JP2020507308A - Methods, apparatus, and systems for analyzing microbial strains of complex heterogeneous communities, determining their functional relevance and interaction, and managing diagnostic and biological conditions based thereon - Google Patents

Abstract

Disclosed are methods, devices, and systems for analyzing microbial strains of a complex heterogeneous community, determining their functional relevance and interaction, and managing diagnostic and biological conditions based thereon. Also disclosed are methods for diagnosing, analyzing, and treating conditions and abnormalities / deviations, including treatments, such as bioreactivity modifiers (such as bioreactivity modifiers, including synthetic microbial ensembles). . [Selection diagram] Fig. 1

Description

  This application claims priority and benefit to US provisional patent application Ser. No. 62 / 439,804, filed Dec. 28, 2016, and filed with US Provisional Patent Application Ser. No. 62 / 560,174 also claims priority and benefit, and the aforementioned application (s) is expressly incorporated herein by reference in its entirety for all purposes.

  This application may include copyright, maskwork, and / or other materials subject to intellectual property protection. The respective owners of such intellectual property do not make any sense to duplicate this disclosure as it appears on the Patent Office's files / records, , All rights reserved.

  Microorganisms coexist naturally as communities and participate in various interactions, resulting in cooperation and competition between individual community members. Advances in microbial ecology have revealed that most communities have high levels of species diversity and complexity. Microorganisms are ubiquitous in the environment and inhabit diverse ecosystems within the biosphere. Individual microbes and their respective communities play unique roles in environments such as the marine region (both deep and marine), soil, and animal tissues (including human tissues).

  The present disclosure is directed to methods, devices, and systems for analyzing microbial strains of a complex heterogeneous community, determining their functional relevance and interaction, and managing diagnostic and biological conditions based thereon. I do. Also disclosed are methods for diagnosing, analyzing, and treating conditions and conditions and abnormalities, including treatments involving synthetic microbial ensembles.

  In one aspect of the present disclosure, a diagnostic method is disclosed. The method may include obtaining at least two samples that share at least one common environmental parameter (sample type, sample position, sample time, etc.). At least one of the at least two samples may be defined as being in a first state, at least one of the at least two samples may be defined as being in a second state, and a second state may be defined. Is different from the first state. For example, in one embodiment, one of the at least two conditions is a healthy condition or a healthy sample source (eg, a sample source having one or more desirable characteristics or metadata). The associated condition, the other condition is an unhealthy / morbid condition, or an unhealthy / morbid sample source (eg, a sample source having one or more undesirable characteristics or metadata; In some cases, particularly conditions associated with a sample source having one or more features or metadata that are undesirable when compared to the corresponding feature (s) or metadata of a healthy sample source. For each sample, the presence of one or more microorganism types in the sample is detected, and the number of each of the one or more microorganism types detected in each sample is determined.

  A unique first marker and its amount in each sample are measured, each unique first marker being a marker for a microorganism strain of the microorganism type to be detected. Based on the number of each microorganism type and the number of unique first markers / each number, the absolute cell number of each microorganism strain in each sample is determined. Next, at least one unique second marker for each microorganism strain is measured and the activity level of that microorganism strain is determined (eg, determined based on the unique second marker above a certain activity threshold). Is done). Depending on the embodiment, the activity level can be numerical, relative, and / or binomial (eg, active / inactive). The absolute cell number of each microbial strain is filtered by the determined activity to obtain a set or list of active microbial strains and their respective absolute cell numbers for each of the at least two samples. A filtered absolute cell number of the active microorganism strain for at least one sample from the first state, and a filtered absolute cell number of the active microorganism strain for at least one sample from the second state; Can be defined or determined by comparing or processing the baseline state (eg, healthy or normal state). Baseline status can be defined or characterized by the presence or absence of the identified taxon and / or strain. In some embodiments, the method comprises or further comprises obtaining at least one additional sample, wherein the status of the additional sample is unknown. Next, for at least one additional sample, the presence of one or more microbial types is detected and the number of each detected microbial type of the one or more microbial types is determined. A unique first marker and its amount are determined, each unique first marker being a marker of a microorganism strain of the microorganism type to be detected. From the number of each microbial type and the number of unique first markers, the absolute cell number of each microbial strain is determined. At least one unique second marker is used for each microbial strain based on the specified threshold to determine the activity level of that microbial strain. The set of active microbial strains and their respective absolute cell numbers are obtained by filtering the absolute cell number of each microbial strain according to the determined activity. The set of active microbial strains and their respective absolute cell numbers for at least one additional sample is then compared to a baseline condition to determine the condition of at least one additional sample (eg, healthy or unhealthy, Normal or abnormal). Next, the determined state of the at least one additional sample is output and / or displayed (eg, output and / or displayed on a display screen or graphical interface).

  According to some other embodiments, the determined state of the at least one additional sample corresponds to a state of the environment associated with the at least one additional sample. Depending on the embodiment, the environment associated with the at least one additional sample may include a geospatial environment, such as a field or pasture, a feed environment or source (eg, a grain silo), Target animals and / or herds. The treatment for the environment associated with the at least one additional sample can be ascertained or determined. In embodiments where the baseline is healthy or the like, the treatment can be configured to change the state of the environment towards the baseline. In some embodiments, the treatment can be configured to change the condition of the environment toward a condition associated with a desired goal or beneficial result. The treatment may be a synthetic ensemble (especially a synthetic ensemble formed according to the methods of the present disclosure), a chemical / biological treatment or drug, a treatment regimen, a combination of two or more of these treatments, and / or the like. May be included. In some embodiments, the baseline status can be updated based on at least one additional sample.

  In another aspect of the present disclosure, an analysis method is disclosed. Such a method may include obtaining at least two sample sets, each sample set including a plurality of samples. In some embodiments, at least one of the at least two sample sets may be defined as being in the first state, and at least one of the at least two sample sets may be defined as the second. The first state is different from the second state, and the range of the sample in the sample set corresponds to the range of the state corresponding to the sample set. In other embodiments, the samples in the sample set are defined as being in their respective states, or the determination or definition of the states is made after analysis. The method then comprises detecting the plurality of microbial types in each sample, determining the absolute cell number of each of the plurality of microbial types detected in each sample among the plurality of microbial types, and determining the unique cell number in each sample. Measuring the first marker and its amount (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected). The method then determines the absolute cell number of each microbial strain present in each sample, the number of each microbial type detected in the sample, and the number and amount of the unique first marker in the sample. And determining at least one unique second marker for each microorganism strain to determine the active microorganism strain in each sample. Next, a set of active microbial strains for each sample of at least two sample sets and their respective absolute cell numbers are obtained. The method comprises analyzing each sample and / or each sample of at least two sample sets for active microbial strains and respective absolute cell numbers to define a baseline condition. Baseline status, in some embodiments, can be defined and / or characterized by the presence or absence of the identified taxon and / or strain.

  Next, at least one additional sample of unknown state is obtained. For at least one additional sample, the method comprises: (1) detecting the presence of one or more microbial types; (2) determining the number of each microbial type detected; Measuring the first marker and its amount (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected); (4) the number of each microorganism type and the unique first marker Determining the absolute cell number of each microbial strain from the number of the microbial strains; Determining the level, and (6) filtering the absolute cell number of each microbial strain according to the determined activity, to obtain a set of active microbial strains and their respective absolute details. To obtain the number, further comprising a. Comparing the set of active microbial strains and their respective absolute cell counts and the baseline status for the at least one additional sample determines the status associated with the at least one additional sample, and the at least one additional sample. The determined state associated with the sample is displayed or output.

  The method comprises selecting a plurality of active microorganism strains based on a baseline condition and a determined condition associated with at least one additional sample, and combining the selected plurality of active microorganism strains with a carrier medium. Forming a synthetic ensemble of active microorganisms, wherein the synthetic ensemble is associated with at least one additional sample when the synthetic ensemble is introduced into an environment associated with the at least one additional sample. Configured to alter the state of the environment.

  According to some embodiments, a method is disclosed for identifying an active microorganism from a plurality of samples, analyzing the identified microorganism with at least one metadata, and creating an ensemble of microorganisms based on the analysis. . In treatment for a disorder or undesirable condition, and / or a change in a biological condition (eg, a change from a diseased condition to a healthy or baseline condition, or a productive or enhanced condition from a baseline or normal condition) An ensemble can be used for (change to state). An embodiment of the method includes determining the absolute cell number of one or more active microbial strains in the sample, wherein the one or more active microbial strains are present in a microbial community in the sample. The one or more microbial strains can be a subtaxon of a microbial type. The sample used in the methods provided herein can be of any environmental origin. For example, in one embodiment, the sample is an animal, soil (eg, bulk soil or rhizosphere), air, saline, freshwater, wastewater sludge, sediment, oil, plant, agricultural product, plant, food or beverage (eg, Cheese, beer, wine, bread, or other fermented foods) or from extreme environments. In another embodiment, the animal sample is a sample of blood, tissue, teeth, sweat, nails, skin, hair, feces, urine, semen, mucus, saliva, gastrointestinal tract, lumen, muscle, brain, tissue, or organ. is there. In one embodiment, a method is provided for determining the absolute cell number of one or more active microbial strains. The method may also be used for definition of condition / biological condition and / or analysis to determine the condition of the sample (and corresponding sample source).

  According to some embodiments, there is provided a method of forming a bioensemble of an active microbial strain configured to alter the properties and / or biological state of a target biological environment. Such methods include obtaining at least two samples (or sample sets) sharing at least one common environmental parameter (sample type, sample time, sample location, sample source type, etc.), and multiple samples in each sample. Detecting the presence of the microbial type. Next, the absolute cell number of each microbial type detected in each sample of the plurality of microbial types is determined (eg, by way of non-limiting examples, the staining procedures discussed herein, cell sorting / FACS), the number and amount of unique first markers in each sample are determined, each unique first marker being a marker for a microorganism strain of the microorganism type to be detected. The absolute cell number of each microbial strain present in each sample is determined based on the number of each detected microbial type in that sample, and the number and amount of unique first markers in that sample. You. The at least one unique second marker is an indicator of activity (eg, metabolic activity), which is measured for each microbial strain to determine the active microbial strain in each sample, and for each of the at least two samples Or a set or list of active microbial strains and their respective absolute cell numbers. The active microbial strain and the respective absolute cell count for each of the at least two samples are analyzed, along with at least one measurement metadata for each of the at least two samples, to provide each active microbial strain and at least one An association with the measurement metadata, the measurement metadata for each sample, and / or the measurement metadata for the sample set (s) is revealed. A plurality of active microbial strains are selected based on the analysis and combined with the carrier medium to form a bioensemble of active microbes, wherein the bioensemble of the active microbe is introduced into the target biological environment. Then, it is configured to change at least one characteristic (corresponding to at least one metadata) of the target biological environment. Depending on the embodiment, the metadata can be a unique environmental parameter or any one environmental parameter, which is the same or relatively similar across samples or sample sets, and has different values across different samples or sample sets. I can do it. For example, dairy cow metadata may include feeding and milk production, and the feeding metadata values may be the same (ie, dairy cows are fed the same feed) while milk production / milk production The composition may be different (ie, a sample from one cow or a sample set from a particular cow group will have a corresponding milk production / sample set from a second cow group or a sample set from another cow group). Having a different average milk production / average milk composition from the milk composition). In some embodiments, a single set of samples can be utilized to define a biological state (such as a baseline state).

  According to some embodiments of the present disclosure, there are provided diagnostic methods and methods for analyzing a microbial community. Such methods include obtaining at least two samples (or at least two sample data), each sample comprising a heterogeneous microbial community, and detecting the presence of multiple microbial types in each sample. , May be included. Next, the absolute cell number of each microbial type detected in each sample of the plurality of microbial types is determined (e.g., determined via FACS or other methods discussed herein). . The number and amount of unique first markers in each sample are determined, each unique first marker being a marker for a microorganism strain of the microorganism type to be detected. The value (activity, concentration, expression, etc.) of one or more unique second markers is measured (the unique second marker is the activity (eg, metabolic activity) of a particular microbial strain of the microorganism type being detected. The activity of each detected microbial strain is determined based on measurements of one or more unique second markers (eg, based on values above a certain set threshold). A respective ratio of each active detection microorganism strain in each sample is determined (eg, based on respective absolute cell numbers, values, etc.). Next, each of the at least two samples of the active detectable microorganism strain (or a subset thereof) is analyzed to determine the activity between each active detectable microbial strain and the other active detectable microbial strain, as well as the respective activity. The relationship between the detected microbial strain and at least one measurement metadata and its strength are revealed. The revealed associations are then displayed or otherwise output and can be used to define a biological state and / or generate a bioensemble. In some embodiments, only relevances above a certain strength or weight are displayed. As set forth in detail throughout this disclosure, a biological condition or condition based on the analysis of the disclosure can be defined for analytical and therapeutic purposes, and the bioensemble, once introduced into the target environment, The ensemble can be configured to be able to change or alter the biological state or property of the target environment (specifically, the property associated with the measurement metadata).

  According to some embodiments of the present disclosure, the method comprises: detecting the presence of a plurality of microbial types in a plurality of samples; and determining an absolute cell count of each of the detected microbial types in each sample; including. The number and amount of unique first markers in each sample can be determined, wherein the unique first markers are microbial strain markers. The value or level of one or more unique second markers is measured, wherein the unique second markers are indicative of the metabolic activity of the particular microorganism strain. Based on the measured value or level, the respective activity of the detected microbial strain for each sample is determined or defined (eg, determined or defined based on the measured value or level above a specified threshold). A weight or cell correction for each detected active microbial strain in the sample is determined (the weight or cell correction is not a relative abundance). In some embodiments, the weighting or cell correction value is the absolute cell number of one strain relative to the sum of all absolute cell numbers of all strains.

  Each of the active detecting microorganism strains of each sample (or set of samples) is analyzed. The analysis is performed between each active detecting microbial strain having a weight and any other active microbial strain having a weight, and between each active microbial strain having a weight and one or more measurement metadata. And clarifying the relationship between them and their strength.

  The revealed relevance (and in some embodiments, related data such as weights and intensities) can be used to define a biological state (such as a baseline state) and / or It can then be displayed or otherwise output and used to generate a synthetic ensemble and / or manage the biological state. In some embodiments, the revealed relevance for each piece of metadata is displayed or output. In some embodiments, the displayed or output association identifies a disease or condition (s) and / or one or more microorganism strains that cause a deviation from the disease or baseline condition, or The association is configured to facilitate its identification. In some embodiments, the displayed or output association identifies or identifies one or more strains of the microorganism for alteration of a biological condition and / or treatment of a disease or disorder. The relevance is configured to facilitate.

  In some embodiments, only relevance above a certain strength or weight (eg, above a specified threshold or reference value) is displayed or output. As will be set forth in greater detail throughout this disclosure, when a synthetic ensemble is introduced into a target environment, the synthetic ensemble modifies the biological state and / or characteristics of the target environment (specifically, measurement metadata and (Related characteristics) can be changed or changed. In some embodiments, the methods described above may be used to modify a biological state or to form a synthetic ensemble of active microbial strains configured to alter the properties of a biological environment. The method may be based on two or more sample sets each having a plurality of environmental parameters, wherein at least one parameter of the plurality of environmental parameters includes a similar common environmental parameter between the two or more sample sets. And the at least one environmental parameter is another environmental parameter that differs between each of the two or more sample sets. In some embodiments, each sample set includes at least one sample that includes a heterogeneous microbial community obtained from a biological sample source. In some embodiments, at least one of the active microbial strains is a sub-taxon of one or more microbial types.

  In some embodiments of the present disclosure, the one or more microbial types are one or more bacteria (eg, mycoplasma, cocci, bacilli, rickettsia, spirirum), fungi (eg, filamentous fungi, yeast), linear Animals, protozoa, archaebacteria, algae, dinoflagellates, viruses (eg, bacteriophages), viroids, and / or combinations thereof. In one embodiment, the one or more microbial strains are one or more bacteria (eg, mycoplasma, cocci, bacilli, rickettsia, spirirum), fungi (eg, filamentous fungi, yeast), nematodes, protozoa , Archaea, algae, dinoflagellates, viruses (eg, bacteriophages), viroids, and / or combinations thereof. In another embodiment, the one or more microorganism strains is one or more fungal species or fungal subspecies. In another embodiment, the one or more microbial strains is one or more bacterial species or bacterial subspecies. In yet another embodiment, the sample is a lumen sample. In some embodiments, the rumen sample is from a cow. In some embodiments, the sample is a gastrointestinal tract sample. In some embodiments, the gastrointestinal tract sample is from a pig or a chicken.

  In some embodiments, the method comprises determining the absolute cell number of one or more active microbial strains in the sample, wherein the presence of the one or more microbial types in the sample is detected, and Alternatively, the absolute number of each of the plurality of microorganism types is determined. Using such embodiments, a biological condition, or a deviation from a predefined baseline condition, can be determined. The number of unique first markers is measured along with the relative amount of each of the unique first markers. As described herein, the unique first marker is a marker for a unique microbial strain. The activity can then be assessed, for example, at the protein or RNA level, by measuring the level of expression of one or more unique second markers. The unique second marker can be the same or different from the unique first marker and is a marker of the activity of the organism strain. Based on the level of expression of one or more of the unique second markers, it is determined which one or more microbial strains (if any) are active. In one embodiment, a microbial strain is considered active if it expresses a unique second marker at a threshold level or at a rate above the threshold level. The absolute cell number of the one or more active microorganism strains is determined by the amount of one or more first markers of the one or more active microorganism strains and the origin of the one or more microorganism strains as a sub-taxon. And the absolute number of the original microbial type.

  In one embodiment, determining the number of each of the one or more biotypes in the sample comprises determining nucleic acid sequencing, centrifugation, light microscopy, fluorescence microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase Subjecting the sample or a portion thereof to a chain reaction (qPCR), or flow cytometry.

  In one embodiment, determining the number of unique first markers in the sample comprises determining the number of unique genomic DNA markers. In another embodiment, determining the number of unique first markers in the sample comprises determining the number of unique RNA markers. In another embodiment, determining the number of unique first markers in the sample comprises determining the number of unique protein markers. In another embodiment, determining the number of unique first markers in the sample comprises determining the number of unique metabolite markers. In another embodiment, determining the number of unique metabolite markers in the sample comprises determining the number of unique carbohydrate markers, unique lipid markers, or a combination thereof.

  In another embodiment, determining the number and amount of unique first markers comprises subjecting genomic DNA from the sample to a high-throughput sequencing reaction. Measurement of the unique first marker, in one embodiment, includes a marker-specific reaction, which is performed, for example, using a primer specific to the unique first marker. In another embodiment, a metagenomic approach is taken.

  In one embodiment, measuring the expression level of one or more unique second markers comprises subjecting RNA (eg, miRNA, tRNA, rRNA, and / or mRNA) in the sample to expression analysis. In another embodiment, the gene expression analysis comprises a sequencing reaction. In yet another embodiment, the RNA expression analysis comprises quantitative polymerase chain reaction (qPCR), meta-transcriptome sequencing, and / or transcriptome sequencing.

  In some embodiments, determining the number of unique second markers in the sample comprises determining the number of unique protein markers. In some embodiments, determining the number of unique second markers in the sample comprises determining the number of unique metabolite markers. In some embodiments, determining the number of unique metabolite markers in the sample comprises determining the number of unique carbohydrate markers. In some embodiments, determining the number of unique metabolite markers in the sample comprises determining the number of unique lipid markers. In some embodiments, the absolute cell number of one or more microbial strains is measured in multiple samples. Using the absolute cell counts of multiple samples, a state or biological state (eg, baseline state) can be defined and / or a predefined biological state (eg, baseline state). From the sample source. In another embodiment, the multiple samples are obtained from the same or similar environment. In some embodiments, multiple samples are obtained at multiple time points. For example, in the management of biological conditions, multiple samples are taken over time for a particular environment or target (such as an animal) to monitor and manage the biological status of the animal, treat, supplement, etc. To direct the target towards a baseline state or other desired biological state, or cause the target to retain it.

  In some embodiments, measuring the level of one or more unique second markers comprises subjecting the sample or a portion thereof to mass spectrometry. In some embodiments, determining the expression level of one or more unique second markers comprises subjecting the sample or a portion thereof to metaribosome profiling and / or ribosome profiling.

  In another aspect of the present disclosure, in a method for determining the absolute cell number of one or more active microbial strains, the determination is made in a plurality of samples, and the absolute cell number level is determined in the one or more metadata ( For example, environment) parameters. Associating an absolute cell number level with one or more metadata parameters includes, in one embodiment, co-occurrence measures, mutual information measures, linkage analysis, and / or the like. In one embodiment, the one or more metadata parameters is the presence of a second active microorganism strain. Thus, in one embodiment of this method, absolute cell values are used to determine the co-occurrence of one or more active microbial strains and environmental parameters in a microbial community. In another embodiment, the absolute cell count level of one or more active microbial strains is related to environmental parameters (such as feeding conditions, pH, nutrients, or temperature) of the environment from which the microbial community is obtained.

  In this aspect, the absolute cell number of one or more active microbial strains is related to one or more environmental parameters. Environmental parameters can be parameters of the sample itself, for example, pH, temperature, amount of protein in the sample, the presence of other microorganisms in the community. In one embodiment, the parameter is a particular genomic sequence of the host from which the sample was obtained (eg, a particular genetic variation). Alternatively, is the environmental parameter a parameter that affects changes in the identity of the microbial community (ie, the “identity” of the microbial community is characterized by the type of microbial strain and / or the number of particular microbial strains in the community? Or the parameters affected by changes in the identity of the microbial community. For example, in one embodiment, the environmental parameter is the food intake of the animal or the amount of milk produced by the lactating ruminant (or the protein or fat content of the milk). In some of the embodiments described herein, the environmental parameters are referred to as metadata parameters.

  In one embodiment, determining the co-occurrence of one or more active microbial strains in the sample comprises creating a matrix of linked linkages indicating the association of one or more environmental parameters with the active microbial strain. Including.

  In one embodiment, determining the co-occurrence of the one or more active organism strains and the metadata parameters comprises a network analysis method and / or a cluster analysis method for measuring the connectivity of the strains in the network; A network is a collection of two or more samples that share common or similar environmental parameters. In some embodiments, the network analysis and / or network analysis method comprises: graph theory, species community rules, eigenvectors / modularity matrices, Gambit of the Group, and / or network measurements; Including one or more of the following. In some implementations, the network measurement includes one or more of an observation matrix, a time-aggregated network, a hierarchical cluster analysis, a node-level feature, and / or a network-level feature. Including. In some embodiments, the node-level features include one or more of order, intensity, intermediary centrality, eigenvector centrality, page rank, and / or reach. In some embodiments, the network-level features include one or more of density, homogeneity / order correlation, and / or transitivity.

  In some embodiments, the network analysis comprises a linkage analysis, a modularity analysis, a robustness measure, an intermediary measure, a connectivity measure, a transitivity measure, a centrality measure, or a combination thereof. In another embodiment, the cluster analysis method includes building a connectivity model, subspace model, distribution model, density model, or centroid model. In another embodiment, network analysis includes predictive modeling of the network via link mining and prediction, population classification, link-based clustering, related similarity, or a combination thereof. In another embodiment, the network analysis includes mutual information between variables, a maximum information coefficient calculation, or other non-parametric methods to establish connectivity. In another embodiment, the network analysis includes population modeling based on differential equations. In another embodiment, the network analysis includes Lotka-Volterra modeling.

  Based on the analysis, strain relationships can be displayed or otherwise output, and / or one or more active related strains are identified for inclusion in a microbial ensemble.

4 illustrates an example of a high-level processing flow of state determination and diagnosis, according to some embodiments. Several implementations of high-level processing flows to screen and analyze microbial strains from complex heterogeneous communities, predict their functional relevance and interactions, and select and synthesize microbial ensembles based thereon The example by a form is shown. 1 illustrates a general process flow according to some embodiments for determining the absolute cell number of one or more active microbial strains. FIG. 3 illustrates a process flow according to some embodiments for analyzing a microbial community, determining and displaying type / strain-metadata relationships, and generating a bioensemble. 4 illustrates an example, according to some embodiments, with visual output of analyzed strains and associations. 4 shows the distribution of MIC scores for rumen bacteria and milk fat efficiency, according to some embodiments. 4 shows the distribution of MIC scores for rumen fungus and milk fat efficiency, according to some embodiments. FIG. 4 shows the distribution of MICs core on rumen bacteria and milk efficiency, according to some embodiments. 4 shows the distribution of MIC scores for rumen fungus and milk efficiency, according to some embodiments. FIG. 7 illustrates a general process flow for determining co-occurrence of one or more active microbial strains and one or more metadata (environment) parameters in a sample (s), according to some embodiments. 1 is a schematic diagram illustrating an example of a system 300 for analyzing and selecting microbial interactions according to some embodiments. FIG. FIG. 3B is an example of a processing flow for use in such a system. Determine multidimensional inter-species interactions and dependencies within the natural microbial community, identify active micro-organisms, and select multiple active micro-organisms to alter specific parameter (s) and / or related indicators Systems and processes for forming an ensemble, assemblage, or other synthetic population of different microorganisms are described in connection with FIGS. 3A and 3B. It is an example of the processing flow for using in the system like FIG. 3A. Determine multidimensional inter-species interactions and dependencies within the natural microbial community, identify active micro-organisms, and select multiple active micro-organisms to alter specific parameter (s) and / or related indicators Systems and processes for forming an ensemble, assemblage, or other synthetic population of different microorganisms are described in connection with FIGS. 3A and 3B. 4 illustrates example data illustrating some aspects of the present disclosure. 4 illustrates example data illustrating some aspects of the present disclosure. Figure 4 shows that the pounds of milk fat produced over the course of the experiment to determine the rumen microbial community component that affects milk fat production in dairy cows is non-linear. Figure 4 shows the correlation between the absolute cell number filtered by activity and pounds of milk fat (lb) of target strain Ascus_713. The absolute cell count filtered by activity and pounds of milk fat (lb) produced over the course of the experiment are shown for the target strain Ascus_7. Figure 3 shows the correlation between the relative, unfiltered amount or abundance of the target strain Ascus_3038, and pounds (lb) of milk fat produced. Figure 3 shows the results of a field test in which a microbial ensemble prepared according to the disclosed method was administered to dairy cows. FIG. 8A shows the average pounds of milk fat produced over time. FIG. 8B shows the average pounds of milk protein produced over time. FIG. 8C shows the average pounds of energy corrected milk yield (ECM) produced over time. 3 shows the results of a bird test according to an embodiment of the present disclosure. 3 shows the results of a horse test according to an embodiment of the present disclosure. 1 shows an overview of an example diagnostic platform workflow according to some embodiments of the present disclosure. AD illustrate embodiments of the present disclosure for identification of equine status and insights into microorganisms. AD illustrate embodiments of the present disclosure for identification of equine status and insights into microorganisms. AD illustrate embodiments of the present disclosure for identification of equine status and insights into microorganisms. AB show examples of embodiments of the present disclosure for identification of cow status and insights into microorganisms. 1 shows an example of an embodiment of the present disclosure for identification of dairy cow status and insight into microorganisms. 1 shows an example of an embodiment of the present disclosure for identification of dairy cow status and insight into microorganisms. 1 shows an example of an embodiment of the present disclosure for identification of dairy cow status and insight into microorganisms.

  The microbial community is central to many different types of ecosystems and environmental processes in the Earth's biogeochemistry, such as by cycling nutrients and sequestering carbon (Falkowski et al. (1998) Science 281, 281). pp. 237-240 (incorporated herein by reference in its entirety for any purpose)). However, the understanding of the molecular and ecological details of these processes and the influencing factors remains lacking because the communities are complex and most members of any given microbial community cannot be cultured. Remains enough.

  Because microbial communities differ qualitatively and quantitatively in composition, each microbial community is unique and its composition depends on the given ecosystem and / or environment in which it resides. The absolute cell number of a microbial community member is affected by changes in the environment in which the community resides, as well as by physiological and metabolic changes (eg, cell division, protein expression, etc.) caused by the microorganism. Changes in environmental parameters and / or the amount of one active microorganism in a community can have far-reaching effects on other microorganisms in the community, as well as on the ecosystem and / or environment in which the community is found. Understanding, predicting and addressing these changes in microbial communities requires identifying the active microorganisms in the sample and the number of active microorganisms in each community. However, so far, the testing of microbial community members has overwhelmingly focused on the proportion of microorganisms in a particular microbial community, rather than on absolute cell number (Segata et al. (2013). Molecular Systems Biology 9, p. 666, which is hereby incorporated by reference in its entirety for any purpose).

  For example, while the composition of microbial communities can be easily determined using high-throughput sequencing techniques, it is necessary to gain a better understanding of how each community is constructed and maintained.

  Microbial communities are involved in critical processes such as the cycle of essential biogeochemical elements (eg, cycle of carbon, oxygen, nitrogen, sulfur, phosphorus, and various metals). Structure, interactions, and dynamics are very important for the presence of the biosphere (Zhou et al. (2015) .mBio 6 (1): e02288-14.Doi: 10.1128 / mBio.02288-14 ( Which is hereby incorporated by reference in its entirety for all purposes)). Such communities are highly heterogeneous and almost always contain a complex mixture of bacteria, viruses, archaea, and other microeukaryotes, such as fungi. Heterogeneous levels of the microbial community in the human environment (such as the intestine and vagina) are associated with diseases such as inflammatory bowel disease and bacterial vaginitis (Nature (2012). Vo. 486, p. 207 ( Which is hereby incorporated by reference in its entirety for all purposes)). Notably, however, even healthy individuals differ significantly in the microorganisms that occupy tissue in such environments (Nature (2012). Vo. 486, p. 207).

  Because many microorganisms are not cultivable or can be difficult / costly to culture, culture-independent techniques (such as nucleic acid sequencing) enhance understanding of the diversity of various microbial communities. did it. A fundamental approach to the study of community microbial diversity was to amplify and sequence the small subunit ribosomal RNA (SSU rRNA or 16s rRNA) gene. These approaches are based, in part, on the universal existence of this gene and a relatively constant evolution rate. Advances in high-throughput methods have led to metagenomics analysis, in which the entire genome of a microorganism is sequenced. In such a way, it is possible to discover new microbial strains without prior knowledge of the community. Metagenomics, metatranscriptomics, metaproteomics, and metabolomics all allow communities to be explored to identify structure and function.

  In addition to classifying microorganisms in the community, which members are active, the number of such organisms, and that the microbial community member (s) co-occur with each other, and the microbial community member (s) and the environment The ability to read the co-occurrence of the parameter (s) (eg, the co-occurrence of two micro-organisms in the community in response to certain changes in the community's environment) is sufficient to identify the uniqueness of the micro-organisms in the micro-organism community. Understand the importance of each environmental factor (eg, climate, nutrient presence, environmental pH) to gender (and their respective numbers), as well as the importance of certain community members to the environment in which the community resides It is assumed that The present disclosure addresses these and other needs.

  As used herein, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, the term "biotype" is intended to mean a single biotype or multiple biotypes. In another example, the term “environmental parameter” may mean a single environmental parameter or multiple environmental parameters, so that the indefinite article “a” or “an” indicates that one environmental parameter exists. Or does not exclude the possibility of multiple environmental parameters, unless the context requires that only one is present.

  Throughout this specification, references to “one embodiment,” “embodiment,” “one aspect,” or “aspect,” “one embodiment,” or “embodiment,” relate to that embodiment. Specific features, structures, or characteristics described herein are meant to be included in at least one embodiment of the present disclosure. Thus, the appearances of the phrase "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Absent. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

  In certain embodiments, the term "about" or "approximately" as used herein, as it modifies a numerical value, means a range of its value ± 10%. Where a range of values is given, each intervening value between the upper and lower limits of the range, up to one tenth of the lower limit, and the description, unless otherwise indicated by context. It is understood that any other stated or intervening values in the range are encompassed by the disclosure. The upper and lower limits of such smaller ranges can be independently included in the smaller ranges, and such upper and lower limits are also included in the disclosure and specifically excluded from the written ranges. Can be subject to any limit value. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the disclosure.

  As used herein, "isolate", "isolated", "isolated microorganism", and like terms, refer to one or more microorganisms that are in a particular environment (e.g., soil, (Water, animal tissue) is intended to mean separated from at least one of its associated materials. Thus, an "isolated microorganism" is not present in the environment in which it naturally occurs, but rather is removed from its natural environment by the application of the various techniques described herein. It is a state that does not occur in nature. Thus, an isolated strain can be present, for example, as a biologically pure culture or as spores (or other forms of the strain) associated with an acceptable carrier.

  As used herein, a "bioreactive modifier" is a composition (e.g., a microorganism comprising one or more active microorganisms) defined by the methods, systems, and / or devices of the present disclosure. Ensembles) that are not naturally present in naturally occurring environments and / or are not consistently found in nature, and / or that are not present in natural products. Refers to a composition having For example, a bioreactive modifier (such as a microbial ensemble (also referred to as a synthetic ensemble or bioensemble)) or a collection of bioreactive modifiers can be identified or generated compounds / compositions and / or 1 It can be formed from one or more isolated microbial strains, together with a suitable vehicle or carrier. The bioreactive modifier can be applied or administered to a target, such as a target environment, a target population, a target individual, a target animal, and / or the like.

  In some embodiments, a bioreactivity modifier (such as a microbial ensemble) according to the present disclosure is selected from a set, subset, and / or group of individual active microbial species or active strains of the species that are interrelated. And / or based on it. Associations and networks are identified by the methods of the present disclosure and are grouped, associated, and / or related based on the performance of one or more common functions, or are recognizable parameters (objectives). Can be described as being involved in or leading to and / or associated with a phenotypic trait (eg, increased milk production in ruminants). In some embodiments, the selected group of microbial ensembles, and / or the selected group of bioreactive modifiers, and / or the bioreactive modifier (such as the microbial ensemble itself) comprise a species of microorganism. , A strain of a microorganism species, or two or more strains of a different species of microorganism. In some cases, the microorganisms can coexist symbiotically in the population, bioreactivity modifier, and / or microbial ensemble.

  In certain aspects of the disclosure, the bioreactive modifier and / or microbial ensemble is one or more isolated microorganisms present as an isolated biologically pure culture. Or based on it. An isolated biologically pure culture of a particular microorganism contains only the individual microorganisms in question, where the culture is substantially free of other organisms (as scientifically justified) It will be understood that it means. The culture may contain different concentrations of the microorganism. It is noted in this disclosure that an isolated biologically pure microorganism is often "necessarily different from low purity or impure material." For example, see Inre Bergstrom, 427 F.R. 2d 1394, (CCPA 1970) (discussed for purified prostaglandins). Inre Bergy, 596 F.R. See also 2d 952 (CCPA 1979) (discussed for purified microorganisms). Parke-Davis & Co. v. H. K. Mulford & Co. , 189 F.R. 95 (SDNY1911) (discussed for purified adrenaline by Learned Hand), aff'd in part, rev'd in part, 196 F.C. 496 (2d Cir. 1912). Each of these documents is incorporated herein by reference in its entirety. Further, in some embodiments, certain quantitative concentration indicators or purity limits that must be achieved in an isolated biologically pure microbial culture to be used in the disclosed microbial ensemble are described in the present specification. It may be necessary in implementing the disclosure. The presence of such purity values is, in certain embodiments, an additional attribute that distinguishes the microorganisms identified by the methods disclosed herein from those that exist in their natural state. For example, Merck & Co. v. Olin Matheson Chemical Corp. , 253 F.C. 2d 156 (4th Cir. 1958) (discussing the purity limits of vitamin B12 produced by microorganisms). That document is incorporated herein by reference.

  As used herein, "carrier", "acceptable carrier", or "pharmaceutical carrier" refers to a diluent, adjuvant, excipient used with or used in a microbial ensemble, Or a medium. Such carriers can be sterile liquids, such as water and oils, including those of petroleum, animal, plant, or synthetic origin, such as peanut oil, soybean oil, mineral oil, sesame oil. , And the like. Preferably, water or aqueous solutions, saline, and aqueous dextrose and glycerol solutions are utilized as carriers, and in some embodiments, for injection. Alternatively, the carrier can be a solid dosage form carrier, which includes, but is not limited to, a binder (for compressed pills), a glidant, an encapsulant, a flavoring agent, and a coloring agent. One or more of the following. The choice of carrier can be made in light of the intended route of administration and standard pharmaceutical practice. Hardee and Baggo (1998. Development and Formulation of Veterinary Dosage Forms. 2nd Ed. CRC Press. 504 pg.). W. Martin (1970. Remington's Pharmaceutical Sciences. 17th Ed. Mack Pub. Co.), and Blaser et al. (U.S. Publication US20110280840A1). Each of these documents is expressly incorporated herein by reference in its entirety.

  The terms "microorganism" and "microbe" are used interchangeably herein and refer to any microorganism that is of a eubacterial, eukaryotic, or archaeal domain. Microbial types include, but are not limited to, bacteria (e.g., mycoplasma, cocci, bacilli, rickettsiae, spirils), fungi (e.g., filamentous fungi, yeast), nematodes, protozoa, archaea, algae, dinoflagellates, Viruses (eg, bacteriophages), viroids, and / or combinations thereof. An organism strain is a sub-taxon of a biotype, which can be, for example, a particular microbial species, subspecies, subtype, genetic variant, pathogenic type, or serotype.

  The term "marker" or "unique marker" as used herein is an indication of the activity of a particular microorganism type, strain, or strain. Markers can be measured in biological samples, including, but not limited to, nucleic acid-based markers (such as ribosomal RNA genes), peptide or protein based markers, and / or metabolites or other metabolites. Includes small molecule markers.

