Patent Application
20120264637
With as many as 1030 microbial genomes globally, across multiple: different environmental and host conditions, variety both within and between microbiomes is well recognized (Huse et al. (2008), PLoS Genetics 4(11): e1000255). As a result of this variety, characterizing the contents of a microbiome is a challenge for current approaches. Firstly, standard culturing techniques are successful in maintaining only a small fraction of the microorganisms in nature. Means of more direct profiling, such as sequencing, face two additional challenges. Both the sheer number of different genomes in a given sample and the degree of homology between members present a complex problem for already laborious procedures.
Biopolymers such as nucleic acids and proteins are often identified in the search for useful genes, to diagnose diseases or to identify organisms. Frequently, hybridization or another binding reaction is used as part of the identification step. As the number of possible targets increases in a sample, the design of systems to detect the different hybridization reactions increases in difficulty along with the analysis of the binding or hybridization data. The design and analysis problems become acute when there are many similar targets in a sample as is the case when the individual species or groups that comprise a microbiome are detected or quantified in a single assay based on a highly conserved polynucleotide. For example, while approximately 98% of bacteria found in the human gut belong to only four bacterial divisions, this includes approximately 36,000 different phylotypes at the strain level, having ≧99% sequence identity (Hattori et al. (2009), DNA Res. 16: 1-12). While possibly containing certain overlapping taxa, the different environments presented by the guts of other hosts are expected to support different microbiomes. In situations where contributions from multiple sub-enviroments are combined, such as a water source potentially contaminated by a variety of sources, just identifying the thousands of taxa is a significant challenge to current methods of detection.
Since the study of microbiomes can offer new insight into origins of environmental change, disease, immunological functions, and physiological functions, improved methods for designing nucleic acids, proteins, or other probes that can recognize specific organisms, or taxa are needed. Similarly, improved methods for data analysis that allow detection and quantification of the members of a microbial community at high confidence levels are also needed.
In one aspect, the invention provides a method for determining a pulmonary condition of a subject. In one embodiment, the method comprises: (a) contacting a sample from said subject with a plurality of different probes; (b) determining hybridization signal strength for each of said probes, wherein said determination establishes a biosignature for said sample; and, (c) determining a pulmonary condition of said subject based on the results of step (b). In some embodiments, step (b) further comprises comparing the biosignature of said sample to a biosignature for one or more pulmonary conditions. In some embodiments, the sample is a pulmonary sample, including but not limited to sputum, endotracheal aspirate, a bronchoalveolar lavage sample, or a swab of the endotrachea. In some embodiments, the method further comprises making a healthcare decision based on the results of step (c). In some embodiments, the biosignature comprises the presence, relative abundance, and/or quantity of one or more OTUs selected from OTUs listed in one or more of Table 3, Table 4, or Table 5. In some embodiments, the biosignature comprises the presence, relative abundance, and/or quantity of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 100, 250, 300, 400, 500, 600, 700, 800, 900, 1000, or more OTUs listed in one or more of Table 3, Table 4, or Table 5. In some embodiments, the pulmonary condition is selected from the group consisting of: healthy, exacerbated COPD, non-exacerbated COPD, and intermediate COPD exacerbation, wherein intermediate COPD exacerbation comprises a prediction of the onset of exacerbation of COPD in said subject. In some embodiments,
In one aspect, the invention provides a method of classification, diagnosis, prognosis, and/or prediction of an outcome of a pulmonary condition in a subject. In one embodiment, the method comprises: (a) isolating nucleic acid material from a sample from said subject; (b) determining hybridization signal strength distributions of negative control probes that do not specifically hybridize to one or more highly conserved polynucleotides in one or more target operational taxon units (OTUs); (c) determining hybridization signal strengths for a plurality of different interrogation probes, each of which is complementary to a section within said one or more highly conserved polynucleotides; (d) using the hybridization signal strengths of the negative and positive probes to determine the probability that the hybridization signal for the different interrogation probes represents the presence, relative abundance, and/or quantity of said one or more OTUs; and, (e) classifying, diagnosing, prognosing, and/or predicting an outcome of said pulmonary condition based on the results of step (d). In some embodiments, the sample is a pulmonary sample, including but not limited to sputum, endotracheal aspirate, a bronchoalveolar lavage sample, or a swab of the endotrachea. In some embodiments, the method further comprises making a healthcare decision based on the results of step (e). In some embodiments, the pulmonary condition is selected from the group consisting of: healthy, exacerbated COPD, non-exacerbated COPD, and intermediate COPD exacerbation, wherein intermediate COPD exacerbation comprises a prediction of the onset of exacerbation of COPD in said subject. In some embodiments, the presence, relative abundance, and/or quantity is detected with a confidence level greater than 95%. Highly conserved polynucleotides include, but are not limited to, 16S rRNA gene, 23S rRNA gene, 5S rRNA gene, 5.8S rRNA gene, 12S rRNA gene, 18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA gene, cox1 gene, nif13 gene, RNA molecules derived therefrom, or a combination thereof.
In one aspect, the invention provides a method for assessing a pulmonary condition of a subject. In one embodiment, the method comprises detecting in a sample from said subject the presence, relative abundance, and/or quantity of one or more OTUs in a single assay, wherein said one or more OTUs are selected from OTUs listed in one or more of Table 3, Table 4, or Table 5; and determining the pulmonary condition of said subject based on said detection. In some embodiments, the presence, relative abundance, and/or quantity of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 100, 250, 300, 400, 500, 600, 700, 800, 900, 1000, or more OTUs listed in one or more of Table 3, Table 4, or Table 5 are detected in a single assay. In some embodiments, the sample is a pulmonary sample, including but not limited to sputum, endotracheal aspirate, a bronchoalveolar lavage sample, or a swab of the endotrachea. In some embodiments, the method further comprises making a healthcare decision based on the determination of the pulmonary condition of the subject. In some embodiments, the pulmonary condition is selected from the group consisting of: healthy, exacerbated COPD, non-1-exacerbated COPD, and intermediate COPD exacerbation, wherein intermediate COPD exacerbation comprises a prediction of the onset of exacerbation of COPD in said subject. In some embodiments, the presence, relative abundance, and/or quantity is detected with a confidence level greater than 95%.
In one aspect, the invention provides a system for practicing the methods of the invention. In one embodiment, the system comprises: (a) negative control probes that do not specifically hybridize to one or more highly conserved polynucleotides in a plurality of target OTUs; and (b) a plurality of different interrogation probes, each of which is complementary to a section within said one or more highly conserved polynucleotides in one or more of said plurality of target OTUs, wherein said plurality of target OTUs consists of OTUs in one or more of Table 3, Table 4, or Table 5. Highly conserved polynucleotides include, but are not limited to, 16S rRNA gene, 23S rRNA gene, 5S rRNA gene, 5.8S rRNA gene, 12S rRNA gene, 18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA gene, cox1 gene, nif13 gene, RNA molecules derived therefrom, or a combination thereof. In some embodiments, the system further comprises a plurality of positive control probes, such as probes comprising sequences selected from SEQ ID NOs: 51-100, and/or the complements thereof.
Probes used in methods and systems of the present invention can be used to detect the presence, absence, relative abundance, and/or quantity of at least 10,000 different OTUs in a single assay. In some embodiments, probes are attached to a substrate. Substrates can comprise any suitable material, including but not limited to glass, plastic, or silicon. Substrates can take any suitable shape, such as a flat surface, a bead, or a microsphere.
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.
The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized; and the accompanying drawings of which:
As used herein, the term “oligonucleotide” refers to a polynucleotide, usually single stranded, that is either a synthetic polynucleotide or a naturally occurring polynucleotide. The length of an oligonucleotide is generally governed by the particular role thereof, such as, for example, probe, primer and the like. Various techniques can be employed for preparing an oligonucleotide, for instance, biological synthesis or chemical synthesis. A nucleic acid of the present invention will generally contain phosphodiester bonds, although in some cases, as outlined below, nucleic acid analogs are included that may have alternate backbones, comprising, for example, phosphoramide (Beaucage, et al., Tetrahedron, 49(10):1925 (1993) and references therein; Letsinger, J. Org. Chem., 35:3800 (1970); Sprinzl, et al., Eur. J. Biochem., 81:579 (1977); Letsinger, et al., Nucl. Acids Res., 14:3487 (1986); Sawai, et al., Chem. Lett., 805 (1984), Letsinger, et al., J. Am. Chem. Soc., 110:4470 (1988); and Pauwels, et al., Chemica Scripta, 26:141 (1986)); phosphorothioate (Mag, et al, Nucleic Acids Res., 19:1437 (1991); and U.S. Pat. No. 5,644,048); phosphorodithioate (Briu, et al., J. Am. Chem. Soc., 111:2321 (1989)); O-methylphosphoroamidite linkages (see Eckstein, Oligonucleotides and Analogues: A Practical Approach, Oxford University Press); and peptide nucleic acid backbones and linkages (see Egholm, J. Am. Chem. Soc., 114:1895 (1992); Meier, et al., Chem. Int. Ed. Engl., 31:1008 (1992); Nielsen, Nature, 365:566 (1993); Carlsson, et al., Nature, 380:207 (1996), all of which are incorporated by reference)). Other analog nucleic acids include those with positive backbones (Denpcy, et al., Proc. Natl. Acad. Sci. USA, 92:6097 (1995)); non-ionic backbones (U.S. Pat. Nos. 5,386,023; 5,637,684; 5,602,240; 5,216,141; and 4,469,863; Kiedrowshi, et al., Angew. Chem. Intl. Ed. English, 30:423 (1991); Letsinger, et al., J. Am. Chem. Soc., 110:4470 (1988); Letsinger, et al., Nucleosides & Nucleotides, 13:1597 (1994); Chapters 2 and 3, ASC Symposium Series 580, “Carbohydrate Modifications in Antisense Research”, Ed. Y. S. Sanghui and P. Dan Cook; Mesmaeker, et al., Bioorganic & Medicinal Chem. Lett., 4:395 (1994); Jeffs, et al., J. Biomolecular NMR, 34:17 (1994); Tetrahedron Lett., 37:743 (1996)); and non-ribose backbones, including those described in U.S. Pat. Nos. 5,235,033 and 5,034,506, and Chapters 6 and 7, ASC Symposium Series 580, “Carbohydrate Modifications in Antisense Research”, Ed. Y. S. Sanghui and P. Dan Cook. Nucleic acids containing one or more carbocyclic sugars are also included within the definition of nucleic acids (see Jenkins, et al., Chem. Soc. Rev., (1995) pp. 169-176). Several nucleic acid analogs are described in Rawls, C & E News, Jun. 2, 1997, page 35. All of these references are hereby expressly incorporated by reference.
The nucleic acid may be DNA, RNA, or a hybrid and may contain any combination of deoxyribo- and ribo-nucleotides, and any combination of bases, including uracil, adenine, thymine, cytosine, guanine, inosine, xanthanine, hypoxanthanine, isocytosine, isoguanine, and base analogs such as nitropyrrole and nitroindole, etc. Oligonucleotides can be synthesized by standard methods such as those used in commercial automated nucleic acid synthesizers and later attached to an array, bead or other suitable surface. Alternatively, the oligonucleotides can be synthesized directly on the assay surface using photolithographic or other techniques. In some embodiments, linkers are used to attach the oligonucleotides to an array surface or to beads.
As used herein, the term “nucleic acid molecule” or “polynucleotide” refers to a compound or composition that is a polymeric nucleotide or nucleic acid polymer. The nucleic acid molecule may be a natural compound or a synthetic compound. The nucleic acid molecule can have from about 2 to 5,000,000 or more nucleotides. The larger nucleic acid molecules are generally found in the natural state. In an isolated state, the nucleic acid molecule can have about 10 to 50,000 or more nucleotides, usually about 100 to 20,000 nucleotides. It is thus obvious that isolation of a nucleic acid molecule from the natural state often results in fragmentation. It may be useful to fragment longer target nucleic acid molecules, particularly RNA, prior to hybridization to reduce competing intramolecular structures. Fragmentation can be achieved chemically, enzymatically, or mechanically. Typically, when the sample contains DNA, a nuclease such as deoxyribonuclease (DNase) is employed to cleave the phosphodiester linkages. Nucleic acid molecules, and fragments thereof, include, but are not limited to, purified or unpurified forms of DNA (dsDNA and ssDNA) and RNA, including tRNA, mRNA, rRNA, mitochondrial DNA and RNA, chloroplast DNA and RNA, DNA/RNA hybrids, biological material or mixtures thereof, genes, chromosomes, plasmids, cosmids, the genomes of microorganisms, e.g., bacteria, yeasts, phage, chromosomes, viruses, viroids, molds, fungi, or other higher organisms such as plants, fish, birds, animals, humans, and the like. The polynucleotide can be only a minor fraction of a complex mixture such as a biological sample.
As used herein, the term “hybridize” refers to the process by which single strands of polynucleotides form a double-stranded structure through hydrogen bonding between the constituent bases. The ability of two polynucleotides to hybridize with each other is based on the degree of complementarity of the two polynucleotides, which in turn is based on the fraction of matched complementary nucleotide pairs. The more nucleotides in a given polynucleotide that are complementary to another polynucleotide, the more stringent the conditions can be for hybridization and the more specific will be the binding between the two polynucleotides. Increased stringency may be achieved by elevating the temperature, increasing the ratio of co-solvents, lowering the salt concentration, and combinations thereof.
As used herein, the terms “complementary,” “complement,” and “complementary nucleic acid sequence” refer to the nucleic acid strand that is related to the base sequence in another nucleic acid strand by the Watson-Crick base-pairing rules. In general, two polynucleotides are complementary when one polynucleotide can bind another polynucleotide in an anti-parallel sense wherein the 3′-end of each polynucleotide binds to the 5′-end of the other polynucleotide and each A, T(U), G, and C of one polynucleotide is then aligned with a T(U), A, C, and G, respectively, of the other polynucleotide. Polynucleotides that comprise RNA bases can also include complementary G/U or U/G basepairs. Two complementary strands may comprise complementary regions comprising all or one or more portions of one or both strands.
As used herein, the term “clustering tree” refers to a hierarchical tree structure in which observations, such as organisms, genes, and polynucleotides, are separated into one or more clusters. The root node of a clustering tree consists of a single cluster containing all observations, and the leaf nodes correspond to individual observations. A clustering tree can be constructed on the basis of a variety of characteristics of the observations, such as sequences of the genes and morphological traits of the organisms. Many techniques known in the art, e.g. hierarchical clustering analysis, can be used to construct a clustering tree. A non-limiting example of the clustering tree is a phylogenetic, taxonomic or evolutionary tree.
As used herein, the terms “operational taxon unit,” “OTU,” “taxon,” “hierarchical cluster,” and “cluster” are used interchangeably. An operational taxon unit (OTU) refers to a group of one or more organisms that comprises a node in a clustering tree. The level of a cluster is determined by its hierarchical order. In one embodiment, an OTU is a group tentatively assumed to be a valid taxon for purposes of phylogenetic analysis. In another embodiment, an OTU is any of the extant taxonomic units under study. In yet another embodiment, an OTU is given a name and a rank. For example, an OTU can represent a domain, a sub-domain, a kingdom, a sub-kingdom, a phylum, a sub-phylum, a class, a sub-class, an order, a sub-order, a family, a subfamily, a genus, a subgenus, or a species. In some embodiments, OTUs can represent one or more organisms from the kingdoms eubacteria, protista, or fungi at any level of a hierarchal order. In some embodiments, an OTU represents a prokaryotic or fungal order.
As used herein, the term “kmer” refers to a polynucleotide of length k. In some embodiments, k is an integer from 1 to 1000. In some embodiments, k is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100, 125, 150, 175, 200, 250, 300, 400, 500, 600, 700, 800, 900, or 1000.
As used herein, the term “perfect match probe” (PM probe) refers to a kmer which is 100% complementary to at least a portion of a highly conserved target gene or polynucleotide. The perfect complementarity usually exists throughout the length of the probe. Perfect probes, however, may have a segment or segments of perfect complementarity that is/are flanked by leading or trailing sequences lacking complementarity to the target gene or polynucleotide.
As used herein, the term “mismatch probe” (MM probe) refers a control probe that is identical to a corresponding PM probe at all positions except for one, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides of the PM probe. Typically, the non-identical position or positions are located at or near the center of the PM probe. In some embodiments, the mismatch probes are universal mismatch probes, e.g., a collection of mismatch probes that have no more than a set number of nucleotide variations or substitutions compared to positive probes. For example, the universal mismatch probes may differ in nucleotide sequence by no more than five nucleotides compared to any one PM probe in the PM probe set. In some embodiments, a MM probe is used adjacent to each test probe, e.g., a PM probe targeting a bacterial 16S rRNA sequence, in the array.
As used herein, the term “probe pair” refers to a PM probe and its corresponding MM probe. In some embodiments, the PM probes and the MM probes are scored in relation to each other during data processing and statistic analysis. As used herein, the term “a probe pair associated with an OTU” is defined as a pair of probes consisting of an OTU-specific PM probe and its corresponding MM probe.
As used herein, a “sample” is from any source, including, but not limited to a biological sample, a gas sample, a fluid sample, a solid sample, or any mixture thereof.
As used herein, a “microorganism” or “organism” includes, but is not limited to, a virus, viroids, bacteria, archaea, fungi, protozoa and the like.
The term “sensitivity” refers to a measure of the proportion of actual positives which are correctly identified as such.
The term “specificity” refers to a measure of the proportion of actual negatives which are correctly identified as such.
The term “confidence level” refers to the likelihood, expressed as a percentage, that the results of a test are real and repeatable, and not random. Confidence levels are used to indicate the reliability of an estimate and can be calculated by a variety of methods.
In one aspect, the invention utilizes a biosignature of OTUs. As used herein, the term “biosignature” refers to an association of the level of one or more members of one or more OTUs with a particular condition, such as a classification, diagnosis, prognosis, and/or predicted outcome of a pulmonary condition in a subject. In one embodiment, the biosignature comprises a determination of the presence, absence, and/or quantity of at least 1, 2, 3, 4, 5, 10, 20, 50, 100, 250, 500, 1000, 5000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 250,000, 500,000 or 1,000,000 OTUs in a sample using a single assay. In some embodiments, the biosignature comprises the presence of or changes in the level of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 75, 100, 125, 150, 175, 200, 250, 300, or more OTUs. In some embodiments, OTUs in a biosignature comprise OTUs selected from one or more of Table 3, Table 4, or Table 5.
In one embodiment, the biosignature is associated with a single condition, for example a single pulmonary condition. In another embodiment, the biosignature is associated with a combination of conditions, for example two or more pulmonary conditions, or one or more pulmonary conditions combined with one or more non-pulmonary conditions. In some embodiments, the condition is chronic obstructive pulmonary disease. A biosignature can be obtained for any sample, including but not limited to: tissue samples; cell culture samples; bacterial culture samples; samples obtained from a subject, including biopsies, body fluids and other excreted material; pulmonary samples; other samples as described herein; materials derived therefrom; and combinations thereof. In some embodiments, the sample is a pulmonary sample. In some embodiments, the pulmonary sample is sputum, endotracheal aspirate, bronchoalveolar lavage sample, a swab of the endotrachea, materials derived therefrom, or combinations thereof. In some embodiments, a biosignature of a test sample is compared to a known biosignature, and a determination is made as to likelihood that the biosignatures are the same. In some embodiments, a biosignature of a sample is compared to a biosignature for a classification, diagnosis, prognosis, and/or predicted outcome of a pulmonary condition. The biosignature to which the biosignature of the test sample is compared can be determined before, after, or at substantially the same time as that of the test sample. Biosignatures can be the result of one or more analyses of one or more samples from a particular source. In some embodiments, a biosignature is indicative of a response to treatment. In some embodiments, a biosignature is used as a basis for the selection of a mode of treatment.
In some embodiments, the biosignature of a test sample is a combination of two or more independent biosignatures, such as 2, 3, 4, 5, 6, 7, 8, 9, 10 or more independent biosignatures. In one embodiment, each of the two or more biosignatures contained in a sample are assayed simultaneously. In a further embodiment, a subset of biosignatures can be evaluated through the use of low-density detection systems, comprising the determination of the presence, absence, and/or level of no more than 10, 25, 50, 100, 250, 500, 1000, 2000, or 5000 OTUs.
In some embodiments, a biosignature comprises a measure of the number of members in one or more bacterial families or OTUs. The number of members may range from 0 to 10000 or more, such as 0 to 5000, 0 to 2500, 0 to 1000, 0 to 2000, 0 to 1000, 0 to 900, 0 to 800, 0 to 700, 0 to 600, 0 to 500, 0 to 400, 0 to 300, 0 to 200, 0 to 100, 0 to 50, 0 to 25, 0 to 20, 0 to 10, or 0 to 5. In some embodiments, a biosignature comprises the presence of 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 2000, 2500, 5000, 10000, or more members of one or more bacterial families or OTUs, or the presence of a range that includes any two of these values as end points. In some embodiments, a biosignature comprises a ratio between numbers of members in two or more bacterial families or OTUs. The numerator and denominator of such ratios may include overlapping sets of bacterial families or OTUs. Ratios of the numbers of members in two or more bacterial families may compare a first set of one or more bacterial families or OTUs to a second set of one or more bacterial families or OTUs, where there is at least one bacterial family or OTU difference between the first and second set. A set of bacterial families or OTUs may include 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 60, 70, 80, 90, 100, or more OTUs. Bacterial families or OTUs may be selected from one or more of Table 3, Table 4, or Table 5. Examples of bacterial families or OTUs include, but are not limited to, Campylobacteraceae, Porphyromonadaceae, Prevotellaceae, Corynebacteriaceae, Enterobacteriaceae, Alteromonadaceae, Peptococc/Acidaminococcacea, Lactobacillaceae, Enterococcaceae, Pasteurellaceae, Flavobacteriaceae, Acidobacteriaceae, Staphylococcaceae, Micrococcaceae, Peptostreptococcaceae, Helicobacteraceae, Streptococcaceae, Pseudomonadaceae, Bacillaceae, Clostridiaceae, Mollicutes, Cyanobacteria, Anaerolineae, Sphingobacteria, Acidobacteria, Flavobacteria, Alphaproteobacteria, Bacteroidetes, Epsilonproteobacteria, Betaproteobacteria, Deltaproteobacteria, Actinobacteria, Gammaproteobacteria, Bacilli, Clostridia, Moraxellaceae, Chloroplasts, Peptostreptococcaceae, Spirochaetaceae, Lachnospiraceae, Verrucomicrobiae, Corynebacteriaceae, Bifidobacteriaceae, Micromonosporaceae, Desulfotomaculum, Dehalococcoidetes, and bacterial families or OTUs as described in the drawings.
In one aspect, the invention provides methods, systems, and compositions for detecting and identifying a plurality of biomolecules and/or organisms in a sample. The invention utilizes the ability to differentiate between individual organisms or OTUs. In one aspect, the individual organisms or OTUs are identified using organism-specific and/or OTU-specific probes, e.g., oligonucleotide probes. More specifically, some embodiments relate to selecting organism-specific and/or OTU-specific oligonucleotide probes useful in detecting and identifying biomolecules and organisms in a sample. In some embodiments, an oligonucleotide probe is selected on the basis of the cross-hybridization pattern of the oligonucleotide probe to regions within a target oligonucleotide and its homologs in a plurality of organisms. The homologs can have nucleotide sequences that are at least 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.5% identical. Such oligonucleotides can be gene, or intergenetic sequences, in whole or a portion thereof. The oligonucleotides can range from 10 to over 10,000 nucleotides in length. In some other embodiments, a method is provided for detecting the presence of an OTU in a sample based at least partly on the cross-hybridization of the OTU-specific oligonucleotide probes to probes specific for other organisms or OTUs. In some embodiments, the biosignature to which a sample biosignature is compared comprises a positive result for the presence of the targets for one or more probes.
In one aspect, the invention provides a diagnostic system for the determination or evaluation of a biosignature of a sample. In one embodiment, the diagnostic system comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 75, 100, 125, 150, 175, 200, 250, 300, or more probes. In another embodiment, the diagnostic system comprises up to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 75, 100, 125, 150, 175, 200, 250, 300, or more probes.
