencing. The results described in EXAMPLE 13 show that the simulated community compositions using rpoB gene-derived SPA fragments and cpn60 gene-derived SPA fragments are very similar. In addition, and unexpectedly, it was discovered that the Cpn60-R571 primer can be used in combination with the RpoB1-R1327 primer in the SPA fragment sequencing methods of the present disclosure to improve the phylogenetic resolution based solely on the rpoB gene. Based on this result a new method is provided, referred to as multi loci SPA fragment sequencing, which combines SPA fragments from multiple phylogenetic identifier genes to analyze the composition of microbial communities. The results of EXAMPLE 13 show that the multi loci SPA fragment sequencing method using two or more phylogenetic identifier genes, such as the rpoB and cpn60 genes, can have advantages over the SPA fragment sequencing method using a single locus. Such advantages include: (1) provision of an internal sample control for the SPA fragment amplification and sequencing process, as similar results for community species abundances are expected independent of the phylogenetic identifier gene; and (2) improvement in phylogenetic resolution for the identification of the community members on the species and subspecies level, as was highlighted in EXAMPLE 13.
The clinically relevant bacterial isolates that can be identified using the methods of the invention include, but are not limited to, Flavobacterium sp., Staphylococcus auricularis, Pseudomonas toyotomiensis, Rheinheimera sediminis, Finegoldia magna, Parvularcula sp., Pseudomonas stutzeri, Pseudomonas soyae, Pseudomonas saponiphila, Pseudomonas sp., Peptoniphilus harei, Quisquilii bacterium sp., Azoarcus sp., Sphingopyxis terrae, uncultured Clostridiales bacterium strain UMGS460, Staphylococcus schweitzeri, Flavobacterium erciyesense, Rhodococcus yananensis, Dietzia massiliensis, Cutibacterium acnes subsp. elongatum, Angustibacter aerolatus, Aerococcus urinae, Klebsiella quasivariicola, Comamonas fuminis, Mycobacterium tuberculosis, Mycobacterium abscessus, Mycobacterium avium, Mycobacterium chimaera, Mycobacterium leprae, Mycobacterium xenopi, Mycobacterium (para)intracellulare, Mycobacterium kansasii, Mycobacterium gilvum, Mycolicibacterium gen. nov. (“fortuitum-vaccae” clade), Mycobacterium gen. (“tuberculosis-simiae” clade), Staphylococcus aureus, Staphylococcus argenteus, Staphylococcus schweitzeri, Pseudomonas aeruginosa, Burkholderia cepacia complex, Burkholderia ubonensis, Burkholderia species Nov., Burkholderia multivorans, Burkholderia pseudomultivorans, Burkholderia pseudomallei, Burkholderia mallei, Trinickia species, Burkholderia thailandensis, Haemophilus influenzae, Haemophilus parainfluenzae, Streptococcus species at the various group and species levels, Streptococcus dysgalactiae, Streptococcus pyogenes, Streptococcus mutans, Streptococcus suis, Streptococcus mitis, Streptococcus pneumoniae, Streptococcus agalactiae, Streptococcus anginosus, Streptococcus intermedius, Streptococcus constellatus, Streptococcus equi subsp. zooepidemicus, Streptococcus oralis, Streptococcus gordonii, Streptococcus uberis, Streptococcus parasanguinis, Streptococcus sanguinis Streptococcus parauberis, Streptococcus infantarius, Streptococcus iniae, Streptococcus salivarius, Streptococcus thermophilus, Streptococcus vestibularis, Streptococcus bovis, Streptococcus gallolyticus subsp. gallolyticus, Streptococcus gallolyticus subsp. macedonicus, Streptococcus gallolyticus subsp. pasteurianus, Streptococcus equinus, Enterococcus faecalis, Enterococcus faecium, Porphyromonas gingivalis, Porphyromonas cangingivalis, Porphyromonas uenonis, Porphyromonas endodontalis, Propionibacterium acidifaciens, Porphyromonas asaccharolytica, Porphyromonas macacae, Prevotella pallens, Prevotella histicola, Prevotella melaninogenica, Prevotella copri, Prevotella intermedia, Prevotella oral, Prevotella nanceiensis, Prevotella salivae, Prevotella nigrescens, Prevotella denticola, Prevotella buccae, Prevotella stercorea, Prevotella oris, Prevotella disiens, Prevotella bryantii, Prevotella shahii, Tannerellaforsythia, Bacteroides fragilis, Helicobacter pylori, Chlamydia trachomatis, Neisseria meningitidis, Neisseria gonorrhoeae, Neisseria subflava, Neisseria perfiava, Neisseria flavescens, Neisseria cinerea, Neisseria lactamica, Neisseria weaver, Neisseria zoodegmatis, Neisseria brasiliensis, Neisseria mucosa, Neisseria animaloris, Aggregatibacter actinomycetemcomitans, Aggregatibacter aphrophilus, Aggregatibacter segnis, Saccharopolyspora species, Bacillus clausii, members of the genera Pseudoxanthomonas and Streptomyces, Fusobacterium nucleatum subsp. polymorphum, Fusobacterium hwasookii, Fusobacterium canifelinum, Fusobacterium nucleatum subsp. animalis, Fusobacterium periodonticum, Fusobacterium necrophorum subsp. funduliforme, Fusobacterium mortiferum, Fusobacterium varium, Fusobacterium nucleatum subsp. nucleatum, Fusobacterium ulcerans, Fusobacterium nucleatum subsp. vincentii, Fusobacterium equinum, Fusobacterium gonidiaformans, Fusobacterium necrogenes, Fusobacterium naviforme, Peptostreptococcus stomatis, Pseudonocardia asaccharolytica, Parvimonas species including Parvimonas oral and Parvimonas micra, Gemella species including Gemella morbillorum, Gemella haemolysans, Gemella palaticanis and Gemella sanguinis, Clostridium difficile, Acinetobacter baumannii, Acinetobacter lactucae, Acinetobacter pittii, Acinetobacter calcoaceticus, Acinetobacter oleivorans, Acinetobacter nosocomialis, Acinetobacter radioresistens, Acinetobacter variabilis, Acinetobacter courvalinii, Acinetobacter ursingii, and members of the Enterobacteriaceae, including Escherichia and Klebsiella species.
This phylogenetic identification of many clinically relevant bacterial isolates at the species level represents a significant improvement over methods such as Kaiju (Menzel et al, 2016) or Kraken (Wood and Salzberg, 2014), which are being used for sequence-read based identification of microorganisms represented by the mcfDNA at the genus level. As is well documented for many pathogenic bacteria, including Mycobacterium species, optimal patient treatment protocols including the use of antibiotics are species-level specific, showing the importance of the level of phylogenetic resolution that is uniquely obtained with the single point amplicon sequencing approach provided herein. Furthermore, by targeting genes that are absent or sufficiently different from the host genome, such as genes conserved in pathogenic fungi that are absent from the human genome (Abadio et al, 2011), the method provided herein can also be used to detect the presence of Eukaryotic infections, such as those caused by parasitic fungi and amoeba. Candidate fungal genes for SPA fragment sequencing include: trr1 that encodes for thioredoxin reductase; rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH; kre2 that encodes for α-1,2-mannosyltransferase; and erg6 that encodes for Δ(24)-sterol C-methyltransferase (Abadio et al, 2011).
In certain instances, disease phenotypes caused by bacteria will depend on the presence of virulence/pathogenicity factors located on mobile genetic elements, including conjugative and/or mobile plasmids, phages, and pathogenicity islands that can be horizontally transferred between bacteria, as is the case for Escherichia coli, Salmonella, Klebsiella, Listeria, Bacillus, pyogenic streptococci and Clostridium perfringens, among others (for review, see Gyles and Boerlin, 2014). As the result of horizontal gene transfer, in some instances phylogenetic information on species composition will be insufficient to predict disease pathology, and therefore needs to be complemented with information on community functionality. SPA fragment sequencing provides the flexibility to address both phylogenetic identification and community functionality: by selecting a degenerate primer that recognizes a conserved DNA region of a specific function, the same protocol outlined in
For instance, the presence in Escherichia coli of the PKS pathogenicity island encoding, among other virulence factors, for genotoxic colibactin synthesis has been linked to increased risk for developing colorectal cancer (Pleguezuelos-Manzano et al, 2020). By designing a primer for SPA fragment amplification that specifically targets the PKS gene cluster essential for colibactin synthesis, the presence of genotoxic Escherichia coli strains (Pleguezuelos-Manzano et al, 2020) can be determined and combined with phylogenetic information for risk assessment of colorectal cancer.
Pan-cancer analyses recently revealed cancer-type-specific fungal ecologies and bacteriome interactions (Narunsky-Haziza et al, 2022). By designing a primer for SPA fragment amplification that specifically targets a human fungal phylogenetic marker such as the nuclear ribosomal internal transcribed spacer region 1 (ITS1) or region 2 (ITS2), the presence of human pathogenic fungi can be determined and combined with bacterial phylogenetic information to for risk assessment of cancer. The amplified mcfDNA that can be generated in the methods provided herein can include mcfDNA from fungal species including one or more members of the Ascomycota, Basidiomycota and Mucoromycota, including Alternaria species, Aspergillus species, Blastomyces species, Candida species, Capnodiales species, Cladosporium species, Malassezia species, Phaeosphaeria species, Pseudozyma species, Saccharomyces species, Sporobolomyces species, Vishniacozyma species, and Yarrowia species. The methods for amplifying mcfDNA provided herein can also be used for detecting viral DNA. For example, a primer for a conserved viral gene can be included in the amplification reaction, where the viral gene primer includes complementarity to a conserved region of the viral gene to determine the presence of the virus. The viral gene can be a human DNA- or RNA-based oncovirus gene. Assessing the risk and better understanding the cause of cancer can be improved by designing primers for SPA fragment amplification that specifically target conserved genes present in human oncoviruses. For example, the method can be used for determining the presence of human DNA-based oncoviruses such as, but not limited to, the Epstein-Barr Virus (EBV), Human Papillomavirus (HPV), Hepatitis B virus (HBV), Human Herpesvirus-8 (HHV-8), and Merkel Cell Polyomavirus (MCPyV).
In one aspect of the invention, phylogenetic and functional information can be obtained simultaneously by including both one or more degenerate primers that target the phylogenetic identifier gene(s) and a primer that targets a functional gene in the same reaction for the SPA fragment amplification step (
Since 100,000 sequencing reads represent the standard depths for amplicon-based sequencing for complex microbial community analysis, the latest Illumina NEXTSEQ instruments allow for an unprecedented number of samples to be sequenced in parallel. For example, the Illumina NEXTSEQ 6000 allows to theoretically collect 20 billion reads with a single run, which would correspond to 100,000 paired-end sequenced samples.
In addition to monitoring of specific diseases, SPA fragment sequencing can be useful as part of the general health screening. Unlike the stool microbiome, the microbiome of colonizing and infecting bacteria will be relatively stable, with changes occurring when the relation between host and microbes is changing. This includes situations of new invasions by infectious and colonizing microorganisms, such as the formation of stomach ulcers, the formation of intestinal polyps/adenomas and their progression into malignancies, gastrointestinal diseases including Irritable Bowel Disease (IBD), various tumors and their specific microbiomes including pancreatic cancer, lung cancer and cervical cancer, Central Nervous System (CNS) diseases including multiple sclerosis (MS) and Alzheimer's disease, minimal residual disease (MRD) monitoring, and other diseases characterized by dysbiotic and inflammatory microbiomes such as cystic fibrosis or tuberculosis, and general risk monitoring of infections in patient populations with a compromised immune system, positioning SPA fragment sequencing as an ideal tool for risk monitoring, early detection, prognostics and evaluation of disease progression. Contrary to PCR based detection methods that monitor for the presence of specific bacteria, SPA fragment sequencing provides an “open” diagnostics approach to detect any bacterium or fungus based on the presence of its mcfDNA in peripheral blood.
In one aspect of the invention, SPA fragment sequencing can provide an important non-invasive method for (early) detection and identification of infectious and colonizing bacteria using mcfDNA from peripheral blood samples, which can subsequently be linked to a broad range of diseases, including: screening for tuberculosis and other diseases caused by Mycobacterium species; determining pulmonary infection risks and causes in cystic fibrosis patients; determining the risk and onset of sepsis in patients with compromised immune systems; detection of opportunistic bacterial pathogens originating from the oral cavity that have been linked to Alzheimer's disease, pancreatic cancer and other serious conditions such as endocarditis; women's health issues including Chlamydia linked to mucopurulent cervicitis, pelvic inflammatory disease, tubal factor infertility, ectopic pregnancy and cervical cancer; detection and monitoring of progression of cancer; monitoring of minimal residual disease after oncology treatments; detection and monitoring of progression and minimal residual disease of breast cancer including triple negative breast cancer; detection of esophageal cancer, precancerous colonic polyps and early stage colorectal cancer, and detection and monitoring of progression and minimal residual disease of gastrointestinal cancers in general; detection and monitoring of progression and minimal residual disease in lung cancer; non-invasive analysis of the microbiome in pancreatic cancer patients to propose treatment protocols and prognostics for long-term survival; detection of Clostridium difficile infections; post-transplant bloodstream infections and Graft versus Host Disease (GvHD); detection of hospital acquired infections by emerging pathogens of clinical concern; detection of an infection in an immune compromised person; or detection of infection or inflammation of the gastrointestinal track in Irritable Bowel Disease (Crohn's disease, Ulcerative colitis); and combinations thereof. Therefore, SPA fragment sequencing represents a quantum leap forward to apply mcfDNA sequencing as a high-resolution, high-throughput and low-cost routine test in disease detection, patient monitoring, risk assessment and large-scale population screenings using mcfDNA informed biomarkers. For example the microbial footprint obtained with SPA fragment sequencing combined with the mutational footprint and methylation footprint that are currently being used as biomarkers for the detection, monitoring and prognostics of cancers, will provide a powerful tool for improved early detection and monitoring of progression of various types of cancer. It is expected that including the microbial footprint will increase the specificity and selectivity of screening tests, e.g. for the detection of early stage adenomas and carcinomas in colorectal cancer. Furthermore, once unique SPA fragments have been identified that correlate with the detection of specific diseases and monitoring of their progression, their sequences can be used to develop species-specific PCR-based screening assays as part of diagnostic platforms.
In addition to using mcfDNA from blood, the SPA fragment sequencing approach provided herein is applicable to analyze microbial DNA compositions in any sample type, especially when in samples having low amounts of small fragment microbial DNA. This includes biopsy samples from solid tumors, skin grafts, and other liquid biopsy samples besides peripheral blood, as well as mcfDNA present in stool samples.
In other instances, the methods and kits provided herein can be used for SPA fragment sequencing as a non-invasive method for (early) detection and identification of infectious and colonizing fungal microbes using mcfDNA from biological samples as described herein. For example, the set of reference microbes in this case includes reference fungal microbes. The method can be used to determine the presence of one or more fungi and/or to determine the fungal community composition. The one or more degenerate primers included in the amplification reaction in this embodiment includes complementarity to a conserved region of a human pathogenic fungal gene or DNA region designated for the set of reference fungal microbes. The conserved human pathogenic fungal gene or DNA region is herein referred to interchangeably for the purposes of the specification and claims as a “fungal phylogenetic marker gene”. In some instances, the fungal phylogenetic marker gene can be ITS1 or ITS2. The microbial community composition that can be calculated based on the percent of the sequences assigned to each species is a fungal community composition. The amplified mcfDNA fragments can include mcfDNA from one or more members of the Ascomycota, Basidiomycota and Mucoromycota, including Alternaria species, Aspergillus species, Blastomyces species, Candida species, Capnodiales species, Cladosporium species, Malassezia species, Phaeosphaeria species, Pseudozyma species, Saccharomyces species, Sporobolomyces species, Vishniacozyma species, and Yarrowia species.
In the SPA fragment sequencing method, a DNA region is identified in a suitable phylogenetic marker gene that has the following characteristics:
An overview of an exemplary SPA primer design method is shown in
A completely conserved nucleotide position will have 100% of one nucleotide and 0% for the other three nucleotides, and a variance of 0.25. A completely non-conserved region will have 25% of each nucleotide and a variance of 0. Primer candidates are prioritized based on their “average sequence variance” scores.
Primer candidates are evaluated for key properties including the level of primer degeneracy and annealing temperature (>50° C.). The sequences from the complete curated marker gene database are aligned to these conserved regions to determine their nucleotide compositions. The conservation of their 3′ nucleotide (must be >99% conserved among entries) and their “average sequence variance” scores are calculated (highly conserved regions have the highest score) and used to rank and select primer leads, prioritizing primers with the highest score.
In the next step, using a curated marker gene database, an algorithm (referred to as “SPA algorithm” in
In the next step, the proposed degenerate primer sequences are matched to the human genome sequence and the number of hits with increased number of allowed mismatches is determined. To minimize annealing to human genomic DNA, a primer should ideally have two or more mismatches with the human genome.
Various modifications and variations of the disclosed methods, compositions, and uses of the invention will be apparent to the skilled person without departing from the scope and spirit of the invention. Although the invention has been disclosed in connection with specific preferred aspects or embodiments, the invention as claimed should not be unduly limited to such specific aspects or embodiments.
The present invention may be implemented using hardware, software, or a combination thereof and may be implemented in one or more computer systems or other processing systems. In one aspect, the invention is directed toward one or more computer systems capable of carrying out the functionality described herein.
The following Examples have been included to provide guidance to one of ordinary skill in the art for practicing representative embodiments of the presently disclosed subject matter. In light of the present disclosure and the general level of skill in the art, those of skill can appreciate that the following Examples are intended to be exemplary only and that numerous changes, modifications, and alterations can be employed without departing from the scope of the presently disclosed subject matter.
SPA Fragment Sequencing Using the 16S rRNA Gene and the rpoB Gene as Phylogenetic Markers
As representative examples, the SPA sequencing approach was successfully demonstrated for the rpoB gene and the 16S rRNA gene as an example of a single-copy and multi-copy phylogenetic marker, respectively.
To validate the RpoB6-F1652 primer and the 16S-V4-R primer for SPA fragment amplification from the rpoB gene and the 16S rRNA gene, the following protocol was followed. Following the steps outlined in
To confirm the presence of mcfDNA in the blood samples, total cfDNA was isolated on 1.0 ml blood plasma and deep sequencing was used to determine the percentage of mcfDNA. In the case of these healthy donors, the percentage of mcfDNA was approximately 0.5% of the total cfDNA (data not shown). This is considerably lower than typically found in blood samples from e.g. cancer patients, where this ranged between approximately 1% to 4% (Poore et al, 2020).
Subsequently, following the supplier's instructions the xGen™ DNA Lib Prep MC kit (IDT) was used for end repair plus 5′-phosphorylation on 10 ng cfDNA fragments followed by the 3′ addition of a deoxy-adenine to create a 3′-sticky end of a single adenine nucleotide (Step 2), after which 20 ng of the asymmetric SPA-linker-UMI-Y was ligated to the repaired cfDNA fragments (Step 3) in a total volume of 16 μl.
The sequences of the two single stranded DNA fragments, SPA-cas1 and SPA-cas2, was used to create the asymmetric SPA-linker-UMI-Y linker cassette are listed in Table 1. The linker cassette was created by the following procedure. First, by annealing equal amounts (4 nmol) of SPA-cas1 and SPA-cas2. The mixture is first heated for 2 min. at 95° C., then for 10 min. at 65° C., 10 min. at 37° C., and finally 20 min. at room temperature. The mixture is kept on ice or stored at 4° C.
To repair the asymmetric linker cassette, a PCR reaction, referred to as PCR1, was performed on the ligation product using two primers: (a) the SPA1-seq-F primer that recognizes the repaired 5′ asymmetrical end of the linker cassette; (b) a primer that recognizes the primer annealing site specific for the conserved region of the phylogenetic marker gene, in this example the RpoB6-SPA-seq-F1652 primer. The forward (SPA1-seq-F) and reverse (e.g. RpoB6-SPA-seq-F1652) primers include a 5′ extension corresponding to the Illumina Read-1 and Read-2 sequences, respectively, to allow sequencing library preparation. The PCR1 was performed in 25 μl reaction containing 1×KAPA HiFi HotStart ReadyMix, 0.2 μM of each primer, and the Linker-cfDNA ligation products. The reaction was run in a thermocycler using the following program: 1 cycle at 95° C. for 10 min, 10 cycles at 98° C. for 20 sec, 65° C. to 50° C. for 30 sec and 72° C. for 15 sec, 35 cycles at 98° C. for 20 sec, 60° C. to 50° C. for 30 sec and 72° C. for 15 sec, and 1 cycle at 72° C. for 1 min. A similar protocol was followed for creating SPA fragments from the 16S rRNA gene using the 16S-seq-V4-R primer.
Once the asymmetric linker cassette was repaired, the SPA1-seq-F primer that recognizes the repaired 5′ asymmetrical end of the linker cassette can anneal and PCR1 amplification is initiated. In the case of the RpoB6-SPA-seq-F1652 primer this will result in the amplification of DNA sequences located downstream of position 1352 of the rpoB gene.
In a second PCR reaction (PCR2), Unique Dual Indexes (UDI) and Illumina sequencing anchors (P5 and P7) were added to the amplified SPA fragments using P5-15-Rd1 and P7-I7-Rd2 primers (see Table 1). The PCR2 was performed in 25 μl reaction containing 1×KAPA HiFi HotStart ReadyMix, 0.2 μM of each primer, and PCR1 bead cleaned products. The reaction was run in a thermocycler using the following program: 1 cycle at 95° C. for 3 min, 8 cycles at 95° C. for 30 sec, 55° C. for 30 sec and 72° C. for 30 sec, and 1 cycle at 72° C. for 5 min. The PCR2 was performed using unique sets of UDI for each sample, subsequently allowing the pooling of the libraries, after which fragments are paired-end sequenced using NGS Illumina sequencing, e.g. on the Illumina NEXTSEQ 1000 (Illumina, Inc., San Diego, CA). This approach resulted in sequenced fragments that all share the sequence of the gene specific primer (e.g., RpoB6-SPA-seq-F1652 primer) followed by sequences that vary in length and nucleotide composition. Sequences derived from the same microorganisms will be identical except for the length of the sequenced fragment, which will vary in function of the distance between the gene specific primer (e.g., RpoB6-SPA-seq-F1652 primer) annealing site and the end of the mcfDNA fragment. A similar protocol was followed for creating SPA fragments from the 16S rRNA gene using the 16S-seq-V4-R primer.