  The term "metabolite" as used herein is a metabolic intermediate or metabolite. In one embodiment, the metabolite is a small molecule. Metabolites have various functions, such as fuel function, structural function, signal transduction function, function of stimulating and inhibiting enzymes, function as a cofactor for enzymes, defense function, And interaction functions with other organisms (such as pigments, odorants, and pheromones). Primary metabolites are directly involved in normal development, development, and reproduction. Secondary metabolites are not directly involved in these processes, but usually have important ecological functions. Examples of metabolites include, but are not limited to, antibiotics and dyes (such as resins and terpenes). Some antibiotics use the primary metabolite as a precursor, for example, actinomycin is created from the primary metabolite tryptophan. As used herein, metabolites include small hydrophilic sugars, large hydrophobic lipids, and complex natural compounds.

  Embodiments of the present disclosure include a diagnostic method. As shown in FIG. 1, such a method may include obtaining at least two samples or data thereon (011), wherein at least two samples have at least one common environmental parameter (sample type, Sample position, sample time, etc.). At least one of the at least two samples may be defined to be in a first state (013), and at least one of the at least two samples may be defined to be in a second state. (015), the second state is different from the first state. For example, in one embodiment, one of the at least two conditions is a healthy condition or a healthy sample source (eg, a sample source having one or more desirable characteristics or metadata). The associated condition, the other condition is an unhealthy / morbid condition, or an unhealthy / morbid sample source (eg, a sample source having one or more undesirable characteristics or metadata; In some cases, particularly conditions associated with a sample source having one or more features or metadata that are undesirable when compared to the corresponding feature (s) or metadata of a healthy sample source. For each sample, the presence of one or more microbial types in the sample is detected (017) and the number of each of the one or more microbial types detected in each sample is determined (019). ).

  Next, a unique first marker and its amount in each sample are measured (021), each unique marker being a marker of a microorganism strain of the microorganism type to be detected. Based on the number of each microorganism type and the number of unique first markers / each number, the absolute cell number of each microorganism strain in each sample is determined (023). Next, at least one unique second marker for each microorganism strain is measured (025) and the activity level of that microorganism strain is determined (027) (eg, a unique second marker above a certain activity threshold). Determined on the basis of the marker). Depending on the embodiment, the activity level can be numerical, relative, and / or binomial (eg, active / inactive). The determined activity filters the absolute cell number of each microbial strain (029) to provide a set or list of active microbial strains and their respective absolute cell numbers for each of the at least two samples. A filtered absolute cell number of the active microorganism strain for at least one sample from the first state, and a filtered absolute cell number of the active microorganism strain for at least one sample from the second state; Can be defined or determined (031) by comparing or processing the baseline state (eg, a healthy state or a normal state). Baseline status can be defined or characterized by the presence or absence of the identified taxon and / or strain. In some embodiments, the method comprises or further comprises obtaining (033) at least one additional sample, wherein the status of the at least one additional sample is unknown. Next, for at least one additional sample, the presence of one or more microbial types is detected (035), and the number of each detected microbial type of the one or more microbial types is determined. (037). A unique first marker and its amount are determined (039), each unique first marker being a marker of a microorganism strain of the microorganism type to be detected. From the number of each microbial type and the number of unique first markers, the absolute cell number of each microbial strain is determined (041). At least one unique second marker is used for each microorganism strain based on the specified threshold to determine the activity level of that microorganism strain (043). The absolute cell number of each microbial strain is filtered (045) according to the determined activity level to obtain a set or list of active microbial strains and their respective absolute cell numbers (047). The set of active microbial strains and their respective absolute cell counts for at least one additional sample is then compared to a baseline condition to determine the condition of at least one additional sample (eg, healthy or unhealthy, Normal or abnormal) is determined (049). Next, the determined state of the at least one additional sample is output and / or displayed (051) (eg, output and / or displayed on a display screen or graphical interface).

  According to some other embodiments, the determined state of the at least one additional sample corresponds to a state of the environment associated with the at least one additional sample. Depending on the embodiment, the environment associated with the at least one additional sample may include a geospatial environment, such as a field or pasture, a feed environment or source (eg, a grain silo), Target animals and / or herds. The treatment for the environment associated with the at least one additional sample can be ascertained or determined. In embodiments where the baseline is healthy or the like, the treatment can be configured to change the state of the environment towards the baseline. In some embodiments, the treatment can be configured to change the condition of the environment toward a condition associated with a desired goal or beneficial result. The treatment may be a synthetic ensemble (especially a synthetic ensemble formed according to the methods of the present disclosure), a chemical / biological treatment or drug, a treatment regimen, a combination of two or more of these treatments, and / or the like. May be included. In some embodiments, the baseline status can be updated based on at least one additional sample.

  In another aspect of the present disclosure, an analysis method is disclosed. Such a method may include obtaining at least two sample sets, each sample set including a plurality of samples. In some embodiments, at least one of the at least two sample sets may be defined as being in the first state, and at least one of the at least two sample sets may be defined as the second. The first state is different from the second state, and the range of the sample in the sample set corresponds to the range of the state corresponding to the sample set. In other embodiments, the samples in the sample set are defined as being in their respective states, or the determination or definition of the states is made after analysis. The method then comprises detecting the plurality of microbial types in each sample, determining the absolute cell number of each of the plurality of microbial types detected in each sample among the plurality of microbial types, and determining the unique cell number in each sample. Measuring the first marker and its amount (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected). In some embodiments, determining the unique first marker and its amount comprises subjecting the genomic DNA from each sample to a high-throughput sequencing reaction and / or metabolizing the genomic DNA from each sample to a metagenomic sequence. Subjecting to at least one of the following. The unique first marker may include at least one of an mRNA marker, an siRNA marker, a ribosomal RNA marker, a sigma factor, a transcription factor, a nucleoside-related protein, and / or a metabolic enzyme. In some embodiments, measuring the unique first marker comprises measuring a unique genomic DNA marker in each sample, measuring a unique RNA marker in each sample, and / or measuring a unique RNA marker in each sample. Measuring at least one of the protein markers. In some embodiments, measuring a unique first marker comprises measuring a unique metabolite marker in each sample, wherein the measuring comprises measuring a unique lipid marker in each sample, and / or Measuring at least one unique carbohydrate marker in the sample.

  The method then determines the absolute cell number of each microbial strain present in each sample, the number of each microbial type detected in the sample, and the number and amount of the unique first marker in the sample. And determining at least one unique second marker for each microorganism strain to determine the active microorganism strain in each sample. In some embodiments, measuring at least one unique second marker for each microbial strain comprises measuring the expression level of at least one unique second marker. In some embodiments, measuring the expression level of the at least one unique second marker comprises subjecting the sample mRNA to gene expression analysis, subjecting each sample or a portion thereof to mass spectrometry, and / or Subjecting the sample or a portion thereof to metaribosome profiling or ribosome profiling.

  Next, a set of active microbial strains for each sample of at least two sample sets and their respective absolute cell numbers are obtained. The method comprises analyzing each sample and / or each sample of at least two sample sets for active microbial strains and respective absolute cell numbers to define a baseline condition. Baseline status, in some embodiments, can be defined and / or characterized by the presence or absence of the identified taxon and / or strain.

  Next, at least one additional sample of unknown state is obtained. For at least one additional sample, the method comprises: (a) detecting the presence of one or more microbial types; (b) determining the number of each microbial type detected; Measuring the first marker and its amount (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected); (d) the number of each microorganism type and the unique first marker Determining the absolute cell number of each microbial strain from the number of microbial strains; Determining the level, and (f) filtering the absolute cell number of each microbial strain according to the determined activity, the set of active microbial strains and their respective absolute details To obtain the number, further comprising a. Comparing the set of active microbial strains and their respective absolute cell counts and the baseline status for the at least one additional sample determines the status associated with the at least one additional sample, and the at least one additional sample. The determined state associated with the sample is displayed or output. Although generally discussed as a single state, for some embodiments and applications, a baseline state or biological state refers to multiple states and / or biological states associated with a particular microbiome. It will be appreciated that more than one condition may be utilized in characterization, identification, and / or treatment of a particular symptom, whether at the individual or herd level.

  The method comprises selecting a plurality of active microorganism strains based on a baseline condition and a determined condition associated with at least one additional sample, and combining the selected plurality of active microorganism strains with a carrier medium. Forming a synthetic ensemble of active microorganisms, wherein the synthetic ensemble is associated with at least one additional sample when the synthetic ensemble is introduced into an environment associated with the at least one additional sample. Configured to alter the state of the environment.

  In one aspect of the present disclosure, a method is disclosed for establishing an association between a plurality of microorganism strains and one or more metadata and / or parameters. As shown in FIG. 1A, sample data for a sample and / or at least two samples is received from at least two sample sources (101), and for each sample, the presence of one or more microbial types is determined (103). The number (cell number) of each of the one or more microbial types detected in each sample is determined (105), and the number and amount of unique first markers in each sample is determined. Each of the unique first markers determined (107) is a marker of a microbial strain. The integration of the number of each microbial type and the number of the first marker yields the absolute cell number of each microbial strain present in each sample (109) and at least one unique for each microbial strain. The level of activity of the respective microbial strain in each sample is determined based on the measured value of the second marker above the specified threshold (111), and the microbial strain is identified as having at least one unique second marker for the strain. If the measurement is above the corresponding threshold, it is considered active. Next, the absolute cell number of each microbial strain is filtered according to the determined activity to obtain a set or list of active microbial strains and their respective absolute cell numbers for each of the at least two samples (113). ). A network analysis of the filtered absolute cell number set or list of active microorganism strains for each of the at least two samples, together with at least one measurement metadata or additional active microorganism strains, is performed (115), wherein the network analysis comprises: Determination of the maximum information coefficient score between each active microorganism strain and any other active microorganism strain, and the maximum information coefficient score between each active microorganism strain and at least one measured metadata or additional active microorganism strain Determination. The active microbial strain can then be classified based on function, expected function, and / or chemistry (117), and a plurality of active microbial strains are identified and output based on the classification (119). In some embodiments, the method further comprises constructing an active microbial ensemble from the plurality of identified microbial strains (121), wherein the microbial ensemble, when applied to the target, includes at least one measurement metadata. It is configured to change the corresponding property. The method may further include identifying at least one pathogen based on the plurality of identified active microorganism strains output (see Example 4 for additional details). In some embodiments, utilizing multiple active microbial strains, when applied to a target, addresses at least one identified pathogen and / or treats a condition associated with at least one identified pathogen. An active microbial ensemble configured to do so can be constructed.

  In one aspect of the present disclosure, there is provided a method for determining the absolute cell number of one or more active microbial strains in one or more samples, wherein the one or more active microbial strains comprises a microorganism in the sample. Exist in the community. The one or more microbial strains is a sub-taxon of one or more biotypes (see method 1000 of FIG. 1B). For each sample, the presence of one or more microbial types in the sample is detected (1001). The absolute number of each of one or more biotypes in the sample is determined (1002). The number of unique first markers is measured along with the amount of each unique first marker (1003). As described herein, the unique first marker is a marker for a unique microbial strain. Next, activity is assessed at the protein and / or RNA level by measuring the expression level of one or more unique second markers (1004). The unique second marker can be the same or different from the unique first marker and is a marker of the activity of the organism strain. Based on the level of expression of one or more of the unique second markers, which microbial strain (if any) is active is determined (1005). The microbial strain expresses the unique second marker at a particular level or above a threshold level (eg, at least about 10%, at least about 20%, at least about 30%, or at least about 40% above the threshold level). %), It is considered active (1005) (various thresholds can be determined based on the particular application and / or embodiment, for example, sample source (s) ( It will be appreciated that the threshold may vary depending on the particular species), the location of the sample origin, the objective metadata, the environment, etc.). The absolute cell number of the one or more active microorganism strains is determined by the amount of one or more first markers of the one or more active microorganism strains and the origin of the one or more microorganism strains as a sub-taxon. And the absolute number of the original biotype.

  Some embodiments of the present disclosure can be configured for analysis of a microbial community. As shown by FIG. 1C, data for two or more samples (and / or sample sets) is obtained (1051) (each sample includes a heterogeneous microbial community) and multiple microbial types in each sample are detected. (1053). Next, the absolute cell number of each microorganism type detected in each sample of the plurality of microorganism types is determined (1055), for example, via FACS or other methods discussed herein. A unique first marker and its amount in each sample are measured (1057), each unique marker being a marker of a microorganism strain of the microorganism type to be detected. The value (activity, concentration, expression, etc.) of one or more unique second markers is measured (1059) (the unique second marker is the activity of a particular microbial strain of the microorganism type to be detected (eg, Metabolic activity) is determined based on the measurement of one or more unique second markers (eg, based on values above a certain set threshold). (1061). A respective ratio of each detected active microbial strain in each sample is determined based on, for example, respective absolute cell numbers, values, etc. (1063). Next, each of the at least two samples of the active detectable microorganism strain (or a subset thereof) is analyzed to determine the activity between each active detectable microbial strain and the other active detectable microbial strain, as well as the respective activity. The biological state (eg, baseline state) and / or association and strength between the detected microbial strain and the at least one measured metadata is determined (1065). The revealed biological states and / or relevance are then displayed or otherwise output (1067) (eg, performed on a graphical display / graphical interface) (eg, FIG. 1D) and the biological It can be used to manage status and / or generate a bioensemble (1069). In some embodiments, the display / output of relevance may be limited such that only relevances above a certain strength or weight are displayed (1066a, 1066b).

  A microbial ensemble according to the present disclosure can be selected from a set, subset, and / or group of individual active microbial species or active strains of the species that are interrelated. Associations and networks are revealed by the methods of the present disclosure and are grouped and / or related based on the performance of one or more common functions, or are recognizable parameters (phenotypic traits of interest). (Eg, increased milk production in ruminants) or can be described as leading to or associated with it. In FIG. 1D, the group associated with dairy cow-related metadata parameters was identified using the Louvain community detection method. Each node represents a particular rumen microorganism strain or metadata parameter. Links between nodes indicate significant association. Unconnected nodes are unrelated microorganisms. Each colored "bubble" indicates a group detected by Louvain analysis. This grouping makes it possible to predict the functionality of the strain based on the group to which it is classified.

  Some embodiments of the present disclosure are configured to leverage mutual information to rank the importance of natural microbial strains that live in the digestive tract of animals to particular animal traits. The maximum information coefficient (MIC) is calculated for all microorganisms and desired animal traits. Associations are scored on a scale of 0 to 1, with 1 indicating strong association between the microbial strain and the animal trait and 0 indicating no association. Use of this score-based cutoff defines those microorganisms that are useful and those that are not useful for improving a particular trait. 1E and 1F show examples of MIC score distributions of rumen microbial strains that share an association with milk fat efficiency. Here, the point at which the curve shifts from an exponential function to a straight line (about 0.45-0.5 for bacteria and about 0.3 for fungi) is the difference between useful and non-useful microbial strains. Cutoff. 1G and 1H show examples of MIC score distributions of rumen microbial strains that share a relationship with milk efficiency. The point at which the curve shifts from exponential to linear (about 0.45-0.5 for bacteria and about 0.25 for fungi) is the cut-off between useful and non-useful microbial strains. Becomes

  As shown in FIG. 2, in another aspect of the present disclosure, the absolute cell number of one or more active microorganisms is determined in the plurality of samples, and the absolute cell number is associated with metadata (environmental parameters) ( 2001-2008). The plurality of samples is subjected to an analysis of the absolute cell number of the one or more active microorganism strains, wherein the one or more active microorganism strains have a threshold level of activity measurement in at least one of the plurality of samples, Or, if it is above the threshold level, it is considered active (2001-2006). Next, the absolute cell number of one or more active microbial strains is associated with a metadata parameter for a particular embodiment and / or application (2008).

  In one embodiment, multiple samples are obtained over time from the same environmental source (eg, obtained over time from the same animal). In another embodiment, the multiple samples are from multiple environmental sources (eg, different animals). In one embodiment, the environmental parameter is the absolute cell number of the second active microorganism strain. In another embodiment, the absolute cell number of one or more active microbial strains is used to determine the co-occurrence of one or more active microbial strains with a second active microbial strain in a microbial community. . In another embodiment, a second environmental parameter is associated with an absolute cell number of one or more active microbial strains and / or an absolute cell number of a second environmental strain.

  Aspects of the disclosed embodiments are discussed throughout this disclosure.

  Importantly, a sample for use in the methods provided herein can be of any type, including a microbial community. For example, samples for use in the methods provided herein include, but are not limited to, animal samples (eg, mammals, reptiles, birds), soil, air, water (eg, marine, freshwater, wastewater sludge). ), Sediments, oils, plants, agricultural products, plants, soil (eg, rhizosphere), foods (eg, cheese, beer, wine, bread), and extreme environmental samples (eg, acid mine drainage, hydrothermal systems). included. In the case of a marine or freshwater sample, the sample may be from the surface of the body of water or from any depth in the body of water (eg, a deep sea sample). The water sample is, in one embodiment, a marine, river, or lake sample.

  In one embodiment, the animal sample is a bodily fluid. In another embodiment, the animal sample is a tissue sample. Animal samples include, but are not limited to, teeth, sweat, nails, skin, hair, feces, urine, semen, mucus, saliva, and gastrointestinal tract. The animal sample can be, for example, a human, primate, cow, pig, dog, cat, rodent (eg, mouse or rat), or bird sample. In one embodiment, the bird sample comprises a sample from one or more chickens. In another embodiment, the sample is a human sample. The human microbiome comprises a collection of microorganisms found on the surface and deep layers of the skin, mammary glands, saliva, oral mucosa, conjunctiva, and gastrointestinal tract. Microorganisms found in microbiomes include bacteria, fungi, protozoa, viruses, and archaea. Different body parts have different microbial diversity. The amount and type of microorganism can be indicative of the health or morbidity of the individual. The number of bacterial taxa is in the thousands, and viruses can be as abundant. The bacterial composition at a given site on the body surface varies from person to person, not only in type, but also in abundance or amount.

In another embodiment, the sample is a lumen sample. Ruminants such as cattle rely on a diverse microbial community to digest their feed. These animals have evolved to use a nutrient-poor diet by having an altered upper gastrointestinal tract (ruminant or rumen), which feed is fermented by the anaerobic microbial community. Is retained in the upper digestive tract. The rumen microbial community is of very high density, containing about 3 × 10 ¹⁰ microbial cells per milliliter. Anaerobic fermentation microorganisms dominate the rumen. The rumen microbial community includes members of all three domains of life (bacteria, archaea, and eukaryotes). Rumen fermentation products are those required by their respective hosts for body maintenance, growth, and milk production (van Houtert (1993). Anim. Feed Sci. Technology. 43, pp. 189-225). (2011) Annu. Rev. Nutr. 31, pp. 299-319, each of which is incorporated by reference in its entirety for all purposes). In addition, milk yield and composition has been reported to be associated with the rumen microbial community (Sandri et al. (2014). Animal 8, pp. 572-579; Palmonari et al. (2010). J. Dairy. Sci. 93, pp. 279-287, each of which is incorporated by reference in its entirety for all purposes). In one embodiment, Jewell et al. (2015). Appl. Environ. Microbiol. 81 pp. A rumen sample is obtained via the process described in 4697-4710, which is hereby incorporated by reference in its entirety for any purpose.

  In another embodiment, the sample is a soil sample (eg, a bulk soil or rhizosphere sample). One gram of soil is estimated to contain tens of thousands of bacterial taxa and up to 1 billion bacterial cells, as well as about 200 million mycelium (Wagg et al. (2010). Proc Natl. Acad. Sci.USA 111, pp. 5266-5270, which is incorporated by reference in its entirety for all purposes). Bacteria, actinomycetes, fungi, algae, protozoa, and viruses are all found in soil. The diversity of the soil microbial community has been linked to the structure and fertility of the soil microenvironment, nutrient acquisition by plants, plant diversity and growth, and the circulation of resources between aboveground and belowground communities. Therefore, by assessing the time course of microbial content of soil samples and the co-occurrence of active microorganisms (and the number of active microorganisms), the environmental metadata parameters (such as nutrient acquisition and / or plant diversity) can be assessed for microorganisms associated with them. Gain insight.

  In one embodiment, the soil sample is a rhizosphere sample, which is a narrow soil area that is directly affected by root secretions and related soil microorganisms. The rhizosphere is a densely populated area where increased microbial activity has been observed and plant roots interact with soil microorganisms through the exchange of nutrients and growth factors (San Miguel et al. (2014). Appl. Microbiol.Biotechnol.DOI 10.1007 / s00253-014-5545-6 (incorporated by reference in its entirety for any purpose). Since plants secrete many compounds into the rhizosphere, analysis of biotypes in the rhizosphere can be useful in determining the characteristics of plants growing there.

  In another embodiment, the sample is a marine or freshwater sample. Marine water contains up to one million microorganisms and thousands of microbial types per milliliter. These numbers can be orders of magnitude higher in coastal waters due to increased productivity and uptake of organic matter and nutrients. Marine microbes function in marine ecosystems, maintain a balance between carbon dioxide production and fixation, and account for over 50% of the Earth's oxygen via marine photosynthetic microorganisms (such as cyanobacteria, diatoms, and picophytoplankton and nanophytoplankton). Is critical for the production of biomass, providing new bioactive compounds and metabolic pathways, and ensuring a sustainable supply of marine products by occupying very important lowest nutrient levels in the marine food web. Organisms found in the marine environment include viruses, bacteria, archaea, and some eukaryotes. Marine viruses can play an important role in controlling marine bacterial populations through lysis by the virus. Marine bacteria are important both as food sources for other small microorganisms and as producers of organic matter. Archaea found in waters through the water column are pelagic archaea, and their abundance is comparable to that of marine bacteria.

  In another embodiment, the sample comprises a sample from an extreme environment, which is an environment that is associated with most life-threatening conditions on earth. Organisms that grow in the extreme environment are called extreme environment microorganisms. The archaebacteria domain includes well-known examples of extreme environment microorganisms, but the eubacteria domain may also have representatives of such microorganisms. Extreme environmental microorganisms include eosinophilic microorganisms that grow at a pH level of 3 or less, alkalophilic microorganisms that grow at a pH level of 9 or more, anaerobic organisms that do not require oxygen for growth (such as Spinoliricus Cinzia), and groundwater in deep underground. Rocks, cracks, aquifers, and cryptoendoliths that inhabit microspaces within faults, halophilic organisms that grow at a salt concentration of at least about 0.2 M, high temperatures (about 80- Extracts energy from hyperthermophilic organisms (e.g., those found in hydrothermal systems) inhabiting at 122 ° C), hypoliths that inhabit under rocks in cold deserts, and reduced mineral compounds such as pyrite, and geochemistry. Autotrophs active in the natural circulation (such as Nitrosomonas europaea), high levels Metal-resistant organisms that are resistant to dissolved heavy metals (such as copper, cadmium, arsenic, and zinc), undernutrition organisms that grow in nutrient-limited environments, and highly-rich organisms that grow in environments with high sugar concentrations ( osmophiles, high pressure piezophiles (or barophiles) (such as those found deep in the ocean or underground), which survive, grow and / or grow at temperatures below about -15 ° C. Or breeding psychrophiles / cryophiles, radioresistant organisms resistant to high levels of ionizing radiation, thermophilic organisms living at temperatures of 45-122 ° C., extremely dry Xerophilic organisms that can grow under defined conditions. Multi-extreme environment microorganisms are organisms that are considered to be extreme-environment microorganisms that fall into more than one category, and include multi-extreme environment microorganisms that are thermophilic and eosinophilic microorganisms (preferably at temperatures of 70-80 ° C. and pH 2-3). included. The archaeon Clen Archaea group includes thermoacidophilic microorganisms.

  The sample may include microorganisms from one or more domains. For example, in one embodiment, the sample comprises a heterogeneous population of bacteria and / or fungi (also referred to herein as bacterial or fungal strains). Additional applications of the teachings of the present disclosure include use in food products, particularly fermented and microbial food products (eg, bread, cheese, wine, beer, kimchi, kombucha, chocolate, etc.).

  Determining the presence and absolute cell number of one or more microorganisms in a sample (eg, the absolute cell number of one or more microorganisms in multiple samples obtained from the same or different environments and / or over multiple time points) In the methods provided herein, the one or more microorganisms can be of any type. For example, the one or more microorganisms can be from an eubacteria domain, an archaea domain, a eukaryote domain, or a combination thereof. Bacteria and archaea are prokaryotes and have a very simple cellular structure with no internal organelles. Bacteria can be classified into Gram-positive / no adventitia, Gram-negative / adventitia, and unclassified phylum. Archaea make up the domain or kingdom of unicellular microorganisms. Although visually similar to bacteria, archaebacteria have genes and several metabolic pathways that are more closely related to those of eukaryotes, notably the enzymes involved in transcription and translation. ing. Other aspects of archaeal biochemistry are unique, such as the presence of ether lipids in the cell membrane. Archaea are divided into four recognized phyla: Taum archaea, Aig archaea, Clen archaea, and Kol archaea.

  Eukaryotic domains include eukaryotes, where eukaryotes are defined by membrane-bound organelles (such as nuclei). Protozoa are unicellular eukaryotes. All multicellular organisms, including animals, plants, and fungi, are eukaryotes. Eukaryotes are classified into four kingdoms: Protista, Plant, Fungi, and Animalia. However, there are several alternative classifications. In another class, eukaryotes are of the kingdom Excavata (various flagellates), the kingdoms of Amoeba (lobose amoeboids and myxomycetes), the kingdom of Opisthoconta (animals, fungi, lagoflagellates). , The lithariae (foraminifera, radiolarians, and various other amoebic protozoa), the chromium albeorata (yellow plants (brown algae, diatoms), hapto plants, crypto plants (or crypto algae), and albeolata), arches It is divided into six kingdoms: Plastidae / primary plants (land plants, green algae, red algae, and gray algae).

  Within the eukaryotic domain, fungi are the main microorganisms in the microbial community. Fungi include microorganisms such as yeast and filamentous fungi, as well as well-known mushrooms. Fungal cells have a cell wall that contains glucans and chitin, which is a unique feature of such organisms. Fungi form a single group of related organisms that share a common ancestor, and this group is called fungi (Eumycota). The fungal kingdom is estimated to be between 1.5 and 5 million species, of which about 5% are formally classified. Most fungal cells grow as tubular elongated filamentous structures called hyphae that can contain multiple nuclei. Some species grow as unicellular yeasts that propagate by budding or fission. The main fungal phyla (sometimes called the division) has been classified primarily based on the characteristics of its sexual reproductive structure. At present, seven phylums, Microsporidian phylum, Phytolacca, Komakkin, Neocalimastics, Glomus, Ascomycota, and Basidiomycota have been proposed.

  The microorganism to be detected and quantified by the methods described herein can also be a virus. Viruses are small infectious agents that replicate only inside living cells of other organisms. Viruses can infect all types of organisms in eukaryotic, bacterial, and archaeal domains. Viral particles (known as virions) contain (i) genetic material, which can be DNA or RNA, (ii) a protein coat that protects such genes, and optionally (iii) proteins when they are extracellular. It consists of two or three parts, a lipid mantle surrounding the coat. With regard to viruses, seven orders have been established: the orders Caudidae, Herpesviruses, Rigamenvirale, Mononegaviruses, Nidoviruses, Picornaviruses, and Timoviruses. The viral genome can be single-stranded (ss) or double-stranded (ds), RNA or DNA, and in the viral genome, reverse transcriptase (RT) may or may not be used. Further, ssRNA viruses can be sense (+) or antisense (-). In this classification, viruses are: I: dsDNA virus (adenovirus, herpes virus, poxvirus, etc.), II: (+) ssDNA virus (parvovirus, etc.), III: dsRNA virus (reovirus, etc.), IV: (+ ) SsRNA virus (picornavirus, togavirus, etc.), V: (−) ssRNA virus (orthomyxovirus, rhabdovirus, etc.), VI: (+) ssRNA-RT virus (retrovirus) with DNA intermediate in life cycle VII: dsDNA-RT virus (such as hepadnavirus).

  The microorganism to be detected and quantified by the methods described herein can also be a viroid. Viroids are the smallest infectious pathogens known and consist solely of circular short strands of single-stranded RNA without a protein coat. Viroids are mostly plant pathogens, some of which are important from an economic point of view. The viroid genome is extremely small in size, having nucleobases ranging from about 246 to about 467.

  According to the methods provided herein, a sample is processed such that the presence of one or more microbial types in the sample is detected (FIG. 1B, 1001; FIG. 2, 2001). The absolute number of one or more microbial biotypes in the sample is determined (FIG. 1B, 1002; FIG. 2, 2002). Determining the presence of one or more biotypes and determining the absolute number of at least one biotype can be performed in parallel or sequentially. For example, for a sample that includes a microbial community that includes bacteria (ie, one microbial type) and fungi (ie, a second microbial type), in one embodiment, one or both of these biotypes in the sample Is detected by the user (FIG. 1B, 1001; FIG. 2, 2001). In another embodiment, the absolute number of at least one biotype in the sample (in this case, the number of bacteria, fungi, or a combination thereof in the sample) is determined by the user (FIGS. 1B, 1002; FIG. 2, FIG. 2002).

  In one embodiment, the sample or a portion thereof is subjected to flow cytometry (FC) analysis to detect the presence and / or number of one or more microbial types (FIGS. 1B, 1001, 1002). FIG. 2, 2001, 2002). In one embodiment with a flow cytometer, individual microbial cells pass through the illumination zone at a rate of at least about 300 cells / second, or at least about 500 cells / second, or at least about 1000 cells / second. However, it will be appreciated that this speed may vary depending on the type of equipment utilized. An electronically gated detector measures the magnitude of the pulse, which is indicative of the degree of scattered light. By electronically sorting the magnitude of these pulses into “bins” or “channels,” the number of cells with certain quantitative characteristics (eg, cell staining characteristics, diameter, cell membrane) can be determined. A histogram can be displayed for the channel number. Such analysis allows one to determine the number of cells in each “bin”, which in the embodiments described herein are “microbial” bins (eg, bacteria, fungi). , Nematodes, protozoa, archaebacteria, algae, dinoflagellates, viruses, viroids, etc.).

  In one embodiment, the sample is stained with one or more fluorescent dyes (the fluorescent dyes are specific for a particular microbial type), which results in a flow cytometer or some other fluorescence-based method. It is possible to detect via detection methods and quantification methods (such as fluorescence microscopy). By this method, the cell number and / or cell volume of a given biotype in a sample can be quantified. In another embodiment, as described herein, the use of flow cytometry to determine a unique first marker and / or a unique second marker for a biotype (enzyme expression, cell surface protein The presence and amount of (eg, the expression of) are determined. The population can also be generated because the variables can produce two or three histograms or contour plots (eg, light scattering versus fluorescence from cell membrane staining versus fluorescence from protein or DNA staining). An impression of the distribution of various target properties between cells throughout can be obtained. Displaying many such multi-parameter flow cytometry data is a common practice and is suitable for use in the methods described herein.

  In one embodiment of processing a sample to detect the presence and number of one or more microbial types, microscopy is utilized (FIGS. 1B, 1001, 1002). In one embodiment, the microscopy is optical microscopy, where visible light and a lens system are used to magnify the image of a small sample. Digital images can be recorded by charge coupled device (CCD) cameras. Other microscopy techniques include, but are not limited to, scanning electron microscopy and transmission electron microscopy. Microbial types are visualized and quantified according to the aspects provided herein.

  In another embodiment of the present disclosure, each sample or a portion thereof is subjected to fluorescence microscopy to detect the presence and number of one or more microbial types. By using different fluorescent dyes, cells in the sample can be stained directly and the total number of cells quantified using epi-fluorescence microscopy as well as flow cytometry as described above. Dyes useful for microbial quantification include, but are not limited to, acridine orange (AO), 4,6-di-amino-2phenylindole (DAPI), and 5-cyano-2,3 ditolyltetrazolium chloride ( CTC). Live cells can be estimated by viability staining methods, which include a green fluorescent SYTO9 ™ dye (permeating all membranes) and a red fluorescent propidium iodide (PI) dye ( LIVE / DEAD (registered trademark) bacterial viability determination kit (Bac-Light (registered trademark)) containing two nucleic acid stains (permeating cells having damaged membranes). Thus, cells with damaged membranes will stain red, while cells without damaged membrane will stain green. Fluorescence in situ hybridization (FISH) is an extension of epi-fluorescence microscopy, which allows for the rapid detection and counting of specific organisms. FISH uses fluorescently labeled oligonucleotide probes (typically 15-25 base pairs) that specifically bind to biological DNA in a sample, and visualizes cells using an epi-fluorescence microscope or confocal laser scanning microscope (CLSM) It becomes possible to do. In the reporter deposition catalyst fluorescence in situ hybridization (CARD-FISH), horseradish peroxidase (HRP) labeled with horseradish peroxidase was used to amplify the intensity of the signal obtained from the microorganism to be tested. The use of probes has improved the FISH method. FISH can be combined with other approaches to characterize microbial communities. One multiplex approach is the high affinity peptide nucleic acid (PNA) -FISH, which enhances the ability of the probe to penetrate an extracellular polymer (EPS) matrix. Another example is LIVE / DEAD-FISH, a combination of a cell viability determination kit and FISH, which has been used to evaluate disinfection efficiency in drinking water distribution systems.

  In another embodiment, each sample or portion thereof is subjected to micro-Raman spectroscopy to determine the presence of the microbial type and the absolute number of at least one microbial type (FIGS. 1B, 1001-1002; FIG. 2). , 2001-2002). Micro-Raman spectroscopy is a non-destructive, label-free technique that allows the detection and measurement of single-cell Raman spectra (SCRS). A typical SCRS provides a unique biochemical “fingerprint” of a single cell. SCRS contains a wealth of information on biomolecules (including nucleic acids, proteins, carbohydrates, and lipids) that can characterize different cell types, physiological changes, and cellular phenotypes become. Raman microscopy examines the scattering of laser light by the chemical bonds of different cell biomarkers. SCRS is the sum of the spectra of all biomolecules in one single cell and indicates the phenotypic profile of the cell. Cellular phenotype is the result of gene expression and usually reflects genotype. Thus, under the same growth conditions, different microbial types can be identified by their Raman spectra, since distinct microbial types yield distinctly different SCRS corresponding to their genotypic differences.

  In yet another embodiment, the sample or a portion thereof is subjected to centrifugation to determine the presence of a microbial type and the number of at least one microbial type (FIGS. 1B, 1001-1002; FIGS. 2, 2001-1). 2002). This process sediments the heterogeneous mixture by using the centrifugal force created by the centrifuge. The high density component of the mixture moves away from the axis of the centrifuge, while the low density component of the mixture moves toward the axis. Centrifugation allows fractionation of the sample into cytoplasmic, membrane and extracellular portions. Centrifugation can also be used to determine the localization information of a biological molecule of interest. In addition, centrifugation can also be used to fractionate total microbial community DNA. Because different prokaryotes differ in their guanine + cytosine (G + C) content in DNA, density gradient centrifugation based on G + C content is a method for identifying biotypes and the number of cells associated with each type. is there. In this approach, a fractional profile of the total community DNA is obtained, indicating the abundance of the DNA as a function of the G + C content. Total community DNA was physically separated into highly purified fractions, each showing a different G + C content, and the fractions were combined with additional molecular techniques (denaturing gradients). Analysis by gel electrophoresis (DGGE) / amplified ribosomal DNA restriction analysis (ARDRA) (see discussion herein), and the like, allows the diversity and 1 The presence / amount of one or more microbial types can be assessed.

  In another embodiment, a sample, or a portion thereof, is subjected to staining to determine the presence of a microbial type and the number of at least one microbial type (FIG. 1B, 1001-1002; FIG. 2, 2001-2002). . The use of stains and dyes allows the visualization of biological tissues, cells, or intracellular organelles. Staining can be used in conjunction with microscopy, flow cytometry, or gel electrophoresis to visualize or mark cells or biological molecules that are unique to different microbial types. In vivo staining is the process of staining living tissue, while in vitro staining stains cells or structures that have been removed from their biological context. Examples of specific staining techniques for use in the methods described herein include, but are not limited to, Gram stain to determine the gram status of bacteria, endogenous endospores to identify the presence of endospores. Spore staining, Thiel-Neelsen staining, hematoxylin and eosin staining for examining thin sections of tissue, papanicolaou staining for examining cell samples derived from various secretions of the body, periodic acid-Schiff staining of carbohydrates, cells Masson trichrome using a three-color staining protocol to distinguish the cells from surrounding connective tissue, Romanovsky staining (or Wright staining, Jenner staining, May-Grunwald solution, Leishmann staining, and Giemsa staining for examining blood or bone marrow samples) Common variants including staining), silver staining to reveal proteins and DNA, sudan for lipids Color, and Conklin staining (Conklin's staining) to detect the true endospores include. Common biological stains include acridine orange for determining cell cycle, bismark brown for acid mucin, carmine for glycogen, carmine aluminum for nuclei, coomassie blue for proteins, nerves Cresyl violet for the acidic components of the cytoplasm, crystal violet for the cell wall, DAPI for the nucleus, cytoplasmic material, cell membrane, some extracellular structures, and eosin for erythrocytes, ethidium bromide for DNA Acid fuchsin for collagen, smooth muscle or mitochondria, hematoxylin for nuclei, Hoechst stain for DNA, iodine for starch, malachite green for bacteria (for Jimenez staining procedure) and spores, Methyl green for chromatin, animal cells Methylene blue, neutral red for Nissl material, Nile blue for nucleus, Nile red for lipophilic material, osmium tetroxide for lipids, rhodamine used in fluorescence microscopy, safranine for nucleus Is included. Stains are also used in transmission electron microscopy to enhance contrast, such as phosphotungstic acid, osmium tetroxide, ruthenium tetroxide, ammonium molybdate, cadmium iodide, carbohydrazide, chloride Ferric, hexamine, indium trichloride, lanthanum nitrate, lead acetate, lead citrate, lead (II) nitrate, periodate, phosphomolybdic acid, potassium ferricyanide, potassium ferrocyanide, ruthenium red, silver nitrate, silver protein silver, gold chloride Includes sodium acid, thallium nitrate, thiosemicarbazide, uranyl acetate, uranyl nitrate, and vanadyl sulfate.

