Patent Application
20190390246
This application may contain material that is subject to copyright, mask work, and/or other intellectual property protection. The respective owners of such intellectual property have no objection to the facsimile reproduction of the disclosure by anyone as it appears in published Patent Office file/records, but otherwise reserve all rights.
Microorganisms coexist in nature as communities and engage in a variety of interactions, resulting in both collaboration and competition between individual community members. Advances in microbial ecology have revealed high levels of species diversity and complexity in most communities. Microorganisms are ubiquitous in the environment, inhabiting a wide array of ecosystems within the biosphere. Individual microorganisms and their respective communities play unique roles in environments such as marine sites (both deep sea and marine surfaces), soil, and animal tissues, including human tissue.
According to some embodiments of the disclosure, methods and systems for forming a synthetic ensemble, synthetic bioensemble, and/or Endomicrobial Supplement (EMS) are disclosed. According to some embodiments, methods of making synthetic microbial ensembles to inhibit bacterial fowl pathogen colonization in the gastrointestinal tract of fowl are disclosed. According to one embodiment, the method comprises: selecting one or more active microorganism strains, the one or more active microorganism strains being identified by processing of a plurality of samples collected from a sample population of fowl, the processing including: for each sample of the plurality of samples: measuring at least one metadata associated with bacterial fowl pathogen colonization; detecting the presence of a plurality of microorganism types and determining an absolute number of cells of detected microorganism types; determining a relative measure of one or more strains of detected microorganism types of the plurality of microorganism types; determining a set of active microorganism strains and respective absolute cell counts based on the absolute number of cells of a detected microorganism type and the relative measure of the one or more microorganism strains for that microorganism type, and filtering by activity level; analyzing the set of active microorganism strains and respective absolute cell counts with the measured metadata via at least one of network analysis, correlation analysis, and cluster analysis to identify relationships between active microorganism strains and measured metadata; and preparing the selected one or more active microorganism strains for inclusion in a synthetic microbial ensemble configured to inhibit bacterial fowl pathogen colonization in a gastrointestinal tract of a fowl when administered thereto; and forming the synthetic microbial ensemble from the prepared one or more active microorganism strains and at least one carrier. According to some embodiments, a method for inhibiting bacterial fowl pathogen colonization in the gastrointestinal tract of fowl is disclosed, the method comprising: administering to a fowl an effective amount of a microbial composition comprising a non-pathogenic Clostridium sp. and/or a Lactobacillus sp.; wherein the fowl administered the effective amount of the microbial composition exhibits a decrease in the incidence of mortality, as compared to a fowl not having been administered the composition.
According to some embodiments, a method for modulating the alpha and/or beta diversity in the microbial population of the gastrointestinal tract of fowl is disclosed, the method comprising: administering to a fowl an effect amount of a microbial composition comprising a Bacillus sp.; wherein the gastrointestinal tract of the fowl exhibits: an increase in the alpha diversity of the microbial population of the gastrointestinal tract of the fowl or a decrease in alpha diversity of the microbial population of the gastrointestinal tract of the fowl; and/or an increase in the beta diversity of the microbial population of the gastrointestinal tract of the fowl or a decrease in alpha diversity of the microbial population of the gastrointestinal tract of the fowl; as compared to a fowl not having been administered the composition. According to some embodiments, a method for modulating the alpha and/or beta diversity in the microbial population of the gastrointestinal tract of fowl comprises: administering to a fowl an effect amount of a microbial composition comprising a non-pathogenic Clostridium sp. and/or Lactobacillus sp.; wherein the gastrointestinal tract of the fowl exhibits: an increase in the alpha diversity of the microbial population of the gastrointestinal tract of the fowl or a decrease in alpha diversity of the microbial population of the gastrointestinal tract of the fowl; and/or an increase in the beta diversity of the microbial population of the gastrointestinal tract of the fowl or a decrease in alpha diversity of the microbial population of the gastrointestinal tract of the fowl; as compared to a fowl not having been administered the composition. According to some embodiments, a chicken feed composition is disclosed, the chicken feed composition comprising (i) chicken feed and (ii) a Bacillus sp.
In some embodiments, such synthetic ensembles contain and/or comprise Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov., and are configured to influence on milk composition and/or yield. Studies based thereon are disclosed, for example, a study where such ensembles/EMS were used to influence milk composition and yield of Holstein cows during lactation. In one study, a total of 16 multiparous, ruminally cannulated Holstein cows were randomly split into 2 groups; Control (CON) and Inoculated (INO). All cows underwent surgery followed by a 10 d recovery and adaptation to new facilities and diet period. Live cultures of EMS were inoculated via ruminal cannula once a day during the treatment (TRT) period, which lasted a total of 32 d. A tendency for a higher milk fat percentage for INO vs. the CON was observed (P=0.0991). Although the treatment by week interaction was not significant, it can be observed that milk fat percentages were numerically similar within the first two weeks. The difference between milk fat percentage was not observed during the post TRT period when cows were not inoculated with microbes. A treatment by week interaction was observed for milk yield (P=0.0025), fat-corrected milk (FCM, P=0.0026), energy-corrected milk (ECM, P=0.0019), and protein yield (PY, P=0.0012). The interaction for yield was mainly the result of milk yield diverging between the two treatments within the first 2 to 3 weeks of the study and coming back together toward the end of the intervention period. These results indicate that under the conditions of this study, and based on the teachings of the disclosure, supplementing multiparous cows with EMS containing Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov., can have a positive effect on cow performance.
In one aspect of the disclosure, a method for identifying active microorganisms from a plurality of samples, analyzing identified microorganisms with at least one metadata, and creating an ensemble of microorganism based on the analysis is disclosed. Embodiments of the method include determining the absolute cell count of one or more active microorganism strains in a sample, wherein the one or more active microorganism strains is present in a microbial community in the sample. The one or more microorganism strains is a subtaxon of a microorganism type. Samples used in the methods provided herein can be of any environmental origin. For example, in one embodiment, the sample is from animal, soil (e.g., bulk soil or rhizosphere), air, saltwater, freshwater, wastewater sludge, sediment, oil, plant, an agricultural product, plant, or an extreme environment. In another embodiment, the animal sample is a blood, tissue, tooth, perspiration, fingernail, skin, hair, feces, urine, semen, mucus, saliva, gastrointestinal tract, rumen, muscle, brain, tissue, or organ sample. In one embodiment, a method for determining the absolute cell count of one or more active microorganism strains is provided.
According to some embodiments, a method of forming a bioensemble of active microorganism strains configured to alter a property in a target biological environment is provided. Such methods can comprise obtaining at least two samples (or sample sets) sharing at least one common environmental parameter (such as sample type, sample time, sample location, sample source type, etc.) and detecting the presence of a plurality of microorganism types in each sample. Then the absolute number of cells of each detected microorganism type of the plurality of microorganism types in each sample is determined (e.g., by way of non-limiting example, the dyeing procedures, cell sorting/FACS, etc., as discussed herein), and measuring a number of unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain of a detected microorganism type. Certain detected microorganisms/strains can be omitted from further processing/analysis, depending on the embodiment, for example, for efficiency. The absolute cell count of some or each microorganism strain present in each sample is determined based on the number of each detected microorganism types in that sample and the number of unique first markers and quantity thereof in that sample. At least one unique second marker, indicative of activity (e.g., metabolic activity) is measured for each microorganism strain to determine active microorganism strains in each sample, and a set or list of active microorganisms strains and their respective absolute cell counts for each of the at least two samples is generated. The active microorganisms strains and respective absolute cell counts for each of the at least two samples with at least one measured metadata for each of the at least two samples are analyzed to identify relationships between each active microorganism strain and at least one measured metadata, measured metadata for each sample, and/or measured metadata for a sample set or the sample sets. Based on the analysis, a plurality of active microorganism strains are selected and combined with a carrier medium to form a bioensemble of active microorganisms, the bioensemble of active microorganisms configured to alter at least one property (that corresponds to the at least one metadata) of a target biological environment when the bioensemble is introduced into that target biological environment. Depending on the embodiment, the metadata can be one or more environmental parameter(s), and can be the same or relatively similar across samples or sample sets, have different values across different samples or sample sets. For example, the metadata for dairy cows could include feed and milk output, and the feed metadata value could be the same (i.e., the cows are fed the same feed) while the milk output could vary (i.e., the sample from one cow or set of samples from a particular herd of cows has an average milk output that is different from milk output corresponding to a sample from a second cow or sample set for a separate herd of cows).
According to some embodiments of the disclosure, methods for analyzing microbial communities are provided. Such methods can comprise obtaining at least two samples (or data for at least two samples), each sample including a heterogeneous microbial community, and detecting the presence of a plurality of microorganism types in each sample. An absolute number of cells of each detected microorganism type of the plurality of microorganism types in each sample is then determined (e.g., via FACS or other methods as discussed herein). A number of unique first markers in each sample, and quantity thereof, are measured, each unique first marker being a marker of a microorganism strain of a detected microorganism type. A value (activity, concentration, expression, etc.) of one or more unique second markers is measured, a unique second marker indicative of activity (e.g., metabolic activity) of a particular microorganism strain of a detected microorganism type, and the activity of each detected microorganism strain is determined based on the measured value of the one or more unique second markers (e.g., based on the value exceeding a specified set threshold). The proportional presence and/or respective ratios of each active detected microorganism strain are determined (e.g., based on the relative quantity of strains for each microorganism type, the number of each microorganism type/respective absolute cell counts per type, the absolute cell count of each detected active microorganism strain, first unique marker values, second unique marker values, etc.). Then each of the active detected microorganism strains (or a subset thereof) of the at least two samples are analyzed to identifying relationships and the strengths thereof between each active detected microorganism strain and the other active detected microorganism strains, and between each active detected microorganism strain and at least one measured metadata. The identified relationships are then displayed or otherwise output, and can be utilized for generation of a bioensemble. In some embodiments, only relationships that exceed a certain strength or weight are displayed. As detailed throughout the disclosure, bioensembles can be configured such that, when introduced into a target environment, a bioensemble can change or alter a property of the target environment (and especially a property that is related to the measured metadata).
According to some embodiments of the disclosure, methods comprise detecting the presence of a plurality of microorganism types in a plurality of samples and determining the absolute number of cells of each of the detected microorganism types in each sample. A number of unique first markers in each sample, and quantity thereof, can be measured, a unique first marker being a marker of a microorganism strain. A value or level of one or more unique second markers is measured, a unique second marker being indicative of metabolic activity of a particular microorganism strain. Based on measured value or level, an activity of each of the detected microorganism strains for each sample is determined or defined (e.g., based on the measured value or level exceeding a specified threshold). A weighted or cell-adjusted value of each active detected microorganism strain in the sample is determined (the weighted or cell-adjusted value is not relative abundance). In some implementations, the weighted or cell-adjusted value is the absolute cell count for a strain relative to the sum of all absolute cell counts for all strains.
Each of the detected active microorganism strains of each sample (or sample sets) is analyzed. The analysis can include identifying relationship and the strengths thereof between each detected active microorganism strain having a weighted value and every other active microorganism strain having a weighted value, and each active microorganism strain having a weighted value and one or more measured metadata.
The identified relationships (an in some embodiments, related data such as weighted values and strengths) can then be displayed or otherwise output, and can be utilized for generation of a synthetic ensemble. In some embodiments, the identified relationships for each metadata are displayed or output. In some embodiments, the displayed or output relationships identify or are configured to facilitate identification of one or more microbial strains responsible for a disease. In some embodiments, the displayed or output relationships identify or are configured to facilitate identification of one or more microbial strains to treat a disease or disorder.
In some embodiments, only relationships that exceed a certain strength or weight (e.g., exceeding a specified threshold or base value) are displayed or output. As detailed throughout the disclosure, synthetic ensembles can be configured such that, when introduced into a target environment, a synthetic ensemble can change or alter a property of the target environment (and especially a property that is related to the measured metadata). In some implementations, the above method can be used to form a synthetic ensemble of active microorganism strains configured to alter a property in a biological environment, and is based on two or more sample sets each having a plurality of environmental parameters, at least one parameter of the plurality of environmental parameters being a common environmental parameter that is similar between the two or more sample sets and at least one environmental parameter being a different environmental parameter that is different between each of the two or more sample sets. In some implementations, each sample set includes at least one sample comprising a heterogeneous microbial community obtained from a biological sample source. In some implementations, at least one of the active microorganism strains is a subtaxon of one or more microorganism types.
In some embodiments of the disclosure, the one or more microorganism types are one or more bacteria (e.g., mycoplasma, coccus, bacillus, rickettsia, spirillum), fungi (e.g., filamentous fungi, yeast), nematodes, protozoans, archaea, algae, dinoflagellates, viruses (e.g., bacteriophages), viroids and/or a combination thereof. In one embodiment, the one or more microorganism strains is one or more bacteria (e.g., mycoplasma, coccus, bacillus, rickettsia, spirillum), fungi (e.g., filamentous fungi, yeast), nematodes, protozoans, archaea, algae, dinoflagellates, viruses (e.g., bacteriophages), viroids and/or a combination thereof. In a further embodiment, the one or more microorganism strains is one or more fungal species or fungal sub-species. In a further embodiment, the one or more microorganism strains is one or more bacterial species or bacterial sub-species. In even a further embodiment, the sample is a ruminal sample. In some embodiments, the ruminal sample is from cattle. In even a further embodiment, the sample is a gastrointestinal sample. In some embodiments, the gastrointestinal sample is from a pig or chicken.