The analysis of the SPA fragment sequences included the following steps:
The database of bacterial rpoB genes was initially created by downloading their nucleotide sequences from the PATRIC database (Wattam et al, 2014) using the version available January 2021. If more than one (incomplete) rpoB gene was found for the same genome, we accepted the longest one, and rejected the shorter one(s). We confirmed for several instances our assumption that multiple rpoB genes in a single strain represented assembly errors, since each bacterium contains only one rpoB gene per genome. Genes were rejected if the genome had no taxonomy or if the gene was not annotated as “DNA-directed RNA polymerase beta subunit (EC 2.7.7.6)”. We evaluated all annotation rejections and found none that seemed to be rejected incorrectly. After January 2021, any new genome added to our genome database is searched for a rpoB gene by annotation, “DNA-directed RNA polymerase beta subunit (EC 2.7.7.6)”, and if found, its nucleotide sequence is added to the database of bacterial rpoB genes. These genomes come from PATRIC and NCBI (National Center for Biotechnology Information; https://www.ncbi.nlm.nih.gov/). Our curated database of bacterial rpoB genes contains 59,069 unique nucleotide sequences as of November 2021. For 16S sequences the 16S_ribosomal_RNA database was downloaded from NCBI.
The lengths of the ASV fragments for the RpoB6-F1652 primer and the 16S-V4-R primer are shown in
Table 2 is a sample of alignment results for the RpoB6-F1652 primer-based SPA fragment sequences, while Table 3 provides a sample of alignment results for the 16S-V4-R primer-based SPA fragment sequences. The presented alignments were required to have an identity of at least 90% across 90% of the bases of the query. E-values represent the probability of the alignment occurring by chance. In the sample results for the 16S-V4-R primer, a SPA fragment as short as 40 nucleotides was aligned with confidence of an E-value of 1.94E-14 against the 16S rRNA gene of Comamonas fiuminis strain CJ34. Both 16S rRNA gene and rpoB gene derived SPA fragments were found for Flavobacterium, Staphylococcus, and Pseudomonas.
Staphylococcus auricularis strain
Flavobacterium sp. strain UBA10157
Pseudomonas toyotomiensis strain
Finegoldia magna BVS033A4
Parvularcula sp. strain NAT21
Pseudomonas stutzeri ATCC 14405 =
Pseudomonas sp. strain NID84
Peptoniphilus harei ACS-146-V-
Quisquiliibacterium sp. CC-CFT501
Azoarcus sp. strain MCMED-G28
Sphingopyxis terrae strain DE15.006
Staphylococcus schweitzeri strain
Flavobacterium erciyesense strain
Rheinheimera sediminis strain
Rhodococcus yananensis strain
Cutibacterium acnes subsp.
elongatum strain K124
Angustibacter aerolatus strain
Aerococcus urinae strain NBRC
Pseudomonas soyae strain JL117
Pseudomonas saponiphila strain
Klebsiella quasivariicola strain
Comamonas fluminis strain CJ34
Primer Selection and SPA Protocol Based on the rpoB Gene as Phylogenetic Marker.
As a representative example, the SPA sequencing approach was successfully demonstrated for design of a rpoB gene specific SPA primer. A total of 50,569 unique rpoB gene sequences were downloaded from the PATRIC database (Wattam et al, 2014) using the version available in January 2021. RpoB gene sequences were identified based on their annotation as “DNA-directed RNA polymerase beta subunit (EC 2.7.7.6)”.
A subset of 50 rpoB gene sequences, representative for a broad range of phylogenetically distinct eubacterial reference microbes, were initially aligned by clustalW to identify conserved nucleotide regions of the rpoB gene, resulting in the identification of several conserved regions as primer candidates. This included the rpoB gene regions 1327-1352, 1528-1550, 1690-1709, 3766-3788 and 3808-3830, as well as the two regions identified by Ogier et al (2019), region 1630-1652 and region 2039-2063. The positions of the regions are based on the nucleotide sequence of the Escherichia coli rpoB gene.
Using the SPA algorithm, the 50,569 unique rpoB genes sequences were aligned to these conserved regions to determine their nucleotide compositions. The conserved nucleotide sequences of the rpoB gene regions 1327-1352, 1528-1550 and 1690-1709 are provided in
To select primers with the least risk for nonspecific annealing to human genomic DNA, the number of putative annealing sites of the proposed degenerate primer sequences to the human genome sequence (Reference: GCF_000001405.40_GRCh38.p14_genomic.fna) with increased number of allowed mismatches is determined. Results for the degenerate primers 16S-V3-F, 16S-V4-R, 16S-V6-R, RpoB6-F1652, RpoB7-R2039 and RpoB-R1327 are shown in Table 5. A primer should not have zero or one mismatch, and ideally no more than 10 instances of two mismatches with the human genome. Based on the results from this analysis, the primer 16S-V3-F showed an unexpectedly high number of putative annealing sites to the human genome, especially compared to the 16S-V4-R primer that also targets the V3-V4 region of the 16S rRNA gene and is, based on this result, considered unsuitable for SPA fragment sequencing.
We subsequently analyzed the minimal length of the variable regions required to have sufficient sequence-based phylogenetic resolution for species level identification, while keeping in mind the size of mcfDNA fragments of approximately 40-100 bp as determined by Burnham et al (2016) and Rassoulian Barrett et al (2020). To do so we calculated the numbers of unique SPA fragments with length of 25, 50, 75, 100 and 200 nucleotides for the regions located downstream of the annealing sites for the RpoB1-R1327 and RpoB7-R2039 primers, and upstream of the RpoB1-F1352 and RpoB6-F1652 primers, respectively. The results are presented in
The RpoB1-R1327 primer, which recognizes the rpoB gene sequence between positions 1327-1352 (positions based on the Escherichia coli rpoB gene sequence) and targets the region upstream of the primer annealing site, was validated in silico for the phylogenetic resolution of 50 nucleotide Single Point Amplification (SPA) fragments as described in EXAMPLES 3 to 9. In EXAMPLES 7 and 9 we also validated the RpoB6-R1630 primer, which recognizes the rpoB gene sequence between positions 1630-1652.
To analyze their phylogenetic resolution, sequences of 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico and analyzed on the genus or species level. Based on the size range of mcfDNA in blood of approximately 40-100 bp (Burnham et al, 2016) it very likely that SPA fragments of approximately 50 nucleotides can be obtained, this in addition to a small number of larger fragments. In EXAMPLES 3 to 9 we demonstrate that 50 nucleotide long SPA fragments provide sufficient phylogenetic resolution to distinguish a wide range of clinically relevant pathogenic bacteria at the species level. To further increase the resolution, we also validated in EXAMPLES 7 and 9 the RpoB6-R1630 primer, which recognizes the rpoB gene sequence between positions 1630-1652.
It should be noted that since we compare the SPA fragments for strain identification against a deduplicated database, the number of strains found for a SPA fragment represents the number of distinct rpoB gene sequences that share a common SPA fragment.
SPA fragment sequences for identification of Mycobacterium species.
Tuberculosis (TB) is an infectious disease for which cfDNA sequencing based diagnostics seems very promising. Clinical recognition of TB is hampered by its long latency and nonspecific presenting symptoms. In addition, people who have received the Bacillus Calmette-Guerin (BCG) vaccine cannot be tested for active TB using routine skin test screening (https://www.cdc.gov/tb/topic/testing/testingbcgvaccinated.htm). Of the estimated 10.4 million active TB cases occurring worldwide in 2016, it is estimated that 40% remained either undiagnosed or unreported, in large part due to inadequate diagnostics. Etiological diagnosis is typically delayed when reliant solely on the acid-fast bacillus (AFB) culture method, while invasive biopsies are often necessary to cultivate the pathogen from deep-seated infections. For an early diagnosis of tuberculosis, researchers have established several targeted Mycobacterium tuberculosis mcfDNA assays (PCR-based methods) to determine the presence of infection by detecting Mycobacterium tuberculosis mcfDNA in blood and urine specimens (Fernández-Carballo et al, 2019). More recently, the performance of deep plasma mcfDNA sequencing was evaluated in patients with tuberculosis infection, including the direct detection in a series of cases of invasive Mycobacterium chimaera infection (Nomura et al, 2019), providing accurate noninvasive microbiologic confirmation in approximately 4 days, which was more than one month faster than standard AFB culture method. Similarly, other successful applications in diseases such as opportunistic Mycobacterium avium or Mycobacterium tuberculosis infections in HIV/AIDS patients (Zhou et al, 2019) and aneurysms infected by Mycobacterium bovis due to Bacille Calmette-Guerin (BCG) instillation (Vudatha et al, 2019) demonstrate that mcfDNA analysis provides a promising, less-invasive diagnostic and monitoring tool for TB. Unfortunately, due to the need for costly deep NGS sequencing, mcfDNA sequencing is not feasible for routine and large-scale screening for TB. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost detection of Mycobacterium tuberculosis and other disease-causing Mycobacterium strains, something SPA fragment sequencing can deliver. As such, TB and the detection of Mycobacterium species represents an important application for SPA fragment sequencing-based detection.
To evaluate its application for the reliable detection of TB and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Mycobacterium species, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Mycobacterium strains. The results are resented in Table 6.
Mycobacterium (My) specific SPA fragment
GTCAGACCACGATGACCGTTCCGGGCGGCGTCGAGGTGCCGGT
GGAAACC (SEQ ID NO: 50)
Mycobacterium tuberculosis
Mycobacterium tuberculosis subsp. africanum
Mycobacterium canettii
Mycobacterium orygis
GTCAGACCACGATGATCGTTCCGGGCGGCGTCGAGGTGCCGGT
GGAAACC (SEQ ID NO: 51)
Mycobacterium tuberculosis subsp. africanum
Mycobacterium tuberculosis
GCCAGACCACGATGACCGCCCCCGGTGGCGTCGAGGTGCCGGT
GGATGTG (SEQ ID NO: 52)
Mycobacterium abscessus
GCCAGACCACGATGACCGCCCCCGGCGGCGTCGAGGTGCCGGT
GGACGTG (SEQ ID NO: 53)
Mycobacterium abscessus
Mycobacterium abscessus subsp. massiliense
GCCAGACCACGATGACCGCCCCCGGCGGCGTCGAGGTGCCGGT
GGATGTG (SEQ ID NO: 54)
Mycobacterium abscessus
GCCAGACCACGATGACCGCCCCCGGGGGCGTCGAGGTGCCGGT
GGATGTT (SEQ ID NO: 55)
Mycobacterium abscessus
GCCAGACCACGATGACCGCCCCCGGGGGCGTCGAGGTGCCGGT
GGATGTG (SEQ ID NO: 56)
Mycobacterium abscessus
GTCAGCCCACGATGACCGTCCCGGGCGGCATCGAGGTGCCGGT
Mycobacterium avium
GTCAGCCCACGATGACCGTCCCCGGCGGCATCGAGGTGCCGGT
GGAGACC (SEQ ID NO: 58)
Mycobacterium avium
Mycobacterium MAC_011194 8550
Mycobacterium MAC_080597_8934
AGCCCGCTGTCATGACTGTCCCCGGCGGCATCGAGGTGCCGGT
GGAGACC (SEQ ID NO: 59)
Mycobacterium chimaera
GTCAGTCGACAATGACTGTCCCAGGTGGGGTAGAAGTGCCAGT
GGAAACT (SEQ ID NO: 60)
Mycobacterium leprae
GGCACGCCACGATGAAGGTCCCCGGTGGCGTCGAGGTGCCGGT
GGAGACC (SEQ ID NO: 61)
Mycobacterium xenopi
GCCAGCCCACGATGACCGTCCCCGGCGGCATCGAGGTGCCGGT
GGAGACC (SEQ ID NO: 62)
Mycobacterium intracellulare
Mycobacterium paraintracellulare
GCCAGGCCACGATGACCGTGCCGGGGGGGGTCGAGGTGCCGGT
GGAAACC (SEQ ID NO: 63)
Mycobacterium kansasii
AGCCCGCCGTCATGACTGTGCCCGGCGGGGTCGAGGTCCCGGT
GGAAACC (SEQ ID NO: 64)
Mycobacterium kansasii
Mycobacterium MK142
Mycobacterium MK21
GTGACCAGACGATGACCGCGCCCGGCGGCTCCGAGGTGCCCGT
CGAGGTC (SEQ ID NO: 65)
Mycobacterium gilvum
CGAGGTG (SEQ ID NO: 66)
Mycobacterium conceptionense
Mycobacterium neworleansense
Mycobacterium nonchromogenicum
GCCAGACCGCGATGACCGCTCCGGGCGGTGTCGAGGTGCCGGT
CGAGACC (SEQ ID NO: 67)
Mycobacterium liflandii
Mycobacterium marinum
Mycobacterium pseudoshottsii
Mycobacterium shottsii
Mycobacterium ulcerans
GCCAGACCTCGATGACGGTGCCCGGCGGTGTCGAGGTGCCGGT
CGAGGTG (SEQ ID NO: 68)
Mycobacterium chlorophenolicum
Mycobacterium chubuense
Mycolicibacterium psychrotolerans
The 50 nucleotide SPA fragments were found to be highly distinctive for clinically relevant Mycobacterium species, including Mycobacterium tuberculosis, Mycobacterium avium, Mycobacterium chimaera and Mycobacterium leprae. For instance, the dataset included 290 Mycobacterium tuberculosis plus Mycobacterium tuberculosis subsp. africanum strains that could be identified by two distinct SPA fragments, SPA fragments My1 and My2. SPA fragment My1 identified 291 strains. In addition to 286 Mycobacterium tuberculosis strains and one Mycobacterium tuberculosis subsp. africanum strain, this fragment was also present in three Mycobacterium canettii strains and one Mycobacterium orygis strain, both members of the Mycobacterium tuberculosis complex and very closely related to Mycobacterium tuberculosis.
The similarities between the strains identified by SPA fragments My1 and My2 was analyzed using whole genome-based Average Nucleotide Identity (Arahal, 2014). The results are presented in
Mycobacterium avium strains, which can cause serious infection in immune compromised patients, such as HIV/AIDS patients, are identified by two distinct SPA fragments, My8 and My9. In addition to recognizing four Mycobacterium avium strains, SPA fragment My9 also identified two metagenome assembled genomes (MAG), Mycobacterium MAC_011194_8550 and Mycobacterium MAC_080597_8934. Based on the specificity of this fragment for Mycobacterium avium it is assumed that the two MAGs are representatives of Mycobacterium avium, as was confirmed by whole genome-based ANI analysis (
The 97 strains belonging to Mycobacterium abscessus and Mycobacterium abscessus subsp. Massiliense could be identified by five distinct 50 nucleotide SPA fragments (My 3 to My7), with no other species being identified. Unique SPA fragments also identified the clinically relevant species Mycobacterium chimaera (My10) and Mycobacterium leprae (My11).
A few SPA fragments identified multiple distinct Mycobacterium species. For instance, eight strains of Mycobacterium conceptionense, Mycobacterium fortuitum (2 strains), Mycobacterium neworleansense, Mycobacterium nonchromogenicum, Mycobacterium vulneris, Mycolicibacterium boenickei, and Mycobacterium senegalense shared the common 50 nucleotide SPA fragment My17. Except for Mycobacterium nonchromogenicum, these strains all belong to the Mycolicibacterium gen. nov. (“fortuitum-vaccae” clade) and are very closely related (Gupto et al, 2018). It is generally accepted in the field that ANI values around 95% correspond to the 70% DNA-DNA hybridization cut-off value, which is widely used to delineate archaeal and bacterial species (Arahal, 2014). Whole genome-based ANI analysis (
These results show that, unexpectedly, despite their relatively short size, sequences of 50 nucleotide long SPA fragments covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of Mycobacterium at the species or clade level (as summarized in Table 7), including the clinically relevant species. This shows the importance and potential of SPA fragment sequencing as a new approach for high-throughput TB screening, based on the (early) detection and identification of infectious Mycobacterium species using mcfDNA from peripheral blood and/or urine samples.
Mycobacterium (My)
Mycobacterium tuberculosis
Mycobacterium abscessus
Mycobacterium avium
Mycobacterium chimaera
Mycobacterium leprae
Mycobacterium xenopi
Mycobacterium (para)intracellulare
Mycobacterium kansasii
Mycobacterium gilvum
Mycolicibacterium gen. nov.
(“fortuitum-vaccae” clade)
Mycobacterium gen.
(“tuberculosis-simiae” clade)
SPA Fragment Sequences for the Detection of Bacterial Pathogens Associated with Pulmonary Infection Risks in Cystic Fibrosis Patients.
Cystic fibrosis (CF), the most common autosomal genetic disease in North America affecting 1:2000 Caucasian individuals, is characterized by chronic lung malfunction, pancreatic insufficiencies and high levels of chloride in sweat. Its high mortality index is evident when lung and spleen are affected, but other organs can also be affected. The persons affected die by progressive bronchiectasis and chronic respiratory insufficiency. CF patients will see a succession of lung inflammation by opportunistic pathogenic bacteria. During the first decade of life of CF patients, Staphylococcus aureus and Hemophilus influenzae are the most common bacteria, but in the second and third decade of life, Pseudomonas aeruginosa is the prevalent bacterium. Other important infectious bacterial pathogens associated with pulmonary infection risks in cystic fibrosis patients include Nontuberculous Mycobacteria (NTM) and Burkholderia cepacia (for review, see Coutinho et al, 2008). Therefore, there is an unmet need for high-resolution, high-throughput and low-cost detection of opportunistic pathogenic bacteria in CF patients, something SPA fragment sequencing can provide. The same is generally true for patients having a compromised immune system.
Mycobacterium species: The most common NTM infecting CF patients are Mycobacterium abscessus (identified by SPA fragments My3 to My7), Mycobacterium avium (identified by SPA fragments My8 and My9), and Mycobacterium (para)intracellulare (identified by SPA fragments My13), with Mycobacterium abscessus the NTM more likely associated with the disease, all of which can be identified by their unique SPA fragments (see Table 7).
Staphylococcus aureus: This is usually the first pathogen to infect and colonize the airways of CF patients. This microorganism is prevalent in children and may cause epithelial damage, opening the way to the adherence of other pathogens such as Pseudomonas aeruginosa. To evaluate its application for the reliable detection of chronic infection in CF patients by Staphylococcus aureus and related species, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Staphylococcus species, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Staphylococcus strains. The results are presented in Table 8
Staphylococcus aureus (Sa) specific SPA fragment
TTGCTTCAATGAGTTACTTCTTTAACTTATTAAGCGGTATTGGAT
ATACA (SEQ ID NO: 69)
Staphylococcus aureus
TCGCTTCAATGAGTTACTTCTTTAACTTATTAAGTGGTATTGGAT
ATACA (SEQ ID NO: 70)
Staphylococcus aureus
Staphylococcus hyicus
TCGCTTCAATGAGTTATTTCTTTAACTTATTAAGTGGTATTGGAT
Staphylococcus argenteus
Staphylococcus aureus
TTGCTTCAATGAGTTATTTCTTTAACTTATTAAGTGGTATTGGAT
ATACA (SEQ ID NO: 72)
Staphylococcus aureus
Staphylococcus schweitzeri
TCGCTTCAATGAGTTACTTCTTTAACTTATTAAGCGGTATTGGAT
ATACA (SEQ ID NO: 73)
Staphylococcus aureus
TCGCTTCAATGAGTTACTTCTTTAATTTATTAAGTGGTATTGGAT
ATACA (SEQ ID NO: 74)
Staphylococcus aureus
GTTGAAACTTGCGCACATGGTTGATGATAAATTACATGCGCGTT
CAACAG (SEQ ID NO: 75)
Staphylococcus aureus
Based on the SPA fragment sequences, four mixed clusters were identified, each with their unique 50 nucleotide fragment (Table 8), that contained Staphylococcus aureus. Whole genome-based ANI analysis on representative members of these four clusters revealed that they grouped in three highly distinct species (
ANI group I, comprised of strains identified by SPA fragments Sa1 and Sa2. With the exception of a single Staphylococcus hyicus strain, the 521 strains identified by Sa1 and Sa2 were all Staphylococcus aureus. Since the Staphylococcus hyicus strain had a 98% ANI score with the Staphylococcus aureus strains, similar to the score between Staphylococcus aureus strains, it also belongs to this species (Arahal, 2014). This confirms that SPA fragments Sa1 and Sa2 are specific for the identification of Staphylococcus aureus strains.
ANI group II, comprised of strains identified by SPA fragment Sa3. These strains had been previously identified as Staphylococcus argenteus and Staphylococcus aureus. Since these strains had ANI scores of 87% to 88% with the ANI group I Staphylococcus aureus strains, they represent a different species (Arahal, 2014), most likely Staphylococcus argenteus. Thus, SPA fragment Sa3 seems to be specific for the identification of Staphylococcus argenteus strains.
ANI group III, comprised of strains identified by SPA fragment Sa4. These strains had been previously identified as Staphylococcus schweitzeri and Staphylococcus aureus. Since these strains had ANI scores of 88% to 89% with the ANI group I Staphylococcus aureus strains and 92% with the ANI group II Staphylococcus argenteus strains, they represent a different species (Arahal, 2014), most likely Staphylococcus schweitzeri. Thus, SPA fragment Sa4 seems to be specific for the identification of Staphylococcus schweitzeri strains.
Despite their relatively short size, 50 nucleotide long SPA sequencing fragments covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of Staphylococcus at the species level (as summarized in Table 9), including the clinically relevant species Staphylococcus aureus and Staphylococcus argenteus.
Staphylococcus (Sa)
Staphylococcus aureus
Staphylococcus argenteus
Staphylococcus schweitzeri
Pseudomonas aeruginosa This species is part of the normal microbial population of the respiratory tract, where it is an opportunistic pathogen in CF patients. Pseudomonas aeruginosa causes infections in more than 50% of CF patients, especially in adult CF patients, as infection has been shown in 20% CF patients 0-2 years old while in 81% in adult groups (>18 years old). To evaluate its application for the reliable detection of chronic infection in CF patients by Pseudomonas aeruginosa and related species, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Pseudomonas aeruginosa species, 50 nucleotide long SPA fragments located upstream of the RpoB11-R1327 priming site were generated in silico for Pseudomonas aeruginosa strains. The results are presented in Table 10.
