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 9,938,558 B2
• Filed: 10/23/2017
• Issued: 04/10/2018
• Est. Priority Date: 06/25/2015
• Status: Active Grant

• Current Assignee: Native Microbials Incorporated
• Original Assignee: Ascus Biosciences, Inc.
• Inventors: Embree, Mallory
• Primary Examiner(s): Ghafoorian, Reza
• Application Number:
  US15/791,391
• Publication Number:
  US 20180044712A1
• Time in Patent Office: 169 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, 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 environmental parameters, are also disclosed.

Claims

1. A method of making a synthetic microbial ensemble to improve at least one target biological property in a target biological environment, comprising:

selecting at least two active microorganism strains including at least one active fungus strain and at least one active bacterium strain, the at least two active microorganism strains being at least two active microorganism strains identified by processing a plurality of samples collected from a sample population, the processing including;

for each sample of the plurality of samples;

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

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

determining an absolute cell count of each microorganism strain based on multiplying the absolute number of each detected microorganism type and relative quantity of unique first markers for that microorganism type;

determining an activity level for each microorganism strain based on measuring at least one unique second marker;

filtering each microorganism strain by the determined activity level to provide a set of active microorganism strains and respective absolute cell counts;

measuring at least one metadata associated with at least one target biological property in a target biological environment for each sample of the plurality of samples collected from the sample population; and

analyzing the set of active microorganism strains and respective absolute cell counts of each sample of the plurality of samples with the at least one measured metadata of each sample of the plurality of samples via network analysis to identify relationships between each active microorganism strain from the set of active microorganism strains and between each active microorganism strain from the set of active microorganism strains and the at least one measured metadata categorizing active microorganism strains based on the identified relationships between active microorganism strains and the at least one measured metadata from which the at least two active microorganism strains can be selected based on at least one of predicted function and chemistry;

preparing the at least one active fungus strain and preparing the at least one active bacterium strain for inclusion in a synthetic microbial ensemble configured to improve the at least one target biological property in a target biological environment when the synthetic microbial ensemble is introduced into the target biological environment; and

forming the synthetic microbial ensemble from the prepared at least one active fungus strain, the prepared at least one active bacterium strain, and at least one stabilizing carrier.

2. The method of claim 1, wherein preparing the at least one active fungus strain includes preservation by vaporization.

3. The method of claim 1, wherein the at least one active fungus strain is a Pichia fungus strain.

4. The method of claim 1, wherein the at least one active fungus strain is a Pichia kudriavzevii.

5. The method of claim 1, wherein preparing the at least one active bacterium strain includes drying spores of the at least one active bacterium strain.

6. The method of claim 1, wherein the at least one active bacterium strain is a Clostridium bacterium strain.

7. The method of claim 1, wherein the at least one active bacterium strain is a Clostridium butyricum.

8. The method of claim 1, wherein the stabilizing carrier includes at least one of calcium carbonate and/or silicon dioxide.

9. The method of claim 1, wherein the target biological environment is the rumen of at least one ruminant, and the target biological property is at least one of improved fiber digestion and increased volatile fatty acid production.

10. A synthetic microbial ensemble feed supplement of active microorganism strains to improve at least one target biological property in a rumen of a ruminant when the synthetic microbial ensemble feed supplement of active microorganism strains administered thereto, comprising:

at least one preserved active Pichia fungus strain having a colony-forming unit (CFU) value of at least 10^7 per gram of synthetic microbial ensemble feed supplement;

at least one active Clostridium bacterium strain having a CFU value of at least 10^6 per gram of synthetic microbial ensemble feed supplement, the at least one active Clostridium bacterium strain composed of dried spores of the at least one active Clostridium bacterium strain;

the at least one active Pichia fungus strain and the at least one active Clostridium bacterium strain corresponding to active microorganism strains identified by network analysis of a plurality of rumen samples collected from a sample population of ruminants, the network analysis including;

for each rumen sample of the plurality of rumen samples;

