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 20170342457A1
• Filed: 11/11/2016
• Published: 11/30/2017
• Est. Priority Date: 06/25/2015
• Status: Active Grant

• Current Assignee: Native Microbials Incorporated
• Original Assignee: Ascus Biosciences, Inc.
• Inventors: EMBREE, Mallory, ZENGLER, Karsten
• Granted Patent: US 10,844,419 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 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

1-87. -87. (canceled)

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

obtaining at least two sample sets sharing at least one common environmental parameter and having at least one different environmental parameter, each of the at least two sample sets comprising a plurality of samples;

for each sample of the at least two sample sets, 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;

determining the absolute cell count of each microorganism strain present in each sample based on the number of each detected microorganism type and the relative number of the corresponding or related unique first markers for that microorganism type;

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 sample of the at least two samples sets;

comparing the filtered absolute cell counts of active microorganisms strains 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, 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 synthetic 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.

89. A method for forming an ensemble of active microorganism strains configured to alter a property or characteristic in an environment based on two or more sample sets that share at least one common or related environmental parameter between the two or more sample sets and that have at least one different environmental parameter between the two or more sample sets, each sample set comprising a plurality of samples each including a heterogeneous microbial community therein, wherein the one or more microorganism strains is a subtaxon of one or more organism types, comprising:

detecting the presence of a plurality of microorganism types in each sample of the two or more sample sets;

determining the absolute number of cells of each of the detected microorganism types in each sample of the two or more sample sets;

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

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

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

calculating the absolute cell count of each detected active microorganism strain in each sample from the proportional quantity of the one or more 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 the unique second marker above the specified threshold;

determining the co-occurrence of each active microorganism strain in the samples with at least one environmental parameter and other active microorganism strain based on maximal information-based nonparametric exploration to identify connectivity of each microorganism strain within a network, the network including the collection of the at least two or more sample sets with at least one common or related environmental parameter;

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

forming an ensemble of active microorganism strains from the selected plurality of active microorganism strains, the ensemble of active microorganism strains configured to selectively alter a property or characteristic of an environment when the ensemble of active microorganism strains is introduced into that environment.

90. The method of claim 89, wherein the at least one environmental parameter comprises a presence, activity and/or quantity of a second microorganism strain.

91. The method of claim 89, wherein at least one measured indicia of at least one common or related environmental factor for a first sample set is different from a measured indicia of the at least one common or related environmental factor for a second sample set.

92. The method of claim 89, wherein each sample set comprises a plurality of samples, and a measured indicia of at least one common or related environmental factor for each sample within a sample set is substantially similar, and an average measured indicia for one sample set is different from the average measured indicia from another sample set.

93. The method of claim 89, wherein the two or more sample sets comprise at least a first sample set and a second sample set, the first sample set collected from a first population and the second sample set collected from a second population.

94. The method of claim 89, wherein the two or more sample sets comprise at least a first sample set and a second sample set, the first sample set collected from a first population at a first time and the second sample set is collected from the first population at a second time different from the first time.

95. The method of claim 89, wherein at least one common or related environmental factor includes nutrient information.

96. The method of claim 89, wherein at least one common or related environmental factor includes dietary information.

97. The method of claim 89, wherein at least one common or related environmental factor includes physiological characteristics.

98. The method of claim 89, wherein at least one common or related environmental factor includes infection information or health status.

99. The method of claim 91, wherein at least one measured indicia is sample pH, sample temperature, abundance of a fat, abundance of a protein, abundance of a carbohydrate, abundance of a mineral, abundance of a vitamin, abundance of a natural product, abundance of a specified compound, bodyweight of the sample source, feed intake of the sample source, weight gain of the sample source, feed efficiency of the sample source, presence or absence of one or more pathogens, physical characteristic(s) or measurement(s) of the sample source, production characteristics of the sample source, or a combination thereof.

100. The method of claim 89, wherein the at least one parameter is at least one of abundance of whey protein, abundance of casein protein, and/or abundance of fats in milk.