TCGATGTGCTCAAGACCCTCGTCGACATCCGTAACGGCAAGGGC
ATCGTC (SEQ ID NO: 76)
Pseudomonas aeruginosa
Pseudomonas FDAARGOS_761
Pseudomonas fluorescens
Pseudomonas HMSC063H08
Pseudomonas HMSC066A08
Pseudomonas HMSC066B03
Pseudomonas HMSC066B11
Pseudomonas HMSC067F09
Pseudomonas HMSC070B12
Pseudomonas HMSC075A08
Pseudomonas RW410
Acinetobacter baumannii
TCGATGTGCTCAAGACCCTGGTCGACATCCGTAACGGCAAGGGC
ATCGTC (SEQ ID NO: 77)
Pseudomonas aeruginosa
Pseudomonas psychrotolerans
Pseudomonas SL25
TCGATGTGCTCAAGACCCTCGTCGATATCCGTAACGGCAAGGGC
ATCGTC (SEQ ID NO: 78)
Pseudomonas aeruginosa
TCGAGGTCCTTAAGACCCTGGTCGATATCCGTAACGGCAAAGGC
ATTGTC (SEQ ID NO: 79)
Pseudomonas aeruginosa
Pseudomonas p99-361
Pseudomonas putida
Based on the SPA fragment sequences, four clusters were identified, each with their unique 50 nucleotide fragment (Table 10), that contained Pseudomonas aeruginosa. ANI analysis on representative members of these four clusters revealed that they grouped in three highly distinct species (
ANI group I, which is comprised of strains identified by SPA fragments Pa1 and Pa2, represents Pseudomonas aeruginosa. Based on their ANI scores of 98% to 99%, the Pseudomonas fluorescens strain NCTC10783 and the Acinetobacter baumannii strain 4300STDY7045820 were previously misclassified and represent Pseudomonas aeruginosa strains. The only strain identified by SPA fragment Pa2 that fell outside of ANI group I was Pseudomonas psychrotolerans strain DSM 15758. This should cause no problem as this species, which grows at lower temperature than P. aeruginosa, is not clinically relevant.
ANI group III, which is comprised of strains identified by SPA fragments Pa4. This group, which includes three Pseudomonas strains, is based on its ANI score (76% to 78%) distinct from the Pseudomonas aeruginosa strains identified by SPA fragments Pa1 and Pa2.
Thus, despite their relatively short size, sequences of 50 nucleotide long SPA fragments covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of Pseudomonas aeruginosa at the species level (as summarized in Table 11).
Pseudomonas aeruginosa
Pseudomonas
aeruginosa
Pseudomonas species
Burkholderia cepacia complex (BCC): A bacterial complex with twenty genomic species (genomovars): genomovar I (B. cepacia), II (B. multivorans), III (B. cenocepacia), IV (B. stabilis), V (B. vietnamiensis), VI (B. dolosa), VII (B. ambifaria), VIII (B. anthina), IX (B. pyrrocinia), and more recently B. stagnalis, B. territorii, B. ubonensis, B. contaminans, B, seminalis, B. metallica, B. arboris, B. lata, B. latens, B. pseudomultivorans, and B. diffusa was reported by Depoorter et al (2016). Infected CF patients show high levels of BCC in the salivary fluid, with transmission rates, prognosis and mortality being distinctly characteristic for each genomovar, as are the treatment strategies. Of the over 20 formally named species within the complex, Burkholderia multivorans (genomovar II) and Burkholderia cenocepacia (genomovar III) together account for approximately 85-97% of all BCC infections in CF (Savi et al, 2019). To evaluate its application for the reliable detection of chronic infection in CF patients by Bulkholderia cepacia and related BCC complex species, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Burkholderia species, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Burkholderia strains. The results are presented in Table 12.
Burkholderia cepacia complex (Bcc) specific SPA fragment
TCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAGGGC
GAAGTG (SEQ ID NO: 80)
Burkholderia strains
Burkholderia ambifaria-(VII)
Burkholderia anthina-(VIII)
Burkholderia cenocepacia-(III)
Burkholderia cepacia-(I)
Burkholderia contaminans-(XIII)
Burkholderia diffusa
Burkholderia lata
Burkholderia latens
Burkholderia metallica
Burkholderia multivorans-(II)
Burkholderia pseudomultivorans
Burkholderia pyrrocinia-(IX)
Burkholderia seminalis
Burkholderia stabilis-(IV)
Burkholderia stagnalis
Burkholderia territorii
Burkholderia thailandensis
Burkholderia ubonensis
Burkholderia vietnamiensis-(V)
Paraburkholderia bannensis
Paraburkholderia caryophylli
Paraburkholderia tropica
Paraburkholderia strains
TCGCGACGATCAAGATCCTCGTCGAACTGCGCAACGGCAAGGGC
GAAGTG (SEQ ID NO: 81)
Burkholderia ambifaria-(VII)
Burkholderia cepacia-(I)
Burkholderia diffusa
Burkholderia pyrrocinia-(IX)
Burkholderia ubonensis
6
Burkholderia strain
10
Paraburkholderia strain
TCGCGACGATCAAGATCCTCGTCGAGTTGCGCAACGGCAAGGGC
GAAGTG (SEQ ID NO: 82)
Burkholderia cenocepacia-(III)
Burkholderia cepacia-(I)
Burkholderia dabaoshanensis*
Burkholderia LK4
TCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAGGGC
GAAGTA (SEQ ID NO: 83)
Burkholderia cepacia-(I)
Burkholderia territorii
TCGCGACGATCAAGATCCTCGTCGAGCTGCGCAATGGCAAGGGC
GAAGTG (SEQ ID NO: 84)
Burkholderia lata
Burkholderia multivorans-(II)
Burkholderia ubonensis
TCGCGACGATCAAGATCCTGGTCGAGCTGCGCAACGGCAAGGGC
GAAGTG (SEQ ID NO: 85)
Burkholderia strains
Burkholderia ubonensis
Burkholderia vietnamiensis-(V)
Paraburkholderia strains
TCGCGACGATCAAGATTCTCGTCGAGCTGCGCAACGGCAAGGGC
GAAGTG (SEQ ID NO: 86)
Burkholderia strains
Burkholderia ubonensis
Based on the SPA fragment sequences, seven clusters were identified, each with their unique 50 nucleotide fragment (Table 12), that contained Burkholderia cepacia. ANI analysis on representative members of various clusters defined by the SPA fragments Bcc1 (, Bcc1 and Bcc2, Bcc1 and Bcc3, and Bcc1, Bcc6 and Bcc7, revealed that 50 nucleotide SPA fragments fail to phylogenetically distinguish between the Burkholderia cepacia complex strains. In addition, a very limited number of strains that fall outside the Burkholderia cepacia complex were found to have similar SPA fragments. ANI analysis confirmed that these strains, such as Parabacteroides strains found to have SPA fragment Bcc1, were not misclassifie
To address the lack of phylogenetic resolution of 50 nucleotide SPA fragments for Burkholderia cepacia complex strains, larger SPA fragments were analyzed. Increasing the SPA fragment length to 75 nucleotides had only a minor effect on the phylogenetic resolution. For instance, the extended 75 nucleotide version of SPA fragment Bcc1 identified 479 strains, with the major difference being the removal of five Paraburkholderia strains. However, increasing the SPA fragment length to 100 nucleotides resulted in the breakup of the SPA fragment Bcc1 group with increased phylogenetic resolution that allowed for differentiation between several species belonging to the Burkholderia cepacia cluster. Since we expect to get for each species a limited number of SPA fragments with sizes around 100 nucleotides, as we showed in EXAMPLE 1, SPA fragment sequencing should allow for classification of Burkholderia cepacia cluster species with sufficient phylogenetic resolution. This is shown in Table 13 and Table 14 for the strains initially identified by the 50 nucleotide SPA fragment Bcc1.
Burkholderia cepacia complex (Bcc) specific SPA fragment
CAGCCGTGACGAGATCACCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 87)
Burkholderia cepacia-(I)
Burkholderia contaminans-(XIII)
Burkholderia ambifaria-(VII)
Burkholderia pyrrocinia-(IX)
Burkholderia stabilis-(IV)
Burkholderia anthina-(VIII)
Burkholderia species
CGGCCGCGACGAGATCACCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 88)
Burkholderia ubonensis
CGGCCGTGACGAAATCACCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 89)
Burkholderia Bp5365
Burkholderia thailandensis ($)
Burkholderia MSMB1588
CGGCCGTGACGAAATCACGGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 90)
Burkholderia multivorans-(II)
Paraburkholderia caryophylli
CGGCCGTGACGAAATCGTCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 91)
Burkholderia vietnamiensis-(V)
Burkholderia ubonensis
Paraburkholderia Cy-641
Paraburkholderia CNPSo
Burkholderia species
CGGCCGTGACGAGATCACCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 92)
Burkholderia ubonensis
Burkholderia stagnalis
Burkholderia pyrrocinia-(IX)
Burkholderia multivorans-(II)
Burkholderia ambifaria-(VII)
Burkholderia species
CGGCCGTGACGAGATCATCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 93)
Burkholderia MSMB1498
Burkholderia MSMB617WGS
Burkholderia MSMB2042
Burkholderia BDU19
Burkholderia BDU18
Burkholderia MSMB0852
CGGCCGTGACGAGATCGTCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 94)
Burkholderia cenocepacia-(III)
Burkholderia territorii
Burkholderia seminalis
Burkholderia cepacia-(I)
Burkholderia metallica
Burkholderia latens
Burkholderia species
CGGCCGTGATGAAATCGTCGGTCCGATGACGCTGCAGGACGACGA
CATTCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 95)
Paraburkholderia species
CGGTCGCGACGAGATCGTCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 96)
Burkholderia ubonensis
CGGTCGTGACGAAATCGTCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 97)
Burkholderia latens
Burkholderia cenocepacia-(III)
GGGCCGTGACGAAATCACCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 98)
Burkholderia pseudomultivorans
Burkholderia TJI49
GGGCCGTGACGAAATCGTCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 99)
Paraburkholderia tropica
Burkholderia vietnamiensis-(V)
Paraburkholderia bannensis
GGGTCGTGACGAAATCACCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 100)
Burkholderia pseudomultivorans
Burkholderia cenocepacia-(III)
CGGCCGCGACGAGATCGTCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 101)
Burkholderia USM
Burkholderia AU16741
CGGCCGCGATGAAATCGTCGGCCCGATGACGCTGCAGGACGACGA
CATCCTCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAG
GGCGAAGTG (SEQ ID NO: 102)
Trinickia 7GSK02
Burkholderia DHOD12
Using 100 nucleotide long SPA sequencing fragments covering the region upstream of the RpoB1-R1327 primer annealing site significantly increased the resolution for phylogenetic identification of Burkholderia cepacia complex species, as is summarized in Table 14.
Burkholderia cepacia
Burkholderia cepacia complex
Burkholderia ubonensis
Burkholderia species Nov.
Burkholderia multivorans-(II)
Burkholderia cepacia complex ($)
Burkholderia cepacia complex
Burkholderia species Nov.
Burkholderia cepacia complex
Paraburkholderia species
Burkholderia ubonensis
Burkholderia cepacia complex
Burkholderia pseudomultivorans
Paraburkholderia species ($)
Burkholderia cepacia complex
Burkholderia species Nov.
Trinickia species
Burkholderia pseudomallei group: Most members of the Burkholderia pseudomallei group including Burkholderia mallei, Burkholderia oklahomensis and
Burkholderia pseudomallei are considered pathogenic. Table 15 shows that two unique SPA fragments, Bpm1 and Bpm2, reliably identified these clinically relevant species. Burkholderia thailandensis, also a member of the Burkholderia pseudomallei complex, is generally considered nonpathogenic. Burkholderia thailandensis could be identified by its own unique SPA fragment, Bpm3. This result, which was also confirmed by the ANI analysis of
pseudomallei group species and the number of strains is indicated. The SPA fragments
Burkholderia pseudomallei (Bpm) specific SPA fragment (50 nucleotides)
TCGCGACGATCAAGATCCTCGTCGAGCTGCGCAACGGCAAGGGC
GAAGTC (SEQ ID NO: 103)
Burkholderia
117
Burkholderia
ABCPW-14
Burkholderia
BDU8
Burkholderia
mallei
Burkholderia
oklahomensis
Burkholderia
pseudomallei
TCGCGACGATCAAGATCCTCGTCGAGTTGCGCAACGGCAAGGGC
GAAGTC (SEQ ID NO: 104)
Burkholderia
pseudomallei
TCGCGACGATCAAGATTCTCGTCGAGCTGCGCAACGGCAAGGGC
GAAGTC (SEQ ID NO: 105)
Burkholderia
thailandensis
Haemophilus influenzae: This species usually infects younger CF patients. For example, in Brazil, 20.4% of CF children between 6 and 12 years old are infected by Haemophilus influenzae. This bacterium hyper-mutates, which can be related to its resistance to antibiotics, making treatment more difficult. To evaluate its application for the reliable detection of chronic infection in CF patients by Haemophilus influenzae and related species, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Haemophilus influenzae species, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Haemophilus influenzae strains. The results are presented in Table 16.
Haemophilus influenzae species. For each SPA fragment, the Haemophilus influenzae species
influenzae strains and Haemophilus strains that shared their SPA fragment are reported.
Haemophilus influenzae -specific (Hi) SPA fragments received a unique numerical identifier
Haemophilus influenza (Hi) specific SPA fragment (50 nucleotides)
TTGCGGTAATGCGTAAATTGATCGACATCCGTAATGGTCGTGGC
GAAGTA (SEQ ID NO: 106)
Haemophilus aegyptius
Haemophilus HMSC066A11
Haemophilus influenzae
TTCGTGTGATGAAAAAACTCATCGATATCCGTAATGGTCGTGGT
GAAGTG (SEQ ID NO: 107)
Haemophilus HMSC068C11
Haemophilus influenzae
Haemophilus parainfluenzae
TTGCGGTAATGCGTAAATTGATTGACATCCGTAATGGTCGTGGC
GAAGTA (SEQ ID NO: 108)
Haemophilus influenzae
TTCGTGTGATGAAAAAACTCATCGACATCCGTAATGGTCGTGGT
GAAGTG (SEQ ID NO: 109)
Haemophilus HMSC61B11
Haemophilus parainfluenzae
TTGCGGTAATGCGTAAATTGATTGACATCCGTAATGGTCGCGGC
GAAGTA (SEQ ID NO: 110)
Haemophilus influenzae
TTCGTGTGATGAAAAAACTCATCGACATCCGTAATGGTCGTGGT
GAAGTA (SEQ ID NO: 111)
Haemophilus influenzae
Haemophilus parainfluenzae
TTGCGGTAATGCGTAAATTAATCGACATCCGTAATGGTCGTGGC
GAAGTA (SEQ ID NO: 112)
Haemophilus haemolyticus
Haemophilus influenzae
TCGCGGTAATGCGTAAATTGATTGACATCCGTAATGGTCGTGGC
GAAGTA (SEQ ID NO: 113)
Haemophilus influenzae
TTGCGGTAATGCGTAAATTAATTGACATCCGTAATGGTCGTGGC
GAAGTA (SEQ ID NO: 114)
Haemophilus influenzae
The species identified by the SPA fragments Hi1, H2, Hi6 and Hi7 were further analyzed by ANI, which resulted in the identification of two distinct ANI groups (
ANI group I, comprised of strains identified by SPA fragments Hi2 and Hi6, represents the Haemophilus parainfluenzae strains. It also shows that Pasteurellaceae HGM20799, which has an ANI score of 94% to 95% with the other strains in this cluster, should be reclassifies as Haemophilus parainfluenzae.
ANI group II, comprised of strains identified by SPA fragments Hi1 and Hi7, represents the Haemophilus influenzae strains. It also shows that the Haemophilus aegyptius strain, which has ANI scores of 97% with the other strains in this cluster, should be reclassifies as Haemophilus influenzae. The Haemophilus haemolyticus strain, which was identified by SPA fragment Hi7, seems to be an outlier in this group with an ANI score of 89% with the other strains in this cluster.
Compared to other species, the ANI scores between members of the same ANI group are relatively low, around 95% instead of 98% to 99%. This might reflect the hyper-mutation phenotype of members of the genus Haemophilus. Overall, sequences of 50 nucleotide long SPA fragments covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of Haemophilus influenzae and Haemophilus parainfluenzae at the species level (as summarized in Table 17).
Haemophilus influenza
Haemophilus
influenzae
Haemophilus
parainfluenzae
Overall, SPA fragments are capable of high resolution phylogenetic identification of opportunistic pathogenic bacteria frequently found to cause infections in CF patients. As such, SPA fragment sequencing represents a powerful tool to evaluate infections in CF patients as their treatment, including the selection of antibiotics, depends on the correct identification of the infectious species.
Opportunistic pathogens of clinical relevance, including Pseudomonas aeruginosa, Mycobacterium abcessus, and Staphylococcus aureus, have been found as the cause of sepsis in patients with compromised immune systems. The successful use of SPA fragments for the high-resolution phylogenetic identification of these species has been described in EXAMPLES 3 and 4.
Streptococcus species, including S. pneumonia, S. pyogenes and S. intermedius are also frequently found as opportunistic pathogens in patients with compromised immune systems, such as HIV/AIDS patients, organ transplant patients or cancer patients undergoing chemotherapy. In addition, other clinically relevant Streptococcus species such as Streptococcus gallolyticus, Streptococcus macedonicus, Streptococcus pasteurianus and Streptococcus equinus, have been linked to cancer. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost detection of opportunistic pathogenic Streptococcus species, something SPA fragment sequencing can provide. To evaluate its application for the reliable detection in peripheral blood of opportunistic pathogenic bacteria leading to sepsis by Streptococcus species, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Streptococcus species, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Streptococcus strains. The results are presented in Table 18.
