METHODS, APPARATUSES, AND SYSTEMS FOR MICROORGANISM STRAIN ANALYSIS OF COMPLEX HETEROGENEOUS COMMUNITIES, PREDICTING AND IDENTIFYING FUNCTIONAL RELATIONSHIPS AND INTERACTIONS THEREOF, AND SELECTING AND SYNTHESIZING MICROBIAL ENSEMBLES BASED THEREON

• US 20170107557A1
• Filed: 12/28/2016
• Published: 04/20/2017
• Est. Priority Date: 06/25/2015
• Status: Active Grant

• Current Assignee: Native Microbials Incorporated
• Original Assignee: Ascus Biosciences, Inc.
• Inventors: ZENGLER, Karsten, EMBREE, Mallory
• Granted Patent: US 10,851,399 B2
• Time in Patent Office: Days
• CPC Class Codes: A23K 20/10 Organic substances
• CPC Class Codes: A23K 50/70 for birds
• CPC Class Codes: C12Q 1/06 Quantitative determination
• CPC Class Codes: C12Q 1/689 for bacteria
• CPC Class Codes: G01N 33/569 for microorganisms, e.g. protozoa, bacteria, viruses
• CPC Class Codes: G16B 40/00 ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
• CPC Class Codes: G16B 40/30 Unsupervised data analysis

Abstract

Methods, apparatuses, and systems for microorganism strain analysis of complex heterogeneous communities, predicting and identifying functional relationships and interactions thereof, and selecting and synthesizing microbial ensembles based thereon are disclosed. Methods for identifying and determining the absolute cell count of microorganism types and strains, along with identifying the network relationships between active microorganisms and metadata, are also disclosed.

Claims

1. A method, comprising:

forming a bioensemble of active microorganism strains configured to alter a property in a target biological environment, the forming including;

obtaining at least two sample sets, each sample set including at least one sample, an each sample sharing at least one common environmental parameter;

detecting a plurality of microorganism types in each sample;

determining an absolute number of cells of each detected microorganism type of the plurality of microorganism types in each sample;

measuring unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain of a detected microorganism type;

determining the absolute cell count of each microorganism strain present in each sample based on the number of each detected microorganism types in that sample and the number of unique first markers and quantity thereof in that sample;

measuring at least one unique second marker for each microorganism strain to determine active microorganism strains in each sample;

generating a set of active microorganisms strains and their respective absolute cell counts for each of the at least two samples;

analyzing the active microorganisms strains and respective absolute cell counts for each sample of the at least two sample sets with at least one measured metadata for each of the at least two sample sets to identify relationships between each active microorganism strain and measured metadata;

selecting a plurality of active microorganism strains from the set of active microorganism strains based on the analysis; and

combining the selected plurality of active microorganism strains with a carrier medium to form a bioensemble of active microorganisms configured to alter a property of a target biological environment, corresponding to the or each measured metadata, when the bioensemble is introduced into that target biological environment.

2. The method of claim 1, wherein analyzing the active microorganisms strains and respective absolute cell counts for each sample of the at least two sample sets with at least one measured metadata is based on maximal information coefficient network analysis to measure connectivity of each active microorganism strain within a network and the at least one measured metadata.

3. The method of claim 1, wherein measuring unique first markers, and quantity thereof, includes at least one of:

subjecting genomic DNA from each sample to a high throughput sequencing reaction; and

/or subjecting genomic DNA from each sample to metagenome sequencing.

4. The method of claim 1, wherein the unique first markers include at least one of an mRNA marker, an siRNA marker, and/or a ribosomal RNA marker.

5. The method of claim 1, wherein the unique first markers include at least one of a sigma factor, a transcription factor, nucleoside associated protein, and/or metabolic enzyme.

6. The method of claim 1, wherein measuring unique first markers includes at least one of measuring unique genomic DNA markers in each sample, measuring unique RNA markers in each sample, and/or measuring unique protein markers in each sample.

7. The method of claim 1, wherein measuring unique first markers includes measuring unique metabolite markers in each sample.

8. The method of claim 7, wherein measuring unique metabolite markers includes at least one of measuring unique lipid markers in each sample and/or measuring unique carbohydrate markers in each sample.

9. The method of claim 1, wherein measuring at least one unique second marker for each microorganism strain includes measuring a level of expression of the at least one unique second marker.

10. The method of claim 9, wherein measuring the level of expression of the at least one unique second marker includes at least one of:

subjecting sample mRNA to gene expression analysis;

subjecting each sample or a portion thereof to mass spectrometry analysis; and

/or subjecting each sample or a portion thereof to metaribosome profiling or ribosome profiling.

11. A processor-implemented method, comprising:

obtaining at least two samples sharing at least one common environmental parameter, each sample including a heterogeneous microbial community;

detecting the presence of a plurality of microorganism types in each sample;

determining an absolute number of cells of each detected microorganism type of the plurality of microorganism types in each sample;

measuring unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain of a detected microorganism type;

measuring a value of one or more unique second markers, a unique second marker indicative of metabolic activity of a particular microorganism strain of a detected microorganism type;

determining the activity of each detected microorganism strain based on the measured value of the one or more unique second markers exceeding a specified threshold;

determining the respective ratios of each active detected microorganism strain in the sample;

analyzing each of the active detected microorganism strains of the at least two samples via a processor, the analysis including identifying relationships and the strengths thereof between each active detected microorganism strain and every other active detected microorganism strain, and each active detected microorganism strain and at least one measured metadata;

displaying, on a graphical interface, identified relationships between active detected microorganism strains and the at least one measured metadata; and

formulating a bioensemble comprising a carrier and at least two active detected microorganism strains based on identified relationships.

12. The processor-implemented method of claim 11, wherein the analysis is based on maximal information coefficient network analysis that measures connectivity of each active microorganism strain to every other active microorganism strain and the at least one measured metadata.

13. The processor-implemented method of claim 11, where an identified relationship is not displayed if the strength thereof does not exceed a specified threshold.

14. The processor-implemented method of claim 11, further comprising assigning each active detected microorganism strains to one of at least two groups based on predicted function thereof.

15. The processor-implemented method of claim 11, further comprising assigning each active detected microorganism strains to one of at least two groups based on chemistry thereof.

16. The processor-implemented method of claim 11, further comprising assigning each active detected microorganism strains to one at least three groups based on predicted function and/or chemistry thereof.

17. The processor-implemented method of claim 11, wherein the analysis includes generating matrices populated with linkages denoting metadata and microorganism strain associations.

18. The processor-implemented method of claim 11, wherein the analysis includes determining co-occurrence of at least one active microorganism strain and another active microorganism strain and/or the at least one measured metadata.