Granted Patent B2

US 10,290,374 · App. 15/645,062 · Granted May 14, 2019

Inventors: Zachary Apte (San Francisco, CA); Jessica Richman (San Fancisco, CA); Daniel Almonacid (San Francisco, CA)

Assignee: uBiome, Inc.

G16H50/20G06F19/00G06F19/3418G16B35/00G16B40/00G16B50/00G16C20/60Y02A90/26

Abstract

Claims (47)

1. A method for characterizing a rheumatoid arthritis condition, the method comprising:

receiving a set of samples from a set of subjects comprising at least one subject associated with the rheumatoid arthritis condition;

for each sample of the set of samples:

determining a microorganism nucleic acid sequence, comprising:

selecting a primer for a nucleic acid,

fragmenting nucleic acid material from the sample, and

amplifying the fragmented nucleic acid material using the selected 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;

transforming the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset into a characterization model for the rheumatoid arthritis condition;

based upon the characterization model, generating a therapy model operable to determine a therapy to improve a state of the rheumatoid arthritis condition; and

at an output device associated with a subject, providing the therapy to the subject with the rheumatoid arthritis condition in accordance with the therapy model.

2. The method of claim 1 , further comprising:

providing a set of sampling kits to the set 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 at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset comprises generating the at least one of 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 selected primer.

4. The method of claim 1 , wherein providing the therapy comprises providing a consumable operable to modulate microbiome composition of the subject towards an equilibrium state to improve the state of the rheumatoid arthritis condition.

5. The method of claim 4 , wherein the consumable comprises at least one of a probiotic and a prebiotic, wherein the at least one of the probiotic and the prebiotic is associated with anti-inflammatory properties.

6. The method of claim 1 , further comprising collecting a supplementary medical imaging dataset associated with the rheumatoid arthritis condition and at least a subset of the set of subjects, wherein the supplementary medical imaging dataset is sampled at a medical imaging system, wherein transforming the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset comprises generating the characterization model based on the supplementary medical imaging dataset and the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset.

7. The method of claim 1 , wherein transforming the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset comprises generating the characterization model based on a set of features extracted from the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset, wherein the set of features is associated with at least one of: relative abundance of different taxonomic groups represented in the microbiome composition diversity dataset, ratio between at least two features associated with the different taxonomic groups and functional diversity, interactions between the different taxonomic groups, and phylogenetic distance between the different taxonomic groups.

8. The method of claim 1 , wherein transforming the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset comprises determining the characterization model for the rheumatoid arthritis condition based on a set of microbiome composition features extracted from the microbiome composition diversity dataset, and wherein the set of microbiome composition features is associated with a set of taxa comprising at least one of: Bacteroides uniformis (species).

9. The method of claim 8 , wherein the set of microbiome composition features is associated with the set of taxa comprising: Alistipes onderdonkii (species).

10. The method of claim 9 , wherein determining the characterization model comprises determining the characterization model for the rheumatoid arthritis condition based on the set of microbiome composition features, and a set of microbiome functional features extracted from the microbiome functional diversity dataset, wherein the microbiome functional features comprises at least one of: clusters of orthologous groups of proteins features, genomic functional features from the Kyoto Encyclopedia of Genes and Genomes (KEGG), chemical functional features, and systemic functional features.

11. A method for characterizing a rheumatoid arthritis condition, the method comprising:

for each sample of a set of samples comprising at least one sample comprising microorganisms associated with the rheumatoid arthritis condition:

determining a microorganism nucleic acid sequence, comprising:

selecting a primer for a nucleic acid, and

amplifying nucleic acid material from the sample based on the selected primer; and

determining a comparison of the microorganism nucleic acid sequence to a reference;

generating at least one of a microbiome composition diversity dataset and a microbiome functional diversity dataset based on the comparisons;

generating a characterization of the rheumatoid arthritis condition based on a set of features extracted from the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset; and

providing a therapy to a subject based on the characterization, wherein the therapy is operable to improve a state of the rheumatoid arthritis condition.

12. The method of claim 11 , wherein determining the microorganism nucleic acid sequence comprises selecting the primer associated with the rheumatoid arthritis condition with a primer selection model, and wherein determining the comparison of the microorganism nucleic acid sequence to the reference comprises determining an alignment of the microorganism nucleic acid sequence to a reference nucleic acid sequence associated with the rheumatoid arthritis condition.

13. The method of claim 11 , further comprising collecting a supplementary blood test dataset associated with the set of samples and informative of the rheumatoid arthritis condition, wherein generating the characterization comprises generating the characterization based on the supplementary blood test dataset and the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset.

14. The method of claim 13 , wherein the supplementary blood test dataset is associated with presence of rheumatoid factor.

15. The method of claim 13 , wherein the set of samples is associated with a population of subjects comprising a first subset exhibiting the rheumatoid arthritis condition and a second subset not exhibiting the rheumatoid arthritis condition, wherein generating the characterization comprises correlating the set of features to the first subset of the population of subjects and the second subset of the population of subjects based on the supplementary blood test dataset.

16. The method of claim 11 , further comprising:

collecting a supplementary dataset associated with an evaluation of synovial tissue inflammation, wherein the evaluation is informative of the rheumatoid arthritis condition; and

determining a risk of the rheumatoid arthritis condition for the subject based on the supplementary dataset and a set of subject microbiome features derived from a subject sample from the subject.

17. The method of claim 11 , wherein providing the therapy comprises providing an inflammatory gene suppression therapy operable to improve the state of the rheumatoid arthritis condition.

18. The method of claim 11 , wherein the set of features is associated with at least one of: Bacteroides uniformis (species) and Alistipes onderdonkii (species).

19. The method of claim 18 , wherein the set of features comprises at least one of: clusters of orthologous groups of proteins features, genomic functional features from the Kyoto Encyclopedia of Genes and Genomes (KEGG), chemical functional features, and systemic functional features.

20. The method of claim 19 , further comprising:

determining a modulator of a biomolecule associated with the set of features and the rheumatoid arthritis condition; and

deriving a therapeutic composition for the rheumatoid arthritis condition based on the modulator,

wherein providing the therapy comprises providing the therapeutic composition to the subject based on the characterization.

Assignments (2)

Continuity (21)

Cited By (1)

US 12,688,255