In some embodiments, the methods include determining the absolute cell count of one or more active microorganism strains in a sample, the presence of one or more microorganism types in the sample is detected and the absolute number of each of the one or more microorganism types in the sample is determined. A number of unique first markers is measured along with the quantity or abundance of each of the unique first markers. As described herein, a unique first marker is a marker of a unique microorganism strain. Activity is then assessed at the protein or RNA level by measuring the level of expression of one or more unique second markers. The unique second marker is the same or different as the first unique marker, and is a marker of activity of an organism strain. Based on the level of expression of one or more of the unique second markers, a determination is made which (if any) one or more microorganism strains are active. In one embodiment, a microorganism strain is considered active if it expresses the second unique marker at threshold level, or at a percentage above a threshold level. The absolute cell count of the one or more active microorganism strains is determined based upon the quantity of the one or more first markers of the one or more active microorganism strains and the absolute number of the microorganism types from which the one or more microorganism strains is a subtaxon.
In one embodiment, determining the number of each of the one or more organism types in the sample comprises subjecting the sample or a portion thereof to nucleic acid sequencing, centrifugation, optical microscopy, fluorescence microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain reaction (qPCR) or flow cytometry.
In one embodiment, measuring the number of first unique markers in the sample comprises measuring the number of unique genomic DNA markers. In another embodiment, measuring the number of first unique markers in the sample comprises measuring the number of unique RNA markers. In another embodiment, measuring the number of unique first markers in the sample comprises measuring the number of unique protein markers.
In another embodiment, measuring the number of unique first markers, and quantity thereof, comprises subjecting genomic DNA from the sample to a high throughput sequencing reaction. The measurement of a unique first marker in one embodiment, comprises a marker specific reaction, e.g., with primers specific for the unique first marker. In another embodiment, a metagenomic approach.
In one embodiment, measuring the level of expression of one or more unique second markers comprises subjecting RNA (e.g., miRNA, tRNA, rRNA, and/or mRNA) in the sample to expression analysis. In a further embodiment, the gene expression analysis comprises a sequencing reaction. In yet another embodiment, the RNA expression analysis comprises a quantitative polymerase chain reaction (qPCR), metatranscriptome sequencing, and/or transcriptome sequencing.
In some embodiments, measuring the number of second unique markers in the sample comprises measuring the number of unique protein markers. In some embodiments, the absolute cell count of the one or more microorganism strains is measured in a plurality of samples. In further embodiments, the plurality of samples is obtained from the same environment or a similar environment. In some embodiments, the plurality of samples are obtained at a plurality of time points.
In some embodiments, measuring the level of one or more unique second markers comprises subjecting the sample or a portion thereof to mass spectrometry analysis. In some embodiments, measuring the level of expression of one more unique second markers comprises subjecting the sample or a portion thereof to metaribosome profiling and/or ribosome profiling.
In another aspect of the disclosure, a method for determining the absolute cell count of one or more active microorganism strains is determined in a plurality of samples, and the absolute cell count levels are related to one or more metadata (e.g., environmental) parameters. Relating the absolute cell count levels to one or more metadata parameters comprises in one embodiment, a co-occurrence measurement, a mutual information measurement, a linkage analysis, and/or the like. The one or more metadata parameters in one embodiment, is the presence of a second active microorganism strain. Accordingly, the absolute cell count values are used in one embodiment of this method to determine the co-occurrence of the one or more active microorganism strains in a microbial community with an environmental parameter. In another embodiment, the absolute cell count levels of the one or more active microorganism strains is related to an environmental parameter such as feed conditions, pH, nutrients or temperature of the environment from which the microbial community is obtained.
In this aspect, the absolute cell count of one or more active microorganism strains is related to one or more environmental parameters. The environmental parameter can be a parameter of the sample itself, e.g., pH, temperature, amount of protein in the sample, the presence of other microbes in the community. In one embodiment, the parameter is a particular genomic sequence of the host from which the sample is obtained (e.g., a particular genetic mutation). Alternatively, the environmental parameter is a parameter that affects a change in the identity of a microbial community (i.e., where the “identity” of a microbial community is characterized by the type of microorganism strains and/or number of particular microorganism strains in a community), or is affected by a change in the identity of a microbial community. For example, an environmental parameter in one embodiment, is the food intake of an animal or the amount of milk (or the protein or fat content of the milk) produced by a lactating ruminant. In some embodiments described herein, an environmental parameter is referred to as a metadata parameter.
In one embodiment, determining the co-occurrence of one or more active microorganism strains in the sample comprises creating matrices populated with linkages denoting one or more environmental parameters and active microorganism strain associations.
In one embodiment, determining the co-occurrence of one or more active organism strains and a metadata parameter comprises a network and/or cluster analysis method to measure connectivity of strains within a network, wherein the network is a collection of two or more samples that share a common or similar environmental parameter. In some embodiments, the network analysis and/or network analysis methods comprise one or more of graph theory, species community rules, Eigenvectors/modularity matrix, Gambit of the Group, and/or network measures. In some implementations, network measures include one or more of observation matrices, time-aggregated networks, hierarchical cluster analysis, node-level metrics and/or network level metrics. In some embodiments, node-level metrics include one or more of: degree, strength, betweenness centrality, Eigenvector centrality, page rank, and/or reach. In some embodiments, network level metrics include one or more of density, homophily/assortativity, and/or transitivity.
In some embodiments, network analysis comprises linkage analysis, modularity analysis, robustness measures, betweenness measures, connectivity measures, transitivity measures, centrality measures or a combination thereof. In another embodiment, the cluster analysis method comprises building a connectivity model, subspace model, distribution model, density model, or a centroid model. In another embodiment, the network analysis comprises predictive modeling of network through link mining and prediction, collective classification, link-based clustering, relational similarity, or a combination thereof. In another embodiment, the network analysis comprises mutual information, maximal information coefficient calculations, or other nonparametric methods between variables to establish connectivity. In another embodiment, the network analysis comprises differential equation based modeling of populations. In another embodiment, the network analysis comprises Lotka-Volterra modeling.
Based on the analysis, strain relationships can be displayed or otherwise output, and/or one or more active relevant strains are identified for including in a microbial ensemble.
Microbial communities are central to environmental processes in many different types ecosystems as well and the Earth's biogeochemistry, e.g., by cycling nutrients and fixing carbon (Falkowski et al. (1998) Science 281, pp. 237-240, incorporated by reference herein in its entirety for all purposes). However, because of community complexity and the lack of culturability of most of the members of any given microbial community, the molecular and ecological details as well as influencing factors of these processes are still poorly understood.
Microbial communities differ in qualitative and quantitative composition and each microbial community is unique, and its composition depends on the given ecosystem and/or environment in which it resides. The absolute cell count of microbial community members is subject to changes of the environment in which the community resides, as well as the physiological and metabolic changes caused by the microorganisms (e.g., cell division, protein expression, etc.). Changes in environmental parameters and/or the quantity of one active microorganism within a community can have far-reaching effects on the other microorganisms of the community and on the ecosystem and/or environment in which the community is found. To understand, predict, and react to changes in these microbial communities, it is necessary to identify the active microorganisms in a sample, and the number of the active microorganisms in the respective community. However, to date, the vast majority of studies of microbial community members have focused on the proportions of microorganisms in the particular microbial community, rather than absolute cell count (Segata et al. (2013). Molecular Systems Biology 9, p. 666, incorporated by reference herein in its entirety for all purposes).
According to some embodiments, methods for making a synthetic bioensemble for a target environment, such as a ruminant animal or herd thereof, can comprise: (1) selecting at least two microorganism strains based on processing a plurality of samples collected from a sample population (such as a population of ruminants, e.g., a herd of dairy cattle), where the processing includes: (A) for each sample of the plurality of samples: (i) detecting the presence of one or more microorganism types and determining a number of each detected microorganism type; (ii) measuring a number of unique first markers, and quantity thereof, each unique first marker being a marker of a microorganism strain; (iii) determining the absolute cell count of each microorganism strain based on the number of each microorganism type and the number of the first markers; (iv) determining an activity level for each microorganism strain based on at least one unique second marker; (v) generating a list of active microorganism strains and their respective absolute cell counts based on absolute cell count and determined activity (e.g., filtering); (B) analyzing (e.g., using one or more analytical methods as disclosed herein) the absolute cell counts of active microorganism strains of each of the samples of the plurality of samples with at least one measured metadata (e.g., for ruminants, milk fat, milk output, etc.) and categorizing active microorganism strains, for example, according to predicted function and/or chemistry (e.g., improved digestion of certain ruminant feeds); (C) identifying at least two distinct microorganism strains, such as at least one fungus strain and a least one bacterium strain, based on the categorization; (2) preparing the at least two distinct microorganism strains (e.g., preparing the at least one fungus strain and preparing the at least one bacterium strain) for inclusion in a synthetic bioensemble configured to alter a property related to, associated with, and/or corresponding to the at least one metadata when in use/when introduced to a target environment (e.g., a dairy cattle herd); and (3) forming the synthetic bioensemble from the prepared at least two distinct microorganism strains (e.g., from the prepared at least one fungus strain and the prepared at least one bacterium strain) and at least one carrier (such as calcium carbonate and/or silicon dioxide, and/or the like). In some embodiments, an initial preparation of the at least two distinct microbial strains comprises a separate preparation for each. For example, a dairy product synthetic bioensemble according to the disclosure can include, comprises, consist of, and/or consist essentially of a fungus strain (such as a Pichia fungus) and a bacterium strain, such as a Clostridium bacterium), and a carrier. In some instances/implementations, the fungi strain and the bacteria strain can be prepared separately, and then mixed with carrier, such as a calcium carbonate and/or silicon dioxide carrier. In some embodiments, the fungi strain(s) (e.g., Pichia fungi and/or Pichia fungi strain) can be dried by preservation by vaporization (PBV), such as PBV in which a disaccharide (e.g., sucrose) and a sugar alcohol (e.g., mannitol) are used to form a glass/sugar matrix in in which the fungi strain (e.g., Pichia and/or Pichia strain) are embedded. In some instances, the result of PBV can be a dry foam that is milled until the fungi strain-containing (e.g., Pichia-containing/Pichia-containing strain) glass is a ready (e.g., becomes a sand-like substance). In some embodiments, preparing includes driving the bacteria strain(s) (e.g., Clostridium bacterial/Clostridium bacteria strain) to spore formation, and spray-drying (e.g., in a phosphate buffered saline solution). In some embodiments, the fungal strain glass (e.g., sugar glass, or alternatively sugar matrix) granules (e.g., Pichia glass granules/Pichia strain glass granules) and the spray dried bacteria strain spores (e.g., Clostridium spores/Clostridium strain spores) are mixed with one or more carriers, to provide a synthetic bioensemble and/or synthetic bioensemble product, which can then be packaged, bagged and/or the like for distribution. In some embodiments, aspects of the method/product can include PBV and glass/glassy states as set forth in U.S. Pat. App. Pub. No. US20080229609 (the entirety of which is herein expressly incorporated by reference for all purposes. In some embodiments, the Pichia is Pichia kudriavzevii. In some embodiments, the Pichia/Pichia strain contains SEQ ID NO:32 and/or is substantially similar to SEQ ID NO:32. In some embodiments, the Clostridium strain is Clostridium butyricum, or a closely related species. In some embodiments, the Clostridium strain contains SEQ ID NO:28, and/or is substantially similar to SEQ ID NO:28. In some embodiments, the synthetic bioensemble product comprises a synthetic bioensemble, the synthetic bioensemble product is formed from method discussed above. Depending on the application or intended use and/or components, the synthetic bioensemble product includes at least one sugar (such as a disaccharide, such as sucrose) and/or sugar alcohol (such as mannitol).
Although microbial community compositions can be readily determined for example, via the use of high throughput sequencing approaches, a deeper understanding of how the respective communities are assembled and maintained is needed.
Microorganism communities are involved in critical processes such as biogeochemical cycling of essential elements, e.g., the cycling of carbon, oxygen, nitrogen, sulfur, phosphorus and various metals; and the respective community's structures, interactions and dynamics are critical to the biosphere's existence (Zhou et al. (2015). mBio 6(1):e02288-14. Doi:10.1128/mBio.02288-14, herein incorporated by reference in its entirety for all purposes). Such communities are highly heterogeneous and almost always include complex mixtures of bacteria, viruses, archaea, and other micro-eukaryotes such as fungi. The levels of microbe community heterogeneity in human environments such as the gut and vagina have been linked to diseases such as inflammatory bowel disease and bacterial vaginosis (Nature (2012). Vo. 486, p. 207, herein incorporated by reference in its entirety for all purposes). Notably however, even healthy individuals differ remarkably in the microbes that occupy tissues in such environments (Nature (2012). Vo. 486, p. 207).
As many microbes may be unculturable or otherwise difficult/expensive to culture, cultivation-independent approaches such as nucleic acid sequencing have advanced the understanding of the diversity of various microbial communities. Amplification and sequencing of the small subunit ribosomal RNA (SSU rRNA or 16s rRNA) gene was the foundational approach to the study of microbial diversity in a community, based in part on the gene's universal presence and relatively uniform rate of evolution. Advances in high-throughput methods have led to metagenomics analysis, where entire genomes of microbes are sequenced. Such methods do not require a priori knowledge of the community, enabling the discovery of new microorganism strains. Metagenomics, metatranscriptomics, metaproteomics and metabolomics all enable probing of a community to discern structure and function.