In one aspect of the invention, a high capacity system is provided for determining a biosignature of a sample by assessing the total microorganism population of a sample in terms of the microorganisms present and optionally their percent composition of the total population. In some embodiments, the system comprises a plurality of probes that are capable of determining the presence or quantity of at least 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, or more different OTUs in a single assay. In some embodiments, one or more OTUs are selected from one or more of Table 3, Table 4, or Table 5. Typically, the probes selectively hybridize to a highly conserved polynucleotide. Usually, the probes hybridize to the same highly conserved polynucleotide or within a portion thereof. Generally, the highly conserved polynucleotide or fragment thereof comprises a gene or fragment thereof. Non-limiting examples of highly conserved polynucleotides comprise nucleotide sequences found in the 16S rRNA gene, 23S rRNA gene, 5S rRNA gene, 5.8S rRNA gene, 12S rRNA gene, 18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA gene, cox1 gene and nifD gene. In other embodiments, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, 15 or more, 20 or more, 25 or more, or 50 or more collections of probes are employed, each of which specifically hybridizes to a different highly conserved polynucleotide or portion thereof. For example, a first collection of probes binds to the same region of the 16S rRNA gene, a second collection of probes binds to the same region of the 16S rRNA gene that is different from the region bound by probes in the first collection, and a third collection of probes binds to the same region of the 23S rRNA gene. The use of two or more collections of probes where each collection recognizes distinct and separate highly conserved polynucleotides or portions thereof allows for the generation and testing of more probes the use of which can provide greater discrimination between species or OTUs.
Highly conserved polynucleotides usually show at least 80%, 85%, 90%, 92%, 94%, 95%, or 97% homology across a domain, kingdom, phylum, class, order, family or genus, respectively. The sequences of these polynucleotides can be used for determining evolutionary lineage or making a phylogenetic determination and are also known as phylogenetic markers. In some embodiments, a biosignature comprises the presence, absence, and/or abundance of a combination of phylogenetice markers. The OTUs detected by the probes disclosed herein can be bacterial, archeal, fungal, or eukaryotic in origin. Additionally, the methodologies disclosed herein can be used to quantify OTUs that are bacterial, archaeal, fungal, or eukaryotic. By combining the various probe sets, a system for the detection of bacteria, archaea, fungi, eukaryotes, or combinations thereof can be designed. Such a universal microorganism test that is conducted as a single assay can provide great benefit for assessing and understanding the composition and ecology of numerous environments, including characterization of biosignatures for various samples, environments, conditions, and contaminants.
In another aspect of the invention, a system is provided that is capable of determining the probability of presence and optionally quantity of at least 10,000, 20,000, 30,000, 40,000, 50,000 or 60,000 different OTUs of a single domain in a single assay. Such a system makes a probability determination with a confidence level greater than 90%, 91%, 92%, 93%, 94%, 95%, 99% or 99.5%. In some embodiments, a biosignature can comprise the combined result of each probability determination.
Some embodiments provide a method of selecting an oligonucleotide probe that is specific for a node in a clustering tree. In some embodiments, the method comprises selecting a highly conserved target polynucleotide and its homologs for a plurality of organisms; clustering the polynucleotides and homologs of the plurality of organisms into a clustering tree; and determining a cross-hybridization pattern of a candidate oligonucleotide probe that hybridizes to a first polynucleotide to each node on the clustering tree. This determination is performed (e.g., in silico) to determine the likelihood that the probe would cross hybridize with homologs of its target complementary sequence. The candidate oligonucleotide probe can be complementary to a highly conserved target polynucleotide, a fragment of the highly conserved target or one of its homologs in one of the plurality of organisms. In some embodiments, a method is provided for the determination of the cross-hybridization pattern of a variant of the candidate oligonucleotide probe to each node on the clustering tree, wherein the variant corresponds to the candidate oligonucleotide probe but comprises at least 1 nucleotide mismatch; and selecting or rejecting the candidate oligonucleotide probe on the basis of the cross-hybridization pattern of the candidate oligonucleotide probe and the cross-hybridization pattern of the variant. In some embodiments, the node is an operational taxon unit (OTU). In some embodiments, the node is a single organism.
Some embodiments provide a method of selecting an OTU-specific oligonucleotide probe for use in detecting a plurality of organisms in a sample. In some embodiments, the method comprises: selecting a highly conserved target polynucleotide and its homologs from the plurality of organisms; clustering the polynucleotides of the target gene and its homologs from the plurality of organisms into one or more operational taxonomic units (OTUs), wherein each OTU comprises one or more groups of similar nucleotide sequence; determining the cross-hybridization pattern of a candidate OTU-specific oligonucleotide probe to the OTUs, wherein the candidate OTU-specific oligonucleotide probe corresponds to a fragment of the target gene or its homolog from one of the plurality of organisms; determining the cross-hybridization pattern of a variant of the candidate OTU-specific oligonucleotide probe to the OTUs, wherein the variant comprises at least 1 nucleotide mismatch from the candidate OTU-specific oligonucleotide probe; and selecting or rejecting the candidate OTU-specific oligonucleotide probe on the basis of the cross-hybridization pattern of the candidate OTU-specific oligonucleotide probe and the cross-hybridization pattern of the variant. In some embodiments, the candidate OTU-specific oligonucleotide probe is selected if the candidate OTU-specific oligonucleotide probe does not cross-hybridize with any polynucleotide that is complementary to probes from other OTUs. In further embodiments, the candidate OTU-specific oligonucleotide probe is selected if the candidate OTU-specific oligonucleotide probe cross-hybridizes with the polynucleotide in no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 30, 40, 50, 100, 200, 500, or 1000 other OTU groups.
Some embodiments provide a method of selecting a set of organism-specific oligonucleotide probes for use in detecting a plurality of organisms in a sample. In some embodiments, the method comprises: identifying a highly conserved target polynucleotide and its homologs in the plurality of organisms; determining the cross-hybridization pattern of a candidate organism-specific oligonucleotide probe to the sequences of the highly conserved target polynucleotide and its homologs in the plurality of organisms, wherein the candidate oligonucleotide probe corresponds to a fragment of the target sequence or its homolog from one of the plurality of organisms; determining the cross-hybridization pattern of a variant of the candidate organism-specific oligonucleotide probe to the sequences of the highly conserved target sequence and its homologs in the plurality of organisms, wherein the variant comprises at least 1 nucleotide mismatch from the candidate organism-specific oligonucleotide probe; and selecting or rejecting the candidate organism-specific oligonucleotide probe on the basis of the cross-hybridization pattern of the candidate organism-specific oligonucleotide probe and the cross-hybridization pattern of the variant of the candidate organism-specific oligonucleotide probe.
In some embodiments, an OTU-specific oligonucleotide probe does not cross-hybridize with any polynucleotide that is complementary to probes from other OTUs. In other embodiments, an OTU-specific oligonucleotide probe cross-hybridizes with the polynucleotide in no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 30, 40, 50, 100, 200, 500, or 1000 other OTU groups. Some embodiments utilize a set of organism-specific oligonucleotide probes for use in detecting a plurality of organisms in a sample. In further embodiments, the candidate organism-specific oligonucleotide probe is selected if the candidate organism-specific oligonucleotide probe only hybridizes with the target nucleic acid molecule of no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 30, 40, 50 unique organisms in the plurality of organisms. In other embodiments, the process is iterative with multiple candidate specific-specific oligonucleotide probes selected. Frequently, the selected organism-specific oligonucleotide probes are clustered and aligned into groups of similar sequences that allow for the detection of an organism with high confidence based on no more than 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 50, or 60 organism-specific oligonucleotide probe matches per OTU. Generally, the candidate organism that the organism-specific oligonucleotide probes detect corresponds to a leaf or node of at least one phylogenetic, genealogic, evolutionary, or taxonomic tree. Knowledge of the position that a candidate organism detected by the organism-specific oligonucleotide probe occupies on a tree provides relational information of the organism to other members of its domain, phylum, class, subclass, order, family, subfamily, or genus.
In some embodiments, the method disclosed herein selects and/or utilizes a set of organism-specific oligonucleotide probes that are a hierarchical set of oligonucleotide probes that can be used to detect and differentiate a plurality of organisms. In some embodiments, the method selects and/or utilizes organism-specific or OTU-specific oligonucleotide probes that allow a comprehensive screen for at least 80%, 85%, 90%, 95%, 99% or 100% of all known bacterial or archaeal taxa in a single analysis, and thus provides an enhanced detection of different desired taxonomic groups. In some embodiments, the identity of all known bacterial or archaeal taxa comprises taxa that were previously identified by the use of oligonucleotide specific probes, PCR cloning, and sequencing methods. Some embodiments provide methods of selecting and/or utilizing a set of oligonucleotide probes capable of correctly categorizing mixed target nucleic acid molecules into their proper operational taxonomic unit (OTU) designations. Such methods can provide comprehensive prokaryotic or eukaryotic identification, and thus comprehensive biosignature characterization.
In some embodiments, the selected OTU-specific oligonucleotide probe is used to calculate the relative abundance of one or more organisms that belong to a specific OTU at differing levels of taxonomic identification. In some embodiments, an array or collection of microparticles comprising at least one organism-specific or OTU-specific oligonucleotide probe selected by the method disclosed herein is provided to infer specific microbial community activities. For example, the identity of individual taxa in a microbial consortium from an anaerobic environment for instance, a marsh, can be determined along with their relative abundance. If the consortium is suspected of harboring microorganisms capable of butanol fermentation, then after providing a suitable feedstock in an anaerobic environment if the production of butanol is noted, then those taxa responsible for butanol fermentation can be inferred by the microorganisms that have abundant quantities of 16S rRNA. The invention provides methods to measure taxa abundance based on the detection of directly labeled 16S rRNA.
In some embodiments, multiple probes are selected for increasing the confidence level and/or sensitivity level of identification of a particular organism or OTU. The use of multiple probes can greatly increase the confidence level of a match to a particular organism. In some embodiments, the selected organism-specific oligonucleotide probes are clustered and aligned into groups of similar sequence such that detection of an organism is based on 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35 or more oligonucleotide probe matches. In some embodiments, the oligonucleotide probes are specific for a species. In other embodiments, the oligonucleotide probe recognizes related organisms such as organisms in the same subgenus, genus, subfamily, family, sub-order, order, sub-class, class, sub-phylum, phylum, sub-kingdom, or kingdom.
Perfect match (PM) probes are perfectly complementary to the target polynucleotide, e.g., a sequence that identifies a particular organism. In some embodiments, a system of the invention comprises mismatch (MM) control probes. Usually, MM probes are otherwise identical to PM probes, but differ by one or more nucleotides. Probes with one or more mismatch can be used to indicate non-specific binding and a possible non-match to the target sequence. In some embodiments, the MM probes have one mismatch located in the center of the probe, e.g., in position 13 for a 25mer probe. The MM probe is scored in relation to its corresponding PM probe as a “probe pair.” MM probes can be used to estimate the background hybridization, thereby reducing the occurrence of false positive results due to non-specific hybridization, a significant problem with many current detection systems. If an array is used, such as an Affymetrix high density probe array or Illumina bead array, ideally, the MM probe is positioned adjacent or close to its corresponding PM probe on the array.
Some embodiments relate to a method of selecting and/or utilizing a set of oligonucleotide probes that enable simultaneous identification of multiple prokaryotic taxa with a relatively high confidence level. Typically, the confidence level of identification is at least 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 99.5%. In general, an OTU refers to an individual species or group of highly related species that share an average of at least 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% 99.5%, or more sequence homology in a highly conserved region. Multiple MM probes may be utilized to enhance the quantification and confidence of the measure. In some embodiments, each interrogation probe of a plurality of interrogation probes has from about 1 to about 20 corresponding mismatch control probes. In further embodiments, each interrogation probe has from about 1 to about 10, about 1 to about 5, about 1 to 4, 1 to 3, 2 or 1 corresponding mismatch probes. These interrogation probes target unique regions within a target nucleic acid sequence, e.g., a 16S rRNA gene, and provide the means for identifying at least about 10, 20, 50, 100, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 250,000, 500,000 or 1,000,000 taxa. In some embodiments, multiple targets can be simultaneously assayed or detected in a single assay through a high-density oligonucleotide probe system. The sum of all target hybridizations is used to identify specific prokaryotic taxa. The result is a more efficient and less time consuming method of identifying unculturable or unknown organisms. The invention can also provide results that could not previously be achieved, e.g., providing results in hours where other methods would require days. In some embodiments, a microbiome (i.e., sample) can be assayed to determine the identity and optionally the abundance of its constituent microorganisms in less than 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 hour.
In some embodiments, the set of OTU-specific oligonucleotide probes comprises from about 1 to about 500 probes for each taxonomic group. In some embodiments, the probes are proteins including antibodies, or nucleic acid molecules including oligonucleotides or fragments thereof. In some embodiments, an oligonucleotide probe corresponds to a nucleotide fragment of the target nucleic acid molecule. In some embodiments, from about 1 to about 500, about 2 to about 200, about 5 to about 150, about 8 to about 100, about 10 to about 35, or about 12 to about 30 oligonucleotide probes can be designed for each taxonomic grouping. In other embodiments, a taxonomic group can have at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, or more probes. In some embodiments, various taxonomic groups can have different numbers of probes, while in other embodiments, all taxonomic groups have a fixed number of probes per group. Multiple probes in a taxonomic group can provide additional data that can be used to make a determination, also known as “making a call” as to whether an OTU is present or not. Multiple probes also allow for the removal of one or more probes from the analysis based on insufficient signal strength, cross hybridization or other anomalies. Removing probes can increase the confidence level of results and further allow for the detection of low abundant microorganisms. The oligonucleotide probes can each be from about 5 to about 100 nucleotides, from about 10 to about 50 nucleotides, from about 15 to about 35 nucleotides, or from about 20 to about 30 nucleotides. In some embodiments, the probes are at least 5-mers, 6-mers, 7-mers, 8-mers, 9-mers, 10-mers, 11-mers, 12-mers, 13-mers, 14-mers, 15-mers, 16-mers, 17-mers, 18-mers, 19-mers, 20-mers, 21-mers, 22-mers, 23-mers, 24-mers, 25-mers, 26-mers, 27-mers, 28-mers, 29-mers, 30-mers, 31-mers, 32-mers, 33-mers, 34-mers, 35-mers, 36-mers, 37-mers, 38-mers, 39-mers, 40-mers, 41-mers, 42-mers, 43-mers, 44-mers, 45-mers, 46-mers, 47-mers, 48-mers, 49-mers, 50-mers, 51-mers, 52-mers, 53-mers, 54-mers, 55-mers, 56-mers, 57-mers, 58-mers, 59-mers, 60-mers, 61-mers, 62-mers, 63-mers, 64-mers, 65-mers, 66-mers, 67-mers, 68-mers, 69-mers, 70-mers, 71-mers, 72-mers, 73-mers, 74-mers, 75-mers, 76-mers, 77-mers, 78-mers, 79-mers, 80-mers, 81-mers, 82-mers, 83-mers, 84-mers, 85-mers, 86-mers, 87-mers, 88-mers, 89-mers, 90-mers, 91-mers, 92-mers, 93-mers, 94-mers, 95-mers, 96-mers, 97-mers, 98-mers, 99-mers, 100-mers or combinations thereof.
Some embodiments provide methods of selecting multiple, confirmatory, organism-specific or OTU-specific probes to increase the confidence of detection. In some embodiments, the methods also select one or more mismatch (MM) probes for every perfect match (PM) probe to minimize the effect of cross-hybridization by non-target regions. The organism-specific and OTU-specific oligonucleotide probes selected by the methods disclosed herein can simultaneously identify thousands of taxa present in an environmental sample and allow accurate identification of microorganisms and their phylogenetic relationships in a community of interest. Systems that use the organism-specific and OTU-specific oligonucleotide probes selected by the methods disclosed herein and the computational analysis disclosed herein have numerous advantages over rRNA gene sequencing techniques. Such advantages include reduced cost per microbiome analysis, and increased processing speed per sample or microbiome from both the physical analysis and the computational analysis point of view. In general, the analysis procedures are not adversely affected by chimeras, are not subject to creating artificial phylotypes, and are not subject to barcode PCR bias. Additionally, quantitative standards can be run with a microbiome sample of the invention.
Some embodiments provide a method for selecting and/or utilizing a set of OTU- or organism-specific oligonucleotide probes for use in an analysis system or bead multiplex system for simultaneously detecting a plurality of organisms in a sample. The method targets known diversity within target nucleic acid molecules to determine microbial community composition and establish a biosignature. The target nucleic acid molecule is typically a highly conserved polynucleotide. In some embodiments, the highly conserved polynucleotide is from a highly conserved gene, whereas in other embodiments the polynucleotide is from a highly conserved region of a gene with moderate or large sequence variation. In further embodiments, the highly conserved region may be an intron, exon, or a linking section of nucleic acid that separates two genes. In some embodiments, the highly conserved polynucleotide is from a “phylogenetic” gene. Phylogenetic genes include, but are not limited to, the 5.8S rRNA gene, 12S rRNA gene, 16S rRNA gene-prokaryotic, 16S rRNA gene-mitochondrial, 18S rRNA gene, 23S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA gene, cox1 gene, and the nifD gene. With eukaryotes, the rRNA gene can be nuclear, mitochondrial, or both. In some embodiments, the 16S-23S rRNA gene internal transcribed spacer (ITS) can be used for differentiation of closely related taxa with or without the use of other rRNA genes. For example, rRNA, e.g., 16S or 23S rRNA, acts directly in the protein assembly machinery as a functional molecule rather than having its genetic code translated into protein. Due to structural constraints of 16S rRNA, specific regions throughout the gene have a highly conserved polynucleotide sequence; although, non-structural segments may have a high degree of variability. Probing the regions of high variability can be used to identify OTUs that represent a single species level, while regions of less variability can be used to identify OTUs that represent a subgenus, a genus, a subfamily, a family, a sub-order, an order, a sub-class, a class, a sub-phylum, a phylum, a sub-kingdom, or a kingdom. The methods disclosed herein can be used to select organism-specific and OTU-specific oligonucleotide probes that offer a high level of specificity for the identification of specific organisms, OTUs representing specific organisms, or OTUs representing specific taxonomic group of organisms. The systems and methods disclosed herein are particularly useful in identifying closely related microorganisms and OTUs from a background or pool of closely related organisms.
The probes selected and/or utilized by the methodologies of the invention can be organized into OTUs that provide an assay with a sensitivity and/or specificity of more than 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98% or 99%. In some embodiments, sensitivity and specificity depends on the hybridization signal strength, number of probes in the OTU, the number of potential cross hybridization reactions, the signal strength of the mismatch probes, if present, background noise, or combinations thereof. In some embodiments, an OTU containing one probe may provide an assay with a sensitivity and specificity of at least 90%, while another OTU may require at least 20 probes to provide an assay with sensitivity and specificity of at least 90%.
Some embodiments relate to methods for phylogenetic analysis system design and signal processing and interpretation for use in detecting and identifying a plurality of biomolecules and organisms in a sample. More specifically, some embodiments relate to a method of selecting a set of organism-specific oligonucleotide probes for use in detecting a plurality of organisms in a sample with a high confidence level. Some embodiments relate to a method of selecting a set of OTU-specific oligonucleotide probes for use in detecting a plurality of organisms in a sample with a high confidence level.
In the case of highly conserved polynucleotides like 16S rRNA that may have only one to a few nucleotides of sequence variability over any 15- to 30-bp region targeted by probes for discrimination between related microbial species, it is advantageous to maximize the probe-target sequence specificity in an assay system. Some embodiments of the present invention provide methods of selecting organism-specific oligonucleotide probes that effectively minimize the influence of cross-hybridization. In one embodiment, the method comprises: (a) identifying sequences of a target nucleic acid molecule corresponding to the plurality of organisms; (b) determining the cross-hybridization pattern of a candidate organism-specific oligonucleotide probe to the target nucleic acid molecule from the plurality of organisms, wherein the candidate oligonucleotide probe corresponds to a sequence fragment of the target nucleic acid molecule from the plurality of organisms; (c) determining the cross-hybridization pattern of a variant of the candidate organism-specific oligonucleotide probe to the target nucleic acid molecule from the plurality of organisms, wherein the variant of the candidate organism-specific oligonucleotide probe comprises at least 1 nucleotide mismatch compared to the candidate organism-specific oligonucleotide probe; and (d) selecting or rejecting the candidate organism-specific oligonucleotide probe on the basis of the cross-hybridization pattern of the candidate organism-specific oligonucleotide probe and the cross-hybridization pattern of the variant of the candidate organism-specific oligonucleotide probe. In some embodiments, a method of selecting a set of OTU-specific oligonucleotide probes for use in detecting a plurality of organisms in a sample is provided. In some embodiments, the method comprises: (a) identifying sequences of a target nucleic acid molecule corresponding to the plurality of organisms; (b) clustering the sequences of the target nucleic acid molecule from the plurality of organisms into one or more Operational Taxonomic Units (OTUs), wherein each OTU comprises one or more groups of similar sequences; (c) determining the cross-hybridization pattern of a candidate OTU-specific oligonucleotide probe to the OTUs, wherein the candidate OTU-specific oligonucleotide probe corresponds to a sequence fragment of the target nucleic acid molecule from one of the plurality of organisms; (d) determining the cross-hybridization pattern of a variant of the candidate OTU-specific oligonucleotide probe to the OTUs, wherein the variant of the candidate OTU-specific oligonucleotide probe comprises at least 1 nucleotide mismatch compared to the candidate OTU-specific oligonucleotide probe; and (e) selecting or rejecting the candidate OTU-specific oligonucleotide probe on the basis of the cross-hybridization pattern of the candidate OTU-specific oligonucleotide probe to the OTUs and the cross-hybridization pattern of the variant of the candidate OTU-specific oligonucleotide probe to the OTUs. In some embodiments, candidate OTU-specific oligonucleotide probe are rejected when the candidate OTU-specific oligonucleotide probe or its variant are predicted to cross-hybridize with other target sequences. In some embodiments, a predetermined amount of predicted cross-hybridization is allowed.
In some embodiments, selected oligonucleotide probes are synthesized by any relevant method known in the art. Some examples of suitable methods include printing with fine-pointed pins onto glass slides, photolithography using pre-made masks, photolithography using dynamic micromirror devices, ink-jet printing, or electrochemistry. In one example, a photolithographic method can be used to directly synthesize the chosen oligonucleotide probes onto a surface. Suitable examples for the surface include glass, plastic, silicon and any other surface available in the art. In certain examples, the oligonucleotide probes can be synthesized on a glass surface at an approximate density from about 1,000 probes per μm2 to about 100,000 probes per μm2, preferably from about 2000 probes per μm2 to about 50,000 probes per μm2, more preferably from about 5000 probes per μm2 to about 20,000 probes per μm2. In one example, the density of the probes is about 10,000 probes per μm2. The number of probes on the array can be quite large e.g., at least 105, 106, 10′, 108 or 109 probes per array. Usually, for large arrays only a relatively small proportion (i.e., less than about 1%, 0.1% 0.01%, 0.001%, 0.00001%, 0.000001% or 0.0000001%) of the total number of probes of a given length target an individual OTU. Frequently, lower limit arrays have no more than 10, 25, 50, 100, 500, 1,000, 5,000, or 10,000, 25,000, 50,000, 100,000 or 250,000 probes.
Typically, the arrays or microparticles have probes to one or more highly conserved polynucleotides. The arrays or microparticles may have further probes (e.g. confirmatory probes) that hybridize to functionally expressed genes, thereby providing an alternate or confirmatory signal upon which to base the identification of a taxon. For example, an array may contain probes to 16S rRNA gene sequences from Yersinia pestis and Vibrio cholerae and also confirmatory probes to Y. pestis cafl virulence gene or V. cholerae zonula occludens toxin (zot) gene. The detection of hybridization signals based on probes binding to 16S rRNA polynucleotides associated with a particular OTU coupled with the detection of a hybridization signal based on a confirmatory probe can provide a higher level of confidence that the OTU is present. For instance, if hybridization signals are detected for the probes associated Y. pestis OTU and the confirmatory probe also displays a hybridization signal for the expression of Y. pestis cafl then the confidence level subscribed to the presence or quantity of Y. pestis will be higher than the confidence level obtained from the use of OTU probes alone.
A range of lengths of probes can be employed on the arrays or microparticles. As noted above, a probe may consist exclusively of a complementary segments, or may have one or more complementary segments juxtaposed by flanking, trailing and/or intervening segments. In the latter situation, the total length of complementary segment(s) can be more important that the length of the probe. In functional terms, the complementary segment(s) of the PM probes should be sufficiently long to allow the PM probes to hybridize more strongly to a target polynucleotide e.g., 16S rRNA, compared with a MM probe. A PM probe usually has a single complementary segment having a length of at least 15 nucleotides, and more usually at least 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 or 30 bases exhibiting perfect complementarity.