  In another embodiment, the sample or a portion thereof is subjected to mass spectrometry (MS) to determine the presence of the microbial type and the number of at least one microbial type (FIGS. 1B, 1001-1002; FIG. 2001-2002). MS can also be used to detect the presence and expression of one or more unique markers in a sample, as discussed below (FIG. 1B, 1003-1004; FIG. 2, 2003-2004). ). Using MS, for example, the presence and amount of protein and / or peptide markers specific to a microbial type are detected, so that the number of each microbial type in the sample is assessed. Quantification can be performed with a stable isotope label or without a label. De novo sequencing of peptides can be performed directly from MS / MS spectra, or with sequence tagging (to obtain short tags that can be matched against a database). MS can reveal post-translational modifications of proteins and also identify metabolites. The MS can be used in combination with chromatography techniques and other separation techniques (gas chromatography, liquid chromatography, capillary electrophoresis, ion mobility, etc.) to increase mass resolution and determinability.

  In another embodiment, a sample or a portion thereof is subjected to lipid analysis to determine the presence of a microbial type and the number of at least one microbial type (FIGS. 1B, 1001-1002; FIG. 2, 2001-2002). ). Fatty acids are present in cell biomass in relatively constant proportions, and microbial cells have signature fatty acids that can distinguish microbial types within a community. In one embodiment, the fatty acids are saponified and then extracted by derivatization to obtain the respective fatty acid methyl ester (FAME), after which the FAME is analyzed by gas chromatography. Next, in one embodiment, the FAME profile is compared to a reference FAME database to identify fatty acids and their corresponding microbial signatures by multivariate statistical analysis.

  In aspects of the methods provided herein, the number of unique first markers in a sample or a portion thereof (eg, an aliquot of a sample) is determined, as well as the amount of each of the unique first markers. (FIG. 1B, 1003; FIG. 2, 2003). The unique marker is that of a microbial strain. It will be appreciated that it is not necessary to analyze the entire sample depending on the particular marker being searched and measured. For example, if the unique marker is unique to a bacterial strain, it is not necessary to analyze the fungal portion of the sample. As noted above, in some embodiments, determining the absolute cell number of one or more biotypes in a sample comprises separating the samples by biotype (eg, performed via flow cytometry). including.

  As used herein, any marker specific to a biological strain can be utilized. For example, markers include, but are not limited to, small subunit ribosomal RNA gene (16S / 18S rDNA), large subunit ribosomal RNA gene (23S / 25S / 28S rDNA), intervening 5.8S gene, cytochrome c oxidase, Beta-tubulin, elongation factors, RNA polymerase, and internal transcription spacer (ITS) can be included.

  The ribosomal RNA gene (rDNA), particularly the small subunit ribosomal RNA gene (ie, 18S rRNA gene for eukaryotes (18S rDNA) and 16S rRNA for prokaryotes (16S rDNA)) It has been a major target for assessing biotypes and strains in the microbial community. However, the large subunit ribosomal RNA gene (28S rDNA) has also been targeted. rDNA is suitable for taxonomic identification because (i) the rDNA is universally present in all known organisms, and (ii) it contains both conserved and variable regions. (Iii) that the database of rDNA sequences available for comparison has been expanded exponentially. In sample community analysis, conserved regions serve as annealing sites for the corresponding universal PCR primers and / or sequencing primers, while variable regions may be used to account for phylogenetic differences. Can be. Furthermore, the high copy number of rDNA in cells also facilitates detection from environmental samples.

  An internal transcribed spacer (ITS) is located between 18S and 28S rDNA, and ITS has also been targeted. Although ITS is transcribed, it is spliced and removed before ribosome assembly. The ITS region is composed of two highly variable spacers, ITS1 and ITS2, and an intervening 5.8S gene. There are multiple copies of this rDNA operon in the genome. The ITS region is highly variable since it does not encode ribosome components.

  In one embodiment, the unique RNA marker can be an mRNA marker, an siRNA marker, or a ribosomal RNA marker.

  A functional gene encoding a protein can also be used herein as a unique first marker. Such markers include, but are not limited to, the recombinase A gene family (bacterial RecA, archaeal RadA and RadB, eukaryotic Rad51 and Rad57, phage UvsX), RNA polymerase β subunit (RpoB) gene (Responsible for initiation and elongation of transcription), and chaperonins. Candidate marker genes have also been identified for bacteria plus archaebacteria: ribosomal protein S2 (rpsB), ribosomal protein S10 (rpsJ), ribosomal protein L1 (rpIA), translation elongation factor EF-2, translation initiation factor IF -2, metalloendopeptidase, ribosomal protein L22, ffh signal recognition particle protein, ribosomal protein L4 / L1e (rpld), ribosomal protein L2 (rplb), ribosomal protein S9 (rpsI), ribosomal protein L3 (rplc), phenylalanyl tRNA synthetase beta subunit, ribosomal protein L14b / L23e (rplN), ribosomal protein S5, ribosomal protein S19 (rpsS), ribosomal protein Protein S16, ribosomal protein L16 / L10E (rplp), ribosomal protein S13 (rpsM), phenylalanyl tRNA synthetase α subunit, ribosomal protein L15, ribosomal protein L25 / L23, ribosomal protein L6 (rplF), ribosomal protein L11 ( rplK), ribosomal protein L5 (rplE), ribosomal protein S12 / S23, ribosomal protein L29, ribosomal protein S3 (rpsC), ribosomal protein S11 (rpsK), ribosomal protein L10, ribosomal protein S8, tRNA pseudouridine synthase B, ribosome Protein L18P / L5E, ribosomal protein S15P / S13e, porphobilinogende Minaze, ribosomal protein S17, ribosomal protein L13 (rplM), phosphoribosyl formylglycine amidine cyclo - ligase (rpsE), ribonuclease HII, and ribosomal protein L24. Other candidate marker genes for bacteria include transcription elongation protein NusA (nusA), rpoB DNA-dependent RNA polymerase subunit beta (rpoB), GTP binding protein EngA, rpoC DNA-dependent RNA polymerase subunit beta ', priA primosome assembly Protein, transcription coupled repair factor, CTP synthase (pyrG), secY protein precursor translocase subunit SecY, GTP-binding protein Obg / CgtA, DNA polymerase I, rpsF 30S ribosomal protein S6, poA DNA-dependent RNA polymerase subunit alpha , Peptide chain terminator 1, rplI 50S ribosomal protein L9, polyribonucleotide nucleotidyl transferase , Tsf elongation factor Ts (tsf), rplQ 50S ribosomal protein L17, tRNA (guanine-N (1)-)-methyltransferase (rplS), rplY putative 50S ribosomal protein L25, DNA repair protein RadA, glucose inhibitory fission protein A Ribosome binding factor A, DNA mismatch repair protein MutL, smpB SsrA-binding protein (smpB), N-acetylglucosaminyltransferase, S-adenosyl-methyltransferase MraW, UDP-N-acetylmuramoylalanine-D-glutamic acid Ligase, rplS 50S ribosomal protein L19, rplT 50S ribosomal protein L20 (rplT), ruvA Holiday junction D A helicase, ruvB Holiday junction DNA helicase B, serS seryl tRNA synthetase, rplU 50S ribosomal protein L21, rpsR 30S ribosomal protein S18, DNA mismatch repair protein MutS, rpsT 30S ribosomal protein S20, DNA repair protein RecN, frr ribosome recycling factor ( frr), recombinant protein RecR, function unknown protein UPF0054, miaA tRNA isopentenyltransferase, GTP binding protein YchF, chromosomal replication initiation protein DnaA, dephospho CoA kinase, 16S rRNA processing protein Rimmm, ATP-cone (domain) protein, -Deoxy-D-xy Loose 5-phosphate reductoisomerase, 2C-methyl-D-erythritol 2,4-cyclodiphosphate synthase, fatty acid / phospholipid synthesis protein PlsX, tRNA (Ile) -lysidine synthetase, dnaG DNA primase (dnaG) , RuvC Holiday junction resolvenase, rpsP 30S ribosomal protein S16, recombinase A recA, riboflavin biosynthesis protein RibF, glycyl tRNA synthetase beta subunit, trmU tRNA (5-methylaminomethyl-2-thiouridylate) -methyltransferase, rpmI 50S ribosome Protein L35, heme uroporphyrinogen decarboxylase, rod-shaped determining protein, rpmA 50S ribosomal protein L27 (rpmA), peptidyl tRNA hydrolase, translation initiation factor IF-3 (infC), UDP-N-acetylmuramyl-tripeptide synthetase, rpmF 50S ribosomal protein L32, rpIL 50S ribosomal protein L7 / L12 (rpIL), leuS leucyl tRNA synthetase, ligA NAD-dependent DNA ligase, cell division protein FtsA, GTP-binding protein TypA, ATP-dependent Clp protease, ATP-binding subunit ClpX, DNA replication and repair protein RecF, and UDP-N-acetylenolpyruvoylglucosamine reductase Is included.

  Phospholipid fatty acids (PLFA) can also be used as unique first markers according to the methods described herein. Because PLFAs are synthesized rapidly during microbial growth, not found in storage molecules, and rapidly degraded during cell death, PLFA can accurately determine the number of members of the current living community. it can. All cells contain fatty acids (FA) that can be extracted and esterified to form fatty acid methyl esters (FAME). When FAME is analyzed using gas chromatography-mass spectrometry, the resulting profile is a "fingerprint" of the microorganisms in the sample. The chemical composition of biological membranes in the eubacteria and eukaryote domains is composed of fatty acids (phospholipid fatty acids (PLFA)) linked to glycerol by ester-type bonds. On the other hand, archaeal membrane lipids are composed of long-chain branched hydrocarbons (phospholipid ether lipids (PLEL)) linked to glycerol by ether-type bonds. This is one of the most widely used non-genetic criteria to distinguish these three domains. In this sense, phospholipids derived from microbial cell membranes (characterized by different acyl chains) are excellent signature molecules because of their ability to associate such lipid structural diversity with specific microbial taxa Things.

  As shown herein, the level of expression of one or more unique second markers (which may be the same or different from the first marker) is measured to determine if the organism strain is active. (FIG. 1B, 1004; FIG. 2, 2004). The unique first markers are those described above. A unique second marker is a marker of microbial activity. For example, in one embodiment, the expression of mRNA or protein of any of the first markers described above is considered to be a unique second marker for the purposes of the present disclosure.

  In one embodiment, a microorganism is considered active if the level of expression of the second marker is above a threshold level (eg, a control level) (FIG. 1B, 1005; FIG. 2, 2005). In one embodiment, the expression level of the second marker is at least about 5%, at least about 10%, at least about 15%, at least about 20% compared to a threshold level (in some embodiments, a control level). , At least about 25%, or at least about 30%, is determined to be active.

  The unique second marker is, in one embodiment, measured at the protein, RNA, or metabolite level. The unique second marker is the same or different from the unique first marker.

  As indicated above, a plurality of unique first markers and unique second markers can be detected according to the methods described herein. Further, detection and quantification of a unique first marker can be performed according to methods known to those of skill in the art in light of the present disclosure (FIGS. 1B, 1003-1004, FIG. 2, 2003-2004).

  In one embodiment, nucleic acid sequencing (eg, gDNA, cDNA, rRNA, mRNA) is used to determine the absolute cell number of a unique first marker and / or a unique second marker. Sequencing platforms include, but are not limited to, Sanger sequencing and high-throughput sequencing available from Roche / 454 Life Sciences, Illumina / Solexa, Pacific Biosciences, Ion Torrent, and Nanopore. Sequencing can be amplicon sequencing of specific DNA or RNA sequences, or shotgun sequencing of the entire metagenome / transcriptome.

  Conventional Sanger sequencing (Sanger et al. (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl. Acad. Sci. USA, 74, pp. 5463-5467, which is incorporated herein by reference in its entirety). ) Is dependent on the selective incorporation of dideoxynucleotides that terminate chain elongation during DNA replication in vitro by DNA polymerases and is suitable for use in the methods described herein.

  In another embodiment, the sample or a portion thereof is subjected to nucleic acid extraction, amplification of a target DNA (such as a rRNA gene) using appropriate primers, and construction of a clone library using a sequencing vector. Next, the selected clones are sequenced by Sanger sequencing, and the nucleotide sequence of the target DNA is searched, whereby the number of unique microorganism strains in the sample can be calculated.

  Roche / 454 Life Sciences' 454 pyrosequencing generates long reads and can be utilized in the methods described herein (Margulies et al. (2005) Nature, 437, pp. 376). 380, U.S. Patent Nos. 6,274,320, 6,258,568, and 6,210,891, each of which is incorporated herein in its entirety for all purposes. . A nucleic acid to be sequenced (eg, genomic DNA / metagenomic DNA fragmented by an amplicon or a nebulizer) has specific adapters added to both ends by PCR or ligation. The DNA with the adapter is immobilized on small beads (ideally one bead will have one DNA fragment) and the beads are suspended in a water-in-oil emulsion. Next, an emulsion PCR step is performed to prepare multiple copies of each DNA fragment, resulting in a set of beads where each bead contains many copies of the same DNA fragment cloned. can get. Next, each of the beads is placed in a well of a fiber optic chip, which also contains the enzymes necessary for a sequencing-by-synthesis reaction. When a base (A, C, G, or T) is added, pyrophosphate is released, and this release generates a flash. By recording the flash, the sequence of a DNA fragment in each well is determined. Approximately 1 million reads per run can be achieved with a maximum read length of 1,000 bases. Pair end-sequencing can be performed, which results in a pair of reads, each resulting from one end of a given DNA fragment. In a multiplex reaction, a molecular barcode can be created and placed between the adapter sequence and the sequence of interest, which allows each sequence to be bioinformatically assigned to a sample.

  Illumina / Solexa sequencing yields an average read length of about 25 base pairs (bp) to about 300 bp (Bennett et al. (2005) Pharmacogenomics, 6: 373-382, Lange et al. (2014) .BMC Genomics). 15, p.63, Fadrosh et al. (2014) Microbiome 2, p.6, Caporaso et al. (2012) ISME J, 6, p.1621-1624, Bentley et al. (2008) Accurate whole human genome seq. using reversible terminator chemistry. Nature, 456: 53-59). This sequencing technique is also sequencing-by-synthesis. In this sequencing technique, a reversible dye terminator and a flow cell having a region to which an oligo is added are used. The DNA fragment to be sequenced has specific adapters at both ends and is flowed over a flow cell in which a number of specific oligonucleotides that hybridize to the ends of the fragment are arranged. Next, each fragment is replicated to prepare a cluster of identical fragments. Next, the reversible dye-terminator nucleotides are flowed over the flow cell, allowing time for binding. Excess nucleotides are washed away and the flow cell is imaged. The reversible terminator can be removed, so that the process can be repeated and nucleotides can be added in subsequent cycles. Pair-end reads of 300 bases each can be achieved. The Illumina platform can generate 4 billion fragments in a single run in a paired-end fashion with 125 bases for each read. For multiplex analysis of samples, barcodes can be used, but index primers are used.

  SOLiD (Sequencing by Ligation and Detection of Oligonucleotides (Life Technologies)) treatment is a “sequencing-by-ligation” technique, in which the presence and absence of a first marker and / or a second marker is determined. Quantities can be used in the methods described herein (FIGS. 1B, 1003-1004; FIGS. 2, 2003-2004) (Peckham et al. SOLiD ™ Sequencing and 2-Base Encoding. San Diego, CA: American Society of Human Genetics, 2007, Mitra et al. (2013) Analysis of the i. testinomic microbiousing SOLiD 16S rRNA gene sequencing and SOLiD shotgun sequencing. Each of which is incorporated herein by reference in its entirety)). A library of DNA fragments is prepared from the sample to be sequenced and used to prepare a population of clonal beads, in which case only one type of fragment will be present on the surface of each magnetic bead. The fragments attached to the magnetic beads will have a universal P1 adapter sequence, so that the starting sequence of every fragment is known and identical. Primers hybridize to the P1 adapter sequence in the library template. A set of four fluorescently labeled dibasic probes compete in ligation to sequencing primers. The specificity of the dinucleotide probe is achieved by ensuring that the first and second bases are all confirmed in each ligation reaction. Ligation, detection and cutting cycles are performed a plurality of times, and the final read length is determined by the number of cycles. The SOLiD platform can generate up to 3 billion reads per run of 75 bases in length. Pair-end sequencing is available and can be used herein, but the length of the second read in the pair is only 35 bases. With separate indexed runs, it is possible to perform multiplex analysis of the sample via a system similar to that used by Illumina.

  The Ion Torrent system is similar to 454 sequencing and is suitable for use in the methods described herein for detecting the presence and amount of a first marker and / or a second marker ( 1B, 1003 to 1004; FIG. 2, 2003 to 2004). In the Ion Torrent system, a microwell plate containing beads to which DNA fragments are bound is used. However, the Ion Torrent system differs from all other systems in the manner in which base incorporation is detected. When a base is added to the growing DNA strand, a proton is released, and the proton causes a slight change in the surrounding pH. A micrometer that senses pH is associated with the wells on the plate, and any such changes are recorded by the micrometer. Different bases (A, C, G, T) flow sequentially through the wells, making it possible to estimate the sequence from each well. The Ion Proton platform can generate up to 50 million reads per run with a read length of 200 bases. The Personal Genome Machine platform produces longer reads, 400 bases. Two-way sequencing is available. Multiplex analysis can be performed via standard in-line molecular barcode sequencing.

  Pacific Biosciences (PacBio) SMRT sequencing uses a single-molecule real-time sequencing approach, and in one embodiment, a book for detecting the presence and amount of a first marker and / or a second marker. Used in the method described in the specification (FIG. 1B, 1003 to 1004; FIG. 2, 2003 to 2004). The PacBio sequencing system sets itself apart from other major next-generation sequencing systems because it does not require an amplification step. In one embodiment, this sequencing is performed on a chip that includes a number of zero mode waveguide (ZMW) detectors. A DNA polymerase is bound to the ZMW detector, and the incorporation of nucleotides labeled with a dye linked via phosphate is imaged in real time in parallel with the synthesis of the DNA strand. With the PacBio system, very long read lengths (about 4,600 bases on average) are obtained, and very many reads are obtained per run (about 47,000). A typical "pair-end" approach is PacBio because reads are typically long enough to cover the fragment multiple times via CCS without the need for independent sequencing from both ends. Not used in Multiplex analysis in PacBio does not require independent reads and is performed according to a standard "in-line" barcode addition model.

  In one embodiment, when the unique first marker is an ITS genomic region, in one embodiment, an automated ribosomal intergenic spacer analysis (ARISA) is used to determine the number and identity of microbial strains in a sample. Used (FIGS. 1B, 1003, 2, 2003) (Ranjard et al. (2003). Environmental Microbiology 5, pp. 1111-1120, incorporated herein by reference in its entirety for any purpose). ). The ITS region has significant heterogeneity in both length and nucleotide sequence. By using a fluorescently labeled forward primer and an automatic DNA sequencer, it is possible to obtain high separation resolution and high throughput. The inclusion of an internal standard in each sample provides accuracy in the global fragment sizing.

  In another embodiment, a fragment length polymorphism (RFLP) of an rDNA fragment amplified by PCR (also known as amplified ribosomal DNA restriction enzyme analysis (ARDRA)) to characterize a unique first marker and its amount in a sample. 1B, 1003, FIG. 2, 2003) (for additional details see Massol-Deya et al. (1995). Mol. Microb. Ecol. Manual. 3.3.2, pp. 1). -18 (herein incorporated by reference in its entirety for any purpose). An rDNA fragment is generated by PCR using common primers, and the resulting rDNA fragment is digested with restriction enzymes, electrophoresed on an agarose gel or acrylamide gel, and stained with ethidium bromide or silver nitrate.

  One of the fingerprinting techniques used in detecting the presence and relative abundance of unique first markers is single-stranded conformational polymorphism (SSCP) (Lee et al. (1996). Appl Environ Microbiol). 62, pp. 3112-3120, Scheinert et al. (1996) .J. Microbiol. Each of these documents is incorporated herein by reference in its entirety). In this technique, a DNA fragment (such as a PCR product obtained using a primer specific to the 16S rRNA gene) is denatured and directly electrophoresed on a non-denaturing gel. Separation is based on size differences and the folded conformation of single-stranded DNA, which affects electrophoretic mobility. Reannealing of DNA strands during electrophoresis can be prevented by a number of strategies, including the use of one phosphorylation primer in PCR followed by lamination of the phosphorylated strand with lambda exonuclease. Specific digestion and magnetic separation of one single strand after denaturation using one biotinylated primer is included. To assess the identity of the main population in a given microbial community, in one embodiment, bands can be cut out and sequenced, or specific probes can be hybridized to the SSCP pattern. Electrophoresis conditions (such as gel matrix, temperature, and the addition of glycerol to the gel) can affect separation.

  In addition to sequencing-based methods, other methods for quantifying the expression of a second marker (eg, gene expression, protein expression) also determine the level of expression of one or more second markers. (FIGS. 1B, 1004; FIG. 2, 2004). For example, quantitative RT-PCR, microarray analysis, linear amplification techniques (such as nucleic acid sequence-based amplification (NASBA)) are all suitable for use in the methods described herein, and those skilled in the art in light of the present disclosure. Can be carried out according to a method known in US Pat.

  In another embodiment, the sample or a portion thereof is subjected to a quantitative polymerase chain reaction (PCR) to detect the presence and amount of the first marker and / or the second marker (FIG. 1B, 1003). -1004; FIG. 2, 2003-2004). After reverse transcription of the transcribed ribosomal RNA and / or messenger RNA (rRNA and mRNA) to complementary DNA (cDNA), the activity of a specific microorganism strain is measured by performing PCR (RT-PCR). .

  In another embodiment, the sample or a portion thereof is subjected to a PCR-based fingerprinting technique to detect the presence and amount of the first marker and / or the second marker (FIGS. 1B, 1003-1003). 1004; FIG. 2, 2003-2004). PCR products can be separated by electrophoresis based on nucleotide composition. Sequence differences between different DNA molecules affect the melting behavior, so that molecules with different sequences will stop migrating at different locations in the gel. Therefore, an electrophoresis profile can be defined by the positions and relative intensities of different bands or peaks, and the electrophoresis profile can be converted into numerical data for calculating a diversity index. After excising the band from the gel, sequencing can also reveal the phylogenetic affiliation of community members. The electrophoresis method is not limited, but denaturant gradient gel electrophoresis (DGGE), temperature gradient gel electrophoresis (TGGE), single-stranded higher-order structure polymorphism (SSCP), restriction fragment length polymorphism analysis (RFLP) or amplified ribosomal DNA restriction enzyme analysis (ARDRA), end-labeled restriction enzyme fragment polymorphism analysis (T-RFLP), automatic analysis of ribosomal intergenic spacer (ARISA), random amplified polymorphic DNA (RAPD), DNA amplification Fingerprinting (DAF) and Bb-PEG electrophoresis may be included.

  In another embodiment, the sample or portion thereof is a chip-based platform (microarray or microfluidics) for determining the amount of a unique first marker and / or the presence / amount of a unique second marker. (FIG. 1B, 1003 to 1004, FIG. 2, 2003 to 2004). The PCR product is amplified from the total DNA in the sample, and the PCR product is directly hybridized to a known molecular probe immobilized on the microarray. After the fluorescently labeled PCR amplicon has hybridized to the probe, a positive signal is scored by using confocal laser scanning microscopy. The microarray approach allows samples to be rapidly evaluated for replication, which is a significant advantage in analyzing microbial communities. Hybridization signal intensity on a microarray can be directly proportional to the amount of target organism. The universal high density 16S microarray (eg, PHYLOCIP) contains approximately 30,000 probes of the 16S rRNA gene targeting several cultured microbial species and “candidate divisions”. Such probes will target all 121 partitioned prokaryotic orders and allow the simultaneous detection of 8,741 bacterial and archaeal taxa. Another microarray used for profiling microbial communities is the functional gene array (FGA). Unlike PHYLOCHP, FGA is primarily designed to detect specific metabolic groups of bacteria. Thus, FGA not only reveals community structure, but also elucidates in situ community metabolic capacity. FGAs are useful in relating microbial community composition to ecosystem function because they contain probes derived from genes whose biological function is known. The FGA, called GEOCHIP, has> 24,000 genes derived from all known metabolic genes involved in various biogeochemical, ecological, and environmental processes (such as ammonia oxidation, methane oxidation, and nitrogen fixation) Probe.

  In one embodiment, a protein expression assay is used in the methods described herein for determining the expression level of one or more second markers (FIGS. 1B, 1004; FIG. 2, 2004). For example, in one embodiment, a mass spectrometry or immunoassay (such as an enzyme-linked immunosorbent assay (ELISA)) is used to quantify the expression level of one or more unique second markers, One or more unique second markers are proteins.

  In one embodiment, a sample or a portion thereof is subjected to bromodeoxyuridine (BrdU) incorporation to determine the level of a unique second marker (FIGS. 1B, 1004; FIG. 2, 2004). BrdU is a synthetic nucleoside analog of thymidine, which can be incorporated into newly synthesized DNA in replicating cells. Next, an antibody specific for this base analog can be used to detect BRdU. Thus, a cell identified by BrdU incorporation is one that is actively replicating its DNA, which is a measure of the activity of a microorganism according to one embodiment of the methods described herein. is there. By combining BrdU incorporation with FISH, the identity and activity of the target cells can be determined.

  In one embodiment, the sample, or a portion thereof, is subjected to microautoradiography (MAR) in combination with FISH to determine the level of a unique second marker (FIGS. 1B, 1004; FIG. 2, 2004). MAR-FISH is based on the incorporation of radioactive substrates into cells, where autoradiography is used to detect active cells and FISH is used to identify cells. Detection and identification of active cells at single cell resolution is performed using a microscope. MAR-FISH provides information on the percentage of total cells, cells targeted by the probe, and cells that take up a given radiolabel. This method provides an assessment of the in-situ function of the target microorganism and is an effective technique for examining the in vivo physiology of the microorganism. A technique developed to quantify cell-specific substrate uptake in combination with MAR-FISH is known as quantitative MAR (QMAR).

  In one embodiment, the sample or a portion thereof is subjected to stable isotope Raman spectroscopy (Raman-FISH) in combination with FISH to determine the level of the unique second marker (FIG. 1B). , 1004; FIG. 2, 2004). In this approach, stable isotope probing, Raman spectroscopy, and FISH are combined to associate metabolic processes with a particular organism. As a result of the influence of the stable isotope uptake ratio by the cell on light scattering, the peak shift of the labeled cell component (including the protein component and the mRNA component) can be measured. Raman spectroscopy can be used to determine whether cells synthesize compounds, including but not limited to oils (such as alkanes), lipids (such as triacylglycerol (TAG), etc.). ), Specific proteins (such as heme proteins, metalloproteins), cytochromes (such as P450, cytochrome c), chlorophyll, chromophores (such as dyes for light-harvesting carotenoids and rhodopsin), organic polymers (such as polyhydroxyalkanoate (PHA)) ), Polyhydroxybutyrate (PHB), hopanoids, steroids, starches, sulfides, sulfates, and secondary metabolites (such as vitamin B12).

In one embodiment, a sample or a portion thereof is subjected to DNA / RNA stable isotope probing (SIP) to determine the level of a unique second marker (FIGS. 1B, 1004; FIG. 2, FIG. 2004). SIP allows to determine the microbial diversity associated with a particular metabolic pathway and is generally applied to test microorganisms involved in the utilization of carbon and nitrogen compounds. The target substrate is labeled with a stable isotope (such as ¹³ C or ¹⁵ N) and added to the sample. Only those microorganisms that can metabolize the substrate will take up the substrate in their cells. Thereafter, ¹³ C-DNA and ¹⁵ N-DNA can be isolated by density gradient centrifugation and used for metagenomic analysis. RNA-based SIP can be a responsive biomarker for use in SIP testing because RNA itself reflects cellular activity.

In one embodiment, the sample, or a portion thereof, is subjected to an isotope array to determine the level of a unique second marker (FIGS. 1B, 1004; FIG. 2, 2004). Isotope arrays allow active microbial communities to be screened functionally and phylogenetically in a high-throughput manner. This approach uses a combination of SIP to monitor substrate uptake profiles and microarray technology to determine the taxonomic identity of the active microbial community. ^The sample is incubated with a ¹⁴ C-labeled substrate, which is incorporated into the microbial biomass during the growth process. ^The rRNA labeled with ¹⁴ C is separated from unlabeled rRNA, and then labeled with a fluorescent dye. After the fluorescently labeled rRNA is hybridized to a phylogenetic microarray, the emitted and fluorescent signals are scanned. Thus, this approach makes it possible to simultaneously test the composition of a microbial community and the consumption of a particular substrate by metabolically active microorganisms of a complex microbial community.

  In one embodiment, the sample, or a portion thereof, is subjected to a metabolomics assay to determine the level of the unique second marker (FIGS. 1B, 1004; FIG. 2, 2004). In metabolomics, a metabolome that is equivalent to a collection of all metabolites (final metabolites of cellular processes) in a biological cell, tissue, organ, or organism is tested. Using this methodology, the presence of microorganisms and / or microbial mediated processes can be monitored, as it allows specific metabolite profiles to be associated with different microorganisms. Intracellular and extracellular metabolite profiles associated with microbial activity can be obtained using techniques such as gas chromatography-mass spectrometry (GC-MS). Complex mixtures of metabolomic samples can be separated by techniques such as gas chromatography, high performance liquid chromatography, and capillary electrophoresis. Metabolite detection includes mass spectrometry, nuclear magnetic resonance (NMR) spectroscopy, ion mobility mass spectrometry, electrochemical detection (in combination with HPLC), and radiolabeling (when combined with thin layer chromatography) Can be implemented.

  According to the embodiments described herein, the presence and number of one or more active microbial strains in a sample is determined (FIGS. 1B, 1006; FIG. 2, 2006). For example, analysis of strain identity information obtained by assaying the number and presence of first markers determines how many unique first markers appear, thereby determining the unique microbial strain. (E.g., by counting the number of sequence reads in a sequencing assay). This value, in one embodiment, is expressed as a percentage of the total sequence reads of the first marker, so that the percentage of a particular microbial strain in a particular microbial type can be obtained. In another embodiment, this ratio is multiplied by the number of microbial types (obtained in step 1002 or step 2002; see FIG. 1B and FIG. 2) to yield 1 in a sample and a given volume. The absolute cell number of one or more microbial strains is obtained.

  The one or more microorganism strains have an expression level of the unique second marker at or above a threshold level (eg, at least about 5%, at least about 10%, at least about 20%, or at least about 30% higher), as described above, is considered active.

  In another aspect of the present disclosure, a method for determining the absolute cell number of one or more microbial strains is performed in a plurality of samples (see FIG. 2, specifically 2007). . For a microorganism strain to be classified as active, it need only be active in one of the samples. The sample may be obtained from the same source over multiple time points, or may be obtained from different environmental sources (eg, different animals).

  In one embodiment, one or more active microbial strains are associated with environmental parameters using absolute cell numbers across samples (FIG. 2, 2008). In one embodiment, the environmental parameter is the presence of a second active microbial strain. The association of one or more active microbial strains to environmental parameters is performed, in one embodiment, by determining the co-occurrence of strains and parameters by network analysis and / or graph theory.

  In one embodiment, the determination of co-occurrence of one or more active microbial strains and environmental parameters comprises determining the co-occurrence of one or more active microbial strains and environmental parameters with a network analysis method for measuring the connectivity between the strain (s) in the network and the environmental parameters. Or, including a cluster analysis method, a network is a collection of two or more samples that share common or similar environmental parameters. Examples of measurements of independence are provided and discussed herein, and for additional details, see Blomqvist, "On a measure of dependency between two random variables," The Annals of Mathematical Technical Review. al. "Nonparametric statistical methods-Wiley series in probability and statistics Texts and references section" (1999), and / or Blum et al. "Distribution free tests of independence based on the sample distribution function" The Annals of Mathematical Techniques, which can be understood from the above publications, and the contents of 98 can be understood from the above-mentioned contents. Is hereby expressly incorporated by reference in its entirety).

  In another embodiment, correlation analysis methods (Pearson correlation, Spearman correlation, Kendall correlation, canonical correlation analysis, likelihood ratio test (eg, Wilks, SS “On”) are used to establish connectivity between variables. The Independence of k Sets of Normally Distributed Statistical Variables "Econometrica, Vol. 3, No. 3, July 1935, pp 309-326, which is hereby incorporated by reference in its entirety for all purposes. By applying the teachings and methods described)) and canonical correlation analysis. Multivariable extensions, maximal correlations of such methods (eg, Alfred Renyi "On measures of dependency" Acta materialatic hungarica 10.3-4 (1959): 441-451, which are expressly incorporated herein by reference in their entirety). Or both (MAC) can be used at appropriate times, depending on the number of variables to be compared. In some embodiments, a maximum correlation analysis and / or other multivariate correlation measurements configured to find multidimensional patterns (eg, “Multivariate Maximum Correlation Analysis,” Nguyen et al., Proceedings of the 31st International). Conference on Machine Learning, Beijing, China, 2014, which is hereby expressly incorporated by reference in its entirety for all purposes, by applying the methods and teachings. . In some embodiments, the network features and analysis (Farine et al, in “Constructing, Conducting and Interpreting Animal Social Network Analysis” ”Journal of Animal Diagnostics, Journal of Animal Diagnostics, Journal of Animal Physics, Vol. /1365-2656.1418, such as those discussed by reference for all purposes, which are expressly incorporated herein by reference in their entirety) can be utilized and adjusted for the present disclosure.

  In some embodiments, the network analysis may be performed using non-parametric techniques (eg, Taskinen et al., “Multivariate nonparametric tests of independence.” Journal of the American Statistical, Academic Association, October 2004, Journal of the American Statistical Association, and the National Association of Medical Sciences). "A Nonparametric Test of Independence Between Two Vectors." Journal of the American Statistical Association, Vol. 92, No. 438, Jun. 1977, Vol. By applying the teachings and methods detailed in each of which are hereby expressly incorporated herein by reference), such non-parametric approaches include a mutual information maximum information coefficient, a maximum information Entropy (MIE; see, for example, Zhang Ya-Hong et al., "Detecting Multivariable Correlation with Maximum Information Entropy [J]", Journal of Electronics, Inc., for all of the following items. , By applying the teachings and methods of U.S. Pat. Quasi-correlation analysis (KCCA; e.g., Bach et al. "Kernel Independent Component Analysis" Journal of Machine Learning Research 3 (2002) 1-48, which is expressly incorporated herein by reference for all purposes). By applying the teachings and methods described in detail in US Pat. "Journal of the American Statistical Association 80, no. 391 (1985): 614-19, doi: 10.2307 / 2288777, which is hereby expressly incorporated by reference in its entirety for any purpose. By applying the teachings and methods described), correlation distance measurements (dcor; see, for example, Szekely et al., "Measuring and Testing Dependence by Correlation of Distances", The Annals of Statistics, 200. Statistical. 6,2769-2794, doi: 10.2114 / 009053700000000505 (Every eye For example, Brownian distance covariance (dcov; e.g., Szekely), by applying the teachings and methods described in detail in US Pat. et al., "Brownian Distance Covariance," The Annals of Applied Statistics, 2009, Vol. 3, No. 4, 1236-1265, Doi: 10.214 / 09-AOAS 312 (herein incorporated by reference in its entirety for all purposes). By incorporating the teachings and methods described in detail in the Hilbert-Schmidt Independence Standard (HSCI / CHSI; eg Gr) etton et al. "A Kernal Two-Sample Test" Journal of Machine Learning Research 13 (2012) 723-773, and Poczos et al. "Copula-based Kernel Dependency Measures" Carnegie Mellow University, Research Showcase @ CMU, Proceedings of the 29th International Conference on Machine Learning (each of these references is hereby expressly incorporated by reference in its entirety for all purposes) by applying the teachings and methods detailed therein. , Randomized dependent coefficients (RDC; see, for example, Lopez-Paz et al. By applying the teachings and methods described in detail in "The Randomized Dependence Coefficient", Advances in Neural Information Processing Systems (2013), which is expressly incorporated by reference in its entirety for any purpose. These methods establish the connectivity of the variables. In some embodiments, one or more of these methods may be performed using a bagging or boosting method or a k-nearest neighbor estimation method (eg, Breiman, “Arcing Classifiers” The Annals of Statistics, 1998, Vol. 3, 801-849, Liu, "Modified Bagging of Maximum Information Coefficient for Genome-wide Identification", Int. J. Data Mining and Bioinformatics, Int. J. Data Mining and Bioinformatics. et al., "Efficient Estimation of Mutual I" The information for the formation for Strongly Dependable Variables "Proceedings of the 18th International Conference on Artificial Intelligence and the references for all of them are: (Which is specifically incorporated herein) by applying the teachings and methods detailed therein.