The ability to not only catalog the microorganisms in a community but to decipher which members are active, the number of those organisms, and co-occurrence of a microbial community member(s) with each other and with environmental parameter(s), for example, the co-occurrence of two microbes in a community in response to certain changes in the community's environment, would allow for the understanding of the importance of the respective environmental factor (e.g., climate, nutrients present, environmental pH) has on the identity of microbes within a microbial community (and their respective numbers), as well as the importance of certain community members have on the environment in which the community resides. The present disclosure addresses these and other needs.
As used in this specification, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, the term “an organism type” is intended to mean a single organism type or multiple organism types. For another example, the term “an environmental parameter” can mean a single environmental parameter or multiple environmental parameters, such that the indefinite article “a” or “an” does not exclude the possibility that more than one of environmental parameter is present, unless the context clearly requires that there is one and only one environmental parameter.
Reference throughout this specification to “one embodiment”, “an embodiment”, “one aspect”, or “an aspect”, “one implementation”, or “an implementation” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.
As used herein, in particular embodiments, the terms “about” or “approximately” when preceding a numerical value indicates the value plus or minus a range of 10%. Where a range of values is provided, it is understood that each intervening value, to the tenth of the unit of the lower limit unless the context clearly dictates otherwise, between the upper and lower limit of that range and any other stated or intervening value in that stated range is encompassed within the disclosure. That the upper and lower limits of these smaller ranges can independently be included in the smaller ranges is also encompassed within the disclosure, subject to any specifically excluded limit in the stated range. 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 microbe,” and like terms, are intended to mean that the one or more microorganisms has been separated from at least one of the materials with which it is associated in a particular environment (for example soil, water, animal tissue). Thus, an “isolated microbe” does not exist in its naturally occurring environment; rather, it is through the various techniques described herein that the microbe has been removed from its natural setting and placed into a non-naturally occurring state of existence. Thus, the isolated strain may exist as, for example, a biologically pure culture, or as spores (or other forms of the strain) in association with an acceptable carrier.
As used herein, “microbial ensemble” refers to a composition comprising one or more active microbes identified by methods, systems, and/or apparatuses of the present disclosure and that does not naturally exist in a naturally occurring environment and/or at ratios or amounts that do not exist in a nature. For example, a microbial ensemble (also synthetic ensemble, bioensemble, and/or endomicrobial supplement (EMS)) or aggregate could be formed from one or more isolated microbe strains, along with an appropriate medium or carrier. Microbial ensembles can be applied or administered to a target, such as a target environment, population, individual, animal, and/or the like.
The microbial ensembles according to the disclosure are selected from sets, subsets, and/or groupings of active, interrelated individual microbial species, or strains of a species. The relationships and networks, as identified by methods of the disclosure, are grouped and/or linked based on carrying out one or more a common functions, or can be described as participating in, or leading to, or associated with, a recognizable parameter, such as a phenotypic trait of interest (e.g. increased milk production in a ruminant). The groups from which the microbial ensemble is selected, and/or the microbial ensemble itself, can include two or more species, strains of species, or strains of different species, of microbes. In some instances, the microbes coexist can within the groups and/or microbial ensemble symbiotically.
In certain aspects of the disclosure, microbial ensembles are or are based on one or more isolated microbes that exist as isolated and biologically pure cultures. It will be appreciated by one of skill in the art, that an isolated and biologically pure culture of a particular microbe, denotes that said culture is substantially free (within scientific reason) of other living organisms and contains only the individual microbe in question. The culture can contain varying concentrations of said microbe. The present disclosure notes that isolated and biologically pure microbes often “necessarily differ from less pure or impure materials.” See, e.g. In re Bergstrom, 427 F.2d 1394, (CCPA 1970) (discussing purified prostaglandins), see also, In re Bergy, 596 F.2d 952 (CCPA 1979) (discussing purified microbes), see also, Parke-Davis & Co. v. H. K. Mulford & Co., 189 F. 95 (S.D.N.Y. 1911) (Learned Hand discussing purified adrenaline), aff'd in part, rev'd in part, 196 F. 496 (2d Cir. 1912), each of which are incorporated herein by reference. Furthermore, in some aspects, implementation of the disclosure can require certain quantitative measures of the concentration, or purity limitations, that must be achieved for an isolated and biologically pure microbial culture to be used in the disclosed microbial ensembles. The presence of these purity values, in certain embodiments, is a further attribute that distinguishes the microbes identified by the presently disclosed method from those microbes existing in a natural state. See, e.g., Merck & Co. v. Olin Mathieson Chemical Corp., 253 F.2d 156 (4th Cir. 1958) (discussing purity limitations for vitamin B12 produced by microbes), incorporated herein by reference.
As used herein, “carrier”, “acceptable carrier”, or “pharmaceutical carrier” refers to a diluent, adjuvant, excipient, or vehicle with which is used with or in the microbial ensemble. Such carriers can be sterile liquids, such as water and oils, including those of petroleum, animal, vegetable, or synthetic origin; such as peanut oil, soybean oil, mineral oil, sesame oil, and the like. Water or aqueous solution saline solutions and aqueous dextrose and glycerol solutions are preferably employed as carriers, in some embodiments as injectable solutions. Alternatively, the carrier can be a solid dosage form carrier, including but not limited to one or more of a binder (for compressed pills), a glidant, an encapsulating agent, a flavorant, and a colorant. The choice of carrier can be selected with regard to the intended route of administration and standard pharmaceutical practice. See Hardee and Baggo (1998. Development and Formulation of Veterinary Dosage Forms. 2nd Ed. CRC Press. 504 pg.); E. W. Martin (1970. Remington's Pharmaceutical Sciences. 17th Ed. Mack Pub. Co.); and Blaser et al. (US Publication US20110280840A1), each of which is herein expressly incorporated by reference in their entirety.
The terms “microorganism” and “microbe” are used interchangeably herein and refer to any microorganism that is of the domain Bacteria, Eukarya or Archaea. Microorganism types include without limitation, bacteria (e.g., mycoplasma, coccus, bacillus, rickettsia, spirillum), fungi (e.g., filamentous fungi, yeast), nematodes, protozoans, archaea, algae, dinoflagellates, viruses (e.g., bacteriophages), viroids and/or a combination thereof. Organism strains are subtaxons of organism types, and can be for example, a species, sub-species, subtype, genetic variant, pathovar or serovar of a particular microorganism.
The term “marker” or “unique marker” as used herein is an indicator of unique microorganism type, microorganism strain or activity of a microorganism strain. A marker can be measured in biological samples and includes without limitation, a nucleic acid-based marker such as a ribosomal RNA gene, a peptide- or protein-based marker, a metabolite, and/or an intermediate or other small molecule marker.
The term “metabolite” as used herein is an intermediate or product of metabolism. A metabolite in one embodiment is a small molecule. Metabolites have various functions, including in fuel, structural, signaling, stimulatory and inhibitory effects on enzymes, as a cofactor to an enzyme, in defense, and in interactions with other organisms (such as pigments, odorants and pheromones). A primary metabolite is directly involved in normal growth, development and reproduction. A secondary metabolite is not directly involved in these processes but usually has an important ecological function. Examples of metabolites include but are not limited to antibiotics and pigments such as resins and terpenes, etc. Some antibiotics use primary metabolites as precursors, such as actinomycin which is created from the primary metabolite, tryptophan. Metabolites, as used herein, include small, hydrophilic carbohydrates; large, hydrophobic lipids and complex natural compounds.
In one aspect of the disclosure, a method for identifying relationships between a plurality of microorganism strains and one or more metadata and/or parameters is disclosed. As illustrated in
In one aspect of the disclosure, a method for determining the absolute cell count of one or more active microorganism strains in a sample or plurality of samples is provided, wherein the one or more active microorganism strains are present in a microbial community in the sample. The one or more microorganism strains is a subtaxon of one or more organism types (see method 1000 at
Some embodiments of the disclosure can be configured for analyzing microbial communities. As illustrated by
Then each of the active detected microorganism strains (or a subset thereof) of the at least two samples are analyzed to identify relationships and the strengths thereof (1065) between and among each active detected microorganism strain and the other active detected microorganism strains, and between each active detected microorganism strain and at least one measured metadata. The identified relationships are then displayed or otherwise output (1067), e.g., on a graphical display/interface (see, e.g.,
Microbial ensembles according to the disclosure can be selected from sets, subsets, and/or groupings of active, interrelated individual microbial species, or strains of a species. The relationships and networks, as identified by methods of the disclosure, are grouped and/or linked based on carrying out one or more a common functions, or can be described as participating in, or leading to, or associated with, a recognizable parameter, such as a phenotypic trait of interest (e.g. increased milk production in a ruminant). In
Some embodiments of the disclosure are configured to leverage mutual information to rank the importance of native microbial strains residing in the gastrointestinal tract of the animal to specific animal traits. The maximal information coefficient (MIC) is calculated for all microorganisms and the desired animal trait. Relationships are scored on a scale of 0 to 1, with 1 representing a strong relationship between the microbial strain and animal trait and 0 representing no relationship. A cut-off based on this score is used to define useful and non-useful microorganisms with respect to the improvement of specific traits.
As provided in
In one embodiment, the plurality of samples is collected over time from the same environmental source (e.g., the same animal over a time course). In another embodiment, the plurality of samples is from a plurality of environmental sources (e.g., different animals). In one embodiment, the environmental parameter is the absolute cell count of a second active microorganism strain. In a further embodiment, the absolute cell count values of the one or more active microorganism strains is used to determine the co-occurrence of the one or more active microorganism strains, with a second active microorganism strain of the microbial community. In a further embodiment, a second environmental parameter is related to the absolute cell count of the one or more active microorganism strains and/or the absolute cell count of the second environmental strain.
Aspects of the disclosed embodiments are discussed throughout the disclosure.
The samples for use with the methods provided herein importantly can be of any type that includes a microbial community. For example, samples for use with the methods provided herein encompass without limitation, an animal sample (e.g., mammal, reptile, bird), soil, air, water (e.g., marine, freshwater, wastewater sludge), sediment, oil, plant, agricultural product, plant, soil (e.g., rhizosphere) and extreme environmental sample (e.g., acid mine drainage, hydrothermal systems). In the case of marine or freshwater samples, the sample can be from the surface of the body of water, or any depth of the body water, e.g., a deep sea sample. The water sample, in one embodiment, is an ocean, river or lake sample.
The animal sample in one embodiment is a body fluid. In another embodiment, the animal sample is a tissue sample. Non-limiting animal samples include tooth, perspiration, fingernail, skin, hair, feces, urine, semen, mucus, saliva, gastrointestinal tract. The animal sample can be, for example, a human, primate, bovine, porcine, canine, feline, rodent (e.g., 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 the collection of microorganisms found on the surface and deep layers of skin, in mammary glands, saliva, oral mucosa, conjunctiva and gastrointestinal tract. The microorganisms found in the microbiome include bacteria, fungi, protozoa, viruses and archaea. Different parts of the body exhibit varying diversity of microorganisms. The quantity and type of microorganisms may signal a healthy or diseased state for an individual. The number of bacteria taxa are in the thousands, and viruses may be as abundant. The bacterial composition for a given site on a body varies from person to person, not only in type, but also in abundance or quantity.
In another embodiment, the sample is a ruminal sample. Ruminants such as cattle rely upon diverse microbial communities to digest their feed. These animals have evolved to use feed with poor nutritive value by having a modified upper digestive tract (reticulorumen or rumen) where feed is held while it is fermented by a community of anaerobic microbes. The rumen microbial community is very dense, with about 3×1010 microbial cells per milliliter. Anaerobic fermenting microbes dominate in the rumen. The rumen microbial community includes members of all three domains of life: Bacteria, Archaea, and Eukarya. Ruminal fermentation products are required by their respective hosts for body maintenance and growth, as well as milk production (van Houtert (1993). Anim. Feed Sci. Technol. 43, pp. 189-225; Bauman et al. (2011). Annu. Rev. Nutr. 31, pp. 299-319; each incorporated by reference in its entirety for all purposes). Moreover, milk yield and composition has been reported to be associated with ruminal microbial communities (Sandri et al. (2014). Animal 8, pp. 572-579; Palmonari et al. (2010). J. Dairy Sci. 93, pp. 279-287; each incorporated by reference in its entirety for all purposes). Ruminal samples, in one embodiment, are collected via the process described in Jewell et al. (2015). Appl. Environ. Microbiol. 81, pp. 4697-4710, incorporated by reference herein in its entirety for all purposes.
In another embodiment, the sample is a soil sample (e.g., bulk soil or rhizosphere sample). It has been estimated that 1 gram of soil contains tens of thousands of bacterial taxa, and up to 1 billion bacteria cells as well as about 200 million fungal hyphae (Wagg et al. (2010). Proc Natl. Acad. Sci. USA 111, pp. 5266-5270, incorporated by reference in its entirety for all purposes). Bacteria, actinomycetes, fungi, algae, protozoa and viruses are all found in soil. Soil microorganism community diversity has been implicated in the structure and fertility of the soil microenvironment, nutrient acquisition by plants, plant diversity and growth, as well as the cycling of resources between above- and below-ground communities. Accordingly, assessing the microbial contents of a soil sample over time and the co-occurrence of active microorganisms (as well as the number of the active microorganisms) provides insight into microorganisms associated with an environmental metadata parameter such as nutrient acquisition and/or plant diversity.