In some arrays or lots of microparticles, all probes are the same length. In other arrays or lots of microparticles, probe length varies between quantification standard (QS) probes, negative control (NC) probes, probe pairs, probe sets (OTUs) and combinations thereof. For example, some arrays may have groups of OTUs that comprise probe pairs that are all 23 mers, together with other groups of OTUs or probe sets that comprise probe pairs that are all 25 mers. Additional groups of probes pairs of other lengths can be added. Thus, some arrays may contain probe pairs having sizes of 15 mers, 16mers, 17mers, 18mers, 19mers, 20mers, 21mers, 22mers, 23mers, 24mers, 25 mers, 26mers, 27 mers, 28mers, 29 mers, 30mers, 31mers, 32mers, 33mers, 34mers, 35mers, 36mers, 37mers, 38mers, 39mers, 40mers or combinations thereof. Other arrays may have different size probes within the same group, OTU, or probe set. In these arrays, the probes in a given OTU or probe set can vary in length independently of each other. Having different length probes can be used to equalize hybridization signals from probes depending on the hybridization stability of the oligonucleotide probe at the pH, temperature, and ionic conditions of the reaction.
In another aspect of the invention, a system is provided for determining the presence or quantity of a plurality of different OTUs in a single assay where the system comprises a plurality of polynucleotide interrogation probes, a plurality of polynucleotide positive control probes, and a plurality of polynucleotide negative control probes. In some embodiments, the system is capable of detecting the presence, absence, relative abundance, and/or quantity of at least 5, 10, 20, 50, 100, 250, 500, 1000, 5000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 250,000, 500,000 or 1,000,000 OTUs in a sample using a single assay. In some embodiments, the polynucleotide positive control probes include 1) probes that target sequences of prokaryotic or eukaryotic metabolic genes spiked into the target nucleic acid sequences in defined quantities prior to fragmentation, or 2) probes complimentary to a pre-labeled oligonucleotide added into the hybridization mix after fragmentation and labeling. The control added prior to fragmentation collectively tests the fragmentation, biotinylation, hybridization, staining and scanning efficiency of the system. It also allows the overall fluorescent intensity to be normalized across multiple analysis components used in a single or combined experiment, such as when two or more arrays are used in a single experiment or when data from two separate experiments is combined. The second control directly assays the hybridization, staining and scanning of the system. Both types of control can be used in a single experiment.
In some embodiments, the QS standards (positive controls) are PM probes. In other embodiments, the QS standards are PM and MM probe pairs. In further embodiments, the QS standards comprise a combination of PM and MM probe pairs and PM probes without corresponding MM probes. In another embodiment, the QS standards comprise at least one, two, three, four, five, six, seven, eight, nine, ten or more MM probes for each corresponding PM probe. In a further embodiment, the QS standards comprise at least one, two, three, four, five, six, seven, eight, nine, ten or more PM probes for each corresponding MM probe. A system can comprise at least 1 positive control probe for each 1, 10, 100, or 1000 different interrogation probes.
In some cases, the spiked-in oligonucleotides that are complementary to the positive control probes vary in G+C content, uracil content, concentration, or combinations thereof. In some embodiments, the G+C % ranges from about 30% to about 70%, about 35% to about 65% or about 40% to about 60%. QS standards can also be chosen based on the uracil incorporation frequency. The QS standards may incorporate uracil in a range from about 1 in 100 to about 60 in 100, about 4 in 100 to about 50 in 100, or about 10 in 100 to about 50 in 100. In some cases, the concentration of these added oligonucleotides will range over 1, 2, 3, 4, 5, 6, or 7 orders of magnitude. Concentration ranges of about 105 to 1014, 106 to 1013, 107 to 1012, 107 to 1011, 108 to 10″, and 108 to 1010 can be employed and generally feature a linear hybridization signal response across the range. In some embodiments, positive control probes for the conduction of the methods disclosed herein comprise polynucleotides that are complementary to the positive control sequences shown in Table 6. Other genes that can be used as targets for positive controls include genes encoding structural proteins, proteins that control growth, cell cycle or reproductive regulation, and house keeping genes. Additionally, synthetic genes based on highly conserved genes or other highly conserved polynucleotides can be added to the sample. Useful highly conserved genes from which synthetic genes can be designed include 16S rRNA genes, 18S rRNA genes, 23SrRNA genes. Exemplary control probes are provided as SEQ ID NOs:51-100.
E. coli biotin synthetase
E. coli biotin synthetase
E. coli bioC protein
E. coli bioC protein
E. coli dethiobiotin synthetase
B. subtilis dapB, dihydrodipicolinate reductase
B. subtilis dapB, dihydrodipicolinate reductase
Saccharomyces, Gene for actin (Act 1p) protein
Saccharomyces, RNA polymerase II mediator
Saccharomyces, TATA-binding protein, general
Saccharomyces, Beta subunit of the oligosaccharyl
Saccharomyces, Ubiquinol-cytochrome-c
In some embodiments, the negative controls comprise PM and MM probe pairs. In further embodiments, the negative controls comprise a combination of PM and MM probe pairs and PM probes without corresponding MM probes. In other embodiments, the negative control probes comprise at least one, two, three, four, five, six, seven, eight, nine, ten or more MM probes for each corresponding negative control PM probe. A system can comprise at least 1 negative control probe for each 1, 10, 100, or 1000 different interrogation probes (PMs).
Generally, the negative control probes hybridize weakly, if at all, to 16S rRNA gene or other highly conserved gene targets. The negative control probes can be complementary to metabolic genes of prokaryotic or eukaryotic origin. Generally, with negative control probes, no target material is spiked into the sample. In some embodiments, negative control probes are from the same collection of probes that are also used for positive controls, but no material complementary to the negative control probes are spiked into the sample, in contrast to the positive control probe methodology. In essence, the control probes are universal control probes and play the role of a positive or negative control probes depending on the system's design. One of skill in the art will appreciate that the universal control probes are not limited to highly conserved sequence analysis systems and have applications beyond the present embodiments disclosed herein.
In a further embodiment, probes to non-highly conserved polynucleotides are added to a system to provide species-specific identification or confirmation of results achieved with the probes to the highly conserved polynucleotides. Usually, these “confirmatory” probes cross hybridize very weakly, if at all, to highly conserved polynucleotides recognized by the perfect match probes. Useful species-specific genes include metabolic genes, genes encoding structural proteins, proteins that control growth, cell cycle or reproductive regulation, housekeeping genes or genes that encode virulence, toxins, or other pathogenic factors. In some embodiments, the system comprises at least 1, 5, 10, 20, 30, 40, 50 60, 70, 80, 90 100, 150, 200, 250, 300, 400, 500, 600, 700, 800, 900, 1000, 5,000 or 10,000 species-specific probes.
In some embodiments, a system of the invention comprises an array. Non-limiting examples of arrays include microarrays, bead arrays, through-hole arrays, well arrays, and other arrays known in the art suitable for use in hybridizing probes to targets. Arrays can be arranged in any appropriate configuration, such as, for example, a grid of rows and columns. Some areas of an array comprise the OTU detection probes whereas other areas can be used for image orientation, normalization controls, signal scaling, noise reduction processing, or other analyses. Control probes can be placed in any location in the array, including along the perimeter of the array, diagonally across the array, in alternating sections or randomly. In some embodiments, the control probes on the array comprise probe pairs of PM and MM probes. The number of control probes can vary, but typically the number of control probes on the array range from 1 to about 500,000. In some embodiments, at least 10, 100, 500, 1,000, 5,000, 10,000, 25,000, 50,000, 100,000, 250,000 or 500,000 control probes are present. When control probe pairs are used, the probe pairs will range from 1 to about 250,000 pairs. In some embodiments, at least 5, 50, 250, 500, 2,500, 5,000, 12,500, 25,000, 50,000, 125,000 or 250,000 control probe pairs are present. The arrays can have other components besides the probes, such as linkers attaching the probes to a support. In some embodiments, materials for fabricating the array can be obtained from Affymetrix (Santa Clara, Calif.), GE Healthcare (Little Chalfont, Buckinghamshire, United Kingdom) or Agilent Technologies (Palo Alto, Calif.)
Besides arrays where probes are attached to the array substrate, numerous other technologies may be employed in the disclosed system for the practice of the methods of the invention. In one embodiment, the probes are attached to beads that are then placed on an array as disclosed by Ng et al. (Ng et al. A spatially addressable bead-based biosensor for simple and rapid DNA detection. Biosensors & Bioelectronics, 23:803-810, 2008).
In another embodiment, probes are attached to beads or microspheres, the hybridization reactions are performed in solution, and then the beads are analyzed by flow cytometry, as exemplified by the Luminex multiplexed assay system. In this analysis system, homogeneous bead subsets, each with beads that are tagged or labeled with a plurality of identical probes, are combined to produce a pooled bead set that is hybridized with a sample and then analyzed in real time with flow cytometry, as disclosed in U.S. Pat. No. 6,524,793. Bead subsets can be distinguished from each other by variations in the tags or labels, e.g., using variability in laser excitable dye content.
In a further embodiment, probes are attached to cylindrical glass microbeads as exemplified by the Illumina Veracode multiplexed assay system. Here, subsets of microbeads embedded with identical digital holographic elements are used to create unique subsets of probe-labeled microbeads. After hybridization, the microbeads are excited by laser light and the microbead code and probe label are read in real time multiplex assay.
In another embodiment, a solution based assay system is employed as exemplified by the NanoString nCounter Analysis System (Geiss G et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nature Biotech. 26:317-325, 2008). With this methodology, a sample is mixed with a solution of reporter probes that recognize unique sequences and capture probes that allow the complexes formed between the nucleic acids in the sample and the reporter probes to be immobilized on a solid surface for data collection. Each reporter probe is color-coded and is detected through fluorescence.
In a further embodiment, branched DNA technology, as exemplified by Panomics QuantiGene Plex 2.0 assay system, is used. Branched DNA technology comprises a sandwich nucleic acid hybridization assay for RNA detection and quantification that amplifies the reporter signal rather than the sequence. By measuring the RNA at the sample source, the assay avoids variations or errors inherent to extraction and amplification of target polynucleotides. The QuantiGene Plex technology can be combined with multiplex bead based assay system such as the Luminex system described above to enable simultaneous quantification of multiple RNA targets directly from whole cells or purified RNA preparations.
An exemplary process 300 for the design of target probes for use in the simultaneous detection of a plurality of microorganisms is illustrated in
Generally, a database for extraction of sequences to be used for probe selection is chosen based on the particular conserved gene or highly homologous sequence of interest, the total number of sequences within the database, the length of the overall sequences or the length of highly conserved regions within the sequences listed in the database, and the quality of the sequences therein. Typically, between two databases of equal sequence number but of different sequence length, the database with longer target regions of highly conserved sequence will generally contain a larger total number of possible sequences that can be compared. In some embodiments, the sequences are at least 300, 400, 500, 600, 700, 800, 900, 1,000, 1,200, 1,400, 1,600, 1,800, 2,000, 4,000, 8,000, 16,000 or 24,000 nucleotides long. Generally, databases with larger number of total sequences provide more material to compare. In a further embodiment, the database contains at least 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 100,000, 200,000, 500,000, 1,000,000 or 2,000,000 sequence listings. A gene of particular interest for probe construction is 16S rDNA (16S rRNA gene). Other conserved genes include 18S rDNA, 23 S rDNA, gyrA, gyrB gene, groEL, rpoB gene, fusA gene, recA gene, sodA, cox1 gene, and nifD gene. In a further embodiment, the spacer region between highly conserved segments of two genes can be used. For example, the spacer region between 16S and 23S rDNA genes can be used in conjunction with conserved sections of the 16S and 23S rDNA.
In some embodiments, the detection of a biosignature comprises the use of probes designed to hybridize with known or discovered targets within one or more OTUs. In some embodiments, targets are selected from a collection of known targets, such as in a database. In some embodiments of the invention, a database used for the selection of probes comprises at least 50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99% or up to 100% of the known sequences of the organisms of interest, e.g., of the bacteria, archaea, fungi, eukaryotes, microorganisms, or prokaryotes of interest. The sequences for each individual organism in the database can include more than 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or more than 95% of the genome of the organism, or of the non-redundant regions thereof. In some embodiments, the database includes up to 100% of the genome of the organisms whose sequenced are contained therein, or of the non-redundant sequences thereof. A listing of almost 40,000 aligned 16S rDNA sequences greater than 1250 nucleotides in length can be found on the Greengenes web application, a publicly accessible database run by Lawrence Berkeley National Laboratory. Other publicly accessible databases include GenBank, Michigan State University's ribosomal database project, the Max Planck Institute for Marine Microbiology's Silva database, and the National Institute of Health's NCBI. Proprietary sequence databases or combinations created by amalgamating the contents of two or more private and/or public databases can also be used to practice the methods of this invention. In some embodiments, a sample is assayed for all targets in one or more chosen databases simultaneously. In other embodiments, a sample is assayed for subsets of targets identified in one or more databases simultaneously. In some embodiments, a biosignature comprises the results of assaying a sample for some or all targets in one or more chosen databases. In other embodiments, a biosignature comprises a subset of the results of assaying a sample for some or all targets in one or more chosen databases.
The analysis of the selected sequences from the database for the detection and removal of chimeras at state 302 is typically performed by generating overlapping fragments and comparing these fragments against each other. Fragments may be retained if they have at least 60%, 70%, 80%, 90%, 95% or 99% sequence identity. It was realized that the above process potentially missed chimeras because the sequence diversity of the selected sequences may be low. By comparing the fragments against a core set of diverse chimera-free sequences, more chimeras can be identified and removed from the sequence set. In cases where one or more sequences are identified that as an ambiguous chimera, e.g., a chimera with a chimeric parent, the chimera is removed and the parent chimera is fragmented and a second comparison cycle is performed. Sequences from a dataset can also be screened for chimeras using a proprietary software program such as Bellerophon3 available from the Greengenes website at greengenes.lbl.gov.
The dataset of retained non-chimeric sequences can then be screened for structural anomalies at state 303 by aligning the retained sequences against the core set of known sequences. Sequences in the retained dataset that have at least 25, 30, 35, 40, 45, 50, 60, 70 or 80 gaps in their alignment when compared against a core set or have insertions of greater than 50, 60, 70, 80, 90, 100, 110, 120, 130, 140, 150, 160, 170, 180, 190, 200, 250, 300 or 400 basepairs when compared against the core set are tagged as having a sequence anomaly and are removed from the dataset.
The screened sequences are then aligned into a multiple sequence alignment (MSA) at state 304 for comparison against the known, chimeric free core set. One alignment tool for performing intensive alignment computations is NAST (Nearest Alignment Space Termination) web tool (DeSantis et al., Nucleic Acids Res. (2006) 34:W394-399). Any appropriate alignment tool can be used to compile the MSAs, for example, clustalw (Thompson et al., Nucleic Acids Res (1994) 22:4673-4680) and MUSCLE (Edgar, Nucleic Acids Res. (2004) 32:1792-1797).
The aligned sequences are searched for sequences harboring PCR primer sequences at state 305 and any so-identified sequences are removed from the dataset.
The aligned sequences can then be clustered at the state 306 to create what is termed a “guide tree.” First, the sequences are converted to a list of kmers. A pair-wise comparison of the lists of kmers is performed and the percent of kmers in common is recorded in a sparse matrix only if a threshold similarity is found. The sparse matrix is clustered e.g., using complete linkage. Clustering includes agglomerative “bottom-up” or divisive “top-down” hierarchical clustering, distance “partition” clustering and alignment clustering. From each cluster, the sequence with the most information content is chosen as a representative. Usually, sequences derived from genome sequencing projects are given priority in cluster creation because they are less likely to be chimeras or have other sequence anomalies. The cyclic process is repeated using only the representatives from the previous cycle. For each new cycle, the threshold for recording in the sparse matrix is reduced. At the final stage, a root node is linked to the final representative sequences in a multifurcated tree. The representative sequences found in each cycle represent a node in the resulting guide tree. All nodes are linked based on their clustering results via a self-referential table allowing rapid access to any hierarchical point in the guide tree. In some embodiments, the results are stored in a database format, e.g., in a Structured Query Language (SQL) compliant format. In the resulting guide tree, each leaf node represents an individual organism and each node above the lowest level of the guide tree represents a candidate OTU.
Typical distance matrixes built from approximately 2×105 sequences can require 40 billion intersections that would require about 40 gigabytes of data space if encoded to disk. Doubling the amount of sequences to 4×105 requires a quadrupling of the file size (approximately 160 GB). The clustering methodology illustrated here using a sparse matrix avoids the need for large files and the expected increase in computing time. Therefore the methodology can be performed more efficiently than conventional sequence clustering methods. Moreover, with distance matrices created from sequence alignments (e.g., DNA alignments), one misalignment can affect many distance values. In contrast, the clustering method illustrated herein is based on the alignment of tuners, and thus the effect of a misalignment on clustering values is significantly reduced.
Following guide tree construction, the dataset of remaining sequences, now termed the “filtered sequence dataset” is used to select candidate probes, e.g., PM probes. First, unsupported sequence polymorphisms are identified and removed from the filtered sequence dataset using a pre-clustering process that uses the guide tree generated above to create clusters over a minimum similarity and under a maximum size. Typically, clustered sequences are at least 80%, 85%, 90%, 95%, 97% or 99% similar. Usually, clusters have no more than 1,000, 500, 200, 100, 80, 60, 50, 40, 30, 20 or 10 sequences. This process allows sequence data outliers to be detected by comparison within near-neighbors and removed from the filtered sequence dataset.
Next, the remaining sequences are fragmented to the desired size to generate candidate target probes. Typically, the fragments range from about 10mer to 100mer, 15mer to about 50mer, about 20mer to about 40mer, about 20mer to about 30mer. Usually, the fragments are at least 15mer, 20mer, 25mer, 30mer, 40mer, 50mer or 100mer in size. Each candidate target probe is required to be found within a threshold fraction of at least one pre-cluster. Generally, threshold fractions of at least 80%, 90% or 95% are used.
All candidate PM probes that are within a threshold fraction of at least one pre-cluster are then evaluated for various biophysical parameters, such as melting temperature (61-80° C.), G+C content (35-70%), hairpin energy over −4 kcal/mol, potential for self-dimerization (>35° C.). Candidate PM probes that fall outside of the setting boundaries of the biophysical parameters are eliminated from the dataset. Optionally, probes can be further filtered for ease of photolithographic synthesis.
The likelihood of cross-hybridization of each PM candidate probe to each non-target input 16s rRNA gene sequence is determined. The cross-hybridization pattern for each PM candidate probe is recorded.
Sequence coverage heuristics are performed at the state 308 are then applied to candidate PM probes with acceptable biophysical parameters.
For each candidate PM probe, corresponding MM probes can be generated at the state 309. Each MM probe differs from its corresponding PM probe by at least one nucleotide. In some embodiments, the MM probe differs from its corresponding PM probe by 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 nucleotides. Within a MM probe, the mismatched nucleotide or nucleotides can include any of the 3 central bases that are not found in the same position or positions in the PM probe. For example, with a 25mer PM probe that has a guanine at the 13th position, i.e., the central nucleotide, the MM probes comprise probes with adenine, thymine, uracil or cytosine at the 13th position. Similarly, with a 25mer PM probe with an adenine at the 12th nucleotide position and a guanine at the 13th nucleotide position when read from the 3′ direction, the possible MM probes comprise probes with guanine at the 12th nucleotide and adenine, thymine or cytosine at the 13th nucleotide position; cytosine at the 12th nucleotide position and adenine, thymine or cytosine at the 13th nucleotide position; and thymine at the 12th nucleotide position and adenine, thymine or cytosine at the 13th nucleotide position. In some embodiments, the mismatched nucleotide or nucleotides include any one or more of the nucleotides in a corresponding PM probe. Increasing the number of MM probes and/or the mis-match positions represented may be used to enhance quantification, accuracy, and confidence.
As describe above for the PM probes, each candidate MM probe is required to meet the set boundaries of one or more biophysical parameters, such as melting temperature, G+C content, hairpin energy, self-dimers and photolithography synthesis steps. Generally, these parameters are identical or substantially similar to the PM probe biophysical parameters.
Candidate MM probes that meet the biophysical parameters and optionally, photolithographic parameters above are then screened for the likelihood of cross-hybridization to a target sequence. Usually, a central kmer length is evaluated. For a 25mer candidate MM, a central kmer from the candidate MM, generally a 15mer, 16mer, 17mer, 18mer, or 19mer is compared against the target sequences. A candidate MM probe that contains a central kmer that is identical to a target sequence is eliminated. Next, candidate PM probes for which no suitable candidate MM probes can be identified are also eliminated.
Each candidate OTU may be evaluated to determine the number of PM probes that are incapable of hybridization to sequences outside the OTU.
In one embodiment, a pre-partition process is performed. A pre-partition is the largest possible Glade (node_id) that does not exceed the max partition size. See
To create candidate sequence clusters, transitive sequence clusters are identified using a sliding threshold of two distance matrixes based on either the count of pairwise unique candidate targets or the count of pairwise common candidate targets. Probes prevalent in a large fraction of the sequences in a candidate sequence cluster, e.g., >=90% of the sequence in the cluster, are identified using the count of sequences containing the PM and the count of sequences with unambiguous data for given PM's locus. For each prevalent probe, a cross-hybridization potential outside the cluster is also tested. All information regarding cluster-PM sets is recorded. Futile clusters are defined as clusters for which only cross-hybridizing probes are identified are removed from the dataset.
Where necessary, probes that are expected to display some degree of cross-hybridization can be selected. Potentially hybridization-prone probes are constrained to reduce the probability that sequences outside the cluster could hybridize to many of the cluster-specific PM probes. A distribution algorithm can be used to examine a graph of probe-sequence interconnections (edges) and to favor sets of probes that minimize overlapping edges.
After solutions from all partitions are completed, a global reconciliation of set solutions across partitions is performed. The sequence clusters are locked as OTUs and each cluster's PM probe set is tested for global cross-hybridization against the other remaining PM probe sets. Probes are ranked for utility based on global cross-hybridization patterns.
The OTUs are assembled and annotated. Typically, each OTU is taxonomically annotated using one term for each rank from domain, kingdom, phylum, sub-phylum, class, sub-class, order, and family. As a result, all the 16S rRNA sequences presented without taxonomic nomenclature and annotated as “environmental samples” or “unclassified” are assigned with taxonomic annotation.
Each genus-level name recognized by NCBI is read and recorded. For each lineage of taxonomic terms, duplicate adjacent terms are removed; domain-level terms are found by direct pattern match; and phylum-level terms are found as rank immediately subordinate to domain. Order-level terms are found by -ales suffix and family-level terms are found by -eae suffix. If a family level-term is unavailable but a genus is identified (e.g., by match to an accepted list), the genus-level term is used to derive a family level-term. All unrecognized terms found between recognized terms are fit into available ranks (new ranks are not created for extra terms). Empty ranks are filled by deriving root terms from subordinate terms and adding pre-determined suffixes. Finally, the family of an OTU is determined by vote from the family assignment of the sequences. Ties are broken by priority sequences (e.g., sequences derived from genome sequencing projects can be given highest priority). All OTUs within a subfamily are compared by kmer distance among the sequences and OTUs are linked into a subfamily whenever a threshold similarity is observed. Each candidate OTU is evaluated to determine the count of targets which are prevalent across the sequences of the candidate OTU and are not expected to hybridize to sequences outside the OTU.
Exemplary PM and MM 25mer probes generated using the disclosed algorithms are provided as SEQ ID Nos. 1-50. It should be noted that the above process is applicable to the selection of probes ranging in size from at least 15 nucleotides to at least 200 nucleotides in length and includes probes that are flanked on one or both sides by common or irrelevant sequences, including linking sequences. Furthermore, probes selected by this process can be further processed to yield probes that are smaller than or larger than the original selected probes. For example, probes listed as SEQ ID Nos. 1-50 can be further processed by removing sequences from the 3′ end, 5′ end or both to produce smaller sequences that are identical to at least a portion of the sequence of the 25mers. In other embodiments, larger probes can be generated by incorporating the sequences of probes identified by the disclosed algorithms, i.e., a 25mer probe can be incorporated into a 30mer or larger, 35mer or larger, 40mer or larger, 45mer or larger, 50mer or larger, 55mer or larger, 60mer or larger, 65mer or larger, 70mer or larger, 75mer or larger, 80mer or larger, 85mer or larger or 90mer or larger probe. Additionally, probes listed as SEQ ID Nos. 1-50 can be shortened on one end and lengthened on the other end to yield probes that range from 10mer to 200mer.