  In some embodiments, the network analysis includes a node-level analysis, where such node-level analysis includes order, strength, intermediary centrality, eigenvector centrality, page rank, and reach. In another embodiment, the network analysis includes network-level features, such as density, homogeneity or order correlation, transitivity, linkage analysis, modularity analysis, robustness measurement, intermediary measurement. , Connectivity measurements, transitivity measurements, centrality measurements, or combinations thereof. In other embodiments, species community rules (e.g., Connor et al., "The Assemblies of Species Communities: Chance or Competition?", Ecology, Vol. 60, No. 6 (Dec., 1979), pp. 1132-1140. (See, for all purposes, incorporated herein by reference in its entirety)), which applies to the Gambit of the Group assumptions (eg, Franks et al.). al. "Sampling Animal Association Networks with the Gambit of the Group" Behav Ecol Sociobiol 2010) by applying the methods and teachings of 64: 493, doi: 10.1007 / x00265-0098-0865-8, which are expressly incorporated herein by reference in their entirety for any purpose. ). In some embodiments, the eigenvector / modularity matrix analysis method is described in, for example, Mark EJ Newman in “Finding community structure in networks using the eigenvectors of matrices” for any purpose. Can be used by adapting the teachings and methods discussed in US Pat.

  In some embodiments, a time aggregation network or a time ordered network is utilized. In another embodiment, the cluster analysis method comprises using community detection in the graph and / or a community detection algorithm (as non-limiting examples, the Louvain algorithm, the Bron-Kerbosch algorithm, the Girvan-Newman algorithm, the Clauset-Newman-Moore algorithm). Algorithm, Pons-Latacy algorithm, and Wakita-Tsurumi algorithm, etc.), including building or constructing an observation matrix, connectivity model, subspace model, distribution model, density model, or centroid model .

  In some embodiments, the cluster analysis method is a heuristic method based on modularity optimization. In another embodiment, the cluster analysis method is based on the Louvain method (eg, Blondel et al. (2008) Fast unfolding of communi- ties in large networks. (Incorporated herein by reference in its entirety), which is applicable for use in the methods disclosed herein).

  In other embodiments, the network analysis includes predictive modeling of the network via link mining and prediction, population classification, link-based clustering, hierarchical cluster analysis, related similarity, or a combination thereof. In another embodiment, the network analysis includes population modeling based on differential equations. In another embodiment, the network analysis includes Lotka-Volterra modeling.

  In some embodiments, associating one or more active microbial strains with environmental parameters in a sample (eg, determining co-occurrence) creates a matrix of coordinated linkages indicating the association of environmental parameters with microbial strains. Including doing.

  In some embodiments, a plurality of sample data obtained in step 2007 (eg, obtained over two or more samples that can be obtained at more than one time point, each time point corresponding to an individual sample) Is counted). In another embodiment, the number of cells of each of the one or more microbial strains in each sample is accumulated in an association matrix (which in some embodiments may be a quantity matrix). In one embodiment, the relevance matrix is used to determine the relevance between active microbial strains in a point-in-time sample using a rule mining approach weighted with relevance (eg, quantity) data. In one embodiment, a filter is applied to exclude rules that are not significant.

  In some embodiments, the absolute cell number of one or more active microbial strains or two or more active microbial strains is related to one or more environmental parameters, for example, through co-occurrence determinations (FIG. 2, 2008). The environmental parameters can be selected according to the sample (s) to be analyzed and are not limited by the methods described herein. Environmental parameters can be parameters of the sample itself, for example, pH, temperature, protein content in the sample. Alternatively, is the environmental parameter a parameter that affects changes in the identity of the microbial community (ie, the “identity” of the microbial community is characterized by the type of microbial strain and / or the number of particular microbial strains in the community? Or the parameters affected by changes in the identity of the microbial community. For example, in one embodiment, the environmental parameter is the food intake of the animal or the amount of milk produced by the lactating ruminant (or the protein or fat content of the milk). In one embodiment, the environmental parameter is the presence, activity, and / or amount of a second microbial strain of the microbial community present in the same sample. In some of the embodiments described herein, environmental parameters are referred to as metadata parameters, and vice versa.

  Other examples of metadata parameters include, but are not limited to, genetic information (eg, DNA mutation information) from the host from which the sample was obtained, sample pH, sample temperature, expression of a particular protein or mRNA, surroundings. Environmental / ecosystem nutrient conditions (eg, level and / or identity of one or more nutrients), susceptibility or resistance to disease, development or progression of disease, susceptibility or resistance of sample to toxins, xenobiotic compounds (Pharmaceutical drug), natural product biosynthesis, or a combination thereof.

  For example, according to one embodiment, a change in the number of microbial strains across multiple samples is calculated according to the method of FIG. 2 (ie, 2001-2007). The change in the number of strains over time of one or more active strains (eg, one or more strains initially identified as active according to step 2006) is tabulated and the direction of the change is taken into account ( That is, a negative value indicates a decrease and a positive value indicates an increase). Cell numbers over time are shown as a network, microbial strains indicate nodes, and volume-weighted rules indicate edges. By utilizing Markov chains and random walks, connectivity between nodes is determined, and clusters are defined. In one embodiment, the clusters are filtered using the metadata to identify clusters associated with the desired metadata (FIG. 2, 2008).

  In another embodiment, the microbial strains are ranked according to their importance by integrating the change in cell number over time with the strains present in the target cluster, with the highest change in cell number being ranked at the top. .

  In one embodiment, the network analysis method and / or the cluster analysis method is used to measure the connectivity of one or more strains in the network, wherein the networks share common or similar environmental parameters. One or more samples are collected. In one embodiment, the network analysis comprises a linkage analysis, a modularity analysis, a robustness measure, a mediation measure, a connectivity measure, a transitivity measure, a centrality measure, or a combination thereof. In another embodiment, network analysis includes predictive modeling of the network via link mining and prediction, social network theory, population classification, link-based clustering, related similarity, or a combination thereof. In another embodiment, the network analysis includes mutual information between variables, a maximum information coefficient calculation, or other non-parametric methods to establish connectivity. In another embodiment, the network analysis includes population modeling based on differential equations. In yet another embodiment, the network analysis includes Lotka-Volterra modeling.

  The cluster analysis method includes constructing a connectivity model, a subspace model, a distribution model, a density model, or a centroid model.

  Network and cluster based analysis (eg, for performing method step 2008 of FIG. 2) may be performed via processors, components, and / or modules. A component and / or module as used herein may be, for example, any assembly, instructions, and / or a set of operably linked electrical components, wherein such component and / or module may be operable. May include, for example, memory, processors, electrical traces, optical connectors, software (implemented in hardware), and / or the like.

  FIG. 3A is a schematic diagram illustrating a platform and system 300 for analyzing, screening, and selecting microorganisms according to one embodiment. Platforms according to the present disclosure may include systems and processes for determining multi-dimensional inter-species interactions and dependencies within a natural microbial community, an example is shown in connection with FIG. 3A. Although FIG. 3A is a structural diagram, certain aspects have been omitted for clarity of explanation, but such aspects will be apparent to one of ordinary skill in the art in light of the context of the present disclosure. Will.

  As shown in FIG. 3A, a platform and system 300 for screening and selecting microorganisms includes one or more processors 310, a database 319, a memory 320, a communication interface 390, a user input device 396, and a peripheral device 397 (with limitations). Data collection and analysis devices (such as FACs), selection / incubation / formulation devices, and / or additional databases / data sources, remote data collection devices (e.g., metadata environmental data (sample characteristics, temperature Devices that can collect such information (including mobile smartphones and other mobile or stationary devices that run apps to collect such information)). Input / output interface, client 393b (which may include a user interface and / or display (such as a graphical display)) and data to user 393a via communication network 392 (eg, LAN, WAN, and / or the Internet). Interface, data collection component 330, absolute count component 335, sample association component 340, activation component 345, network analysis component 350, and strain selection / microorganism ensemble generation component 355, and biological A status / diagnosis component 360 may be included. In some embodiments, the microorganism screening system 300 can be a single physical device. In other embodiments, the microbial screening system 300 may include a plurality of physical devices (eg, operably linked by a network), each of which may include the components and components shown in FIG. 3A. And / or may include one or more modules. In some embodiments, the screening systems include, for example, U.S. Patent Application Publication Nos. 2016/0110515, 2016/0230217, and 2016/0224749, each of which is referenced for any purpose. , Which are expressly incorporated herein by reference in their entirety) and can be utilized in diagnosis and therapy by applying the teachings and methods detailed therein.

  Each component or module in the microbial screening system 300 can be operably linked to each of the remaining components and / or modules. Each of the components and / or modules in the microbial screening system 300 may be hardware and / or software capable of performing one or more specific functions associated with the component and / or module (e.g., stored on hardware). And / or implemented in hardware).

  Memory 320 may be, for example, random access memory (RAM) (eg, dynamic RAM, static RAM), flash memory, removable memory, hard drive, database, and / or the like. In some embodiments, memory 320 may include, for example, a database (eg, as shown at 319), a process, an application, a virtual machine, and / or some other software component, software program, and / or software module. (Either stored in hardware and / or implemented in hardware), or may include hardware components / hardware modules, such as microbial screening processes and / or microbial screening and ensemble generation (E.g., a data collection component 330, an absolute count component 335, a sample association component 340, an activity coordinator, etc.). Component 345, the network analysis component 350, strain selection / microbial ensemble generation component 355 is configured to run through (and / or similar modules)). In such embodiments, instructions for performing the microbial screening and / or ensemble generation process and / or related methods may be stored in memory 320 and executed by processor 310. In some embodiments, data collected via data collection component 330 may be stored in database 319 and / or memory 320.

  The processor 310 is configured to, for example, control the operation of the communication interface 390, write data to and read data from the memory 320, and execute instructions stored in the memory 320. Can be. Processor 310 performs and / or controls the operation of, for example, data collection component 330, absolute count component 335, sample association component 340, activation component, and network analysis component 350 (described in further detail herein). It can also be configured to do so. In some embodiments, the data collection component 330, the absolute count component 335, the sample association component 340, the activation component 345, the network analysis component 350, and the strain selection / ensemble generation component 355 control the processor (s) 310. Below, it can be configured to perform a microbial screening, selection, and synthetic ensemble generation process (described in further detail herein) based on a method or process stored in memory 320.

  Communication interface 390 may include one or more ports of microbial screening system 300 and / or may be configured to manage it (eg, manage via input / output interface (s) 395). In some cases, for example, communication interface 390 (eg, a network interface card (NIC)) may include one or more line cards, each of which is a device (eg, peripheral device 397 and a peripheral device 397). And / or a user input device 396). A port included in the communication interface 390 may be any port capable of providing active communication with a connected device or active communication via the network 392 (eg, communication with an end user device 393b, a host device, a server, etc.). Entity. In some embodiments, such ports need not necessarily be hardware ports, but may also be virtual ports or ports defined by software. Communication network 392 can be any network or combination of networks capable of transmitting information (eg, data and / or signals), including, for example, a telephone network, an Ethernet network, , Fiber optic networks, wireless networks, and / or cellular networks. Communication may be via a network, such as a Wi-Fi or wireless local area network ("WLAN") connection, a wireless wide area network ("WWAN") connection, and / or a cellular connection, and the like. is there. The network connection can be a wired connection, such as an Ethernet connection, a digital subscriber line (“DSL”) connection, a broadband coaxial connection, and / or a fiber optic connection, for example. For example, microbial screening system 300 can be a host device configured to be accessed by one or more computing devices 393b via network 392. In such a manner, the computing device can send information to and / or receive information from the microorganism screening system 300 via the network 392. Such information can be, for example, information collected, correlated, determined, analyzed, and / or generated by the microbial screening system 300 to generate an ensemble of active microbes among the microbes that have been networked. Is described in further detail herein. Similarly, the computing device can be configured to retrieve and / or request decision information from the microorganism screening system 300.

  In some embodiments, communication interface 390 includes and / or can be configured to include input / output interface 395. The input / output interface may receive, communicate with, and / or connect to, user input devices, peripheral devices, cryptographic processor devices, and / or the like. In some cases, one output device may be a video display, which includes, for example, an interface (eg, a digital visual interface (DVI) electrical circuit and cable) that receives signals from the video interface. A cathode ray tube (CRT) or liquid crystal display (LCD), LED, or plasma based monitor may be included. In such embodiments, communication interface 390 may be configured to receive data and / or information and to transmit microbial screening modifications, commands, and / or instructions, among other functions.

  Data collection component 330 may include any hardware and / or configured to collect, process, and / or normalize data for analysis of multidimensional inter-species interactions and dependencies within the natural microbial community. Alternatively, the analysis may be software components and / or modules (eg, stored in memory (eg, memory 320) and / or executed in hardware (eg, processor 310)), and the above analysis may be performed by absolute number counting component 335. , The sample association component 340, the activation component 345, the network analysis component 350, and / or the strain selection / ensemble generation component 355. In some embodiments, the data collection component 330 can be configured to determine the absolute cell number of one or more active organism strains in a given volume of the sample. The data collection component 330 can use the marker sequence to identify active strains in the absolute cell count data set based on the absolute cell count of one or more active microbial strains. Data collection component 330 can continuously collect data for a period of time to indicate the dynamics of the microbial population in the sample. The data collection component 330 can aggregate data over time and accumulate the cell number of each active organism strain in a quantity matrix present in a memory (such as the memory 320).

  The sample association component 340 and the network analysis component 350 can be configured to collectively determine multidimensional interspecies interactions and dependencies within the natural microbial community. The sample association component 340 may include any hardware and / or software components configured to associate metadata parameters (with environmental parameters, eg, by co-occurrence) with the presence of one or more active microbial strains. (Eg, stored in memory (eg, memory 320) and / or executed on hardware (eg, processor 310)). In some embodiments, the sample association component 340 can associate one or more active organism strains with one or more environmental parameters.

  The network analysis component 350 may include any hardware and / or software components (such as memory) configured to determine that one or more active microbial strains in a sample co-appear in response to environmental (metadata) parameters. (Eg, stored in memory 320) and / or executed on hardware (eg, processor 310). In some embodiments, the association between the data collected by the data collection component 330 and one or more active microbial strains and one or more environmental parameters determined by the sample association component 340, Based on the network analysis component 350, the linkage indicating the association of the environmental parameter with the microbial strain, the absolute cell number of one or more active microbial strains, and the expression of one or more unique second markers Creating a leveled matrix can indicate one or more networks of heterogeneous populations of microbial strains. For example, network analysis uses quantitative data-weighted rule mining techniques to reveal the association between active microbial strains and metadata parameters in a sample (eg, the association of two or more active microbial strains). The relevance (amount and / or abundance) matrix can be used to In some embodiments, the network analysis component 350 can apply filters to select and / or exclude rules. The network analysis component 350 can calculate the change in cell number of the active strain over time, taking into account the direction of the change (ie, a negative value indicates a decrease and a positive value indicates an increase). . The network analysis component 350 can show the matrix as a network, where microbial strains show nodes and quantity-weighted rules show edges. Network analysis component 350 can leverage Markov chains and random walks to determine connectivity between nodes and define clusters. In some embodiments, the network analysis component 350 can use the metadata to filter the clusters to identify clusters associated with the desired metadata. In some embodiments, the network analysis component 350 can rank the target microbial strain by integrating the change in cell number over time with the strain present in the target cluster, where the cell number change is greatest. Are ranked at the top.

  In some embodiments, the network analysis comprises a linkage analysis, a modularity analysis, a robustness measure, an intermediary measure, a connectivity measure, a transitivity measure, a centrality measure, or a combination thereof. In another embodiment, a cluster analysis method can be used, which includes building a connectivity model, subspace model, distribution model, density model, or centroid model. In another embodiment, network analysis includes predictive modeling of the network via link mining and prediction, population classification, link-based clustering, related similarity, or a combination thereof. In another embodiment, the network analysis includes mutual information between variables, a maximum information coefficient calculation, or other non-parametric methods to establish connectivity. In another embodiment, the network analysis includes population modeling based on differential equations. In another embodiment, the network analysis includes Lotka-Volterra modeling.

  FIG. 3B illustrates an example of a logic flow according to one embodiment of the present disclosure. Initially, a plurality of samples and / or sample sets are acquired and / or obtained (3001). As used herein, a “sample” may refer to one or more samples, a set of samples, a plurality of samples (eg, from a particular population), which may be easier to understand. And consequently, when discussing two or more different samples, it will be understood that each sample may include a plurality of sub-samples (eg, the first sample and the second sample are discussed). When performed, the first sample may include two, three, four, five, or more subsamples obtained from the first population, and the second sample may include a second sample. A sub-sample obtained from the population or a sub-sample obtained from the first population but obtained at a different time point (for example, one week or one month after obtaining the first sub-sample) is 2 , Three, four, five, or more). Once the sub-samples have been obtained, the individual acquisition indicators and parameters for each sub-sample can be managed and accumulated, including environmental parameters, qualitative and / or quantitative findings, population Member identity is included (thus, for example, when samples are obtained from the same population at two or more different time points, such sub-samples are paired by identity, thus, The acquired subsample is associated with a subsample obtained from the same animal at time 2).

  For each sample, sample set, and / or subsample, cells are stained based on the target biotype (3002), and each sample / subsample or portion thereof is weighed, serially diluted (3003), and processed. (3004), the number of cells of each microorganism type in each sample / subsample is determined. In one exemplary embodiment, a cell sorter can be used to count individual bacterial and fungal cells from a sample, such as those from an environmental sample. As part of the present disclosure, the development of specific dyes has made it possible to count microorganisms that could not previously be counted by conventional methods. Using a specific dye in accordance with the methods of the present disclosure stains the cell wall (eg, of bacterial and / or fungal nature) and allows the target cell to be removed from a larger population based on the cell characteristics using a laser. Individual groups can be counted. In one particular example, an environmental sample is prepared, diluted with an isotonic buffer, and stained with the following dyes: (a) For bacteria, the following dyes can be used for staining-DNA: Sybr green, respiration: 5-cyano-2,3-ditolyltetrazolium chloride and / or CTC, cell wall: malachite green and / or crystal violet, (b) For fungi, the following dyes can be used for staining: -Cell wall: Calcoflor White, Congo Red, Trypan Blue, Direct Yellow 96, Direct Yellow 11, Direct Black 19, Direct Orange 10, Direct Red 23, Direct Red 81, Direct Green 1, Direct Violet 51, Wheat Germ Agglutinin- WGA, reactive Yellow 2, Reactive Yellow 42, Reactive Black 5, Reactive Orange 16, Reactive Red 23, Reactive Green 19, and / or reactive violet 5.

  Direct dyes and reactive dyes are typically reminiscent of dyeing cellulose-based materials (ie, cotton, flax, and viscose rayon), but in the development of the present disclosure, such dyes are referred to as dyes. It has been advantageously discovered that it can also be used for dyeing chitin and chitosan. The reason why the dye can be used for staining chitin and chitosan is that chitin and chitosan each have a chain in which acetylglucosamine is linked by β- (1 → 4) bond, and D-glucosamine and N-acetyl-D-glucosamine. Is due to the presence of chains linked by β- (1 → 4) bonds. When chains are built from these subunits, a flat fibrous structure is formed, much like cellulose chains. Direct dye attachment to chitin molecules and / or chitosan molecules occurs via Van der Waals forces between the dye and the fiber molecule. The larger the contact surface area between these two molecules, the stronger the interaction. On the other hand, the reactive dye forms a covalent bond to chitin and / or chitosan.

  Each of the stained samples is loaded into FACs for counting (3004). Equipped with nozzles of a specific size (for example, 100 μm (selected according to the embodiment and application)) to generate a flow of individual droplets (for example, about 1/10 microliter (0.1 μL)) The sample can be subjected to analysis via the microfluidic chip. These variables (nozzle size, droplet formation) can be optimized for each target microbial type. Ideally, each droplet encapsulates a single cell, or "event," and when each droplet is illuminated by a laser, all of the dye is excited and emits light of a different wavelength. . FACs can detect each emission optically and plot it as an event (eg, can be plotted on a 2D graph). A typical graph consists of one axis corresponding to the size of the event (determined by "forward scatter") and another axis corresponding to the intensity of the fluorescence. "Gates" can be applied around individual groups on such graphs, and events within such gates can be counted.

  FIG. 3C shows an example of data from fungi stained with direct yellow, yeast monoculture (3005a) (positive control, left), E. coli. E. coli (3005b) (negative control, middle) and environmental sample (3005c) (experimental, right). In this figure, the complexity of the event is measured by "backscatter" (BSC-A), and the fluorescence emission intensity from direct yellow is measured by FITC. Each dot represents one event, and the density of the event is indicated by a color change from green to red. Gate B shows the normal area where the target event (in this case, the fungus stained with direct yellow) is expected to be seen.

3B, two or more samples from one or more sources (samples obtained over time from individual animals or a single geographic location, geographic location, breed, performance, food Obtaining a sample obtained from two or more groups that differ in disease, etc., a sample obtained from one or more groups experiencing a physiological disturbance or event, and / or the like). Starting at (3001), the absolute number can be established by analyzing the sample using flow cytometry (including staining (3002) above). Samples are weighed, serially diluted (3003) and processed using FACs (3004). Next, the results of the FACs are processed to determine the absolute number of the desired biotype in each sample (3005). The following code fragment illustrates an example of such a processing methodology, according to one embodiment:

  Total nucleic acid is isolated from each sample (3006). The nucleic acid sample eluate is split into two parts (typically bisected) and each part is purified enzymatically to yield purified DNA (3006a) or purified RNA Is obtained (3006b). Purified RNA is stabilized by enzymatic conversion to cDNA (3006c). By preparing a sequencing library (eg, an ILLUMINA sequencing library) for both the purified DNA and the purified cDNA using PCR, the appropriate barcode and adapter regions are added, and the desired biotype A marker region suitable for measurement is amplified (3007). Library quality can be assessed and quantified, and then all libraries can be pooled and sequenced.

Quality trimming and merging are performed on the raw sequencing reads (3008). The processed reads are deduplicated and clustered to provide a complete set or list of unique strains present in multiple samples (3009). This set or list can be used to taxonomically identify each strain present in multiple samples (3010). Sequencing libraries derived from DNA samples can be identified, and sequencing reads derived from the identified DNA libraries identify which strains are present in each sample and identify the respective strains in each sample. It is mapped back to a set or list of deduplicated strains to quantify the number of reads (3011). Next, the quantified lead list is integrated (3013) with the absolute cell number of the target microbial type to determine the absolute number or cell number of each strain. The following code fragment illustrates an example of such a processing methodology, according to one embodiment:

A sequencing library obtained from the cDNA sample is identified (3014). Next, the sequencing reads from the identified cDNA library are mapped back to the list of deduplicated strains to determine which strains are active in each sample. If the number of reads is below a specified or specified threshold (3015), the strain is considered inactive or identified and excluded from further analysis (3015a). If the number of reads is above the threshold (3015), the strain is considered active or identified and left for analysis (3015b). The inactive strains are then filtered from the results (3013) to obtain a set or list of active strains for each sample and their respective absolute / cell counts (3016). The following code fragment illustrates an example of such a processing methodology, according to one embodiment:

  Qualitative and quantitative metadata (eg, environmental parameters, etc.) for each sample (set of samples, subsamples, etc.) is identified, searched, and / or obtained (3017) and stored in a database (eg, 319). Saved (3018). Appropriate metadata can be identified and the database can be queried depending on the application / embodiment to derive specific and / or related metadata for each sample to be analyzed (3019). The sample matrix then creates one large species and metadata by merging a subset of the metadata with the set or list of active strains and their corresponding absolute / cell numbers (3020).

Next, the maximum information coefficient (MIC) is calculated between the stock and the metadata (3021a) and between the stocks (3021b). The results are pooled to create a set or list of all associations and their corresponding MIC scores (3022). If the relevance is below a given threshold (3023), it is considered / relevant (3023b). If the relevance is above a given threshold (3023), it is considered / identified as relevant (3023a) and further submitted to network analysis (3024). The following code fragment illustrates an example of such an analysis methodology, according to one embodiment:

  Based on the results of the network analysis, a biological state is defined and / or an active strain is selected (3025), wherein the selection comprises a product (eg, an ensemble, an assembly, and / or an ensemble) containing the selected strain. Other synthetic populations). The results of the network analysis can also be used to obtain information on the diagnosis and / or selection of the strain for conducting additional product composition tests.

  The use of thresholds is discussed above with respect to analysis and determination. Thresholds, in addition to statistical thresholds (ie, significance tests), may be (1) determined empirically depending on the embodiment and application (e.g., specific portions with low read levels or significant The cutoff value is set to be a number that removes the undesired part and is determined based on the distribution level), (2) any non-zero value, (3) based on the ratio / percentile, (4) Only those strains in which the normalized number of reads of the second marker (ie, activity) exceeds the normalized number of reads of the first marker (number of cells), and (5) the activity and the amount or the number of cells And (6) the normalized number of reads for the second marker (activity) is the average number of reads for the second marker (activity) for the entire sample (and / or sample set). To exceed And / or any magnitude of the threshold value. In the following, an example of setting a threshold value for the distribution of measurements of a second marker based on RNA in connection with measurements of a first marker based on DNA according to one embodiment is shown in detail.

  The intestinal contents of one male Cobb500 were obtained and subjected to analysis according to the present disclosure. Briefly, the total number of bacterial cells in a sample was determined using FACs (eg, 3004). Total nucleic acids were isolated from fixed small intestine samples (eg, 3006). A DNA (first marker) sequencing library and a cDNA (second marker) sequencing library were prepared (eg, 3007) and loaded into the ILLUMINA MISEQ. Quality filtering, deduplication, clustering, and quantification were performed on raw sequencing reads from each library (eg, 3008). By integrating the list of quantified strains from both DNA-based and cDNA-based libraries with cell count data, the absolute cell number of each strain in the sample was established (eg, 3013). . Although cDNA does not necessarily provide a direct measure of strain quantity (ie, highly active strains may have many copies of the same RNA molecule), in this example, a cDNA-based library Was maintained with the cell count data to maintain the same normalization procedure used for the DNA library.

  After analysis, the cDNA-based library identified 702 strains (46 were unique) and the DNA-based library identified 1140 strains. Using 0 as the activity threshold (ie, leaving any non-zero value), 57% of the strains that had the first DNA-based marker in this sample had both a cDNA-based second marker and Was related. Such strains are identified / considered to be an active part of the microbial community, and only such strains are subject to further analysis. If the threshold is stricter and only those strains whose second marker value exceeds the first marker value are considered active, only 289 strains (25%) meet the threshold. Strains meeting this threshold correspond to those above the DNA (first marker) line in FIG. 3D.

  The present disclosure includes various methods of identifying a plurality of interacting active microbial strains and one or more parameters or metadata and selecting the identified microorganisms for use in a microbial ensemble. Is associated with the performance of or affects the common function, or is involved in a recognizable parameter, such as a phenotypic trait of interest (eg, increased milk production in ruminants), or Includes a selected subset of the microbial community of individual microbial species or strains of a species that can guide or be described as being related thereto. The present disclosure also includes various systems and devices that implement and / or facilitate such methods.

  In some embodiments, the methods share at least one common characteristic (sample geographic location, sample type, sample source, sample source individual, sample target animal, sample time, breed, food, temperature, etc.) And at least two with at least one different feature (sample geographical / temporal location different from the common feature, sample type, sample source, sample source individual, sample target animal, sample timing, breed, food, temperature, etc.) Obtaining one sample. For each sample, the presence of one or more microbial types is detected, the number of each of the one or more microbial types detected in each sample is determined, and a unique first type in each sample is determined. The number and amount of the markers are measured, each unique first marker being a marker of a microorganism strain. Thereafter, the number of each microbial type and the number of the first marker are integrated to obtain the absolute cell number of each microbial strain present in each sample, and at least one unique for each microbial strain. The second marker is measured based on the specified threshold to determine the level of activity of that microbial strain in each sample, and the absolute cell number is filtered by the determined activity to provide at least two samples. A set or list of active microbial strains for each and their respective absolute cell numbers are obtained, and the filtered absolute cell numbers of the active microbial strains for each of at least two samples are compared to each other, or at least two The expected function and / or compared to at least one measurement metadata for each of the samples Or at least two groups, at least three groups, at least four groups, at least five groups, at least six groups, at least seven groups, at least eight groups, at least nine groups, at least ten groups The active microorganism strain is classified into one of a group, at least 15 groups, at least 20 groups, at least 25 groups, at least 50 groups, at least 75 groups, or at least 100 groups. For example, the comparison can be a network analysis that reveals a connection between each microorganism strain, a connection between each microorganism strain and the metadata, and / or a connection between the metadata and the microorganism strain. . The at least one microorganism is configured to be selected from at least two groups and combined to alter a property corresponding to the at least one metadata (eg, a target property (eg, milk production of a cow or cow population)). An ensemble of microorganisms can be formed. Ensemble formation includes isolating the or each microbial strain, selecting a pre-isolated microbial strain based on the analysis, and / or incubating / growing a particular microbial strain based on the analysis. And combining the strains to include the strains in a specific amount / number and / or ratio and / or medium / carrier (s) based on the application to form a microbial ensemble. The ensemble is a suitable vehicle, carrier, and / or medicament that enables the microorganisms in the ensemble to be delivered in such a way that it can affect the recipient (eg, increase milk production). A carrier may be included.

  The number of unique first markers is determined by measuring the number of unique genomic DNA markers in each sample, measuring the number of unique RNA markers in each sample, the number of unique protein markers in each sample. Measuring the number of unique metabolite markers in each sample (measuring the number of unique lipid markers in each sample, and / or the number of unique carbohydrate markers in each sample) Measuring).

  In some embodiments, determining the number and amount of unique first markers comprises subjecting the genomic DNA from each sample to a high-throughput sequencing reaction and / or analyzing the genomic DNA from each sample. Subject to metagenomic sequencing. The unique first marker may include at least one of an mRNA marker, an siRNA marker, and / or a ribosomal RNA marker. The unique first marker may additionally or alternatively include at least one of a sigma factor, a transcription factor, a nucleoside-related protein, and / or a metabolic enzyme.

  In some embodiments, measuring the at least one unique second marker comprises measuring the expression level of the at least one unique second marker in each sample and analyzing the mRNA in the sample for gene expression analysis. Subjecting the subject to an application. Gene expression analysis can include sequencing reactions, quantitative polymerase chain reaction (qPCR), meta-transcriptome sequencing, and / or transcriptome sequencing.

  In some embodiments, determining the expression level of the at least one unique second marker comprises subjecting each sample or a portion thereof to mass spectrometry, and / or metabolizing each sample or a portion thereof to metaribosome profiling or Subject to ribosome profiling. The one or more microbial types include bacteria, archaea, fungi, protozoa, plants, other eukaryotes, viruses, viroids, or combinations thereof, and one or more microbial strains include: One or more bacterial strains, archaebacteria strains, fungal strains, protozoan strains, plant strains, other eukaryotic strains, virus strains, viroid strains, or combinations thereof, are included. The one or more microorganism strains may be one or more fungal species or fungal subspecies, and / or the one or more microorganism strains may be one or more bacterial species or bacterial subspecies. .

  In some embodiments, determining the number of each of the one or more microbial types in each sample comprises sequencing, centrifuging, light microscopy, fluorescence microscopy, staining, mass spectroscopy of each sample or a portion thereof. , Microfluidics, quantitative polymerase chain reaction (qPCR), gel electrophoresis, and / or flow cytometry.

  The unique first markers are 5S ribosomal subunit gene, 16S ribosomal subunit gene, 23S ribosomal subunit gene, 5.8S ribosomal subunit gene, 18S ribosomal subunit gene, 28S ribosomal subunit gene, cytochrome c oxidase It may include phylogenetic markers including subunit genes, β-tubulin genes, elongation factor genes, RNA polymerase subunit genes, internal transcribed spacers (ITS), or combinations thereof. The number of unique markers and the amount thereof can be determined by subjecting genomic DNA derived from each sample to a high-throughput sequencing reaction, subjecting genomic DNA to genomic sequencing, and / or subjecting genomic DNA to amplicon sequencing. Serving.

  In some embodiments, the at least one different feature is an acquisition time at which each of the at least two samples was acquired such that an acquisition time of the first sample and an acquisition time of the second sample were different; An acquisition position (geographical position difference and / or individual sample target / animal acquisition difference) at which each of the at least two samples was acquired such that the acquisition position of the sample and the acquisition position of the second sample were different; including. The at least one common feature may include a sample source type where the sample source type of the first sample and the sample source type of the second sample are the same. The sample source type is an animal type, an organ type, a soil type, a water type, a sediment type, an oil type, a plant type, an agricultural product type, a bulk soil type, a soil rhizosphere type, a plant part type, and / or the like, One of the following. In some embodiments, at least one common feature includes that each of the at least two samples is a gastrointestinal tract sample, and such a gastrointestinal tract sample may in some embodiments be a rumen sample. In some embodiments, the common / different features provided herein may alternatively be different / common features between certain samples. In some embodiments, the at least one common feature includes an animal sample source type, and each sample includes additional common features, such that each sample is a tissue sample, a blood sample, a tooth sample. , Sweat samples, nail samples, skin samples, hair samples, fecal samples, urine samples, semen samples, mucus samples, saliva samples, muscle samples, brain samples, or organ samples.

  In some embodiments, the above method may further comprise obtaining at least one additional sample from the target based on the at least one measurement metadata, wherein the at least one additional sample from the target comprises: Share at least one common feature with at least two samples. Next, for at least one additional sample from the target, the presence of one or more microbial types is detected and the number of each detected microbial type of the one or more microbial types is determined. The number and amount of unique first markers are measured, and the number of each microbial type and the number of first markers are integrated to obtain the absolute cell number of each microbial strain present. Measuring at least one unique second marker for each microbial strain to determine the level of activity of that microbial strain and filtering the absolute cell number according to the determined activity to obtain a target-derived A set or list of active microbial strains and their respective absolute cell numbers for at least one additional sample is obtained. In such embodiments, the selection of at least one microbial strain from the at least two groups comprises the set or list of active microbial strain (s) and the respective absolute for at least one additional sample from the target. The ensemble that is based on the cell number, such that the resulting ensemble is configured to alter a property of the target corresponding to the at least one metadata. For example, if such an embodiment is used, a microbial ensemble can be revealed from a sample obtained from a Holstein cow and a target sample obtained from a Jersey cow or buffalo, and the original sample Analysis reveals the same, substantially similar, or similar network associations between the same or similar microbial strains from the target sample (s).

  In some embodiments, the filtered absolute cell number of the active microbial strain for each of the at least two samples and at least one measured metadata or additional active microbial strain for each of the at least two samples. The comparison involves determining the co-occurrence of one or more active microbial strains in each sample with at least one measured metadata or additional active microbial strain. The at least one measurement metadata may include one or more parameters, wherein the one or more parameters include sample pH, sample temperature, fat abundance, protein abundance, carbohydrate abundance, mineral abundance. Abundance, vitamin abundance, natural product abundance, specific compound abundance, sample source weight, sample source feed intake, sample source weight gain, sample source feed efficiency, one or more At least one of the presence or absence of a pathogen, the physical characteristic (s) or measured value (s) of the sample source, the production characteristics of the sample source, or a combination thereof. Parameters can also include abundance of whey protein, abundance of casein protein, and / or abundance of fat in milk produced by the sample source.

  In some embodiments, the determination of the co-occurrence of one or more active microbial strains and at least one measured metadata or additional active microbial strains in each sample is determined by determining the meta-occurrence in two or more sample sets. Creating a matrix of linkages indicating the relevance of the data to the microbial strain, the absolute cell count of one or more active microbial strains, and measurements of one or more unique second markers This may include indicating one or more networks of the homogeneous microbial community (s). Determining the co-occurrence of one or more active microbial strains and at least one measured metadata or additional active microbial strain, and classifying the active microbial strain, comprises determining the connectivity of each microbial strain in the network. May include a network analysis and / or a cluster analysis to measure the at least two samples that share common characteristics, measurement metadata, and / or related environmental parameters. The network analysis and / or cluster analysis may include linkage analysis, modularity analysis, robustness measurement, mediation measurement, connectivity measurement, transitivity measurement, centrality measurement, or a combination thereof. Cluster analysis may include building connectivity models, subspace models, distribution models, density models, and / or centroid models. Network analysis, in some embodiments, involves predictive modeling of network (s) via link mining and prediction, population classification, link-based clustering, related similarity, combinations thereof, and / or the like. May be included. Network analysis may include population modeling and / or Lotka-Volterra modeling based on differential equations. The analysis can be a heuristic method. In some embodiments, the analysis may be a Louvain method. Network analysis may include non-parametric methods for establishing connectivity between variables and / or calculating mutual information and / or maximum information coefficients between variables to establish connectivity.