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

measuring a quantity of unique first markers, 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 from the absolute number of each detected microorganism type and relative quantity of unique first markers of that microorganism type;

determining an activity level for each microorganism strain based on at least one unique second marker;

filtering each microorganism strain by the determined activity level to provide a list of active microorganism strains and their respective absolute cell counts;

measuring at least one metadata associated with a rumen biological property for each of the plurality of rumen samples collected from the sample population of ruminants; and

analyzing the active microorganisms strains and respective absolute cell counts of each rumen sample of the plurality of rumen samples with the at least one measured metadata to identify relationships between each microorganism strain and between each active microorganism strain and the at least one measured metadata, categorizing active microorganism strains based on the identified relationships between active microorganism strains and the at least one measured metadata from which the at least two active microorganism strains can be selected based on at least one of predicted function and chemistry; and

at least one stabilizing carrier for the at least one preserved active Pichia fungus strain and the at least one active Clostridium bacterium strain.

11. A method to improve rumen function in a ruminant with a synthetic microbial ensemble supplement, comprising:

selecting at least two active microorganism strains, including at least one active Pichia fungus strain and a least one active Clostridium bacterium strain, the selected at least two active microorganism strains corresponding to at least two active microorganism strains identified by network analysis of a plurality of rumen samples collected from a sample population of ruminants, the network analysis including;

for each rumen sample of the plurality of rumen samples;

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

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

determining the absolute cell count of each microorganism strain based on the absolute number of each detected microorganism type and relative quantity of unique first markers;

determining an activity level for each microorganism strain based on at least one unique second marker;

generating a list of active microorganism strains and their respective absolute cell counts;

measuring at least one metadata associated with rumen function for each rumen sample of the plurality of rumen samples collected from the sample population of ruminants; and

analyzing the absolute cell counts of active microorganisms strains of each of the rumen samples of the plurality of rumen samples with the at least one measured metadata to identify relationships between each active microorganism strain and between each active microorganism strain the at least one measured metadata, categorizing active microorganism strains based on the identified relationships between active microorganism strains and the at least one measured metadata from which at least two active microorganism strains can be selected based on at least one of predicted function and chemistry;

preparing the at least one active Pichia fungus strain and preparing the at least one active Clostridium bacterium strain for inclusion in a synthetic microbial ensemble supplement configured to improve rumen function when administered to a ruminant as a feed supplement;

forming the synthetic microbial ensemble supplement from the prepared at least one active Pichia fungus strain and at least one active Clostridium bacterium strain, and at least one stabilizing carrier; and

administering the synthetic microbial ensemble supplement to at least one ruminant as a feed supplement to improve rumen function of the at least one ruminant.

12. The method of claim 11, wherein the at least one active Pichia fungus strain includes a Pichia kudriavzevii.

13. The method of claim 11, wherein the at least one active Clostridium bacterium strain includes a Clostridium butyricum.

14. The method of claim 11, wherein the stabilizing carrier includes calcium carbonate.

15. The method of claim 11, wherein the improved rumen function includes at least one of improved fiber digestion and increased volatile fatty acid production.

16. The method of claim 11, wherein the at least one active Pichia fungus strain of the synthetic microbial ensemble supplement has a CFU value of at least 10^6 per gram of synthetic microbial ensemble supplement.

17. The method of claim 11, wherein the at least one active Clostridium bacterium strain of the synthetic microbial ensemble supplement has a CFU value of at least 10^5 per gram of synthetic microbial ensemble supplement.

18. The method of claim 11, wherein the at least one ruminant is a dairy cow and administering the synthetic microbial ensemble supplement as a feed supplement includes providing a daily dosage of synthetic microbial ensemble supplement starting at calving.

19. The method of claim 11, wherein the at least one ruminant is a dairy cow and administering the synthetic microbial ensemble supplement as a feed supplement includes providing a daily dosage of at least 1 g synthetic microbial ensemble supplement.