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1. (US20170286620) Method and system for microbiome-derived diagnostics and therapeutics
注意: このテキストは、OCR 処理によってテキスト化されたものです。法的な用途には PDF 版をご利用ください。

Claims

1. A method for characterizing a set of gastrointestinal conditions comprising a Crohn's disease condition, an irritable bowel syndrome (IBS) condition, an inflammatory bowel disease (IBD) condition, and an ulcerative colitis condition, the method comprising:
receiving a set of samples from a population of subjects comprising at least one subject associated with at least one of the set of gastrointestinal conditions;
for each sample of the set of samples:
determining a microorganism nucleic acid sequence, comprising:
selecting a primer for a nucleic acid sequence;
fragmenting nucleic acid material based on the sample; and
amplifying the fragmented nucleic acid material based on the primer; and
determining an alignment of the microorganism nucleic acid sequence to a reference nucleic acid sequence;
generating a microbiome composition diversity dataset and a microbiome functional diversity dataset for the population of subjects based upon the alignments;
collecting a supplementary dataset, associated with at least a subset of the population of subjects, wherein the supplementary dataset is informative of a characteristic associated with the at least one of the set of gastrointestinal conditions;
generating a set of characterizations for the set of gastrointestinal conditions based upon the supplementary dataset and at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset;
based upon the set of characterizations, generating a therapy model that determines a therapy for correcting the at least one of the set of gastrointestinal conditions; and
at an output device associated with a subject, providing the therapy to the subject based upon the therapy model, wherein the therapy modulates microbiome composition of the subject towards an equilibrium state.
2. The method of claim 1, further comprising:
providing a set of sampling kits to the population of subjects, each sampling kit of the set of sampling kits comprising a sample container configured to receive the sample from a collection site,
wherein determining the microorganism nucleic acid sequence comprises determining the microorganism nucleic acid sequence with a bridge amplification substrate of a next generation sequencing platform of a sample processing system, and
wherein generating the microbiome composition diversity dataset and the microbiome functional diversity dataset comprises generating the microbiome composition diversity dataset and the microbiome functional diversity dataset at computing devices operable to communicate with the next generation sequencing platform.
3. The method of claim 2, wherein determining the microorganism nucleic acid sequence comprises performing, at a library preparation subsystem of the sample processing system, multiplex amplification with the fragmented nucleic acid material based on the primer.
4. The method of claim 1, wherein selecting the primer comprises selecting the primer for the nucleic acid sequence associated with a 16S RNA sequence.
5. The method of claim 4, wherein generating the set of characterizations comprises generating the set of characterizations based on a set of features associated with the 16S RNA sequence and comprising a relative abundance of different taxonomic groups represented in the microbiome composition diversity dataset.
6. The method of claim 5, wherein the population of subjects comprises a first subset exhibiting the at least one of the set of gastrointestinal conditions and a second subset not exhibiting the at least one of the set of gastrointestinal conditions, and wherein the set of features comprises a comparison between the first subset and the second subset for the relative abundance of different taxonomic groups.
7. The method of claim 1, further comprising determining, for the subject, a set of risks for the set of gastrointestinal conditions based on the set of characterizations and at least one of a subject microbiome composition diversity dataset and a subject microbiome functional diversity dataset associated with a subject sample from the subject, wherein the therapy is operable to reduce the set of risks.
8. The method of claim 1, wherein generating the set of characterizations for the set of gastrointestinal conditions comprises generating the set of characterizations based on a set of microbiome composition features extracted from the microbiome composition diversity dataset, wherein the set of microbiome composition features are associated with a set of taxa comprising at least one of: Alistipes (genus), Barnesiella (genus), Bifidobacterium (genus), Clostridium (genus), Lactobacillus (genus), Odoribacter (genus), Prevotella (genus), Flavonifractor (genus), Roseburia (genus), Ruminococcus (genus), Veillonella (genus), Akkermansia (genus), Bacteroides (genus), Pseudobutyrivibrio (genus), Collinsella (genus), Coprococcus (genus), Desulfovibrionales (order), Dialister (genus), Faecalibacterium (genus), and Streptococcus (genus).
9. The method of claim 8, wherein the set of gastrointestinal conditions comprises a gastrointestinal symptom comprising at least one of abdominal pain, bloating, gastrointestinal discomfort, and alteration of bowel habits, wherein selecting the primer comprises selecting the primer for the nucleic acid sequence associated with the gastrointestinal symptom, and wherein generating the set of characterizations comprises generating a characterization of the gastrointestinal symptom.
