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 9,540,676 B1
• Filed: 07/22/2016
• Issued: 01/10/2017
• Est. Priority Date: 06/25/2015
• Status: Active Grant

• Current Assignee: Native Microbials Incorporated
• Original Assignee: Ascus Biosciences, Inc.
• Inventors: Embree, Mallory, Zengler, Karsten
• Primary Examiner(s): Ghafoorian, Reza
• Application Number:
  US15/217,575
• Publication Number:
  US 20160376627A1
• Time in Patent Office: 172 Days
• US Class Current: 1/1
• 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 50/10 for ruminants
• CPC Class Codes: A23K 50/30 for swines
• 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: 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 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

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 for forming a synthetic ensemble of active microorganism strains configured to alter a property in a biological environment, based on two or more sample sets each having a plurality of environmental parameters, at least one parameter of the plurality of environmental parameters being a common environmental parameter that is similar between the two or more sample sets and at least one environmental parameter being a different environmental parameter that is different between each of the two or more sample sets, each sample set including at least one sample comprising a heterogeneous microbial community obtained from a biological sample source, at least one of the active microorganism strains being a subtaxon of one or more organism types, the method comprising:

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

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

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

measuring the level of expression of one or more unique RNA markers, wherein a unique RNA marker is a marker of activity of a microorganism strain;

determining activity of each of the detected microorganism strains for each sample based on the level of expression of the one or more unique RNA markers exceeding a specified threshold to identify one or more active microorganism strains;

calculating the absolute cell count of each detected active microorganism strain in each sample by multiplying the respective quantity of each of the one or more unique first markers and the absolute number of cells of the microorganism types from which the one or more microorganism strains is a subtaxon, the one or more active microorganism strains expressing one or more unique RNA markers above the specified threshold;

analyzing the active microorganism strains of the two or more sample sets, the analyzing including conducting nonparametric network analysis of each of the active microorganism strains for each of the two or more sample sets, the at least one common environmental parameter, and the at least one different environmental parameter, the nonparametric network analysis including (1) determining the maximal information coefficient score between each active microorganism strain and every other active microorganism strain and (2) determining the maximal information coefficient score between each active microorganism strain and the at least one different environmental parameter;

selecting a plurality of active microorganism strains from the one or more active microorganism strains based on the nonparametric network analysis; and

forming a synthetic ensemble of active microorganism strains comprising the selected plurality of active microorganism strains and a microbial carrier medium, the ensemble of active microorganism strains configured to selectively alter a property of a biological environment when the synthetic ensemble of active microorganism strains is introduced into that biological environment.

2. The method of claim 1, wherein at least one unique first marker is a phylogenetic marker including a 5S ribosomal subunit gene, a 16S ribosomal subunit gene, a 23S ribosomal subunit gene, a 5.8S ribosomal subunit gene, a 18S ribosomal subunit gene, a 28S ribosomal subunit gene, a cytochrome c oxidase subunit gene, a beta-tubulin gene, an elongation factor gene, an RNA polymerase subunit gene, an internal transcribed spacer (ITS), or a combination thereof.

3. The method of claim 1, wherein each sample set comprises a plurality of samples, and a measured indicia of at least one environmental parameter for each sample within a sample set is substantially similar to each other sample within that sample set, and the measured indicia for one sample set is different from an average measured indicia for that environmental parameter from another sample set.

4. The method of claim 3, wherein the measured indicia of the at least one environmental parameter is one or more of:

pH data, temperature data, fat abundance data, protein abundance data, carbohydrate abundance data, mineral abundance data, vitamin abundance data, sample source bodyweight data, sample source feed intake data, sample source weight gain data, sample source feed efficiency data, pathogen presence or absence data, sample source physical characteristic data, sample source physical measurement data, sample source chemical measurement data, sample source production data.

5. The method of claim 1, wherein at least one environmental parameter of the plurality of environmental parameters comprises a presence, activity and/or quantity of a second microorganism strain.

6. The method of claim 1, wherein at least one measured indicia of at least one environmental parameter of the plurality of environmental parameters for a first sample set is different from a measured indicia of that at least one environmental parameter for a second sample set.

7. The method of claim 1, wherein each sample set comprises a plurality of samples, and a first sample set is collected from a first population and a second sample set is collected from a second population.

8. The method of claim 1, wherein each sample set comprises a plurality of samples, and a first sample set is collected from a first population at a first time and a second sample set is collected from the first population at a second time different from the first time.

9. The method of claim 1, wherein measuring the number of unique first markers in each sample comprises measuring the number of unique genomic DNA markers.

10. The method of claim 1, wherein measuring the number of unique first markers in each sample comprises measuring the number of unique protein markers.

11. The method of claim 1, wherein determining the absolute number of cells of each of the detected microorganism types in each sample includes subjecting a sample portion to staining, microfluidics, microfabrication, and/or flow cytometry.

12. The method of claim 1, wherein analyzing the active microorganism strains of the two or more sample sets further includes cluster analysis.

13. The method of claim 1, wherein analyzing the active microorganism strains of the two or more sample sets further includes Louvain method cluster analysis.