Probes selected by the above process also include probes that comprise one or more base substitutions, for example uracil in the place of thymine; incorporate one or more base analogs such as nitropyrrole and nitroindole; comprise of one or more sugar substitutions, e.g., ribose in the place of deoxyribose, or any combination thereof. Similarly, probes selected by the process of the invention, may further comprise alternate backbone chemistry, for example, comprising of phosphoramide.
The size of the collection of putative probes generated by the methodologies of the invention is partially dependent on the length of the particular highly conserved sequence with longer sequences like that of 23 S rRNA gene allowing for a greater number of homologous sequences than a smaller highly conserved sequence such as 16S rRNA gene. In some embodiments, the length of the highly conserved sequence is at least 100 bp, 250 bp, 500 bp, 1,000 bp, 2,000 bp, 4,000 bp, 8,000 bp, 10,000 bp, or 20,000 bp. Additionally, the size of the collection of putative probes generated by the methodologies of the invention is also dependent on the size of the collection of homologous sequences in one or more databases from which sequences are selected for the analysis and generation of probes. Larger collections of homologous sequences, by providing a larger pool of sequences that can be analyzed, allow for the generation of more putative probes. In some embodiments, the starting collection of homologous sequences in one or more databases contains at least 100,000, 250,000, 500,000, 1,000,000, 2,000,000, 5,000,000 or 10,000,000 sequences. The size of the collection of putative probes is further dependent on the length of the desired probe, because the probe length decreases, as the number of probes that bind to unique sequences increases. Depending on the particular highly conserved sequence, the size of the database and the length of the desired probe, collections of putative probes of at least 100, 1,000, 10,000, 25,000, 50,000, 100,000, 250,000, 500,000, 1,000,000, 2,000,000, 5,000,000 or 10,000,000 probes can be generated.
Detection systems can be constructed from the putative probes generated by the above methods. The detection system can have any number of probes and range from 1 probe to all the probes selected by the methodology. In some embodiments, the detection system comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 36, 40, 45, 50, 55, 60, 65, 70, 80, 90, 100, 125, 150, 200, 300, 400, 500, 1000, 2,000, 5,000, 10,000, 20,000, 40,000, 50,000, 100,000, 200,000, 500,000, 1,000,000 or 2,000,000 probes. Systems with large number of probes can be used to identify relevant microorganisms in a sample, e.g., an environment or clinical sample, and/or to generate a biosignature. In another embodiment, once relevant microorganisms are known, detection systems with low (e.g., 1-10,000) to medium (e.g., 10,000-100,000) numbers of probes can be designed for special purpose applications, such as determining one or more specific biosignatures. In some embodiments, knowledge of the identity of relevant microorganisms can be used to select further probes to these microorganisms. If, for instance, five 25mer probes in a first set of probes hybridize to a relevant microorganism, then variants of these five probes can be generated and tested (e.g. in silico) for their binding and biophysical characteristics. Alternately, identification of relevant microorganisms can lead to the generation of new probes that are unlike the probes first used to identify the microorganisms. For example, once novel microorganisms are identified, antibodies can be generated for specific applications.
To select OTU-specific probes, e.g., oligonucleotide probes specific for organisms that are included within a hierarchical node, additional PM probes can be chosen for each hierarchical node that has more than one child node. To qualify targets for selection to a certain node, a threshold fraction of sequences within a node matching a PM set are enforced. Examples of the threshold fractions included 0.2%, 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, and 10%. Coverage of direct sub-nodes (children) is also enforced. For example, each target should be representative of at least 25% of at least one sub-node.
The specificity of the probes selected by the methods disclosed herein can be validated experimentally in a number of ways. For example, the hybridization signal of a probe in the presence of the target sequence can be measured and compared to the background signal. Target sequences can be derived from one or more pure cultures or from environmental or clinical samples that are known to contain the target sequence. A specific taxa can be identified as present in a sample if a majority (about 70% to about 100%, about 80% to about 100% or about 90% to about 100%) of the probes on the array have a hybridization signal at least about 50 times, 100 times, 150 times, 200 times, 250 times, 300 times, 350 times, 400 times, 450 times, 500 times, or 1,000 times greater than that of the background. Also, the hybridization signal of the probe can be compared to the hybridization signal of one or more of its mismatch probes. A PM:MM ratio of at least 1.05, 1.10, 1.15, 1.20, 1.25, 1.30, 1.40, 1.45, or 1.50 can indicate that the PM probe, can selectively hybridize to its target sequence. An additional way to test the ability of a probe to selectively hybridize to its target is to calculate a pair difference score (d), further explained below. A pair difference score above 1.0 indicates that the probe can selectively hybridize to the target compared to one of its mismatch probes.
The methods disclosed herein can be used to select and/or utilize organism-specific and/or OTU-specific oligonucleotide probes for biomolecules, such as proteins, DNA, RNA, DNA or RNA amplicons, and native rRNA from a target nucleic acid molecule. In some embodiments, probes are designed to be antisense to the native rRNA so that rRNA from samples can be placed on the array to identify actively metabolizing organisms in a sample with no bias from PCR amplification. Actively metabolizing organisms have significantly higher numbers of ribosomes used for the production of proteins, compared to quiescent or dead organisms. Therefore, in some embodiments, the capacity of one or more organisms to make proteins at a particular point in time can be measured. In this way, the array system of the present embodiments can be used to directly identify the metabolizing organisms within diverse communities.
In some embodiments, the sample used can be an ecosystems sample. Ecosystems include microbiomes associated with plants, animals, and humans. Animal and human associated microbiomes include those found in the gastrointestinal tract, respiratory system, nares, urogenital tract, mammary glands, oral cavity, auditory canal, feces, urine, and skin. In some embodiments, the sample can be any kind of clinical or medical sample. For example, samples from blood, urine, feces, nares, the lungs, the gut, other bodily fluids or excretions, materials derived therefrom, or combinations thereof of mammals may be assayed using the array system. Also, the probes selected by the methods disclosed herein and the array system of the present embodiments can be used to identify an infection in the blood of an animal. The probes selected by the methods disclosed herein and the array system of the present embodiments can also be used to assay medical samples that are directly or indirectly exposed to the outside of the body, such as the lungs, ear, nose, throat, the entirety of the digestive system or the skin of an animal. In some embodiments, a sample includes cell culture samples and/or bacterial culture samples. In some embodiments, a sample comprises a pulmonary sample from a subject, including but not limited to sputum, endotracheal aspirate, bronchoalveolar lavage sample, a swab of the endotrachea, materials derived therefrom, or combinations thereof.
Techniques and systems to obtain genetic sequences from multiple organisms in a sample, such as an ecosystem, medical, or clinical sample, are well known by persons skilled in the art. Many commercially available DNA extraction and purification kits can also be used. Samples, with lower than 2 pg purified DNA may require amplification, which can be performed using conventional techniques known in the art, such as a whole community genome amplification (WCGA) method (Wu et al., Appl. Environ. Microbiol. (2006) 72, 4931-4941). In some embodiments, highly conserved sequences such as those found in the 16S RNA gene, 23S RNA gene, 5S RNA gene, 5.8S rRNA gene, 12S rRNA gene, 18S rRNA gene, 28S rRNA gene, gyrB gene, rpoB gene, fusA gene, recA gene, cox1 gene and nifD gene are amplified. Usually, amplification is performed using PCR, but other types of nucleic acid amplification can be employed. Generally, amplification is performed using a single pair of universal primers specific to a highly conserved sequence. For redundancy or for increased amount of total amplicon concentration, two or more universal probe pairs each specific to a different highly conserved sequence can be used. Representative PCR primers include: bacterial primers 27F and 1492R. In some embodiments, a nucleic acid sample is amplified using a collection of primers each comprising one or more nucleotide positions selected at random from two or more different nucleotides. In some embodiments, primers, nucleotides, or other reagents used in an amplification reaction are labeled to produced labeled amplification products.
A gel electrophoresis method can also be used to isolate community RNA (McGrath et al., J. Microbiol. Methods (2008) 75:172-176). Samples with lower than 5 pg purified RNA may require amplification, which can be performed using conventional techniques known in the art, such as a whole community RNA amplification approach (WCRA) (Gao et al., Appl. Environ. Microbiol. (2007) 73:563-571) to obtain cDNA. In some embodiments, sampling and DNA extraction are conducted as previously described (DeSantis et al., Microbial Ecology, 53(3):371-383, 2007).
In some embodiments, DNA; total RNA, or a fraction thereof, including rRNA, 16S rRNA, and 23S rRNA; or combinations thereof are directly labeled and used without any amplification.
Techniques and means for generating oligonucleotide probes to be used on analysis systems, beads or in other systems are well-known by persons skilled in the art. For example, the oligonucleotide probes can be generated by synthesis of synthetic polynucleotides or oligonucleotides, e.g., using N-phosphonate or phosphoramidite chemistries (Froehler et al., Nucleic Acid Res. 14:5399-5407 (1986); McBride et al., Tetrahedron Lett. 24:246-248 (1983)). Synthetic sequences are typically between about 10 and about 500 bases in length, more typically between about 15 and about 100 bases, and most preferably between about 20 and about 40 bases in length. In some embodiments, synthetic nucleic acids include non-natural bases, such as, but by no means limited to, inosine. An example of a suitable nucleic acid analogue is peptide nucleic acid (see, e.g., Egholm et al., Nature 363:566-568 (1993); U.S. Pat. No. 5,539,083). In some embodiments, at least 10, 25, 50, 100, 500, 1,000, 5,000, 10,000, 20,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000 100,000, 200,000, 500,000, 1,000,000 or 2,000,000 probes are included on the array. In further embodiments, each PM probe has one or more corresponding MM probe present on the array. Typically, each PM-MM probe pair is associated with an OTU. In some embodiments, at least 10, 25, 50, 100, 500, 1,000, 5,000, 10,000, 20,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000 100,000, 200,000 or 500,000 probe pairs are placed on the array. Generally, sets of probe pairs have at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34 or 35 probe pairs present.
In some embodiments, positive control probes that are complementary to particular sequences in the target sequences (e.g., 16S rRNA gene) are used as internal quantification standards (QS) and included in the system. In other embodiments, positive control probes, also known as internal DNA quantification standards (QS) probes are probes that hybridize to spiked-in nucleic acid sequence targets. Usually, the sequences are from metabolic genes. In some embodiments, negative control (NC) probes, e.g., probes that are not complementary or do not appreciably hybridize to sequences in the target sequences (e.g., 16S rRNA gene) are included on the array. Unlike the QS probes, no target material is spiked into the sample mix for the NC probes, prior to sample processing.
In some embodiments, the probes are synthesized separately and then attached to a solid support or surface, which may be made, e.g., from glass, latex, plastic (e.g., polypropylene, nylon, polystyrene), polyacrylamide, nitrocellulose, gel, silicon, or other porous or nonporous material. In some embodiments, the surface is spherical or cylindrical as in the case of microbeads or rods. In other embodiments, the surface is planar, as in an array or microarray. For example, the method described generally by Schena et al, Science 270:467-470 (1995) can be used for attaching the nucleic acids to a surface by printing on glass plates. In other embodiments, typically used for making high-density oligonucleotide arrays, thousands of oligonucleotides complementary to defined sequences are synthesized in situ at defined locations on a surface by photolithographic techniques (see e.g., Fodor et al., 1991, Science 251:767-773; Pease et al., 1994, Proc. Natl. Acad. Sci. U.S.A. 91:5022-5026; Lockhart et al., 1996, Nature Biotechnology 14:1675; U.S. Pat. Nos. 5,578,832; 5,556,752; and 5,510,270) or other methods for rapid synthesis and deposition of defined oligonucleotides (e.g., Blanchard et al., Biosensors & Bioelectronics 11:687-690). In some of these methods, oligonucleotides (e.g., 25-mers) of known sequence are synthesized directly on a surface such as a derivatized glass slide. Other methods for making analysis systems are also available, e.g., by masking (Maskos and Southern, 1992, Nuc. Acids. Res. 20:1679-1684). Embodiments of the present invention are applicable to any type of array, for example, bead-based arrays, arrays on glass plates or derivatized glass slides as discussed above, and dot blots on nylon hybridization membranes.
Embodiments of the invention are applicable for use in any analysis system, including but not limited to bead or solution multiplex reaction platforms, or across multiple platforms, for example, Affymetrix GeneChip® Arrays, Illumina BeadChip® Arrays, Luminex xMAP® Technology, Agilent Two-Channel Arrays, MAGIChips (Analysis systems of Gel-immobilized Compounds) or the NanoString nCounter Analysis System. The Affymetrix (Santa Clara, Calif., USA) platform DNA arrays can have the oligonucleotide probes (approximately 25mer) synthesized directly on the glass surface by a photolithography method at an approximate density of 10,000 molecules per μm2 (Chee et al., Science (1996) 274:610-614). Spotted DNA arrays use oligonucleotides that are synthesized individually at a predefined concentration and are applied to a chemically activated glass surface. In general, oligonucleotide lengths can range from a few nucleotides to hundreds of bases in length, but are typically from about 10mer to 50mer, about 15mer to 40mer, or about 20mer to about 30mer in length.
Oligonucleotides produced using techniques known in the art can be built on and/or coupled to microspheres, beads, microbeads, rods, or other microscopic particles for use in arrays, flow cytometry, and other multiplex assay systems. Numerous microparticles are commercially available from about 0.01 to 100 micrometers in diameter. Generally, microparticles from about 0.1-50 μm, about 1-20 μm, or about 3-10 μm are preferred. The size and shapes of microparticles can be uniform or they can vary. In some embodiments, sublots of different sizes, shapes or both are conjugated to probes before combining the sublots to make a final mixed lot of labeled microparticles. The individual sublots can therefore be distinguished and classified based on their size and shape. The size of the microparticles can be measured in practically any flow cytometry apparatus by so-called forward or small-angle scatter light. The shape of the particle can be also discriminated by flow cytometry, e.g., by high-resolution slit-scanning method.
Microparticles can be made out of any solid or semisolid material including glass, glass composites, metals, ceramics, or polymers. Frequently, the microparticles are polystyrene or latex material, but any type of polymeric material is acceptable including but not limited to brominated polystyrene, polyacrylic acid, polyacrylonitrile, polyacrylamide, polyacrolein, polybutadiene, polydimethylsiloxane, polyisoprene, polyurethane, polyvinylacetate, polyvinylchloride, polyvinylpyridine, polyvinylbenzylchloride, polyvinyltoluene, polyvinylidene chloride, polydivinylbenzene, polymethylmethacrylate, or combinations thereof. Microparticles can be magnetic or non-magnetic and may also have a fluorescent dye, quantum dot, or other indicator material incorporated into the microparticle structure or attached to the surface of the microparticles. Frequently, microparticles may also contain 1 to 30% of a cross-linking agent, such as divinyl benzene, ethylene glycol dimethacrylate, trimethylol propane trimethacrylate, or N,N′ methylene-bis-acrylamide or other functionally equivalent agents known in the art.
In one embodiment, the nucleic acid targets are labeled so that a laser scanner tuned to a specific wavelength of light can measure the number of fluorescent molecules that hybridized to a specific DNA probe. For arrays, the nucleic acid targets are typically fragmented to between 15 and 100 nucleotides in length and a biotinylated nucleotide is added to the end of the fragment by terminal DNA transferase. At a later stage, the biotinylated fragments that hybridize to the oligonucleotide probes are used as a substrate for the addition of multiple phycoerythrin fluorophores by a sandwich (Streptavidin) method. For some arrays, such as those made by AGILENT or NIMBLEGEN, the purified community DNA can be fluorescently labeled by random priming using the Klenow fragment of DNA polymerase and more than one fluorescent moiety can be used (e.g. controls could be labeled with Cy3, and experimental samples labeled with Cy5 for direct comparison by hybridization to a single analysis system). Some labeling methods incorporate the molecular label into the target during an amplification or enzymatic step to produce multiple labeled copies of the target.
In some embodiments, the detection system is able to measure the microbial diversity of complex communities without PCR amplification, and consequently, without the inherent biases associated with PCR amplification. Actively metabolizing cells typically contain about 20,000 or more ribosomes for protein assembly compared to quiescent or dead cells that have few. In some embodiments, rRNA can be purified directly from a sample and processed with no amplification step, thereby reducing or avoiding bias caused by preferential amplification of some sequences over others. Thus, in some embodiments, the signal from the analysis system can reflect the true number of rRNA molecules that are present in the samples. This can be expressed as the number of cells multiplied by the number of rRNA copies within each cell. The number of cells in a sample can then be inferred by several different methods, such as, for example, quantitative real-time PCR, or FISH (fluorescence in situ hybridization.). Then the average number of ribosomes within each cell may be calculated.
Hybridizations can be carried out under conditions well-known by persons skilled in the art. See Rhee et al. (Appl. Environ. Microbiol. (2004) 70:4303-4317) and Wu et al. (Appl. Environ. Microbiol. (2006) 72:4931-4941). The temperature can be varied to reduce or increase stringency and allow the detection of more or less divergent sequences. Robotic hybridization and stringency wash stations can be used to give more consistent results and reduce processing time. In some embodiments, the hybridization and washing process can be accomplished in less than about half an hour, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 14 hours, 16 hours, 18 hours, 20 hours or 24 hours. Generally, hybridization and washing times are reduced for microparticle based detection systems owing to the greater accessibility of the probes to the target molecules. Generally, hybridization times may be reduced for low complexity assays and/or assays for which there is an excess of target analytes.
After hybridization, arrays can be scanned using any suitable scanning device. Non-limiting examples of conventional microarray scanners include GeneChip Scanner 3000 or GeneArray Scanner, (Affymetrix, Santa Clara, Calif.); and ProScan Array (Perkin Elmer, Boston, Mass.); and can be equipped with lasers having resolutions of 10 pm or finer. The scanned image displays can be captured as a pixel image, saved, and analyzed by quantifying the pixel density (intensity) of each spot on the array using image quantification software (e.g., GeneChip Analysis system Analysis Suite, version 5.1 Affymetrix, Santa Clara, Calif.; and ImaGene 6.0, Biodiscovery Inc. Los Angeles, Calif., USA). For each probe, an individual signal value can be obtained through imaging parsing and conversion to xy-coordinates. Intensity summaries for each feature can be created and variance estimations among the pixels comprising a feature can be calculated.
With flow cytometry based detection systems, a representative fraction of microparticles in each sublot of microparticles can be examined. The individual sublots, also known as subsets, can be prepared so that microparticles within a sublot are relatively homogeneous, but differ in at least one distinguishing characteristic from microparticles in any other sublot. Therefore, the sublot to which a microparticle belongs can readily be determined from different sublots using conventional flow cytometry techniques as described in U.S. Pat. No. 6,449,562. Typically, a laser is shined on individual microparticles and at least three known classification parameter values measured: forward light scatter (C1) which generally correlates with size and refractive index; side light scatter (C2) which generally correlates with size; and fluorescent emission in at least one wavelength (C3) which generally results from the presence of fluorochrome incorporated into the labeled target sequence. Because microparticles from different subsets differ in at least one of the above listed classification parameters, and the classification parameters for each subset are known, a microparticle's sublot identity can be verified during flow cytometric analysis of the pool of microparticles in a single assay step and in real-time. For each sublot of microparticles representing a particular probe, the intensity of the hybridization signal can be calculated along with signal variance estimations after performing background subtraction.
Simultaneous detection of at least 500, 1,000, 5,000, 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, or more taxa with a high level of confidence can incorporate techniques to de-convolute the signal intensity of numerous probe sets into probability estimates. In some embodiments, the methods, compositions, and systems of the invention enable detection in one assay the presence or absence of a microorganism in a community of microorganisms, such as an environmental or clinical sample when the microorganism comprises less than 0.05% of the total population of microorganisms. In some embodiments, detection includes determining the quantity of the microorganism, e.g., the percentage of the microorganism in the total microorganism population. De-convolution techniques can include the incorporation of NC probe pairs into the analysis system and the use of the data to fit the hybridization signals from the QS probe pairs to the hybridization distribution of the NC probe pairs.
De-convolution techniques can allow the detection and quantification of nucleic acids in a sample and by inference, the detection and quantification of microorganisms in a sample. In one aspect of the invention, a system is provided for determining the presence or quantity of a microorganism in a sample comprising contacting a sample with a plurality of probes, detecting the hybridization signals of the sample nucleic acids with the probes and de-convoluting the signals to determine the presence, absence and/or quantity of a particular nucleic acid present in a population of nucleic acids where the particular nucleic acid is present at less than 0.01% of the total nucleic acid population. In some embodiments, the particular nucleic acid is at least 80%, 85%, 90%, 91%, 92%, 93%, 94%, 95%, 96% or 97% homologous to other nucleic acids in the population.
In some embodiments, the data output from an imaged or scanned sample is de-convoluted and analyzed using the following methods. Using an array as an illustrative example, the hybridization signals are converted to xy-coordinates with intensity summaries and variance estimates generated for the pixels using commercial software. The data is outputted using a standard data format like a CEL file (Affymetrix), or a Feature Report file (NimbleGen).
The hybridization signals undergo background subtraction. Typically, the background intensity is computed independently for each quadrant as the average signal intensity of the least intense 2% of the probes in the quadrant. Other threshold values may also be used, e.g., 0.5%, 1%, 3%, 4%, 5% or 10%. Background intensity is then subtracted from all probes in a quadrant before further computation is performed. This noise removal procedure can be done on a quadrant-by-quadrant basis or across a whole array.
In some embodiment, array signals are normalized to allow for the comparison of results achieved in different experiments or for the comparison of replicate experiments. Normalization can be achieved by a number of methods. In one embodiment, reproducibility between different probes for the same target are evaluated using a Position Dependent Nearest Neighbor (PDNN) model as described in Zhang L. et al., A model of molecular interactions on short oligonucleotide analysis systems, Nat. Biotechnol. 2003, 21(7):818-821. The PDNN model allows estimation of the sequence specific noise signal and a non-specific background signal, and thus enables estimation of the true intensity for the probes.
In other embodiments, per-array models of signal and background distributions using responses observed from comparison of the PM and MM probe pairs and the internal DNA quantification standards (QS) probe pairs are created. In one embodiment, the probability that each probe pair is “positive” is determined by calculating a difference score, d, for each probe pair. d may be defined as:
In some embodiments, the internal DNA quantification standards (QS) and negative control (NC) probe pairs are binned and sorted by attributes of the probes. Examples of the attributes of the probes that can be used in the embodiments of the present invention include, but are not limited to binding energy; base composition, including A+T count, G+C count, and T count; sequence complexity; cross-hybridization binding energy; secondary structure; hair-pin forming potential; melting temperature; and length of the probe. These attributes of the probes may affect hybridization properties of the probes, for example, A+T count may affect hydrogen bonding of the probe, and T count may affect the length and base composition of the fragments produced by the use of DNase. Fragmentation with other enzyme systems may be influenced by the composition of other bases.
In one embodiment, QS and NC probe pairs are binned and sorted based on the individual probe's A+T count and T count. For each bin (A+T count by T count), the d values from the negative control probes are fit to a normal distribution to derive the scale (mean) and shape (standard deviation). Then, the d values from QS are fit to a gamma distribution to derive scale and shape. For each array, multiple density plots are produced by this process. Two examples of density plots generated from two different probe bins within the same array are shown in
The parameters derived from gamma and normal distributions are used to derive a pair response score, r, for each probe pair. r is an indicator of the probability that a probe pair is positive, i.e., the probability for a probe pair to be responsive to the target sequence. r may be defined as:
Each set of interrogation probe pairs, e.g., an OTU, can be scored based on pair response scores, cross-hybridization relationships or both. In some embodiments, the system removes data from at least a subset of probe pair sets before making a final call on the presence or quantity of said microorganisms. In one embodiment, the data is removed based on interrogation probe cross hybridization potential. In one embodiment, the scoring of probe pairs is performed by a two-stage process as discussed below.
For example, a two stage analysis can be performed wherein only probe pairs that pass a first stage are analyzed in the next stage. In the first stage, the distribution of r across each set of probe pairs, R, is determined. For each set of probe pairs that is associated with an OTU, the r values of all probe pairs are ranked within the set, and percentage of probe pairs that meet one or more threshold r values are determined. Frequently, three threshold determinations are made at 25% increments across the total range of ranked probe pairs (interquartile Q1, Q2, and Q3); however, any number of threshold determinations or percentage increments can be used. For example, a determination may use one increment at 70% in which probe pairs must pass a threshold value of 80%.
