METHODS, APPARATUSES AND SYSTEMS FOR ANALYZING MICROORGANISM STRAINS FROM COMPLEX HETEROGENEOUS COMMUNITIES, PREDICTING AND IDENTIFYING FUNCTIONAL RELATIONSHIPS AND INTERACTIONS THEREOF, AND SELECTING AND SYNTHESIZING MICROBIAL ENSEMBLES BASED THEREON

• US 20180223325A1
• Filed: 04/09/2018
• Published: 08/09/2018
• Est. Priority Date: 06/25/2015
• Status: Active Grant

• Current Assignee: Native Microbials Incorporated
• Original Assignee: Ascus Biosciences, Inc.
• Inventors: EMBREE, Mallory
• Granted Patent: US 10,870,877 B2
• Time in Patent Office: Days
• CPC Class Codes: A23K 10/16 Addition of microorganisms or extracts thereof, e.g. single-cell proteins, to feeding-stuff compositions A23K10/12 takes precedence
• CPC Class Codes: A23K 10/18 of live microorganisms
• CPC Class Codes: A23K 20/10 Organic substances
• CPC Class Codes: A23K 50/10 for ruminants
• CPC Class Codes: A23K 50/30 for swines
• CPC Class Codes: A23K 50/70 for birds
• CPC Class Codes: A23K 50/75 for poultry
• CPC Class Codes: A61K 35/74 Bacteria therapeutic use of a bacterial protein A61K38/00
• CPC Class Codes: C12Q 1/06 Quantitative determination
• CPC Class Codes: C12Q 1/6874 involving nucleic acid arrays, e.g. sequencing by hybridisation
• CPC Class Codes: C12Q 1/689 for bacteria
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: C12Q 2600/178 miRNA, siRNA or ncRNA
• CPC Class Codes: G01N 2333/33 from Clostridium (G)
• CPC Class Codes: G01N 2333/37 from fungi
• CPC Class Codes: G01N 33/569 for microorganisms, e.g. protozoa, bacteria, viruses
• CPC Class Codes: G16B 20/00 ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
• CPC Class Codes: G16B 20/20 Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
• 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

Abstract

Methods, apparatuses, and systems for screening, analyzing and selecting microorganisms from complex heterogeneous communities, predicting and identifying functional relationships and interactions thereof, synthesizing microbial ensembles based thereon, and forming and administering endomicrobial feed supplements 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 environmental parameters, are also disclosed.

Claims

1. A method, comprising:

improving rumen function in a ruminant by administering an endomicrobial feed supplement thereto, the endomicrobial feed supplement including a synthetic microbial ensemble, including;

selecting at least three active microorganism strains, the selected at least three active microorganism strains corresponding to at least three active microorganism strains identified by an analysis of a plurality of rumen samples collected from a sample population of ruminants, the analysis including;

detecting the presence of one or more microorganism types and determining an absolute number of each detected microorganism type for each sample;

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

determining the absolute cell count of each microorganism strain in each sample from the absolute number of each detected microorganism type and relative quantity of unique first markers;

determining an activity level for each microorganism strain in each sample;

generating a set of active microorganism strains and their respective absolute cell counts for each sample;

measuring at least one metadata associated with rumen function for each sample;

identifying network relationships between active microorganism strains and between active microorganism strains the at least one measured metadata using associated absolute cell count data of active microorganisms strain; and

categorizing active microorganism strains based on the identified network relationships of the active microorganism strains and the at least one measured metadata from which active microorganism strains can be selected based on at least one of predicted function and chemistry;

preparing the at least three active microorganism strains for inclusion in an endomicrobial feed supplement;

forming the endomicrobial feed supplement from the prepared at least three active microorganism strains and at least one medium; and

administering the endomicrobial feed supplement to a ruminant to improve rumen function of the ruminant.

2. A processor-implemented method, comprising:

receiving data for a plurality of samples, each sample of the plurality of samples having at least one characteristic in common with every other sample of the plurality of samples, each sample of the plurality of samples having at least one characteristic different from every other sample of the plurality of samples, and each sample of the plurality of samples including a heterogeneous microbial community having a plurality of microorganism types, the received data including unique first marker data and unique second marker data;

determining, from the received data for the plurality of samples, an absolute number of cells of each microorganism type of the plurality of microorganism types for each sample of the plurality of samples;

determining a relative quantity of microorganism strains for each microorganism type of the plurality of microorganism types for each sample of the plurality of samples using the associated unique first marker data;

determining an activity of each microorganism strain of each microorganism type of the plurality of microorganism types for each sample of the plurality of samples using the associated unique second marker data;

determining absolute cell counts of each active microorganism strain in each sample of the plurality of samples using the absolute number of cells of each microorganism type, the relative quantity of microorganism strains thereof, and the associated activity of each microorganism strain;

analyzing each of the active microorganism strains from each sample of the plurality of samples via at least one processor, the analysis including determining relationships and the strengths thereof between each active microorganism strain and corresponding absolute cell count and every other active microorganism strain and absolute cell count, and each active microorganism strain and absolute cell count and at least one measured metadata; and

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

3. The processor-implemented method of claim 2, where an identified relationship is not displayed if the strength thereof does not exceed a specified threshold.

4. The processor-implemented method of claim 2, further comprising assigning each active microorganism strain to one of at least two groups based on predicted function and/or chemistry thereof.

5. The processor-implemented method of claim 2, wherein the analysis includes generating matrices populated with linkages denoting metadata and microorganism strain associations.

6. The processor-implemented method of claim 2, 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 measure metadata.

7. The processor-implemented method of claim 6, wherein determining the co-occurrence is based on graph theory.

8. The processor-implemented method of claim 2, wherein the absolute number of cells of each microorganism type of the plurality of microorganism types for each sample of the plurality of samples is determined via flow cytometry.

9. The processor-implemented method of claim 2, wherein the unique first marker data includes unique genomic DNA marker data.

10. The processor-implemented method of claim 2, wherein the unique first marker data includes unique genomic RNA marker data.