14. A method of forming an active microorganism bioensemble of active microorganism strains configured to alter a property in a target biological environment, comprising:

obtaining at least two samples sharing at least one common environmental parameter and having at least one different environmental parameter;

for each sample, detecting the presence of one or more microorganism types in each sample;

determining a number of each detected microorganism type of the one or more microorganism types in each sample;

measuring a number of 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;

multiplying the number of each microorganism type by the proportional number of the unique first markers to yield the absolute cell count of each microorganism strain present in each sample;

measuring at least one unique second marker for each microorganism strain based on a specified threshold to determine an activity level for that microorganism strain in each sample;

filtering the absolute cell count of each microorganism strain by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for each of the at least two samples;

comparing the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with at least one measured metadata for each of the at least two samples, the comparison including determining the co-occurrence of the active microorganism strains in each sample with the at least one measured metadata, determining the co-occurrence of the active microorganism strains and the at least one measured metadata in each sample including creating matrices populated with linkages denoting metadata and microorganism strain relationships, the absolute cell count of the active microorganism strains, and the measure of the unique second markers, to represent one or more heterogeneous microbial community networks;

grouping the active microorganism strains into at least two groups according to predicted function and/or chemistry based on at least one of nonparametric network analysis and cluster analysis identifying connectivity of each active microorganism strain and measured metadata within an active heterogeneous microbial community network;

selecting at least one microorganism strain from each of the at least two groups; and

combining the selected microorganism strains and with a carrier medium to form a bioensemble of active microorganisms configured to alter a property corresponding to the at least one metadata of target biological environment when the bioensemble is introduced into that target biological environment.

15. The method of claim 14, further comprising:

obtaining at least one further sample, based on the at least one measured metadata, wherein the at least one further sample shares at least one environmental parameter with the at least two samples; and

for the at least one further sample, detecting the presence of one or more microorganism types, determining a number of each detected microorganism type of the one or more microorganism types, measuring a number of unique first markers and quantity thereof, multiplying the number of each microorganism type by the proportional number of the unique first markers to yield the absolute cell count of each microorganism strain present, measuring at least one unique second marker for each microorganism strain to determine an activity level for that microorganism strain, filtering the absolute cell count by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for the at least one further sample;

wherein comparing the filtered absolute cell counts of active microorganisms strains comprises comparing the filtered absolute cell counts of active microorganism strains for each of the at least two samples and the at least one further sample with the at least one measured metadata, such that the selection of the active microorganism strains is at least partially based on the list of active microorganisms strains and their respective absolute cell counts for the at least one further sample.

16. The method of claim 14, wherein the nonparametric network analysis includes:

determining a maximal information coefficient score between each active microorganism strain and every other active microorganism strain, and

determining a maximal information coefficient score between each active microorganism strain and the at least one measured metadata.

17. The method of claim 14, wherein measuring the at least one unique second marker includes measuring a level of expression of the at least one unique second marker in each sample.

18. The method of claim 17, wherein measuring the level of expression of the at least one unique second marker in each sample includes subjecting mRNA in the sample to gene expression analysis.

19. The method of claim 18, wherein the gene expression analysis includes a sequencing reaction.

20. The method of claim 18, wherein the gene expression analysis includes a quantitative polymerase chain reaction (qPCR), metatranscriptome sequencing, and/or transcriptome sequencing.

21. The method of claim 17 wherein measuring the level of expression of the at least one unique second marker includes subjecting at least a portion of each sample to at least one of mass spectrometry analysis, metaribosome profiling, and/or ribosome profiling.

22. The method of claim 14, wherein the one or more microorganism types includes bacteria, archaea, fungi, protozoa, plant, other eukaryote, viruses, viroids, or a combination thereof.

23. The method of claim 14, wherein the one or more microorganism strains is one or more bacterial strains, archaeal strains, fungal strains, protozoa strains, plant strains, other eukaryote strains, viral strains, viroid strains, or a combination thereof.

24. The method of claim 14, wherein the cluster analysis comprises Louvain method cluster analysis.

25. The method of claim 14, wherein the at least one different environmental parameter includes a collection time at which each of the at least two samples was collected, such that the collection time for a first sample is different from the collection time of a second sample.

26. The method of claim 14, wherein the at least one different environmental parameter includes a collection location at which each of the at least two samples was collected, such that the collection location for a first sample is different from the collection location of a second sample.

27. The method of claim 14, wherein the at least one common environmental parameter includes a common sample source type, such that the sample source type for a first sample is the same as the sample source type of a second sample, the common sample source type including one or more of:

an animal species type, an animal breed type, a tissue type, an organ type, a blood sample, a tooth sample, a perspiration sample, a water sample, a nail sample, a skin sample, a hair sample, a plant sample, a feces sample, a soil sample, a urine sample, a semen sample, a mucus sample, a saliva sample, a muscle sample, a brain sample, a gastrointestinal sample.

28. The method of claim 14, wherein the at least one measured metadata includes one or more of pH, temperature, amount of a fat, amount of milk fat, amount of a protein, amount of whey protein, amount of casein protein, amount of a carbohydrate, amount of a mineral, amount of a vitamin, bodyweight, amount of milk, feed intake, weight gain, feed efficiency, feed digestibility, lesion scores, diet, and/or presence or absence of one or more pathogens.

29. The method of claim 14, wherein the at least one measured metadata comprises a presence, activity and/or quantity of a second microorganism strain.

30. The method of claim 14, wherein measuring the number of unique first markers, and quantity thereof, includes at least one of:

subjecting genomic DNA from each sample to a high throughput sequencing reaction, and subjecting genomic DNA from each sample to metagenome sequencing.