10. The method of claim 8, wherein the set of microbiome composition features are associated with the set of taxa further comprising at least one of: Clostridiaceae (family), Prevotellaceae (family), Oscillospiraceae (family), Gammaproteobacteria (class), Proteobacteria (phylum), Eggerthella (genus), Anaerosporobacter (genus), Erysipelothrix (genus), Legionella (genus), Parabacteroides (genus), Barnesiella (genus), Actinobacillus (genus), Haemophilus (genus), Megasphaera (genus), Marvinbryantia (genus), Butyricicoccus (genus), Bilophila (genus), Oscillibacter (genus), Butyricimonas (genus), Sarcina (genus), Pectobacterium (genus), Eubacterium (genus), Subdoligranulum (genus), Cronobacter (genus), Lachnospira (genus), Blautia (genus, Peptostreptococcaceae (family), Veillonellaceae (family), Erysipelotrichaceae (family), Christensenellaceae (family), Erysipelotrichales (order), Erysipelotrichia (class), Actinobacillus porcinus (species), Pasteurellaceae (family), Pasteurellales (order), Flavonifractor plautii (species), Lactobacillales (order), Lachnospiraceae bacterium 2_1_58FAA (species), Bacilli (class), bacterium NLAE-zl-P430 (species), Parasutterella (genus), Parasutterella excrementihominis (species), Coriobacteriaceae (family), uncultured Coriobacteriia bacterium (species), Coriobacteriales (order), Bacteroides fragilis (species), Holdemania (genus), Porphyromonadaceae (family), Chlamydiae/Verrucomicrobia group (superphylum), Eggerthella lenta (species), Verrucomicrobia (phylum), Bacteroidales (order), Bacteroidia (class), Bacteroidetes (phylum), Bacteroidetes/Chlorobi group (superphylum), Verrucomicrobiae (class), Verrucomicrobiales (order), Verrucomicrobiaceae (family), Dorea (genus), Deltaproteobacteria (class), delta/epsilon subdivisions (subphylum), Bacillales incertae sedis (no rank), Desulfovibrionales (order), Eubacteriaceae (family), Acidaminococcaceae (family), Rhodospirillales (order), Rhodospirillaceae (family), Bacillales (order), Alistipes putredinis (species), and Bacillaceae (family), Selenomonadales (order), Gammaproteobacteria (class), Negativicutes (class), bacterium NLAE-zl-P562 (species), Enterobacteriales (order), Enterobacteriaceae (family), Streptococcaceae (family), Cronobacter sakazakii (species), Streptococcus (genus), Burkholderiales (order), Betaproteobacteria (class), Sutterellaceae (family), Ruminococcaceae (family), butyrate-producing bacterium SR1/1 (species), Sphingobacteriales (order), Bacillales Family XI. Incertae Sedis, Oceanospirillales (order), Finegoldia (genus), Rikenellaceae (family), Bilophila wadsworthia (species), Clostridiales (order), Clostridia (class), Clostridium lavalense (species), Odoribacter splanchnicus (species), organismal metagenomes (no rank), Anaerostipes (genus), Actinobacteria (class), bacterium NLAE-zl-H54 (species), Actinobacteridae spp. (no rank), Roseburia sp. 11SE38 (species), Bifidobacteriaceae (family), Bifidobacteriales (order), Finegoldia magna (species), Finegoldia (genus), and Peptoniphilus (genus).
11. The method of claim 8, wherein generating the set of characterizations for the set of gastrointestinal conditions comprises generating the set of characterizations based on the set of microbiome composition features and a set of microbiome functional diversity features extracted from the microbiome functional diversity dataset, wherein the set of microbiome functional diversity features comprise at least one of: a clusters of orthologous groups of proteins (COG) feature set and a Kyoto Encyclopedia of Genes and Genomes (KEGG) feature set.