Typically, to differentiate signal from noise, an OTU is considered to pass Stage 1 if Q1, Q2, and Q3 of the set of probe pairs that is associated with this OTU surpass the threshold of Q1min, Q2min, and Q3min, respectively. That is, for an OTU to pass Stage 1, the r value of 75% of the probe pairs in the set of probe pairs that is associated with that OTU has to be at least Q1min, the r value of 50% of the probe pairs in that set of probe pairs have to be at least Q2min, and the r value of 25% of the probe pairs in that set of probe pairs have to be at least Q3min. Q1min is at least about 0.5, about 0.55, about 0.6, about 0.65, about 0.7, about 0.75, about 0.8, about 0.82, about 0.84, about 0.86, about 0.88, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. Q2min is at least about 0.5, about 0.55, about 0.6, about 0.65, about 0.7, about 0.75, about 0.8, about 0.82, about 0.84, about 0.86, about 0.88, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94; about 0.95, about 0.96, about 0.97, about 0.98, or about 0.99. Q3min is at least about 0.5, about 0.55, about 0.6, about 0.65, about 0.7, about 0.75, about 0.8, about 0.82, about 0.84, about 0.86, about 0.88, about 0.90, about 0.91, about 0.92, about 0.93, about 0.94, about 0.95, about 0.96, about 0.97, about 0.98, about 0.99, about 0.992, about 0.994, about 0.996, about 0.998, or about 0.999. In some embodiments, Q2min, and Q3min are determined empirically from spike-in experiments. For example, Q1min, Q2min, and Q3min are chosen to allow 2 pM amplicon concentration to pass. In one embodiment, Q1min, Q2min, and Q3min are 0.98, 0.97, and 0.82, respectively. These threshold numbers were empirically derived using DNase to fragment the sample sequences. Since DNase has a T-bias, the use of other enzymes may require a shift in the threshold numbers and can be empirically derived.
In the second stage only the OTUs passing the first are considered as potential sources of cross-hybridization. In some embodiments, for each OTU, only probe-pairs with r>0.5 (these are the probe pairs considered as to be likely responsive to the target sequence) are further analyzed. In other instances, only probe pairs with r>0.6, 0.7, 0.8, or 0.9 are considered responsive and are further analyzed. Probe pairs that are unlikely to be responsive (i.e., r<0.5) are not analyzed further even if their set R, was responsive overall. R0.5 represents the subset of probe pairs in which all probe pairs have r>0.5. Typically, based on the interquartile Q1, Q2 and Q3 values chosen at Stage 1, most of the probe pairs in the OTUs passing Stage 1 are analyzed. In other embodiments, only the probe-pairs with r>0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, or 0.90 are further analyzed.
For each probe pair in the R0.5 subset, the count of putatively cross-hybridizing OTUs (i.e., the number of OTUs with which the probe pair can cross-hybridize) is determined. In this process, only the OTUs that have passed Stage 1 are considered as potential sources of cross-hybridization. Each probe pair in the R0.5 subset is penalized by dividing its r value by the count of putatively cross-hybridizing OTUs to determine its modified possibility of being positive. The modified possibility of being positive for a probe pair may be represented by a rx value. rx may be defined as:
rx is proportional to the response of the probe pair and the specificity of the probe pair given the community observed during the first stage. rx value can range from 0 to 1. For each set of probe pairs associated with an OTU, rx are calculated for each probe pair and ranked within the set. Interquartile Q1, Q2, Q3 values for the distribution of rx value in each set of probe pairs are determined. The taxon represented by the OTU is considered to be present if Q1 is greater than Qx1, Q2 is greater than Qx2, or Q3 is greater than Qx3. Qx1 is at least about 0.5, at least about 0.55, at least about 0.6, at least about 0.65, at least about 0.7 at least about 0.75, at least at least about 0.8, at least about 0.85, at least about 0.90, at least about 0.95, or at least about 0.97. Qx2 is at least about 0.5, at least about 0.55, at least about 0.6, at least about 0.65, at least about 0.7 at least about 0.75, at least at least about 0.8, at least about 0.85, at least about 0.90, at least about 0.95, or at least about 0.97. Qx3 is at least about 0.5, at least about 0.55, at least about 0.6, at least about 0.65, at least about 0.7 at least about 0.75, at least at least about 0.8, at least about 0.85, at least about 0.90, at least about 0.95, or at least about 0.97. In one embodiment, Qx1 is at least 0.66, that is, 75% of the probe pairs in the set of the probe pairs have a rx value that is at least 0.66.
A two stage hybridization signal analysis procedure can be performed on hybridization signals from any array or microparticle generated data set, including data generated from the use of any combination of probes selected using the disclosed methodologies. In some embodiments, the second stage of the procedure penalizes probes based on the number of cross-hybridizations, the intensity of the cross-hybridization signals or a combination of the two.
The method disclosed herein is useful for hierarchical probe set scoring. An OTU may be present at a node at any hierarchical level on a clustering tree. As used herein, an OTU is a group of one or more organisms, such as a domain, a sub-domain, a kingdom, a sub-kingdom, a phylum, a sub-phylum, a class, a sub-class, an order, a sub-order, a family, a subfamily, a genus, a subgenus, a species, or any cluster. In some embodiments, a R0.5 set is collected for each node on the phylogenetic tree and consists of all unique probes from subordinate R0.5 sets. For example, for calculating rx values for probe pairs in a R0.5 set for an OTU representing an “order,” the count of putatively cross-hybridizing equally-ranked taxa (i.e., “order” node) containing at least one sequence with cross-hybridization potential is used as the denominator in Eqn. 3.
In some embodiments, the OTUs at the leaf level (e.g., species, sub-genus or genus) are first analyzed. Then each successive level of nodes in the clustering tree is analyzed. In one embodiment, the analysis is performed up to the domain level. In another embodiment, the analysis is performed up to the phylum level. In yet another embodiment, the analysis is performed up to the kingdom level. Penalization for cross-hybridization in Eqn. 3 is only performed for probes on the same taxonomy level. All present taxa are quantified using the mean scaled PM probe intensity after discarding the highest and lowest value of the set R (HybScore). In some embodiments, only taxa present at a first level are analyzed further.
In some embodiments, a summary abundance score is determined. Corrected abundance scores are created based on G+C content and uracil incorporation. Generally, probes with higher G+C content produce a higher hybridization signal that is typically compensated for correcting the abundance scores.
The probability of detection for each taxonomic node is determined by summarizing terminal node detection and the breadth of cross-hybridization relationships. Hierarchical probes are scored for evidence of novel organisms based on cluster analysis.
In some embodiments, the system is capable of analyzing other data in conjunction with that obtained from the analysis of probe hybridization signal strength. In some embodiments, the system can analyze sequencing reaction data including that obtained with high-through put sequencing techniques. In some embodiments, the sequencing data is from same regions of the same highly conserved sequence analyzed by the method disclosed herein using probes.
Numerous subject-derived samples can be assayed to determine the sample's microbiome composition. By having an assay system capable of detecting in a single assay the presence and optionally quantity of at least 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 200,000, 500,000 or 1,000,000 bacterial or archeal taxa, a complete picture of a microbiotic ecosystem can be achieved quickly and at relatively low cost providing the ability to examine numerous subjects.
The elucidation of a specific microbiome associated with an ecosystem, animal, human, organ system, condition, and the like allows for the generation of a “signature,” “biosignature,” or “fingerprint” of the particular environment sampled, terms used interchangeably herein. If the biosignature is from a normal or healthy system or subject, or is from a subject free from a condition under examination, then the associated biosignature can be used as a reference for the comparison of later samples from the same or other subjects to monitor for changes that are associated with an abnormal or unhealthy state or condition. For example, if a later biosignature of a subject shows that the microbiome has shifted away from that associated with a healthy pulmonary status, then preemptive measures could be taken to prevent a continued shift, for example by identifying a disease-related organism or OTU and taking steps to treat it.
Similarly, a biosignature of an environment can be compared to a biosignature generated from a pool of samples that represent an average or normal biosignature for a population or collection of environments. For example, a sample from an unhealthy individual could be assayed and the microbial biosignature compared to the biosignature seen in a healthy population at large. If one or more microorganisms are detected in the unhealthy individual that are either not seen in the general population or not seen at the same prevalence then therapeutic measures can be taken to selectively eliminate or reduce in number the microorganisms associated with the unhealthy state. For instance, the microflora of the respiratory system can be compared between individuals that suffer from chronic obstructed pulmonary disease (COPD), such as during an exacerbation of the disease, and individuals not suffering from COPD or having COPD that is in remission. If the individuals with exacerbated COPD are shown to have one or more dominant pulmonary microorganisms compared to the other individuals, then an available drug and/or dietary therapy that specifically targets the prevalent, abnormal microorganisms can be administered. Alternatively or additionally, the pulmonary microorganism population in the COPD sufferer can be shifted through the introduction of large numbers of the microorganisms associated with healthy pulmonary status. Once a relationship is known between the prevalence of a particular microorganism or group of microorganisms (e.g. one or more OTUs) and a disease state, then disease progression or treatment response can also be monitored, diagnosed, and/or predicted using the present systems and methods.
Numerous microbiomes of animals or humans can be analyzed with the present systems and methods including the gut, respiratory system, urogenital tract, mammary glands, skin, oral cavity, auditory canal, and skin. Clinical samples such as blood, sputum, nares, feces, and urine can be used with the method. From the analysis of normal individuals and those suffering from a disease or condition, a large database of fingerprints or biosignatures can be assembled. By comparing the biosignatures between healthy and disease related states, associations can be made as to the influence and importance of individual components of the microbiome.
Once these associations are made, treatments can be designed and tested to alter the composition of the microbiota seen in the disease state. Additionally, by regularly monitoring the microbial composition of an affected organ system in a diseased individual, disease progress or response to therapy can be observed and if need, additional therapeutic measures taken to alter the microbiome composition to one that is more representative of that seen in a healthy population.
An interesting property of bacteria that has great importance in healthcare, water quality and food safety is quorum sensing. Many bacteria are able to sense the presence of other members of their species or related species and upon reaching a specific density the bacteria start producing various virulence or pathogenicity factors. In other words, the bacteria's gene expression is coordinated as a group. For example, some bacteria produce exopolysaccharides that are known as “slime layers.” The secretion of exopolysaccharidse can decrease the ability of white blood cells to phagocytize the microorganisms and make the microorganisms more resistant to therapeutics or cleaning agents. Traditional methodologies require the detection of specific gene expression in order to detect or study quorum sensing and other population induced effects. The present systems and methods can be used to understand the changes that occur in a microbiome that are associated with a given effect such as biofilm formation or toxicity production. One can develop protocols with the present systems and methods to look for and determine conditions that lead to quorum sensing. For example, testing samples at various timepoints and under varying conditions can lead to determining how and when to intervene or reverse population induced expression of virulence or pathogenicity factors.
In one embodiment, a method is provided to identify a new indicator species for an environmental or health condition with the present systems and methods. The condition can be that of a normal or healthy state. Alternatively, the indicator species can be for an unhealthy or abnormal condition. To identify a new indicator species, a normal sample is simultaneously assayed to determine the presence or quantity of each OTU associated with all known bacteria, archae, or fungi; this test result is compared to the results achieved in the simultaneous assay of sample from the environment of the condition where the presence or quantity of each OTU associated with all known bacteria, archae, or fungi was determined. Microorganisms that change in abundance at least 2-fold, 3-fold, 4-fold, 5-fold, 10-fold, 20-fold, 50-fold or 100-fold, either increasing in abundance or decreasing in abundance represent putative indicator species for a condition.
In other embodiments, methods are provided for identifying indicators species associated with a disease state, disease progression, treatment regimen, probiotic administration, including progression of disease. In some embodiments the disease is COPD. In some embodiments, the disease relates to a level of COPD activity in a subject, such as a subject having COPD that is not exacerbated (e.g. non-exacerbated COPD), COPD that is exacerbated (e.g. exacerbated COPD), or changing in the level of disease activity (e.g. intermediate COPD exacerbation). Intermediate COPD exacerbation may be indicative of a transition away from an exacerbated state, and may be used as an indication of successful response to therapy. Intermediate COPD exacerbation may be indicative of a transition towards an exacerbated state. Where intermediate COPD exacerbation indicates an onset of COPD exacerbation, intermediate COPD exacerbation can comprise a prediction of the onset of exacerbation of COPD in a subject. A prediction in onset can comprise a prediction in time to onset, for example, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or more days before an onset of COPD exacerbation; or 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or more weeks before an onset of COPD exacerbation. A prediction of onset of COPD exacerbation may be used as a basis for taking medical action, such as therapeutic action, including but not limited to the administration of a therapeutic compound. In other embodiments, methods are provided for monitoring a change in the environment or health status associated with introducing one or more new microorganisms into a community. For example, measures to increase a particular microorganism's percentage of the gut microbiome in an individual, such as feeding a person yogurt or a food supplement containing L. casei, can be monitored using the present methods and systems.
The ability to identify and quantitate the microorganisms in a sample can be combined with a gene expression technology such as a functional gene array to correlate populations with observed gene expression. Similarly, microbiome composition analysis can be correlated with the presence of chemicals, proteins including enzymes, toxins, drugs, antibiotics or other sample constituents. For instance, nucleic acids isolated from a soil sample can be analyzed to elucidate the microbiome composition (e.g. biosignature) and also to identify expressed genes. In the bare, nutrient-poor soils on the Antarctic, this analysis associated chitinase and mannanase expression with Bacteroidetes and CH4-related genes with Alphaproteobacteria. (Yergeau et al., Environmental microarray analyses of Antarctic soil microbial communities. ISME J. 3:340-351, 2009). Significant correlations were also found between taxon abundances and C- and N-cycle gene abundance. From this data, one can predict that certain organisms or groups of organisms are required or account for the majority of an expected or observed enzymatic or degradative process. For example, members of the Bacteroidetes phylum probably degrade the majority of environmental chitin, a major constituent of exoskeletons of insect and arthropods and also of fungi cell walls, at the sample locale.
This methodology can be used to identify new antibiotic producing organisms, even ones that are unculturable. For instance, soil extracts can be tested for antibiotic activity. If a positive extract is found, a sample of the soil from which a portion was extracted for antibiotic can be analyzed for microbial composition and perhaps gene expression. Major constituents of the microbiome could be correlated with antibiotic activity with the correlation strengthened through gene expression data allowing one to predict that a particular organism or group of organisms is responsible for the observed antibiotic activity.
In one aspect, the invention provides a method for determining a condition in a sample. In one embodiment, the method comprises a) contacting said sample with a plurality of different probes; b) determining hybridization signal strength for each of said probes, wherein said determination establishes a biosignature for said sample; and, c) comparing the biosignature of said sample to a biosignature for COPD, including COPD exacerbation. In some embodiments, a method is provided for making a prediction about a sample comprising a) determining microorganism population data as the probability of the presence or absence of at least 100 OTUs of microorganisms in said sample; b) determining gene expression data of one or more genes by said microorganisms in said sample and c) using said expression data and population data to make a prediction about said sample. In some embodiments, the prediction entails the identity of a microorganism responsible for a characteristic or condition observed in an environment.
Other combined analysis methods include the use of a diffusion chamber to retain microorganisms in a sample while one or more constituents or parameters of the sample are changed. For instance, the salinity or pH of the sample can be changed abruptly or gradually over time. Following specific time intervals, the microbiome of the sample in the diffusion chamber can be determined. Microorganisms that cannot tolerate the new environment conditions will die, become reduced in number due to unfavorable conditions or predation, or remain static in their numbers. In contrast, microorganisms that can tolerate the new conditions will at least maintain their number or thrive, perhaps becoming a dominant population. Use of a diffusion chamber coupled with a system capable of detecting the presence or quantity of at least 10,000 OTUs can allow the identification of microorganisms that perish or fail to thrive when placed in a new environment. Such microorganisms are termed “transient”, meaning that their percent composition of the microbiome changes quickly. The identification of transient microorganisms can be used to ascertain the time and/or place they were introduced into an environment. Different transient microorganisms can have different half-lives for a particular condition.
Diffusion chambers can also take the form of a semi-permeable capsule, tube, rod, or sphere or other solid or semi-solid object. A microbiome or a select group of bacteria can be placed inside the capsule, that is then sealed and introduced into an environment for a specified period of time. Upon removal, the capsule is opened and the microbiome or select group of bacteria sampled to ascertain changes in the presence or quantity of the individual constituents. The capsule can be removed once or periodically to sample the microbiome. Alternatively, multiple single use capsules with identical quantities of the microbiome can be used, each one removed and sampled at a different time point. Microbiomes placed in capsules or other semi-permeable containers can be introduced into a living organism, usually through an orifice, to measure changes to the microbiome composition associated with a particular organ or system environment. For example, a semi-permeable capsule or tube containing a microbiome can be introduced into the gastrointestinal system through the mouth or anus. A microbiome from a healthy individual can be introduced in this manner into an unhealthy individual, such as a patient suffering from Crohn's disease or irritable bowel syndrome to ascertain the effect of the unhealthy condition on the normal, healthy individual associated microbiome. In this manner, the efficacy of drug effectiveness and treatment protocols could also be evaluated based on the effects of the gut ecology on a known microbiome.
In some embodiments, probes are selected for constructing special purpose systems including those with arrays or microparticles. Typically, special purpose “low density” systems, are designed for use in a specific environment or for a particular application and usually feature a reduced number of probes, “down-selected” probes, that are specific to organisms that are known or expected to be present in the particular environment, such as associated with a particular biosignature. In some cases the biosignature is fecal contamination. Typically, a low density system comprises no more than 10, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000 or 10,000 down selected probes or 5, 10, 25, 50, 100, 250, 500, 1,000, 2,500 or 5,000 down selected probes probe pairs (PM and MM probes). In some embodiments, only 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 probes are used per OTU. In further embodiments, only PM probes are used. Generally, these down-selected probes have robust hybridization signals and few or no cross hybridizations. In some embodiments, the collection of down selected probes have a median cross hybridization potential number of less than 20, 15, 10, 8, 7, 6, 5, 4, 3, 2, or 1 per probe. Frequently the down selected probes belong to OTUs that have reduced numbers of probes. In some embodiments, the OTUs of a down select probe collection have a median number of less than 25, 20, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3 or 2 probes per OTU. Generally, low density systems feature probes that recognize no more than 10, 25, 50, 100, 250, 500, 1,000, 2,000, or 5,000 taxa. For a set number of probes, a number of design strategies can be employed for low density systems. One approach is to maximize the number of OTUs identified, e.g., use one probe per OTU with no mismatch probes. Another approach is to select probes based on the desired confidence level. Here, multiple probes for each OTU along with corresponding mismatch probes may be required to achieve at least 95% confidence level for the presence and quantity of each OTU. The probes for a particular low density application can be selected by applying a sample from an appropriate environment to a high density analysis system, e.g., a detection system that can in a single assay determine the probability of the presence or quantity of at least 10,000, 20,000, 30,000, 40,000, 50,000, 60,000, 70,000, 80,000, 90,000, 100,000, 250,000, 500,00 or 1,000,000 OTUs of a single domain, such as bacteria, archea, or fungi, or alternatively, for each known OTU of a single domain. Probes associated with prevalent OTUs can be selected for a low density system. Alternately, the OTUs seen in a sample of interest can be compared with a control sample and shared OTUs subtracted out with the probes associated with the remaining OTUs selected for the low density system. Additionally, probes can be selected based on a change in prevalence of OTUs between the environment of interest and a control environment. For example, OTUs that are at least 2-fold 5-fold, 10-fold, 100-fold or 1,000-fold more abundant in the sample of interest compared to the control sample are included in the down selected probe set. Using this information, a down selected array, bead multiplex system or other low density assay system is designed.
“Low density” assays systems can be used to identify select microorganisms and determine the percentage composition of various select microorganisms in relation to each other. Low density assay systems can be constructed using probes selected through the disclosed methodologies. These low density systems can identify at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 500, 1000 or more microorganisms. Representative microorganisms to be identified and optionally quantified are listed in Table 7. Additional representative microorganisms to be identified and optionally quantified are listed in Tables 3-5.
Listeria monocytogenes
Salmonella enterica subsp.
enterica serovar
Enteritidis
Pseudomonas aeruginosa
Low density assays systems are useful for numerous environmental and clinical applications. Exemplary applications are listed in Table 7. Medical conditions that can be identified, diagnosed, prognosed, tracked, or treated based on data obtained with a low density system include but are not limited to, cystic fibrosis, chronic obstructive pulmonary disease, Crohn's Disease, irritable bowel syndrome, cancer, rhinitis, stomach ulcers, colitis, atopy, asthma, neonatal necrotizing enterocolitis, obesity, periodontal disease and any disease or disorder caused by, aggravated by or related to the presence, absence or population change of a microorganism. Through the judicious selection of OTUs to be included in a system, the system becomes a diagnostic device capable of diagnosing one or more conditions or diseases with a high level of confidence producing very low rates of false positive or false negative readings.
In some embodiments, the low density systems also feature confirmatory probes that are specific (complimentary) for genes or sequences expressed in specific organisms. For example, the call virulence gene of Yersinia pestis and the zonula occludens toxin (zot) gene of Vibrio cholerae and also confirmatory probes to Y. pestis or V. cholerae.
As used herein a “kit” refers to any delivery system for delivering materials or reagents for carrying out a method of the invention. In the context of assays, such delivery systems include systems that allow for the storage, transport, or delivery of arrays or beads with probes, reaction reagents (e.g., probes, enzymes, etc. in the appropriate containers) and/or supporting materials (e.g., buffers, written instructions for performing the assay etc.) from one location to another. For example, kits include one or more enclosures (e.g., boxes) containing the relevant reaction reagents and/or supporting materials for assays of the invention.
In one aspect of the invention, kits for analysis of nucleic acid targets are provided. According to one embodiment, a kit includes a plurality of probes capable of determining the presence or quantity over 10, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 30,000, 40,000 50,000 or 60,000 different OTUs in a single assay. Such probes can be coupled to, for example, an array or plurality of microbeads. In some aspects a kit comprises at least 5, 10, 15, 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 50,000, 100,000, 200,000, 500,000, 1,000,000 or 2,000,000 interrogation probes selected using the disclosed methodologies and/or for use in the identification and/or comparison of a biosignature of one or more samples.
The kit can also include reagents for sample processing. In some embodiments, the reagents comprise reagents for the PCR amplification of sample nucleic acids including primers to amplify regions of a highly conserved sequence, such as regions of the 16S rRNA gene. In some embodiments, the reagents comprise reagents for the direct labeling of RNA, such as rRNA. In further embodiments, the kit includes instructions for using the kit. In other embodiments, the kit includes a password or other permission for the electronic access to a remote data analysis and manipulation software program. Such kits will have a variety of uses, including environmental monitoring, diagnosing disease, monitoring disease progress or response to treatment, and identifying a contamination source and/or the presence, absence, or amount of one or more contaminants.
The method or system may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. The method or system may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network.
With reference to
Components of computer 102 may include, but are not limited to, a processing unit 104, a system memory 106, and a system bus 108 that couples various system components including the system memory to the processing unit 104.
Computer 102 typically includes a variety of computer readable media. Computer readable media includes both volatile and nonvolatile media, removable and non-removable media and a may comprise computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.
The system memory 106 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 110 and random access memory (RAM) 112. A basic input/output system 114 (BIOS), containing the basic routines that help to transfer information between elements within computer 102, such as during start-up, is typically stored in ROM 110. RAM 112 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 104.
The computer 102 may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only,
The drives and their associated computer storage media discussed above and illustrated in
The computer 102 can be integrated into an analysis system, such as a microarray or other probe system described herein. Alternatively, the data generated by an analysis system can be imported into the computer system using various means known in the art.
The computer 102 may operate in a networked environment using logical connections to one or more remote computers or analysis systems. The remote computer may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 102. The logical connections depicted in
In further aspects of the invention, computer-implemented methods are provided for analyzing the presence or quantity of over 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 30,000, 40,000 50,000 or 60,000 different OTUs in a single assay. In one embodiment, computer executable logic is provided for determining the presence or quantity of one or more microorganisms in a sample comprising: logic for analyzing intensities from a set of probes that selectively binds each of at least 20, 50, 100, 200, 500, 1,000, 2,000, 5,000, 10,000, 20,000, 30,000, 40,000 50,000 or 60,000 unique and highly conserved polynucleotides and determining the presence of at least 97% of all species present in said sample with at least 90%, 95%, 96%, 97%, 98%, 99% or 99.5% confidence level.