Streptococcus species. For each SPA fragment, the Streptococcus species and the number of
Streptococcus species (St) specific SPA fragment (50 nucleotides) sequence
TTGCTGAGATGAGCTACTTCCTCAACTTGGCTGAAGGACTTGGC
CGTGTA (SEQ ID NO: 115)
Streptococcus pneumoniae
Streptococcus pseudopneumoniae
Streptococcus mitis
Streptococcus D19
Streptococcus OH4692_COT-348
TGGCAGAAATGTCTTACTTCTTGAACCTTGCTGAAGGTCTTGGAA
AAGTT (SEQ ID NO: 116)
Streptococcus dysgalactiae
Streptococcus NCTC
Streptococcus pyogenes
TGGCAGAAATGTCTTACTTCTTGAACCTTGCAGAAGGTCTTGGA
AAAGTT (SEQ ID NO: 117)
Streptococcus pyogenes
TGGCAGAAATGTCATACTTCTTGAACCTTGCTGAAGGTCTTGGA
AAAGTT (SEQ ID NO: 118)
Streptococcus pyogenes
TAGCTGAAATGTCTTATTTCCTTAACTTGGCTGAGGGTCTAGGTA
AAGTT (SEQ ID NO: 119)
Streptococcus mutans
TGGCTGAAATGAGCTACTTCCTCAACTTGGCTGAGGGTCTTGGT
CGTGTA (SEQ ID NO: 120)
Streptococcus suis
TGGCTGAAATGAGCTACTTCCTCAACTTGGCTGAAGGACTTGGT
CGCGTA (SEQ ID NO: 121)
Streptococcus suis
TTGCCGAGATGAGCTACTTCCTCAACTTGGCTGAAGGACTTGGC
CGTGTA (SEQ ID NO: 122)
Streptococcus mitis
Streptococcus pneumoniae
TTGCTGAGATGAGCTACTTCCTCAACTTGGCTGAAGGCCTTGGC
CGTGTA (SEQ ID NO: 123)
Streptococcus mitis
Streptococcus pneumoniae
Streptococcus pseudopneumoniae
TTGCTGAGATGAGTTACTTCCTCAACTTGGCTGAAGGACTTGGC
CGTGTA (SEQ ID NO: 124)
Streptococcus mitis
Streptococcus pneumoniae
TTGCTGAAATGAGCTACTTCCTCAACTTGGCTGAAGGACTTGGC
CGTGTA (SEQ ID NO: 125)
Streptococcus mitis
Streptococcus pneumoniae
Streptococcus pseudopneumoniae
Streptococcus UMB0029
TTGCTGAGATGAGCTACTTCCTCAACTTGGCTGAAGGGCTTGGC
CGTGTA (SEQ ID NO: 126)
Streptococcus mitis
Streptococcus pneumoniae
TAGCAGAGATGTCATACTTCTTAAACCTTGCAGAGGGTATCGGT
AAGGTA (SEQ ID NO: 127)
Streptococcus agalactiae
TGGCTGAGATGAGCTACTTCCTCAACTTAGCAGAAGGCATCGGC
CGTGTG (SEQ ID NO: 128)
Streptococcus anginosus
Streptococcus AS20
Streptococcus constellatus
Streptococcus FDAARGOS_146
Streptococcus HMSC067A03
Streptococcus intermedius
TGGCTGAGATGAGCTACTTCCTCAACTTAGCAGAGGGCATCGGC
CGTGTG (SEQ ID NO: 129)
TGGCTGAGATGAGCTACTTCCTCAACTTAGCAGAAGGCATCGGC
CGTGTA (SEQ ID NO: 130)
Streptococcus intermedius
TGGCTGAGATGAATTACTTCTTGAACCTCGCTGAAGGACTTGGT
CGTGTG (SEQ ID NO: 131)
Streptococcus anginosus
Streptococcus constellatus
Streptococcus HF-100
Streptococcus HF-2466
Streptococcus KCOM
TGGCTGAGATGTCTTATTTCCTTAACCTTGCTGAAGGTCTTGGAA
AGGTC (SEQ ID NO: 132)
Streptococcus equi
TTGCAGAGATGAGCTACTTCCTTAACTTGGCAGAAGGTATCGGA
CGTGTG (SEQ ID NO: 133)
Streptococcus FDAARGOS 256
Streptococcus GMDIS
Streptococcus GMD3S
Streptococcus mitis
Streptococcus oralis
Streptococcus pneumoniae
Streptococcus UMGS867
TTGCAGAGATGAGCTACTTCCTCAACTTGGCTGAAGGTATCGGA
CGTGTG (SEQ ID NO: 134)
Streptococcus GMD5S
Streptococcus HMSC066F01
Streptococcus mitis
Streptococcus oralis
TGGCTGAGATGAGCTACTTCCTCAACTTGGCAGAAGGTATCGGT
CGTGTG (SEQ ID NO: 135)
Streptococcus gordonii
Streptococcus mitis
Streptococcus oligofermentans
TTGCAGAGATGAGCTACTTCCTCAACTTGGCGGAAGGTATCGGA
CGTGTG (SEQ ID NO: 136)
Streptococcus CM6
Streptococcus mitis
Streptococcus NPS
Streptococcus oralis
TGGCTGAAATGTCATACTTCTTAAATCTTTCTGAAGGGATTGGAA
AAGTT (SEQ ID NO: 137)
Streptococcus uberis
TGGCAGAAATGAGCTATTTCTTGAACCTTGCAGAAGGTATTGGC
CGCGTG (SEQ ID NO: 138)
Streptococcus HMSC061E03
Streptococcus HMSC072C09
Streptococcus HMSC072G04
Streptococcus JCVI_31A_bin.20
Streptococcus parasanguinis
TGGCAGAAATGAGCTATTTCTTGAACCTTGCAGAAGGCCTTGGC
CGTGTA (SEQ ID NO: 139)
Streptococcus parasanguinis
TGGCTGAGATGAGCTACTTCCTCAACTTGGCTGAAGGCATTGGT
CGCGTG (SEQ ID NO: 140)
Streptococcus sanguinis
TTGCAGAAATGTCTTATTTCTTAAACCTTTCTGAAGGTATTGGTA
AAGTA (SEQ ID NO: 141)
Streptococcus parauberis
TGGCTGAAATGTCATACTTCCTTAACCTTGCTGAAGGTCTAGGTA
AAGTT (SEQ ID NO: 142)
Streptococcus CNU
Streptococcus infantarius
Streptococcus KCJ4932
Streptococcus KCJ4950
Streptococcus SL1232
Streptococcus UBA11297
TTGCAGAAATGTCATATTTCTTGAACCTTGCAGAGGGTCTTGGAA
AAGTT (SEQ ID NO: 143)
Streptococcus iniae
TGGCTGAAATGAGCTACTTCCTCAACCTTGCTGAAGGTATCGGT
AAAGTA (SEQ ID NO: 144)
Streptococcus 1004_SSPC
Streptococcus equinus
Streptococcus FDAARGOS_192
Streptococcus HMSC068F04
Streptococcus HMSC072D03
Streptococcus salivarius
Streptococcus thermophilus
Streptococcus vestibularis
TGGCTGAAATGAGTTACTTCCTCAACCTTGCTGAAGGTATCGGT
AAAGTA (SEQ ID NO: 145)
Streptococcus CCH5-D3
Streptococcus HMSC10E12
Streptococcus MGYG-HGUT-02550
Streptococcus salivarius
TGGCTGAAATGAGCTACTTCCTCAACCTTGCTGAAGGTATCGGT
AAAGTT (SEQ ID NO: 146)
Streptococcus CCH8-H5
Streptococcus HMSC064H09
Streptococcus JCVI_32_bin.27
Streptococcus salivarius
TGGCTGAAATGTCATACTTCCTTAATCTTGCTGAAGGTCTTGGTA
AAGTT (SEQ ID NO: 147)
Streptococcus bovis
Streptococcus gallolyticus subsp. gallolyticus
Streptococcus gallolyticus subsp. macedonicus
Streptococcus gallolyticus subsp. pasteurianus
TGGCAGAAATGTCTTACTTCCTTAACCTTGCTGAAGGTCTAGGTA
AAGTT (SEQ ID NO: 148)
Streptococcus AS08sgBPME_176
Streptococcus equinus
Streptococcus gallolyticus
TGGCTGAAATGTCATACTTCCTTAACCTTGCTGAAGGTCTTGGTA
AAGTT (SEQ ID NO: 149)
Streptococcus gallolyticus subsp. gallolyticus
Overall, 50 nucleotide long SPA sequencing fragments covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of Streptococcus at the cluster or species level (as summarized in Table 20). For instance, unique SPA fragments were able to phylogenetically identify Streptococcus mutans, the cause of dental cavities; Streptococcus suis, a pathogen in pigs that can cause severe systemic infection in humans; Streptococcus agalactiae and Streptococcus equi, the causative agent of strangles which is the most frequently diagnosed infectious disease of horses; and Streptococcus parauberis, an important fish pathogen. When multiple species were identified by the same 50 nucleotide SPA fragment, whole genome-based ANI analysis on representative members was used to confirm the results based on the SPA fragments. Representative examples are shown in
The ANI results shown in
Similarly, based on ANI results, the SPA fragments St1, St8, St9, St10, St11 and St12 can be used to identify bacterial strains belonging to the Streptococcus mitis, Streptococcus pneumoniae and Streptococcus pseudopneumoniae cluster. Members of this cluster have previously been referred to as the viridans group streptococci (VGS), Streptococcus mitis group, and based on their ANI analysis, group together. A second group of strains, identified by the SPA fragments St19, St20 and St22, represents bacterial strains previously identified as Streptococcus mitis and Streptococcus oralis (
As shown in
The whole genome-based ANI analysis for the Streptococcus equinus, Streptococcus salivarius, Streptococcus thermophilus and Streptococcus vestibularis strains identified by SPA fragments St30, St31 and St32 is shown in
Streptococcus gallolyticus subsp. gallolyticus (formerly known as Streptococcus bovis type I) has recently been recognized as the main causative agent of septicemia and infective endocarditis in elderly and immunocompromised persons. It also has been strongly associated to colorectal cancer (CRC; defined as carcinomas and premalignant adenomas) (Boleij et al, 2011; Pasquereau-Kotula et al, 2018). Several previous studies failed to clearly attribute an association between Streptococcus bovis and CRC; this can, however, be explained by the lack of a proper distinction between Streptococcus bovis type I (Streptococcus gallolyticus strains), type 11.1 (Streptococcus infantarius) and type I1.2 (Streptococcus gallolyticus subsp. macedonicus and Streptococcus gallolyticus subsp. pasteurianus), with Streptococcus bovis type I being prevalently associated to CRC, and to a lesser extend Streptococcus bovis type I1.2 (Abdulamir et al, 2011). The phylogenetic resolution of 50 nucleotide SPA fragments allowed to discriminate between Streptococcus infantarius (SPA fragment St28) and Streptococcus gallolyticus (SPA fragments St33 and St35) strains. Therefore, SPA fragment sequencing represents a promising approach for CRC screening based on the presence of Streptococcus gallolyticus strains (Streptococcus bovis type I and I1.2) in peripheral blood. The whole genome-based ANI analysis presented in
Since Enterococcus faecalis and Enterococcus faecium also belong to the Lancefield group D “Streptococci” (Table 20), the phylogenetic resolution of their 50 nucleotide SPA fragments was determined (Table 19). SPA fragments Ef1 and Ef2 were found to be specific for Enterococcus faecalis, while SPA fragments Ef3 and Ef4 were found to be specific for Enterococcus faecium. The results of the whole genome-based ANI analysis, shown in
Enterococcus faecalis and Enterococcus faecium strains. For each SPA fragment, the
Enterococcus faecalis and Enterococcus faecium species and the number of strains is indicated.
Enterococcus faecalis and Enterococcus faecium (Ef) specific SPA fragment
TTGCTTCAATGAGCTACTTCTTCAACTTAATGGAAGATATCGGCA
ATGTC (SEQ ID NO: 150)
Enterococcus faecalis
TTGCTTCAATGAGCTACTTCTTCAACTTAATGGAAGATATCGGTA
ATGTC (SEQ ID NO: 151)
Enterococcus faecalis
Streptococcus pneumoniae
TTGCTTCAATGAGCTATTTCTTGAACTTGATGGAAGGTATCGGCA
ATGTC (SEQ ID NO: 152)
Enterococcus faecium
Enterococcus lactis
Enterococcus N4D85
TTGCTTCAATGAGCTATTTCTTGAACTTGATGGAAGGTATCGGCA
ATGTT (SEQ ID NO: 153)
Enterococcus faecium
Enterococcus FM11-1
Streptococcus species.
Streptococcus
Streptococcus mitis,
Streptococcus
pneumoniae,
Streptococcus
pseudopneumoniae
S. mitis
Streptococcus
dysgalactiae or
Streptococcus
pyogenes
Streptococcus
pyogenes
Streptococcus
mutans
S. mutans
Streptococcus suis
Streptococcus mitis,
Streptococcus
pneumoniae
S. mitis
Streptococcus
agalactiae
Streptococcus
S. anginosus
anginosus,
Streptococcus
intermedius,
Streptococcus
constellatus
Streptococcus
S. anginosus
intermedius
Streptococcus
S. anginosus
anginosus,
Streptococcus
constellatus
Streptococcus
equi. subsp.
zoopidemicus
Streptococcus oralis,
Streptococcus
pneumoniae
S. mitis
Streptococcus oralis
S. mitis
Streptococcus gordonii
S. sanguinis
Streptococcus sanguinis
Streptococcus uberis
Streptococcus
parasanguinis
S. sanguinis
Streptococcus sanguinis
Streptococcus sanguinis
S. sanguinis
Streptococcus sanguinis
Streptococcus
parauberis
Streptococcus
Streptococcus
Streptococcus
infantarius
bovis/
Streptococcus
equinus
Streptococcus
bovis biotype
Streptococcus iniae
Streptococcus
Streptococcus
salivarius,
Streptococcus
thermophilus,
Streptococcus
Streptococcus
vestibularis
salivarius
Streptococcus
salivarius
Streptococcus
salivarius
Streptococcus bovis
Streptococcus
Streptococcus
bovis/
gallolyticus subsp.
Streptococcus
gallolyticus
equinus
Streptococcus
gallolytics subsp.
macedonicus
Streptococcus
gallolytics subsp.
Streptococcus
pasteurianus
bovis
Streptococcus equinus
Streptococcus
bovis/
Streptococcus
equinus
Streptococcus
Streptococcus
Gallolytics subsp.
bovis/
gallolyticus
Streptococcus
equinus
Enterococcus faecalis
Enterococcus faecalis
Enterococcus faecium
Enterococcus faecium
These results show that, unexpectedly, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of clinically relevant Streptococcus and Ernterococcus at the species or group level (as summarized in Table 20), thus providing an important method for (early) detection and identification of these infectious species using mcfDNA from peripheral blood samples. The high phylogenetic resolution of the SPA fragments can be directly linked to the standard of care for the most appropriate antibiotic regime to treat infections by Streptococcus and Enterococcus species, further demonstrating the clinical relevance of SPA fragment sequencing.
In addition, clinically relevant Streptococcus species including Streptococcus gallolyticus, Streptococcus macedonicus, Streptococcus pasteurianus and Streptococcus equinus have been linked to cancer. Therefore, the detection of Streptococcus species in peripheral blood is important for detection and prognostics of various types of cancer, as will also be discussed in EXAMPLE 7.
Furthermore, the analysis of EXAMPLE 5 shows the promise of SPA fragment sequencing as a new approach for assessing the risk of sepsis in immune compromised individuals, based on the (early) detection and identification of infectious and opportunistic pathogenic bacterial species using mcfDNA from peripheral blood samples.
SPA Fragment Sequences to Identify Opportunistic Bacterial Pathogens Originating from the Oral Cavity.
The oral cavity represents a source of opportunistic pathogenic bacteria that can have significant health implications when entering the body. Porphyromonas gingivalis is an example of an oral pathogen that has received a lot of attention. Not only is this bacterium the cause of gingivitis (Socransky et al, 1998; Chen et al, 2018), but several studies have implicated this bacterium in Alzheimer's disease (Dominy et al, 2019; Kanagasingam et al, 2020). Therefore, in the fight against Alzheimer's disease there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood, something SPA fragment sequencing can provide. To evaluate its application for high-resolution detection of Porphyromonas gingivalis in peripheral blood, saliva or stool to complement risk screening for developing Alzheimer's disease, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Porphyromonas gingivalis strains, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Porphyromonas strains. The results are presented in Table 21.
Porphyromonas gingivalis strains and related species. For each SPA fragment, the
TTGAGATCATCAAGTATCTTATTGAGTTAGTAAATTCCAAGGCAT
CAGTA (SEQ ID NO: 154)
Porphyromonas gingivalis
CTGCGATCATTGCTCATCTCGTAGAGTTGAAGAACAGCAAGCAG
GTCGTC (SEQ ID NO: 155)
Porphyromonas cangingivalis
TGGCCATCATCAAGTACCTCATCGGGCTTGTCAACTCTAAGGAG
GTCGTC (SEQ ID NO: 156)
Porphyromonadaceae
TGGCCATCATCAAGTACCTCATCGGGCTTGTCAACTCTAAGGAA
GTCGTC (SEQ ID NO: 157)
Porphyromonadaceae
TTGCTATCATACGCCACCTGATCAAGCTCGTCAATGGTAAGGCA
CCTGTC (SEQ ID NO: 158)
Porphyromonas uenonis
TTGCGATCATACGTCATCTGATCAAGCTCGTCAATGGTAAGGCT
CCTGTC (SEQ ID NO: 159)
Porphyromonadaceae
TTTCCATTGTTAACCACCTTCTATTGTTAGCAACAACGGGTGCTA
ACGTT (SEQ ID NO: 160)
Porphyromonas endodontalis
Propionibacterium acidifaciens
TTGCGATCATACGTCACTTGATCAAGCTCGTCAATGGTAAGGCT
CCAGTC (SEQ ID NO: 161)
Porphyromonas asaccharolytica
TGGCCATCATCAAGTACCTCATCGGTCTTGTCAACTCTAAGGAG
GTCGTC (SEQ ID NO: 162)
Porphyromonadaceae JCVI 49 bin. 7
TTGAAATTATTAAATATCTGATTCAATTAGTTAACTCCAAAGCGG
TGGTG (SEQ ID NO: 163)
Porphyromonas macacae
As shown in Table 21, the 50 nucleotide SPA fragments generated in silico for Porphyromonas gingivalis strains and related species distinguish Porphyromonas at the species level, as was also confirmed by whole genome-based ANI analysis (
Prevotella are bacteria that inhabit many parts of the body. Although common in the gut microbiome, if found elsewhere, they may be a sign of infection. Prevotella oris represents an example of an opportunistic pathogenic bacterium that has been associated with several serious oral and systemic infections. Prevotella oris can been identified in clinical specimens by bacterial culture and biochemical tests, which are generally unreliable (Riggo and Lennon, 2007). Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood, something SPA fragment sequencing can provide. To evaluate its application for the reliable detection in peripheral blood of Prevotella species, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Prevotella strains, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Prevotella strains. The results are presented in Table 22.
Prevotella species. For each SPA fragment, the Prevotella species and the number of strains is
Prevotella-specific (Pr) SPA fragments received a unique numerical identifier for reference in
Prevotella (Pr) specific SPA fragment (50 nucleotides) sequence
TCGCTATCATTAAGTATTTGATAAATCTTGTAAATTCAAATGCAA
CAGTT (SEQ ID NO: 164)
Prevotella pallens
TTGAAATTATCAAGTACCTTATAAGTCTTGTAAATTCAAATGCTA
CAGTC (SEQ ID NO: 165)
Prevotella histicola
TTGAGATTATTAAGTATCTTATCAGCCTTATCAATTCAAATGCTA
CGGTT (SEQ ID NO: 166)
Prevotella melaninogenica
TTGAGATCATTAAATATCTTATTCAGCTGATCAACTCTAGTGCAA
CAGTT (SEQ ID NO: 167)
Prevotella copri
TTGAGATTATTAAATATCTTATTCAGCTGATTAACTCTAGTGCAA
CAGTT (SEQ ID NO: 168)
Prevotella copri
TCGAGATTATCAAGTATTTGATAAACCTCGTAAATTCGAATGCAA
CAGTT (SEQ ID NO: 169)
Prevotella intermedia
TCGAGATTATCAAGTATTTGATTAACCTCGTAAATTCGAATGCAA
CAGTT (SEQ ID NO: 170)
Prevotella intermedia
TTGAGATTATCAAGTACCTCATTAGCTTAGTCAATTCAAATGCAA
CCGTT (SEQ ID NO: 171)
Prevotella oral
TCGCAATTATACGATACTTGATTCAGCTTATCAATTCGAATGCAA
CAGTC (SEQ ID NO: 172)
Prevotella nanceiensis
TTGCGATTATCAAATATCTCATTCAGCTTGTCAATTCTAATGTTA
CAGTT (SEQ ID NO: 173)
Prevotella salivae
TTGCGATTATCAAATACCTTATTCAGCTTGTCAATTCTAATGTTA
CAGTT (SEQ ID NO: 174)
Prevotella salivae
TCGCGATTATAAAATATTTGATAAACCTTGTGAATTCAAATGCCA
CTGTT (SEQ ID NO: 175)
Prevotella nigrescens
TTGAAATCATCAAATATCTCATCAGCCTGATCAACTCAAATGCCA
CGGTT (SEQ ID NO: 176)
Prevotella denticola
TTGAGATTATCAAATATCTGATTCAGCTGATTAACTCCAATGCTA
CTGTA (SEQ ID NO: 177)
Prevotella buccae
TTGCCATCATCCGCTATCTCATCCAGTTGGTTAACTCTAACGCAA
CTGTT (SEQ ID NO: 178)
Prevotella stercorea
TTGAAATCATAAAATATCTCATCCAGTTGGTTAATTCCAATGCCA
CTGTT (SEQ ID NO: 179)
Prevotella oris
TTGAGATTATCAAATATTTGATAAACCTCATCAATTCTAACGCAA
CTGTT (SEQ ID NO: 180)
Prevotella disiens
TTGCTATTATCAAGTACTTGATTAAGCTTGTTAATTCTCAGGCTA
CTGTT (SEQ ID NO: 181)
Prevotella bryantii
TTGAAATTATCAAATATCTCATTCAGCTGGTTAACTCTAATGCAA
CCGTG (SEQ ID NO: 182)
Prevotella shahii
As shown in Table 22, the 50 nucleotide SPA fragments generated in silico for Prevotella strains distinguish Prevotella at the species level. These results show that unexpectedly, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of clinically relevant Prevotella at the species level (Table 22), including Prevotella oris, thus providing an important method for its (early) detection as an infecting pathogen using mcfDNA from peripheral blood samples.
SPA Fragment Sequences of Bacteria Linked to Tumor Microbiomes and their Use as Biomarkers for Cancer Detection and Progression Monitoring.
Several clinically relevant bacteria have been identified as playing a key role in the onset and progression of cancer, such as Streptococcus bovis type I (Streptococcus gallolyticus strains) which has been associated with CRC (see Example 5). Therefore, the use of SPA fragment sequencing for screening of peripheral blood or stool of cancer patients for the presence of bacteria as biomarkers for the detection, monitoring of disease progression, prognostics for survival and minimal residual disease, will provide important information complementary to customary blood biopsy- and stool-based detection and monitoring approaches that use cfDNA and focus on the methylation and mutation footprints in specific genetic loci as tumor biomarkers.
Contrary to PCR-based detection methods that monitor for the presence of specific bacteria, SPA fragment sequencing provides an “open” diagnostics approach to detect any bacterium based on the presence of its mcfDNA in peripheral blood. Due to its high phylogenetic resolution, SPA fragment sequencing can be used to identify novel microbiome signatures in blood and stool as biomarkers for the (early) detection of cancer. Once these signatures have been identified and validated as cancer-relevant biomarkers, SPA fragment sequencing is ideally positioned as a novel high-resolution, high-throughput and low-cost approach for population screening, e.g. adults between the ages 45 to 85, with a focus on (early) detection. In what follows, examples are provided for SPA fragments as biomarkers to detect and monitor the progression of cancer based on the presence of microbial signatures characterized by bacteria that have been associated with specific cancers and their developmental stage.
Risk screening for esophageal cancer: Esophageal cancer is the eighth most common cause of cancer deaths worldwide. Tannerella forsythia and Porphyromonas gingivalis, both of which have been implicated in periodontal diseases as part of red complex of periodontal pathogens, have been found to be associated with an increased risk of esophageal cancer (Malinowski et al, 2019). As shown in Table 21 of EXAMPLE 6, Porphyromonas gingivalis strains can be specifically identified by SPA fragment Pg1. To evaluate its application for the reliable detection in peripheral blood, saliva and stool of Tannerella forsythia to complement risk screening for developing esophageal cancer, and to analyze the discriminatory power of SPA fragment sequencing for high resolution phylogenetic identification of infecting Tannerella forsythia strains, 50 nucleotide long SPA fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Tannerella forsythia strains. The results are presented in Table 23.
Tannerella forsythia and the related species Tannerella oral. For each SPA fragment, the
Tannerella species and the number of strains is indicated. The SPA fragments representing 10
Tannerella strains are reported. Tannerella (forsythia)-specific (Tf) SPA fragments received a
Tannerella forsythia (Tf) specific SPA fragment (50 nucleotides) sequence
TTGAGATTATCAAATATCTGATTGAATTGATCAACTCGAAGGCGG
TGGTA (SEQ ID NO: 183)
Tannerella forsythia
TTGAGATTATCAAATATCTGATTGAACTGATTAATTCGAAGGCAG
TTGTA (SEQ ID NO: 184)
Tannerella forsythia
TCGAAATCATCAAATACCTCATCGAGCTGATCAACTCCAAGGCG
GTTGTT (SEQ ID NO: 185)
Tannerella oral
As shown in Table 23, the 50 nucleotide SPA fragments generated in silico for Tannerella strains distinguish between Tannerella forsythia and the related species Tannerella oral. These results show that unexpectedly, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of the clinically relevant species Tannerella forsythia (Table 23). Therefore, SPA fragments for Tannerella forsythia and Porphyromonas gingivalis can be used as biomarkers using mcfDNA from peripheral blood, saliva and stool samples for the risk profiling and (early) detection of esophageal cancer.
Risk screening for precancerous colonic polyps: The common commensal bacterium, nontoxigenic Bacteroides fragilis (NTBF), is enriched in patients with precancerous colonic polyps. NTBF isolated from polyps is enriched in genes involved in LPS biosynthesis, which may allow for its increased ability to activate the immune system and cause inflammation (Kordahi et al, 2021). Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood and stool samples, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the early detection of Bacteroides fragilis as an indicator species for the presence of precancerous colonic polyps, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Bacteroides fragilis strains. The results are presented in Table 24.