  For some embodiments, a method for forming an ensemble of active microbial strains constructed to alter the properties or characteristics of an environment based on two or more sample sets, the two or more samples comprising: The sets share at least one common or related environmental parameter between the two or more sample sets and have at least one different environmental parameter between the two or more sample sets; The method wherein the sample set comprises at least one sample comprising a heterogeneous microbial community, and wherein the one or more microorganism strains is a sub- taxon of one or more biotypes, comprises: Detecting the presence, determining the absolute cell number of each of the detected microbial types in each sample, and identifying a unique first marker in each sample. Measuring the number and amount of the car, wherein the first marker-specific, a marker of microbial strains. Next, the expression level of one or more unique second markers is measured at the protein or RNA level, wherein the unique second markers are markers of the activity of the microbial strain, and the one or more unique second markers are Determining the activity of the detected microbial strain for each sample based on the expression level of the second marker above the specified threshold, the amount of the one or more first markers and the one or more microbial strains The absolute cell number of each active microorganism strain detected in each sample is calculated based on the absolute cell number of the microorganism type from which the subtaxon is derived, and one or more active microorganism strains are Express a unique second marker above the specified threshold. Next, the co-occurrence of the active microbial strain and the at least one environmental parameter in the sample is determined based on a maximum information coefficient network analysis to determine the connectivity of each microbial strain in the network, wherein the network comprises at least Two or more sample sets are grouped together with at least one common or related environmental parameter. A plurality of active microbial strains are selected from the one or more active microbial strains based on the network analysis, and an ensemble of active microbial strains is formed from the selected plurality of active microbial strains. When an ensemble of microbial strains is introduced into an environment, it is configured to selectively alter the characteristics or characteristics of that environment. For some embodiments, at least one measurement indicator of at least one common or related environmental factor for the first sample set is at least one common or related environmental factor for the second sample set. It is different from the measurement identification index. For example, if the sample / sample set is from a dairy cow, the first sample set may be from a dairy cow fed a herbage diet, while the second sample set may be from a dairy cow fed a corn diet. Can be derived from A sample set may be a single sample, but may alternatively be a plurality of samples, wherein the measurement indicator of at least one common or related environmental factor for each sample in the sample set is substantially (E.g., all samples in one set were obtained from a herd feeding herd) and the average measurement indicator for one set of samples was derived from another set of samples. (The first set of samples is from a herd that consumes forage and the second set of samples is from a herd that consumes corn). There may be additional differences and similarities considered in the analysis, such differences and varieties, food differences, performance differences, age differences, feed additive differences, growth stage differences, These include differences in physiological characteristics, differences in health status, differences in elevation, differences in environmental temperature, differences in seasons, differences in antibiotics, and the like. In some embodiments, each sample set includes a plurality of samples, a first sample set is obtained from a first population, and a second sample set is obtained from a second population, In additional or alternative embodiments, each sample set includes a plurality of samples, the first sample set is obtained from a first population at a first time point, and the second sample set is obtained at a first time point. At a second time point different from the first population. For example, a first set of samples may be obtained at a first time from a herd of cows on a forage diet, and a second set of samples may be obtained immediately after the first set of samples is obtained. The feed can be obtained at a second point in time when the feed is switched to corn (eg, two months later). In such embodiments, the sample can be obtained, analysis performed on a population, and / or the sample can include unique referral information for the individual animal, such that the individual Changes that have occurred in animals can be identified, and data with an increased level of granularity can be obtained. In some embodiments, a synthetic ensemble of active microbial strains configured to alter the properties of a biological environment comprises two or more samples (or sample sets, each set comprising at least one sample). Wherein each has a plurality of environmental parameters (and / or metadata), wherein at least one of the plurality of environmental parameters is between two or more samples or sample sets. And at least one environmental parameter is another environmental parameter that is different between each of the two or more samples or sample sets, and wherein each sample set is obtained from a biological sample source. At least one of the active microbial strains comprises one or more heterogeneous microbial communities. Methods that are subclasses of physical types include detecting the presence of multiple microbial types in each sample, determining the absolute cell number of each detected microbial type in each sample, Determining the number and amount of one marker (the specific first marker is a marker of a microbial strain), measuring the level (eg, expression level) of one or more specific second markers (The specific second marker is a marker of the activity of the microbial strain) based on the level of one or more specific second markers above a specified threshold (eg, expression level) Determining the activity of each of the detected microbial strains for the sample to identify one or more active microbial strains, determining the amount of each of the one or more unique first markers (relative amount, (E.g., example amount, percentage amount, etc.) and the absolute cell count of each or corresponding microbial type from which one or more microbial strains are a sub-taxon, from each active microbial strain detected in each sample Calculating the absolute cell number (the calculation is a numerical operation such as multiplication, dot operator, and / or other operations) (one or more active microbial strains exceed one specified threshold) Analyzing the active microbial strains of two or more sample sets (having or expressing a plurality of unique second markers) (analysis is performed for each active microbial strain of each of the two or more sample sets) Performing a non-parametric network analysis of at least one common environmental parameter and at least one different environmental parameter. The peak analysis determines the maximum information coefficient score between each active microbial strain and any other active microbial strain, and the maximum information coefficient between each active microbial strain and at least one different environmental parameter. Determining a score), selecting a plurality of active microbial strains from the one or more active microbial strains based on non-parametric network analysis, and determining the selected plurality of active microbial strains and microbial carrier medium. Forming a synthetic ensemble of active microbial strains, including an ensemble of active microbial strains that selectively alters the characteristics of the biological environment when the synthetic ensemble of the active microbial strain is introduced into the biological environment. ) Is included. Depending on the embodiment or implementation, at least two samples or sample sets comprise three samples, four samples, five samples, six samples, seven samples, eight samples, nine samples, ten samples, 11 samples, 12 samples, 13 samples, 14 samples, 15 samples, 16 samples, 17 samples, 18 samples, 19 samples, 20 samples, 21 samples, 22 samples, 23 samples Samples, 24 samples, 25 samples, 26 samples, 27 samples, 28 samples, 29 samples, 30 samples, 35 samples, 40 samples, 45 samples, 50 samples, 60 samples, It may include 70 samples, 80 samples, 90 samples, 100 samples, 150 samples, 200 samples, 300 samples, 400 samples, 500 samples, 600 samples, and / or the like. The total number of samples may depend on the embodiment / embodiment, and is less than 5, 5-10, 10-15, 15-20, 20-30, 30-40, 40-50, 50-60, 60-. 70, 70-80, 80-90, 90-100, less than 100, more than 100, less than 200, more than 200, less than 300, more than 300, less than 400, more than 400, less than 500, more than 500, less than 1000, more than 1000, It may be less than 5000, less than 10,000, less than 20,000, and so on.

  In some embodiments, the at least one common or related environmental factor includes nutrient information, food information, animal characteristics, infection information, health status, and / or the like.

  At least one measurement discriminator may include sample pH, sample temperature, fat abundance, protein abundance, carbohydrate abundance, mineral abundance, vitamin abundance, natural abundance, specific compound abundance. Abundance, sample source weight, sample source feed intake, sample source weight gain, sample source feed efficiency, presence or absence of one or more pathogens, sample source physical characteristic (s) or Measurement value (s), production characteristics of the sample source, abundance of whey protein in milk produced by the sample source, abundance of casein protein produced by the sample source, and / or fat in milk produced by the sample source. Abundance, or a combination thereof, may be included.

  Determining the number of unique first markers in each sample comprises, depending on the embodiment, determining the number of unique genomic DNA markers, measuring the number of unique RNA markers, and / or determining the number of unique proteins. Measuring the number of markers. The plurality of microbial types can include one or more bacteria, archaea, fungi, protozoa, plants, other eukaryotes, viruses, viroids, or combinations thereof.

  In some embodiments, determining the absolute number of each of the microbial types in each sample comprises sequencing, centrifugation, light microscopy, fluorescence microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain reaction ( qPCR), gel electrophoresis and / or flow cytometry. In some embodiments, the one or more active microbial strains are selected from one or more bacteria, archaea, fungi, protozoa, plants, other eukaryotes, viruses, viroids, or combinations thereof. Subgroups of one or more microbial types. In some embodiments, the one or more active microbial strains is one or more bacterial strains, archaebacteria strains, fungal strains, protozoan strains, plant strains, other eukaryotic strains, virus strains, viroids Strain, or a combination thereof. In some embodiments, the one or more active microbial strains is one or more bacterial species or bacterial subspecies. In some embodiments, the one or more active microbial strains is one or more fungal species or fungal subspecies.

  In some embodiments, the at least one unique first marker is a 5S ribosomal subunit gene, a 16S ribosomal subunit gene, a 23S ribosomal subunit gene, a 5.8S ribosomal subunit gene, an 18S ribosomal subunit gene, Includes phylogenetic markers including 28S ribosomal subunit gene, cytochrome c oxidase subunit gene, beta-tubulin gene, elongation factor gene, RNA polymerase subunit gene, internal transcribed spacer (ITS), or a combination thereof .

  In some embodiments, determining the number and amount of unique first markers comprises subjecting the genomic DNA from each sample to a high-throughput sequencing reaction and / or analyzing the genomic DNA from each sample. Subject to metagenomic sequencing. In some embodiments, a unique first marker may include an mRNA marker, an siRNA marker, and / or a ribosomal RNA marker. In some embodiments, the unique first marker may include a sigma factor, a transcription factor, a nucleoside-related protein, a metabolic enzyme, or a combination thereof.

  In some embodiments, measuring the expression level of one or more unique second markers comprises subjecting the mRNA in each sample to a gene expression analysis, and in some embodiments, the gene expression analysis comprises , A sequencing reaction. In some embodiments, the gene expression analysis comprises quantitative polymerase chain reaction (qPCR), meta-transcriptome sequencing, and / or transcriptome sequencing.

  In some embodiments, measuring the expression level of one or more unique second markers comprises subjecting each sample or a portion thereof to mass spectrometry, metaribosome profiling, and / or ribosome profiling. .

  In some embodiments, determining the expression level of at least one or more unique second markers comprises subjecting each sample or a portion thereof to metaribosome profiling or ribosome profiling (Ribo-Seq) ( For example, Ingolia, NT, S. Ghaemmaghammi, JR Newman, and J. S. Weissman, 2009, "Genome-wide analysis in vivo of radio resourcing radio communication. , Ingolia, NT, 2014, "Ribosome profiling: new views o. translation, from single codons to genome scale "Nat.Rev.Genet.15: 205-213 See (each of these references are incorporated by reference in their entirety for all purposes)). Ribo-seq is a molecular approach that can be used to determine protein synthesis in vivo at the genomic scale. In this method, as the footprint ribosome binds and interacts with mRNA, it is directly measured which transcript is actively transcribed through it. Next, the bound region of the mRNA is processed and subjected to a high-throughput sequencing reaction. Ribo-seq has been shown to have a strong correlation with quantitative proteomics (eg, Li, GW, D. Burkhardt, C. Gross, and J.S. Weissman. 2014 "Quantifying absolute protein synthesis"). See rates reviews principals underlying allocation of cellular resources "Cell 157: 624-635, which is expressly incorporated herein by reference in its entirety).

  The type of sample source may be animal, soil, air, saline, freshwater, wastewater sludge, sediment, oil, plant, agricultural product, bulk soil, soil rhizosphere, plant part, vegetable, extreme environment, or a combination thereof, One of the following. In some embodiments, each sample is a gastrointestinal tract sample and / or a rumen sample. In some embodiments, the sample is a tissue sample, blood sample, tooth sample, sweat sample, nail sample, skin sample, hair sample, fecal sample, urine sample, semen sample, mucus sample, saliva sample, muscle sample, brain It can be a sample, a tissue sample, and / or an organ sample.

  Depending on the embodiment, the microbial ensemble of the present disclosure can include two or more substantially pure microbes or microbial strains (a desired microbial / microbial strain mixture) and can be administered to a target (e.g., a microbiota). It can also include any additional ingredients that can be administered to the animal to recover). Microbial ensembles prepared in accordance with the present disclosure are those in which the microorganism is in the target environment (eg, the gastrointestinal tract of an animal, where the ensemble is resistant to low pH and is configured to grow in the gastrointestinal tract environment). Can be administered with a substance that allows it to survive. In some embodiments, the microbial ensemble may include one or more substances that increase the number and / or activity of one or more desired microbes or microbial strains, wherein the strains comprise the microorganisms included in the ensemble. / A strain that is present in the strain or a strain that is not present therein. Examples of such substances include, but are not limited to, fructooligosaccharides (eg, oligofructose, inulin, inulin-type fructans), galactooligosaccharides, amino acids, alcohols, and mixtures thereof (Ramirez-Farias et al. 2008. Br. J. Nutr. 4: 1-10, and Pool-Zobel and Sauer 2007. J. Nutr. 137: 2580-2584 and supplemental, each of which is referred to for any purpose. Which is incorporated herein in its entirety)).

  Microbial strains identified by the methods of the present disclosure can be cultured / grown prior to inclusion in an ensemble. Media can be used for such growth, and such media can include any medium suitable for supporting the growth of microorganisms, including, but not limited to, gastrin supplementation. Natural or artificial, including agar, LB media, serum, and / or tissue culture gel. It will be appreciated that the media can be used alone or in combination with one or more other media. The medium can be used with or without the addition of exogenous nutrients. The medium can be modified or enriched with additional compounds or components, which can assist, for example, in the interaction and / or selection of a particular group of microorganisms and / or strains thereof. Component. For example, antibiotics (such as penicillin) or sterilants (eg, quaternary ammonium salts and oxidizing agents) may be included, and / or physical conditions (salts, nutrients (eg, organic and inorganic minerals (eg, phosphorus, nitrogen) Salts, ammonia, potassium, and micronutrients (such as cobalt and magnesium), pH, and / or temperature) can be modified.

  As discussed above, systems and devices may be configured in accordance with the present disclosure, and in some embodiments, such systems and devices may include a processor and a memory, wherein the memory has processor-readable instructions / issues. Storing the instructions implements the method (s). In one embodiment, the system and / or device is configured to perform the method. A processor embodiment of the method is also disclosed, which is discussed with reference to FIG. 3A. For example, a method performed by a processor may include receiving sample data from at least two samples sharing at least one common feature and having at least one different feature, for each sample, one or more samples in each sample. Determining the presence of the microbial type, determining the number of cells of each microbial type detected in each sample among the one or more microbial types, determining the number of unique first markers in each sample and the number thereof Determining the amount (each unique first marker is a marker of a microorganism strain), integrating the number of each microbial type with the number of the first marker via one or more processors Obtaining the absolute cell number of each microbial strain present in each sample, at least one unique second for each microbial strain. Determining the level of activity of the respective microbial strain in each sample based on the measured values above the identified threshold value of the microorganism (the microbial strain is such that the measured value of at least one unique second marker for that strain raises the corresponding threshold value). If so, then identify the activity), filter the absolute cell number of each microbial strain by the determined activity to obtain a list of active microbial strains for each of at least two samples and their respective absolute Obtaining the cell number, the filtered absolute number of one or more active microbial strains for each of the at least two samples, along with at least one measurement metadata or additional active microbial strains for each of the at least two samples. Analyze and analyze functions, predictive functions, and / or chemical Classifying active microbial strains based on the classification, identifying a plurality of active microbial strains based on the classification, and outputting the plurality of identified active microbial strains (the identified active microbial strains are For constructing an active microorganism configured to alter a property of the target corresponding to at least one measurement metadata when applied to the at least one measurement metadata). In some embodiments, the output can be utilized in the production, synthesis, evaluation, and / or testing of synthetic and / or transgenic microorganisms and microbial strains. Some embodiments may include a processor-readable and non-transitory computer-readable medium having stored thereon instructions for implementing and / or facilitating the performance of method (s). In some embodiments, the analysis and screening methods, devices, and systems according to the present disclosure can be used to identify the microorganisms and strains (such as pathogens) of interest, as described in Example 4 below. Will be discussed. In such situations, it is envisioned that known symptom metadata (such as a lesion score) will be used in network analysis of the sample.

  The state and phenotype of the host can be intrinsically related to the composition of the microbiome present in the host. By learning these composition measurements in association with host data, biomarkers can be identified to accurately predict patient outcomes and state changes. In some embodiments, the diagnostic tools used to determine status utilize readily available samples, and the time required to apply and analyze such diagnostic tools is short, and in some embodiments, such diagnostic tools Is a candidate for substituting the method using culture.

  There are a variety of methods available for determining the genotype / phenotype of a microbiome, including, but not limited to, metabolomics, amplicon metagenomics, metagenomics, metatranscriptomics, and / or Or proteomics. On the other hand, each measurement value is decomposed into a table in which a row indicates a sample and a column indicates a measurement item. For example, amplicon metagenomics is broken down into a table with rows for samples and columns with OTUs (ie, microorganisms), which summarize the measurements for that sample. In some cases, the measured variables are called features, and the table has a sample dimension for each feature. The table of measurements can be referred to as target data, and the external data for each sample is referred to as a label. The label data can be arranged to correspond to the target row and includes at least one column (s).

  According to some embodiments, the first step in some diagnostic methods involves pre-processing the target dataset. Even in the measurement-specific case and in the measurement / model-specific case, it may be possible to use various normalization methods. In such cases, the table may contain significant outliers, with one sample missing due to high abundance features not found in other samples. The presence of samples with significant outliers can result in poor model performance. Disclosed herein are various methods for addressing outliers, such as correcting the data set to minimize the effects of the outliers, or completely excluding outliers. (Eg, Iglewicz 1983, Art et al. 1982, Janssen et al. 1995, Girman 1994, McLachlan and Peel 2004, each of which is expressly incorporated herein by reference for all purposes). Thing). Outliers can also arise from sparse data with many missing values, which can be common in biological measurements. This can be corrected for via matrix interpolation, matrix decomposition, and / or other methodologies that allow for the approximation of missing values (eg, Keshavan et al. 2009, Kapur et al. 2016, Mazmmer et al. 2010, each of which is expressly incorporated herein by reference for all purposes). If the absolute amount is unknown, a correction can be made to the composition, for example, using a centered log and inverse log ratio transform (see, for example, Morton et al. 2017). thing). In some cases, irrelevant features can be significantly reduced from the signals for a particular set of labels via feature selection (see, eg, Baraniuk 2007). Feature selection utilizes relevance measures (such as MIC and Hoffding). On the other hand, there are many feature extraction methods in which features regarded as having no relevance are excluded or their weights are reduced, and the weight of features with high importance is increased.

  According to some embodiments, machine learning can be utilized as part of the disclosed method, and in particular, in determining the mechanism in the target data associated with the label, also in the target data associated with the label. Machine learning can also be used in biomarker discovery. Machine learning can be subdivided into supervised and unsupervised machine learning methods. In supervised machine learning, labels are directly integrated into the modeling process for both developing and validating models. Unsupervised machine learning refers to a class of machine learning whose labels are unknown or not incorporated, in which the data is analyzed purely based on target data features.

  Unsupervised machine learning includes many methods for measuring the unique structure of target data between samples or features. The main goal of most unsupervised machine learning methods (such as manifold learning (Criminisi et al. 2012), clustering (Kluger et al. 2003), and decomposition (Bowmans et al. 2015)) is to determine the unique label of the data. Is to determine the number. Such methods are most commonly used in dimension reduction in diagnostic tools, where dimension reduction allows the sample in the target data to be viewed in a lower, visible (ie, 1-3 dimensional) dimension. . In the microbiome, the most common dimensional methods used are principal coordinate analysis (PCoA) for different distance matrices (Lozupone et al. 2011), principal component analysis (Jolliffe 1986), and linear discriminant analysis (Ye et al. 2005). In addition, all but the PCoA dimension reduction technique can be used as a pre-processing stage for supervised machine learning.

  Although supervised machine learning is a broad method classification, certain methods disclosed herein, including but not limited to the following, are particularly useful for the microbiome-related analysis of the present disclosure. In the supervised machine learning class and the prediction model category, there are two subcategories, regression and classification. Regression represents the case where the labels are continuous. The classification can be binomial if there are two possible labels, or multi-class if there are several possible labels. In either finding category, each label must appear more than once in any given column.

  In some embodiments, model optimization is maximized by pre-processing the target data as needed, and the label data is processed to include no missing entries. Next, each column in the label data is separated and evaluated as either continuous regression, binomial classification, or multi-class classification. The method is determined according to the subclass, and this determination is generally, but not limited to, a random forest (Breiman 2001), a nearest neighbor (Indyk and Motwani 1998), a neural network (supervised) (Mφller 1993), a support vector machine. (Smola and Scholkopf 2004), or using a Gaussian process (Neumann et al. 2009). The model is cross-validated by splitting the target and label data into a training dataset (eg, 80%) and a test dataset (eg, 20%). This cross-validation is performed iteratively, with (split) shuffling the features and samples at each iteration. At each iteration, a metric of model performance is calculated between predictions from training data to target data (Taylor 2001). The performance criterion is used for both adjusting the parameters of the model (also called hyperparameter adjustment) and verifying the predictive power of the model.

  After a highly predictive model is obtained, an automated prediction platform is developed in addition to the high-throughput biomarker probe. In some embodiments, the entire community of measurements is utilized to provide accurate results, where the input measurements are used to generate predictions. The developed prediction model is used to predict labels from new data after training on the entire known data set. A prediction can be obtained with associated reliability and probability distribution. This can be done in an automatic function that visualizes the prediction from the input sample. In some embodiments, the feature selection or model reveals small subgroups or single features with high predictive power. In such embodiments, developing a high-throughput probe can quickly identify features associated with the prediction. For example, in the case of amplicon metagenomics, a single microorganism or small community of microorganisms can directly determine a condition and predict patient outcome. High throughput probes can be real-time PCR primers that can reveal the abundance or presence of a particular feature.

  It is contemplated that the systems and methods described herein can be implemented by software (stored in memory and / or executed in hardware), hardware, or a combination thereof. The hardware components and / or hardware modules may include, for example, a general purpose processor, a field programmable gate array (FPGA), and / or an application specific integrated circuit (ASIC). Software components and / or software modules (executing in hardware) can be represented in various software languages (eg, computer code), including Unix utilities, C, C ++, and the like. , Java, JavaScript (eg, ECMAScript6), Ruby, SQL, SAS, the R programming language / software environment, Visual Basic, and other object-oriented, procedural. Or other types of programming languages and development tools. Examples of computer code include, but are not limited to, microcode or microinstructions, machine instructions (such as those generated by a compiler), code used to generate web services, and execution by a computer using an interpreter. Includes files that contain high-level instructions to be performed. Additional examples of computer code include, but are not limited to, control signals, encryption codes, and compression codes.

  Some of the embodiments described herein relate to devices with non-transitory computer-readable media (which may also be referred to as non-transitory processor-readable media or processor-readable memory). Computer-readable media are those on which instructions or computer code for performing various operations performed by the computer reside. Computer-readable media (or processor-readable media) is a non-transitory device in the sense that it does not include e.g., evanescently propagating signals (e.g., propagating electromagnetic waves that carry information over a transmission medium (e.g., space or cable)). It is transient. The media and computer code (which may also be referred to as code) may be those designed and constructed for a particular purpose (s). Examples of non-transitory computer readable media include, but are not limited to, magnetic storage media (such as hard disks, floppy disks, and magnetic tapes), optical storage media (compact disks / digital video disks (CD / DVD), compact discs-read only memory (CD-ROM) and holographic devices), magneto-optical storage media (such as optical discs), carrier signal processing components and / or carrier signal processing modules, and storing and executing program code Hardware devices (such as application specific integrated circuits (ASICs), programmable logic devices (PLDs), read-only memory (ROM), and random access memory (RAM) devices). Other embodiments described herein relate to computer program products, which can include, for example, instructions and / or computer code discussed herein.

  While various embodiments of FIG. 3A are described above, it will be understood that such embodiments are provided by way of example only, and not limitation. If the above methods and steps indicate certain events occurring in a particular order, the order of the particular steps may be modified. Further, certain steps may be performed concurrently, if possible, in parallel, or may be performed sequentially as described above. Although various embodiments are described as having a particular feature and / or combination of components, any of the features and / or components of any of the embodiments described herein may be derived. Other embodiments with combinations or sub-combinations are possible. Furthermore, while various embodiments are described as having a particular entity associated with a particular computing device, in other embodiments, the entities associated with other computing devices and / or different computing devices may be different.

Experimental Data and Examples The present disclosure is further illustrated by reference to the following experimental data and examples. It should be noted, however, that these experimental data and examples, like the embodiments above, are illustrative and should not be construed as limiting the scope of the present disclosure in any manner.

Example 1
Reference is made to the steps shown in FIG.

  2000: Exfoliate cells from the rumen sample of the cow from the matrix. This can be accomplished by vigorous blending or mixing of the sample via sonication or vortexing, followed by differential centrifugation to remove the matrix from the cells. Centrifugation may include a gradient centrifugation step using Nycodenz or Percoll.

  2001: An organism is stained with a fluorescent dye that targets a particular biotype. By using flow cytometry, different populations are distinguished based on staining characteristics and size.

  2002: The absolute number of organisms in the sample is determined, for example, by flow cytometry. At this stage, information is obtained about how many biotypes (such as bacteria, archaea, fungi, viruses, or protists) are present in a given volume.

  2003: Take a dairy cow rumen sample and lyse cells adhering to the matrix directly through bead breaking. Purify all nucleic acids. All purified nucleic acids are treated with RNAse to obtain purified genomic DNA (gDNA). The use of qPCR amplifies specific markers from bulk gDNA while adding a sequencing adapter and barcode to each marker. Stopping the qPCR reaction early in the exponential amplification minimizes the bias associated with PCR. The samples are pooled and multiplex sequencing is performed on the pooled samples using Illumina Miseq.

  2004: Lyse cells directly from bead rumen samples adhered to matrix via bead disruption. Purify total nucleic acid using a column-based approach. All purified nucleic acids are treated with DNAse to obtain purified RNA. The total RNA is converted to cDNA using reverse transcriptase. By using qPCR, a specific marker is amplified from the bulk cDNA, and at the same time, a sequencing adapter and a barcode are added to each marker. Stopping the qPCR reaction early in the exponential amplification minimizes the bias associated with PCR. The samples are pooled and multiplex sequencing is performed on the pooled samples using Illumina Miseq.

  2005: Process sequencing results (fastq file) by excluding low quality base pairs and truncated reads. Analyze the DNA-based dataset using a customized UPARSE pipeline and match sequencing reads against existing database entries to identify strains within the population. Specific sequences are added to the database. Analyze the RNA-based data set using a customized UPARSE pipeline. Identify active strains using up-to-date databases.

  2006: Using the unique strain data obtained in the previous step (2005), the number of reads representing each strain is determined and indicated as a percentage of all reads. Multiplying this ratio by the cell number (2002) determines the absolute cell number of each biotype in the sample and in a given volume. Marker sequences present in the RNA-based dataset are used with appropriate thresholds to identify active strains in the absolute cell count dataset. Strains that do not meet the threshold are excluded from the analysis.

  2007: Repeating 2003-2006 establishes a time course showing the kinetics of the microbial population in multiple dairy cow rumens. The temporal data is aggregated and the cell number and metadata of each active organism strain for each sample is stored in a quantity or abundance matrix. The use of a quantity matrix reveals the association between active strains in a sample at a particular time point using a rule mining technique weighted by quantity data. Apply filters to exclude rules that are not significant.

  2008: The change in cell number of the active strain over time is calculated, taking into account the direction of the change (ie, a negative value indicates a decrease and a positive value indicates an increase). The matrix is shown as a network, where the biological strains indicate nodes and the rules weighted by quantity indicate edges. Utilizing Markov chains and random walks, the connectivity between nodes is determined, and clusters are defined. The clusters are filtered using the metadata to identify clusters associated with the desired metadata (environment parameter (s)). The target organism strain is ranked by integrating the change in cell number over time with the strains present in the target cluster, and the one with the largest change in cell number is ranked at the top.

Example 2
Experimental Design and Materials and Methods Objective: To determine the components of the rumen microbial community that affect milk fat production in dairy cows.

  Animals: Eight lactating Holstein dairy cows with cannulated lumens were housed in individual tystals for use in experiments. The cows were fed twice a day and milked twice a day, keeping the cows free to drink fresh water. One dairy cow (dairy cow 1) was excluded from the study after the first induction of food-induced loss of milk fat due to complications due to pre-experimental miscarriage.

  Experimental Design and Processing: The experiments used a crossover design with two groups and one experimental period. The experimental period spanned 38 days, 10 days of co-variation / washout period, and 28 days for data collection and sample acquisition. The data collection period was configured so that the induction period of food-induced decrease in milk fat mass (MFD) was 10 days and the recovery period was 18 days. After the first experimental period, period 10 was started after all cows had a 10-day washout period.

  Highly starch degradable (70% degradable), high levels of polyunsaturated fatty acids (PUFA, 3.7%), low fiber content (29% NDF), originated from food using fully mixed feed (TMR) Induced MFD. During the recovery period, two foods with different starch degradability were used. Four dairy cows were randomly assigned to a recovery food with high fiber content (NDF 37%), low PUFA content (2.6%) and high starch degradability (70% degradable). The remaining four cows were fed a restorative food with a high fiber content (NDF 37%) and a low PUFA content (2.6%) but low starch degradability (35%).

  During the 10-day covariation period and the 10-day washout period, dairy cows were fed foods with high fiber content, low PUFA content, and low starch degradability.

  Samples and measurements: Throughout the covariation period, the washout period, and the sample acquisition period, milk production, dry matter intake, and feed efficiency were measured daily for each animal. The nutrient composition of the TMR sample was measured. During the acquisition period, milk samples were acquired and analyzed every three days. Analysis of the samples was performed for milk component concentrations (milk fat, milk protein, lactose, milk urea nitrogen, somatic cell count, and solids) and fatty acid composition.

  During the acquisition period, rumen samples were obtained and analyzed every three days for microbial community composition and activity. On days 0, 7, and 10 after food-induced MFD, 0, 2, 4, 6, 8, 10 and 12 hours after feeding on that day After 14 hours, 16 hours, 18 hours, 20 hours, and 22 hours, samples were intensively collected from the lumen. Similarly, during the recovery period, on days 16 and 28, 0 hour, 2 hours, 4 hours, 6 hours, 8 hours, 10 hours, 12 hours, 14 hours after feeding on that day. After hours, 16 hours, 18 hours, 20 hours, and 22 hours, samples were intensively obtained from the lumen. Analysis of rumen contents was performed for pH, acetic acid, butyric acid, propionic acid, isoacid, and long chain and CLA isomer concentrations.

  Preparation and Sequencing of Rumen Sample: After obtaining the rumen sample, centrifugation (4,000 rpm) of the rumen sample was performed at 4 ° C. for 20 minutes in a swinging bucket type centrifuge. The supernatant was decanted and aliquots of the rumen contents sample (1-2 mg) were each added to a 1.7 mL sterile tube pre-filled with 0.1 mm glass beads. A second aliquot was obtained and stored in an empty 1.7 mL sterile tube for cell counting.

  A rumen sample (first aliquot) containing glass beads was homogenized using bead crushing to dissolve the microorganism. DNA and RNA were extracted and purified from each sample and prepared for sequencing on Illumina Miseq. Samples were sequenced using paired-end chemistry and 300 base pairs were sequenced on both ends of the library. The rumen samples (the second aliquot) in empty tubes were stained and run on a flow cytometer to quantify the number of cells of each microbial type in each sample.

  Sequencing read processing and data analysis: By performing quality trimming and processing of the sequencing reads, bacterial species present in the rumen were identified based on marker genes. The absolute data on the number of active microbial species within the rumen microbial community was determined by integrating the counting and activity data sets with the sequencing reads. By using mutual information, the time course of dairy cow production characteristics (including pounds of milk produced) was linked to the distribution of active microorganisms in each sample over the course of the experiment. The maximum information coefficient (MIC) score was calculated between pounds of milk fat produced and the absolute cell number of each active microorganism. The microorganisms were ranked by MIC score and the microorganism with the highest MIC score was selected as the target species most relevant to the pound of milk produced.

  Test cases to determine enumeration data, activity data, and the effect of number and activity on the final result were performed by excluding the appropriate dataset from sequencing analysis. To assess the effect of using linear correlation rather than MIC on target selection, the Pearson coefficient of pounds of milk fat produced relative to the relative abundance of all microorganisms and the absolute cell number of active microorganisms was also calculated.

Results and discussion Relative abundance versus absolute cell number

  The MIC score for the top 15 target species (absolute cell count, Table 2) for the data set with cell number data and the top 15 target species (relative abundance, Table 1) for the data set without cell number data Was identified based on Activity data was not used in this analysis to highlight the effect of cell number data on final selection of targets. Finally, the top eight targets were identical between the two data sets. Five of the seven remaining strains were on both lists, but in a different order. The MIC scores calculated for each of the five strains were identical between the two lists, despite differences in ranking. Two strains (ascus_111 and ascus_288) that were present in the absolute cell count list but not in the relative abundance list were ranked 91 and 16 in the relative abundance list, respectively. Two strains (ascus_102 and ascus_252) that are present in the relative abundance list but not in the absolute cell count list were ranked 50 and 19, respectively, in the absolute cell count list. These four strains actually differed in their MIC scores in each of the lists, thus this explains that their rankings have shifted and have affected other stocks in the list accordingly. Is to give.

Integrating cell number data did not always affect the final MIC score assigned to each line. This means that although the microbial population actually changed daily in the lumen and changed over the 38 days of the experiment, the change was always in the range of 10 ⁷ to 10 ⁸ cells / milliliter. Can be attributed to the fact that It is assumed that as the change in population number increases, it is clear that the range affecting the final MIC score will increase.

Comparison of inert and active species

  To evaluate the effect of filtering strains based on activity data, a dataset utilizing relative abundance with activity data (Table 3) and a dataset utilizing relative abundance without activity data (Table 1) ) And a dataset utilizing the absolute cell number with activity data (Table 4) and a dataset utilizing the absolute cell number without activity data (Table 2).

  In the case of relative abundance, ascus_126, ascus_1366, ascus_1780, ascus_299, ascus_1139, ascus_127, ascus_341, and ascus_252 were considered as target strains before the application of the activity data. These eight strains (53% of the first 15 top targets) fell below the top 15 and fell down after integration of activity data. A similar trend was observed for absolute cell numbers. Ascus — 126, ascus — 1366, ascus — 1780, ascus — 299, ascus — 1139, ascus — 127, and ascus — 341 (46% of the first 15 top targets) fell out of the top 15 and dropped down after integration of the active dataset.

  When compared to the cell number data set, the activity data set had a much more significant impact on target ranking and selection. When integrating these data sets together, if a sample proves to be inert, it is essentially turned to "0" and is no longer considered part of the analysis. For this reason, the distribution of points in a sample can become severely altered or distorted after integration, which in turn has a significant effect on the final MIC score and thus the order of the target microorganism.

Relative abundance and no activity vs. absolute cell number and activity

  Ultimately, the methods defined herein utilize both cell number data and activity data to identify microorganisms that are highly associated with relevant metadata features. Of the top 15 targets selected using both methods (Table 4, Table 1), only 7 strains were found in both lists. Eight strains (53%) were unique to the list including absolute cell number and activity. The top three targets on both lists were consistent in both strains and ranks. However, two of the three differed in the MIC scores in both lists, indicating that although they were affected by the integration of the active dataset, their effects were not enough to reverse their ranking. Suggests that it was not enough.

Comparison between linear correlation method and nonparametric method

  Pearson coefficients and MIC scores were calculated between pounds of milk fat produced and the absolute cell number of active microorganisms in each sample (Table 5). By ranking strains by either MIC (Table 5a) or Pearson coefficient (Table 5b), the target strain with the highest association with milk fat production was selected. Both the MIC score and the Pearson coefficient are reported in each case. Six strains were found in both lists, which means that 9 unique strains (60%) were identified using the MIC approach. The order of the strains between the lists was different, and the top three target strains identified by each method were also unique.

  Like the Pearson coefficient, MIC scores are reported over a range of 0 to 1, with 1 indicating a very strong association between the two variables. Here, the top 15 targets showed MIC scores ranging from 0.97 to 0.74. On the other hand, the Pearson coefficient for this correlation test case was in the range of 0.53 to 0.45, which was substantially lower than that of the mutual information test case. This difference may be due to differences inherent in each analysis method. Correlation is a linear estimate that measures the variance of points around a straight line, while mutual information exploits a probability distribution to measure the similarity between two distributions. Over the course of the experiment, the pounds of milk fat produced varied non-linearly (FIG. 4). For this particular function, it may be more appropriate to indicate the mutual information and approximate it compared to the correlation. To investigate this, the top target strains identified using correlation and mutual information (Ascus_713 (FIG. 5) and Ascus_7 (FIG. 6), respectively) were plotted to show the relationship between strains and milk fat mass. We determined how accurately each method predicted gender. If the two variables show a strong correlation, a single line with little or no point variance when plotted against each other indicates the two variables. In FIG. 5, the correlation between Ascus — 713 and the amount of milk fat is weak, which is indicated by the wide spread of the points. Again, mutual information measures how similar two point distributions are. When Ascus_7 is plotted with milk fat mass (FIG. 6), it is clear that the distribution of these two points is very similar.

Comparison between the method of the present invention as a whole and the conventional method

  Traditional approaches to analyzing microbial communities rely on using relative abundance data without capturing activity information and ultimately end up with a simple correlation of microbial species to metadata. (See, for example, US Patent No. 9,206,680, which is incorporated herein by reference in its entirety for all purposes). Here, we show how incorporation of each dataset additionally affects the final list of targets. When the method described herein was applied in its entirety, a completely different set of targets was selected by the method described herein compared to the conventional method (Tables 5a and 5c). Ascus-3038, which was selected as the top target strain using conventional techniques, was plotted against milk fat mass to visualize the strength of the correlation (FIG. 7). As in the previous example, the correlation shown by Ascus_3038 for milk fat content was also weak.