In one embodiment, computer executable logic is provided for determining probability that one or more organisms, from a set of different organisms, are present in a sample. The computer logic comprises processes or instructions for determining the likelihood that individual interrogation probe intensities are accurate based on comparison with intensities of negative control probes and positive control probes; a process or instructions for determining likelihood that an individual OTU is present based on intensities of interrogation probes from OTUs that pass a first quantile threshold; and a process or instructions for penalizing one or more OTUs that have passed the first quantile threshold based on their potential for cross-hybridizing with other probes that have also passed the first quantile threshold.
In a further embodiment, computer executable logic is provided for determining the presence of one or more microorganisms in a sample. The logic allows for the analysis of a set of at least 1000 different interrogation perfect probes. The logic further provides for the discarding of information from at least 10% of the interrogation perfect match probes in the process of making the determination. In some embodiments, the computer executable logic is stored on computer readable media and represents a computer software product.
In other embodiments, computer software products are provided wherein computer executable logic embodying aspects of the invention is stored on computer media like hard drives or optical drives. In one embodiment, the computer software products comprise instructions that when executed perform the methods described herein for determining candidate probes.
In further embodiments, computer systems are provided that can perform the methods of the inventions. In some embodiments, the computer system is integrated into and is part of an analysis system, like a flow cytometer or a microarray imaging device. In other embodiments, the computer system is connected to or ported to an analysis system. In some embodiments, the computer system is connected to an analysis system by a network connection.
After processing, the results are stored in an exchangeable binary format for later use or sharing. Additionally, hybridization scores and OTU probability values may be exported to a tab delimited file or in a format compatible with UniFrac (Lozupone, et al., UniFrac—an online tool for comparing microbial community diversity in a phylogenetic context, BMC Bioinformatics, 7, 371; 2006) for further statistical analysis of the detected sample communities.
In some embodiments, multiple, interactive views of the data are available, including taxonomic trees, heatmaps, hierarchical clustering, parallel coordinates (time series), bar plots, and multidimensional scaling scatterplots. In some embodiments, the taxonomy tree displays the mean intensities for each detected OTU and displays the leaves of the tree as a heatmap of samples. The tree may be dynamically pruned by filtering OTUs below a certain intensity or probability threshold. Additionally, the tree may be summarized at any level from phylum to subfamily. In other embodiments, the user can hierarchically cluster both OTUs and samples using any of the standard distance and linkage methods from the integrated C Clustering Library (de Hoon, et al., Open source clustering software, Bioinformatics, 20, 1453-1454; 2004), and the resulting dendrograms displayed in a secondary heatmap window. In some embodiments, a third window is provided that displays interactive bar plots of differential OTU intensities to facilitate pairwise comparison of samples. For any two samples, the height of the difference bars displays either the absolute or relative difference in mean intensity between OTUs. The bars may be grouped and sorted along the horizontal axis by any taxonomic rank for easy identification and comparison. Synchronized selection and filtering affords users the unique ability to seamlessly navigate between multiple views of the data. For example, users can select a cluster in the hierarchical clustering window and simultaneously view the selected organisms in the taxonomy tree, immediately revealing both their phylogenetic and environmental relationship. In further embodiments, the data from the analysis system, i.e., analysis system or flow cytometer, can be co-analyzed and displayed with high-throughput sequencing data. In some embodiments, for each organism identified as present in the sample, the user is able to view a list of other environments where the particular organism is found.
In some embodiments, the screen displays are dynamic and synchronized to allow the selection or filtration of OTUs with changes to any view simultaneously reflected in all other views. Additionally, OTUs confirmed by 16S rRNA gene, 18S rRNA gene, or 23S rRNA gene sequencing can be co-displayed in all views.
In some aspects of the invention, a business method is provided wherein a client images an array or scans a lot of microparticles and sends a file containing the data to a service provider for analysis. The service provider analyzes the data and provides a report to the user in return for financial compensation. In some embodiments, the user has access to the service provider's analysis system and can manipulate and adjust the analysis parameters or the display of the results.
In another aspect of the invention, a business method is provided wherein a client sends a sample to be processed, imaged or scanned and the data analyzed for the presence or quantity of organisms. The service provider sends a report to the client in return for financial compensation. In some embodiments of the invention, the client has access to a suite of data analysis and display programs for the further analysis and viewing of the data. In further embodiments, the service provider first provides a system or kit to the client. The kit can include a system to assay a majority, or the entirety of the microbiome present or the system can contain “down-selected” probes designed for particular applications. After sample processing and imaging, the client sends the data for analysis by the service provider. In some embodiments of the invention, the client report is electronic. In other embodiments, the client is provided access to a suite of data analysis and display programs for the further viewing, manipulation, comparison and analysis of the data. In some embodiments, the client is provided access to a proprietary database in which to compare results. In other embodiments, the client is provided access to one or more public databases, or a combination of private and public database for the comparison of results. In some embodiments, the proprietary database includes the pooled results (fingerprints, biosignatures) for normal samples or the pooled results from particular abnormal situations such as a disease state. In some embodiments, the biosignatures are continuously and automatically updated upon receipt of a new sample analysis.
In some embodiments, the database further comprises highly conserved sequence listings. In some embodiments, the database is updated automatically as new sequence information becomes available, for instance, from the National Institutes of Health's Human Microbiome Project. In further embodiments, probe sets are automatically updated based on the new sequence information. Continuous upgrading of the sequence information and refinement of the probe sets allow for increasing accuracy and resolution in determining the composition of microbiomes and the quantity of their individual constituents. In some embodiments, the system compares earlier microbiome biosignatures with later microbiome biosignatures from the same or substantially similar environments and analyzes the changes in probe set composition and hybridization signal analysis parameters for information that is useful in improving or refining the discrimination between related OTUs, identification and quantification of microbiome constituents, or increasing accuracy of the determinations.
In some embodiments, the database compiles information about specific microbiomes, for example, the microbiota associated with healthy and unhealthy human intestinal microflora including, age, gender and general health status of host, geographical location of host, host's diet (i.e., Western, Asian or vegetarian), water source, host's occupation or social status, host's housing status.
In some embodiments, the reference healthy/normal signatures for adults, male and female, and children can be used as benchmarks to identify presymptomatic and symptomatic disease states, response to treatments/therapies, infection, and/or secondary infection associated with disease.
In some embodiments, the client is provided with a diagnosis or treatment recommendation based on the comparison between the client's sample microbiome and one or more reference microbiome.
The following examples are given for the purpose of illustrating various embodiments of the invention and are not meant to limit the present invention in any fashion. The present examples, along with the methods described herein are presently representative of preferred embodiments, are exemplary, and are not intended as limitations on the scope of the invention. Changes therein and other uses which are encompassed within the spirit of the invention as defined by the scope of the claims will occur to those skilled in the art.
Following sample preparation, application, incubation and washing, using standard techniques, PhyloChip G3 arrays were scanned using a GeneArray Scanner from Affymetrix. The scan was captured as a pixel image using standard AFFYMETR1X® software (GCOS v1.6 using parameter: Percentile v6.0) that reduces the data to an individual row in a text-encoded table for each probe. See Table 8.
Each analysis system had approximately 1,016,000 cells, with 1 probe sequence per cell. The analysis system scanner recorded the signal intensity across the array, which ranges from 0 to 65,000 arbitrary units (a.u) in a regular grid with −30-45 pixels per cell. A 2 pixel margin was used between adjacent cells, leaving approximately 25-40 pixels per probe of usable signal. From these pixels, the AFFYMETR1X® software computed the 75th percentile average pixel intensity (denoted as the “MEAN”), the standard deviation of signal intensity among the about 25-40 pixels (denoted as the “STDV”), and the number of pixels used per cell (denoted as “NPIXELS”). Any cells that had pixels that were three standard deviations apart in signal intensity were classified as outliers.
The analysis systems were divided into a user-defined number of horizontal and vertical divisions. By default, four horizontal and four vertical divisions were created resulting in 16 regularly spaced sectors for independent background subtraction. The background intensity was computed independently for each quadrant, as the average signal intensity of the least intense 2% (by default) of probes in that quadrant. The background intensity was then subtracted from all probes before further computation.
The noise value was estimated according to recommendations in the AFFYMETRIX® GeneChip User Guide v3.3. Noise (N) was due to variations in pixel intensity signals observed by the scanner as it read the array surface and was calculated as the standard deviation of the pixel intensities within each of the identified background cells divided by the square root of the number of pixels comprising that cell. The average of the resulting quotients was used for N in the calculations described below:
The intensities of all probes were then scaled so that the average observed signal intensity of the spiked in probes had a pre-determined signal strength. This was accomplished by finding a scaling factor (Sf) in order to force the mean response of the corresponding PM probes to a target mean using the equation below:
Typically, the pre-determined signal strengths ranged from about 0 to about 65,000. Once the scaling factor was derived, all cell intensities were multiplied by the scaling factor.
The noise (N) was scaled by the same factor: Ns=N×Sf; where Ns=scaled noise, N=unscaled noise, and Sf=scaling factor.
As an alternative or optional step, MM probes with high hybridization signal responses were identified and the probe pair eliminated where:
After classifying an OTU as “present”, the present call was propagated upwards through the taxonomic hierarchy by considering any node (subfamily, family, order, etc.) as ‘present’ if at least one of its subordinate OTUs was present.
Hybridization intensity was the measure of OTU abundance and was calculated in arbitrary units for each probe set as the trimmed average (maximum and minimum values removed before averaging) of the PM minus MM intensity differences across the probe pairs in a given probe set.
The dry weather water flow in the lower Mission Creek and Laguna watersheds of Santa Barbara, Calif., a place associated with elevated fecal indicator bacteria concentrations and human fecal contamination will be sampled with an array of the present invention. The goal is to characterize whole bacterial community composition and biogeographic pattern in an urbanized creek, 2) compare taxa detected by molecular methods to conventional fecal indicator bacteria, and 3) elucidate reliable groups of bacterial taxa to be used in culture-independent community-based fecal contamination monitoring (indicator species for fecal contamination).
The watersheds flow through an urbanized area of downtown Santa Barbara. Places to be sampled include storm drains, sections of the flowing creek, lagoon (M2, M4) and ocean. Additionally sites include where Old Mission Creek tributary discharges into Mission Creek. The dry creek flow can have many sources including underground springs in the upstream reaches, urban runoff associated with irrigation and washing, groundwater seepage, sump or basement pumps, and potentially illicit sewer connections. Sampling will be done during a period when there will not have been rain for at least 48 hours prior to or during the sampling. Besides the watershed samples, human feces and sewage will be sampled.
Sample description, collection and extraction. Water samples are collected over 3-5 days from a watershed during a period of dry weather. Additionally, fecal samples including human feces sewage inflow are collected. Dissolved oxygen (DO), pH, temperature and salinity are measured along with each sampling. Water samples are filtered in the lab on 0.22 pm filters and extracted for DNA using the UltraClean Water DNA kit (MoBio Laboratories), and archived at −20° C. Concentrations (by IDEXX) of Total Coliforms, E. coli, and Enterococcus spp., as well as quantitative PCR (qPCR) measurements of Human-specific Bacteroides Marker (HBM) are also performed.
16S rRNA gene amplification for analysis system analysis. The 16S rDNA is amplified from the gDNA using non-degenerate Bacterial primers 27F.jgi and 1492R. Polymerase chain reaction (PCR) is carried out using the TaKaRa Ex Taq system (Takara Bio Inc, Japan). The amplification protocol is previously described (Brodie et al., Application of a High Density Oligonucleotide Analysis system Approach to Study Bacterial Population Dynamics during Uranium Reduction and Reoxidation. Applied Environ Microbio. 72:6288-6298, 2006).
Analysis system processing, and image data analysis. Analysis system analysis is performed using a high-density phylogenetic analysis system (PhyloChip). The protocols are previously reported (Brodie et al., 2006). Briefly, amplicons are concentrated to a volume less than 400 by isopropanol precipitation. The DNA amplicons are fragmented with DNAse, biotin labeled, denatured, and hybridized to the DNA analysis system at 48° C. overnight (>16 hr). The arrays are subsequently washed and stained. Arrays are scanned using a GeneArray Scanner (Affymetrix, Santa Clara, Calif., USA). The CEL files obtained from the Affymetrix software that produces information about the fluorescence intensity of each probe (PM, MM, and control probes) are analyzed using the CELanalysis software designed by Todd DeSantis (LBNL, Berkeley, USA).
PhyloChip data normalization. All statistical analyses are carried out in R (Team RCD (2008) R: A language and environment for statistical computing)). To correct for variation associated with quantification of amplicon target (quantification variation), and downstream variation associated with target fragmentation, labeling, hybridization, washing, staining and scanning (analysis system technical variation) a two-step normalization procedure is developed: First, for each PhyloChip experiment, a scaling factor best explaining the intensities of the spiked control probes under a multiplicative error model is estimated using a maximum-likelihood procedure. The intensities in each experiment are multiplied with its corresponding optimal scaling factor. In addition, the intensities for each experiment are corrected for the variation in total array intensity by dividing the intensities by its corresponding total array intensity separately for bacteria and archea.
Statistical Analysis. All statistical analyses were carried out in R. Bray-Curtis distances were calculated using normalized fluorescence intensity with the bcdist function in the ecodist package (Goslee S C & Urban D L (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22(7):1-19). Mantel correlation between Bray-Curtis distance matrices of community data, geographical distance and environmental variables are calculated using the mantel function in the vegan package. Pearson's correlation is calculated with 1000 permutations of the Monte Carlo (randomization) test. Non-metric multidimensional scaling (NMDS) is performed using the metaMDS function of the vegan package. A relaxed neighbor-joining tree is generated using Clearcut (Evans J, Sheneman L, & Foster J A (2006) Relaxed neighbor-joining: a fast distance-based phylogenetic tree. Construction method. Mol Evol 62:785-792.). Separate clearcut trees are generated for the ‘resident’ and ‘transient’ communities for each site. Unweighted UniFrac distances (Lozupone C & Knight R (2005) UniFrac: a new phylogenetic method for comparing microbial communities. Applied and Environmental Microbiology 71(12):8228-8235) are calculated for each of the sites.
Fecal Taxa. Taxa that are present in all three fecal samples, and in all 27 water samples are tabulated separately. The list of ‘Fecal Taxa’ is derived by removing those taxa found in all water samples from the taxa that are present in all three fecal samples.
Transient and resident subpopulations. Taxa that are present in at least one sample from each site across the sampling period are tabulated and variances of the fluorescence intensities for those taxa are generated. The taxa in the top deciles are defined as the ‘transient’ subpopulation, and taxa in the bottom deciles were defined as the ‘resident’ subpopuation.
BBC:A. The number of taxa in the classes of Bacilli, Bacteroidetes, Clostridia, and a-proteobacteria are tallied. The ratio is calculated using the following formula:
The count for unique taxa in each of the class is normalized by dividing by the total taxa in each class detected by the analysis system.
Aligned sequences from published studies are downloaded from Greengenes (DeSantis T Z, et al. (2006) Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Applied and Environmental Microbiology 72(7):5069-5072) and re-classified using PhyloChip taxonomy. The counts of unique taxa are tallied for each Bacterial class. BBC:A are calculated using the formula above. If no taxon is detect for a class, the count for the class is set as 0.5.
Mission Creek samples are delineated into three habitat types: ocean, estuarine lagoon, and fresh water (creeks and storm drain effluent). Bray-Curtis distances of the watershed samples and three fecal samples (two sewage and one human feces) are calculated. Non-metric multidimensional scaling (NMDS) ordination and plotting of the first two axes are used to display the distances between samples. Bacterial communities are clearly separated by habitat types. The drain samples are most similar to the fecal samples. Lagoon samples are most similar to the ocean samples.
Signature taxa that account for the majority of differences in bacterial communities observed between habitats are identified by comparing the detected taxa at the class level among all habitat types. The number of taxas in each habitat type are divided by the total detected for each sample type to obtain a percent detection. Comparing the fecal samples to samples taken above the urban zone or those from the lagoon or ocean show that there are lower fractions of α-proteobacteria and higher fractions of Bacilli and Clostridia. Moreover, five classes are only detected in the fecal samples: Solibacteres, Unclassified Acidobacteria, Chloroflexi-4, Coprothermobacteria and Fusobacteria. Chloroflexi-3 are only detected in creek samples, and Thermomicrobia, Unclassified Termite group 1, and Unclassified Chloroflexi only in the ocean samples. The top 10 classes with the highest standard deviations across the four habitats are (in descending order): Clostridia, α-proteobacteria, Bacilli, γ-proteobacteria, β-proteobacteria, Actinobacteria, Flavobacteria, Bacteroidetes, Cyanobacteria, and c-proteobacteria. Of those classes, Clostridia, Bacilli, and Bacteroidetes fractions are higher, but a-proteobacteria fractions were lower. These four taxa can be used as indicators of fecal contamination.
Subpopulations of taxa are identified that fluctuate the most between samplings. These are term “transient” populations. Populations that remain stable the sampling period are term “resident” populations. A comparison of taxa found in the “transient” and “resident” subpopulations illustrate differences in community composition from site to site. The six major orders (Enterobacteriales, Lactobacillales, Actinomycetales, Bacteroidales, Clostridiales and Bacillales) of the Fecal Taxa are compared to further dissect the distribution of fecal bacteria over time. The number of transient Enterobacteriales in samples from some sites are extremely high compare to the rest of the sites. While others have high resident subpopulations of Bacillales. Bacteria are identified that are ubiquitous and not affected by changes in the environmental variables measured, as measured by PhyloChip. Bacteria classes that have similar numbers of taxa throughout the watershed and fecal samples included Verrucomicrobiae, Planctomycetacia, α-proteobacteria, Anaerolinaea, Acidobacteria, Sphingobacteria, and Spirochaetes
Bacilli, Bacteroidetes and Clostridia to α-proteobacteria Ratio
Four bacterial classes: Bacilli, Bacteroidetes, Clostridia and α-Proteobacteria are identified as having the highest variance among the habitat types and are further developed as fecal indicators.
The combined percentage of Bacilli, Bacteroidetes and Clostridia represent about 20-35% of total classes detected in the fecal samples, whereas their percentages at sites with expected cleaner water such as creek, lagoon and ocean are less than 10-15%. At least 45% of the taxa detected in creek water, lagoon and ocean samples are α-Proteobacteria. These microorganisms were classified as Clean Water Taxa as the percentage of Proteobacteria found in fecal samples is significantly lower at about 35-45%. The ratio of Bacilli, Bacteroidetes and Clostridia to α-proteobacteria (BBC:A) for fecal samples is about 3-5-fold higher than the ratios found in other habitat types. The BBC:A ratios are calculated for each site, and exhibit the same pattern as Fecal Taxa counts across all sites with ocean water having the lowest BBC:A of about 0.75-0.90 with samples close to observed sites of fecal contamination at around 1.50 to about 1.90.
This ratio contains non-coliform associated bacteria, and avoids the potential of false positive fecal detection due to growth of coliforms in the environment. Bacteroidetes and Clostridia are well known fecal-associated anaerobic bacteria. Bacilli are not especially fecal-associated but have been found in aerobic thermophilic swine wastewater bioreactors (Juteau P, Tremblay D, Villemur R, Bisaillon J G, & Beaudet R (2005) Analysis of the bacterial community inhabiting an aerobic thermophilic sequencing batch reactor (AT-SBR) treating swine waste Applied Microbiology and Biotechnology 66:115-122.). Therefore, the presence of Bacilli, Bacteroides and Clostridiales is a good indication of wastewater-, waste treatment-, and human-derived fecal pollution. α-proteobacteria are mostly phototrophic bacteria that are abundant in the environment, and play key roles in global carbon, sulfur and nitrogen cycles. Many α-proteobacteria thrive under low-nutrient conditions, and will be a good proxy for non-fecal bacteria found in non contaminated aquatic environments.
The results compare well to BBC:A found in other fecal-associated sources that are analyzed by the PhyloChip with mouse cecum, cow colon, sewage contaminated groundwater, human colon, and secondary sewage. These sources have BBC:A of above 1.2. In contrast, anaerobic groundwater has a BBC:A of 0.80-0.99.
To confirm the value of the BBC:A ratio for detecting fecal contamination, published studies of bacterial communities obtained by sequencing are analyzed. Ratios from mammalian guts, anaerobic digester sludge, ocean, Antarctic lake ice, and drinking water also demonstrate that there are differences between fecal and non-fecal samples. Mammalian gut samples have BBC:A ranging from about 10 to about 260. Anaerobic digester sludge samples have BBC:A of at least 1 to about 10. These results may reflect the highly-selected community in anaerobically-digested waste activated sludge in wastewater treatment. Non-fecal samples have BBC:A from 0 to 0.94. The sequencing results confirm that a BBC:A threshold of 1.0 can be used as a cutoff for identifying fecal pollution in water with values of 1 and above indicating polluted water. This method of calculating a BBC:A value offers numerous advantages including speed, as culturing is not required, greater detection ability as it can detect microorganisms that are currently unculturable and also avoids expense and technical problems associated with PCR cloning and high through-put sequencing.
The BBC:A ratio can be used to track the source of fecal pollution as the number usually increases in samples obtained from sites closer to a source of fecal pollution.
An array system, “PhyloChip”, was fabricated with some of the organism-specific and OTU-specific 16s rRNA probes selected by the methods described herein. The PhyloChip array consisted of 1,016,064 probe features, arranged as a grid of 1,008 rows and columns. Of these features, −90% were oligonucleotide PM or MM probes with exact or inexact complementarity, respectively, to 16s rRNA genes. Each probe is paired with a mismatch control probe to distinguish target-specific hybridization from background and non-target cross-hybridization. The remaining probes were used for image orientation, normalization controls, or for pathogen-specific signature amplicon detection using additional targeted regions of the chromosome. Each high-density 16s rRNA gene microarray was designed with additional probes that (1) targeted amplicons of prokaryotic metabolic genes spiked into the 16s rRNA gene amplicon mix in defined quantities just prior to fragmentation and (2) were complementary to pre-labelled oligonucleotides added into the hybridization mix. The first control collectively tested the target fragmentation, labeling by biotinylation, array hybridization, and staining/scanning efficiency. It also allowed the overall fluorescent intensity to be normalized across all the arrays in an experiment. The second control directly assayed the hybridization, staining and scanning.
Complementary targets to the probe sequences hybridize to the array and fluorescent signals were captured as pixel images using standard AFFYMETRIX® software (GeneChip Microarray Analysis Suite, version 5.1) that reduced the data to an individual signal value for each probe and was typically exported as a human readable CEL' file. Background probes were identified from the CEL file as those producing intensities in the lowest 2% of all intensities. The average intensity of the background probes was subtracted from the fluorescence intensity of all probes. The noise value (N) was the variation in pixel intensity signals observed by the scanner as it reads the array surface. The standard deviation of the pixel intensities within each of the identified background probe intensities was divided by the square root of the number of pixels comprising that feature. The average of the resulting quotients was used for N in the calculations described below.
Using previous methods, probe pairs scored as positive are those that meet two criteria: (i) the fluorescence intensity from the perfectly matched probe (PM) was at least 1.3 times greater than the intensity from the mismatched control (MM), and (ii) the difference in intensity, PM minus MM, was at least 130 times greater than the squared noise value (>130 N2). The positive fraction (PosFrac) was calculated for each probe set as the number of positive probe pairs divided by the total number of probe pairs in a probe set. An OTU was considered ‘present’ when its PosFrac for the corresponding probe set was >0.92 (based on empirical data from clone library analyses). Replicate arrays cuold be used collectively in determining the presence of each OTU by requiring each to exceed a PosFrac threshold. Present calls were propagated upwards through the taxonomic hierarchy by considering any node (subfamily, family, order, etc.) as ‘present’ if at least one of its subordinate OTUs was present.
Hybridization intensity was the measure of OTU abundance and was calculated in arbitrary units for each probe set as the trimmed average (maximum and minimum values removed before averaging) of the PM minus MM intensity differences across the probe pairs in a given probe set. All intensities<1 were shifted to 1 to avoid errors in subsequent logarithmic transformations.
The analysis methods described in Example 1 can also be applied to a sample that has been applied to the presently described PhyloChip G3 array.