Bacteroides fragilis and related species. For each SPA fragment, the Bacteroides species and
Bacteroides fragilis (Bf) specific SPA fragment (50 nucleotides) sequence
TTGAGATCATTAAATATCTGATTGAGTTGATTAACTCTAAAGCAG
ATGTG (SEQ ID NO: 186)
Bacteroides fragilis
Bacteroides NSJ-2
Bacteroides PHL
Bacteroides UW
TCGAGATCATCAAATATCTGATTGAGCTGATTAATTCAAAAGCAG
ATGTA (SEQ ID NO: 187)
Bacteroides fragilis
TCGAGATCATCAAATATCTGATTGAGCTGATTAACTCAAAAGCAG
ATGTA (SEQ ID NO: 188)
Bacteroides 2_1_16
Bacteroides 3_2_5
Bacteroides cellulosilyticus
Bacteroides fragilis
As shown in Table 24, the 50 nucleotide SPA fragments generated in silico for Bacteroides fragilis strains and related species distinguish Bacteroides fragilis at the species level, as was also confirmed by whole genome-based ANI analysis presented in
Risk screening for precancerous stomach ulcers: Stomach ulcers, caused by Helicobacter pylori, are a cause for stomach cancer when left untreated. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood and stool, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the early detection of Helicobacter pylori as an indicator species for the presence of stomach ulcers and potentially early-stage stomach cancer, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Helicobacter pylori strains. The results are presented in Table 25.
Helicobacter pylori. For each SPA fragment the number of Helicobacter pylori strains is
Helicobacter pylori-specific (Hp) SPA fragments received a unique numerical identifier for
Helicobacter pylori (Hp) specific SPA fragment (50 nucleotides) sequence
TCACCACCGTTAAATACCTCATGAAAATCAAAAACAATCAGGGC
AAGATT (SEQ ID NO: 189)
Helicobacter pylori
TCACCACCGTTAAATACCTCATGAAGATCAAAAACAATCAAGGC
AAGATT (SEQ ID NO: 190)
Helicobacter pylori
TCACCACCGTTAAATACCTCATGAAGATCAAAAACAATCAGGGC
AAGATT (SEQ ID NO: 191)
Helicobacter pylori
As shown in
Women's health risk screening: Chlamydia trachomatis, a bacterium which is commonly transmitted sexually, is the major cause of mucopurulent cervicitis, pelvic inflammatory disease, tubal factor infertility, and ectopic pregnancy. Thus, the healthcare costs due to complications caused by Chlamydia trachomatis are enormous.
Cervical cancer is the most common cancer in women worldwide. Infection with Chlamydia trachomatis greatly increases the risk of cervical cancer (Anttila et al, 2001). Although infections with oncogenic strains of human papillomavirus remain the prime cause of cervical cancer, coinfections with some strains of Chlamydia trachomatis and Neisseria gonorrhoeae seem to contribute to that risk and the severity of the disease, especially high-grade squamous intraepithelial cervical lesions (De Abreu et al, 2016). This finding is important because chlamydia, though frequently asymptomatic, is one of the most common sexually transmitted diseases and can be treated with appropriate antibiotics. In the United States, between four million and eight million new cases of chlamydia are reported yearly.
Neisseria gonorrhoeae is a bacterial pathogen responsible for gonorrhea and various sequelae that tend to occur when asymptomatic infection ascends within the genital tract or disseminates to distal tissues. Like Chlamydia trachomatis, Neisseria gonorrhoeae is an important sexually transmitted pathogen and a major cofactor in HIV-1 infection. Global rates of gonorrhea continue to rise, facilitated by the emergence of broad-spectrum antibiotic resistance that has recently afforded the bacteria ‘superbug’ status. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of these bacteria in peripheral blood and vaginal smear samples, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the early detection of Chlamydia trachomatis and Neisseria gonorrhoeae as indicator species for women's health issues including the risk to develop cervical cancer, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Chlamydia trachomatis and Neisseria gonorrhoeae strains. The results are presented in Table 26 and Table 27.
Chlamydia trachomatis (Ct) specific SPA fragment
GACAAACCCTGTCGCAGAATTGACGCACAAGCGTCGTCTGTCAG
CATTAG (SEQ ID NO: 192)
Chlamydia trachomatis
TAAGATCCACGCTCGTTCTATAGGACCTTACTCTCTCGTTACGCA
GCAAC (SEQ ID NO: 193)
Chlamydia trachomatis
These results indicate that unexpectedly, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of the clinically relevant species Chlamydia trachomatis (Table 26)
Neisseria species (Ne) specific SPA fragment
TCGCCTCGATTGCGACTTTGGTCGAGTTGCGTAACGGCCATGGC
GAAGTG (SEQ ID NO: 194)
Neisseria gonorrhoeae
Neisseria meningitidis
TCGCCTCGATTGCGACTTTGGTCGAGTTGCGTAACGGCCATGGT
GAAGTT (SEQ ID NO: 195)
Neisseria meningitidis
TCGCCTCGATTGCGACTTTGGTCGAGCTGCGTAACGGTCACGGC
GAAGTG (SEQ ID NO: 196)
Neisseria lactamica
TTGCTTCTATTGCGACATTGGTTGAACTGCGTAACGGTCATGGC
GAAGTA (SEQ ID NO: 197)
Neisseria flavescens
Neisseria perflava
Neisseria subflava
TCGCCTCGATTGCGACTTTGGTCGAGTTGCGTAACTACCATGGC
GAAGTG (SEQ ID NO: 198)
Neisseria gonorrhoeae
TTGTTTCAATTGCTACCTTAGTTGAATTACGTAATCATAATGATG
GTGTT (SEQ ID NO: 199)
Neisseria weaver
TTGCATCAATTGCTACTTTAGTTGAATTGCGAAACGGTCATGGCG
AAGTG (SEQ ID NO: 200)
Neisseria mucosa
TGGCTTCGATTGCAACGTTGGTTGAGTTGCGTAACGGTCACGGT
GAAGTG (SEQ ID NO: 201)
Neisseria 10009
Neisseria 10022
TGGCTTCCATCGCCACTTTGGTGGAGTTGCGCAACGGGCATGGC
GAAGTG (SEQ ID NO: 202)
Neisseria shayeganii
TCGCTTCGATTGCCACTTTGGTTGAATTGCGTAACGGTCACGGC
GAAGTG (SEQ ID NO: 203)
Neisseria brasiliensis
Neisseria N95_16
TTGTTTCTATTGCCACTTTAGTTGAGCTGCGTAATGGACATGGTG
AAGTA (SEQ ID NO: 204)
Neisseria zalophi
Except for SPA fragments Ne1 and Ne4, the Neisseria-specific (Ne) SPA fragments were found to be species specific. The major combined group, identified by SPA fragment Ne1, was formed by Neisseria gonorrhoeae and Neisseria meningitidis strains. Neisseria meningitidis (meningococcus) causes significant morbidity and mortality in children and young adults worldwide through epidemic or sporadic meningitis and/or septicemia.
To improve the phylogenetic resolution of SPA fragment sequencing for Neisseria species, 50 nucleotide long fragments located upstream of the RpoB6-R1630 priming site were generated in silico for Neisseria strains. As shown in Table 4, the region upstream of the RpoB6-R1630 priming site has less sequence variance than the region upstream of the RpoB1-R1327 priming site. However, we found that this region provided a high degree of phylogenetic resolution of several c112linically important bacteria, including strains belonging to the genus Neisseria. An overview of the phylogenetic resolution of RpoB6-R1630-based SPA fragment sequencing for Neisseria species is provided in Table 28.
Neisseria species (Ne) specific SPA fragment
AACGCCGCGTATCCGCATTGGGTCCGGGCGGTTTGACCCGCGAA
CGTGCA (SEQ ID NO: 205)
Neisseria meningitidis
AACGCCGTGTATCTGCATTGGGCCCGGGCGGTTTGACCCGCGAA
CGTGCC (SEQ ID NO: 206)
Neisseria gonorrhoeae
AACGCCGCGTATCTGCATTGGGTCCGGGCGGTTTGACCCGCGAA
CGTGCC (SEQ ID NO: 207)
Neisseria meningitidis
AACGCCGCGTATCCGCATTGGGTCCGGGCGGTTTGACCCGCGAA
CGTGCC (SEQ ID NO: 208)
Neisseria meningitidis
AACGCCGTGTATCTGCATTGGGTCCGGGCGGTTTGACCCGCGAA
CGTGCA (SEQ ID NO: 209)
Neisseria meningitidis
AACGCCGTGTATCTGCATTGGGCCCGGGCGGTTTGACTCGCGAA
CGTGCA (SEQ ID NO: 210)
Neisseria meningitidis
Neisseria subflava
Neisseria perflava
Neisseria flavescens
Neisseria cinerea
AACGCCGTGTATCTGCATTGGGTCCGGGCGGTTTGACCCGCGAA
CGTGCC (SEQ ID NO: 211)
Neisseria gonorrhoeae
AACGCCGTGTATCTGCGTTGGGTCCGGGCGGTTTGACCCGCGAA
CGTGCA (SEQ ID NO: 212)
Neisseria lactamica
AGCGTCGTGTGTCTGCTTTAGGTCCAGGTGGTTTGACACGTGAA
CGTGCA (SEQ ID NO: 213)
Neisseria weaveri
AGCGTCGTGTGTCTGCTTTAGGTCCGGGTGGTTTGACACGTGAA
CGTGCA (SEQ ID NO: 214)
Neisseria zoodegmatis
AACGTCGTGTATCTGCATTGGGTCCGGGCGGTTTGACCCGCGAA
CGTGCA (SEQ ID NO: 215)
Neisseria meningitidis
AACGTCGTGTTTCTGCCTTGGGCCCGGGTGGTTTGACCCGTGAG
CGTGCC (SEQ ID NO: 216)
Neisseria 10022
Neisseria 10009
AACGTCGTGTTTCTGCTTTGGGTCCAGGCGGTTTGACCCGTGAA
CGTGCT (SEQ ID NO: 217)
Neisseria N95_16
Neisseria brasiliensis
AACGCCGTGTATCCGCATTGGGTCCGGGCGGCTTGACCCGCGAA
CGTGCA (SEQ ID NO: 218)
Neisseria meningitidis
AACGCCGTGTATCTGCATTGGGCCCTGGTGGTTTGACTCGCGAA
CGTGCA (SEQ ID NO: 219)
Neisseria mucosa
Neisseria JCVI_22A_bin.7
AGCGTCGTGTGTCTGCTTTAGGTCCGGGCGGTTTGACACGTGAA
CGTGCG (SEQ ID NO: 220)
Neisseria animaloris
As shown in Table 28, SPA fragments generated in silico for Neisseria species from the region upstream of the RpoB6-R1630 priming site allowed to distinguish with high phylogenetic resolution between Neisseria gonorrhoeae and Neisseria meningitidis strains. The practical implications of using an alternative primer annealing site or a combination of two primers that target different phylogenetic identifier regions are discussed in EXAMPLE 9.
Overall, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB6-R1630 primer annealing site allow for high resolution phylogenetic identification of the clinically relevant species Neisseria gonorrhoeae (Table 28). Therefore, SPA fragments for Chlamydia trachomatis and Neisseria gonorrhoeae can be used as biomarkers using mcfDNA from peripheral blood and/or vaginal smear samples for the risk profiling and (early) detection of women's health issues related to these bacteria including the risk to develop cervical cancer.
Prognostic correlations with the microbiome of breast cancer subtypes: There are four subtypes of breast cancer (BC) that are based on the status of the estrogen receptor, progesterone receptor, and human epidermal growth (Her2) expression in cancerous breast cells. As shown by Banerjee et al (2021), the subtypes of BC have specific viromes and microbiomes, with estrogen receptor positive (ER+) and triple negative (TN) tumors showing the most and least diverse microbiomes, respectively. These specific microbial signatures allowed successful discrimination between the different BC subtypes. Furthermore, Banerjee et al (2021) demonstrated correlations between the presence and absence of specific microbes in BC subtypes with the clinical outcomes. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of bacteria associated with breast cancer subtypes in peripheral blood, something SPA fragment sequencing can provide.
TN BC (15-20% of BC patients) is the most aggressive of all the BCs, is non-responsive to treatment, is highly angiogenic, highly proliferative, and has the lowest survival rate. TN breast cancer showed decreased microbial diversity and increased levels of Aggregatibacter species; significant levels of this species were not detected in other BC types. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the detection of Aggregatibacter species as indicator and prognostics species for TN breast cancer, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Aggregatibacter strains. The results are presented in Table 29.
Aggregatibacter species (Ag) specific SPA fragment
TCAGTGTGATGAAAAAATTGATTGATATCCGTAATGGCCGTGGT
GAAGTG (SEQ ID NO: 221)
Aggregatibacter actinomycetemcomitans
TCAGTGTGATGAAGAAACTGATTGATATTCGTAATGGTCGCGGT
GAAGTG (SEQ ID NO: 222)
Aggregatibacter aphrophilus
TCAGTGTGATGAAGAAATTGATTGATATCCGTAATGGCCGTGGT
GAAGTG (SEQ ID NO: 223)
Aggregatibacter actinomycetemcomitans
TCAGTGTGATGAAAAAACTGATTGATATTCGTAATGGTCGCGGA
GAAGTG (SEQ ID NO: 224)
Aggregatibacter aphrophilus
TAAGTGTCATGAAGAAATTGATCGAAATTCGTAACGGTCGTGGT
GAAGTG (SEQ ID NO: 225)
Aggregatibacter segnis
The results presented in Table 29 showed that the Aggregatibacter species could be identified by their unique SPA fragments. This was further confirmed by whole genome ANI analysis (
The whole genome-based ANI results in
Prognostic correlations with the microbiome of pancreatic cancer: Pancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDAC), is an aggressive disease with a poor prognosis. Chandra and McAllister (2021) pointed out the importance of microbial biomarkers for risk prognosis for pancreatic cancer. Risk factors for pancreatic cancer included periodontal disease and oral microbial dysbiosis, with abundances of Porphyromonas gingivalis, Aggregatibacter actinomycetemcomitans, Neisseria elongate and Streptococcus mitis as indicator species. As discussed previously, 50 nucleotide SPA fragments covering the region upstream of the RpoB1-R1327 primer annealing site can be used to successfully identify these species.
Of specific interest is the tumor microbiome composition of PDAC patients, as it holds clues for their treatment options and long-term survival. Geller et al (2017) reported the presence of bacteria in human PDACs and demonstrated that intra-tumoral Gamma-proteobacteria, among the most common bacteria detected in human pancreatic tumors, reduce the efficacy of chemotherapeutic drugs like gemcitabine, which these bacteria can metabolize into its inactive form via their cytidine deaminase. Thus, one application of SPA fragment sequencing would be to link phylogenetic identification to metabolic strain models, thereby predicting the impact of the tumor microbiome on drug metabolism and efficacy.
Riquelme et al (2019) profiled intra-tumoral bacteria from patients with resected PDAC and compared short-term and long-term survivors. Long-term survivors had greater intra-tumoral microbial α-diversity than did those who died of the disease within 5 years after resection. Overall tumor microbial characterization revealed a microbial composition similar to the one in human PDAC previously described by Geller et al (2017), but unique enrichment in the following microbes was found in tumors from long-term survivors: Pseudoxanthomonas, Streptomyces, Saccharopolyspora and Bacillus clausii, the last two species have documented immunomodulatory functions that might play a role in slowing down the disease progression. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of these bacteria in peripheral blood, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the detection of Pseudoxanthomonas, Streptomyces, Saccharopolyspora and Bacillus clausii species as indicator and prognostics species as prognostics for long term survival of PDAC patients, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Pseudoxanthomonas, Streptomyces, Saccharopolyspora and Bacillus clausii strains. Unique SPA fragments were found able to identify Pseudoxanthomonas and Streptomyces at the genes level, and Saccharopolyspora and Bacillus clausii at the species level. The results for Bacillus clausii are presented in Table 30.
Bacillus clausii (Bcl) specific SPA fragment
TCGCTTCCATCAGCTATTTCTTCAACTTGCTGCATGGTGTCGGCG
ATACA (SEQ ID NO: 226)
Bacillus clausii
TCGCTTCCATCAGCTATTTCTTCAACTTGTTGCATGGTGTCGGCG
ATACA (SEQ ID NO: 227)
Bacillus clausii
These results show that overall, unexpectedly, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of clinically relevant Pseudoxanthomonas, Streptomyces, Saccharopolyspora and Bacillus clausii strains. Therefore, SPA fragments can be used as biomarkers using mcfDNA from peripheral blood samples for the risk profiling and prognostics for long-term survival of PDAC patients.
Prognostic correlations with the microbiome of lung cancer: Lung cancer is the most common cancer, excluding nonmelanoma skin cancer, and the most common cause of cancer-related death in the world, with approximately 1.8 million diagnoses and 1.6 million deaths per year. Peters et al (2019) pointed out the importance of microbial biomarkers for risk prognosis for lung cancer, observing that greater abundance of family Koribacteraceae in normal long tissue was associated with increased recurrence-free survival (RFS) and long-term disease-free survival (DFS), whereas greater abundance of family Lachnospiraceae, and genera Faecalibacterium and Ruminococcus (from Ruminococcaceae family), and Roseburia and Ruminococcus (from Lachnospiraceae family) were associated with reduced RFS and DFS. Taxa associated only with RFS (P<0.05) included family S24-7 (increased RFS), and family Bacteroidaceae and genus Bacteroides (reduced RFS). Taxa associated only with DFS (P<0.05) included family Sphingomonadaceae and genus Sphingomonas (increased DFS), and family Ruminococcaceae (reduced DFS). However, this study was performed using 16S rRNA gene sequencing and lacked the phylogenetic resolution to identify biomarker species at the species level. The 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB1-R1327 primer annealing site allow for the high resolution phylogenetic identification at the species level of the clinically relevant bacteria associated with the prognosis for recurrence-free survival (RFS) and long-term disease-free survival (DFS) of lung cancer patients. SPA sequencing is therefore well positioned to monitor disease progression and prognosis for lung cancer patients.
Risk screening for gastrointestinal tumors: Fusobacterium spp. is important in the development and progression of gastrointestinal tumors. In line with this, Poore et al (2020) showed that the Fusobacterium genus was overabundant in primary tumors compared to normal solid-tissue. Furthermore, pan-cancer analyses also showed an overabundance of Fusobacterium when comparing all broadly-defined gastrointestinal (GI) cancers against non-GI cancers in both primary tumor tissue and adjacent normal solid-tissue, pointing to Fusobacterium species as a biomarker for GI cancer. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood and stool samples, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the early detection of Fusobacterium species as biomarker for the risk to develop gastrointestinal cancer. 50) nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Fusobacterium species. The results are presented in Table 31.
Fusarium species (Fs) specific SPA fragment
CTATTAAATATGTTATAGAGCTTAATAATGGTGATCAAAATGTTC
ATACT (SEQ ID NO: 228)
Fusobacterium canifelinum
Fusobacterium nucleatum
Fusobacterium OBRC1
CTATTAAATATGTTATAGATCTTAATAATGGCGATCAAAATGTTC
ATACT (SEQ ID NO: 229)
Fusobacterium HMSC065F01
Fusobacterium nucleatum
CAATGAAATATGTTACTGACCTTTATAATGGTGACCAAAATGTTC
ATACA (SEQ ID NO: 230)
Fusobacterium periodonticum
CGATACAATATGTCATTGATTTAAATAATGGGGAATCTCATGTCC
ATACC (SEQ ID NO: 231)
Fusobacterium necrophorum
CAATGAAATATGTTACTGACCTTTATAATGGTGATCAAAATGTTC
ATACA (SEQ ID NO: 232)
Fusobacterium periodonticum
TAGCTACAATGAAGTATGTAATTAACTTAAATAATGGAAATGGAC
ATACT (SEQ ID NO: 233)
Fusobacterium FSA-380-WT-2B
Fusobacterium mortiferum
CTATTAAGTATGTTATAGAGCTAAATAATGGTGACCAAAATGTTC
ATACT (SEQ ID NO: 234)
Fusobacterium hwasookii
Fusobacterium nucleatum
CTATTAGATATGTTATAGATCTTAATAATGGCGATCAAAATGTTC
ATACT (SEQ ID NO: 235)
Fusobacterium nucleatum
TAGGAACAATGAAATATGTAATTAATCTAAATAATGGAAATGGAC
ACACT (SEQ ID NO: 236)
Fusobacterium UBA10773
Fusobacterium varium
CTATTAAGTATGTTATAGAACTTAATAATGGTGAACAAAATGTTC
ATACT (SEQ ID NO: 237)
Fusobacterium nucleatum
TTGGAACAATGAAATATGTAATTAATCTAAATAATGGAAATGGAC
ATACT (SEQ ID NO: 238)
Fusobacterium ulcerans
CTATTAAATATGTTATAGAACTTAATAATGGTGATCAAAATGTTC
Fusobacterium nucleatum
CTATTAAATATGTTATAGATCTTAATAATGGTGATCAAAATGTTC
ATACT (SEQ ID NO: 240)
Fusobacterium CM1
Fusobacterium nucleatum
CTATTAAATATGTAATAGAGCTTAATAATGGTGATCAAAATGTTC
ATACT (SEQ ID NO: 241)
Fusobacterium nucleatum
CGATTCAATATGTCATTGATTTAAATAATGGAGAATCCCATGTAC
ATACA (SEQ ID NO: 242)
Fusobacterium equinum
Fusobacterium gonidiaformans
TTGGAACAATGAAATATGTAATTAATTTGAATAATGGAAATGGGC
ATACT (SEQ ID NO: 243)
Fusobacterium varium
TTGCAACTATGAAGTATGTAATTAATTTAAACAATGGAAATGGAC
ATACT (SEQ ID NO: 244)
Fusobacterium necrogenes
TCGCCTCCATCAATTACAACATGCATATCGAGGAGGGCATCGGC
AGCAAC (SEQ ID NO: 245)
Fusobacterium naviforme
As shown in Table 31, the 50 nucleotide SPA fragments generated in silico for Fusobacterium strains mostly allowed to distinguish Fusobacterium at the (sub) species level, as was also confirmed by whole genome-based ANI analysis. The following exceptions were observed: In addition to identifying Fusobacterium nucleatum subsp. polymorphum, SPA fragment Fs1 also identified the closely related Fusobacterium canifelinum. Whole genome-based ANI analysis confirmed the similarity between these two species. In addition to identifying Fusobacterium hwasookii, SPA fragment Fs7 also identified the closely related Fusobacterium nucleatum subsp. polymorphum. Whole genome-based ANI analysis confirmed the similarity between these two species; it also showed that Fusobacterium nucleatum ChDC F128 strain should be reclassified as Fusobacterium hwasookii. Whole genome-based ANI analysis also showed that Fusobacterium equinum and Fusobacterium gonidiaformans, both identified by SPA fragment Fs15, represent the same species. A summary of the Fusobacterium species (Fs) specific SPA fragments as phylogenetic identifiers at the (sub)species level is provided in Table 32.