12. The method of claim 11, wherein the set of microbiome functional diversity features comprises at least one of: a COG (D) code derived feature, a COG (I) code derived feature, a COG (J) code derived feature, a cell growth and death KEGG pathway derived feature, an endocrine system KEGG pathway derived feature, a folding, sorting, and degradation KEGG pathway derived feature, a metabolism KEGG pathway derived feature, a metabolism of terpenoids and polyketides KEGG pathway derived feature, a replication and repair KEGG pathway derived feature, a translation KEGG pathway derived feature, an amino acid related enzymes KEGG pathway derived feature, an aminoacyl-tRNA biosynthesis KEGG pathway derived feature, a homologous recombination KEGG pathway derived feature, a nucleotide excision repair KEGG pathway derived feature, a PPAR signaling pathway KEGG pathway derived feature, a peptidoglycan biosynthesis KEGG pathway derived feature, a prion diseases KEGG pathway derived feature, a ribosome KEGG pathway derived feature, a translation factors KEGG pathway derived feature, a large subunit ribosomal protein L20 KEGG derived feature, a Mg 2+-importing ATPase KEGG derived feature, a peptidyl-tRNA hydrolase PTH1 family KEGG derived feature, a large subunit ribosomal protein L13 KEGG derived feature, a type IV pilus assembly protein PilQ KEGG derived feature, a superoxide dismutase, Cu—Zn family KEGG derived feature, a transposase KEGG derived feature, a transposase IS30 family KEGG derived feature, a COG (B) code derived feature, a signal transduction KEGG pathway derived feature, a base excision repair KEGG pathway derived feature, a cell cycle— Caulobacter KEGG pathway derived feature, a N-Glycan biosynthesis KEGG pathway derived feature, an Oxidative phosphorylation KEGG pathway derived feature, a putative glycerol-1-phosphate prenyltransferase KEGG derived feature, a 5,10-methylenetetrahydromethanopterin reductase KEGG derived feature, a glutamate:Na + symporter ESS family KEGG derived feature, a putative transposase KEGG derived feature, a diacylglycerol kinase KEGG derived feature, an uncharacterized protein KEGG derived feature, a LPPG:FO 2-phospho-L-lactate transferase KEGG derived feature, a phosphosulfolactate synthase KEGG derived feature, a UDP-N-acetyl-D-glucosamine dehydrogenase KEGG derived feature, a hypothetical protein KEGG derived feature, a proline dehydrogenase KEGG derived feature, pcoC KEGG derived feature, a carboxylate-amine ligase KEGG derived feature, and an isocitrate lyase KEGG derived feature.
13. A method for characterizing at least one of a set of gastrointestinal conditions for a subject, the method comprising:
for each sample of a set of samples associated with a set of subjects comprising at least one subject associated with the at least one of the set of gastrointestinal conditions:
determining a microorganism nucleic acid sequence, comprising:
selecting a primer for a nucleic acid sequence; and
amplifying nucleic acid material from the sample based on the primer; and
determining an alignment of the microorganism nucleic acid sequence to a reference nucleic acid;
generating at least one of a microbiome composition diversity dataset and a microbiome functional diversity dataset based on the alignments;
receiving a supplementary dataset associated with the at least one of the set of gastrointestinal conditions and at least a subset of the set of subjects;
generating a characterization for the at least one of the set of gastrointestinal conditions based upon the supplementary dataset and the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset;
based upon the characterization, determining a therapy for the subject; and
providing the therapy to the subject, wherein the therapy is operable to modulate microbiome composition of the subject in association with the at least one of the set of gastrointestinal conditions.
14. The method of claim 13, wherein selecting the primer comprises selecting the primer complementary to the nucleic acid sequence associated with a 16S RNA sequence, and wherein generating the characterization comprises determining a relative abundance of different taxonomic groups represented in the microbiome composition based on a feature derived from the 16S RNA sequence.
15. The method of claim 13, further comprising:
collecting a behavioral supplementary dataset for the subject; and
determining a cause for the at least one of the set of gastrointestinal conditions for the subject based on the behavioral supplementary dataset and a subject sample from the subject, wherein providing the therapy comprises providing the therapy based on the behavioral supplementary dataset.