101. The method of claim 89, wherein measuring the number of unique first markers in each sample comprises measuring the number of unique genomic DNA markers.

102. The method of claim 89, wherein measuring the number of unique first markers in each sample comprises measuring the number of unique RNA markers.

103. The method of claim 89, wherein measuring the number of unique first markers in the sample comprises measuring the number of unique protein markers.

104. The method of claim 89, wherein the plurality of microorganism types includes one or more bacteria, archaea, fungi, protozoa, plant, other eukaryote, virus, viroid, or a combination thereof.

105. The method of claim 89, wherein determining the absolute cell number of each of the microorganism types in each sample includes subjecting the sample or a portion thereof to sequencing, centrifugation, optical microscopy, fluorescent microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain reaction (qPCR), gel electrophoresis and/or flow cytometry.

106. The method of claim 89, wherein one or more active microorganism strains is a subtaxon of one or more microbe types selected from one or more bacteria, archaea, fungi, protozoa, plant, other eukaryote, virus, viroid, or a combination thereof.

107. The method of claim 89, wherein one or more active 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.

108. The method of claim 89, wherein one or more active microorganism strains is one or more fungal species, fungal subspecies, bacterial species and/or bacterial subspecies.

109. The method of claim 89, wherein at least one unique first marker comprises a phylogenetic marker comprising 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.

110. The method of claim 89, wherein measuring the number of unique first markers, and quantity thereof, comprises subjecting genomic DNA from each sample to a high throughput sequencing reaction.

111. The method of claim 89, wherein measuring the number of unique first markers, and quantity thereof, comprises subjecting genomic DNA from each sample to metagenome sequencing.

112. The method of claim 89, wherein a unique first marker comprises an mRNA marker, an siRNA marker, or a ribosomal RNA marker.

113. The method of claim 89, wherein a unique first marker comprises a sigma factor, a transcription factor, nucleoside associated protein, metabolic enzyme, or a combination thereof.

114. The method of claim 89, wherein measuring the level of expression of one or more unique second markers comprises subjecting mRNA in the sample to gene expression analysis.

115. The method of claim 114, wherein the gene expression analysis comprises a sequencing reaction.

116. The method of claim 114, wherein the gene expression analysis comprises a quantitative polymerase chain reaction (qPCR), metatranscriptome sequencing, and/or transcriptome sequencing.

117. The method of claim 89, wherein measuring the level of expression of one or more unique second markers includes subjecting each sample or a portion thereof to mass spectrometry analysis.

118. The method of claim 89, wherein measuring the level of expression of one or more unique second markers comprises subjecting the sample or a portion thereof to metaribosome profiling, and/or ribosome profiling.

119. The method of claim 89, wherein the source type for the samples is one of animal, soil, air, saltwater, freshwater, wastewater sludge, sediment, oil, plant, an agricultural product, bulk soil, soil rhizosphere, plant part, vegetable, an extreme environment, or a combination thereof.

120. The method of claim 89, wherein each sample is a gastrointestinal sample.

121. The method of claim 89, wherein each sample is one of a tissue sample, blood sample, tooth sample, perspiration sample, fingernail sample, skin sample, hair sample, feces sample, urine sample, semen sample, mucus sample, saliva sample, muscle sample, brain sample, or organ sample.

122. A method, comprising:

obtaining at least two sample sets sharing at least one common environmental parameter and having at least one different environmental parameter, each of the at least two sample sets comprising a plurality of samples;

for each sample of the at least two sample sets, 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;

determining the absolute cell count of each microorganism strain present in each sample based on the number of each detected microorganism type and the relative number of the corresponding or related unique first markers for that microorganism type;

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 sample of the at least two samples sets;

comparing the filtered absolute cell counts of active microorganisms strains 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, 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, the nonparametric network analysis includes maximal information-based nonparametric exploration;

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 synthetic 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.