A Latin Square Validation was carried out on the PhyloChip G3 array. The novel PhyloChip microarray (G3) was manufactured containing multiple probes for each known Bacterial and Archaeal taxon. The array was challenged with triplicate mixtures of 26 organisms combined in known but randomly assigned concentrations spanning over several orders of magnitude using a Latin Square experimental design. Probe-target complexes were quantified by flourescence intensity. To monitor community dynamics within the environment, water samples were taken from the San Francisco Bay (CA) at two time points following a point-source sewage spill. Entire 16S rRNA gene amplicon pools (−100 billion molecules/time point) were evaluated with the array. Three replicates were tested on different days with 78 Latin Square chips and 1 Quantitative Standards only control. The amplicon concentration range was >4.5 log10. The target concentration was from 0.25 pM to 477.79 pM, increasing 37% per step plus a 0 pM (26 different concentrations). Each chip contained all 26 targets, each with a different concentration 0-66 ng each for 243 ng total spike. The Latin Square matrix is not shown.
The validation shows that the novel PhyloChip G3 array is capable of excellent organism detection and quantification in a sample over the prior G2 array.
This example describes profiling of airway bacterial communities of a cohort of patients with chronic obstructive pulmonary disease (COPD), using apparatuses and methods of the invention.
Subject selection and sample collection. Potential subjects for this study were screened from a database of airway specimens collected between August 2004 and April 2006 from mechanically ventilated patients admitted to the intensive care units at Moffitt-Long Hospital (University of California, San Francisco), who were enrolled in a study of Pseudomonas aeruginosa in intubated patients (Flanagan et al., 2007, J. Clin Microbiol 45: 1954-1962). Subjects admitted to the ICU with a primary diagnosis of “COPD exacerbation” were identified for inclusion in this study. Available endotracheal aspirates (ETAs) from eight patients were processed for 16S rRNA PhyloChip analysis, as described in the herein and also detailed below. To compare results from PhyloChip analysis with conventional clinical cultures, results were obtained for quantitative clinical laboratory bacterial cultures (blood agar, chocolate agar, and EMB media) performed on minibronchoalveolar lavage (m-BAL) airway samples, collected within 1-5 days of the ETA specimen analyzed by PhyloChip, as previously described (Flanagan et al., 2007). In general, m-BALs possess a similar bacterial community composition to that of ETAs obtained concurrently from the same patient. Clinical data (Table 1) were recorded in a secure database, including whether a diagnosis of pneumonia by conventional clinical and radiologic criteria was made during the patient's hospitalization and the time frame between diagnosis and collection of airway samples. The Committee on Human Research at UCSF approved all study protocols, and all patients or their surrogates provided written, informed consent.
amini-BAL, minibronchoalveolar lavage clinical culture. The most recent, available culture data were obtained from within 1-5 days prior to the endotracheal aspirate sample analyzed by PhyloChip.
bDetected by PhyloChip; *≧10,000 colony-forming units on quantitative mini-BAL culture. PA, Pseudomonas aeruginosa; KP, Klebsiella pneumoniae; SA, Staphylococcus aureus; EA, Enterobacter aerogenes; SM, Stenotrophomonas maltophilia; AF, Aspergillus fumigatus.
DNA extraction, 16S rRNA gene amplification, PhyloChip processing. Total DNA eas extracted from ETAs (200 μL) using a bead-beating step (5.5 ms−1 for 30 seconds, FastPrep system) (MP Biomedicals, Cleveland, Ohio) prior to nucleic acid extraction using the Wizard Genomic DNA Purification kit (Promega, Madison, Wis.). Twelve, 25-cycle PCR reactions, containing 100 ng of DNA, 2.5 mM each of dNTPs, 1.5 μM each primer (Bact-27F and Bact-1492R) and 0.02 U/μL of ExTaq (Takara Bio, Japan), were performed for each sample across a gradient of annealing temperatures (48-58° C.), to maximize the diversity recovered. The resulting PCR products were pooled and gel-purified using the MinElute Gel Extraction kit (Qiagen, Chatsworth, Calif.). Known concentrations of synthetic 16S rRNA gene fragments and non-16S rRNA gene fragments were spiked into the pooled, purified PCR product, which served as internal standards for normalization. A total of 250 ng of purified PCR product per sample was fragmented, biotin-labeled, and hybridized to the microarray as described in Example 2. Washing, staining, and scanning of arrays were conducted according to standard Affymetrix protocols. Background subtraction, noise calculations and scaling were carried out as described in Examples 1 and 2.
Analysis of PhyloChip data. Detection and quantification criteria for each taxon were applied, as described in Examples 1 and 2. Briefly, probe-pairs consisting of a perfectly matched and mismatched cross hybridization control probe (containing a mismatch at the 13th nucleotide) were scored as positive if they met two criteria: (1) the fluorescence intensity of the perfectly matched probe was times greater than that of the mismatched probe, and (2) the difference in intensity in each probe pair was 130 times greater than the squared noise value for that array. The positive fraction (pf)) of probe sets (minimum of 11, median of 24 probe-pairs per taxon) was calculated, and a taxon was considered “present” if the calculated pf was 90%. Statistical analyses were performed in the R environment (www.Rproject.org), using the ecological community analysis package vegan (version 1.16-1). Log-transformed fluorescence intensities were used to calculate Bray-Curtis dissimilarity measures of ecological distance. Nonmetric multidimensional scaling (NMDS), a nonparametric ordination method that maps community relatedness, in this case using the Bray-Curtis distance metric, was used to assess variability in bacterial community structure. The function adonis (Anderson, 2001, Aust. Ecol. 26: 32-46.), which conducts a matrix-based nonparametric analysis of variance, was applied to explore relationships between community composition and clinical variables, including age, gender, number of intubation days, presence of pneumonia, time frame between pneumonia diagnosis and sample collection, antibiotic and corticosteroid treatments, and survival to ICU discharge. Between-group differences in taxon abundance were assessed by two-tailed t-testing with significance adjusted for false discovery using q-values. Taxa exhibiting q values<0.05, a p-value ≦0.02 and a change of >1,000 fluorescence units (log-fold change in 16S rRNA copy number) were considered statistically and biologically significant. Phylogenetic trees were constructed using representative 16S rRNA sequences from the Greengenes database. A neighbor-joining tree with nearest-neighbor interchange was produced using FastTree (Price et al., 2009, Mol Biol Evol 26: 1641-1650) and uploaded to the Interactive Tree of Life project (itol.embl.de) for annotation.
Quantitative polymerase chain reaction (Q-PCR). To confirm that changes in array fluorescence intensities were reflective of changes in target organism abundance, triplicate, Q-PCR reactions were performed for selected taxa containing species of interest, using a Stratagene MxP3000 real-time system and the QuantiTect SYBR Green PCR kit (Qiagen). Primers for taxa containing selected species of interest were designed based on PhyloChip probes for the target taxon (Table 2). Reaction conditions included use of 10 ng of DNA extract and 40 cycles of using the annealing temperatures listed in Table 2 for each primer set. Regression analyses of inverse cycle threshold values plotted against PhyloChip fluorescence intensities were determined for each targeted taxon.
P. aeruginosa
S. maltophilia
H. cetorum
C. mucosalis
16S rRNA PhyloChip analysis identified a total of 1,213 bacterial taxa present in airway samples from COPD patients obtained during the course of acute exacerbations (the complete list is provided in Table 3). Despite recent or ongoing exposure to antibiotics across the group, the mean number of taxa detected in each sample was 411±246 taxa (SD). Identified taxa represented a diverse group of species belonging to 38 bacterial phyla and 140 distinct families (
Interpersonal variation in bacterial richness (number of taxa detected) was noted across the patient samples (
Given the variation in bacterial richness among samples, which suggested differences in bacterial community composition, NMDS was used to assess variation in bacterial community structure (based on Bray-Curtis dissimilarity measures) across the sample cohort. This revealed two distinct groups of patient samples and confirmed that patient 8 represented a structurally distinct airway community (
A common core of 75 bacterial taxa representing 27 classified bacterial families was identified in all patients analyzed (
Quantitative PCR was performed to validate that changes in reported array fluorescence intensities for targeted taxa correlated with changes in target species copy number for a selection of known airway pathogens (P. aeruginosa and Stenotrophomonas maltophilia) and two characteristic gastrointestinal organisms (Campylobacter mucosalis and Helicobacter cetorum). Regression analysis of species abundance determined by Q-PCR and array fluorescence intensity demonstrated strong concordance between the two independent methods for each target organism (Table 9), confirming their presence in these COPD airway samples and the ability of the array to accurately reflect changes in organism relative abundance.
P. aeruginosa
S. maltophilia
Campylobacter mucosalis
Helicobacter cetorum
This examples describes the characterization of bacterial microbiota of the airway microbiome around the time of acute COPD exacerbations.
Twenty-five sputum specimens collected over periods before, during, and after acute exacerbations in five patients were analyzed using a PhyloChip microarray, as described herein. Data was analyzed for changes in community diversity, relative abundance of individual OTUs, and association with clinical variables using repeated measures ANOVA, ordination and cluster analysis methods, and Spearman rank correlations, performed using R statistical software.
Three subjects had exacerbations deemed infectious-related by a clinician and treated with oral steroids plus antibiotics (e.g. azithromycin), while two individuals were treated with oral steroids and decongestants only. Five time points per subject were analyzed, spanning a pre-exacerbation clinically stable period (range: 12-126 days before exacerbation onset), at exacerbation before start of new treatments, and post-exacerbation when the subject was clinically stable/improved (range: 25-70 days after exacerbation onset). There were significant changes in bacterial diversity over time across all subjects (p=0.018), which also correlated strongly with clinical symptom scores (Spearman rho=0.5, p≦0.01). Greatest bacterial diversity was observed in samples from at the onset of exacerbation, particularly in subjects with ‘infectious-related’ events and in whom diversity decreased significantly following antibiotic therapy. Leading up to exacerbation, increased diversity was reflected by all of the following: 1) significant changes in the relative abundance of multiple, existing (i.e. detected in a previous sample from the subject) bacterial OTUs; 2) expansions in existing OTUs or classes of bacteria through the addition of new members; and 3) the appearance of new OTUs not previously detected in the subject. Microarray analysis confirmed culture-based identification and increased abundance of individual species previously implicated in exacerbations. However, members of additional OTUs, including other potentially pathogenic species, demonstrated contemporaneous shifts in relative abundance, including the Enterobacteriaceae family, Actinobacteria and Clostridia classes of bacteria.
In this example, methods and apparatus of the present invention are used to determine the biosignature and disease state of a subject having an unknown medical condition. A sample can be collected and nucleic acid extracted as described in Example 4. The sample is then tested for the presence, absence, and/or quanitity of OTUs using an array as described in Example 4. The resulting biosignature is then compared to biosignatures for numerous conditions, such as COPD exacerbation, such as a biosignature as determined in Example 4. Based on a comparison of the biosignatures, a clinician makes a diagnosis of healthy, exacerbated COPD, non-exacerbated COPD, or intermediate exacerbated COPD. Based on the diagnosis, and/or on the biosignature, the then prescribes a treatment for the condition, such as a therapeutic compound.
While preferred embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed in practicing the invention. It is intended that the following claims define the scope of the invention and that methods and structures within the scope of these claims and their equivalents be covered thereby.
Acidobacterium capsulatum
Varibaculum cambriense str. CCUG 44998
Actinomyces naeslundii
Actinomyces odontolyticus str. CCUG 28084
Saccharothrix texasensis str. NRRL B-16107T
Bifidobacterium psychraerophilum str. T16
Bifidobacterium pseudocatenulatum str. JCM1200
Bifidobacterium adolescentis str. E-981074T
Bifidobacterium thermacidophilum porcinum subsp. suis
Bifidobacteriaceae genomosp. C1
Bifidobacterium breve str. KB 92
Cellulomonas gelida str. DSM 20111T
Beutenbergia cavernosa str. DSM 12333
Atopobium vaginae VA14183_00
Corynebacterium xerosis str. DSM 20743
Corynebacterium tuscaniae str. ISS-5309
Corynebacterium jeikeium str. ATCC 43734
Corynebacterium mucifaciens National
Corynebacterium simulans National Microbiology
Corynebacterium tuberculostearicum str. CIP102346
Corynebacterium spheniscorum str. CCUG 45512
Brachybacterium nesterenkovii str. DSM 9573
Kineococcus aurantiacus str. IFO 15268
Microbacterium lacticum
Arthrobacter psychrolactophilus
Arthrobacter oxydans str. DSM 20119
Arthrobacter globiformis
Arthrobacter sp str. AC-51
Arthrobacter agilis str. DSM 20550
Arthrobacter nicotianae str. SB42
Citricoccus sp. str. 2216.25.22
Micrococcus luteus str. HN2-11
Rothia mucilaginosa str. DSM
Rothia dentocariosa str. ChDC B200
Rothia dentocariosa str. ATCC 17931
Kocuria roseus
Couchioplanes subsp. caeruleus str. IFO13939
Mycobacterium cf. xenopi ‘Hymi_Wue Tb_939/99’ str.
Mycobacterium holsaticum str. 1406
Mycobacterium pyrenivorans str. DSM 44605
Mycobacterium chelonae str. CIP 104535T
Mycobacterium tuberculosis str. NCTC 7416 H37Rv
Mycobacterium terrae str. ATCC 15755
Nocardia transvalensis str. DSM 43405
Streptomonospora salina str. YIM90002
Cellulosimicrobium cellulans str. NCIMB 11025
Promicromonospora sukumoe str. DSM 44121
Saccharomonospora azurea str. M. Goodfel
Nonomuraea polychroma str. IFO 14345
Actinomadura pelletieri str. IMSNU 22169T
Actinomadura fulvescens str. DSM 43923T
Georgenia muralis str. 1A-C
Jonesia quinghaiensis str. DSM 15701
Arthrobacter ureafaciens str. DSM 20126
Bacteroides distasonis
Bacteroides uniformis
Bacteroides fragilis str. YCH46
Blattabacterium species
Flexibacter japonensis str. IFO 16041
Microscilla arenaria str. IFO 15982
Flavobacterium aquatile
Tenacibaculum maritimum str. IFO 15946
Tenacibaculum ovolyticum str. IAM14318
Riftia pachyptila's tube clone R103-B20
Capnocytophaga gingivalis str. ChDC OS45
Capnocytophaga granulosa str. LMG 12119; FDC
Aequorivita antarctica str. QSSC9-14
Flavobacterium sp. str. V4.MS.29 = MM_2747
Cytophaga uliginosa
Psychroserpens burtonensis str. S2-64
Cytophaga sp. I-545
Cytophaga sp. I-1787
Microscilla sericea str. IFO 16561
Flexibacter tuber str. IFO 16677
Hongiella mannitolivorans str. IMSNU 14012 JC2050
Hymenobacter sp. str. NS/50
Flexibacter flexilis subsp. pelliculosus str. IFO
Cytophaga sp. str. BHI60-57B
Porphyromonas endodontalis str. ATCC 35406
Dysgonomonas wimpennyi str. ANFA2
Prevotella tannerae str. 29-1
Prevotella denticola str. ATCC 35308
Bacteroidaceae str. A42
Prevotella sp. str. E7_34
Sphingobacteriaceae str. Ellin160
Paralvinella palmiformis mucus secretions clone
Paralvinella palmiformis mucus secretions clone
Chlamydophila pneumoniae str. AR39
Chlorobium ferrooxidans DSM 13031 str. KofoX
Chlorobium phaeovibrioides str. 2631
Chlorobium limicola str. M1
Paralvinella palmiformis mucus secretions clone
Coprothermobacter sp. str. Dex80-3
Emiliania huxleyi str. Plymouth Marine Laborator
Cyanidium caldarium str. 14-1-1
Euglena tripteris str. UW OB
Lepocinclis fusiformis str. ACOI 1025
Adiantum pedatum
Calypogeia muelleriana
Mitrastema yamamotoi
Solanum nigrum
Epifagus virginiana - chloroplast
Pisum sativum - chloroplast
Synechococcus sp. str. PCC 6312
Oscillatoria sancta str. PCC 7515
Hapalosiphon welwitschii
Nodularia sphaerocarpa str. UTEX B 2093
Oscillatoria spongeliae str. 520 bg
Plectonema sp. str. F3
Thermus sp. str. C4
Vulcanithermus mediatlanticus str. TR
Aerococcus viridans
Abiotrophia defectiva str. GIFU12707
Abiotrophia para-adiacens str. TKT1
Trichococcus flocculiformis str. DSM 2094
Nostocoida limicola I str. Ben206
Marinilactibacillus psychrotolerans str. O21
Desemzia incerta str. DSM 20581
Carnobacterium alterfunditum
Trichococcus pasteurii str. KoTa2
Bacillus schlegelii str. ATCC 43741T
Bacillus algicola str. KMM 3737
Bacillus sp. str. 2216.25.2
Bacillus sp. str. SAFN-006
Bacillus vulcani str. 3S-1
Geobacillus thermocatenulatus str. DSM 730
Bacillus thermoleovorans
Geobacillus jurassicus str. DS1
Geobacillus thermoleovorans str. B23
Geobacillus stearothermophilus
Geobacillus stearothermophilus str. 46
Geobacillus stearothermophilus str. T10
Geobacillus thermodenitrificans str. DSM 466
Bacillus caldotenax str. DSM 406
Geobacillus sp. str. YMTC1049
Bacillus aeolius str. 4-1
Bacillus acidogenesis str. 105-2
Bacillus sporothermodurans str. M215
Bacillus niacini str. IFO15566
Bacillus siralis str. 171544
Bacillus senegalensis str. RS8; CIP 106 669
Bacillus firmus CV93b
Bacillus sp. 6160m-C1
Bacillus megaterium str. QM B1551
Bacillus pumilus str. S9
Pseudobacillus carolinae
Bacillus sp. str. TGS437
Bacillus subtilis str. IAM 12118T
Bacillus sp. str. TGS750
Bacillus mojavensis str. M-1
Bacillus sonorensis str. NRRL B-23155
Bacillus licheniformis str. KL-068
Bacillus licheniformis str. DSM 13
Bacillus subtilis subsp. Marburg str. 168
Bacillus subtilis
Bacillus luciferensis str. LMG 18422
Bacillus silvestris str. SAFN-010
Planococcus maritimus str. TF-9
Carnobacterium sp. str. D35
Caryophanon latum str. DSM 14151
Clostridium caminithermale str. DVird3
Tepidibacter thalassicus str. SC 562
Clostridium paradoxum str. DSM 7308T
Clostridium glycolicum str. DSM 1288
Clostridium papyrosolvens str. DSM 2782
Clostridium sp. str. JC3
Lachnospiraceae bacterium 19gly4
Clostridium novyi str. NCTC538
Clostridium acetobutylicum str. ATCC 824 (T)
Clostridium chauvoei str. ATCC 10092T
Clostridium sardiniense str. DSM 600
Enterococcus mundtii str. LMG 10748
Enterococcus saccharolyticus str. LMG 11427
Enterococcus ratti str. ATCC 700914
Vagococcus lutrae str. m1134/97/1; CCUG 39187
Tetragenococcus muriaticus
Enterococcus solitarius str. DSM 5634
Melissococcus plutonius str. NCDO 2440
Enterococcus cecorum str. ATCC43198
Enterococcus dispar str. LMG 13521
Erysipelothrix rhusiopathiae str. Pecs 56
Bacillus clausii str. GMBAE 42
Halobacillus yeomjeoni str. MSS-402
Halobacillus salinus str. HSL-3
Amphibacillus xylanus str. DSM 6626
Salibacillus sp. str. YIM-kkny16
Gracilibacillus sp. str. YIM-kkny13
Clostridium amygdalinum str. BR-10
Clostridium nexile
Butyrivibrio fibrisolvens str. LP1265
Butyrivibrio fibrisolvens str. OB156
Pseudobutyrivibrio ruminis str. pC-XS2
Butyrivibrio fibrisolvens str. NCDO 2249
Lachnospira pectinoschiza
Faecalibacterium prausnitzii str. ATCC 27766
Lactobacillus kitasatonis str. KM9212
Lactobacillus crispatus str. DSM 20584 T
Lactobacillus crispatus str. ATCC33197
Lactobacillus suntoryeus str. LH
Lactobacillus jensenii str. KC36b
Lactobacillus kalixensis str. Kx127A2; LMG
Lactobacillus reuteri str. DSM 20016 T
Lactobacillus frumenti str. TMW 1.666
Lactobacillus pontis str. LTH 2587
Lactobacillus fermentum str. MD-9
Pediococcus inopinatus str. DSM 20285
Pediococcus pentosaceus
Lactobacillus letivazi str. JCL3994
Lactobacillus suebicus str. CECT 5917T
Lactobacillus brevis
Lactobacillus paralimentarius str. DSM 13238
Lactobacillus saerimneri str. GDA154 LMG 22087
Lactobacillus subsp. aviarius
Lactobacillus salivarius str. RA2115
Lactobacillus cypricasei str. LMK3
Lactobacillus casei
Lactobacillus perolens str. L532
Lactobacillus sakei
Weissella koreensis S-5673
Leuconostoc ficulneum str. FS-1
Mycoplasma gypsbengalensis str. Gb-V33
Mycoplasma salivarium str. PG20(T)
Mycoplasma pulmonis str. UAB CTIP
Paenibacillus nematophilus str. NEM1b
Paenibacillus sp. str. MB 2039
Brevibacillus borstelensis str. LMG 15536
Brevibacillus sp. MN 47.2a
Ammoniphilus oxalaticus str. RAOx-FF
Ammoniphilus oxalivorans str. RAOx-FS
Selenomonas ruminantium str.JCM6582
Selenomonas ruminantium str.S20
Centipeda periodontii str. HB-2
Allisonella histaminiformans str. MR2
Veillonella dispar str. DSM 20735
Mogibacterium neglectum str. ATCC 700924
Finegoldia magna str. ATCC 29328
Peptostreptococcus sp. str. E3_32
Finegoldia magna
Peptoniphilus lacrimalis str. CCUG 31350
Anaerococcus vaginalis str. CCUG 31349
Bacillus sp. clone ML615J-19
Bacillus sp. str. C-59-2
Staphylococcus auricularis str. MAFF911484
Staphylococcus saprophyticus
Staphylococcus caprae str. DSM 20608
Staphylococcus haemolyticus str. CCM2737
Staphylococcus sp str. AG-30
Staphylococcus pettenkoferi str. B3117
Staphylococcus sciuri
Staphylococcus succinus str. SB72
Micrococcus luteus B-P 26
Macrococcus lamae str. CCM 4815
Lactococcus Il1403 subsp. lactis str. IL1403
Streptococcus equi subsp. zooepidemicus str.
Streptococcus agalactiae str. 2603V/R
Streptococcus bovis str. B315
Streptococcus salivarius str. ATCC 7073
Streptococcus macedonicus str. ACA-DC 206
Streptococcus thermophilus str. DSM 20617
Streptococcus downei str. ATCC 33748
Streptococcus bovis str.ATCC 43143
Streptococcus constellatus str. ATCC27823
Streptococcus bovis str. HJ50
Streptococcus gallinaceus str. CCUG 42692
Streptococcus suis str. 8074
Streptococcus cristatus str. ATCC 51100
Streptococcus mitis str. Sm91
Streptococcus mutans str. UA96
Streptococcus gordonii str. ATCC 10558
Thermoactinomyces sp. str. 700375
Ferribacter thermoautotrophicus
Desulfotomaculum thermobenzoicum str. DSM 6193
Ferribacter thermoautotrophicus str.
Desulfotomaculum thermoacetoxidans str. DSM
Desulfotomaculum solfataricum str. V21
Pelotomaculum sp. str. JT
Isobaculum melis CCUG 37660T
Leptotrichia amnionii str. AMN-1
Sneathia sanguinegens str. CCUG 41628T
Fusobacterium nucleatum subsp. vincentii str.