Table 5: Top 15 target strains when using mutual information or correlation

Example 3
Increasing Total Milk Fat, Total Milk Protein, and Energy Corrected Milk (ECM) in Dairy Cows Example 3 aims to increase the total amount of milk fat and milk protein produced by lactating ruminants, as well as the calculated ECM. Is shown. As used herein, ECM refers to the amount of energy in milk based on milk volume, milk fat, and milk protein. In the ECM, the milk component is corrected to 3.5% fat and 3.2% protein, resulting in equal animal performance and comparing production over time at the individual animal and herd level. . The formula used to calculate the ECM in connection with the present disclosure is:
ECM = (0.327 x pounds of milk) + (12.95 x pounds of fat) + (7.2 x pounds of protein)

  Utilizing the disclosed methods to identify active correlated microorganisms / microbial strains, from which microbial ensembles are generated and the methodology presented herein can be applied to the production of milk fat and milk protein produced by lactating ruminants. It has been demonstrated that the total amount increases. These increases were realized without the need for additional hormones.

  In this example, a microbial ensemble containing two isolated microorganisms (Ascusb_X and Ascusf_Y) identified and generated according to the above disclosure was administered to mid-lactating Holstein dairy cows for 5 weeks. Dairy cows were randomly assigned to two groups of eight cows, one of which was a control group receiving a buffer without a microbial ensemble. The second group (experimental group) was administered a microbial ensemble containing Ascusb_X and Ascusf_Y once a day for 5 weeks. Each cow was housed in a separate breeding area with free access to feed and water. The food was a milk-rich food. Dairy cows were allowed free access to food and weighed at the end of the day, and the anorexic foods the previous day were weighed and discarded. Body weight measurements were performed on a PS-2000 scale from Salter Brecknell (Fairmont, MN).

  The dairy cow was cannulated so that it reached the lumen of the dairy cow. Dairy cows were given a further recovery period of at least 10 days after cannulation prior to receiving control or experimental doses.

The control group was administered using 20 ml of neutral buffered saline, and the experimental group was administered using about 10 ⁹ cells suspended in 20 ml of neutral buffered saline. did. The control group was administered once the saline 20ml per day, the experimental group were administered the saline 20ml, further comprising a microorganism ensemble according to 10 ⁹ microbial cells.

  Samples were obtained from the rumen of all dairy cows on days 0, 7, 14, 21 and 35, where the day before microbial administration was day 0. Note that the experimental and control doses were performed that day after samples were obtained from the lumen. Daily sample acquisition from the rumen commenced on day 0 and recordings were taken by inserting a pH meter from Hanna Instruments (Woonsocket, RI) into the acquired rumen fluid. In obtaining a rumen sample, both microparticles and liquid were obtained via cannula from the central, dorsal, ventral, anterior, and posterior regions of the lumen, and all five samples were Pooled in a 15 ml conical vial containing 1.5 ml stop solution (95% ethanol, 5% phenol). Fecal samples were also obtained on each sample acquisition day, and feces were obtained from the rectum using a palpation sleeve. The cows were weighed at the time of each sample acquisition.

  Fecal samples are placed in 2 oz vials, stored frozen, and analyzed to determine apparent neutral detergent fiber (NDF) digestibility, apparent starch digestibility, and apparent protein digestibility. Were determined. In obtaining a rumen sample, both the liquid portion and the fine particle portion of the lumen were obtained, and each of them was stored in a 15 ml conical tube. Cells were fixed with 10% stop solution (mixture of 5% phenol / 95% ethanol), kept at 4 ° C., kept on ice and sent to Ascus Biosciences (San Diego, California).

  Milk production was measured twice a day (once in the morning and once in the night). Milk composition (such as% fat and% protein) was measured twice a day (once in the morning and once at night). Protein fat and solids were analyzed by near-infrared spectroscopy, and milk urea nitrogen (MUN) and somatic cell count (SCC) were analyzed by Tulare Dairy Herd Improvment Association (DHIA) (Tulare, California). The sample was further analyzed. Feed intake and rumen pH of individual cows were determined once a day.

  Samples of complete mixed feed (TMR) were obtained on the last day of the application period, after which weekly acquisitions were continued. Sample acquisition was performed using the quadrant method, samples were stored in vacuum-sealed bags, and the vacuum-sealed bags were sent to Cumberland Valley Analytical Services (Hagerstown, MD) for analysis with the NIR1 package. The final day of buffer and / or microbial bioensemble administration was on day 35, but all other measurements and sample acquisitions continued as described until day 46.

  FIG. 8A shows that cows receiving a microbial ensemble based on the disclosed method increased mean milk fat production by 20.9% compared to cows receiving a buffer alone. FIG. 8B shows that cows receiving the microbial ensemble increased mean milk protein production by 20.7% compared to cows receiving the buffer alone. FIG. 8C shows that cows receiving the microbial ensemble increased mean energy corrected milk production by 19.4%. The increase seen in FIGS. 8A-C decreased in significance after stopping administration of the ensemble (indicated by the vertical line across the data points).

Example 4
Detection of Clostridium perfringens as the causative agent of pathogenesis in broilers Various levels of Clostridium perfringens were administered to 160 male Cobb500 (Table 6a). These male Cobb500 were bred for 21 days, sacrificed and scored for lesions to determine the progression of necrotic enteritis and C. cerevisiae. The effect of perfringens was quantified.

Experimental design

  The birds were housed in an environmentally controlled facility and housed on a wooden floor rearing area (approximately 4 feet x 4 feet minus 2.25 square feet for feeder space). In this example, floor space and bird densities were [approximately 0.69 square feet per bird] and provided temperature, lighting, feeders, and water. Birds were placed in a clean breeding area containing wood chips at an appropriate depth to provide a comfortable environment for the chick. During the test, additional wood chips were added to the rearing area if the wood chips became too moist as a comfortable condition for the test birds. Lighting was via white light and a commercial lighting program was used as described below.

  The environmental conditions for birds (ie, bird density, temperature, lighting, feeder, and water space) were the same for all treatment groups. A robust (plastic) divider approximately 24 inches high was provided between the rearing areas in each rearing area to prevent bird migration and bacterial spread between the rearing areas.

Vaccination and treatment administration:

  Birds were vaccinated at the hatchery to prevent Marek's disease. At the time of acceptance (day 0 of the study), birds were vaccinated by spray application to prevent Newcastle disease and infectious bronchitis. The vaccine manufacturer, lot number, and expiration date were documented and submitted with the final report.

water:

  Water was supplied continuously throughout the study via one Plasson waterer per breeding area. The water supply was checked twice a day and washed as needed to ensure that clean water was constantly and reliably supplied to the birds.

Feeding:

  Permanent feeding was conducted throughout the study through a suspended tube feeder approximately 17 inches in diameter per breeding area. For the first approximately four days, a chick feeder tray was placed in each breeding area. Upon receipt (Day 0), birds were placed under their respective treated food intakes according to the experimental design. The feed added to and removed from the breeding area from day 0 to the end of the test was weighed and recorded.

Daily observations:

  The test facility, breeding area, and birds were observed at least twice daily for overall herd status, lighting, water, feed, ventilation, and unexpected events. If any abnormal condition or behavior was observed in any of the observations twice a day, it was documented and it was attached to the test record. The minimum and maximum temperatures of the test facility were recorded once a day.

Breeding area cards:

  Two cards were attached to each breeding area. The rearing area number was identified by one card, and the processing number was indicated by the second card.

Animal Handling:

  Animals were kept under ideal housing compatible conditions. Animals were handled in such a way as to reduce damage and unnecessary stress. Strictly implemented humane measures.

Veterinary management, intervention, and euthanasia:

  Birds that developed a clinically significant comorbidity unrelated to the study procedure were excluded from the study at the discretion of the study director or nominee and euthanized according to the SOP on site. In addition, moribund or injured birds were also euthanized with the authority of a field veterinarian or qualified technician. Both exclusion reasons were documented. If the animal died or was excluded and was euthanized for humane reasons, this was recorded on the mortality sheet in the breeding area, an autopsy was performed and submitted to document the reasons for exclusion.

  Animals were euthanized by cervical dislocation if euthanasia was deemed necessary by the study director.

Mortality and screening:

  Any birds that were found dead or excluded and slaughtered were weighed and necropsied. This started on day 0 of the study. Sorted birds that had no access to feed or water were sacrificed, weighed, and documented. Body weight and probable cause of death and necropsy findings were recorded in mortality records in the breeding area.

Body weight and feed intake:

  Approximately on days 14 and 21, birds were weighed separately by breeding area. The feed remaining in each breeding area was weighed and recorded on days 14 and 21 of the test. Feed intake between days 14-21 was calculated.

Weight gain and feed requirement:

  The average weight of birds on breeding area and on an individual basis was summarized on each weighing day. The average feed demand was calculated on the 21st day of the study (ie, on days 0-21) using the total feed consumption of the breeding area divided by the total weight of the surviving birds. The corrected feed demand was calculated using the total weight of surviving birds plus the weight of birds that died or were excluded from the breeding area divided by the total feed consumption in that breeding area.

Administration of CLOSTRIDIUM PERFRINGENS

Administration method:

  In this test, cultures of Clostridium perfringens (CL-15, type A, alpha toxin and beta 2 toxin) were administered via feed. For mixing with the culture, feed from feeders in each breeding area was used. Before placing the culture in the breeding area, the treated feed was removed from the birds for approximately 4-8 hours. For each bird breeding area, a broth culture at a concentration of about 2.0-9.0 × 10 8 cfu / ml was mixed with a fixed amount of feed (about 25 g / bird) in a feeder tray at a fixed amount based on the study design. . All breeding areas where administration was performed were treated identically. Most of the culture-feed was consumed within 1-2 hours. The non-treated group also had feed removed at the same time as the treated group, so that birds were treated similarly in all treatments.

Administration of Clostridium:

  Culture growth of Clostridium perfringens (CL-15) was performed at about 37 ° C. for about 5 hours in a liquid thioglycolic acid medium containing starch. CL-15 is a field strain of Clostridium perfringens from a pandemic among broilers in Colorado. Fresh broth cultures were prepared and used each day. For each breeding area of birds, an overnight culture of broth culture was mixed in a fixed volume with a fixed volume of treated feed in a feeder tray (see dosing section). The amount of feed, the volume and quantification of the inoculum culture, and the number of days of dosing were entered in the final report. All breeding areas should be treated identically. C. The administration of perfringens cultures to birds was carried out for one day (day 17 of the study).

Collected data:
-Intestinal contents for analysis with the Ascus platform method according to the present disclosure.
-Body weight of individual birds and of individual birds and feed efficiency by rearing area (approx. Days 14 and 21).
-The amount of feed added to each breeding area and the amount of feed removed from each breeding area (Day 0 to end of test).
-Mortality: gender, body weight, and probable cause of death (Day 0-study completed).
-Birds excluded: Reasons for sorting, gender, and body weight (Day 0 to end of study).
-Daily observation of facilities and birds, daily facility temperature.
-Lesion score of 5 birds / house area (approximately day 21).

Lesion scoring:

C. Four days after the last administration of the perfringens culture, five birds from each breeding area were randomly selected by selecting the first captured bird, sacrificed, and scored for intestinal lesions for necrotic enteritis. Attached. Lesions were scored as follows:
-0 = normal: no NE lesions, small intestine has normal elasticity (curls back to normal position after opening).
-1 = mild: the small intestinal wall is thin and flaccid (leave flat when opened, does not roll back to its normal position after opening) and has excess mucus overlying the mucous membrane.
-2 = moderate: the intestinal wall is significantly reddened and swollen, there are few ulcers and necrosis in the intestinal membrane, and there is an excess of mucus.
-3 = severe: there is a region (s) where there is frequent necrosis and ulceration of the small intestinal membrane, marked bleeding is present, and a layer of fibrin and necrotic debris is present on the mucosa (having the appearance of a Turkish towel) .
-4 = dead or moribund: birds likely to die within 24 hours and with an NE lesion score of 2 or more.

result

  The methods disclosed above (eg, those discussed with reference to FIGS. 1A, 1B, and 2, as well as those discussed throughout this specification) and conventional correlation techniques (those discussed above) Was used to analyze the results. Strain level microbial abundance and activity were determined for the small intestine contents of each bird, and such profiles were analyzed for two different bird characteristics (individual lesion score and average lesion score of the breeding area).

  For analysis of individual lesion scores, 37 birds were used. For this analysis, 40 birds were scored, but only 37 birds had sufficient intestinal material for analysis. The same sequencing reads and the same sequencing analysis pipeline were used for both the Ascus technique of the present disclosure and the conventional technique. However, in the Ascus method, as described in detail above, activity information was integrated in addition to the cell number information of each sample.

  Using the Ascus mutual information approach, the association between the abundance of active strains and the individual lesion scores of 37 broilers was scored. For the conventional procedure, Pearson correlations were calculated between 37 broiler strains and individual lesion scores. C., the causative strain. Confirmation of perfringens was performed via an exhaustive alignment search against a list of organisms identified from the pool of samples. Next, the ranking of this particular strain was confirmed by the results of each analysis method. In the Ascus technique, C. albicans administered in experiments was used. perfringens was identified as the top strain associated with individual lesion scores. This strain was previously identified as the top 26 strain associated with the individual lesion score. By using the disclosed methods / techniques, C.I. Since perfringens has been successfully identified as the causative pathogen, the first and / or second markers indicative of this pathogenic strain can be used as indicators of the pathogenic and / or undesired state in future samples. . The abundance of the marker can also be used as an indicator of the severity of the pathological condition.

  For analysis of the average lesion score, 102 birds were used. As before, the same sequencing reads and the same sequencing analysis pipeline were used for both the Ascus technique and the conventional technique. Again, in the Ascus technique, activity information was integrated in addition to cell number information for each sample.

  Using the Ascus mutual information approach, the association between the abundance of active strains and the average lesion score for each breeding area was scored. For the conventional approach, Pearson correlations were calculated between strains in each breeding area and the average lesion score. C., the causative strain. Confirmation of perfringens was performed via an exhaustive alignment search against a list of organisms identified from the pool of samples. Next, the ranking of this particular strain was confirmed by the results of each analysis method. In the Ascus technique, C. albicans administered in experiments was perfringens was identified as the top 4 strain associated with the average lesion score in the breeding area. In the conventional method, C.I. perfringens was identified as the top 15 strain associated with the average lesion score in the breeding area. C. by measuring bulk / average. Since the fluctuation of the infection level of perfringens is hidden, the measurement accuracy of the average lesion score in the breeding area is low as compared with the individual lesion scores. When the analysis of individual lesion scores was compared to the average breeding area lesion score analysis, this decline was as expected. The following shows the collected metadata.

Example 5
Selection of Active Microbial Strain Ensembles to Alter the Composition of Broiler Gastrointestinal Microbiomes toward a More Productive State 96 male Cobb500 were bred for 21 days. Body weight and food intake were determined for individual birds and cecal exfoliates were obtained after sacrifice. Processing of this cecal sample using the methods of the present disclosure revealed an ensemble of microorganisms that, when administered to a broiler in a production setting, would increase feed efficiency.

Experimental design

  120 Cobb 500 chicks were divided and placed in breeding areas based on food treatment. Birds were placed in the rearing area on the floor for each treatment on days 0-14. The test facility was divided into one block containing two housing areas and 48 blocks each containing two individual cages. The treatment was assigned to the breeding area / cage using the complete randomized block method, and the breeding area / cage treatment was maintained throughout the study. Processing was identified by a numeric code. Birds were randomly assigned to cage / house area. The specific treatment groups are as shown below in Table 9.

Containment:

  Allocation of treatments to cages / rearing areas was performed using a computer program. The computer generated assignments are as follows:

  The birds were housed in an environmentally controlled facility and housed in a large concrete floor breeding area (4 feet x 8 feet) composed of sturdy plastic (4 feet high) along with clean straw debris. On day 14, 96 birds were transferred to cages in the same environmentally controlled facility. Each cage was 24 inches x 18 inches x 24 inches.

  Lighting was via white light and a commercially available lighting program was used. The time of continuous light every 24 hours is as shown in Table 10 below.

  The environmental conditions for birds (ie, 0.53 square feet), temperature, lighting, feeder, and water space were the same for all treatment groups.

  To prevent bird migration, each breeding area was inspected to ensure that there was no opening greater than 1 inch in the approximately 14 inch high divider between breeding areas.

Vaccination:

  Birds were vaccinated at the hatchery to prevent Marek's disease. At the time of acceptance (day 0 of the study), birds were vaccinated by spray application to prevent Newcastle disease and infectious bronchitis. The vaccine manufacturer, lot number, and expiration date were documented and submitted with the final report.

water:

  Water was supplied continuously throughout the test. In the floor breeding area, water was supplied via an automatic bell-type waterer. In the battery cage, water was supplied via one nipple-type water supply. The water supply was checked twice a day and washed as needed to ensure that clean water was always supplied to the birds.

Feeding:

  Food was supplied continuously throughout the study. In the raising area on the floor, the feed was fed via a hanging tube feeder of about 17 inches in diameter. In the battery cage, it was fed through one feeder trough (9 inches x 4 inches). For the first four days, chick feeder trays were placed in each rearing area on the floor.

Daily observations:

  The test facility, breeding area, and birds were observed at least twice daily for overall herd status, lighting, water, feed, ventilation, and unexpected events. The minimum and maximum temperatures of the test facility were recorded once a day.

Mortality and screening:

  Any birds that were found dead or excluded and sacrificed were necropsied. This started on day 0 of the study. Sorted birds that were unable to obtain food or water were sacrificed and necropsied. Probable causes of death and autopsy findings were recorded in mortality records in the breeding area.

Body weight and feed intake:

  Approximately 96 birds were weighed individually daily. The diet remaining in each cage was weighed daily and recorded on days 14-21. Food intake for each cage was determined daily.

Weight gain and feed requirement:

  Cage-based weight gain and treatment-based average weight gain on days 14-21 were determined. The total feed consumption of the cage divided by the bird's weight was used to calculate the daily and total feed requirements for the 14 to 21 day period. The average treated feed demand during the 14 to 21 day period was determined by averaging the individual feed demand from each cage that received the treatment.

Veterinary management, intervention, and euthanasia:

  Animals that developed, were injured, and whose condition could affect the outcome of the study, had significant co-morbidities were excluded from the study and euthanized when the decision was made. Six days after dosing, all birds in the cage were removed and scored for lesions.

Collected data:

  Bird weight and feed demand (performed individually daily between days 14-21).

  The amount of feed added to the breeding area of the floor and the cage and the amount of feed removed from the breeding area of the floor and the cage (Day 0 to end of test).

  Mortality: Estimated cause of death (Day 0 to end of study).

  Birds excluded: Reasons for selection (Day 0 to end of study).

  Daily observation of facility and birds, daily facility temperature.

  Cecal contents from each bird (day 21).

result

  The results were analyzed using the methods disclosed above (eg, those discussed with reference to FIGS. 1A, 1B, and 2, as well as those discussed throughout this specification). Strain level microbial abundance and activity were determined for the cecal contents of each bird. A total of 22,461 unique strains were detected across all 96 broiler caecal samples. A list of active microbial strains and their respective absolute cell numbers were created by filtering the absolute cell number of each strain by activity threshold. On average, only 48.3% of the above strains were considered active in their respective broilers at the time of slaughter. After filtering, the profile of each bird's active microbes is integrated with a variety of tri-metadata, including feed efficiency, final body weight, and the presence / absence of salinomycin in food, to provide all the capabilities of these traits Ensemble selected to improve.

  Using the mutual information technique of the present disclosure, the association between the absolute cell number of an active strain and a performance measure, as well as the association between two different active strains, was scored for all 96 birds. Attached. After application of the threshold, 4039 metadata-strain relationships were considered significant, and 8842 strain-strain relationships were considered significant. Then, using these links (weighted by the MIC score) as edges (metadata and stocks as nodes), a network was created for subsequent community detection analysis. A node was classified into subgroups by applying a community detection algorithm according to the Louvain method to this network.

  In the Louvain method, network modularity is optimized by first removing a node from its current subgroup and populating it into an adjacent subgroup. If the modularity of the node adjacent to the node improves, a reassignment of the node to the new subgroup is performed. If the modularity of the groups improves, the subgroup with the most effective change is selected. This step is repeated for every node in the network until no new allocations are made. In the next stage, a new coarse-grained network is created, i.e. the discovered sub-groups become new nodes. Edges between nodes are defined by the sum of all of the low-level nodes in each subgroup. From here, the first and second steps are repeated until the change in which the modularity can be optimized can no longer occur. Examining both maxima (ie, groups that occur in the repetitive phase) and maxima (ie, final grouping) isolates the subgroups that occur within the entire microbial community and the potential A hierarchy can be revealed.

Modularity:

Where A is the metadata-strain relevance and stock-strain relevance matrices, k _i = Σ _j Aij is the total link weight added to node i, and m = １ ／ Σ _ij A _ij . If node i and node j are assigned to the same community, Kronecker's delta δ (c _i , c _j ) will be 1, otherwise 0.

Calculate the change in modularity when a node moves:

ΔQ is the increment of the modularity of subgroup C. Ｉｎ _in is the sum of the link weights at C, Σ _tot is the sum of the link weights associated with the nodes at C, k _i is the sum of the link weights at node i, and k _{i and in} are the sum of the weights of the links connected to the node in C from I, and m is the sum of the weights of all the links in the network.

  Using the Louvain community detection method, five different subgroups were detected in the chicken microbial community. Although there is a vast amount of microbial diversity in nature, much less functional diversity exists. The presence of similarities and duplications in metabolic capacity creates redundancy. Microbial strains that respond to the same environmental stimuli or nutrients may have a similar tendency (which is captured by the methods of the present disclosure), and such microorganisms will eventually be grouped together. Become. The resulting classification and stratification reveal a functional prediction of the strain, based on the group to which such strain is classified after community detection analysis. This classification can also be used to define better / productive conditions. Once this state is established, it can be used to define and represent future sample states.

  After the strain classification is completed, the microorganism strain derived from the sample is cultured. Because of the technical difficulties involved in isolating and growing pure cultures from heterogeneous microbial communities, in the laboratory, only a few strains have passed both the activity and relevance thresholds of the disclosed method. Only some will grow purely. After the culture is completed, an ensemble of microbial strains is selected based on whether a pure culture is present and on which subgroup the strains fall. Create an ensemble that is as functionally diverse as possible, ie, select strains to be diverse subgroups in the ensemble. These ensembles are then tested in efficacy and on-site tests to determine the effectiveness of the ensemble of the strain as a product and, if it is demonstrated that the ensemble of the strain contributes to production, the ensemble of the strain is It can be manufactured and distributed as a product.

Example 6
Identification of Active Microbial Strains by Using Small Samples As will be shown in detail below, the number of samples that are effective for identifying active microbial strains can be as few as two. Specifically, for all comparisons, including comparison of two samples, C. as the active microbial strain and causative agent of intestinal lesions and necrotizing enteritis. The following experiments show that perfringens is correctly identified by the method of the present disclosure.

Experimental design

  The birds were housed in an environmentally controlled facility and placed in a concrete floor rearing area (approximately 4 feet x 4 feet minus 2.25 square feet for feeder space), where the rearing area contained floors. The space and bird densities were [approximately 0.55 square feet / bird (day 0), approximately 0.69 square feet / bird (day 21 (after lesion scoring))], temperature, humidity, lighting, Feeder and water space was provided to be similar for all test groups. Placing birds in a clean breeding area, including clean wood chips at the appropriate depth, provided a comfortable environment for the chick. Additional wood chips were added to the rearing area to keep the birds comfortable. Illumination via white light and a commercial lighting program were used as follows.

  The environmental conditions for birds (ie, bird density, temperature, lighting, feeder, and water space) were the same for all treatment groups. A robust (plastic) divider approximately 24 inches high was provided between the rearing areas in each rearing area to prevent bird migration and bacterial spread between the rearing areas.

Vaccination and treatment administration:

  Birds were vaccinated at the hatchery to prevent Marek's disease. At the time of acceptance (day 0 of the study), birds were vaccinated by spray application to prevent Newcastle disease and infectious bronchitis. The vaccine manufacturer, lot number, and expiration date were documented and submitted with the final report.

water:

  Water was supplied continuously throughout the study via one Plasson waterer per breeding area. The water supply was checked twice a day and washed as needed to ensure that clean water was constantly and reliably supplied to the birds.

Feeding:

  Permanent feeding was conducted throughout the study through a suspended tube feeder approximately 17 inches in diameter per breeding area. For the first approximately four days, a chick feeder tray was placed in each breeding area. Upon receipt (Day 0), birds were placed under their respective treated food intakes according to the experimental design. The feed added to and removed from the breeding area from day 0 to the end of the test was weighed and recorded.

Daily observations:

  The test facility, breeding area, and birds were observed at least twice daily for overall herd status, lighting, water, feed, ventilation, and unexpected events. If an abnormal condition or behavior was identified in any of the two observations per day, it was documented and included in the document along with the test records. The minimum and maximum temperatures of the test facility were recorded once a day.

Breeding area cards:

  Two cards were attached to each breeding area. The rearing area number was identified by one card, and the processing number was indicated by the second card.

Animal Handling:

  Animals were kept under ideal housing compatible conditions. Animals were handled in such a way as to reduce damage and unnecessary stress. Strictly implemented humane measures.

Veterinary management, intervention, and euthanasia:

  Birds that have developed a clinically significant comorbidity unrelated to the study procedure may be excluded from the study at the discretion of the study director or designated person and euthanized according to the SOP on site. In addition, moribund or injured birds may also be euthanized with the authority of a field veterinarian or qualified technician. Exclusion reasons are documented. If the animal died or was excluded and was euthanized for humane reasons, this was recorded on the mortality sheet in the breeding area, an autopsy was performed and submitted to document the reasons for exclusion.

  Animals were euthanized by cervical dislocation if euthanasia was deemed necessary by the study director.

Mortality and screening:

  Any birds that were found dead or excluded and slaughtered were weighed and necropsied. This started on day 0 of the study. Sorted birds that had no access to feed or water were sacrificed, weighed, and documented. Body weight and probable cause of death and autopsy findings were recorded in mortality records in the rearing area.

Administration of CLOSTRIDIUM PERFRINGENS

Administration method:

In this test, cultures of Clostridium perfringens (CL-15, type A, alpha toxin and beta 2 toxin) were administered via feed. For mixing with the culture, feed from feeders in each breeding area was used. The treated feed was removed from the birds for approximately 4-8 hours before placing the culture in the breeding area. For each bird breeding area, a broth culture at a concentration of about 2.0-9.0 × 10 ⁸ cfu / ml was applied in a fixed amount based on the study design in a fixed amount of feed (about 25 g / bird) and feeder tray. Mixed. All breeding areas where administration was performed were treated identically. Most of the culture-feed was consumed within 1-2 hours. The non-treated group also had feed removed at the same time as the treated group so that birds were treated similarly in all treatments.

Administration of Clostridium:

  Culture growth of Clostridium perfringens (CL-15) was performed at about 37 ° C. for about 5 hours in a liquid thioglycolic acid medium containing starch. CL-15 is a field strain of Clostridium perfringens from a pandemic among broilers in Colorado. Fresh broth cultures were prepared and used each day. For each breeding area of birds, an overnight culture of broth culture was mixed in a fixed amount with a fixed amount of treated feed in a feeder tray. The amount of feed, the volume and quantification of the inoculum culture, and the number of days of dosing were noted in the final report. All breeding areas should be treated identically. C. The administration of the culture of perfringens to the birds is to be carried out for one day (day 17 of the test).

Collected data

  Intestinal contents for analysis by the method of the present application.

  Body weight of individual birds and feeding efficiency by individual breeding area (approximately days 14 and 21).

  Feed amount added to each breeding area and feed amount removed from each breeding area (from day 0 to end of test).

  Mortality: gender, weight, and probable cause of death (Day 0 to study end).

  Excluded birds: sorting reason, gender, and body weight (Day 0 to end of study).

  Daily observation of facility and birds, daily facility temperature.

  Lesion score of 5 birds / reared area (approximately day 21).

  Samples obtained from 48 birds scored for lesions.

Lesion scoring:

  C. Four days after the last administration of the perfringens culture, five birds from each breeding area were randomly selected by selecting the first captured bird, sacrificed, and scored for intestinal lesions for necrotic enteritis. Attached. Lesions were scored as follows:

  0 = normal: no NE lesions, small intestine has normal elasticity (curls back to normal position after opening).

  1 = mild: small intestinal wall is thin and flaccid (leave flat when opened, does not roll back to normal position after opening), and has excess mucus covering mucous membranes.

  2 = moderate: the intestinal wall is significantly reddened and swollen, there are few ulcers and necrosis in the intestinal membrane, and there is an excess of mucus.

  3 = Severe: There is a region (s) where there is frequent necrosis and ulceration of the small intestine, marked haemorrhage, and a layer of fibrin and necrotic debris is present on the mucosa (having the appearance of a Turkish towel).

  4 = Dead or moribund: birds likely to die within 24 hours and with an NE lesion score of 2 or more.

result

  The results were analyzed using the method of the present application. The absolute cell number and activity of the microorganism at the strain level was determined for the contents of the small intestine of all 48 birds. By the method of the present application, not only the absolute cell number information but also the activity information of each sample was integrated.

  Using the mutual information approach of the present application, the association between the absolute cell number of the active strain and the individual lesion scores of 10 randomly selected broilers was scored. One sample was randomly excluded from the dataset and the analysis was repeated. This was repeated until there were only two broiler samples to compare.

C., the causative strain. Confirmation of perfringens was performed via an exhaustive alignment search against the list of organisms identified from the pool of samples. Table 12 shows C.D. for all strains analyzed. The ranking of perfringens is indicated (ranking 1 means that the strain is most involved in inducing lesion scores):

  Table 12 shows that using the disclosed method, C. as the active microbial strain and causative pathogen in the lesion score for all comparisons, including comparison of the two samples. perfringens was correctly identified. As the number of samples was reduced, the number of false positives (ie, other strains were also identified as the causative pathogen) increased, and this increase began when seven samples were compared. At the time of comparison of these seven samples, C.I. Two strains, including perfringens, tied first. This trend continues until the number of comparative samples is reduced to two, and in the comparison of two samples, C.I. Thirty-one strains, including perfringens, became the top tie.

  In general, the use of additional samples can reduce the number of noise / false positives, while further analysis and processing of the remaining strains, including those from a total of 31 identified strains, can also reduce the C.I. perfringens can be identified as the causative strain. The methods of the present disclosure can be performed on a small number of samples, depending on the embodiment, configuration, and application, and depending on the sample source, sample type, metadata, complexity of the target microbiome, etc. Can be changed.

Example 7
Platform for Diagnosing Broilers Infected with Clostridium perfringens This test provides an example of using the present disclosure to provide a diagnosis. The purpose of this study was to determine the differences in microbial composition in broilers during necrotic enteritis when Clostridium perfringens was administered at various levels. For additional details on Clostridium perfringens, see Al-Sheikhly et al. "The interaction of Clostridium perfringens and itits toxins in the production of necrotic enteritis of chickens" is incorporated by reference in its entirety for all purposes; Can be.

  The test utilized 160 Cobb 500 broilers over a 21 day test period. All Cobb 500 commercial production broilers used were male and approximately one day old at the time of acceptance (day 0). Cobb 500 chickens were obtained from Siloam Springs North. The chickens were divided into four treatments, the number of birds per breeding area was 20, and the number of breeding areas per treatment was 2.

  In this test, a feed additive phytase 2500 (obtained from Nutra Blend, LLC) (lot no. 06115A07) was utilized. The phytase 2500 used is commercially available, has a concentration of 2,500 FTU / g, a content level of 0.02%, and is stored in a safe and temperature-controlled dry area. The method of administration was via feed and was carried out for 21 days.

  Samples of the basic feed and the processed food were duplicated (sample size: about 300 g). One sample for the base food and each treated food was submitted to the assay sponsor.

Experimental design

Test group

  The test facility was divided into two blocks containing four breeding areas. Treatments were assigned to breeding areas / cages using the complete randomized block method. Specific treatment groups were designed as shown in Table 13.

Containment

  Allocation of treatments to cages / rearing areas was performed using a computer program. The computer generated assignments are as shown below in Table 14:

  The birds were housed in an environmentally controlled facility and housed on a wooden floor rearing area (approximately 4 feet x 4 feet minus 2.25 square feet for feeder space). , Floor space and bird density was approximately 0.69 square feet per bird, and temperature, lighting, feeder, and water space were supplied to be similar for all test groups. Birds were placed in a clean breeding area containing wood chips at an appropriate depth to provide a comfortable environment for the chick. During the test, additional wood chips were added to the rearing area if the wood chips became too moist as a comfortable condition for the test birds. Lighting was via white light and a commercial lighting program was used as shown in Table 15.

  A robust (plastic) divider approximately 24 inches high was provided between the rearing areas in each rearing area to prevent bird migration and bacterial spread between the rearing areas.

Vaccination

  Birds were vaccinated at the hatchery to prevent Marek's disease. On day 0 of the study, birds were vaccinated by spray application to prevent Newcastle disease and infectious bronchitis. No other vaccinations were performed during the study, except in the experimental design. Vaccination records (vaccine origin, type, lot number, and expiration date) were maintained along with the test records. No vaccinations or medications other than those disclosed herein were utilized.

water

  Water was supplied continuously throughout the study via one Plasson waterer per breeding area. The water supply was checked twice a day and washed as needed to ensure that clean water was always supplied to the birds.

feed

  Permanent feeding was conducted throughout the study through a suspended tube feeder approximately 17 inches in diameter per breeding area. For the first four days, chick feeder trays were placed in each rearing area on the floor. Upon receipt (Day 0), birds were placed under their respective treated food intakes according to the experimental design. The feed added to and removed from the breeding area from day 0 to the end of the test was weighed and recorded.

Daily observation

  The test facility, breeding area, and birds were observed at least twice daily for overall herd status, lighting, water, feed, ventilation, and unexpected events. If any abnormal condition or abnormal behavior was confirmed in any of the observations twice a day, it was recorded in the test record. The minimum and maximum temperatures of the test facility were recorded once a day.

Breeding area card

  Two cards were attached to each breeding area. The breeding area number is identified by one card, and the processing number is indicated by the second card.

Animal handling

  Animals were kept under ideal housing compatible conditions. Animals were handled in such a way as to reduce damage and unnecessary stress. Strictly implemented humane measures.

Veterinary management, intervention, and euthanasia

  Birds that developed a clinically significant comorbidity unrelated to the study procedure were excluded from the study at the discretion of the responsible or designated person and euthanized according to local standard operating procedures. In addition, moribund or injured birds may also be euthanized with the authority of a field veterinarian or qualified technician. All exclusion reasons have been documented. If animals died or were excluded and were euthanized for humane reasons, this was recorded on the mortality sheet in the breeding area, an autopsy was performed, and submitted to document the reasons for exclusion. Animals were euthanized via cervical dislocation if it was determined that euthanasia was necessary.

Mortality and sorting

  Any birds that were found dead were excluded, weighed and necropsied. This started on day 0 of the study. Birds unable to obtain food or water were sacrificed and necropsied. Body weight and probable cause of death and autopsy findings were recorded in mortality records in the rearing area.

Body weight and feed intake

  Approximately on days 14 and 21, birds were weighed separately by breeding area. The feed remaining in each breeding area was weighed and recorded on days 14 and 21 of the test. Feed intake between days 14-21 was calculated.

Weight gain and feed requirement

  The average weight of birds on breeding area and on an individual basis was summarized on each weighing day. The average feed demand was calculated on day 21 of the study using the total feed consumption of the breeding area divided by the total weight of the surviving birds. The corrected feed demand was calculated using the total weight of surviving birds plus the weight of birds that died or were excluded from the breeding area divided by the total feed consumption in that breeding area.

Acquisition of digestive tract contents

  On day 21, each bird was euthanized by cervical dislocation and the following was obtained using the described procedure, and gloves were changed between each bird.

  Immediately place the contents of one cecum into a 1.5 ml tube pre-filled with 150 μl of stop solution.

  Immediately place the contents of the small intestine in a 1.5 ml tube pre-filled with 150 μl of stop solution.

  The gizzard is excised from the GI tract, the contents are removed with forceps and placed in a 1.5 ml tube pre-filled with 150 μl of stop solution.

  The sac is excised from the GI tract, the contents are stripped from the mucosal lining with forceps / scraper and placed in a 1.5 ml tube pre-filled with 150 μl of stop solution.

  Store all samples at 4 ° C. until sent.

Scale

  The scales used for weighing feed and feed additives were approved and / or certified by Colorado. Each time the balance was used, the balance was checked using a reference weight according to CQR standard operating procedures.

Administration of Clostridium perfringens

Administration method

Cultures of Clostridium perfringens were obtained from Microbial Research, Inc. In this test, C.I. Cultures of perfringens (CL-15, type A, alpha toxin and beta 2 toxin) were administered via feed. For mixing with the culture, feed from feeders in each breeding area was used. The treated feed was removed from the birds for approximately 4-8 hours before placing the culture in the breeding area. For each bird breeding area, a broth culture at a concentration of about 2.0-9.0 × 10 ⁸ cfu / ml was applied in a fixed amount based on the study design in a fixed amount of feed (about 25 g / bird) and feeder tray. All breeding areas where mixing and dosing were performed identically. Most of the culture-feed was consumed within 1-2 hours. The non-treated group also had feed removed at the same time as the treated group so that birds were treated similarly in all treatments.