The soil sample in one embodiment is a rhizosphere sample, i.e., the narrow region of soil that is directly influenced by root secretions and associated soil microorganisms. The rhizosphere is a densely populated area in which elevated microbial activities have 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 all purposes). As plants secrete many compounds into the rhizosphere, analysis of the organism types in the rhizosphere may be useful in determining features of the plants which grow therein.
In another embodiment, the sample is a marine or freshwater sample. Ocean water contains up to one million microorganisms per milliliter and several thousand microbial types. These numbers may be an order of magnitude higher in coastal waters with their higher productivity and higher load of organic matter and nutrients. Marine microorganisms are crucial for the functioning of marine ecosystems; maintaining the balance between produced and fixed carbon dioxide; production of more than 50% of the oxygen on Earth through marine phototrophic microorganisms such as Cyanobacteria, diatoms and pico- and nanophytoplankton; providing novel bioactive compounds and metabolic pathways; ensuring a sustainable supply of seafood products by occupying the critical bottom trophic level in marine foodwebs. Organisms found in the marine environment include viruses, bacteria, archaea and some eukarya. Marine viruses may play a significant role in controlling populations of marine bacteria through viral lysis. Marine bacteria are important as a food source for other small microorganisms as well as being producers of organic matter. Archaea found throughout the water column in the ocean are pelagic Archaea and their abundance rivals that of marine bacteria.
In another embodiment, the sample comprises a sample from an extreme environment, i.e., an environment that harbors conditions that are detrimental to most life on Earth. Organisms that thrive in extreme environments are called extremophiles. Though the domain Archaea contains well-known examples of extremophiles, the domain bacteria can also have representatives of these microorganisms. Extremophiles include: acidophiles which grow at pH levels of 3 or below; alkaliphiles which grow at pH levels of 9 or above; anaerobes such as Spinoloricus cinzia which does not require oxygen for growth; cryptoendoliths which live in microscopic spaces within rocks, fissures, aquifers and faults filled with groundwater in the deep subsurface; halophiles which grow in about at least 0.2M concentration of salt; hyperthermophiles which thrive at high temperatures (about 80-122° C.) such as found in hydrothermal systems; hypoliths which live underneath rocks in cold deserts; lithoautotrophs such as Nitrosomonas europaea which derive energy from reduced mineral compounds like pyrites and are active in geochemical cycling; metallotolerant organisms which tolerate high levels of dissolved heavy metals such as copper, cadmium, arsenic and zinc; oligotrophs which grow in nutritionally limited environments; osmophiles which grow in environments with a high sugar concentration; piezophiles (or barophiles) which thrive at high pressures such as found deep in the ocean or underground; psychrophiles/cryophiles which survive, grow and/or reproduce at temperatures of about −15° C. or lower; radioresistant organisms which are resistant to high levels of ionizing radiation; thermophiles which thrive at temperatures between 45-122° C.; xerophiles which can grow in extremely dry conditions. Polyextremophiles are organisms that qualify as extremophiles under more than one category and include thermoacidophiles (prefer temperatures of 70-80° C. and pH between 2 and 3). The Crenarchaeota group of Archaea includes the thermoacidophiles.
The sample can 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).
In the methods provided herein for determining the presence and absolute cell count of one or more microorganisms in a sample, for example the absolute cell count of one or more microorganisms in a plurality of samples collected from the same or different environments, and/or over multiple time points, the one or more microorganisms can be of any type. For example, the one or more microorganisms can be from the domain Bacteria, Archaea, Eukarya or a combination thereof. Bacteria and Archaea are prokaryotic, having a very simple cell structure with no internal organelles. Bacteria can be classified into gram positive/no outer membrane, gram negative/outer membrane present and ungrouped phyla. Archaea constitute a domain or kingdom of single-celled microorganisms. Although visually similar to bacteria, archaea possess genes and several metabolic pathways that are more closely related to those of eukaryotes, notably the enzymes involved in transcription and translation. Other aspects of archaeal biochemistry are unique, such as the presence of ether lipids in their cell membranes. The Archaea are divided into four recognized phyla: Thaumarchaeota, Aigarchaeota, Crenarchaeota and Korarchaeota.
The domain of Eukarya comprises eukaryotic organisms, which are defined by membrane-bound organelles, such as the nucleus. Protozoa are unicellular eukaryotic organisms. All multicellular organisms are eukaryotes, including animals, plants and fungi. The eukaryotes have been classified into four kingdoms: Protista, Plantae, Fungi and Animalia. However, several alternative classifications exist. Another classification divides Eukarya into six kingdoms: Excavata (various flagellate protozoa); amoebozoa (lobose amoeboids and slime filamentous fungi); Opisthokonta (animals, fungi, choanoflagellates); Rhizaria (Foraminifera, Radiolaria, and various other amoeboid protozoa); Chromalveolata (Stramenopiles (brown algae, diatoms), Haptophyta, Cryptophyta (or cryptomonads), and Alveolata); Archaeplastida/Primoplantae (Land plants, green algae, red algae, and glaucophytes).
Within the domain of Eukarya, fungi are microorganisms that are predominant in microbial communities. Fungi include microorganisms such as yeasts and filamentous fungi as well as the familiar mushrooms. Fungal cells have cell walls that contain glucans and chitin, a unique feature of these organisms. The fungi form a single group of related organisms, named the Eumycota that share a common ancestor. The kingdom Fungi has been estimated at 1.5 million to 5 million species, with about 5% of these having been formally classified. The cells of most fungi grow as tubular, elongated, and filamentous structures called hyphae, which may contain multiple nuclei. Some species grow as unicellular yeasts that reproduce by budding or binary fission. The major phyla (sometimes called divisions) of fungi have been classified mainly on the basis of characteristics of their sexual reproductive structures. Currently, seven phyla are proposed: Microsporidia, Chytridiomycota, Blastocladiomycota, Neocallimastigomycota, Glomeromycota, Ascomycota, and Basidiomycota.
Microorganisms for detection and quantification by the methods described herein can also be viruses. A virus is a small infectious agent that replicates only inside the living cells of other organisms. Viruses can infect all types of life forms in the domains of Eukarya, Bacteria and Archaea. Virus particles (known as virions) consist of two or three parts: (i) the genetic material which can be either DNA or RNA; (ii) a protein coat that protects these genes; and in some cases (iii) an envelope of lipids that surrounds the protein coat when they are outside a cell. Seven orders have been established for viruses: the Caudovirales, Herpesvirales, Ligamenvirales, Mononegavirales, Nidovirales, Picornavirales, and Tymovirales. Viral genomes may be single-stranded (ss) or double-stranded (ds), RNA or DNA, and may or may not use reverse transcriptase (RT). In addition, ssRNA viruses may be either sense (+) or antisense (−). This classification places viruses into seven groups: I: dsDNA viruses (such as Adenoviruses, Herpesviruses, Poxviruses); II: (+) ssDNA viruses (such as Parvoviruses); III: dsRNA viruses (such as Reoviruses); IV: (+)ssRNA viruses (such as Picornaviruses, Togaviruses); V: (−)ssRNA viruses (such as Orthomyxoviruses, Rhabdoviruses); VI: (+)ssRNA-RT viruses with DNA intermediate in life-cycle (such as Retroviruses); VII: dsDNA-RT viruses (such as Hepadnaviruses).
Microorganisms for detection and quantification by the methods described herein can also be viroids. Viroids are the smallest infectious pathogens known, consisting solely of short strands of circular, single-stranded RNA without protein coats. They are mostly plant pathogens, some of which are of economical importance. Viroid genomes are extremely small in size, ranging from about 246 to about 467 nucleobases.
According to the methods provided herein, a sample is processed to detect the presence of one or more microorganism types in the sample (
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 microorganism types (
In one embodiment, a sample is stained with one or more fluorescent dyes wherein a fluorescent dye is specific to a particular microorganism type, to enable detection via a flow cytometer or some other detection and quantification method that harnesses fluorescence, such as fluorescence microscopy. The method can provide quantification of the number of cells and/or cell volume of a given organism type in a sample. In a further embodiment, as described herein, flow cytometry is harnessed to determine the presence and quantity of a unique first marker and/or unique second marker of the organism type, such as enzyme expression, cell surface protein expression, etc. Two- or three-variable histograms or contour plots of, for example, light scattering versus fluorescence from a cell membrane stain (versus fluorescence from a protein stain or DNA stain) can also be generated, and thus an impression may be gained of the distribution of a variety of properties of interest among the cells in the population as a whole. A number of displays of such multiparameter flow cytometric data are in common use and are amenable for use with the methods described herein.
In one embodiment of processing the sample to detect the presence and number of one or more microorganism types, a microscopy assay is employed (
In another embodiment of the disclosure, in order to detect the presence and number of one or more microorganism types, each sample, or a portion thereof is subjected to fluorescence microscopy. Different fluorescent dyes can be used to directly stain cells in samples and to quantify total cell counts using an epifluorescence microscope as well as flow cytometry, described above. Useful dyes to quantify microorganisms include but are not limited to acridine orange (AO), 4,6-di-amino-2 phenylindole (DAPI) and 5-cyano-2,3 Dytolyl Tetrazolium Chloride (CTC). Viable cells can be estimated by a viability staining method such as the LIVE/DEAD® Bacterial Viability Kit (Bac-Light™) which contains two nucleic acid stains: the green-fluorescent SYTO 9™ dye penetrates all membranes and the red-fluorescent propidium iodide (PI) dye penetrates cells with damaged membranes. Therefore, cells with compromised membranes will stain red, whereas cells with undamaged membranes will stain green. Fluorescent in situ hybridization (FISH) extends epifluorescence microscopy, allowing for the fast detection and enumeration of specific organisms. FISH uses fluorescent labelled oligonucleotides probes (usually 15-25 basepairs) which bind specifically to organism DNA in the sample, allowing the visualization of the cells using an epifluorescence or confocal laser scanning microscope (CLSM). Catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH) improves upon the FISH method by using oligonucleotide probes labelled with a horse radish peroxidase (HRP) to amplify the intensity of the signal obtained from the microorganisms being studied. FISH can be combined with other techniques to characterize microorganism communities. One combined technique is high affinity peptide nucleic acid (PNA)-FISH, where the probe has an enhanced capability to penetrate through the Extracellular Polymeric Substance (EPS) matrix. Another example is LIVE/DEAD-FISH which combines the cell viability kit with FISH and has been used to assess the efficiency of disinfection in drinking water distribution systems.
In another embodiment, each sample, or a portion thereof is subjected to Raman micro-spectroscopy in order to determine the presence of a microorganism type and the absolute number of at least one microorganism type (
In yet another embodiment, the sample, or a portion thereof is subjected to centrifugation in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In another embodiment, the sample, or a portion thereof is subjected to staining in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In another embodiment, the sample, or a portion thereof is subjected to mass spectrometry (MS) in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In another embodiment, the sample, or a portion thereof is subjected to lipid analysis in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In the aspects of the methods provided herein, the number of unique first makers in the sample, or portion thereof (e.g., sample aliquot) is measured, as well as the quantity of each of the unique first markers (
Any marker that is unique to an organism strain can be employed herein. For example, markers can include, but are not limited to, small subunit ribosomal RNA genes (16S/18S rDNA), large subunit ribosomal RNA genes (23S/25S/28S rDNA), intercalary 5.8S gene, cytochrome c oxidase, beta-tubulin, elongation factor, RNA polymerase and internal transcribed spacer (ITS).
Ribosomal RNA genes (rDNA), especially the small subunit ribosomal RNA genes, i.e., 18S rRNA genes (18S rDNA) in the case of eukaryotes and 16S rRNA (16S rDNA) in the case of prokaryotes, have been the predominant target for the assessment of organism types and strains in a microbial community. However, the large subunit ribosomal RNA genes, 28S rDNAs, have been also targeted. rDNAs are suitable for taxonomic identification because: (i) they are ubiquitous in all known organisms; (ii) they possess both conserved and variable regions; (iii) there is an exponentially expanding database of their sequences available for comparison. In community analysis of samples, the conserved regions serve as annealing sites for the corresponding universal PCR and/or sequencing primers, whereas the variable regions can be used for phylogenetic differentiation. In addition, the high copy number of rDNA in the cells facilitates detection from environmental samples.
The internal transcribed spacer (ITS), located between the 18S rDNA and 28S rDNA, has also been targeted. The ITS is transcribed but spliced away before assembly of the ribosomes. The ITS region is composed of two highly variable spacers, ITS1 and ITS2, and the intercalary 5.8S gene. This rDNA operon occurs in multiple copies in genomes. Because the ITS region does not code for ribosome components, it is highly variable. In one embodiment, the unique RNA marker can be an mRNA marker, an siRNA marker or a ribosomal RNA marker.
