Method and system for microbiome-derived characterization, diagnostics and therapeutics for cutaneous conditions

Patent metadata

Field	Value
Publication number	US-10410749-B2
Application number	US-201615098153-A
Country	US
Kind code	B2
Filing date	Apr 13, 2016
Priority date	Oct 21, 2014
Publication date	Sep 10, 2019
Grant date	Sep 10, 2019

Abstract

A method for at least one of characterizing, diagnosing, and treating a cutaneous condition in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the cutaneous condition based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the cutaneous condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

First claim

We claim:

1. A method for at least one of characterizing, diagnosing, and treating a cutaneous condition in at least a subject, the method comprising:
processing nucleic acid content of each of an aggregate set of samples, from a population of subjects, with a fragmentation operation, a multiplexed amplification operation using a set of primers, a sequencing analysis operation, and an alignment operation;
generating a microbiome composition dataset and a microbiome functional diversity dataset upon processing of the aggregate set of samples;
receiving a supplementary dataset informative of the cutaneous condition within the population of subjects;
transforming the supplementary dataset and features extracted from the microbiome composition dataset and the microbiome functional diversity dataset into a characterization model of the cutaneous condition;
generating a therapy model, associated with the characterization model, configured to improve a state of the cutaneous condition; and
providing a therapy to the subject, upon characterizing the subject with the cutaneous condition according to the characterization model and the therapy model.

2. The method of claim 1 , wherein generating the characterization model comprises performing a statistical analysis to assess a set of microbiome composition features and microbiome functional features having variations across a first subset of the population of subjects exhibiting the cutaneous condition and a second subset of the population of subjects not exhibiting the cutaneous condition.

3. The method of claim 2 , wherein generating the characterization model of the cutaneous condition comprises generating a characterization that is diagnostic of a skin condition.

4. The method of claim 3 , wherein generating the characterization model of the cutaneous condition comprises generating the characterization of at least one of: acne and dermatomyositis.

5. The method of claim 4 , wherein generating the characterization that is diagnostic of acne comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from a set of taxa including: Phascolarctobacterium (genus).

6. The method of claim 5 , wherein generating the characterization further comprises determining presence of features derived from a set of functions.

7. The method of claim 6 , wherein generating the set of functions includes a lepB TPP signal peptidase KEGG orthology-derived feature.

8. The method of claim 4 , wherein generating the characterization that is diagnostic of dermatomyositis comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from a set of taxa including at least one of: Faecalibacterium (genus), Ruminococcaceae (family), and Bacteroidia (class).

9. The method of claim 8 , wherein generating the characterization further comprises determining presence of features derived from a set of functions.

10. The method of claim 9 , wherein generating the set of functions includes a first feature related to energy metabolism, a second feature related to immune system, and a third feature related to D-Alanine metabolism.

11. The method of claim 2 , wherein generating the characterization model of the cutaneous condition comprises generating a characterization that is diagnostic of at least one of: a hair condition, a nail condition, and a cutaneous gland condition.

12. The method of claim 2 , wherein generating the characterization model comprises:
extracting candidate features associated with a set of functional aspects of microbiome components indicated in the microbiome composition dataset to generate the microbiome functional diversity dataset; and
characterizing the cutaneous condition in association with a subset of the set of functional aspects, the subset derived from at least one of clusters of orthologous groups of proteins features, genomic functional features, chemical functional features, and systemic functional features.

13. A method for characterizing a cutaneous condition in a subject, the method comprising:
upon processing an aggregate set of samples from a population of subjects, generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects, the microbiome functional diversity dataset indicative of systemic functions present in microbiome components of the aggregate set of samples;
transforming features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset into a characterization model of the cutaneous condition, wherein the characterization model is diagnostic of at least one of a skin condition;
generating a therapy model, associated with the characterization model, configured to improve a state of the cutaneous condition; and
providing a therapy to the subject with the cutaneous condition based upon the therapy model.

14. The method of claim 13 , wherein transforming the features extracted from the at least one of the microbiome composition dataset and the microbiome functional diversity dataset into the characterization model comprises analyzing a set of features form the microbiome composition dataset with a statistical analysis, wherein the set of features includes features associated with interactions between different taxonomic groups represented in the microbiome composition dataset and phylogenetic distance between taxonomic groups represented in the microbiome composition dataset.

15. The method of claim 13 , wherein transforming the features extracted from the at least one of the microbiome composition dataset and the microbiome functional diversity dataset into the characterization model comprises performing a statistical analysis with at least one of a Kolmogorov-Smirnov test and a Welch's t-test to assess a set of microbiome composition features and microbiome functional features having variations across a first subset of the population of subjects exhibiting the cutaneous condition and a second subset of the population of subjects not exhibiting the cutaneous condition.

16. The method of claim 13 , wherein transforming the features extracted from the at least one of the microbiome composition dataset and the microbiome functional diversity dataset into the characterization model of the skin condition comprises generating a characterization of at least one of: acne and dermatomyositis.

17. The method of claim 16 , wherein generating the characterization that is diagnostic of acne comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from 1) a set of taxa including: Phascolarctobacterium (genus) and 2) a set of functions including a first functional feature related to lepB TPP signal peptidase.

18. The method of claim 16 , wherein generating the characterization that is diagnostic of dermatomyositis comprises generating the characterization upon processing the aggregate set of samples and determining presence of features derived from 1) a set of taxa including at least one of: Faecalibacterium (genus), Ruminococcaceae (family), and Bacteroidia (class) and 2) a set of functions including a first functional feature related to energy metabolism, a second feature related to immune system, and a third feature related to D-Alanine metabolism.

19. The method of claim 13 , further comprising diagnosing the subject with the cutaneous condition upon pr

Assignees

• Ubiome Inc

Inventors

• Apte Zachary
• Almonacid Daniel
• Richman Jessica
• Behbahani Siavosh Rezvan

Classifications

• C12Q1/6874

  involving nucleic acid arrays, e.g. sequencing by hybridisation · CPC title

• G16H40/67

  for remote operation · CPC title

• G16B50/00

  ICT programming tools or database systems specially adapted for bioinformatics · CPC title

• G16H10/40

  for data related to laboratory analysis, e.g. patient specimen analysis · CPC title

• G16B40/00 Primary

  ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding · CPC title