Cytophaga sp. str. Dex80-43
Cytophaga sp. str. Dex80-64
Bacillus sp. clone ML1228J-1
Gluconacetobacter europaeus str. ZIM B028 V3
Acidithiobacillus ferrooxidans str. D2
Acidithiobacillus albertensis str. DSM 14366
Acidithiobacillus ferrooxidans str. ATCC 19859
Aeromonas ichthiosmia
Aeromonas allosaccharophila str. CECT 4199
Aeromonas sp. PAR2A
Aeromonas culicicola str. MTCC 3249
Haemophilus piscium str. NCIMB 1952
Aeromonas sobria str. NCIMB 12065
Aeromonas molluscorum str. 849T
Alcaligenes defragrans str. PD-19
Alcaligenes faecalis str. M3A
Achromobacter subsp. denitrificans str. DSM
Alcaligenes faecalis 5659-H
Brackiella oedipodis str. LMG 1945 R8846
Alcaligenes sp. str. VKM B-2263 dcm6
Alcanivorax sp. str. K3-3 (MBIC 4323)
Alcanivorax sp. str. Haw1
Microbulbifer sp. str. JAMB-A94
Alteromonadaceae isolate str. LA50
Marinobacter lipolyticus str. SM-19
Marinobacter sp. str. SBS
Colwellia piezophila str. Y223G
Idiomarina loihiensis str. GSP37
Aestuariibacter salexigens str. JC2042
Agarivorans albus str. MKT 89
Alteromonas marina str. SW-47
Alteromonas stellipolaris str. LMG 21861
Alteromonadaceae clone PH-B55N
Pseudoalteromonas sp. str. Bdeep-1
Alteromonas sp. str. MS23
Pseudoalteromonas antarctica str. N-1
Alteromonas sp. str. NIBH P1M3
Pseudoalteromonas carrageenovora str. ATCC
Pseudoalteromonas sp. str. E36
Pseudoalteromonas agarivorans str. KMM 255
Pseudoalteromonas haloplanktis str. ATCC 14393
Pseudoalteromonas ruthenica str. KMM300
Alteromonas sp. str. NIBH P2M11
Pseudoalteromonas porphyrae str. S2-65
Pseudoalteromonas luteoviolacea str. NCIMB 1893T
Shewanella hanedai str. CIP 103207T
Moritella viscosa str. NVI 88/478T
Shewanella algae str. 43940
Shewanella algae str. ACM 4733
Shewanella gaetbuli str. TF-27
Psychromonas profunda str. 2825
Wolbachia pipientis
Wolbachia sp
Wolbachia sp. Dlem16SWol
Rhinocyllus conicus endosymbiont
Wolbachia pipientis
Bartonella schoenbuchensis str. R1
Bartonella quintana str. Toulouse
Bartonella henselae str. Houston-1
Scrippsiella trochoidea NEPCC 15
Beijerinckia indica
Methylosinus sporium
Methylosinus trichosporium
Methylocella tundrae str. Y1
Oligotropha carboxidovorans str. S23
Nitrobacter hamburgensis str. X14
Rhodopseudomonas palustris str. GH
Rhodopseudomonas palustris str. ATCC 17001
Afipia genosp. 4 str. G3644
Rhodopseudomonas rhenobacensis str. Klemme Rb
Bradyrhizobium japonicum HA1
Bradyrhizobium japonicum str. USDA 38
Bradyrhizobium elkanii str. USDA 76
Bradyrhizobium str. YB2
Afipia genosp. 2 str. G4438
Afipia genosp. 10 str. G8996
Bradyrhizobium elkanii str. SEMIA 6028
Bradyrhizobium sp. str. KKI14
Bradyrhizobium japonicum SD5
Bradyrhizobium japonicum str. IAM 12608
Ochrobactrum anthropi str. ESC1
Ochrobactrum gallinifaecis str. Iso 196
Burkholderia glathei str. ATCC 29195T
Burkholderia hospita str. LMG 20598T
Burkholderia sp.
Burkholderia caribensis str. MWAP71
Burkholderia caryophylli str. ATCC 25418
Riftia pachyptila's tube clone R103-B70
Arcobacter cryaerophilus
Sulfurospirillum deleyianum str. Spirillum 5175
Campylobacter sp. str. NO2B
Campylobacter gracilis
Campylobacter showae
Campylobacter subsp. fetus
Campylobacter helveticus
Campylobacter showae str. LMG 12636
Cardiobacterium hominis
Asticcacaulis excentricus str. ATCC15261
Brevundimonas intermedia str. MBIC2712
Brevundimonas vesicularis str. IAM 12105T
Brevundimonas diminuta str. DSM 1635
Brevundimonas diminuta str. IAM 12691T
Brevundimonas bacteroides str. CB7
Brevundimonas subvibrioides str. CB81
Brevundimonas sp. str. FWC40
Allochromatium sp. AT2202
Thiocapsa litoralis
Thiococcus sp. AT2204
Comamonas testosteroni str. SMCC B329
Xylophilus ampelinus str. ATCC 33914
Pseudomonas lanceolata str. ATCC 14669T
Delftia tsuruhatensis str. AD9
Variovorax paradoxus
Hydrogenophaga flava str. DSM 619T
Acidovorax sp. str. OS-6
Acidovorax konjaci str. DSM 7481
Acidovorax delafieldii str. ATCC 17505
Acidovorax facilis str. CCUG 2113
Acidovorax avenae subsp. cattleyae str. NCPPB
Acidovorax defluvii str. BSB411
nephridia Octolasion lacteum clone Ol2-2
Aquaspirillum metamorphum str. DSM 1837
Anoxobacterium dechloraticum
Desulfobacterium cetonicum str. DSM 7267 oil
Desulfobacter curvatus str. DSM 3379
Riftia pachyptila's tube clone R103-B13
Desulfonauticus submarinus str. 6N
Desulfomicrobium baculatum str. DSM 1742
Desulfovibrio sp. str. Ac5.2
Desulfovibrio giganteus str. DSM 4370
Desulfovibrio desulfuricans
Halorhodospira neutrophila str. SG 3304
Opitutus sp. str. SA-9
Buchnera sp
Alterococcus agarolyticus str. ADT3; CCRC17102
Salmonella subsp. enterica serovar Waycross str.
Salmonella typhimurium LT2 str. SGSC1412
Erwinia chrysanthemi str. 573
Pectobacterium subsp. atrosepticum str. GSPB
Erwinia amylovora EA G-5
Erwinia amylovora str. DSM 30165
Pantoea cedenensis str. A34
Erwinia amylovora str. BC199(=Ea528)
Kluyvera ascorbata 69
Morganella morganii str. AP28
Citrobacter freundii str. CDC 621-64
Morganella morganii str. ATCC35200
Pectobacterium cypripedii str. ATCC 29267
Antonina pretiosa symbiont
Pantoea agglomerans str. A40
Baumannia cicadellinicola
Pantoea subsp. stewartii str. GSPB 2626
Vryburgia amaryllidis symbiont
Dysmicoccus neobrevipes symbiont
Melanococcus albizziae symbiont
Amonostherium lichtensioides symbiont
Erium globosum symbiont
Baumannia cicadellinicola
Pectobacterium subsp. carotovorum str. E155
Serratia marcescens subsp. sakuensis str. KRED
Buttiauxella warmboldiae str. DSM 9404
Enterobacter cloacae Nr. 3
Enterobacteriaceae CF01Ent-1
Klebsiella oxytoca str. ChDC OS31
Enterobacter ludwigii str. EN-119 = DSMZ 16688
Enterobacter sp. CC1
Enterobacter intermedius str. JCM1238
Enterobacter nimipressuralis str. LMG 10245-T
Raoultella planticola 7
Australicoccus grevilleae symbiont
Raoultella planticola str. DR3
Klebsiella pneumoniae str. ASR1
Klebsiella pneumoniae str. DSM 30104
Cyphonococcus alpinus symbiont
Serratia odorifera str. DSM 4582
Serratia proteamaculans str. DSM 4543
Serratia entomophila str. DSM 12358
Aranicola proteolyticus
Serratia fonticola str. DSM 4576
Planococcus ficus symbiont
Heteropsylla texana symbiont
Photorhabdus asymbiotica str. ATCC 43949
Erwinia chrysanthemi str. 580
Photorhabdus asymbiotica subsp. australis str. MB
Hafnia alvei
Rahnella aquatilis k 8
Rahnella geno sp. 3 str. DSM 30078
Rahnella aquatilis str. ATCC 33989
Yersinia aldovae str. A125
Dermacentor variabilis symbiont
Caedibacter taeniospiralis
Chromohalobacter israelensis str. ATCC 43985 T
Halomonas sp. str. TNB I20
Halomonas sp. Ko502
Halomonas desiderata str. FB2
Halomonas variabilis str. ANT9112
Halomonas sp. SK1
Flexispira rappini FH 9702248
Helicobacter heilmannii str. MM2
Helicobacter aurati str. MIT 97-5075c
Helicobacter cetorum str. MIT 99-5656
Helicobacter suncus str. Kaz-2
Helicobacter felis str. Dog-1
Helicobacter heilmannii str. C4S
Helicobacter pullorum str. NCTC 12826
Helicobacter rodentium str. MIT 96-1312
Helicobacter pylori str. ATCC 49396T
Helicobacter sp. blood isolate 964
Helicobacter rappini W.Tee-Bat
Helicobacter winghamensis str. NLEP 97-1611
Helicobacter rappini W.Tee-Yu
Sulfurimonas autotrophica str. OK5
Riftia pachyptila's tube clone R76-B51
Hyphomicrobium aestuarii str. DSM 1564
Dechlorospirillum sp. str. SN1
Methylobacterium thiocyanatum str. ALL/SCN-P
Methylobacter psychrophilus str. Z-0021
Psychrobacter frigidicola str. DSM 12411
Moraxella oblonga str. IAM 14971
Psychrobacter psychrophilus CMS 28
Alkanindiges hongkongensis str. HKU9
Acinetobacter junii str. S33
Acinetobacter tandoii str. 4N13
Acinetobacter haemolyticus
Myxococcus fulvus str. Mx f2
Aquaspirillum serpens str. IAM 13944
Neisseria sp. str. CCUG 46910
Nitrosomonas sp. str. Nm86
Nitrosomonas europaea str. ATCC 19718
Nitrosomonas eutropha str. Nm57
Herbaspirillum sp. str. NAH4
Massilia timonae timone
Diaphorina citri symbiont
Paucimonas lemoignei str. ATCC 17989T
Collimonas fungivorans str. Ter331
Oxalobacter formigenes str. OXB ovinen rumen
Aquaspirillum arcticum str. IAM 14963
Janthinobacterium agaricidamnosum str. W1r3T
Herbaspirillum seropedicae str. DSM 6445 ATCC
Pasteurella multocida subsp. gallicida str. MCCM
Pasteurella sp. str. 91985
Haemophilus influenzae str. R2866
Haemophilus influenzae str. M9741
Haemophilus quentini str. MCCM 02026
Haemophilus influenzae str. M11105
Actinobacillus indolicus str. H1419
Haemophilus parasuis 427
Acidithiobacillus thiooxidans str. KCTC 8928P
Actinobacillus lignieresii
Actinobacillus capsulatus
Mannheimia sp. R19.2 str. R19.2; CCUG 38463
Haemophilus segnis str. MCCM 00337
Histophilus somni str. CCUG 12839
Mesorhizobium mediterraneum str. PECA20
Phyllobacterium trifolii str. PETP02
Mesorhizobium tianshanense str.-1BS; USDA 3592
Ahrensia kielensis str. IAM12618
Phyllobacterium myrsinacearum HM35
Aminobacter aminovorans str. DSM7048T
Pseudaminobacter salicylatoxidans str. KTC001
Thiomicrospira sp. str. Milos-T2
Thiomicrospira crunogena str. XCL-2
Riftia pachyptila's tube clone R76-B23
Methylophaga alcalica str. M39
Methylophaga sp. str. V4.ME.29 = MM_2343
Acanthamoeba sp. UWC6 symbiont
Pseudoalteromonas sp
Pseudoalteromonas sp. str. 05
Lyrodus pedicellatus symbiont
Lyrodus pedicellatus symbiont
Pseudomonas citronellolis str. TERIDB26
Pseudomonas aeruginosa str. PAO1
Pseudomonas sp. str. P400Y-1
Paederus fuscipes endosymbiont
Pseudomonas aeruginosa str. #47
Pseudomonas citronellolis str. TERIDB18
Pseudomonas sp. str. KNA6-5
Pseudomonas stutzeri str. KC
Pseudomonas stutzeri str. A1501
Pseudomonas stutzeri HY-105
Anabaena circinalis AWQC118C isolate str.
Pseudomonas fulva str. IAM 1587
Pseudomonas sp. str. 2N1-1
Agrobacterium agile str. IAM12615
Pseudomonas sp. str. KY
Pseudomonas flavescens str. B62
Pseudomonas monteilii str. CIP 104883
Pseudomonas cf. monteilii 9
Cellvibrio subsp. mixtus str. ACM 2601
Pseudomonas sp. str. dcm7B
Pseudomonas syringae pv. broussonetiae str. KOZ
Pseudomonas cichorii str. ATCC 10857T
Pseudomonas koreensis str. Ps 9-14
Pseudomonas fluorescens str. CHA0
Pseudomonas syringae pv. theae str. PT1
Pseudomonas sp. str. AC-167
Pseudomonas synxantha str. DSM 13080 G
Pseudomonas sp. B65
Pseudomonas marginalis str. ATCC 10844T
Pseudomonas putida str. ATCC 17472
Pseudomonas extremorientalis str. KMM3447
Pseudomonas fulgida str. DSM 14938 = LMG 2146
Pseudomonas tolaasii str. LMG 2342T ( )
Pseudomonas sp. SK-1-3-1
Pseudomonas psychrophila str. E-3
Wautersia basilensis str. DSM 11853
Wautersia paucula str. LMG 3413
Cupriavidus necator
Ralstonia detusculanense str. APF11
Ralstonia insidiosa str. CCUG 46388
Mycoplana dimorpha str. IAM 13154
Sinorhizobium fredii str. ATCC35423
Sinorhizobium meliloti str. 1021
Ensifer adhaerens str. LMG 20582
Rhizobium tropici str. LMG 9517
Rhizobium mongolense str. USDA 1832
Rhizobium gallicum str. FL27
Rhizobium etli str. USDA 2667 ATCC 14483
Agrobacterium tumefaciens TG14
Rhizobium sp. str. SH19312
Agrobacterium tumefaciens str. C58 Cereon
Agrobacterium tumefaciens C4
Rhizobium huautlense str. SO2 ( )
Roseobacter clone NAC11-3
Loktanella vestfoldensis str. LMG 22003
Scrippsiella trochoidea NEPCC 15
Sulfitobacter sp. BIO-11
Leisingera methylohalidivorans str. MB2
Paracoccus alcaliphilus str. JCM 7364
Rhodobacter sphaeroides str. 2.4.1
Scrippsiella trochoidea NEPCC 15
Zoogloea resiniphila str. PIV-3A2y
Thauera aromatica str. LG356
Thauera selenatis str. ATCC 55363T
Rickettsia bellii str. strains 369-C and G2D42
Shewanella benthica str. DB21MT-2
Moritella abyssi str. 2693
Shewanella sp. str. MTW-1
Sphingobium chungbukense str. DJ77
Sphingobium yanoikuyae str. GIFU9882
Afipia genosp. 13 str. G8991
Sphingomonas phyllosphaerae str. FA1
Sphingomonas sp. str. SAFR-027
Sphingomonas paucimobilis str. GIFU2395
Sphingomonas asaccharolytica str. IFO 10564-T
Sphingopyxis flavimaris str. SW-151
Novosphingobium capsulatum str. GIFU11526
Lutibacterium anuloederans str. LC8
Anaerobiospirillum sp. str. 3J102
Desulfacinum hydrothermale str. MT-96
Beggiatoa sp. str. MS-81-1c
Beggiatoa alba str. B18LD; ATCC 33555
Beggiatoa sp. str. AA5A
Anabaena circinalis AWQC118C isolate str.
Pseudovibrio denitrificans str. DN34
Rhizobiales str. A48
Blastochloris sulfoviridis str. GN1
Bosea thiooxidans TJ1
Shinella zoogloeoides str. ATCC 19623
Candidatus Pelagibacter ubique str. HTCC1002
Kaistobacter koreensis str. PB229
Chitinimonas taiwanensis str. cf
Lucina nassula gill symbiont
Seepiophila jonesi symbiont
Marinobacter hydrocarbonoclasticus str. ATCC
Rheinheimera baltica str. OS140 Baltic # 166
Shewanella algae str. ATCC 51192
Salmonella bongori str. JEO 4162
Paralvinella palmiformis mucus secretions clone P. palm
Photobacterium leiognathi str. LN101
Vibrio gallicus str. CIP 107867; HT 3-3
Vibrio pomeroyi str. LMG 20537
Vibrio aestuarianus str. KT0901
Vibrio aestuarianus str. 01/151
Azorhizobium caulinodans str. ORS 571
Dyemonas todaii str. XD10
Xanthomonas axonopodis pv. citri str. MA
Pseudoxanthomonas mexicana str. AMX 26B
Stenotrophomonas rhizophila str. e-p10
Stenotrophomonas maltophilia str. LMG 11104
Leptospira interrogans serovar Copenhageni str.
Spirochaeta sp. str. BHI80-158
Spironema culicis str. BR91
Treponema sp. str. 7CPL208
Treponema sp
Treponema sp. str. III:C:BA213
Treponema primitia str. ZAS-1
Mixotricha paradoxa is flagellate hindgut
Mastotermes darwiniensis clone mp4 of
Flexistipes sp. str. E3_33
Synergistes sp. P1 str. P4G_18
Geothermobacterium ferrireducens
Thermosipho sp. str. MV1063
Prosthecobacter dejongeii
Fucophilus fucoidanolyticus str. SI-1234
Candidatus Xiphinematobacter brevicolli
aS-F, Subfamily identification;
bTaxon ID, PhyloChip Taxon identification number;
cRepresentative species, Taxon bacterial species identifier.
Veillonella dispar str. DSM 20735
Bacillus firmus CV93b
Butyrivibrio fibrisolvens str. OB156
Clostridium caminithermale str.
Butyrivibrio fibrisolvens str. NCDO
Clostridium nexile
Clostridium glycolicum str. DSM
Lachnospira pectinoschiza
Streptococcus bovis str. B315
Streptococcus cristatus str. ATCC
Staphylococcus auricularis str.
Enterococcus mundtii str. LMG
Bacillus niacini str. IFO15566
Caryophanon latum str. DSM
Streptococcus mitis str. Sm91
Brevibacillus borstelensis str. LMG
Streptococcus salivarius str. ATCC
Enterococcus ratti str. ATCC
Trichococcus flocculiformis str.
Nostocoida limicola I str. Ben206
Pseudobacillus carolinae
Lactobacillus kitasatonis str.
Bacillus sp. clone ML615J-19
Vagococcus lutrae str. m1134/97/1;
Streptococcus macedonicus str.
Lactobacillus subsp. aviarius
Bacillus algicola str. KMM 3737
Streptococcus thermophilus str.
Tetragenococcus muriaticus
Streptococcus bovis str. HJ50
Bacillus silvestris str. SAFN-010
Bacillus subtilis str. IAM 12118T
Micrococcus luteus B-P 26
Weissella koreensis S-5673
Streptococcus constellatus str.
Marinilactibacillus psychrotolerans
Planococcus maritimus str. TF-9
Pediococcus inopinatus str. DSM
Lactobacillus sakei
Lactobacillus frumenti str. TMW
Bacillus megaterium str. QM B1551
Desemzia incerta str. DSM 20581
Streptococcus gallinaceus str.
Lactobacillus pontis str. LTH 2587
Staphylococcus saprophyticus
Streptococcus downei str. ATCC
Bacillus senegalensis str. RS8; CIP
Staphylococcus caprae str. DSM
Bacillus schlegelii str. ATCC
Staphylococcus haemolyticus str.
Streptococcus mutans str. UA96
Bacillus clausii str. GMBAE 42
Lactobacillus letivazi str. JCL3994
Staphylococcus sp str. AG-30
Brevibacillus sp. MN 47.2a
Staphylococcus pettenkoferi str.
Bacillus sp. str. 2216.25.2
Bacillus mojavensis str. M-1
Staphylococcus sciuri
Amphibacillus xylanus str. DSM
Lactobacillus salivarius str. RA2115
Bacillus sonorensis str. NRRL B-
Lactococcus Il1403 subsp. lactis
Bacillus sp. str. C-59-2
Streptococcus suis str. 8074
Lactobacillus suebicus str. CECT
Lactobacillus perolens str. L532
Staphylococcus succinus str. SB72
Bacillus acidogenesis str. 105-2
Bacillus licheniformis str. KL-068
Carnobacterium alterfunditum
Trichococcus pasteurii str. KoTa2
Streptococcus equi subsp.
zooepidemicus str. Tokyo1291
Bacillus licheniformis str. DSM 13
Streptococcus bovis str.ATCC
Bacillus subtilis subsp. Marburg
Bacillus subtilis
Clostridium chauvoei str. ATCC
Clostridium sardiniense str. DSM
Clostridium novyi str. NCTC538
Cytophaga sp. I-1787
Paralvinella palmiformis mucus
Flexibacter flexilis subsp.
pelliculosus str. IFO 16028
Spirochaeta sp. str. BHI80-158
aS-F, Subfamily identification;
bTaxon ID, PhyloChip Taxon identification number;
cRepresentative species, Taxon bacterial species identifier.
Marinobacter lipolyticus str. SM-19
Idiomarina loihiensis str. GSP37
Pseudoalteromonas ruthenica str. KMM300
Psychromonas profunda str. 2825
Wolbachia sp
Arcobacter cryaerophilus
Sulfurospirillum deleyianum str. Spirillum 5175
Campylobacter showae
Brevundimonas diminuta str. DSM 1635
Brevundimonas sp. str. FWC40
Emiliania huxleyi str. Plymouth Marine Laborator PML 92
Desulfomicrobium baculatum str. DSM 1742
Dysmicoccus neobrevipes symbiont
Helicobacter cetorum str. MIT 99-5656
Helicobacter suncus str. Kaz-2
Helicobacter felis str. Dog-1
Helicobacter heilmannii str. C4S
Leptospira interrogans serovar Copenhageni str. Fiocruz L1-130
Psychrobacter frigidicola str. DSM 12411
Psychrobacter psychrophilus CMS 28
Alkanindiges hongkongensis str. HKU9
Methylophaga alcalica str. M39
Pseudomonas aeruginosa str. PAO1
Pseudomonas aeruginosa str. #47
Pseudomonas stutzeri str. A1501
Pseudomonas flavescens str. B62
Pseudomonas fluorescens str. CHA0
Pseudomonas sp. SK-1-3-1
Sphingobacteriaceae str. Ellin160
Sphingopyxis flavimaris str. SW-151
Thermoactinomyces sp. str. 700375
Fjonesia
Beggiatoa sp. str. MS-81-1c
Prosthecobacter dejongeii
Synergistes sp. P1 str. P4G_18
Paralvinella palmiformis mucus secretions cloneP. palm C 84 proteobacterium
Jonesia quinghaiensis str. DSM 15701
Arthrobacter ureafaciens str. DSM 20126
Dyemonas todaii str. XD10
Candidatus Xiphinematobacter brevicolli
aS-F, Subfamily identification;
bTaxon ID, PhyloChip Taxon identification number;
cRepresentative species, Taxon bacterial species identifier.
This application is related to and claims priority to the following co-pending U.S. provisional patent applications: U.S. Application Ser. No. 61/259,565 [Attorney Docket No. IB-2733P1], filed on Nov. 9, 2009; U.S. Application Ser. No. 61/317,644 [Attorney Docket No. IB-2733P2], filed on Mar. 25, 2010; U.S. Application Ser. No. 61/347,817 [Attorney Docket No. IB-2733P3], filed on May 24, 2010; U.S. Application Ser. No. 61/252,620 [Attorney Docket No. IB-2229P4], filed Oct. 16, 2009; each of which are incorporated herein by reference. This application is related to the co-pending international application having application number PCT/US2010/040106 [Attorney Docket No. IB-2733PCT], filed on Jun. 25, 2010, which is incorporated herein by reference.
This invention was made with Government support under Contract No. DE-AC02-05CH11231 awarded by the Department of Energy; a grant from the Department of Homeland Security and Agreement Number 07-576-550-0 from State of California Water Quality Board, and a grant from the National Institutes of Health having Award Number AI075410. The government has certain rights in this invention.
| Filing Document | Filing Date | Country | Kind | 371c Date |
|---|---|---|---|---|
| PCT/US10/02755 | 10/15/2010 | WO | 00 | 6/27/2012 |
| Number | Date | Country | |
|---|---|---|---|
| 61317644 | Mar 2010 | US | |
| 61259565 | Nov 2009 | US | |
| 61220937 | Jun 2009 | US | |
| 61347817 | May 2010 | US | |
| 61347817 | May 2010 | US | |
| 61317644 | Mar 2010 | US | |
| 61259565 | Nov 2009 | US | |
| 61252620 | Oct 2009 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | PCT/US10/40106 | Jun 2010 | US |
| Child | 13502108 | US |