Protein-coding functional genes 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, archaea RadA and RadB, eukaryotic Rad51 and Rad57, phage UvsX); RNA polymerase β subunit (RpoB) gene, which is responsible for transcription initiation and elongation; chaperonins. Candidate marker genes have also been identified for bacteria plus archaea: ribosomal protein S2 (rpsB), ribosomal protein S10 (rpsJ), ribosomal protein L1 (rplA), 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 S7, 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, ribosomal protein L18P/L5E, ribosomal protein S15P/S13e, Porphobilinogen deaminase, ribosomal protein S17, ribosomal protein L13 (rplM), phosphoribosylformylglycinamidine cyclo-ligase (rpsE), ribonuclease HII and ribosomal protein L24. Other candidate marker genes for bacteria include: transcription elongation protein NusA (nusA), rpoB DNA-directed RNA polymerase subunit beta (rpoB), GTP-binding protein EngA, rpoC DNA-directed RNA polymerase subunit beta′, priA primosome assembly protein, transcription-repair coupling factor, CTP synthase (pyrG), secY preprotein translocase subunit SecY, GTP-binding protein Obg/CgtA, DNA polymerase I, rpsF 30S ribosomal protein S6, poA DNA-directed RNA polymerase subunit alpha, peptide chain release factor 1, rplI 50S ribosomal protein L9, polyribonucleotide nucleotidyltransferase, tsf elongation factor Ts (tsf), rplQ 50S ribosomal protein L17, tRNA (guanine-N(1)-)-methyltransferase (rplS), rplY probable 50S ribosomal protein L25, DNA repair protein RadA, glucose-inhibited division protein A, ribosome-binding factor A, DNA mismatch repair protein MutL, smpB SsrA-binding protein (smpB), N-acetylglucosaminyl transferase, S-adenosyl-methyltransferase MraW, UDP-N-acetylmuramoylalanine-D-glutamate ligase, rplS 50S ribosomal protein L19, rplT 50S ribosomal protein L20 (rplT), ruvA Holliday junction DNA helicase, ruvB Holliday junction DNA helicase B, serS selyl-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), recombination protein RecR, protein of unknown function UPF0054, miaA tRNA isopentenyltransferase, GTP-binding protein YchF, chromosomal replication initiator protein DnaA, dephospho-CoA kinase, 16S rRNA processing protein RimM, ATP-cone domain protein, 1-deoxy-D-xylulose 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 Holliday junction resolvase, rpsP 30S ribosomal protein S16, Recombinase A recA, riboflavin biosynthesis protein RibF, glycyl-tRNA synthetase beta subunit, trmU tRNA (5-methylaminomethyl-2-thiouridylate)-methyltransferase, rpml 50S ribosomal protein L35, hemE uroporphyrinogen decarboxylase, Rod shape-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.
Phospholipid fatty acids (PLFAs) can also be used as unique first markers according to the methods described herein. Because PLFAs are rapidly synthesized during microbial growth, are not found in storage molecules and degrade rapidly during cell death, it provides an accurate census of the current living community. All cells contain fatty acids (FAs) that can be extracted and esterified to form fatty acid methyl esters (FAMEs). When the FAMEs are analyzed using gas chromatography-mass spectrometry, the resulting profile constitutes a ‘fingerprint’ of the microorganisms in the sample. The chemical compositions of membranes for organisms in the domains Bacteria and Eukarya are comprised of fatty acids linked to the glycerol by an ester-type bond (phospholipid fatty acids (PLFAs)). In contrast, the membrane lipids of Archaea are composed of long and branched hydrocarbons that are joined to glycerol by an ether-type bond (phospholipid ether lipids (PLELs)). This is one of the most widely used non-genetic criteria to distinguish the three domains. In this context, the phospholipids derived from microbial cell membranes, characterized by different acyl chains, are excellent signature molecules, because such lipid structural diversity can be linked to specific microbial taxa.
As provided herein, in order to determine whether an organism strain is active, the level of expression of one or more unique second markers, which can be the same or different as the first marker, is measured (
In one embodiment, if the level of expression of the second marker is above a threshold level (e.g., a control level) or at a threshold level, the microorganism is considered to be active (
Second unique markers are measured, in one embodiment, at the protein, RNA or intermediate level. A unique second marker is the same or different as the first unique marker.
As provided above, a number of unique first markers and unique second markers can be detected according to the methods described herein. Moreover, the detection and quantification of a unique first marker is carried out according to methods known to those of ordinary skill in the art (
Nucleic acid sequencing (e.g., gDNA, cDNA, rRNA, mRNA) in one embodiment is used to determine absolute cell count of a unique first marker and/or unique second marker. Sequencing platforms include, but are not limited to, Sanger sequencing and high-throughput sequencing methods available from Roche/454 Life Sciences, Illumina/Solexa, Pacific Biosciences, Ion Torrent and Nanopore. The sequencing can be amplicon sequencing of particular DNA or RNA sequences or whole metagenome/transcriptome shotgun sequencing.
Traditional Sanger sequencing (Sanger et al. (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl. Acad. Sci. USA, 74, pp. 5463-5467, incorporated by reference herein in its entirety) relies on the selective incorporation of chain-terminating dideoxynucleotides by DNA polymerase during in vitro DNA replication and is amenable for use with the methods described herein.
In another embodiment, the sample, or a portion thereof is subjected to extraction of nucleic acids, amplification of DNA of interest (such as the rRNA gene) with suitable primers and the construction of clone libraries using sequencing vectors. Selected clones are then sequenced by Sanger sequencing and the nucleotide sequence of the DNA of interest is retrieved, allowing calculation of the number of unique microorganism strains in a sample.
454 pyrosequencing from Roche/454 Life Sciences yields long reads and can be harnessed in the methods described herein (Margulies et al. (2005) Nature, 437, pp. 376-380; U.S. Pat. Nos. 6,274,320; 6,258,568; 6,210,891, each of which is herein incorporated in its entirety for all purposes). Nucleic acid to be sequenced (e.g., amplicons or nebulized genomic/metagenomic DNA) have specific adapters affixed on either end by PCR or by ligation. The DNA with adapters is fixed to tiny beads (ideally, one bead will have one DNA fragment) that are suspended in a water-in-oil emulsion. An emulsion PCR step is then performed to make multiple copies of each DNA fragment, resulting in a set of beads in which each bead contains many cloned copies of the same DNA fragment. Each bead is then placed into a well of a fiber-optic chip that also contains enzymes necessary for the sequencing-by-synthesis reactions. The addition of bases (such as A, C, G, or T) trigger pyrophosphate release, which produces flashes of light that are recorded to infer the sequence of the DNA fragments in each well. About 1 million reads per run with reads up to 1,000 bases in length can be achieved. Paired-end sequencing can be done, which produces pairs of reads, each of which begins at one end of a given DNA fragment. A molecular barcode can be created and placed between the adapter sequence and the sequence of interest in multiplex reactions, allowing each sequence to be assigned to a sample bioinformatically.
Illumina/Solexa sequencing produces average read lengths of about 25 basepairs (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 sequencing using reversible terminator chemistry. Nature, 456:53-59). This sequencing technology is also sequencing-by-synthesis but employs reversible dye terminators and a flow cell with a field of oligos attached. DNA fragments to be sequenced have specific adapters on either end and are washed over a flow cell filled with specific oligonucleotides that hybridize to the ends of the fragments. Each fragment is then replicated to make a cluster of identical fragments. Reversible dye-terminator nucleotides are then washed over the flow cell and given time to attach. The excess nucleotides are washed away, the flow cell is imaged, and the reversible terminators can be removed so that the process can repeat and nucleotides can continue to be added in subsequent cycles. Paired-end reads that are 300 bases in length each can be achieved. An Illumina platform can produce 4 billion fragments in a paired-end fashion with 125 bases for each read in a single run. Barcodes can also be used for sample multiplexing, but indexing primers are used.
The SOLiD (Sequencing by Oligonucleotide Ligation and Detection, Life Technologies) process is a “sequencing-by-ligation” approach, and can be used with the methods described herein for detecting the presence and quantity of a first marker and/or a second marker (
The Ion Torrent system, like 454 sequencing, is amenable for use with the methods described herein for detecting the presence and quantity of a first marker and/or a second marker (
Pacific Biosciences (PacBio) SMRT sequencing uses a single-molecule, real-time sequencing approach and in one embodiment, is used with the methods described herein for detecting the presence and quantity of a first marker and/or a second marker (
In one embodiment, where the first unique marker is the ITS genomic region, automated ribosomal intergenic spacer analysis (ARISA) is used in one embodiment to determine the number and identity of microorganism strains in a sample (
In another embodiment, fragment length polymorphism (RFLP) of PCR-amplified rDNA fragments, otherwise known as amplified ribosomal DNA restriction analysis (ARDRA), is used to characterize unique first markers and the quantity of the same in samples (
One fingerprinting technique used in detecting the presence and abundance of a unique first marker is single-stranded-conformation polymorphism (SSCP) (see Lee et al. (1996). Appl Environ Microbiol 62, pp. 3112-3120; Scheinert et al. (1996). J. Microbiol. Methods 26, pp. 103-117; Schwieger and Tebbe (1998). Appl. Environ. Microbiol. 64, pp. 4870-4876, each of which is incorporated by reference herein in its entirety). In this technique, DNA fragments such as PCR products obtained with primers specific for the 16S rRNA gene, are denatured and directly electrophoresed on a non-denaturing gel. Separation is based on differences in size and in the folded conformation of single-stranded DNA, which influences the electrophoretic mobility. Reannealing of DNA strands during electrophoresis can be prevented by a number of strategies, including the use of one phosphorylated primer in the PCR followed by specific digestion of the phosphorylated strands with lambda exonuclease and the use of one biotinylated primer to perform magnetic separation of one single strand after denaturation. To assess the identity of the predominant populations in a given microbial community, in one embodiment, bands are excised and sequenced, or SSCP-patterns can be hybridized with specific probes. Electrophoretic conditions, such as gel matrix, temperature, and addition of glycerol to the gel, can influence the separation.
In addition to sequencing based methods, other methods for quantifying expression (e.g., gene, protein expression) of a second marker are amenable for use with the methods provided herein for determining the level of expression of one or more second markers (
In another embodiment, the sample, or a portion thereof is subjected to a quantitative polymerase chain reaction (PCR) for detecting the presence and quantity of a first marker and/or a second marker (
In another embodiment, the sample, or a portion thereof is subjected to PCR-based fingerprinting techniques to detect the presence and quantity of a first marker and/or a second marker (
In another embodiment, the sample, or a portion thereof is subjected to a chip-based platform such as microarray or microfluidics to determine the quantity of a unique first marker and/or presence/quantity of a unique second marker (
A protein expression assay, in one embodiment, is used with the methods described herein for determining the level of expression of one or more second markers (
In one embodiment, the sample, or a portion thereof is subjected to Bromodeoxyuridine (BrdU) incorporation to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to microautoradiography (MAR) combined with FISH to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to stable isotope Raman spectroscopy combined with FISH (Raman-FISH) to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to DNA/RNA stable isotope probing (SIP) to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to isotope array to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to a metabolomics assay to determine the level of a second unique marker (
According to the embodiments described herein, the presence and respective number of one or more active microorganism strains in a sample are determined (
The one or more microorganism strains are considered active, as described above, if the level of second unique marker expression is at a threshold level, higher than a threshold value, e.g., higher than at least about 5%, at least about 10%, at least about 20% or at least about 30% over a control level.
In another aspect of the disclosure, a method for determining the absolute cell count of one or more microorganism strains is determined in a plurality of samples (
The absolute cell count values over samples are used in one embodiment to relate the one or more active microorganism strains, with an environmental parameter (
In one embodiment, determining the co-occurrence of one or more active microorganism strains with an environmental parameter comprises a network and/or cluster analysis method to measure connectivity of strains or a strain with an environmental parameter within a network, wherein the network is a collection of two or more samples that share a common or similar environmental parameter. Examples of measurement of independence are provided and discussed herein, and additional details can be understood by configuring the teachings and methods of: Blomqvist “On a measure of dependence between two random variables” The Annals of Mathematical Statistics (1950): 593-600; Hollander et 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 Statistics (1961): 485-498; the entirety of each of the aforementioned publications being herein expressly incorporated by reference for all purposes.
In another embodiment, correlation methods including Pearson correlation, Spearman correlation, Kendall correlation, Canonical Correlation Analysis, Likelihood ratio tests (e.g., by adapting the teachings and methods detailed in Wilks, S. S. “On the Independence of k Sets of Normally Distributed Statistical Variables” Econometrica, Vol. 3, No. 3, July 1935, pp 309-326, the entirety of which is herein expressly incorporated by reference for all purposes), and canonical correlation analysis are used establish connectivity between variables. Multivariate extensions of these methods, Maximal correlation (see, e.g., Alfred Renyi “On measures of dependence” Acta mathematica hungarica 10.3-4 (1959): 441-451, herein expressly incorporated by reference in its entirety), or both (MAC) can be used when appropriate, depending on the number of variables being compared. Some embodiments utilize Maximal Correlation Analysis and/or other multivariate correlation measures configured for discovering multi-dimensional patterns (for example, by adapting the methods and teachings of “Multivariate Maximal Correlation Analysis,” Nguyen et al., Proceedings of the 31st International Conference on Machine Learning, Beijing, China, 2014, which is herein expressly incorporated by reference in its entirety for all purposes). In some embodiments, network metrics and analysis, such as discussed by Farine et al, in “Constructing, Conducting and Interpreting Animal Social Network Analysis” Journal of Animal Ecology, 2015, 84, pp. 1144-1163. doi:10.1111/1365-2656.12418 (the entirety of which is herein expressly incorporated by reference for all purposes) can be utilized and configured for the disclosure.