Administration of Clostridium

  C. Perfringens (CL-15) was grown in a liquid thioglycolic acid medium containing starch at about 37 ° C. for about 5 hours. CL-15 is C. cerevisiae derived from a pandemic among broilers in Colorado. perfringens. Fresh broth cultures were prepared and used each day. For each breeding area of birds, an overnight culture of broth culture was mixed in a fixed volume with a fixed volume of treated feed in a feeder tray (see dosing section). The amount of feed, the volume and quantification of the inoculum culture, and the number of days of dosing were entered in the final report. All breeding areas were treated identically. C. The administration of perfringens cultures to birds was carried out for one day (day 17 of the study). Quantification on cultures was performed by Microbiological Research, Inc, and the results were entered in the final report. No target mortality was determined for this study.

Lesion scoring

  C. Four days after the last administration of the perfringens culture, five birds from each breeding area were randomly selected by selecting the first captured bird, sacrificed, and scored for intestinal lesions for necrotic enteritis. Attached. Lesions were scored as follows:

  0 = normal: no NE lesions, small intestine has normal elasticity (curls back to normal position after opening).

  1 = mild: small intestinal wall is thin and flaccid (leave flat when opened, does not roll back to normal position after opening), and has excess mucus covering mucous membranes.

  2 = moderate: the intestinal wall is significantly reddened and swollen, there are few ulcers and necrosis in the intestinal membrane, and there is an excess of mucus.

  3 = Severe: There is a region (s) where there is frequent necrosis and ulceration of the small intestine, marked haemorrhage, and a layer of fibrin and necrotic debris is present on the mucosa (having the appearance of a Turkish towel).

  4 = Dead or moribund: birds likely to die within 24 hours and with an NE lesion score of 2 or more.

disposal

Excessive test articles

  Reports were continuously made on the test articles obtained and used for this test. Excess test articles were disposed of or returned to the sponsor. Documentation was submitted along with the test records.

feed

  Reports were continuously made on all foods. The amount mixed, used, and discarded was documented. Unused feed was disposed of for disposal and / or disposal in large garbage containers for commercial transport to a local landfill. The disposal was noted in the test report.

Test animal

  Reports were continuously made on birds obtained for testing. Carcasses and slaughtered birds during and at the end of the test were sent to a treatment plant at the end of the test and disposed of. Documentation of the disposition was submitted with the test report. Food from the animals used in this study was prevented from entering the human food chain.

Sample analysis

  A portion of each digestive tract content sample was stained and subjected to a flow cytometer to quantify the number of cells of each microbial type in each sample. Microorganisms were lysed by homogenizing another portion of the same digestive tract content sample with bead disruption. DNA and RNA were extracted and purified from each sample and prepared for sequencing on Illumina Miseq. Samples were sequenced using paired-end chemistry and 300 base pairs were sequenced on both ends of the library. By using sequencing reads, C.I. The number of cells of each active microbial member present in each bird after infection with perfringen was quantified.

  Necrotizing enteritis is severe necrosis of the intestinal mucosa; caused by toxins produced by perfringens. Therefore, C.I. To assess the platform's ability to diagnose disease, presence, and activity of perfringens, the platform's ability was analyzed in relation to the lesion score of each bird from which samples were taken. All organs were analyzed, and the results indicated that the small intestine was C. perfrigens infection was shown to be the best predictor. This was as expected because the small intestine is the primary location for pathogen establishment.

  The results are shown in FIG. Birds that scored 1 or more during the lesion scoring period were C.I. in all birds except one. perfrigens was detected. C. perfrigens abundance has a tendency to correlate with the lesion score measured for each bird, The higher the abundance of perfrigens, the more likely the bird score would be “4”. The GI tracts of birds with a lesion score of “0” include C.I. perfringens actually existed. Analysis of the activity, despite its presence, shows that in these birds C. It was revealed that perfringens was not active. These results indicate that the disclosed methods and systems are useful in the case of birds with necrotizing enteritis. This shows that the amount and activity of perfringens can be detected. This information can be used as a diagnostic to predict the causative agent of the necrotizing enteritis pandemic in broilers, as well as the severity of the disease in the individual birds with the disease.

Example 8

Changes in Rumen Microbial Composition After Administration of Microbial Composition The methods of the present disclosure have been applied to increase the total amount of milk fat and milk protein produced by lactating ruminants, as well as the calculated ECM.

  The methodologies of the present disclosure set forth herein are based on utilizing the disclosed isolated microorganisms, ensembles, and compositions comprising the same, the total amount of milk fat and milk protein produced by lactating ruminants. Has increased. These increases were realized without the need for additional hormones.

  In this example, a microbial ensemble containing two isolated microorganisms (one bacterium and one fungus) identified and synthesized according to the methods of the present disclosure was administered to mid-lactating Holstein dairy cows for five weeks.

  Dairy cows were randomly assigned to two groups of eight cows, one of which was a control group receiving a buffer without a microbial ensemble. The second group (experimental group) received the microbial ensemble once a day for 5 weeks. Each cow was housed in a separate breeding area with free access to feed and water. The food was a milk-rich food. Dairy cows were fed ad libitum and weighed feed at the end of each day, and the anorexic food the previous day was weighed and discarded. Body weight measurements were performed on a PS-2000 scale from Salter Brecknell (Fairmont, MN).

  The dairy cow was cannulated so that it reached the lumen of the dairy cow. Dairy cows were given a further recovery period of at least 10 days after cannulation prior to receiving control or experimental doses.

And performs administration respectively 20ml neutral buffered saline, was, as carried out administering respectively which were suspended about 10 ⁹ cells to the physiological saline. The control group was administered once the saline 20ml per day, the experimental group were administered the saline 20ml, further comprising a microorganism ensemble according to 10 ⁹ microbial cells.

  Samples were obtained from the rumen of all dairy cows on days 0, 7, 14, 21 and 35, where the day before microbial administration was day 0. Note that the experimental and control doses were performed that day after samples were obtained from the lumen. Daily sample acquisition from the rumen commenced on day 0 and recordings were taken by inserting a pH meter from Hanna Instruments (Woonsocket, RI) into the acquired rumen fluid. In obtaining a rumen sample, both microparticles and liquid were obtained via cannula from the central, dorsal, ventral, anterior, and posterior regions of the lumen, and all five samples were Pooled in 15 ml conical vials containing 1.5 ml of stop solution (95% ethanol, 5% phenol), stored at 4 ° C., kept on ice and sent to Ascus Biosciences (Vista, California).

  A portion of each rumen sample was stained and run on a flow cytometer to quantify the number of cells of each microbial type in each sample. Another portion of the same rumen sample was homogenized by bead disruption to lyse the microorganism. DNA and RNA were extracted and purified from each sample and prepared for sequencing on Illumina Miseq. Samples were sequenced using paired-end chemistry and 300 base pairs were sequenced on both ends of the library. Using sequencing reads, the number of cells of each active microbial member present in each of the control and experimental animal rumens was quantified over the course of the experiment.

  Both bacteria and fungi colonized the rumen, and in some animals, both bacteria and fungi were active in the rumen about 3-5 days after daily administration. This colony formation was observed in the experimental group but not in the control group. The rumen is a dynamic environment in which the chemistry of the cumulative rumen microbial community is highly intertwined. The artificial addition of a microbial ensemble can affect the overall structure of the community. To assess this promising effect, analysis of the entire microbial community over the course of the experiment revealed high-level taxonomic changes in the microbial community population.

  No characteristic trend was observed over time in the fungal population except for the increased number of fungal cells administered in the experimental animals. On the other hand, the bacterial population actually changed more as expected. Percentage composition of the microbial population was calculated and compared to assess the high level of trends over individual animals over time. Only genera with a community composition greater than 1% were analyzed. It was found that the percentage composition of genera containing known fibrinolytic bacteria (including the genus Luminococcus) was increased in experimental animals compared to control animals. It is evident that volatile fatty acid producing genera, including the Clostridium cluster XIVa, the genera Clostridium, the genus Pseudobutyrivibrio, the genus Butyricimonas, and the genus Lachnospira, also had elevated abundance in experimental animals. It became. The change observed for Prevotella was the largest. Members of this genus have been shown to be involved in the degradation of cellobiose, pectin, and various other structural carbohydrates in the rumen. One species of Prevotella is also involved in converting plant lignin to advantageous antioxidants (of Prevotella origin).

  In order to more directly measure the quantitative time course of the lumen, the bulk changes in the population were revealed at the cellular level by integrating cell number data with sequencing data. The fold change in cell number was determined by dividing the average number of cells in each genus of the experimental group by the average number of cells in each genus of the control group. Many genera that had fallen below the threshold in previous analyzes were captured by cell count analysis. Promicromonospora, Rhodopirellula, Olivibacter, Wiktivallis, Nocardia, Lentisfaera, Eubacterium, Pedobacter, Butyricimonas, Mogiumbium ), And Desulfovibrio all increased on average at least 10-fold in experimental animals. Prevotella, Lachnospira, Butyricoccus, Clostridium XIVa, Rosebria, Clostridium in a narrow sense, and Pseudobutyl vibrio were found to have increased about 1.5-fold in experimental animals.

Example 9
Determination of equine fecal microbiota in horses with colic and healthy controls in the same rearing area

  Horses are often diagnosed with colic, and a good bowel disorder that causes severe abdominal pain in the animal. The causes of colic are highly variable. Colic can be caused by obstruction due to ingestion of indigestible objects, gas, or gastrointestinal torsion. Some colic is associated with abnormalities in microbial communities present in the digestive tract of animals. In most cases, it is very difficult to diagnose the exact cause of colic, especially chronic cases. Here, feces of 20 horses were analyzed by the disclosed method to diagnose animals with microbe-based colic.

  Twenty horses (10 controls, 10 experiments) were assayed over a two month period. Samples were obtained from the animals so that the samples were duplicated. For each of the horses having colic, a sample of a control horse bred at the same breeding ground was obtained. A horse having the same migration history as a horse having colic, receiving no antimicrobial agent in the past 6 months, and having no episode of colic was used as a control.

  Each horse owner signed a letter of consent and obtained research cooperation. A physical examination was performed on each horse to measure heart rate, respiratory rate, body temperature, mucosal color, capillary refill time, and gastrointestinal belly. All other abnormalities found on examination were reported. Blood was obtained for complete blood count and chemistry panel, and fecal samples were obtained by inserting swabs 4-6 cm into the rectum of the animals. Cells and fecal material were obtained by gently rubbing the swab against the inner wall of the rectum. The swab was then completely immersed in a tube pre-filled with stop solution and immediately transferred to a new sterile 1.7 mL tube. The excess swab was removed and the tube was closed. This operation was repeated to obtain a duplicate sample. For horses with colic, feces were also obtained during rectal examination. Feces were stored in a 50 mL conical pre-filled with 15 mL of stop solution. Where possible, gastric juice and gastric contents were obtained.

  During transport, all samples were stored at 4 ° C./ice. Swabs were stored at −20 ° C. while returning to the laboratory and maintained at −20 ° C. until shipment.

Acquisition data includes:
-Age, breed, main use-Blood test results-Food / feed / supplement regimen-Type of containment-Migration history-Anthelmintic history-Treatment-Medication and medical history (especially whether colic has recurred) ( Consequently including any episode of anesthesia)
• Optional additional information on equine symptoms / behavior, pathogen tests (Salmonella, Clostridium) • Final diagnosis

  All fecal samples were analyzed using the methods of the present disclosure.

  Weese et al. Have shown that female horses have a tendency to develop colic episodes due to large volvulus when the relative abundance of proteobacteria in their feces is higher compared to control horses without colic. ("Changes in the faecal microbiota of mares precede the development of post partum colic" Equine Veterinary Journal 47.6: 2015-64-649 is hereby incorporated by reference in its entirety for every purpose.) Torsion of the large colon is one of the most severe forms of colic and should be prevented if the food and management of the animal is altered before the colic progresses and becomes more severe. In many cases, early detection is not possible and horses with large volvulus are euthanized if they undergo invasive surgery or colic recurs.

  Analysis revealed that only a few of the 10 horses had microbial colic, which was supported by veterinary diagnosis. Specifically, one horse was diagnosed as having colic due to large volvulus. As can be seen in FIG. 10, in horse 3 with colic, the level of highly active proteobacteria (pink bars) was actually elevated compared to all other horses. Further analysis showed that this proteobacterium was a distant relative of Helicobacter equiorum. Previous studies have not been able to correlate this species with pathogenicity (see, for example, Moyaert et al. "Helicobacter equiorum: prevalence and significance for hoses and humans", FEMS Immunology. 14-16, which are expressly incorporated herein by reference for all purposes), the results here show that this species plays a role in the development of colic due to large volvulus It is shown that. Thus, although horses suffer from a wide variety of colic, the disclosed methods can diagnose animals with microbial-based colic. FIG. 10 shows the relative abundance of active microorganisms in equine feces at the phylum level. Proteobacteria are indicated by a light pink color. Horse 3 with colic (a horse diagnosed with colic due to volvulus of the large colon) is indicated by a red rectangle.

  FIG. 11 provides an overview of an example diagnostic platform workflow according to some embodiments.

Example 10
Identification of horse status and insights into microbes The purpose of this study was to understand biomarkers and possible biological mechanisms and distinguish between multiple states of colic (ie, bacterial and non-bacterial equine colic). And distinguished). Multiple samples were taken from a total of 60 patients, revealing that 30 of these patients had some form of colic. The remaining 30 patients had no other diagnostic symptoms and revealed to be healthy.

Sample processing:
At each sample acquisition point, a stool sample was obtained, immediately added to a 15 ml conical tube pre-filled with a stabilizing solution, and stored at 4 ° C. After mixing the solution by inverting several times, it was immediately stored at 4 ° C. The stool sample is centrifuged at 4,000 rpm for 15 minutes, the supernatant is decanted, 0.5 mL aliquoted, and a PowerViral® environmental RNA / DNA isolation kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA). ) For extraction of total RNA and total DNA. The decanted supernatant was snap frozen in liquid nitrogen for downstream metabolomics processing.

16S rRNA.
The 16S rRNA gene was amplified using 27F and 534R modified for Illumina sequencing, and the ITS region was amplified using ITS5 and ITS4 modified for Illumina sequencing. These amplifications were performed according to the standard protocol of Q5® high fidelity DNA polymerase (New England Biolabs, Inc., Ipswich, Mass., USA). After amplification, the PCR products were verified using standard agarose gel electrophoresis and purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA). Purified amplicon libraries were quantified and sequenced on a MiSeq platform (Illumina, San Diego, CA, USA) according to standard protocols (see, eg, Flores et al. 2014). Raw fastq reads were demultiplexed on the MiSeq platform (Illumina, San Diego, CA, USA). All total cell counts were performed on a SH800S cell sorter (Sony, San Jose, CA, USA). All raw sequencing data was subjected to trimming of the adapter sequence and quality filtering with fred33 with a cutoff value of 20, and these treatments were performed using Trim Galore (see, for example, Krueger 2015). Thing). Next, all remaining sequences were filtered for PhiX, low complexity reads, and crosstalk (see, eg, Edgar 2016). USEARCH UNOISE and SINTAX (v 10.0.240) (e.g., Edgar and Flyvjerg 2015, Edgar) using the RDP 16S rRNA database (see, e.g., Cole et al. 2014) in conjunction with target sequences for DY20 and DY21. 2016), taxonomic sequence clustering and classification by 16S was performed.

Activity measurement.
CDNA synthesis was performed on the RNA sample after DNase I treatment (New England Biolabs, Inc., Ipswich, MA, USA). Random primer mixtures (New England Biolabs, Inc., Ipswich, MA, USA), Superscript® IV reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA), and Ras according to the manufacturer's protocol. (Promega, Madison, WI, USA) was used for cDNA synthesis. The 16S rRNA gene was amplified using 27F and 534R modified for Illumina sequencing, and the ITS region was amplified using ITS5 and ITS4 modified for Illumina sequencing. These amplifications were performed according to standard protocols. After amplification, the PCR product was verified and purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA). The purified amplicon library was quantified with a Qubit® DNA HS kit (Thermo Fisher Scientific, Waltham, Mass., USA) and sequenced on a MiSeq platform (Illumina, San Diego, Calif., USA) according to standard protocols. Raw fastq reads were demultiplexed on the MiSeq platform (Illumina, San Diego, CA, USA).

Cell staining and counting.
An aliquot of each sample was set aside in a new 1.7 mL tube and weighed. 1 mL of sterile PBS was added to each sample and the cells were separated from the fibrous rumen contents by performing bead breakage without beads. Next, the sample was centrifuged to remove large debris. An aliquot of the supernatant was diluted with PBS and stained. Counting beads were added to each tube (Spherotech ACFP-70-10). The stained samples were then processed on a Sony SH800 cell sorter (Sony, San Jose, CA, USA) to determine the number of fungal and bacterial cells per gram of the original sample.

Biomarker identification.
Absolute cell numbers were used to determine absolute cell numbers and inactive OTUs were filtered via normalization by cDNA sequencing. Sample output results were processed in OTU tables and pre-processed via matrix interpolation. After imputation, data learning was performed on health (bacteria colic versus non-bacterial colic or healthy) and the ROC in a 10-fold validation was greater than 0.9. Data was visualized with PCoA dimension reduction. In addition, common pathogenic biomarkers were screened from the OTU table. Finally, the composition was compared between health conditions.

Case study.
New samples for screening were presented and run on the platform using the methods of the present disclosure. The distribution based on the predicted health status was obtained from the random forest machine learning model (FIG. 12a). General pathogenic biomarkers revealed that no markers were present in very high abundance (FIG. 12b). PCoA revealed that the sample was included in the distribution of colic (FIG. 12c). Finally, a comparison of the composition of the sample states with the presentation sample revealed that the presentation sample was consistent with the colic composition (FIG. 12d). The sample and subsequent analysis suggest that the horse from which the sample was obtained was colic at the time of sample acquisition.

Example 11
Identification of dairy cow status and insights into microorganisms The purpose of this study was to obtain dairy cow rumen biomarkers and possible biological mechanisms related to production and other important external factors. A total of 5,000 samples were obtained from different climates, geographies, varieties, feed systems, and health conditions. In addition, several healthy conditions predominantly induced by food type (ie, TMR, pTMR, pasture) were sampled and compared to some common unhealthy conditions (ie, reduced milk fat mass).

Sample processing.
At each sample acquisition point, a stool sample was obtained, immediately added to a 15 ml conical tube pre-filled with a stabilizing solution, and stored at 4 ° C. After mixing the solution by inverting several times, it was immediately stored at 4 ° C. The stool sample is centrifuged at 4,000 rpm for 15 minutes, the supernatant is decanted, 0.5 mL aliquoted, and a PowerViral® environmental RNA / DNA isolation kit (Mo Bio Laboratories, Inc., Carlsbad, CA, USA). ) For extraction of total RNA and total DNA. The decanted supernatant was snap frozen in liquid nitrogen for downstream metabolomics processing.

16S rRNA.
The 16S rRNA gene was amplified using 27F and 534R modified for Illumina sequencing, and the ITS region was amplified using ITS5 and ITS4 modified for Illumina sequencing. These amplifications were performed according to the standard protocol of Q5® high fidelity DNA polymerase (New England Biolabs, Inc., Ipswich, Mass., USA). After amplification, the PCR products were verified using standard agarose gel electrophoresis and purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA). Purified amplicon libraries were quantified and sequenced on a MiSeq platform (Illumina, San Diego, CA, USA) according to standard protocols (see, eg, Flores et al. 2014). Raw fastq reads were demultiplexed on the MiSeq platform (Illumina, San Diego, CA, USA). All total cell counts were performed on a SH800S cell sorter (Sony, San Jose, CA, USA). All raw sequencing data was subjected to trimming of the adapter sequence and quality filtering with fred33 with a cutoff value of 20, and these treatments were performed using Trim Galore (see, for example, Krueger 2015). Thing). Next, all remaining sequences were filtered for PhiX, low complexity reads, and crosstalk (see, eg, Edgar 2016). USEARCH UNOISE and SINTAX (v 10.0.240) (e.g., Edgar and Flyvjerg 2015, Edgar) using the RDP 16S rRNA database (see, e.g., Cole et al. 2014) in conjunction with target sequences for DY20 and DY21. 2016), taxonomic sequence clustering and classification by 16S was performed.

Activity measurement.
CDNA synthesis was performed on the RNA sample after DNase I treatment (New England Biolabs, Inc., Ipswich, MA, USA). Random primer mixtures (New England Biolabs, Inc., Ipswich, MA, USA), Superscript® IV reverse transcriptase (Thermo Fisher Scientific, Waltham, MA, USA), and Ras according to the manufacturer's protocol. (Promega, Madison, WI, USA) was used for cDNA synthesis. The 16S rRNA gene was amplified using 27F and 534R modified for Illumina sequencing, and the ITS region was amplified using ITS5 and ITS4 modified for Illumina sequencing. These amplifications were performed according to standard protocols. After amplification, the PCR product was verified and purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA). The purified amplicon library was quantified with a Qubit® DNA HS kit (Thermo Fisher Scientific, Waltham, Mass., USA) and sequenced on a MiSeq platform (Illumina, San Diego, Calif., USA) according to standard protocols. Raw fastq reads were demultiplexed on the MiSeq platform (Illumina, San Diego, CA, USA).

Cell staining and counting.
An aliquot of each sample was set aside in a new 1.7 mL tube and weighed. 1 mL of sterile PBS was added to each sample and the cells were separated from the fibrous rumen contents by performing bead breakage without beads. Next, the sample was centrifuged to remove large debris. An aliquot of the supernatant was diluted with PBS and stained. Counting beads were added to each tube (Spherotech ACFP-70-10). The stained samples were then processed on a Sony SH800 cell sorter (Sony, San Jose, CA, USA) to determine the number of fungal and bacterial cells per gram of the original sample.

Construction of biomarkers and prediction models.
Absolute cell numbers were used to determine absolute cell numbers and inactive OTUs were filtered via normalization by cDNA sequencing. Next, the data was complemented via matrix completion. Data was visualized with PCoA dimension reduction. This clearly shows that some healthy states are clustered tightly on the left side in foods based on TMR and on the right side in foods based on pTMR, with the unhealthy group widely distributed below. (FIG. 13b). Learning on animal data for microbial composition via partial least squares regression was first performed. The accuracy of the resulting model was high, with an R-squared value of greater than 0.9 and a mean square error of less than 1. This made it possible to predict composition based on nutritional, geographical and climatic inputs. Through manipulation of such data, predictions of microbial composition can be obtained. Nutritional, geographical, and climatic data were predicted from microbial composition using random forest machine learning, with a ROC of> 0.9 in 10-fold validation. Both of these methods can be used in tandem and can learn and predict either sample metadata or sample microbial composition. This is suitable for many healthy and unhealthy conditions, so that any condition can be optimized predictively.

Case study.
Samples were presented from rumen samples of healthy dairy cows consuming pTMR food. Ruminal samples were analyzed using the described method and sequenced on an Illumina Miseq. This sample was found to fall into a healthy distribution after the PCoA dimension reduction (Figure 13a). Furthermore, when the NDF and pH of the rumen were optimized in silico, the rumen composition was found to be more productive among those consuming pTMR foods (FIG. 13b). This means that altering these two variables via feed changes or feed additives will ensure that the microbial composition of the animal from which the sample is obtained is consistent with that of the closest, most productive state Suggest that. Learning the microbial composition could also predict unmeasured external factors to reveal possible mismanagement in health (FIG. 14A), food (FIG. 14B), and climate (FIG. 14C). Was.

  Although generally discussed as a single state, for some embodiments and applications, a state (eg, a baseline state) or a biological state may be associated with multiple states and / or states associated with a particular microbiome. It can refer to a biological condition, defining a baseline, defining a particular condition, characterizing a sample, identifying potential problems, and / or treating a particular symptom (individual or group (eg, herd) level) It will be understood that multiple states are also available. For example, in the colic example above, there may be multiple causes of colic, such as reflected in the microbiome. In some embodiments, comparisons according to the present disclosure may utilize the following conditions: control (healthy), microbial colic, and non-microbial colic (and in some embodiments, multiple Different states / lower states).

Additional embodiment examples

Embodiment A1
Obtaining at least two samples sharing at least one common characteristic and having at least one different characteristic; for each sample, detecting the presence of one or more microbial types in each sample; Determining the number of each microbial type detected in each sample of the one or more microbial types, determining the number and amount of the unique first marker in each sample (the unique first marker) Each of the markers is a marker of a microorganism strain), integrating the number of each of the respective microorganism types and the number of the first marker to obtain the absolute cell number of each of the microorganism strains present in each sample; At least one unique second marker for each microorganism strain is measured based on the specified threshold to determine the level of activity of said microorganism strain in each sample. Determining a list of active microbial strains for each of the at least two samples and their respective absolute cell numbers by filtering the absolute cell numbers according to the determined activity; the at least two samples Comparing the filtered absolute cell count of said active microorganism strain for each of said at least one measured metadata or additional active microorganism strain for each of said at least two samples, wherein said active microorganism strain is: Classifying into at least two groups based on expected function and / or chemistry, selecting at least one microorganism strain from said at least two groups, and said selected from said at least two groups By combining at least one microorganism strain, Also be formed ensemble configuration microorganisms to alter the corresponding characteristic one metadata, including the method.

Embodiment A2
The method of embodiment A1, wherein measuring the number of unique first markers comprises measuring the number of unique genomic DNA markers in each sample.
Embodiment A3
The method of embodiment A1, wherein measuring the number of unique first markers comprises measuring the number of unique RNA markers in each sample.
Embodiment A4
The method of embodiment A1, wherein measuring the number of unique first markers comprises measuring the number of unique protein markers in each sample.
Embodiment A5
The method of embodiment A1, wherein determining the number of unique first markers comprises determining the number of unique metabolite markers in each sample.
Embodiment A6
The method of embodiment A5, wherein measuring the number of unique metabolite markers comprises measuring the number of unique lipid markers in each sample.
Embodiment A7
The method of embodiment A5, wherein measuring the number of unique metabolite markers comprises measuring the number of unique carbohydrate markers in each sample.
Embodiment A8
The method of embodiment A1, wherein measuring the number and amount of the unique first markers comprises subjecting genomic DNA from each sample to a high-throughput sequencing reaction.
Embodiment A9
The method of embodiment A1, wherein measuring the number and amount of the unique first markers comprises subjecting genomic DNA from each sample to metagenomic sequencing.
Embodiment A10
The method of embodiment A1, wherein the unique first marker comprises at least one of an mRNA marker, an siRNA marker, and / or a ribosomal RNA marker.
Embodiment A11
The method of embodiment A1, wherein the unique first marker comprises at least one of a sigma factor, a transcription factor, a nucleoside-related protein, and / or a metabolic enzyme.

Embodiment A12
Embodiment A1-A11, wherein measuring the at least one unique second marker comprises measuring an expression level of the at least one unique second marker in each sample. Method.
Embodiment A13
The method of embodiment A12, wherein measuring the expression level of the at least one unique second marker comprises subjecting the mRNA in the sample to gene expression analysis.
Embodiment A14
The method of embodiment A13, wherein the gene expression analysis comprises a sequencing reaction.
Embodiment A15
The method of embodiment A13, wherein the gene expression analysis comprises quantitative polymerase chain reaction (qPCR), meta-transcriptome sequencing, and / or transcriptome sequencing.
Embodiment A16
The method of embodiment A12, wherein measuring the expression level of the at least one unique second marker comprises subjecting each sample or a portion thereof to mass spectrometry.
Embodiment A17
The method of embodiment A12, wherein measuring the expression level of the at least one unique second marker comprises subjecting each sample or a portion thereof to metaribosome or ribosome profiling.

Embodiment A18
The embodiment A1-A17, wherein the one or more microbial types comprise bacteria, archaea, fungi, protozoa, plants, other eukaryotes, viruses, viroids, or combinations thereof. The described method.
Embodiment A19
The one or more microorganism strains is one or more bacterial strains, archaebacteria strains, fungal strains, protozoan strains, plant strains, other eukaryotic strains, virus strains, viroid strains, or combinations thereof. The method of any one of embodiments A1-A18.
Embodiment A20
The one or more microorganism strains is one or more fungal species or fungal subspecies, and / or the one or more microorganism strains is one or more bacterial species or bacterial subspecies. The method of embodiment A19.

Embodiment A21
Determining the number of each of the one or more microbial types in each sample comprises sequencing, centrifuging, light microscopy, fluorescence microscopy, staining, mass spectrometry, microfluidics, quantification of each sample or a portion thereof. The method of any one of embodiments A1 to A20, comprising subjecting to a quantitative polymerase chain reaction (qPCR), gel electrophoresis, and / or flow cytometry.

Embodiment A22
The unique first marker is a 5S ribosomal subunit gene, 16S ribosomal subunit gene, 23S ribosomal subunit gene, 5.8S ribosomal subunit gene, 18S ribosomal subunit gene, 28S ribosomal subunit gene, cytochrome c oxidation. The method of embodiment A1, comprising a phylogenetic marker comprising an enzyme subunit gene, a β-tubulin gene, an elongation factor gene, an RNA polymerase subunit gene, an internal transcribed spacer (ITS), or a combination thereof.

Embodiment A22a
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker.
Embodiment A22b
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 5S ribosomal subunit gene.
Embodiment A22c
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 16S ribosomal subunit gene.
Embodiment A22d
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 23S ribosomal subunit gene.
Embodiment A22e
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 5.8S ribosomal subunit gene.
Embodiment A22f
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising an 18S ribosomal subunit gene.
Embodiment A22g
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 28S ribosomal subunit gene.
Embodiment A22h
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a cytochrome c oxidase subunit gene.
Embodiment A22i
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a β-tubulin gene.
Embodiment A22j
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising an elongation factor gene.
Embodiment A22k
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising an RNA polymerase subunit gene.
Embodiment A221
The method of embodiment A1, wherein the unique first marker does not include a phylogenetic marker that includes an internal transcribed spacer (ITS).

Embodiment A23
The method of embodiment A22, wherein measuring the number and amount of the unique markers comprises subjecting genomic DNA from each sample to a high-throughput sequencing reaction.
Embodiment A24
The method of embodiment A22, wherein measuring the number and amount of the unique markers comprises subjecting genomic DNA to genomic sequencing.
Embodiment A25
The method of embodiment A22, wherein measuring the number and amount of the unique markers comprises subjecting the genomic DNA to amplicon sequencing.

Embodiment A26
Embodiment A1-A25 wherein the at least one different feature comprises an acquisition time at which each of the at least two samples was acquired such that an acquisition time of a first sample and an acquisition time of a second sample are different. A method according to any one of the preceding claims.

Embodiment A27
Embodiments A1-A25 wherein the at least one different feature comprises an acquisition position at which each of the at least two samples was acquired such that an acquisition position of a first sample and an acquisition position of a second sample are different. A method according to any one of the preceding claims.

Embodiment A28
The method of any one of embodiments A1-A27, wherein the at least one common feature includes a sample source type in which the sample source type of the first sample and the sample source type of the second sample are the same. Method.
Embodiment A29
The sample source type is one of an animal type, an organ type, a soil type, a water type, a sediment type, an oil type, a plant type, an agricultural product type, a bulk soil type, a soil rhizosphere type, or a plant part type. The method of embodiment A28.

Embodiment A30
The method of any one of embodiments A1-A27, wherein the at least one common feature comprises that each of the at least two samples is a gastrointestinal tract sample.

Embodiment A31
The at least one common feature includes an animal sample source type, and each sample has additional common features, such that each sample is a tissue sample, a blood sample, a tooth sample, a sweat sample, a nail. The method according to any one of embodiments A1-A27, which is a sample, skin sample, hair sample, fecal sample, urine sample, semen sample, mucus sample, saliva sample, muscle sample, brain sample, or organ sample.

Embodiment A32
Obtaining at least one additional sample from the target based on the at least one measurement metadata (where the at least one additional sample from the target has at least one common characteristic with the at least two samples) Sharing), as well as detecting the presence of one or more microbial types for the at least one additional sample from the target, each microorganism detected in the one or more microbial types Determining the number of types, measuring the number and amount of unique first markers, integrating the number of each respective microbial type with the number of the first marker to determine each microorganism present Obtaining the absolute cell number of the strain, measuring at least one unique second marker for each microbial strain to determine the activity level of said microbial strain Determining a list of active microbial strains and their respective absolute cell numbers for the at least one additional sample from the target by filtering the absolute cell number according to the determined activity. The method of any one of embodiments A1 to A31, further comprising the step of selecting the at least one microbial strain from each of the at least two groups, wherein the at least one microorganism strain is derived from the target. Based on the list of active microbial strains and their respective absolute cell numbers for one additional sample, such that the formed ensemble alters the properties of the target corresponding to the at least one metadata The method as described above.

Embodiment A33
Comparing the filtered absolute cell count of the active microbial strain for each of the at least two samples with at least one measured metadata or additional active microbial strain for each of the at least two samples comprises: The embodiment of any one of embodiments Al to A32, comprising determining a co-occurrence of said one or more active microbial strains in said sample and said at least one measurement metadata or additional active microbial strain. the method of.
Embodiment A34
The at least one measurement metadata includes one or more parameters, wherein the one or more parameters are sample pH, sample temperature, fat abundance, protein abundance, carbohydrate abundance, mineral Abundance, vitamin abundance, natural product abundance, specific compound abundance, sample source weight, sample source feed intake, sample source weight gain, sample source feed efficiency, At least one of the presence or absence of one or more pathogens, the physical characteristic (s) or measurement (s) of the sample source, the production characteristics of the sample source, or a combination thereof. The method of embodiment A33.
Embodiment A35
The method of embodiment A34, wherein the one or more parameters are at least one of whey protein abundance, casein protein abundance, and / or fat abundance in milk.

Embodiment A36
Determining, in each sample, the co-occurrence of the one or more active microbial strains and the at least one measurement metadata, the linkage indicating a relationship between the metadata and the microbial strain; The absolute cell number of the active microbial strain, as well as the measurement of the one or more unique second markers, creates a cohesive matrix to indicate one or more networks of the heterogeneous microbial community (s). The method of any one of embodiments A33 to A35, comprising:
Embodiment A37
The method of embodiment A36, wherein the at least one measurement metadata comprises the presence, activity, and / or amount of a second microorganism strain.

Embodiment A38
Determining the co-occurrence of the one or more active microbial strains and the at least one measurement metadata and classifying the active microbial strain measures the connectivity of each microbial strain in a network Embodiments A33-A37 comprising a network analysis and / or a cluster analysis for the at least two samples sharing a common characteristic, measurement metadata, and / or relevant environmental parameters. The method according to any one of the preceding claims.
Embodiment A39
The method of embodiment A38, wherein the at least one measurement metadata comprises the presence, activity, and / or amount of a second microorganism strain.
Embodiment A40
Embodiment A38 or the embodiment, wherein the network analysis and / or cluster analysis comprises linkage analysis, modularity analysis, robustness measurement, mediation measurement, connectivity measurement, transitivity measurement, centrality measurement, or a combination thereof. The method according to A39.
Embodiment A41
The method of any one of embodiments A38-A40, wherein the cluster analysis includes building a connectivity model, subspace model, distribution model, density model, or centroid model.

Embodiment A42
The method of embodiment A38 or A39, wherein the network analysis comprises predictive modeling of the network via link mining and prediction, population classification, link-based clustering, related similarity, or a combination thereof.
Embodiment A43
The method of embodiment A38 or embodiment 3A9, wherein the network analysis includes population modeling based on differential equations.
Embodiment A44
The method of embodiment A43, wherein the network analysis comprises Lotka-Volterra modeling.
Embodiment A45
The method of embodiment A38 or embodiment A39, wherein the cluster analysis is a heuristic method.
Embodiment A46
The method of embodiment A45, wherein the heuristic method is the Louvain method.

Embodiment A47
The method of embodiment A38 or A39, wherein the network analysis comprises a non-parametric method for establishing connectivity between variables.
Embodiment A48
The method of embodiment A38 or embodiment A39, wherein the network analysis includes calculating mutual information between variables and / or a maximum information coefficient to establish connectivity.

Embodiment A49
A method for forming an ensemble of active microbial strains configured to alter environmental characteristics or characteristics based on two or more sample sets, wherein the two or more sample sets comprise the two or more sample sets. Sharing at least one common or related environmental parameter between the sample sets and having at least one different environmental parameter between the two or more sample sets, each sample set comprising a heterogeneous microbial community. Wherein the one or more microbial strains are a sub-taxon of one or more biotypes, and wherein the method detects the presence of a plurality of microbial types in each sample. Determining the absolute cell number of each of the microbial types detected in each sample, measuring the number and amount of unique first markers in each sample. Doing (the unique first marker is a marker of a microbial strain), measuring the expression level of one or more unique second markers at the protein or RNA level (the unique second marker) Is a marker of the activity of the microorganism strain), determines the activity of the detected microorganism strain for each sample based on the expression level of the one or more unique second markers above a specified threshold. Detecting in each sample based on the amount of the one or more first markers and the absolute cell number of the microbial type from which the one or more microbial strains are a sub-taxon Calculating the absolute cell count of each active microbial strain identified (where said one or more active microbial strains express said unique second marker above said specified threshold) ), Co-occurrence of said active microbial strain in said sample with at least one environmental parameter or additional active microbial strain is determined by a maximum information coefficient network analysis to determine the connectivity of each microbial strain in the network (Wherein said network is a collection of said at least two or more sample sets together with at least one common or related environmental parameter), said one or more activities based on said network analysis Selecting a plurality of active microbial strains from the microbial strains, and forming an ensemble of active microbial strains from the selected plurality of active microbial strains. Selectively introduces the characteristics or characteristics of the environment when introduced Configured to change).