These results show that unexpectedly, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB-R1327 primer annealing site allow for high resolution phylogenetic identification of clinically relevant Fusobacterium at the (sub)species level (Table 32), thus providing an important method for its (early) detection using mcfDNA from peripheral blood and stool samples. This shows the importance of SPA fragment sequencing as a new approach as part of risk screening for broadly-defined gastrointestinal (GI) cancers based on the (early) detection and identification of Fusobacterium species.
Fusobacterium species
Fusobacterium nucleatum subsp.
polymorphum, Fusobacterium hwasookii,
Fusobacterium canifelinum
Fusobacterium nucleatum subsp. animalis
Fusobacterium periodonticum
Fusobacterium necrophorum subsp.
funduliforme
Fusobacterium mortiferum
Fusobacterium varium
Fusobacterium nucleatum subsp. nucleatum
Fusobacterium ulcerans
Fusobacterium nucleatum subsp.
polymorphum
Fusobacterium nucleatum subsp. vincentii
Fusobacterium equinum*, Fusobacterium
gonidiaformans*
Fusobacterium necrogenes
Fusobacterium naviforme
Several studies successfully demonstrated that including the microbial footprint increases the specificity and sensitivity of screening tests for the detection of early-stage adenomas and carcinomas in colorectal cancer. For example, a metagenomics-based classification model, using abundance changes of Fusobacterium nucleatum ssp. vincentii and Fusobacterium nucleatum ssp. animalis, Peptostreptococcus stomatis and Pseudonocardia asaccharolytica in CRC patients versus healthy controls combined with standard CRC diagnostics improved CRC-detection sensitivity for the guaiac-based fecal occult blood test (gFOBT) by >45% (Zeller et al, 2014). A microbiota-based random forest model using abundance changes of Fusobacterium, Peptostreptococcus, Porphyromonas, Prevotella, Parvimonas, Bacteroides and Gemella species complemented the fecal immunochemical test (FIT) (Baxter et al, 2016). The microbiota-based random forest model detected 91.7% of cancers and 45.5% of adenomas while FIT alone detected 75.0% and 15.7%, respectively. Of the colonic lesions missed by FIT, the model detected 70.0% of cancers and 37.7% of adenomas.
The present inventors confirmed that Peptostreptococcus stomatis and Pseudonocardia asaccharolytica can be identified by their single unique SPA fragments; that Parvimonas species, including Parvimonas oral and Parvimonas micra could be identified by a single SPA fragment; and that Gemella species, including Gemella morbillorum, Gemella haemolysans, Gemella palaticanis and Gemella sanguinis each had their unique SPA fragment. Therefore, combining tumor-specific biomarkers (including mutational footprint, methylation footprint, and blood detection in stool) with the quantitative detection of biomarker microorganisms using SPA fragment sequencing at the species and subspecies level will significantly increase the sensitivity and specificity of colorectal cancer screening. In addition, a further application of the SPA sequencing method is that once unique SPA fragments have been identified that correlate with the detection of specific diseases and monitoring of their progression, the unique SPA fragment sequences can be used to develop species-specific screening assays as part of PCR-based diagnostic platforms.
In certain instances, disease phenotypes caused by bacteria will depend on specific metabolic properties; as a result, accurate disease detection, monitoring and prognostics will require additional functional insights besides phylogenetic identification and community composition. For example, a random forest-based model using abundance changes of Fusobacterium nucleatum, Peptostreptococcus stomatis, Pseudonocardia asaccharolytica, Prevotella species, Parvimonas species, Gemella morbillorum and other bacteria, combined with gFOBT, improved the sensitivity/specificity of CRC detection (Thomas et al, 2019). This study also found that the choline trimethylaminelyase gene, which encodes Trimethylamine (TMA) synthesis from dietary quaternary amines (mainly choline and camitine), was overabundant in the microbiomes of CRC patients (P=0.001), identifying a relationship between gut microbiome choline metabolism and CRC. Trimethylamine (TMA) has previously been associated with atherosclerosis and severe cardiovascular disease. Importantly, SPA fragment sequencing provides the flexibility to address both phylogenetic identification and community functionality. For example, this is performed by selecting a degenerate primer that recognizes a conserved DNA region of a specific function, the same protocol outlined in
Risk screening for developing Clostridium difficile infection: Clostridium difficile is the leading cause of health-care-associated infective diarrhea. Due to increased use of antibiotics that disrupt the healthy gut microbiome, creating a niche for Clostridium difficile to thrive, the incidence of Clostridium difficile infection (CDI) has been rising worldwide with subsequent increases in morbidity, mortality, and health care costs. Asymptomatic colonization with Clostridium difficile is common and a high prevalence has been found in specific cohorts, e.g., hospitalized patients, adults in nursing homes and in infants. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood stool samples, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the early detection of Clostridium difficile as biomarker for the risk to develop CDI, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Clostridium difficile strains. The results are presented in Table 33.
Clostridium difficile strain (Cd) specific SPA fragment
TAGCTTCAATAAGTTATGAGTTCAATATATTCTATAATATAGGA
AATATT (SEQ ID NO: 246)
Clostridium difficile
Clostridium UMGS188
The results in Table 33 show that Clostridium difficile strains can be identified by the highly specific SPA fragment Cd1, thus providing an important method for its (early) detection using mcfDNA from peripheral blood samples. This shows the importance of SPA fragment sequencing as a novel approach as part of risk screening, e.g. after surgery or prolonged treatment with broad spectrum antibiotics, for developing CDI based on the (early) detection and identification of Clostridium difficile in peripheral blood and/or stool samples.
Risk screening for developing hospital-acquired infections: Acinetobacter baumannii is an opportunistic bacterial pathogen primarily associated with hospital-acquired infections. The recent increase in incidence, coupled with a dramatic increase in the incidence of multidrug-resistant (MDR) strains, has significantly raised the profile of this emerging opportunistic pathogen. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of this bacterium in peripheral blood, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the early detection of Acinetobacter baumannii as biomarker for the risk to developing a hospital-acquired infection from this pathogen, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were generated in silico for Acinetobacter baumannii strains. The results are presented in Table 34.
Acinetobacter baumannii species (Ab) specific SPA fragment
TCGATGTATTACGTACATTGGTTGAAATCCGTAACGGTAAAGGT
GAAGTC (SEQ ID NO: 247)
Acinetobacter baumannii
Klebsiella pneumoniae
Acinetobacter calcoaceticus
Acinetobacter pittii
Acinetobacter Tr-809
TTGATGTATTACGTACATTAGTTGAAATCCGTAACGGTAAAGGTG
AAGTC (SEQ ID NO: 248)
Acinetobacter baumannii
Acinetobacter BS1
Acinetobacter cl
Acinetobacter calcoaceticus
Acinetobacter lactucae
Acinetobacter NRRL
Acinetobacter pittii
TCGATGTATTACGTACGTTGGTTGAAATCCGTAACGGTAAAGGC
GAAGTA (SEQ ID NO: 249)
Acinetobacter baumannii
Acinetobacter nosocomialis
TCGATGTATTACGTACATTAGTTGAAATCCGTAACGGTAAAGGTG
AAGTC (SEQ ID NO: 250)
Acinetobacter AC1-2
Acinetobacter ACIN00229
Acinetobacter baumannii
Acinetobacter calcoaceticus
Acinetobacter oleivorans
Acinetobacter UBA11343
Acinetobacter V2
TCGATGTATTACGTACTTTAGTTGAAATTCGTAACGGTAAGGGTG
AGGTC (SEQ ID NO: 251)
Acinetobacter baumannii
Acinetobacter radioresistens
TTGATGTATTACGTACATTGGTTGAAATCCGTAACGGTAAAGGTG
AAGTC (SEQ ID NO: 252)
Acinetobacter baumannii
Acinetobacter NRRL
Acinetobacter pittii
Acinetobacter vivianii
TCGATGTGTTACGTACTTTAGTTGAAATTCGTAACGGTAAGGGTG
AGGTC (SEQ ID NO: 253)
Acinetobacter baumannii
Acinetobacter radioresistens
Acinetobacter baumannii
Acinetobacter FDAARGOS 541
Acinetobacter nosocomialis
Acinetobacter RQ Bin 15
CTGATGTATTAAAAACATTAGTAGAAATCCGTAACGGTAAAGGT
GAAGTC (SEQ ID NO: 255)
Acinetobacter ACNIH1
Acinetobacter baumannii
Acinetobacter GFQ9D192M
Acinetobacter variabilis
TTGATGTACTGCGTACATTGGTAGAAATCCGTAACGGTAAAGGT
GAAGTC (SEQ ID NO: 256)
Acinetobacter baumannii
Acinetobacter courvalinii
TTGATGTACTGCGTACATTGGTTGAAATCCGTAACGGTAAAGGT
GAAGTC (SEQ ID NO: 257)
Acinetobacter baumannii
Acinetobacter C16S1
TCGATGTATTACGTACATTGGTTGAAATCCGTAATGGTAAAGGTG
AAGTC (SEQ ID NO: 258)
Acinetobacter baumannii
CTGATGTACTACGTACATTGGTTGAGATTCGTAACGGTAAAGGT
GAAGTT (SEQ ID NO: 259)
Acinetobacter baumannii
Acinetobacter ursingii
As shown in Table 34, the 50 nucleotide SPA fragments generated in silico for Acinetobacter baumannii strains, especially SPA fragment Ab1, largely allowed to distinguish Acinetobacter baumannii at the species level. However, several SPA fragments identified both Acinetobacter baumannii and related species, as well as some unexpected strains including Klebsiella pneumonia strains identified by SPA fragment Ab1. To clarify this result, whole genome-based ANI analysis was performed on selected Acinetobacter baumannii strains and representatives from related species that were identified by the same SPA fragments. Where available, the genomes sequences of the Acinetobacter species type strains were included in this analysis, of which the results are shown in
ANI group I, which contains the strains identified by SPA fragment Ab1 (
ANI group II, which contains Acinetobacter baumannii and Acinetobacter nosocomialis strains identified by SPA fragments Ab3 and Ab8 (
ANI group III, which contains Acinetobacter lactucae and Acinetobacter pittii strains identified by SPA fragment Ab2 (
ANI group IV, which contains closely related Acinetobacter calcoaceticus and Acinetobacter oleivorans strains identified by SPA fragments Ab2 and Ab4, as well as a strain identified by SPA fragment Ab4 that was misclassified as Acinetobacter baumannii (
ANI group V, which contains Acinetobacter baumannii and Acinetobacter radioresistens strains identified by SPA fragments Ab5 and Ab7 (
ANI group VI, which contains Acinetobacter baumannii and Acinetobacter courvalinii strains identified by SPA fragment Ab10 (
ANI group VII, which contains Acinetobacter baumannii and Acinetobacter ursingii strains, including the Acinetobacter ursingii type strain DSM 16037, identified by SPA fragment Ab13 (
ANI group VIII, which contains Acinetobacter baumannii and Acinetobacter variabilis strains identified by SPA fragment Ab9 (
Overall, these results confirm the phylogenetic resolution of 50 nucleotide SPA fragments to not only correctly identify Acinetobacter baumannii, but also point out strains that have been previously misclassified. A summary of the Acinetobacter baumannii strains and related species (Ab) specific SPA fragments as phylogenetic identifiers at the species level is provided in Table 35. These results show that unexpectedly, despite their relatively short size, 50 nucleotide long SPA fragment sequences covering the region upstream of the RpoB1-R1327 primer annealing site allow for high resolution phylogenetic identification of clinically relevant Acinetobacter strains at the species level, thus providing an important method for their (early) detection using mcfDNA from peripheral blood samples. This shows the importance of SPA fragment sequencing as a new approach as part of risk screening for hospital acquired infections based on the (early) detection and identification of Acinetobacter species.
Acinetobacter baumannii
Acinetobacter baumannii
Acinetobacter lactucae,
Acinetobacter pittii
Acinetobacter calcoaceticus,
Acinetobacter
oleivorans
Acinetobacter nosocomialis
Acinetobacter radioresistens
Acinetobacter vivianii,
Acinetobacter pittii
Acinetobacter variabilis
Acinetobacter courvalinii
Acinetobacter ursingii
In addition to the previous examples, the example presented below demonstrates how the SPA fragment sequencing method is generalizable and adaptable to improve phylogenetic resolution in a targetable fashion, which is informed by the existing knowledgebase of sequence variation at the species and subspecies level. Just as a lens can be refocused, resolution can be redirected to identify new taxa and subspecies of interest.
To address a limited number of cases where 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site fail to identify bacteria at the genus or species level, the combination of two SPA fragments can be used to improve the phylogenetic resolution. In the example provided for the Enterobacteriaceae, this is done by generating SPA fragments from two distinct regions of the rpoB gene and combining this information. However, the same can be achieved by combining the information of SPA fragments generated from two or more separate conserved housekeeping genes, including the prokaryotic genes coding for the DNA gyrase subunit B (gyrB), the chaperone protein (GroEL), the heat shock protein 60 (hsp60), the superoxide dismutase A protein (sodA), the TU elongation factor (tuf), the 60 kDa chaperonin protein (cpn60), and DNA recombinase proteins (including recA, recE). Practically, the same protocol as outlined in
Screening for Enterobacteriaceae: The Enterobacteriaceae represents a group of often closely related bacteria, many of clinical importance. Key genera involve Escherichia, Shigella, Klebsiella, Salmonella and Serratia, many of which have been linked to sometimes life threatening and lethal infections, especially in immune compromised patients, including transplant patients where these bacteria are linked to post-transplant bloodstream infections, Graft versus Host Disease (GvHD), and increased mortality. Therefore, there is an unmet need for high-resolution, high-throughput and low-cost early detection of these bacteria in peripheral blood and other biopsy samples, something SPA fragment sequencing can provide. To analyze the discriminatory power of SPA fragment sequencing for the detection of Enterobacteriaceae, 50 nucleotide long fragments located upstream of the RpoB1-R1327 priming site were initially generated in silico for members of the Enterobacteriaceae. The results for the two SPA fragments able to identify the largest number of strains are presented in Table 36.
Enterobacteriaceae (Ent) specific SPA fragment
TTGATGTTATGAAAAAGCTCATCGATATCCGTAACGGTAAAGGC
GAAGTC (SEQ ID NO: 260)
Escherichia coli
Shigella flexneri
Shigella sonnei
Escherichia fergusonii
Escherichia albertii
Shigella dysenteriae
Shigella boydii
Enterobacteriaceae strains
TCGAAGTGATGAAGAAGCTCATCGATATCCGTAACGGTAAAGGC
GAAGTG (SEQ ID NO: 261)
Klebsiella pneumoniae
Enterobacter cloacae
Enterobacter asburiae
Klebsiella quasipneumoniae
Leclercia adecarboxylata
Serratia fonticola
Enterobacter kobei
Enterobacter mori
Enterobacter bugandensis
Klebsiella aerogenes
Enterobacter roggenkampii
Yokenella regensburgei
Escherichia coli
Lelliottia nimipressuralis
Enterobacteriaceae strains
As shown in Table 36, the 50 nucleotide SPA fragments generated in silico for strains belonging to the Enterobacteriaceae from the region upstream of the RpoB1-R1327 priming site failed to phylogenetically distinguish between strains on the genus level. This prompted us to evaluate if a combination of SPA fragments generated from two distinct regions of the rpoB gene would improve the phylogenetic identification of Enterobacteriaceae at the genus and species level. The results are presented in Table 37 and Table 38 for strains initially identified by SPA fragments Ent1 and Ent2, respectively.
Enterobacteriaceae (Ent) specific SPA fragment
TTGATGTTATGAAAAAGCTCATCGATATCCGTAACGGTAAAGGCG
AAGTC (SEQ ID NO: 260)
AACGTCGTATCTCCGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 262)
Escherichia coli
Shigella flexneri
Shigella dysenteriae
Shigella boydii
Shigella sonnei
Escherichia strains
AACGTCGTATCTCGGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 263)
Escherichia coli
Shigella boydii
AACGTCGTATCTCCGCACTCGGCCCGGGTGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 264)
Escherichia coli
Shigella sonnei
AACGTCGTATCTCCGCACTCGGCCCAGGTGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 265)
Escherichia coli
AGCGTCGTATCTCCGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 266)
Escherichia fergusonii
Escherichia coli
Escherichia 0.2392
Escherichia HH41S
Escherichia 94.0001
AACGTCGTATCTCGGCACTTGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 267)
Escherichia coli
AACGTCGTATCTCCGCACTCGGCCCTGGCGGTCTGACTCGTGAA
CGCGCG (SEQ ID NO: 268)
Escherichia albertii
AACGTCGTATCTCGGCCCTTGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 269)
Escherichia coli
AACGTCGTATCTCAGCACTCGGCCCAGGTGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 270)
Escherichia coli
CGTGCA (SEQ ID NO: 271)
Escherichia coli
AACGTCGTATTTCCGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 272)
Escherichia coli
AACGTCGTATCTCTGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 273)
Escherichia coli
Escherichia MOD1-EC6475
Escherichia 4726-5
Escherichia 93.0816
AACGTCGTATCTTCGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 274)
Escherichia coli
AACGTCGTATCTCCGCACTCGGTCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 275)
Escherichia coli
Shigella sonnei
AACGTCGTATCTCTGCACTCGGTCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 276)
Escherichia MR
Escherichia coli
Enterobacteriaceae (Ent) specific SPA fragment
TCGAAGTGATGAAGAAGCTCATCGATATCCGTAACGGTAAAGGC
GAAGTG (SEQ ID NO: 261)
AACGTCGTATCTCCGCACTCGGCCCAGGCGGTCTGACCCGTGAG
CGCGCA (SEQ ID NO: 277)
Klebsiella pneumoniae
Klebsiella quasipneumoniae
Klebsiella aerogenes
Serratia liquefaciens
Klebsiella 18A069
Enterobacteriaceae S05
Klebsiella 01A030
AACGTCGTATCTCTGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 278)
Enterobacter cloacae
Enterobacter kobei
Enterobacter bugandensis
Enterobacter asburiae
Enterobacter roggenkampii
Lelliottia nimipressuralis
Enterobacter 725m/11
Enterobacter ODB01
Enterobacter AM17-18
Enterobacter mori
Enterobacter 35730
Leclercia adecarboxylata
Enterobacter 44593
Enterobacter GN02366
Enterobacter 50588862
Enterobacter M4-VN
Enterobacter RHBSTW-00901
Enterobacter N18-03635
Enterobacter T2
Enterobacter Acro-832
Enterobacter WCHEn090040
Enterobacter DC1
Enterobacter Tr-810
Enterobacter E12
Enterobacter WCHEs120002
Enterobacter GN02186
Leclercia LK8
Enterobacter GN02266
Enterobacter 35669
Enterobacter GN02283
AACGTCGTATCTCTGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGCGCA (SEQ ID NO: 279)
Enterobacter cloacae
Enterobacter asburiae
Enterobacter SECR19-1250
Klebsiella pneumoniae
Enterobacter kobei
Enterobacter mori
Enterobacter RHBSTW-01064
Enterobacter DC3
Enterobacter WCHECI1597
Enterobacter GN02174
Enterobacter 35699
Enterobacter JMULE2
AGCGTCGTATCTCTGCACTCGGCCCAGGCGGTCTGACCCGTGAG
CGCGCA (SEQ ID NO: 280)
Leclercia adecarboxylata
Enterobacteriaceae w17
Leclercia LSNIH1
Enterobacteriaceae w6
Leclercia 1106151
AACGTCGTATCTCTGCATTGGGCCCAGGCGGTCTGACCCGTGAA
CGTGCC (SEQ ID NO: 281)
Serratia fonticola
Serratia 3ACOL1
AACGTCGTATCTCTGCACTCGGCCCAGGCGGTCTGACTCGTGAA
CGCGCA (SEQ ID NO: 282)
Enterobacter cloacae
Enterobacter WCHEn045836
Enterobacter GN02534
AGCGTCGTATCTCTGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGCGCA (SEQ ID NO: 283)
Enterobacter cloacae
Enterobacteriaceae ATCC
Enterobacter A11
Enterobacter BIDMC92
AACGTCGTATCTCTGCACTCGGCCCAGGCGGTCTGACCCGTGAG
CGCGCA (SEQ ID NO: 284)
Enterobacter asburiae
Leclercia LSNIH6
Enterobacter SES19
Enterobacter mori
Leclercia LSNIH7
Enterobacter NFIX59
AACGTCGTATTTCTGCACTCGGCCCAGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 285)
Enterobacter cloacae
Enterobacter GN02225
Enterobacter GN02204
Enterobacter asburiae
Enterobacter 42202
CGTGCA (SEQ ID NO: 286)
Yokenella regensburgei
Enterobacter asburiae
Enterobacter cloacae
AACGTCGTATCTCTGCACTCGGCCCGGGCGGTCTGACCCGTGAG
CGCGCA (SEQ ID NO: 287)
Enterobacter mori
Enterobacter cloacae
Escherichia coli
Enterobacter tabaci
AGCGTCGTATCTCTGCACTCGGCCCGGGCGGTCTGACCCGTGAG
CGCGCA (SEQ ID NO: 288)
Leclercia UBA9585
Leclercia adecarboxylata
Enterobacter UMGS201
AACGTCGTATCTCCGCACTCGGCCCGGGCGGTCTGACCCGTGAA
CGTGCA (SEQ ID NO: 289)
Enterobacter asburiae
Kluyvera SCKS090646
Enterobacter cloacae
AGCGTCGTATCTCTGCATTGGGCCCAGGCGGTCTGACCCGTGAA
CGTGCC (SEQ ID NO: 290)
Serratia fonticola
Comparing the results from Table 36 and Table 37 shows the improved phylogenetic classification of species that clustered together for SPA fragment Ent1 after they were further classified using 50 nucleotide SPA fragments generated from the region upstream of the position of the RpoB6-R1630 priming site. For instance, the 1006 Escherichia coli strains previously identified by SPA fragment Ent1 broke into several subgroups, most of the 32 Shigella sonnei strains ended up in different groups, as did the 14 Escherichia fergusonii and the 13 Escherichia albertii strains. The results from whole genome-based ANI show that strains identified by SPA fragments Ent3*, Ent4*, Ent5* and Ent16*, despite representing different species, are very closely related with ANI scores of >0.97. Members of the genus Shigella have high genomic similarity to Escherichia coli and are often considered to be atypical members of this species. In line with the observation that many Shigella and Escherichia coli strains were identified by the same SPA fragment, Shigella species were reclassified as Escherichia species in the Genome Taxonomy Database (GTDB) using an operational average nucleotide identity (ANI)-based approach nucleated around type strains (Parks et al, 2021).