In some embodiments, network analysis comprises nonparametric approaches (e.g., by adapting the teaching and methods detailed in Taskinen et al. “Multivariate nonparametric tests of independence.” Journal of the American Statistical Association 100.471 (2005): 916-925; and Gieser et al. “A Nonparametric Test of Independence Between Two Vectors.” Journal of the American Statistical Association, Vol. 92, No. 438, June, 1977, pp 561-567; entirety of each of being herein expressly incorporated by reference for all purposes), including mutual information Maximal Information Coefficient, Maximal Information Entropy (MIE; e.g., by adapting the teachings and methods of Zhang Ya-hong et al. “Detecting Multivariable Correlation with Maximal Information Entropy[J]” Journal of Electronics & Information Technology, 2015-01 (37(1): 123-129), the entirety of which is herein expressly incorporated by reference for all purposes), Kernel Canonical Correlation Analysis (KCCA; e.g., by adapting the teachings and methods detailed in Bach et al. “Kernel Independent Component Analysis” Journal of Machine Learning Research 3 (2002) 1-48, the entirety of which is herein expressly incorporated by reference for all purposes), Alternating Conditional Expectation or backfitting algorithms (ACE; e.g., by adapting the teaching and methods detailed in Breiman et al. “Estimating Optimal Transformations for Multiple Regression and Correlation: Rejoinder.” Journal of the American Statistical Association 80, no. 391 (1985): 614-19, doi:10.2307/2288477, the entirety of which is herein expressly incorporated by reference for all purposes), Distance correlation measure (dcor; e.g., by adapting the teaching and methods detailed in Szekely et al. “Measuring and Testing Dependence by Correlation of Distances” The Annals of Statistics, 2007, Vol. 35, No. 6, 2769-2794, doi:10.1214/009053607000000505, the entirety of which is herein expressly incorporated by reference for all purposes), Brownian distance covariance (dcov; e.g., by adapting the teaching and methods detailed in Szekely et al. “Brownian Distance Covariance” The Annals of Applied Statistics, 2009, Vol. 3, No. 4, 1236-1265, Doi:10.1214/09-AOAS312, the entirety of which is herein expressly incorporated by reference for all purposes), Hilbert-Schmidt Independence Criterion (HSCI/CHSI; e.g., by adapting the teachings and methods detailed in Gretton 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 which is herein expressly incorporated by reference in their entireties for all purposes), Randomized Dependence Coefficient (RDC; e.g., by adapting the teaching and methods detailed in Lopez-Paz et al. “The Randomized Dependence Coefficient” Advances in Neural Information Processing Systems (2013), the entirety of which is herein expressly incorporated by reference for all purposes) to establish connectivity between variables. In some embodiments, one or more of these methods can be coupled to bagging or boosting methods, or k nearest neighbor estimators (e.g., by adapting the teaching and methods detailed in: Breiman, “Arcing Classifiers” The Annals of Statistics, 1998, Vol. 26, No. 3, 801-849; Liu, “Modified Bagging of Maximal Information Coefficient for Genome-wide Identification” Int. J. Data Mining and Bioinformatics, Vol. 14, No. 3, 2016, pp. 229-257; and/or Gao et al. “Efficient Estimation of Mutual Information for Strongly Dependent Variables” Proceedings of the 18th International Conference on Artificial Intelligence and Statistics (AISTATS), 2015, San Diego, Calif., JMLR: W&CP Volume 38; each of which is herein expressly incorporated by reference in its entirety for all purposes).
In some embodiments, the network analysis comprises node-level analysis, including degree, strength, betweenness centrality, eigenvector centrality, page rank, and reach. In another embodiment, the network analysis comprises network level metrics, including density, homophily or assortativity, transitivity, linkage analysis, modularity analysis, robustness measures, betweenness measures, connectivity measures, transitivity measures, centrality measures or a combination thereof. In others embodiments, species community rules (see, e.g., Connor et al. “The Assembly of Species Communities: Chance or Competition?” Ecology, Vol. 60, No. 6 (December, 1979), pp. 1132-1140, the entirety of which is herein incorporated by reference for all purposes) are applied to the network, which can include leveraging Gambit of the Group assumptions (e.g., by applying the methods and teachings of Franks et al. “Sampling Animal Association Networks with the Gambit of the Group” Behav Ecol Sociobiol (2010) 64:493, doi:10.1007/x00265-0098-0865-8, the entirety of which is herein expressly incorporated by reference for all purposes). In some embodiments, eigenvectors/modularity matrix analysis methods can be used, e.g., by configuring the teachings and methods as discussed by Mark E J Newman in “Finding community structure in networks using the eigenvectors of matrices” Physical Review E 74.3 (2006): 036104, the entirety of which is herein expressly incorporated by reference for all purposes.
In some embodiments, time-aggregated networks or time-ordered networks are utilized. In another embodiment, the cluster analysis method comprises building or constructing an observation matrix, connectivity model, subspace model, distribution model, density model, or a centroid model, using community detection in graphs, and/or using community detection algorithms such as, by way of non-limiting example, the Louvain, Bron-Kerbosch, Girvan-Newman, Clauset-Newman-Moore, Pons-Latapy, and/or Wakita-Tsurumi algorithms.
In some embodiments, the cluster analysis method is a heuristic method based on modularity optimization. In a further embodiment, the cluster analysis method is the Louvain method (see, e.g., the method described by Blondel et al. (2008) Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, Volume 2008, October 2008, incorporated by reference herein in its entirety for all purposes, and which can be adapted for use in the methods disclosed herein).
In other embodiments, the network analysis comprises predictive modeling of network through link mining and prediction, collective classification, link-based clustering, hierarchical cluster analysis, relational similarity, or a combination thereof. In another embodiment, the network analysis comprises differential equation based modeling of populations. In another embodiment, the network analysis comprises Lotka-Volterra modeling.
In some embodiments, relating the one or more active microorganism strains to an environmental parameter (e.g., determining the co-occurrence) in the sample comprises creating matrices populated with linkages denoting environmental parameter and microorganism strain associations.
In some embodiments, the multiple sample data obtained at step 2007 (e.g., over two or more samples which can be collected at two or more time points where each time point corresponds to an individual sample) is compiled. In a further embodiment, the number of cells of each of the one or more microorganism strains in each sample is stored in an association matrix (which can be in some embodiments, a quantity matrix). In one embodiment, the association matrix is used to identify associations between active microorganism strains in a specific time point sample using rule mining approaches weighted with association (e.g., quantity) data. Filters are applied in one embodiment to remove insignificant rules.
In some embodiments, the absolute cell count of one or more, or two or more active microorganism strains is related to one or more environmental parameters (
Other examples of metadata parameters include but are not limited to genetic information from the host from which the sample was obtained (e.g., DNA mutation information), sample pH, sample temperature, expression of a particular protein or mRNA, nutrient conditions (e.g., level and/or identity of one or more nutrients) of the surrounding environment/ecosystem), susceptibility or resistance to disease, onset or progression of disease, susceptibility or resistance of the sample to toxins, efficacy of xenobiotic compounds (pharmaceutical drugs), biosynthesis of natural products, or a combination thereof.
For example, according to one embodiment, microorganism strain number changes are calculated over multiple samples according to the method of
In a further embodiment, microorganism strains are ranked according to importance by integrating cell number changes over time and strains present in target clusters, with the highest changes in cell number ranking the highest.
Network and/or cluster analysis method in one embodiment, is used to measure connectivity of the one or more strains within a network, wherein the network is a collection of two or more samples that share a common or similar environmental parameter. In one embodiment, network analysis comprises linkage analysis, modularity analysis, robustness measures, betweenness measures, connectivity measures, transitivity measures, centrality measures or a combination thereof. In another embodiment, network analysis comprises predictive modeling of network through link mining and prediction, social network theory, collective classification, link-based clustering, relational similarity, or a combination thereof. In another embodiment, network analysis comprises mutual information, maximal information coefficient calculations, or other nonparametric methods between variables to establish connectivity. In another embodiment, network analysis comprises differential equation based modeling of populations. In yet another embodiment, network analysis comprises Lotka-Volterra modeling.
Cluster analysis method comprises building a connectivity model, subspace model, distribution model, density model, or a centroid model.
Network and cluster based analysis, for example, to carry out method step 2008 of
As shown in
Each component or module in the microbe screening system 300 can be operatively coupled to each remaining component and/or module. Each component and/or module in the microbe screening system 300 can be any combination of hardware and/or software (stored and/or executing in hardware) capable of performing one or more specific functions associated with that component and/or module.
The memory 320 can be, for example, a random-access memory (RAM) (e.g., a dynamic RAM, a static RAM), a flash memory, a removable memory, a hard drive, a database and/or so forth. In some embodiments, the memory 320 can include, for example, a database (e.g., as in 319), process, application, virtual machine, and/or some other software components, programs and/or modules (stored and/or executing in hardware) or hardware components/modules configured to execute a microbe screening process and/or one or more associated methods for microbe screening and ensemble generation (e.g., via the data collection component 330, the absolute count component 335, the sample relation component 340, the activity component 345, the network analysis component 350, the strain selection/microbial ensemble generation component 355 (and/or similar modules)). In such embodiments, instructions of executing the microbe screening and/or ensemble generation process and/or the associated methods can be stored within the memory 320 and executed at the processor 310. In some embodiments, data collected via the data collection component 330 can be stored in a database 319 and/or in the memory 320.
The processor 310 can be configured to control, for example, the operations of the communications interface 390, write data into and read data from the memory 320, and execute the instructions stored within the memory 320. The processor 310 can also be configured to execute and/or control, for example, the operations of the data collection component 330, the absolute count component 335, the sample relation component 340, the activity component, and the network analysis component 350, as described in further detail herein. In some embodiments, under the control of the processor(s) 310 and based on the methods or processes stored within the memory 320, the data collection component 330, absolute count component 335, sample relation component 340, activity component 345, network analysis component 350, and strain selection/ensemble generation component 355 can be configured to execute a microbe screening, selection and synthetic ensemble generation process, as described in further detail herein.
The communications interface 390 can include and/or be configured to manage one or multiple ports of the microbe screening system 300 (e.g., via input out interface(s) 395). In some instances, for example, the communications interface 390 (e.g., a Network Interface Card (NIC)) can include one or more line cards, each of which can include one or more ports (operatively) coupled to devices (e.g., peripheral devices 397 and/or user input devices 396). A port included in the communications interface 390 can be any entity that can actively communicate with a coupled device or over a network 392 (e.g., communicate with end-user devices 393b, host devices, servers, etc.). In some embodiments, such a port need not necessarily be a hardware port, but can be a virtual port or a port defined by software. The communication network 392 can be any network or combination of networks capable of transmitting information (e.g., data and/or signals) and can include, for example, a telephone network, an Ethernet network, a fiber-optic network, a wireless network, and/or a cellular network. The communication can be over a network such as, for example, a Wi-Fi or wireless local area network (“WLAN”) connection, a wireless wide area network (“WWAN”) connection, and/or a cellular connection. A network connection can be a wired connection such as, for example, an Ethernet connection, a digital subscription line (“DSL”) connection, a broadband coaxial connection, and/or a fiber-optic connection. For example, the microbe screening system 300 can be a host device configured to be accessed by one or more compute devices 393b via a network 392. In such a manner, the compute devices can provide information to and/or receive information from the microbe screening system 300 via the network 392. Such information can be, for example, information for the microbe screening system 300 to collect, relate, determine, analyze and/or generate ensembles of active, network-analyzed microbes, as described in further detail herein. Similarly, the compute devices can be configured to retrieve and/or request determined information from the microbe screening system 300.
In some embodiments, the communications interface 390 can include and/or be configured to include input/output interfaces 395. The input/output interfaces can accept, communicate, and/or connect to user input devices, peripheral devices, cryptographic processor devices, and/or the like. In some instances, one output device can be a video display, which can include, for example, a Cathode Ray Tube (CRT) or Liquid Crystal Display (LCD), LED, or plasma based monitor with an interface (e.g., Digital Visual Interface (DVI) circuitry and cable) that accepts signals from a video interface. In such embodiments, the communications interface 390 can be configured to, among other functions, receive data and/or information, and send microbe screening modifications, commands, and/or instructions.
The data collection component 330 can be any hardware and/or software component and/or module (stored in a memory such as the memory 320 and/or executing in hardware such as the processor 310) configured to collect, process, and/or normalize data for analysis on multi-dimensional interspecies interactions and dependencies within natural microbial communities performed by the absolute count component 335, sample relation component 340, activity component 345, network analysis component 350, and/or strain selection/ensemble generation component 355. In some embodiments, the data collection component 330 can be configured to determine absolute cell count of one or more active organism strains in a given volume of a sample. Based on the absolute cell count of one more active microorganism strains, the data collection component 330 can identify active strains within absolute cell count datasets using marker sequences. The data collection component 330 can continuously collect data for a period of time to represent the dynamics of microbial populations within a sample. The data collection component 330 can compile temporal data and store the number of cells of each active organism strain in a quantity matrix in a memory such as the memory 320.