16. The method of claim 13,
wherein the at least one of the set of gastrointestinal conditions comprises a Crohn's disease condition,
wherein generating the characterization comprises generating the characterization based on a set of microbiome composition features extracted from the microbiome composition diversity dataset and on a set of microbiome functional diversity features extracted from the microbiome functional diversity dataset,
wherein the set of microbiome composition features are associated with a first set of taxa comprising at least one of: Clostridium (genus), Flavonifractor (genus), Prevotella (genus), Clostridiaceae (family), Prevotellaceae (family), Oscillospiraceae (family), Gammaproteobacteria (class), and Proteobacteria (phylum), and
wherein the set of microbiome functional diversity features comprises a first set of functional features comprising at least one of: a clusters of orthologous groups of proteins (COG) (D) code derived feature, a COG (I) code derived feature, a COG (J) code derived feature, a cell growth and death Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway derived feature, an endocrine system KEGG pathway derived feature, a folding, sorting, and degradation KEGG pathway derived feature, a metabolism KEGG pathway derived feature, a metabolism of terpenoids and polyketides KEGG pathway derived feature, a replication and repair KEGG pathway derived feature, a translation KEGG pathway derived feature, an amino acid related enzymes KEGG pathway derived feature, an aminoacyl-tRNA biosynthesis KEGG pathway derived feature, a homologous recombination KEGG pathway derived feature, a nucleotide excision repair KEGG pathway derived feature, a PPAR signaling pathway KEGG pathway derived feature, a peptidoglycan biosynthesis KEGG pathway derived feature, a prion diseases KEGG pathway derived feature, a ribosome KEGG pathway derived feature, a translation factors KEGG pathway derived feature, a large subunit ribosomal protein L20 KEGG derived feature, a Mg 2+-importing ATPase KEGG derived feature, a peptidyl-tRNA hydrolase PTH1 family KEGG derived feature, a large subunit ribosomal protein L13 KEGG derived feature, a type IV pilus assembly protein PilQ KEGG derived feature, a superoxide dismutase, Cu—Zn family KEGG derived feature, a transposase KEGG derived feature, and a transposase IS30 family KEGG derived feature.
17. The method of claim 16,
wherein the at least one of the set of gastrointestinal conditions comprises an irritable bowel syndrome (IBS) condition,
wherein the set of microbiome composition features are associated with a second set of taxa comprising at least one of: Flavonifractor (genus), Odoribacter (genus), Blautia (genus), and Finegoldia (genus), and
wherein the set of microbiome functional diversity features comprises a second set of functional features comprising at least one of: pcoC KEGG derived feature, a carboxylate-amine ligase KEGG derived feature, and an isocitrate lyase KEGG derived feature.
18. The method of claim 17,
wherein the at least one of the set of gastrointestinal conditions comprises an inflammatory bowel disease (IBD) condition,
wherein the set of microbiome composition features are associated with a third set of taxa comprising at least one of: Clostridium (genus), Ruminococcus (genus), Clostridiaceae (family), Veillonellaceae (family), Selenomonadales (order), Gammaproteobacteria (class), Negativicutes (class), and Proteobacteria (phylum), and
wherein the set of microbiome functional diversity features comprises a third set of functional features comprising at least one of: a UDP-N-acetyl-D-glucosamine dehydrogenase KEGG derived feature, a putative glycerol-1-phosphate prenyltransferase KEGG derived feature, a hypothetical protein KEGG derived feature, a proline dehydrogenase KEGG derived feature, and the transposase IS30 family KEGG derived feature.
19. The method of claim 18,
wherein the at least one of the set of gastrointestinal conditions comprises an ulcerative colitis condition,
wherein the set of microbiome composition features are associated with a fourth set of taxa comprising at least one of: Clostridium (genus), Lachnospira (genus), Blautia (genus), Dialister (genus), Ruminococcus (genus), Clostridiaceae (family), Peptostreptococcaceae (family), Veillonellaceae (family), Erysipelotrichaceae (family), Christensenellaceae (family), Erysipelotrichales (order), Gammaproteobacteria (class), and Erysipelotrichia (class), and
wherein the set of microbiome functional diversity features comprises a fourth set of functional features comprising at least one of: a COG (B) code derived feature, the COG (I) code derived feature, a cell growth and death KEGG pathway derived feature, the metabolism of terpenoids and polyketides KEGG pathway derived feature, a signal transduction KEGG pathway derived feature, the translation KEGG pathway derived feature, a base excision repair KEGG pathway derived feature, a cell cycle— Caulobacter KEGG pathway derived feature, a N-Glycan biosynthesis KEGG pathway derived feature, an Oxidative phosphorylation KEGG pathway derived feature, the putative glycerol-1-phosphate prenyltransferase KEGG derived feature, a 5,10-methylenetetrahydromethanopterin reductase KEGG derived feature, a glutamate:Na + symporter ESS family KEGG derived feature, a putative transposase KEGG derived feature, a diacylglycerol kinase KEGG derived feature, an uncharacterized protein KEGG derived feature, a LPPG:FO 2-phospho-L-lactate transferase KEGG derived feature, and a phosphosulfolactate synthase KEGG derived feature.
20. The method of claim 19, wherein providing the therapy comprises providing a consumable operable to selectively modulate a population size of a desired taxon associated with at least one of the first, the second, the third, and the fourth set of taxa, and at least one of the first, the second, the third, and the fourth set of functional features.