SPA fragment Ent7* identified Escherichia coli and Escherichia fergusonii strains, and SPA fragment Ent9* identified Escherichia albertii strains. Based on whole genome-based ANI it can also be concluded that Shigella boydii strain 60_SBOY (Ent4) should be assigned as Escherichia coli, that Escherichia coli strain 102606_aEPEC (Ent9) should be reassigned as Escherichia albertii, and that Escherichia coli strain JL_F4_1 (Ent16) and Shigella sonnei strain ECSW+02 (Ent16) represent the same species with an ANI score of 1.00.
Similarly, comparing the results from Table 36 and Table 38 shows the improved phylogenetic classification of species that clustered together for SPA fragment Ent2 after they were further classified using 50 nucleotide SPA fragments generated from the region upstream of the position of the RpoB6-R1630 priming site. For instance, SPA fragment Ent18* specifically grouped closely related Klebsiella pneumoniae, Klebsiella quasipneumoniae and Klebsiella aerogenes strains. This was confirmed by whole genome-based ANI as shown in
In addition to Klebsiella pneumoniae and Klebsiella quasipneumoniae strains, SPA fragment Ent2 identified closely related Enterobacter sp. strains that could be further classified using 50 nucleotide SPA fragments generated from the region upstream of the position of the RpoB6-R1630 priming site, as was confirmed by whole genome-based ANI. Based on the ANI results it can be concluded that many strains that were previously identified as Enterobacter cloacae represent in fact different but closely related species. However, the strains designated as Enterobacter cloacae identified by SPA fragments Ent20* and Ent23* represent true Enterobacter cloacae; this also includes the Enterobacter cloacae ATCC 13047 type-strain. SPA fragment Ent20* also identifies Enterobacter asburiae strains. However, based on their ANI score of 0.88 with Enterobacter cloacae ATCC 13047, the strains identified by SPA fragment Ent24* represent a different species, which is confirmed by their unique SPA fragment.
SPA fragment Ent19* grouped closely related Enterobacter sp. strains, including Enterobacter kobei strains, Enterobacter roggenkampii strains, Enterobacter bugandensis strains, and Enterobacter asburiae strains. Based on whole genome ANI, Leclercia adecarboxylata UMB0660 identified by SPA fragment Ent19* represents an Enterobacter bugandensis strain. In addition to SPA fragment Ent19*, Enterobacter asburiae strains were identified by SPA fragment Ent20*, Ent25*, Ent26*, Ent30*, and Ent27*, which also identified the reference strain Enterobacter asburiae 35734 and the type-strain Yokenella regensburgei ATCC 49455
SPA fragment Ent20* identified strains from the closely related species Enterobacter cloacae and Enterobacter asburiae. Serratia fonticola strains were specifically identified by SPA fragments Ent22* and Ent31*. SPA fragment Ent28* was found to be specific for Enterobacter mori, while SPA fragments Ent21* and Ent29* were found to be specific for Leclercia adecarboxylata and a closely related Leclercia species; this species was also identified by SPA fragment Ent25*. The results also show that Leclercia adecarboxylata strain UMB0660, identified by SPA fragment Ent19*, should be reassigned to Enterobacter bugandensis. The results for the Enterobacteriaceae specific SPA fragments are summarized in Table 39.
Enterobacteriaceae species
Escherichia coli, Shigella flexneri,
Shigella dysenteriae,
Shigella boydii, Shigella sonnei
Escherichia coli
Escherichia coli, Shigella sonnei
Escherichia coli,
Escherichia fergusonii
Escherichia albertii
Klebsiella pneumoniae,
Klebsiella quasipneumoniae
Enterobacter kobei, Enterobacter
bugandensis, Enterobacter asburiae,
Enterobacter roggenkampii
Enterobacter cloacae,
Enterobacter asburiae
Leclercia adecarboxylata,
Leclercia sp. Nov.
Serratia fonticola
Enterobacter cloacae
Enterobacter sp. Nov.
Leclercia sp. Nov., Enterobacter asburiae
Enterobacter asburiae, Enterobacter kobei
Yokenella regensburgei, Enterobacter
asburiae
Enterobacter mori
Leclercia adecarboxylata
Enterobacter asburiae
To show the synergy of using two SPA fragments generated from two distinct regions of the rpoB gene for phylogenetic identification of closely related bacteria we compared the phylogenetic classification of 121 Escherichia coli strains and related species belonging to different phylotypes as described by Fang et al (2018). This includes Escherichia coli phylotype B2 strains, which are prevalent in IBD patients and have distinct metabolic capabilities that allow them to colonize mucosa. The results are presented in
In certain instances, disease phenotypes caused by bacteria will depend on the presence of virulence/pathogenicity factors located on mobile genetic elements, including conjugative and/or mobile plasmids, phages, and pathogenicity islands that can be horizontally transferred between bacteria, as is the case for Escherichia coli, Salmonella, Klebsiella, Listeria, Bacillus, pyogenic streptococci and Clostridium perfringens, among others (for review, see Gyles and Boerlin, 2014). As the result of horizontal gene transfer, phylogenetic information on species composition will be insufficient to predict disease pathology, and therefore needs to be complemented with information on community functionality. For instance, the presence in Escherichia coli of the PKS pathogenicity island encoding, among other virulence factors, for genotoxic colibactin synthesis has been linked to increased risk for developing colorectal cancer (Pleguezuelos-Manzano et al, 2020). As discussed for colorectal cancer in Example 7, multiplex SPA fragment sequencing provides the flexibility to address both phylogenetic identification and community functional in a same amplification step. By designing a primer for SPA fragment amplification that specifically targets the PKS gene cluster essential for colibactin synthesis, the presence of genotoxic Escherichia coli strains (Pleguezuelos-Manzano et al, 2020) can be determined, and combined with phylogenetic information be used for improved risk assessment and detection of colorectal cancer.
vDescription of the community used for the simulations: To understand the sensitivity of the SPA fragment sequencing method, the gut microbiome community of a person suffering from intestinal complications was used for in silico simulations. The assumption was that this microbiome would leave a similar signature in the mcfDNA. This consortium (see Table 40), whose composition was determined using long-read PacBio sequencing, is interesting as it includes six Metagenome Assembled Genomes (MAGs) representing closely related Faecalibacterium strains that based on their Average Nucleotide Identity (ANI) represent five different species/subspecies (
Alistipes onderdonkii strain D10-10
Clostridia bacterium strain
Blautia sp. AF19-10LB
Roseburia intestinalis ERR321618-bin.7
Dorea longicatena strain MSK.11.4
Lachnospiraceae bacterium strain
Roseburia inulinivorans strain
Roseburia inulinivorans strain
Faecalibacterium
sp. strain
S04C.meta.bin_2
Bacteroidaceae bacterium strain
Bacteroides caccae strain BIOML-A1 *2
Parabacteroides merdae strain
Parabacteroides distasonis strain LMAG:27
Bacteroides caccae strain BIOML-A2 *2
Coprococcus
comes strain MSK.16.14
Ruminococcaceae bacterium
Alistipes finegoldii DSM 17242
uncultured
Faecalibacterium
sp. strain
UMGS184
Agathobaculum butyriciproducens strain
Eubacterium sp. 38_16
Subdoligranulum sp. strain S08B.meta.bin_8
Anaerostipes hadrus strain S01C.meta.bin_9
Ruminococcus sp. D40t1_170626_H2 *3
Blautia faecis strain MSK.11.45 *3
Bifidobacterium longum subsp.
Acetatifactor sp. strain COPD172
Firmicutes bacterium AM31-12AC
Faecalibacterium prausnitzii strain
Ruminococcus sp. strain UBA10663
Bacteroides ovatus strain OF01-19AC *4
Bacteroides sp. AM30-16
Bifidobacterium pseudocatenulatum strain
Alistipes obesi MGYG-HGUT-01415
Faecalibacterium
sp. Marseille-P9312 *5
Faecalibacterium prausnitzii
strain
COPD315 *5
Ruminococcus sp. AM40-10AC
Blautia wexlerae strain
Paraprevotella clara CAG:116 strain
Ruminococcus sp. CAG:9
Bacteroides ovatus AF26-20AA *4
Faecalibacterium
prausnitzii
strain
COPD342
Blautia massiliensis strain MSK.13.24
Bacteroides ovatus strain
Agathobacter sp. strain COPD130
Bacteroides vulgatus strain VPI-5710
Bacteroides stercoris strain AM51-2BH
Faecalibacterium species are marked in bold.
In silico generation of the SPA fragments for the individual community members: To demonstrate the discriminatory power of SPA fragment sequencing targeting the RpoB gene, 25 base pair and 50 base pair long SPA fragments located 3′ of the RpoB1-R1327 primer annealing site were generated in silico for each of the community members. The results for the 25 base pair long SPA fragments that identified more than one bacterial strain present in the community are presented in Table 41. Identical results were obtained for the 50 base pair SPA fragments. It should be noted that for the simulations, we still consider that all strains can be identified by their individual SPA fragments.
Using the sequences of either the 25 or 50 base pair long SPA fragments, 50 of the 52 strains in the community could be identified on the species level by their unique SPA fragments. Four SPA fragments obtained in silico with the RpoB1-R1327 primer identified multiple but very closely related strains (Table 41), as was confirmed by their identical genome taxonomy. Based on genome taxonomy and ANI it was concluded that each recognized strains belonging to the same species, and that their assignment to different MAGs was most likely the result of binning errors.
The six Faecalibacterium strains, classified on whole genome-based ANI as belonging to five different (sub)species (
Bacteroides ovatus strain 1001275B_160808_G11
Bacteroides ovatus strain AF26-20AA
Bacteroides ovatus strain OF01-19AC
Blautia faecis strain MSK.11.45
Ruminococcus sp. D40t1_170626_H2
Roseburia inulinivorans strain AF28-15
Roseburia inulinivorans strain SRR5519173-bin.6
Bacteroides caccae strain BIOML-A1
Bacteroides caccae strain BIOML-A2
Faecalibacterium prausnitzii strain COPD315
Faecalibacterium sp. Marseille-P9312
Description of the parameters to simulate the effect of SPA fragment length on community composition: Four simulations, each having 30 trials, were run with varying average length of mcfDNA fragments (40, 60, 80 and 100 base pairs). For the simulations we used of 1 ml liquid biopsy sample containing 100 ng/ml cfDNA and assumed that 1% of the total cfDNA represents mcfDNA (1 ng/ml). These estimates are considered realistic; for instance, in patients with metastatic breast cancer, the median plasma cfDNA concentration was found to be 112 ng/ml (Fernandez-Garcia et al, 2019). To be very conservative, we also estimate that due to technical limitations only 10% of the mcfDNA is effectively processed. As such, the simulations assume that fragments are only generated from 0.1 ng mcfDNA.
For each genome in the microbial community, length weighted relative abundance of total sample fragments was determined to account for the larger number of mcfDNA fragments generated from larger bacterial genomes. This abundance was subsequently used to determine the number of mcfDNA fragments per genome. The mcfDNA fragment sizes are randomly selected using a truncated normal distribution with fragment sizes between 1 and 200 base pairs. The fragment ends (start and end positions) were randomly selected from the genome. If a fragment contains the SPA primer annealing site, an in silico SPA fragment is generated from the 3′-end of the SPA primer to the end of the fragment (
As described herein above, SPA fragments of 50 base pairs or longer, obtained using the RpoB1-R1327 primer, provide high resolution phylogenetic identification for most bacteria at the species and subspecies level. Therefore, the “number of SPA fragments generated with length 50 base pairs or greater” is used as one of the criteria to determine the sensitivity of the method for species identification in function of the various parameters. It should also be noted that many more SPA fragments with smaller length will be generated.
As previously shown herein above SPA fragments with length 25 base pairs or greater, obtained using the RpoB1-R1327 primer, show good resolution at the genus level. Therefore, the “relative abundance numbers of SPA fragment with length 25 base pairs or greater” will be used to calculate the community composition.
The parameters used in the four simulations are presented in Table 42. The following formula is used to calculate the “total number of cfDNA molecules”, based on X ng cfDNA with an average length of Y bp for the mcfDNA: (X ng×[6.022×1023] molecules/mol)/(Y bp×[1×109]ng/g×618 g/mol).
Simulation of fragment size distributions: We first evaluated the distribution of fragment sizes. To do so, we simulated the size distribution of a million mcfDNA fragments based on a truncated normal distribution with averages of 40, 60, 80 and 100 nucleotides in length, respectively. The results are presented in
Simulation of SPA fragment generation for species identification and community composition analysis: For each simulation, the trial was repeated 30 times. The Wilcoxon rank sum test was performed on each of the simulations, by genome, with the two null hypotheses being: “the count of SPA fragments of 50 base pairs or greater was less than 3” (key criterium for species identification); or “the count of SPA fragments of 25 base pairs or greater was less than 10” (key criterium for species abundance). The results for the simulations using mcfDNA fragments with an average length of 40 base pairs or 60 base pairs are presented in Table 43 and Table 44, respectively; the RpoB1-R1327 was used to create the SPA fragments targeting the rpoB gene for phylogenetic identification.
Based on the results presented in Table 43, the null hypotheses “the count of 3 SPA fragments of 50 base pairs or greater was less than 3” gets accepted for the simulation using mcfDNA fragments with an average length of 40 base pairs. This indicates that for the conditions used in this simulation no reliable strain identification can be obtained at the species and subspecies level based on the presence of SPA fragments of 50 base pairs or greater. However, the null hypothesis “the count of 10 SPA fragments of 25 base pairs or greater was less than 10” gets rejected for strains that are present at approximately 1.25% or above with a p-value <0.05. This indicates that under the simulated conditions, using mcfDNA with an average fragment length of 40 base pairs, species present at approximately 1.25% in the community can be reliably identified by their 25 base pairs or greater SPA fragments at the genus level, and in many cases at the species level. In addition, the relative abundances of these species can be calculated.
Based on the results presented in Table 44, the null hypotheses “the count of SPA fragments of 50 base pairs or greater was less than 3” (key criterium for species identification) and “the count of SPA fragments of 25 base pairs or greater was less than 10” (key criterium for species abundance) both get rejected with a p-value <0.0001. This indicates that mcfDNA fragments with an average length of 60 base pairs can be reliably used for the identification of strains at the species and subspecies level, when the strains represent approximately 0.5% of the microbial community composition. In addition, mcfDNA fragments with an average length of 60 base pairs can be used to determine the community composition for species present at approximately 0.5%. Very similar results were obtained for the simulations using average mcfDNA fragment lengths of 80 base pairs and 100 base pairs.
On average, approximately 14,500 mcfDNA fragments that contain the RpoB1-R1327 primer annealing site were generated per trial for the simulation using mcfDNA fragments with an average length of 60 base pairs, of which approximately 5650 fragments would generate SPA fragments of 25 base pairs or greater. This should provide ample targets for the amplification step in the SPA fragment protocol, and subsequent sequencing.
Conclusions: Overall, the simulations show that mcfDNA fragments with an average length of 60 base pairs can be reliably used for the identification of strains at the species and subspecies level when they are present at 0.5% or above in the microbial community detectable in liquid biopsy samples, including peripheral blood. On average, strain abundances measured based on SPA fragments were within 1.4% of the actual abundance. For strains with less than 1% abundance, the average error was 1.8%, ranging from 0.1% to 7.2%; for strains with an abundance of 1% or higher, the average error was 1.2%, ranging from <0.1% to 4.5%.
indicates data missing or illegible when filed
Several studies have shown that high resolution phylogenetic identification of bacteria is a prerequisite to accurately link bacteria to specific disease phenotypes, including the development of adenomas and early-stage carcinomas in colorectal cancer. Therefore, one of the key requirements for SPA fragment sequencing is high-resolution identification of microbial species in liquid biopsy samples at the species and subspecies level. We therefore tried to answer the following questions:
Description of the community used for the simulations: To understand the specificity of the SPA fragment sequencing method, the same gut community described in EXAMPLE 10 was used for the simulations. The 52-member community, whose composition was obtained with PacBio sequencing, is described in Table 45. The sequences of the SPA fragments obtained for each of the community members are also presented. SPA fragments that were identical between multiple community members are highlighted in grey.
Bacteroides
stercoris
Bacteroides
vulgatus VPI-
Agathobacter
Bacteroides
ovatus
Blautia
massiliensis
Alistipes
putredinis
Faecali-
bacterium
prausnitzii
Bacteroides
ovatus AF26-
Ruminococcus
Paraprevotella
clara
rectale
Blautia
wexlerae
Ruminococcus
Faecali-
bacterium
prausnitzii
Faecali-
bacterium
Alistipes obesi
Bifido-
bacterium_
pseudo-
catenulatum
Bacteroides
Bacteroides
ovatus OF01-
Ruminococcus
Faecali-
bacterium
prausnitzii
Firmicutes
bacterium
Acetatifactor
bacterium
longum
Blautia faecis
Blautia faecis
lactaris
Anaerostipes
hadrus
Eubacterium
Subdoli-
granulum sp.
Agathobaculum
butyrici-
producens
Faecali-
bacterium sp.
Alistipes
finegoldii
Clostridiales
bacterium
Rumino-
coccaceae
bacterium
Eubacterium
Dialister sp.
Coprococcus
comes
Bacteroides
caccae
Parabacteroids
distasonis
Para-
bacteroides
merdae
Bacteroides
caccae
Faecali-
bacterium sp.
Bacteroidaceae
bacterium
Roseburia
inulinivorans
Roseburia
inulinivorans
Lachno-
spiraceae
bacterium
Dorea
longicatena
Roseburia
intestinalis
Clostridia
bacterium
Blautia sp.
Alistipes
onderdonkii
Specificity analysis of SPA fragments obtained using the RpoB1-R1327 primer: To analyze the phylogenetic specificity of the SPA fragments listed in Table 45, we compared them to a phylogenetic gene database containing over 50,000 unique RpoB gene entries. The results of this comparison are presented in Table 46 and show the following:
Overall, the results shows that SPA fragments generated 3′ of the RpoB1-R1327 primer annealing site have very high phylogenetic specificity to reliably classify bacteria at both the taxonomic genus and species level.
Bacteroides
Bacteroides
Bacteroides
stercoris
stercoris
Phocaeicola
Phocaeicola
Phocaeicola
vulgatus
vulgatus
Agathobacter
Agathobacter
Agathobacter
faecis
faecis
Bacteroides
Bacteroides
Bacteroides ovatus
ovatus
Bacteroides
xylanisolvens
Blautia_A
Blautia_A
Blautia_A
massiliensis
massiliensis
Alistipes
Alistipes
Alistipes putredinis
putredinis
Faecalibacterium
Faecalibacterium
Faecalibacterium
prausnitzii_C
prausnitzii_C
Bacteroides
Bacteroides
Bacteroides ovatus
ovatus
Bacteroides
xylanisolvens
Blautia_A
Blautia_A
Blautia_A
wexlerae_A
wexlerae_A
Blautia_A wexlerae
Blautia_A
Paraprevotella
Paraprevotella
Paraprevotella clara
clara
Agathobacter
Agathobacter
Agathobacter rectalis
rectalis
Fusicatenibacter
Fusicatenibacter
Fusicatenibacter
saccharivorans
saccharivorans
Blautia_A
Blautia_A
Blautia_A
Faecalibacterium
Faecalibacterium
Faecalibacterium
prausnitzii_G
prausnitzii_G
Faecalibacterium
Faecalibacterium
Faecalibacterium
prausnitzii_G
prausnitzii_G
Alistipes
Alistipes
Alistipes communis
communis
Bifidobacterium
Bifidobacterium
Bifidobacterium
pseudocatenulatum
pseudocatenulatum
Bacteroides
Bacteroides
Bacteroides
uniformis
uniformis
Bacteroides
Bacteroides
Bacteroides ovatus
ovatus
Bacteroides
xylanisolvens
Ruminococcus_D
Ruminococcus_D
Ruminococcus_D
bicirculans
bicirculans
Faecalibacterium
Faecalibacterium
Faecalibacterium
prausnitzii_J
prausnitzii_J
Faecalibacterium
prausnitzii
Schaedlerella
Schaedlerella
Schaedlerella
Acetatifactor
Acetatifactor
Acetatifactor
Bifidobacterium
Bifidobacterium
Bifidobacterium
longum
longum subsp.
longum
Bifidobacterium
longum subsp.
infantis
Blautia_A faecis
Blautia_A
Blautia_A faecis
Blautia_A faecis
Blautia_A
Blautia_A faecis
Mediterraneibacter
Mediterraneibacter
Mediterraneibacter
lactaris
lactaris
Anaerostipes
Anaerostipes
Anaerostipes hadrus
hadrus
Anaerostipes
hadrus_B
Anaerobutyricum
Anaerobutyricum
Anaerobutyricum
soehngenii
soehngenii
Gemmiger
Gemmiger
Gemmiger formicilis
formicilis
Agathobaculum
Agathobaculum
Agathobaculum
butyriciproducens
butyriciproducens
Faecalibacterium
Faecalibacterium
Faecalibacterium
Alistipes
Alistipes
Alistipes finegoldii
finegoldii
Gemmiger
Gemmiger
Gemmiger qucibialis
qucibialis
Lachnospira
Lachnospira
Lachnospira
Dialister invisus
Dialister
Dialister invisus
Bariatricus comes
Bariatricus
Bariatricus comes
Bacteroides
Bacteroides
Bacteroides caccae
caccae
Parabacteroides
Parabacteroides
Parabacteroides
distasonis
distasonis
Parabacteroides
Parabacteroides
Parabacteroides
merdae
merdae
Bacteroides
Bacteroides
Bacteroides caccae
caccae
Faecalibacterium
Faecalibacterium
Faecalibacterium
prausnitzii_D
prausnitzii_D
Barnesiella
Barnesiella
Barnesiella
intestinihominis
intestinihominis
Roseburia
Roseburia
Roseburia
inulinivorans
Roseburia
Roseburia
Roseburia
Roseburia
inulinivorans
inulinivorans
Roseburia
Dorea_A
Dorea_A
Dorea_A longicatena
longicatena
Roseburia
Roseburia
Roseburia intestinalis
intestinalis
Blautia_A
Blautia_A
Blautia_A
Alistipes
Alistipes onderdonkii
onderdonkii
Alistipes megaguti
Alistipes shahii
Simulation of sensitivity and specificity analysis of deep NGS sequencing of mcfDNA fragments followed by taxonomic classification using read-based metagenome analysis methods: The current approach to analyze microbial signatures in cfDNA involves deep NGS sequencing. After filtering out the human DNA reads, the mcfDNA reads are analyzed; this is customary done using read-based taxonomic classifiers. To understand the usefulness of read-based taxonomic classifiers for mcfDNA informed community analysis we simulated mcfDNA fragments and classified them with either Kaiju (Menzel et al, 2016) or Kraken 2 (Wood et al, 2019), two commonly used read-based taxonomic classifiers. For this simulation we used the assumption that on a routine basis 100 cfDNA samples were sequenced in parallel on a NovaSeq 6000 NGS sequencer. Since the maximum capacity of the NovaSeq 6000 is approximately 20 billion reads, this would enable sequencing of a maximum of 200 million cfDNA fragments per sample. This is in line with the numbers published by Poore et al (2020). Based on the assumption that 1% of the cfDNA represents mcfDNA fragments, around 2 million mcfDNA fragments sequence reads will be generated per sample.