The sample relation component 340 and the network analysis component 350 can be configured to collectively determine multi-dimensional interspecies interactions and dependencies within natural microbial communities. The sample relation component 340 can be any hardware and/or software component (stored in a memory such as the memory 320 and/or executing in hardware such as the processor 310) configured to relate a metadata parameter (environmental parameter, e.g., via co-occurrence) to presence of one or more active microorganism strains. In some embodiments, the sample relation component 340 can relate the one or more active organism strains to one or more environmental parameters.
The network analysis component 350 can be any hardware and/or software component (stored in a memory such as the memory 320 and/or executing in hardware such as the processor 310) configured to determine co-occurrence of one or more active microorganism strains in a sample to an environmental (metadata) parameter. In some embodiments, based on the data collected by the data collection component 330, and the relation between the one or more active microorganism strains to one or more environmental parameters determined by the sample relation component 340, the network analysis component 350 can create matrices populated with linkages denoting environmental parameters and microorganism strain associations, the absolute cell count of the one or more active microorganism strains and the level of expression of the one or more unique second markers to represent one or more networks of a heterogeneous population of microorganism strains. For example, the network analysis can use an association (quantity and/or abundance) matrix to identify associations between an active microorganism strain and a metadata parameter (e.g., the associations of two or more active microorganism strains) in a sample using rule mining approaches weighted with quantity data. In some embodiments, the network analysis component 350 can apply filters to select and/or remove rules. The network analysis component 350 can calculate cell number changes of active strains over time, noting directionality of change (i.e., negative values denoting decreases, positive values denoting increases). The network analysis component 350 can represent matrix as a network, with microorganism strains representing nodes and the quantity weighted rules representing edges. The network analysis component 350 can use leverage markov chains and random walks to determine connectivity between nodes and to define clusters. In some embodiments, the network analysis component 350 can filter clusters using metadata in order to identify clusters associated with desirable metadata. In some embodiments, the network analysis component 350 can rank target microorganism strains by integrating cell number changes over time and strains present in target clusters, with highest changes in cell number ranking the highest.
In some embodiments, the network analysis includes linkage analysis, modularity analysis, robustness measures, betweenness measures, connectivity measures, transitivity measures, centrality measures or a combination thereof. In another embodiment, a cluster analysis method can be used including building a connectivity model, subspace model, distribution model, density model, or a centroid model. In another embodiment, the network analysis includes predictive modeling of network through link mining and prediction, collective classification, link-based clustering, relational similarity, or a combination thereof. In another embodiment, the network analysis comprises mutual information, maximal information coefficient calculations, or other nonparametric methods between variables to establish connectivity. In another embodiment, the network analysis includes differential equation based modeling of populations. In another embodiment, the network analysis includes Lotka-Volterra modeling.
For each sample, sample set, and/or subsample, the cells are stained based on the target organism type 3002, each sample/subsample or portion thereof is weighed and serially diluted 3003, and processed 3004 to determine the number of cells of each microorganism type in each sample/subsample. In one exemplary implementation, a cell sorter can be used to count individual bacterial and fungal cells from samples, such as from an environmental sample. As part of the disclosure, specific dyes were developed to enable counting of microorganisms that previously were not countable according to the traditional methods. Following the methods of the disclosure, specific dyes are used to stain cell walls (e.g., for bacteria and/or fungi), and discrete populations of target cells can be counted from a greater population based on cellular characteristics using lasers. In one specific example, environmental samples are prepared and diluted into isotonic buffer solution and stained with dyes: (a) for bacteria, the following dyes can be used to stain—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 to stain—Cell wall: Calcofluor 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.
In the development of this disclosure, it was advantageously discovered that although direct and reactive dyes are typically associated with the staining of cellulose-based materials (i.e., cotton, flax, and viscose rayon), they can also be used to stain chitin and chitosan because of the presence of β-(1→4)-linked N-acetylglucosamine chains, and β-(1→4)-linked D-glucosamine and N-acetyl-D-glucosamine chains, respectively. When these subunits assemble into a chain, a flat, fiber-like structure very similar to cellulose chains is formed. Direct dyes adhere to chitin and/or chitosan molecules via Van der Waals forces between the dye and the fiber molecule. The more surface area contact between the two, the stronger the interaction. Reactive dyes, on the other hand, form a covalent bond to the chitin and/or chitosan.
Each dyed sample is loaded onto the FACs 3004 for counting. The sample can be run through a microfluidic chip with a specific size nozzle (e.g., 100 μm, selected depending on the implementation and application) that generates a stream of individual droplets (e.g., approximately 1/10th of a microliter (0.1 μL)). These variables (nozzle size, droplet formation) can be optimized for each target microorganism type. Ideally, encapsulated in each droplet is one cell, or “event,” and when each droplet is hit by a laser, anything that is dyed is excited and emits a different wavelength of light. The FACs optically detects each emission, and can plot them as events (e.g., on a 2D graph). A typical graph consists of one axis for size of event (determined by “forward scatter”), and the other for intensity of fluorescence. “Gates” can be drawn around discrete population on these graphs, and the events in these gates can be counted.
Returning to
The total nucleic acids are isolated from each sample 3006. The nucleic acid sample elutate is split into two parts (typically, two equal parts), and each part is enzymatically purified to obtain either purified DNA 3006a or purified RNA 3006b. Purified RNA is stabilized through an enzymatic conversion to cDNA 3006c. Sequencing libraries (e.g., ILLUMINA sequencing libraries) are prepared for both the purified DNA and purified cDNA using PCR to attach the appropriate barcodes and adapter regions, and to amplify the marker region appropriate for measuring the desired organism type 3007. Library quality can be assessed and quantified, and all libraries can then be pooled and sequenced.
Raw sequencing reads are quality trimmed and merged 3008. Processed reads are dereplicated and clustered to generate a set or list of all of the unique strains present in the plurality of samples 3009. This set or list can be used for taxonomic identification of each strain present in the plurality of samples 3010. Sequencing libraries derived from DNA samples can be identified, and sequencing reads from the identified DNA libraries are mapped back to the set or list of dereplicated strains in order to identity which strains are present in each sample, and quantify the number of reads for each strain in each sample 3011. The quantified read list is then integrated with the absolute cell count of target microorganism type in order to determine the absolute number or cell count of each strain 3013. The following code fragment shows an exemplary methodology for such processing, according to one embodiment:
Sequencing libraries derived from cDNA samples are identified 3014. Sequencing reads from the identified cDNA libraries are then mapped back to the list of dereplicated strains in order to determine which strains are active in each sample. If the number of reads is below a specified or designated threshold 3015, the strain is deemed or identified as inactive and is removed from subsequent analysis 3015a. If the number of reads exceeds the threshold 3015, the strain is deemed or identified as active and remains in the analysis 3015b. Inactive strains are then filtered from the output 3013 to generate a set or list of active strains and respective absolute numbers/cell counts for each sample 3016. The following code fragment shows an exemplary methodology for such processing, according to one embodiment:
Qualitative and quantitative metadata (e.g., environmental parameters, etc.) is identified, retrieved, and/or collected for each sample 3017 (set of samples, subsamples, etc.) and stored 3018 in a database (e.g., 319). Appropriate metadata can be identified, and the database is queried to pull identified and/or relevant metadata for each sample being analyzed 3019, depending on the application/implementation. The subset of metadata is then merged with the set or list of active strains and their corresponding absolute numbers/cell counts to create a large species and metadata by sample matrix 3020.
The maximal information coefficient (MIC) is then calculated between strains and metadata 3021a, and between strains 3021b. Results are pooled to create a set or list of all relationships and their corresponding MIC scores 3022. If the relationship scores below a given threshold 3023, the relationship is deemed/identified as irrelevant 3023b. If the relationship is above a given threshold 3023, the relationship deemed/identified as relevant 3023a, and is further subject to network analysis 3024. The following code fragment shows an exemplary methodology for such analysis, according to one embodiment:
Based on the output of the network analysis, active strains are selected 3025 for preparing products (e.g., ensembles, aggregates, and/or other synthetic groupings) containing the selected strains. The output of the network analysis can also be used to inform the selection of strains for further product composition testing.
The use of thresholds is discussed above for analyses and determinations. Thresholds can be, depending on the implementation and application: (1) empirically determined (e.g., based on distribution levels, setting a cutoff at a number that removes a specified or significant portion of low level reads); (2) any non-zero value; (3) percentage/percentile based; (4) only strains whose normalized second marker (i.e., activity) reads is greater than normalized first marker (cell count) reads; (5) log 2 fold change between activity and quantity or cell count; (6) normalized second marker (activity) reads is greater than mean second marker (activity) reads for entire sample (and/or sample set); and/or any magnitude threshold described above in addition to a statistical threshold (i.e., significance testing). The following example provides thresholding detail for distributions of RNA-based second marker measurements with respect to DNA-based first marker measurements, according to one embodiment.
The small intestine contents of one male Cobb500 was collected and subjected to analysis according to the disclosure. Briefly, the total number of bacterial cells in the sample was determined using FACs (e.g., 3004). Total nucleic acids were isolated (e.g., 3006) from the fixed small intestine sample. DNA (first marker) and cDNA (second marker) sequencing libraries were prepared (e.g., 3007), and loaded onto an ILLUMINA MISEQ. Raw sequencing reads from each library were quality filtered, dereplicated, clustered, and quantified (e.g., 3008). The quantified strain lists from both the DNA-based and cDNA-based libraries were integrated with the cell count data to establish the absolute number of cells of each strain within the sample (e.g., 3013). Although cDNA is not necessarily a direct measurement of strain quantity (i.e., highly active strains may have many copies of the same RNA molecule), the cDNA-based library was integrated with cell counting data in this example to maintain the same normalization procedure used for the DNA library.
After analysis, 702 strains (46 unique) were identified in the cDNA-based library and 1140 strains were identified in the DNA-based library. If using 0 as the activity threshold (i.e. keeping any nonzero value), 57% of strains within this sample that had a DNA-based first marker were also associated with a cDNA-based second marker. These strains are identified as/deemed the active portion of the microbial community, and only these strains continue into subsequent analysis. If the threshold is made more stringent and only strains whose second marker value exceed the first marker value are considered active, only 289 strains (25%) meet the threshold. The strains that meet this threshold correspond to those above the DNA (first marker) line in
The disclosure includes a variety of methods identifying a plurality of active microbe strains that influence each other as well as one or more parameters or metadata, and selecting identified microbes for use in a microbial ensemble that includes a select subset of a microbial community of individual microbial species, or strains of a species, that are linked in carrying out or influence a common function, or can be described as participating in, or leading to, or associated with, a recognizable parameter, such as a phenotypic trait of interest (e.g. increased milk production in a ruminant). The disclosure also includes a variety of systems and apparatuses that perform and/or facilitate the methods.
In some embodiments, the method, comprises: obtaining at least two samples sharing at least one common characteristic (such as sample geolocation, sample type, sample source, sample source individual, sample target animal, sample time, breed, diet, temperature, etc.) and having a least one different characteristic (such as sample geolocation/temporal location, sample type, sample source, sample source individual, sample target animal, sample time, breed, diet, temperature, etc., different from the common characteristic). For each sample, detecting the presence of one or more microorganism types, determining a number of each detected microorganism type of the one or more microorganism types in each sample; and measuring a number of unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain. This is followed by integrating the number of each microorganism type and the number of the first markers to yield the absolute cell count of each microorganism strain present in each sample; measuring at least one unique second marker for each microorganism strain based on a specified threshold to determine an activity level for that microorganism strain in each sample; filtering the absolute cell count by the determined activity to provide a set or list of active microorganisms strains and their respective absolute cell counts for each of the at least two samples; comparing the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with each other and with at least one measured metadata for each of the at least two samples and categorizing the active microorganism strains into one of 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 10 groups, 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, based on predicted function and/or chemistry. For example, the comparison can be network analysis that identifies the ties between the respective microbial strains and between each microbial strain and metadata, and/or between the metadata and the microbial strains. At least one microorganism can be selected from the at least two groups, and combined to form an ensemble of microorganisms configured to alter a property corresponding to the at least one metadata (e.g., a property in a target, such as milk production in a cow or cow population). Forming the ensemble can include isolating the microorganism strain or each microorganism strain, selecting a previously isolated microorganism strain based on the analysis, and/or incubating/growing specific microorganism strains based on the analysis, and combining the strains, including at particular amounts/counts and/or ratios and/or media/carrier(s) based on the application, to form the microbial ensemble. The ensemble can include an appropriate medium, carrier, and/or pharmaceutical carrier that enables delivery of the microorganisms in the ensemble in such a way that they can influence the recipient (e.g., increase milk production).
Measurement of the number of unique first markers can include measuring the number of unique genomic DNA markers in each sample, measuring the number of unique RNA markers in each sample, measuring the number of unique protein markers in each sample, and/or measuring the number of unique intermediate markers in each sample.
In some embodiments, measuring the number of unique first markers, and quantity thereof, includes subjecting genomic DNA from each sample to a high throughput sequencing reaction and/or subjecting genomic DNA from each sample to metagenome sequencing. The unique first markers can include at least one of an mRNA marker, an siRNA marker, and/or a ribosomal RNA marker. The unique first markers can additionally or alternatively include at least one of a sigma factor, a transcription factor, nucleoside associated protein, and/or metabolic enzyme.
In some embodiments, measuring the at least one unique second marker includes measuring a level of expression of the at least one unique second marker in each sample, and can include subjecting mRNA in the sample to gene expression analysis. The gene expression analysis can include a sequencing reaction, a quantitative polymerase chain reaction (qPCR), metatranscriptome sequencing, and/or transcriptome sequencing.