Embodiment A50
The method of embodiment A49, wherein the at least one environmental parameter comprises the presence, activity, and / or amount of a second microorganism strain.
Embodiment A51
At least one measurement indicator of at least one common or related environmental factor for a first sample set is different from the measurement indicator of the at least one common or related environmental factor for a second sample set; The method according to embodiment A49 or embodiment A50.

Embodiment A52
Each sample set includes a plurality of samples, and the measurement identifiers of at least one common or related environmental factor for each sample in the sample set are substantially similar, and the average measurement identification for one sample set The method of embodiment A49 or embodiment A50, wherein the indicator is different from the average measurement identification indicator from another sample set.
Embodiment A53
Embodiment A49 or Embodiment A50 wherein each sample set comprises a plurality of samples, a first sample set is obtained from a first population, and a second sample set is obtained from a second population. The described method.
Embodiment A54
Each sample set includes a plurality of samples, a first sample set is obtained from a first population at a first time point, and a second sample set is a second time point different from the first time point. The method of embodiment A49 or embodiment A50, wherein the method is obtained from the first population at
Embodiment A55
The method of any one of embodiments A49-A54, wherein the at least one common or related environmental factor comprises nutrient information.

Embodiment A56
The method of any one of embodiments A49-A54, wherein the at least one common or related environmental factor comprises food information.
Embodiment A57
The method of any one of embodiments A49-A54, wherein the at least one common or related environmental factor comprises an animal characteristic.
Embodiment A58
The method of any one of embodiments A49-A54, wherein the at least one common or related environmental factor comprises infection information or health status.

Embodiment A59
The at least one measurement discriminator is a sample pH, a sample temperature, a fat abundance, a protein abundance, a carbohydrate abundance, a mineral abundance, a vitamin abundance, a natural abundance, a specific compound abundance. Abundance, weight 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 characteristics of the sample source The method of embodiment A51, wherein the method (s) or measurement (s), production characteristics of the sample source, or a combination thereof.

Embodiment A60
The method of embodiment A49 or embodiment A50, wherein the at least one parameter is at least one of whey protein abundance, casein protein abundance, and / or fat abundance in milk. .
Embodiment A61
The method of any one of embodiments A49-A60, wherein measuring the number of the unique first marker in each sample comprises measuring the number of unique genomic DNA markers.
Embodiment A62
The method of any one of embodiments A49-A60, wherein measuring the number of the unique first marker in the sample comprises measuring the number of unique RNA markers.
Embodiment A63
The method of any one of embodiments A49-A60, wherein measuring the number of the unique first marker in the sample comprises measuring the number of unique protein markers.

Embodiment A64
Any one of embodiments A49-A63 wherein the plurality of microbial types comprises one or more bacteria, archaebacteria, fungi, protozoa, plants, other eukaryotes, viruses, viroids, or combinations thereof. The method described in one.
Embodiment A65
Determining the absolute cell number of each of the microbial types in each sample comprises sequencing, centrifuging, light microscopy, fluorescence microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain The method of any one of embodiments A49-A64, comprising subjecting to a reaction (qPCR), gel electrophoresis, and / or flow cytometry.
Embodiment A66
The one or more active microorganism strains are one or more selected from one or more bacteria, archaea, fungi, protozoa, plants, other eukaryotes, viruses, viroids, or combinations thereof. The method of any one of embodiments A49-A65, which is a sub-taxon of a microbial type.

Embodiment A67
The one or more active microbial strains are one or more bacterial, archaeal, fungal, protozoan, plant, other eukaryotic, viral, viroid, or a combination thereof. The method of any one of embodiments A49-A65.
Embodiment A68
The method of any one of embodiments A49-A67, wherein the one or more active microbial strains is one or more fungal species, fungal subspecies, bacterial species, and / or bacterial subspecies.
Embodiment A69
The at least one unique first marker is a 5S ribosomal subunit gene, 16S ribosomal subunit gene, 23S ribosomal subunit gene, 5.8S ribosomal subunit gene, 18S ribosomal subunit gene, 28S ribosomal subunit gene, cytochrome Embodiments A49-A68 comprising a phylogenetic marker comprising a 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. A method according to any one of the preceding claims.

Embodiment A70
The method of embodiment A49 or embodiment A50, wherein measuring the number and amount of the unique first markers comprises subjecting genomic DNA from each sample to a high-throughput sequencing reaction.
Embodiment A71
The method of embodiment A49 or embodiment A50, wherein measuring the number and amount of the unique first markers comprises subjecting genomic DNA from each sample to metagenomic sequencing.
Embodiment A72
The method of embodiment A49 or embodiment A50, wherein the unique first marker comprises an mRNA marker, an siRNA marker, or a ribosomal RNA marker.
Embodiment A73
The method of embodiment A49 or A50, wherein the unique first marker comprises a sigma factor, a transcription factor, a nucleoside-related protein, a metabolic enzyme, or a combination thereof.

Embodiment A74
The method of any one of embodiments A49-A73, wherein measuring the expression level of the one or more unique second markers comprises subjecting the mRNA in the sample to gene expression analysis.
Embodiment A75
The method of embodiment A74, wherein the gene expression analysis comprises a sequencing reaction.
Embodiment A76
The method of embodiment A74, wherein the gene expression analysis comprises quantitative polymerase chain reaction (qPCR), meta-transcriptome sequencing, and / or transcriptome sequencing.

Embodiment A77
Any one of Embodiments A49-A68 and A74-A76 wherein measuring the expression level of the one or more unique second markers comprises subjecting each sample or a portion thereof to mass spectrometry. The method described in.
Embodiment A78
Embodiments A49-A68 and A74-E wherein measuring the expression level of the one or more unique second markers comprises subjecting the sample or a portion thereof to metaribosome profiling and / or ribosome profiling. The method of any one of A76.

Embodiment A79
The type of source of the sample is animal, soil, air, saline, freshwater, wastewater sludge, sediment, oil, plant, agricultural product, bulk soil, soil rhizosphere, plant part, vegetable, extreme environment, or a combination thereof. The method of any one of embodiments A49-A78, wherein the method is one of:

Embodiment A80
The method of any one of embodiments A49-A78, wherein each sample is a gastrointestinal tract sample.
Embodiment A81
Each sample is a tissue sample, blood sample, tooth sample, sweat sample, nail sample, skin sample, hair sample, fecal sample, urine sample, semen sample, mucus sample, saliva sample, muscle sample, brain sample, or organ sample. The method of any one of embodiments A49-A78, one of which.

Embodiment A82
A method performed by a processor, the method comprising: receiving sample data from at least two samples sharing at least one common characteristic and having at least one different characteristic; Determining the presence of one or more microbial types, determining the number of each of the one or more microbial types detected in each sample, a unique first marker in each sample Determining the number and the amount of each (each unique first marker is a marker of a microorganism strain), integrating the number of said respective microorganism type and the number of said first marker via a processor Obtaining the absolute cell number of each microbial strain present in each sample, and determining at least one unique Determining the level of activity of the respective microbial strain in each sample based on the measured values of the markers above the specified threshold (the microbial strain is characterized in that the measured value of at least one unique second marker for said strain is a corresponding threshold) If more than, identify as active), filtering the absolute cell number of said respective microbial strain according to said determined activity, to obtain a list of active microbial strains for each of said at least two samples; Obtaining its respective absolute cell number, the filtered of the active microbial strain for each of the at least two samples, together with at least one measurement metadata or additional active microbial strain for each of the at least two samples. Network analysis of absolute cell numbers is performed by at least one process. (The network analysis determines a maximum information coefficient score between each active microbial strain and any other active microbial strain, and at least one of each active microbial strain and each Determining the maximum information coefficient score between the measured metadata or additional active microbial strains), classifying said active microbial strains based on expected function and / or chemistry, Identifying the active microorganism strain of the above, and outputting the plurality of identified active microorganism strains.

Embodiment A83
The method performed by the processor of embodiment A82, further comprising constructing an active microbial ensemble configured to change a property corresponding to the at least one measurement metadata when applied to the target.
Embodiment A84
The output plurality of active microbial strains is used to construct an active microbial ensemble, and the active microbial ensemble is configured to change a property corresponding to the at least one measurement metadata when applied to a target. The method performed by the processor of embodiment A82, wherein the method is performed.
Embodiment A85
The method performed by the processor of embodiment A82, further comprising identifying at least one pathogen based on the output plurality of identified active microorganism strains.
Embodiment A86
The output plurality of active microbial strains is further used in the construction of an active microbial ensemble, wherein the active microbial ensemble, when applied to a target, targets the at least one identified pathogen and the at least one identified pathogen The method performed by a processor according to any one of embodiments A82-A85, configured to treat and / or prevent a condition associated with.

Embodiment A87
A method of forming an active microbial bioensemble of an active microbial strain configured to alter the properties of a target biological environment, said method sharing at least one common characteristic and at least one different characteristic Obtaining at least two samples having, detecting, for each sample, the presence of one or more microbial types in each sample, detecting each of the one or more microbial types detected in each sample. Determining the number of microbial types, measuring the number and amount of unique first markers in each sample (each unique first marker is a marker of a microbial strain); Integrating the number of the first marker with the number of the first marker to obtain the absolute cell number of each microorganism strain present in each sample; Measuring at least one unique second marker based on the identified threshold to determine the level of activity of the microorganism strain in each sample, and filtering the absolute cell number according to the determined activity. Obtaining a list of active microbial strains for each of said at least two samples and their respective absolute cell counts; a filtered absolute cell count of said active microbial strains for each of said at least two samples; Comparing at least one measurement metadata for each of the two samples, wherein the comparing determines a co-occurrence of the active microbial strain and the at least one measurement metadata in each sample. Comprising, in each sample, the active microorganism strain and the at least one The determination of co-occurrence with the constant metadata is a matrix comprising a linkage indicating the association of the metadata with the microbial strain, the absolute cell number of the active microbial strain, and the measurements of the unique second marker. Creating one or more heterogeneous microbial community networks), and determining the connectivity of each active microbial strain within the active heterogeneous microbial community network with the measured metadata Grouping said active microbial strains into at least two groups according to functions and / or chemistry predicted based on at least one of analysis and cluster analysis, at least one microbial strain from each of said at least two groups And combining the selected microorganism strain with a carrier Forming a bio-ensemble of active microorganisms with the medium, wherein the bio-ensemble corresponds to the at least one metadata of the target biological environment when the bio-ensemble is introduced into the target biological environment Configured to change properties).

Embodiment A88
Obtaining at least one additional sample based on the at least one measurement metadata (the at least one additional sample shares at least one characteristic with the at least two samples); and Detecting, for an additional sample, the presence of one or more microbial types, determining the number of each microbial type detected among the one or more microbial types, a unique first marker Measuring the number and amount of said, integrating said respective number of microbial types and said number of said first marker to obtain the absolute cell number of each microbial strain present, for each microbial strain Measuring at least one unique second marker to determine the level of activity of the microbial strain; The method of embodiment A87, further comprising filtering the cell number to obtain a list of active microbial strains and their respective absolute cell numbers for the at least one additional sample. The comparison of the filtered absolute cell number of the microbial strain, together with the at least one measurement metadata, includes the filtered absolute cell number of the active microbial strain for each of the at least two samples, and the at least one additional Comparing the filtered active microbial cell number of the active microbial strain for a sample, such that the selection of the active microbial strain comprises the list of active microbial strains for the at least one additional sample; Said at least partially based on their respective absolute cell numbers, Law.

Embodiment A89
A method for forming a synthetic ensemble of active microbial strains configured to alter the properties of a biological environment, the method comprising the steps of: Wherein at least one of the plurality of environmental parameters is a similar common environmental parameter between the two or more sample sets, and at least one environmental parameter is between each of the two or more sample sets. Wherein each set of samples comprises at least one sample comprising a heterogeneous microbial community obtained from a biological sample source, wherein at least one of said active microbial strains comprises one or more A sub-taxon of a biotype, wherein said method detects the presence of a plurality of microbial types in each sample; Determining the absolute cell number of each of the issued microbial types, measuring the number and amount of unique first markers in each sample (the unique first markers are markers of the microorganism strain) Measuring the expression level of one or more unique RNA markers (the unique RNA marker is a marker of the activity of the microbial strain) above a specified threshold of said one or more unique RNA markers Determining the activity of each of the detected microbial strains for each sample based on the expression level; determining the amount of the one or more first markers; Calculating the absolute cell count of each active microbial strain detected in each sample based on the absolute cell count of the microbial type of origin from Analyzing the active microorganism strains of the two or more sample sets, wherein the plurality of active microorganism strains express one or more unique RNA markers above the specified threshold; Performing a non-parametric network analysis of each of the active microbial strains of each of the two or more sample sets, the at least one common environmental parameter, and the at least one different environmental parameter; The parametric network analysis comprises: (1) determining a maximum information coefficient score between each active microbial strain and any other active microbial strain; and (2) each active microbial strain and the at least one different environmental parameter. Determining the maximum information coefficient score between the non-parametric Selecting a plurality of active microbial strains from the one or more active microbial strains based on network analysis and forming a synthetic ensemble of active microbial strains comprising the selected plurality of active microbial strains and a microbial carrier medium (The ensemble of the active microbial strain is configured to selectively alter the properties of the biological environment when the synthetic ensemble of the active microbial strain is introduced into the biological environment). , Said method.

Embodiment A90
A method of forming an active microbial bioensemble configured to alter the properties of a target biological environment, said method sharing at least one common environmental parameter and having at least one different environmental parameter. Obtaining one sample, for each sample, detecting the presence of one or more microbial types in each sample, and for each microbial type detected in each sample among the one or more microbial types. Determining a number, measuring the number and amount of a unique first marker in each sample (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected); Based on the number of each microorganism type and the proportional / relative number of unique first markers corresponding to or associated with said microorganism type. Determining the absolute cell number of each microorganism strain present in each sample, measuring at least one unique second marker for each microorganism strain based on a specified threshold, Determining a level of activity of the strain, filtering the absolute cell number of the respective microbial strain by the determined activity, thereby listing a list of active microbial strains and the respective absolute cells for each of the at least two samples. Obtaining a number, comparing the filtered absolute cell number of the active microbial strain for each of the at least two samples with at least one measurement metadata for each of the at least two samples (the The comparison comprises comparing the active microorganism strain in each sample with the at least one Determining the co-occurrence of the active microorganism strain and the at least one measured metadata in each sample, wherein determining the co-occurrence of the metadata and the microorganism strain in each sample. The absolute cell number of the active microbial strain, as well as the measurement of the unique second marker, to create a coherent matrix to indicate one or more heterogeneous microbial community networks). Function and / or chemistry predicted based on at least one of non-parametric network analysis and cluster analysis that reveals connectivity between each active microbial strain in the active heterogeneous microbial community network and the measured metadata Grouping said active microbial strains into at least two groups according to said at least two Selecting at least one microbial strain from each of the groups, combining the selected microbial strains and combining with a carrier medium to form a synthetic bioensemble of active microorganisms, wherein the synthetic bioensemble comprises: Configured to change a property corresponding to the at least one metadata of the target biological environment upon introduction into the target biological environment).

  While this disclosure has been described with reference to specific embodiments thereof, it will be understood that various changes can be made and equivalent substitutions can be made without departing from the true spirit and scope of this disclosure. One skilled in the art will understand. In addition, many modifications may be made to adapt a particular situation, material, composition of matter, process, or process step (s) to the spirit and scope of the described embodiments and the purpose of the disclosure. All such modifications are intended to be within the scope of the present disclosure. The patents, patent applications, patent application publications, journal articles, and protocols referred to herein are incorporated by reference in their entirety for any purpose. Such documents include the following PCT application publications: WO / 2016/210251, WO / 2017/120495, and WO / 2017/181203.

  Although various embodiments are described and illustrated herein, performing the functions described herein and / or obtaining the results described herein, and / or the advantages described herein. Various other methods and / or structures for achieving one or more of the above will readily occur to those skilled in the art. Each such variation and / or modification is considered to be within the scope of the present disclosure. More generally, the parameters, dimensions, materials, and configurations described herein are provided by way of example, and the actual parameters, dimensions, materials, and / or configurations may be modified by the teachings of the disclosure. One of ordinary skill in the art will readily appreciate that it will depend on the particular application (s) or implementation (s) used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, equivalents to the specific embodiments described herein. Therefore, the foregoing embodiments are provided by way of example only, and are intended to be practiced other than as specifically described and claimed, within the scope of the appended claims and their equivalents. It should be understood that the forms can be implemented. Embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and / or method described herein. Further, any combination of two or more such features, systems, articles, materials, kits, and / or methods may include any combination of such features, systems, articles, materials, kits, and / or methods with each other. If they do not conflict, they are included in the scope of the present disclosure.

  The embodiments described above can be implemented in any of a number of ways. For example, embodiments may be implemented using hardware, software, or a combination thereof. When embodied in software, software code may execute on any suitable processor or collection of processors, such a processor or collection of processors may be provided on a single computer or may include multiple processors. It does not matter whether it is distributed across computers.

  Further, it will be appreciated that the disclosed method can be used in combination with a computer. Such a computer may be embodied in any of several forms, such as a rack-mounted computer, desktop computer, laptop computer, or tablet computer. Further, the computer can be incorporated into a device that is not generally considered a computer, but that has the appropriate processing power, such as a tablet, personal digital assistant (PDA), smartphone, or any other suitable mobile device. Includes fixed or stationary electronic devices.

  Also, a computer may have one or more input and output devices, including one or more displays. Such devices can be used, among other things, to provide a user interface. Examples of output devices that can be used to provide a user interface include a printer or display screen for visually displaying the output results, and a speaker or other sound generating device for displaying the output results in sound. Is included. Examples of input devices that can be used for the user interface include a keyboard, and a pointing device (such as a mouse, touchpad, and digitizing tablet). As another example, a computer may receive input information via voice recognition or in other sound formats.

  Such computers may be interconnected by one or more networks in any suitable form, including local or wide area networks (such as corporate networks), and intelligent networks (IN). Or include the Internet. Such a network may be based on any suitable technology, may operate according to any suitable protocol, and may include a wireless network, a wired network, or a fiber optic network.

  The various methods and processes (and / or portions thereof) outlined herein may be implemented as software executable on one or more processors utilizing any one of a variety of operating systems or platforms. Can be coded. Further, such software may be written using any of a number of suitable programming languages and / or programming or scripting tools, and may include executable machine language code or frameworks or virtual machines. It can also be compiled as intermediate code executed by

  In this regard, computer readable storage media (or multiple computer readable storage media) encoded with one or more programs (eg, computer memory, one or more floppy disks, compact disks, optical disks) , Magnetic tape, flash memory, circuitry of a field programmable gate array or other semiconductor device, or other non-transitory or tangible computer storage media). Such one or more programs, when executed on one or more computers or other processors, can perform the methods of implementing the various embodiments of the present disclosure described above. The computer-readable medium (s) may be portable, so that the program (s) stored thereon may be loaded onto one or more different computers or other processors and discussed above. Various aspects of the present disclosure may be implemented.

  The term "program" or "software" refers to any of the computer-executable instructions that can be used to program a computer or other processor to perform various aspects of the embodiments discussed above. Is used herein in a generic sense to refer to a computer code or set of the type Further, according to one aspect, one or more computer programs that, when executed, implement the methods of the present disclosure need not reside on a single computer or processor, but may implement the various aspects of the present disclosure. Will be distributed in a modular fashion across several different computers or processors.

  Computer-executable instructions can take many forms (eg, program modules) that are executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the program modules can be integrated or distributed in various embodiments as needed.

  Also, the data structures can be stored on the computer readable medium in any suitable format. For ease of explanation, a data structure may be shown to have fields associated with locations in the data structure. Similarly, such an association can be achieved by making a reserved assignment of the fields such that a location on the computer-readable medium indicates an association between the fields. However, any suitable mechanism for establishing the association between the information in the fields of the data structure can be used, including such mechanisms for establishing the association between pointers, tags, or data elements. That use the above mechanism.

  Also, various concepts of the disclosure may be embodied as one or more methods, and examples of such methods are provided. Acts performed as part of a method can be ordered in any suitable way. Thus, embodiments may be configured to perform acts in a different order than that illustrated, even if the acts were shown as continuous in the example embodiments. , One that performs several actions simultaneously.

  It will be appreciated that all definitions defined and used herein take precedence over the definition of a dictionary, the definition of a document incorporated by reference, and / or the ordinary meaning of a defined term.

  Flow diagrams are used herein. The use of flow diagrams is not intended to limit the order of operations performed. The objects described herein may show different components included in other different components, or different components linked to other different components. It will be appreciated that such described construction modes are merely exemplary, and in fact many other construction modes may be utilized that achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is efficiently “coupled” to achieve the desired functionality. Thus, any two components herein that are combined to achieve a particular functionality are "coupled" to each other to achieve the desired functionality, regardless of the manner of construction or the intermediate components. Can be considered. Similarly, any two components so coupled may be considered “operably linked” or “operably coupled” to one another to achieve a desired functionality, Any two components that can be combined in this manner can also be considered "operably coupleable" with one another to achieve the desired functionality. Specific examples of things that can be operatively coupled include, but are not limited to, components that can be physically paired and / or interact physically, and / or interact wirelessly. And / or wirelessly interacting components and / or logically interacting and / or logically interacting components are included.

  It will be understood that the indefinite articles "a" and "an" as used herein and in the claims mean "at least one" unless a different definition is explicitly stated.

  The phrase "and / or" as used herein and in the claims may refer to the united element, i.e., "any of the elements that may or may not be discretely connected. Or both. Multiple elements listed with "and / or" should also be construed in the same fashion, i.e., "one or more" of the elements so conjoined. In addition to the elements specifically identified by the "and / or" clause, other elements may optionally be present, whether or not associated with those elements specifically identified. Thus, by way of non-limiting example, reference to "A and / or B" when used in conjunction with non-limiting words (such as "comprises"), in one embodiment, includes only A (elements other than B) , And in another embodiment, only B (optionally including elements other than A), and in yet another embodiment both A and B (optionally including other elements). (Including selection).

  It will be understood that "or" as used herein and in the claims has the same meaning as "and / or" as defined above. For example, when items in a list are separated by "or" or "and / or", "or" or "and / or" is inclusive, i.e., several elements or a series of elements. In addition to including at least one of the following, and optionally including additional items not in the list. Only those terms that are explicitly indicated to be different (such as "only one of" or "exactly one of" or, when used in the claims, "consisting of") , Several elements or exactly one element of a series. In general, as used herein, the term "or" refers to the term exclusivity ("any," "one of," "only one of," or "exactly one of" Preceded by, etc.) shall be construed only as referring to an exclusive option (ie, "one or the other but not both"). "Consisting essentially of," when used in the claims, shall have its ordinary meaning as used in the field of patent law.

  The phrase "at least one" as used in the specification and claims with respect to one or more lists of elements is selected from any one or more of the elements present in the list of elements. Means at least one element, but does not necessarily include at least one of every and every element specifically listed in the list of elements and does not exclude any combination of elements present in the list of elements. It will be understood that. This definition optionally states that an element other than the specifically identified element in the list of elements referred to by the phrase "at least one" is associated with the specifically identified element. It is also acceptable that it can be present. Thus, as a non-limiting example, "at least one of A and B" (or equivalently "at least one of A or B", or equivalent "at least one of A and / or B" ""), In one embodiment, refers to at least one A (optionally including a plurality) A where B is absent (and optionally including elements other than B), and in another embodiment , A refers to at least one B (optionally including a plurality of Bs) (optionally including elements other than A), and in yet another embodiment at least one A (optionally absent) And / or at least one B (optionally including a plurality of B) (in addition to optionally including other elements).

  As used in the claims and herein above, "comprising", "including", "carrying", "having", "containing", "including" Transitional phrases such as "involving", "holding", "composed of", and the like are all non-limiting, ie, include, but are not limited to It will be understood that it has meaning. Only the transitional phrases “consisting of” and “consisting essentially of” shall be the limiting or semi-limiting transitional phrases, respectively, as described in Section 2111.03 of the United States Patent Office Handbook for Patent Examination. Is done.

Claims (35)

Obtaining at least two sample sets (each sample set includes a plurality of biological samples, at least one sample set of the at least two sample sets is defined as being in a first state; At least one sample set of the at least two sample sets is defined as being in a second state, wherein the first state is different from the second state);
Detecting multiple microbial types in each sample;
Determining the absolute cell number of each microorganism type detected in each sample among the plurality of microorganism types,
Measuring a unique first marker and its amount in each sample (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected);
The absolute cell count of each microbial strain present in each sample, the absolute cell count of each detected microbial type in the sample, and the number and relative amount of the unique first marker in the sample, To be determined based on
Measuring at least one unique second marker for each microorganism strain to determine the active microorganism strain in each sample;
Obtaining a set of active microbial strains and their respective absolute cell counts for each sample of said at least two sample sets;
Analyzing the active microbial strains and their respective absolute cell numbers for each sample of the at least two sample sets to define a baseline condition (where the baseline condition is the presence or absence of the identified taxon and / or strain) Absent or with specific abundance or activity),
Obtaining at least one additional sample of unknown status, wherein said at least one additional sample is a biological sample from a biological sample source;
For the at least one additional sample,
Detecting the presence of one or more microbial types;
Determining the absolute cell count of each detected microbial type;
Measuring a unique first marker and its amount (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected);
Determining the absolute cell number of each microbial strain from the number of each respective microbial type and the amount of the unique first marker;
Measuring at least one unique second marker for each microorganism strain based on the identified threshold to determine the activity level of said microorganism strain;
Obtaining a set of active microbial strains and their respective absolute cell numbers for said at least one additional sample;
Comparing the set of active microbial strains and their respective absolute cell counts for the at least one additional sample to the baseline condition to determine a condition associated with the at least one additional sample;
Outputting / displaying the determined state associated with the at least one additional sample;
Determining a treatment for the biological sample source based on the determined condition associated with the at least one additional sample if the determined condition is substantially different from the baseline condition; Administering the treatment to the biological sample source;
Including, methods. Wherein the treatment is a bioreactive modifier, wherein the bioreactive modifier comprises a synthetic microbial ensemble,
Selecting one or more active microbial strains based on the baseline condition and the determined condition associated with the at least one additional sample; and Forming the synthetic microbial ensemble in combination with a carrier medium (the synthetic microbial ensemble, when administered to the biological sample source, changes the state of the biological sample source toward the baseline state) Is configured to be)
The method of claim 1, further comprising: Obtaining at least two samples sharing at least one common parameter (at least one of the at least two samples is defined as being in a first state and at least one of the at least two samples is One is defined as being in a second state, wherein the second state is different from the first state);
For each sample, detecting the presence of one or more microbial types in said sample;
Determining a total number of respective microorganism types detected in each sample of the one or more microorganism types;
Measuring a unique first marker and its amount in each sample (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected);
Determining the absolute cell number of each microorganism strain in each sample from the total number of said respective microorganism types and the relative number of said unique first marker;
Measuring at least one unique second marker for each microorganism strain based on the identified threshold to determine the level of activity of said microorganism strain in each sample;
Filtering the absolute cell counts of said respective microbial strains according to said determined activity to obtain a set of active microbial strains and their respective absolute cell counts for each of said at least two samples;
A filtered absolute cell number of the active microbial strain for the at least one sample from the first state, and a filtered of the active microbial strain for the at least one sample from the second state; Defining / determining a baseline state by comparing the absolute cell number with the absolute cell number (the baseline state is defined by the presence or absence or a specific abundance or activity of the identified taxon and / or strain). ),
Obtaining at least one additional sample (the state of said additional sample is unknown);
For the at least one additional sample,
Detecting the presence of one or more microbial types;
Determining a number of each of the one or more microorganism types detected among the microorganism types;
Measuring a unique first marker and its amount (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected);
Determining the absolute cell number of each microorganism strain from the number of each of the microorganism types and the number of the unique first markers;
Measuring at least one unique second marker for each microorganism strain based on the identified threshold to determine the activity level of said microorganism strain;
Filtering the absolute cell count of the respective microbial strain according to the determined activity to obtain a set of active microbial strains and their respective absolute cell counts;
Determining the status of the at least one additional sample by comparing the set of active microbial strains and their respective absolute cell numbers for the at least one additional sample to the baseline status;
Outputting / displaying the determined state of the at least one additional sample;
Including, methods.   4. The method of claim 3, wherein the determined condition of the at least one additional sample corresponds to a condition of an environment associated with the at least one additional sample.   5. The method of claim 4, further comprising determining a treatment for the environment associated with the at least one additional sample, wherein the treatment is configured to change the state of the environment toward the baseline. The described method.   5. The method of claim 4, further comprising determining a treatment for the environment associated with the at least one additional sample, wherein the treatment is configured to change the state of the environment away from a current state. The method described in.   7. The method of claim 5 or claim 6, wherein treating comprises changing management or lifestyle.   7. A method according to claim 5 or claim 6, wherein the treatment comprises altering a feed component or a feeding regimen.   7. The method of claim 5 or claim 6, wherein the treatment comprises administering a drug or performing a therapy.   7. The method according to claim 5 or claim 6, wherein the treatment comprises a medical intervention.   Claim 3, Claim 4, Claim 5, Claim 6, Claim 7, Claim 8, Claim 9, Claim 9, further comprising updating the baseline state based on the at least one additional sample. Or the method according to claim 1.   Claim 3, Claim 4, Claim 5, Claim 6, Claim 7, Claim 8, Claim 9, wherein defining the baseline condition comprises defining a threshold for a particular microbial strain. The method according to claim 1, claim 10 or claim 11.   Claim 3, Claim 4, Claim 5, Claim 6, Claim 7, Claim 8, Claim 9, wherein defining the baseline condition comprises defining a threshold of a group of microbial strains. The method according to claim 1, claim 10 or claim 11.   Claim 3, Claim 4, Claim 5, Claim 6, Claim 7, Claim 8, Claim 9, Claim 10, Claim 10, wherein defining the baseline state includes supervised machine learning. 14. The method according to claim 11, 12, or 13.   Claim 3, Claim 4, Claim 5, Claim 6, Claim 7, Claim 8, Claim 9, Claim 10, Claim 10, wherein defining the baseline state includes unsupervised machine learning. 14. The method according to claim 11, 12, or 13.   4. The method of claim 3, wherein comparing the set of active microbial strains and their respective absolute cell numbers to the baseline condition for the at least one additional sample comprises determining a relative amount of a particular microbial strain. Claim 1, Claim 5, Claim 6, Claim 7, Claim 8, Claim 8, Claim 9, Claim 10, Claim 11, Claim 12, Claim 13, Claim 14, or Claim 15 The method described in the section.   The method of claim 1, wherein comparing the set of active microbial strains and their respective absolute cell numbers to the baseline condition for the at least one additional sample comprises determining a relative amount of a particular group of microbial strains. 3, Claim 4, Claim 5, Claim 6, Claim 7, Claim 8, Claim 9, Claim 10, Claim 11, Claim 12, Claim 13, Claim 14, or Claim 15 The method according to item 1.   A comparison of the set of active microbial strains and their respective absolute cell counts to the baseline state for the at least one additional sample is performed using a reduced dimension matrix, dissimilarity matrix, distance matrix, or covariance matrix. Claim 3, Claim 4, Claim 5, Claim 6, Claim 7, Claim 8, Claim 9, Claim 10, Claim 11, Claim 12, which includes utilizing at least one of the following. The method according to any one of claims 13, 14, 14, 15, 16, or 17.   6. The method of claim 3, 4, or 5, wherein comparing the set of active microbial strains and their respective absolute cell counts to the baseline state for the at least one additional sample comprises supervised machine learning. , Claim 6, claim 7, claim 8, claim 9, claim 10, claim 11, claim 12, claim 13, claim 14, claim 15, claim 16, claim 17, or The method according to claim 1.   6. The method of claim 3, wherein the comparing the set of active microbial strains and their respective absolute cell numbers to the baseline state for the at least one additional sample comprises unsupervised machine learning. , Claim 6, claim 7, claim 8, claim 9, claim 10, claim 11, claim 12, claim 13, claim 14, claim 15, claim 16, claim 17, or The method according to claim 1. Obtaining at least two sample sets (each sample set includes a plurality of samples, wherein at least one sample set of the at least two sample sets is defined as being in a first state; and At least one sample set of the one sample set is defined to be in a second state, wherein the first state is different from the second state);
Detecting multiple microbial types in each sample;
Determining the absolute cell number of each microorganism type detected in each sample among the plurality of microorganism types,
Measuring a unique first marker and its amount in each sample (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected);
The absolute cell count of each microbial strain present in each sample, the absolute cell count of each detected microbial type in the sample, and the number and relative amount of the unique first marker in the sample, To be determined based on
Measuring at least one unique second marker for each microorganism strain to determine the active microorganism strain in each sample;
Obtaining a set of active microbial strains and their respective absolute cell counts for each sample of said at least two sample sets;
Analyzing the active microbial strains and their respective absolute cell numbers for each sample of the at least two sample sets to define a baseline condition (where the baseline condition is the presence or absence of the identified taxon and / or strain) Absent or with specific abundance or activity),
Obtaining at least one additional sample of unknown status;
For the at least one additional sample,
Detecting the presence of one or more microbial types;
Determining the absolute cell count of each detected microbial type;
Measuring a unique first marker and its amount (each unique first marker is a marker of a microorganism strain of the microorganism type to be detected);
Determining the absolute cell number of each microbial strain from the number of each respective microbial type and the amount of the unique first marker;
Measuring at least one unique second marker for each microorganism strain based on the identified threshold to determine the activity level of said microorganism strain;
Obtaining a set of active microbial strains and their respective absolute cell numbers for said at least one additional sample;
Comparing the set of active microbial strains and their respective absolute cell numbers for the at least one additional sample to the baseline condition to determine a condition associated with the at least one additional sample; And outputting / displaying the determined state associated with the at least one additional sample;
Including, methods. Selecting a plurality of active microbial strains based on the baseline condition and the determined condition associated with the at least one additional sample; and combining the selected plurality of active microbial strains with a carrier medium. Combining to form a synthetic ensemble of active microorganisms (the synthetic ensemble, when introduced into an environment associated with the at least one additional sample, alters a state of the environment associated with the at least one additional sample) Is configured to
23. The method of claim 22, further comprising:   23. The method of claim 21 or claim 22, wherein determining the unique first marker and its amount comprises subjecting genomic DNA from each sample to a high-throughput sequencing reaction.   23. The method of claim 21 or claim 22, wherein determining the unique first marker and its amount comprises subjecting genomic DNA from each sample to metagenomic sequencing.   25. The one of claim 21, claim 22, claim 23, or claim 24, wherein the unique first marker comprises at least one of an mRNA marker, an siRNA marker, and / or a ribosomal RNA marker. The method described in.   25. The unique first marker comprises at least one of a sigma factor, a transcription factor, a nucleoside-related protein, and / or a metabolic enzyme. The method according to item 1.   25. The method of claim 21, claim 22, claim 23, or claim 24, wherein measuring a unique first marker comprises measuring a unique genomic DNA marker in each sample.   25. The method of claim 21, claim 22, claim 23, or claim 24, wherein measuring a unique first marker comprises measuring a unique RNA marker in each sample.   25. The method of any one of claims 21, 22, 22, or 24, wherein measuring a unique first marker comprises measuring a unique protein marker in each sample.   30. The method of any one of claims 21 to 29, wherein measuring at least one unique second marker for each microbial strain comprises measuring the expression level of the at least one unique second marker. .   31. The method of claim 30, wherein measuring the expression level of the at least one unique second marker comprises subjecting the sample mRNA to gene expression analysis.   31. The method of claim 30, wherein determining the expression level of the at least one unique second marker comprises subjecting each sample or a portion thereof to mass spectrometry.   31. The method of claim 30, wherein determining the expression level of the at least one unique second marker comprises subjecting each sample or a portion thereof to metaribosome profiling or ribosome profiling. A method performed by a processor, wherein the method comprises:
Receiving sample data for a plurality of samples (the sample data includes a list of detected microbial types and a corresponding absolute cell number of each microbial type detected in each sample, and unique first marker data ( The specific first marker data includes the relative amount of each detected microbial strain of each microbial type in each sample) and the specific second marker data (the specific second marker data comprises: Including activity information of each microbial strain in the sample).
Obtaining a set of active microbial strains and their respective absolute cell numbers for each sample based on said sample data using one or more processors;
Processing the set of active microbial strains and their respective absolute cell counts using the one or more processors to identify a baseline condition (where the baseline condition is the identified taxon and / Or the presence or absence of a strain or a specific amount or activity),
Receiving additional data of at least one additional sample of unknown status (where the additional data of the at least one additional sample is a list of detected microbial types and detected in the at least one additional sample) The corresponding absolute cell number of each microbial type and the unique first marker data (where the unique first marker data is the microbial strain of each microbial type detected in the at least one additional sample). (Including relative amounts) and unique second marker data (where the unique second marker data includes activity information of each microbial strain in the at least one additional sample).
An additional set of active microbial strains and their respective absolute cell numbers for the at least one additional sample based on the additional data of the at least one additional sample using the one or more processors. To get
Determining the status of the at least one additional sample based on the additional set of active microbial strains for the at least one additional sample and an analysis comparing the respective absolute cell counts to the baseline status. Determining using one or more processors; and displaying the determined state associated with the at least one additional sample using the one or more processors;
The above method, comprising:   If the determined condition is substantially different from the baseline condition, at least one treatment is performed based on the determined condition associated with the at least one additional sample. 35. The processor-implemented method of claim 34, further comprising displaying using a processor, wherein the at least one action is an action for adjusting the condition of the at least one additional sample. Method.