For each genome in the microbial community of Table 40, the length weighted relative abundance of total sample fragments was determined to account for the larger number of mcfDNA fragments generated from larger genomes. This abundance was subsequently used to determine the number of mcfDNA fragments generated per genome. The mcfDNA fragment sizes were randomly selected from a truncated normal distribution with fragment sizes between 1 and 200 base pairs and an average of 60 base pairs; these represents the same parameters as used for the SPA fragment simulation and matches best with the reported size distribution for mcfDNA fragments (Burnham et al, 2016). The fragment start and end positions were randomly selected from the genomes.
The results of the taxonomic assignment of fragments by Kaiju and Kraken 2 to different phylogenetic levels, ranging from phylum to species, is presented in Table 47. The community compositions determined by PacBio sequencing and the SPA fragment sequencing simulation using the RpoB1-R1327 primer are included for reference. Based on the results presented in Table 47 it can be concluded that Kaiju and Kraken 2 failed to correctly assign short mcfDNA reads to their taxonomic classification or to correctly deconvolute the community composition. This is in contrast to the results obtained for the SPA fragment sequencing simulation, which closely matched the community composition obtained by PacBio sequencing that was used as input for all three simulations. It is also important to remember that for all three simulations, similar mcfDNA fragments with an average length of 60 base pairs and a similar size distribution were used.
Further details on the phylogenetic assignment of mcfDNA reads to the genus level by Kaiju and Kraken 2 are presented in Table 48 and Table 49, respectively. In the original community, all 52 members are present at a relative abundance ranging from 3.541 to 21.61 (see Table 40). Of the reads, 40.77 and 38.04 could be assigned by Kaiju and Kraken 2, respectively, to the genus level, represented by genera with a relative abundance of 0.01% or above. This number is in line with the results published by Poore et al (2020), with 35.8% of the mcfDNA reads being assigned to the genus level. A further comparison of the genus level taxonomic assignment is provided in Table 50.
Bacteroides
Faecalibacterium
Alistipes
Roseburia
Ruminococcus
Paraprevotella
Bifidobacterium
Parabacteroides
Blautia
Eubacterium
Clostridium
Coprococcus
Subdoligranulum
Dorea
Dialister
Butyricicoccus
Gemmiger
Prevotella
Fusicatenibacter
Clostridioides
Barnesiella
Anaerobutyricum
Anaerostipes
Oscillibacter
Lachnoclostridium
Bacteroides
Alistipes
Faecalibacterium
Blautia
Bifidobacterium
Paraprevotella
Parabacteroides
Ruminococcus
Roseburia
Anaerobutyricum
Anaerostipes
Lachnoclostridium
Clostridium
Eubacterium
Prevotella
Butyricimonas
Clostridioides
Mordavella
Paenibacillus
Faecalitalea
Muribaculum
Barnesiella
Butyrivibrio
Streptococcus
Longibaculum
Streptomyces
Pseudomonas
Alloprevotella
Tannerella
Odoribacter
Duncaniella
Porphyromonas
Proteiniphilum
Chryseobacterium
Flavobacterium
Capnocytophaga
Hymenobacter
Mucilaginibacter
Sphingobacterium
Pedobacter
Chitinophaga
Pseudobutyrivibrio
Ruthenibacterium
Flavonifractor
Hungatella
Flintibacter
Dysosmobacter
Oscillibacter
Staphylococcus
Lactobacillus
Enterococcus
Corynebacterium
Citrobacter
Acinetobacter
Vibrio
Burkholderia
Campylobacter
Ruminococcus, respectively, to which they previously belonged.
Bacteroides
Blautia
Faecalibacterium
Agathobacter
Alistipes
Phocaeicola
Bacteroides
Bacteroides
Agathobaculum/
Paraprevotella
Bifidobacterium
Fusicatenibacter
Gemmiger
Roseburia
Parabacteroides
Lachnospira
Ruminococcus
Schaedlerella
Acetatifactor
Mediterraneibacter
Ruminococcus
Ruminococcus
Anaerostipes
Anaerobutyricum
Oscillospiraceae
Bariatricus
Dialister
Barnesiella
Dorea
Based on the results presented in Table 50 it can be concluded that all three simulations identified the most abundant genera, including Bacteroides, Blautia, Faecalibacterium, Alistipes, Phocaeicola, Agathobaculum Paraprevotella, Bifidobacterium and Fusicatenibacter. However, compared to the input data for the simulations, the numbers for their relative abundances are imprecise for Kaiju and Kraken 2. This becomes even more obvious for low abundant species. In addition, the read-based taxonomic classification tools fail to provide any meaningful insights when multiple closely related species are present.
Species and subspecies level insights are required to draw meaningful conclusions between microbial signatures and diseases, including cancer detection and prognostics. The simulated compositions on the species level of the gut microbiome community using Kaiju or Kraken 2 for taxonomic classification of in silico generated mcfDNA fragments were very imprecise. For Bacteroides stercoris, the dominant species present at 21.61% in the community, Kaiju was able to match 2.6% of the mcfDNA fragments to this species, while Kraken 2 failed to link any mcfDNA fragments to this species. This clearly shows that read-based taxonomic classification tools are lacking the sensitivity and specificity required to analyze microbial signatures present in mcfDNA from biopsy samples.
Conclusion: Short DNA fragments with an average length of approximately 60 base pairs are an intrinsic property of mcfDNA. In contrast to the result from the simulation using SPA fragment sequencing-based analysis, where the fragments were generated using the RpoB1-R1327 primer, simulations using deep metagenome sequencing of cfDNA fragments followed by taxonomic classification of mcfDNA using read-based metagenome analysis methods showed that the current read-based tools are unsuitable for taxonomic classification of the short sequencing reads obtained from mcfDNA. As such this approach lacks the sensitivity and specificity to provide meaningful insights for disease detection and progression monitoring. An approach to overcome this limitation would require very deep sequencing and assembly of short reads into larger fragments. In addition to a significantly higher sequencing cost, limitations in the assembly of short sequencing reads makes this approach unsuitable for scalable application to the routine analysis of microbial patterns in biopsy samples.
As concluded from EXAMPLE 11, SPA fragment sequences obtained with the primer RpoB1-R1327 provided excellent phylogenetic resolution for gut microbiome bacteria at the genus level and in most instances at the species and subspecies level. However, in some instances, it failed to discriminate between very closely related species, such as Bacteroides ovatus and Bacteroides xylanisolvens, and Alistipes onderdonkii, Alistipes finegoldii and Alistipes shahii.
Design of the Cpn60-R571 SPA primer: To further improve the phylogenetic resolution compared to SPA fragment sequencing based on the rpoB gene (using primer RpoB1-R1327) we analyzed the 60 kDa chaperonin protein gene (cpn60 gene, also referred to as the groEL gene) for SPA fragment sequencing. Using the method described herein above and exemplified in Example 2, a conserved region spanning position 571 to 593 (position numbers based on the Escherichia coli cpn60 gene) was identified for SPA fragment sequencing; this primer annealing region is located downstream of a hypervariable DNA region to be used for phylogenetic identification. The degenerate nucleotide sequence of this region is presented in
Cpn60-R571 primer: 5′ CCN.YKR.TCR.AAB.YGC.ATN.CCY.TC 3′ (SEQ ID NO: 3)
As described herein above, a conserved primer annealing region is located adjacent to at least one of a 25 nucleotide-long or a 50 nucleotide-long variable region with preferably an average sequence variance of <0.1 and <0.075, respectively. As can be seen in Table 51, the 25 nucleotide-long variable region located upstream of the Cpn60-R571 primer annealing site has an average sequence variance of 0.0851.
In silico sensitivity analysis for Cpn60-R571-based SPA fragment sequences: Using a similar consortium (see Table 40) and parameters for the simulations as described in EXAMPLE 10, a simulation was performed to determine the sensitivity of SPA fragment sequencing using the Cpn60-R571 primer annealing site. The 52-member community, whose composition was obtained with PacBio sequencing, is described in Table 52. The sequences of the SPA fragments obtained for each of the community members are also presented. The 50 base pair SPA fragments that are identical for multiple closely related community members are highlighted in grey. Based on the results from EXAMPLE 10, mcfDNA fragments with an average sequence length of 60 base pairs were used in this simulation. The results from the simulation using the Cpn60-R571 primer showed that mcfDNA fragments with an average length of 60 base pairs can be reliably used to determine the microbial community composition when the strains are present at approximately 0.5%0 (Table 53). These results are very similar to the results that were obtained for the simulation using the RpoB1-R1327 primer (Table 44).
Bacteroides vulgatus
Agathobacter sp.
Bacteroides ovatus
Blautia massiliensis
Alistipes putredinis
Faecalibacterium
prausnitzii strain
prausnitzii strain
Bacteroides ovatus
Blautia wexlerae
Paraprevotella clara
rectale strain
Blautia wexlerae
Ruminococcus sp.
Faecalibacterium
prausnitzii strain
Faecalibacterium sp.
Alistipes obesi strain
Bifidobacterium
pseudocatenulatum
Bacteroides sp.
Bacteroides ovatus
Ruminococcus sp.
Faecalibacterium
prausnitzii strain
Firmicutes
bacterium AM31-
Acetatifactor sp.
Bifidobacterium
longum subsp.
Blautia faecis strain
Ruminococcus sp.
lactaris strain
Anaerostipes hadrus
Eubacterium sp.
Subdoligranulum sp.
Agathobaculum
butyriciproducens
Faecalibacterium sp.
Alistipes finegoldii
bacterium strain
Ruminococcaceae
bacterium strain
Eubacterium sp.
Coprococcus comes
Bacteroides caccae
Parabacteroides
distasonis strain
Parabacteroides
merdae strain
Bacteroides caccae
Faecalibacterium sp.
Bacteroidaceae
bacterium strain
Roseburia
inulinivorans strain
Roseburia
inulinivorans strain
Lachnospiraceae
bacterium strain
Dorea longicatena
Roseburia
intestinalis strain
Clostridia bacterium
Blautia sp. AF19-
Alistipes
onderdonkii strain
Specificity analysis of SPA fragments obtained using the Cpn60-R571 primer: To analyze the phylogenetic specificity of the Cpn60-SPA fragments listed in Table 52, we compared them to the reference phylogenetic gene database which contains over 40,000 unique cpn60 gene entries. We also compared the results with those obtained using the RpoB1-R1327 primer. The results of this comparison are presented in Table 54 and show the following:
Unexpectedly, the phylogenetic resolution on the species level was gene dependent and, therefore, combining the results from multiple phylogenetic genes will result in better phylogenetic deconvolution of the community. As shown in Table 54, in several cases where SPA fragments derived from a single phylogenetic identifier gene failed to provide species level resolution, the combination of rpoB and cpn60 gene-derived SPA fragments from the same species allowed for improved phylogenetic resolution at the species level. Improved phylogenetic identification on the species level by rpoB gene-derived SPA fragments (compared to cpn60 gene-derived SPA fragments) was observed for Blautia_A massiliensis (rpoB_SPA5 fragment), Faecalibacterium prausnitzii_C (rpoB_SPA7 fragment), Blautia_A sp003480185 (rpoB_SPA12 fragment), Acetatifactor sp900066565 (rpoB_SPA20 fragment), Bacteroides caccae (rpoB_SPA35 fragment), and Faecalibacterium prausnitzii_D (rpoB_SPA38 fragment); and improved phylogenetic identification on the species level by cpn60 gene-derived SPA fragments (compared to rpoB gene-derived SPA fragments) was observed for Bacteroides ovatus (cpn60_SPA4 fragment), Roseburia sp900552665 (cpn60_SPA39 fragment) and Alistipes onderdonkii (cpn60_SPA46 fragment); and on the subspecies level for Faecalibacterium prausnitzii_J (cpn60_SPA17 fragment). Thus, using the combination of rpoB and cpn60 gene-derived SPA fragments, species-level taxonomic classification ambiguities were solved for Faecalibacterium, Acetatifactor and Bacteroides, and remained for Blautia_A species (rpob_SPA8 and cpn60_SPA8 fragments) and Roseburia species (rpob_SPA40 and cpn60_SPA40 fragments); and subspecies-level taxonomic classification ambiguities were solved for Faecalibacterium prausnitzii and remained for Bifidobacterium longum (rpob_SPA21 and cpn60_SPA20 fragments) and Anaerostipes hadrus (rpob_SPA24 and cpn60_SPA23 fragments).
Based on this result a new method is provided, referred to as multi loci SPA fragment sequencing, which combines SPA fragments from multiple phylogenetic identifier genes to analyze the composition of microbial communities as is described in EXAMPLE 14
Bacteroides
Bacteroides
Bacteroides
Bacteroides
Bacteroides
stercoris
stercoris
Phocaeicola
stercoris
Phocaeicola
Phocaeicola
Phocaeicola
Phocaeicola
vulgatus
vulgatus
vulgatus
Agathobacter
Agathobacter
Agathobacter
Agathobacter
Agathobacter
faecis
faecis
faecis
Bacteroides
Bacteroides
Bacteroides
Bacteroides
Bacteroides
ovatus
ovatus
ovatus
Bacteroides
xylanis
olvens
Blautia_A
Blautia_A
Blautia_A
Blautia_A
Blautia_A
massiliensis
massiliensis
massiliensis
Blautia_A
Blautia_A
Alistipes
Alistipes
Alistipes
Alistipes
Alistipes
putredinis
putredinis
putredinis
Faecali-
Faecali-
Faecali-
Faecali-
Faecali-
bacterium
bacterium
bacterium
bacterium
bacterium
prausnitzii_C
prausnitzii_C
prausnitzii_C
Faecali-
bacterium
prausnitzii
Faecali-
bacterium
sp003449675
Faecali-
bacterium
prausnitzii_A
Bacteroides
Bacteroides
Bacteroides
Bacteroides
Bacteroides
ovatus
ovatus
ovatus
Bacteroides
xylanis
olvens
Blautia_A
Blautia_A
Blautia_A
Blautia_A
Blautia_A
wexlerae_A
wexlerae
wexlerae_A
Blautia_A
Blautia_A
wexlerae_A
wexlerae
Blautia_A
Blautia_A
wexlerae_B
Blautia_A
Blautia_A
Blautia_A
Paraprevotella
Parapre
Paraprevotella
Paraprevotella
Paraprevotella
clara
votella
clara
clara
Agathobacter
Agathobacter
Agathobacter
Agathobacter
Agathobacter
rectalis
rectalis
rectalis
Fusicateni-
Fusicateni-
Fusicateni-
Fusicateni-
Fusicateni-
bacter
bacter
bacter
bacter
bacter
saccharivorans
saccharivorans
saccharivorans
Blautia_A
Blautia_A
Blautia_A
Blautia_A
Blautia_A
wexlerae
Blautia_A
wexlerae_A
Blautia_A
wexlerae_B
Blautia_A
Blautia_A
Blautia_A
Faecali-
Faecali-
Faecali-
Faecali-
Faecali-
bacterium
bacterium
bacterium
bacterium
bacterium
prausnitzii_G
prausnitzii_G
prausnitzii_G
Faecali-
Faecali-
Faecali-
Faecali-
Faecali-
bacterium
bacterium
bacterium
bacterium
bacterium
prausnitzii_G
prausnitzii_G
prausnitzii_G
Alistipes
Alistipes
Alistipes
Alistipes
Alistipes
communis
communis
communis
Bifido-
Bifido-
Bifido-
Bifido-
Bifido-
bacterium
bacterium
bacterium
bacterium
bacterium
pseudo
pseudo
pseudo
catenulatum
catenulatum
catenulatum
Bacteroides
Bacteroides
Bacteroides
Bacteroides
Bacteroides
uniformis
uniformis
uniformis
Bacteroides
Bacteroides
Bacteroides
Bacteroides
Bacteroides
ovatus
ovatus
ovatus
Bacteroides
xylanis
olvens
Ruminococcus_D
Ruminococcus_D
Ruminococcus_D
Ruminococcus_D
Ruminococcus_D
bicirculans
bicirculans
bicirculans
Faecali-
Faecali-
Faecali-
bacterium
bacterium
bacterium
prausnitzii_J
prausnitzii_J
Schaedlerella
Schaedlerella
Schaedlerella
Schaedlerella
Schaedlerella
Acetatifactor
Acetatifactor
Acetatifactor
Acetatifactor
Acetatifactor
Acetatifactor
Bifido-
Bifido-
Bifido-
Bifido-
Bifido-
bacterium
bacterium
bacterium
bacterium
bacterium
longum
longum
Bifido-
Bifido-
bacterium
bacterium
infantis
infantis
Bifido-
bacterium
imperatoris
Blautia_A
Blautia_A
Blautia_A
Blautia_A
Blautia_A
faecis
faecis
faecis
Blautia_A
Blautia_A
Blautia_A
Blautia_A
Blautia_A
faecis
faecis
faecis
Mediterranei-
Mediterranei-
Mediterranei-
Mediterranei-
Mediterranei-
bacter
bacter
bacter
bacter
bacter
lactaris
lactaris
lactaris
Anaerostipes
Anaerostipes
Anaerostipes
Anaerostipes
Anaerostipes
hadrus
hadrus
hadrus
Anaerostipes
Anaerostipes
hadrus_B
hadrus_B
Anaero-
Anaero-
Anaero-
Anaero-
Anaero-
butyricum
butyricum
butyricum
butyricum
butyricum
soehngenii
soehngenii
soehngenii
Gemmiger
Gemmiger
Gemmiger
Gemmiger
Gemmiger
formicilis
formicilis
formicilis
Agatho-
Agatho-
Agatho-
Agatho-
Agatho-
baculum
baculum
baculum
baculum
baculum
butyrici-
butyrici-
butyrici-
producens
producens
producens
Faecali-
Faecali-
Faecali-
Faecali-
Faecali-
bacterium
bacterium
bacterium
bacterium
bacterium
Alistipes
Alistipes
Alistipes
Alistipes
Alistipes
finegoldii
finegoldii
finegoldii
Gemmiger
Gemmiger
Gemmiger
Gemmiger
Gemmiger
qucibialis
qucibialis
qucibialis
Lachnospira
Lachnospira
Lachnospira
Lachnospira
Lachnospira
Dialister
Dialister
Dialister
Dialister
Dialister
invisus
invisus
invisus
Bariatricus
Bariatricus
Bariatricus
Bariatricus
Bariatricus
comes
comes
comes
Bacteroides
Bacteroides
Bacteroides
Bacteroides
Bacteroides
caccae
caccae
caccae
Bacteroides
Para-
Para-
Para-
Para-
Para-
bacteroides
bacteroides
bacteroides
bacteroides
bacteroides
distasonis
distasonis
distasonis
Para-
Para-
Para-
Parabac
Para-
bacteroides
bacteroides
bacteroides
teroides
bacteroides
merdae
merdae
merdae
Bacteroides
Bacteroides
Bacteroides
Bacteroides
Bacteroides
caccae
caccae
caccae
Bacteroides
Faecali-
Faecali
Faecali-
Faecali-
Faecali-
bacterium
bacterium
bacterium
bacterium
bacterium
prausnitzii_D
prausnitzii_D
prausnitzii_D
Faecali-
bacterium
Barnesiella
Barnesiella
Barnesiella
Barnesiella
Barnesiella
intestini-
intestini-
intestini-
hominis
hominis
hominis
Roseburia
Roseburia
Roseburia
Roseburia
Roseburia
inulini-
vorans
Roseburia
Roseburia
Roseburia
Roseburia
Roseburia
Roseburia
inulini-
inulini-
inulini-
vorans
vorans
vorans
Roseburia
Roseburia
Dorea_A
Dorea_A
Dorea_A
Dorea_A
Dorea_A
longicatena
longicatena
longicatena
Roseburia
Roseburia
Roseburia
Roseburia
Roseburia
intestinalis
intestinalis
Blautia_A
Blautia_A
Blautia_A
Blautia_A
Blautia_A
Alistipes
Alistipes
Alistipes
Alistipes
Alistipes
onderdonkii
onderdonkii
onderdonkii
Alistipes
megaguti
Alistipes
shahii
As concluded from EXAMPLE 11 and EXAMPLE 13, SPA fragment sequences obtained with the primers RpoB1-R1327 and Cpn60-R571 provided excellent phylogenetic resolution for gut microbiome bacteria at the genus level and in many instances at the species and subspecies level. However, in some instances, these SPA fragments failed to discriminate between very closely related species and subspecies. To further improve the phylogenetic resolution of SPA fragment sequencing we provide a new approach, referred to as “Multi Loci SPA Fragment Sequencing”; In this approach two or more phylogenetic identifier genes are targeted using different gene-specific SPA primers in the same amplification reaction via multiplexing PCR. One example of a protocol is as follows:
The processing and analysis of the SPA fragment sequences can include the following steps:
This application is a continuation application of International Application No. PCT/US2023/011406, filed Jan. 24, 2023, which claims the benefit of U.S. Provisional Application No. 63/302,313 filed Jan. 24, 2022, and U.S. Provisional Application No. 63/340,004 filed May 10, 2022, both of which are incorporated herein by reference in entirety.
| Number | Date | Country | |
|---|---|---|---|
| 63302313 | Jan 2022 | US | |
| 63340004 | May 2022 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US2023/011406 | Jan 2023 | WO |
| Child | 18780156 | US |