In some embodiments, measuring the level of expression of the at least one unique second marker includes subjecting each sample or a portion thereof to mass spectrometry analysis and/or subjecting each sample or a portion thereof to metaribosome profiling, or ribosome profiling. The one or more microorganism types includes bacteria, archaea, fungi, protozoa, plant, other eukaryote, viruses, viroids, or a combination thereof, and the one or more microorganism strains includes one or more bacterial strains, archaeal strains, fungal strains, protozoa strains, plant strains, other eukaryote strains, viral strains, viroid strains, or a combination thereof. The one or more microorganism strains can be one or more fungal species or sub-species, and/or the one or more microorganism strains can be one or more bacterial species or sub-species.
In some embodiments, determining the number of each of the one or more microorganism types in each sample includes subjecting each sample or a portion thereof to sequencing, centrifugation, optical microscopy, fluorescent microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain reaction (qPCR), gel electrophoresis, and/or flow cytometry.
Unique first markers can include a phylogenetic marker comprising a 5S ribosomal subunit gene, a 16S ribosomal subunit gene, a 23S ribosomal subunit gene, a 5.8S ribosomal subunit gene, a 18S ribosomal subunit gene, a 28S ribosomal subunit gene, a cytochrome c oxidase subunit gene, a β-tubulin gene, an elongation factor gene, an RNA polymerase subunit gene, an internal transcribed spacer (ITS), or a combination thereof. Measuring the number of unique markers, and quantity thereof, can include subjecting genomic DNA from each sample to a high throughput sequencing reaction, subjecting genomic DNA to genomic sequencing, and/or subjecting genomic DNA to amplicon sequencing.
In some embodiments, the at least one different characteristic includes: a collection time at which each of the at least two samples was collected, such that the collection time for a first sample is different from the collection time of a second sample, a collection location (either geographical location difference and/or individual sample target/animal collection differences) at which each of the at least two samples was collected, such that the collection location for a first sample is different from the collection location of a second sample. The at least one common characteristic can include a sample source type, such that the sample source type for a first sample is the same as the sample source type of a second sample. The sample source type can be one of animal type, organ type, soil type, water type, sediment type, oil type, plant type, agricultural product type, bulk soil type, soil rhizosphere type, plant part type, and/or the like. In some embodiments, the at least one common characteristic includes that each of the at least two samples are gastrointestinal samples, which can be, in some implementations, ruminal samples. In some implementations, the common/different characteristics provided herein can be, instead, different/common characteristics between certain samples. In some embodiments, the at least one common characteristic includes animal sample source type, each sample having a further common characteristic such that each sample is a tissue sample, a blood sample, a tooth sample, a perspiration sample, a fingernail sample, a skin sample, a hair sample, a feces sample, a urine sample, a semen sample, a mucus sample, a saliva sample, a muscle sample, a brain sample, or an organ sample.
In some embodiments, the above method can further comprise obtaining at least one further sample from a target, based on the at least one measured metadata, wherein the at least one further sample from the target shares at least one common characteristic with the at least two samples. Then, for the at least one further sample from the target, detecting the presence of one or more microorganism types, determining a number of each detected microorganism type of the one or more microorganism types, measuring a number of unique first markers and quantity thereof, integrating the number of each microorganism type and the number of the first markers to yield the absolute cell count of each microorganism strain present, measuring at least one unique second marker for each microorganism strain to determine an activity level for that microorganism strain, filtering the absolute cell count by the determined activity to provide a set or list of active microorganisms strains and their respective absolute cell counts for the at least one further sample from the target. In such embodiments, the selection of the at least one microorganism strain from the at least two groups is based on the set or list of active microorganisms strain(s) and the/their respective absolute cell counts for the at least one further sample from the target such that the formed ensemble is configured to alter a property of the target that corresponds to the at least one metadata. For example, using such an implementation, a microbial ensemble could be identified from samples taken from Holstein cows, and a target sample taken from a Jersey cow or water buffalo, where the analysis identified the same, substantially similar, or similar network relationships between the same or similar microorganism strains from the original sample and the target sample(s).
In some embodiments, comparing the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with at least one measured metadata or additional active microorganism strain for each of the at least two samples includes determining the co-occurrence of the one or more active microorganism strains in each sample with the at least one measured metadata or additional active microorganism strain. The at least one measured metadata can include one or more parameters, wherein the one or more parameters is at least one of sample pH, sample temperature, abundance of a fat, abundance of a protein, abundance of a carbohydrate, abundance of a mineral, abundance of a vitamin, abundance of a natural product, abundance of a specified compound, bodyweight of the sample source, feed intake of the sample source, weight gain of the sample source, feed efficiency of the sample source, presence or absence of one or more pathogens, physical characteristic(s) or measurement(s) of the sample source, production characteristics of the sample source, or a combination thereof. Parameters can also include abundance of whey protein, abundance of casein protein, and/or abundance of fats in milk produced by the sample source.
In some embodiments, determining the co-occurrence of the one or more active microorganism strains and the at least one measured metadata or additional active microorganism strain in each sample can include creating matrices populated with linkages denoting metadata and microorganism strain associations in two or more sample sets, the absolute cell count of the one or more active microorganism strains and the measure of the one or more unique second markers to represent one or more networks of a heterogeneous microbial community or communities. Determining the co-occurrence of the one or more active microorganism strains and the at least one measured metadata or additional active microorganism strain and categorizing the active microorganism strains can include network analysis and/or cluster analysis to measure connectivity of each microorganism strain within a network, the network representing a collection of the at least two samples that share a common characteristic, measured metadata, and/or related environmental parameter. The network analysis and/or cluster analysis can include linkage analysis, modularity analysis, robustness measures, betweenness measures, connectivity measures, transitivity measures, centrality measures, or a combination thereof. The cluster analysis can include building a connectivity model, subspace model, distribution model, density model, and/or a centroid model. Network analysis can, in some implementations, include predictive modeling of network(s) through link mining and prediction, collective classification, link-based clustering, relational similarity, a combination thereof, and/or the like. The network analysis can comprise differential equation based modeling of populations and/or Lotka-Volterra modeling. The analysis can be a heuristic method. In some embodiments, the analysis can be the Louvain method. The network analysis can include nonparametric methods to establish connectivity between variables, and/or mutual information and/or maximal information coefficient calculations between variables to establish connectivity.
For some embodiments, the method for forming an ensemble of active microorganism strains configured to alter a property or characteristic in an environment based on two or more sample sets that share at least one common or related environmental parameter between the two or more sample sets and that have at least one different environmental parameter between the two or more sample sets, each sample set comprising at least one sample including a heterogeneous microbial community, wherein the one or more microorganism strains is a subtaxon of one or more organism types, comprises: detecting the presence of a plurality of microorganism types in each sample; determining the absolute number of cells of each of the detected microorganism types in each sample; and measuring the number of unique first markers in each sample, and quantity thereof, wherein a unique first marker is a marker of a microorganism strain. Then, at the protein or RNA level, measuring the level of expression of one or more unique second markers, wherein a unique second marker is a marker of activity of a microorganism strain, determining activity of the detected microorganism strains for each sample based on the level of expression of the one or more unique second markers exceeding a specified threshold, calculating the absolute cell count of each detected active microorganism strains in each sample based upon the quantity of the one or more first markers and the absolute number of cells of the microorganism types from which the one or more microorganism strains is a subtaxon, wherein the one or more active microorganism strains expresses the second unique marker above the specified threshold. The co-occurrence of the active microorganism strains in the samples with at least one environmental parameter is then determined based on maximal information coefficient network analysis to measure connectivity of each microorganism strain within a network, wherein the network is the collection of the at least two or more sample sets with at least one common or related environmental parameter. A plurality of active microorganism strains from the one or more active microorganism strains is selected based on the network analysis, and an ensemble of active microorganism strains is formed from the selected plurality of active microorganism strains, the ensemble of active microorganism strains configured to selectively alter a property or characteristic of an environment when the ensemble of active microorganism strains is introduced into that environment. For some implementations, at least one measured indicia of at least one common or related environmental factor for a first sample set is different from a measured indicia of the at least one common or related environmental factor for a second sample set. For example, if the samples/sample sets are from cows, the first sample set can be from cows fed on a grass diet, while the second sample set can be from cows fed on a corn diet. While one sample set could be a single sample, it could alternatively be a plurality of samples, and a measured indicia of at least one common or related environmental factor for each sample within a sample set is substantially similar (e.g., samples in one set all taken from a herd on grass feed), and an average measured indicia for one sample set is different from the average measured indicia from another sample set (first sample set is from a herd on grass feed, and the second sample set is samples from a herd on corn feed). There may be additional difference and similarities that are taken into account in the analysis, such as differing breeds, differing diets, differing performance, differing age, differing feed additives, differing growth stage, differing physiological characteristics, differing state of health, differing elevations, differing environmental temperatures, differing season, different antibiotics, etc. While in some embodiments each sample set comprises a plurality of samples, and a first sample set is collected from a first population and a second sample set is collected from a second population, in additional or alternative embodiments, each sample set comprises a plurality of samples, and a first sample set is collected from a first population at a first time and a second sample set is collected from the first population at a second time different from the first time. For example, the first sample set could be taken at a first time from a herd of cattle while they were being feed on grass, and a second sample set could be taken at a second time (e.g., 2 months later), where the herd had been switched over to corn feed right after the first sample set was taken. In such embodiments, the samples can be collected and the analysis performed on the population, and/or can include specific reference to individual animals so that the changes that happened to individual animals over the time period could be identified, and a finer level of data granularity provided. In some embodiments, a method for forming a synthetic ensemble of active microorganism strains configured to alter a property in a biological environment, based on two or more samples (or sample sets, each set comprising at least one sample), each having a plurality of environmental parameters (and/or metadata), at least one parameter of the plurality of environmental parameters being a common environmental parameter that is similar between the two or more samples or sample sets and at least one environmental parameter being a different environmental parameter that is different between each of the two or more samples or sample sets, each sample set including at least one sample comprising a heterogeneous microbial community obtained from a biological sample source, at least one of the active microorganism strains being a subtaxon of one or more organism types, comprises: detecting the presence of a plurality of microorganism types in each sample; determining the absolute number of cells of each of the detected microorganism types in each sample; measuring the number of unique first markers in each sample, and quantity thereof, a unique first marker being a marker of a microorganism strain; measuring the level (e.g., level of expression) of one or more unique second markers, wherein a unique second marker is a marker of activity of a microorganism strain; determining activity of each of the detected microorganism strains for each sample based on the level (e.g., level of expression) of the one or more unique second markers exceeding a specified threshold to identify one or more active microorganism strains; calculating the absolute cell count of each detected active microorganism strain in each sample from the quantity (relative quantity, proportional number, proportional quantity, percentage quantity, etc.) of each of the one or more unique first markers and the absolute number of cells of the respective or corresponding microorganism types from which the one or more microorganism strains is a subtaxon (wherein the calculating is mathematical function such as multiplication, dot operator, and/or other operation), the one or more active microorganism strains having or expressing one or more unique second markers above the specified threshold; analyzing the active microorganism strains of the two or more sample sets, the analyzing including conducting nonparametric network analysis of each of the active microorganism strains for each of the two or more sample sets, the at least one common environmental parameter, and the at least one different environmental parameter, the nonparametric network analysis including determining the maximal information coefficient score between each active microorganism strain and every other active microorganism strain and determining the maximal information coefficient score between each active microorganism strain and the at least one different environmental parameter; selecting a plurality of active microorganism strains from the one or more active microorganism strains based on the nonparametric network analysis; and forming a synthetic ensemble of active microorganism strains comprising the selected plurality of active microorganism strains and a microbial carrier medium, the ensemble of active microorganism strains configured to selectively alter a property of a biological environment when the synthetic ensemble of active microorganism strains is introduced into that biological environment. Depending on the embodiment or implementation, the at least two samples or sample sets can comprise three samples, four samples, five samples, six samples, seven samples, eight samples, nine samples, ten samples, eleven samples, twelve samples, thirteen samples, fourteen samples, fifteen samples, sixteen samples, seventeen samples, eighteen samples, nineteen samples, twenty samples, twenty one samples, twenty two samples, twenty three samples, twenty four samples, twenty five samples, twenty six samples, twenty seven samples, twenty eight samples, twenty nine samples, thirty samples, thirty five samples, forty samples, forty five samples, fifty samples, sixty samples, seventy samples, eighty samples, ninety samples, one hundred samples, one hundred fifty samples, two hundred samples, three hundred samples, four hundred samples, five hundred samples, six hundred samples, and/or the like. The total number of samples can, depending on the embodiment/implementation, can be less than 5, from 5 to 10, 10 to 15, 15 to 20, 20 to 30, 30 to 40, 40 to 50, 50 to 60, 60 to 70, 70 to 80, 80 to 90, 90 to 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, less than 5000, less than 10000, less than 20000, and so forth.
In some embodiments, at least one common or related environmental factor includes nutrient information, dietary information, animal characteristics, infection information, health status, and/or the like.
The at least one measured indicia can include sample pH, sample temperature, abundance of a fat, abundance of a protein, abundance of a carbohydrate, abundance of a mineral, abundance of a vitamin, abundance of a natural product, abundance of a specified compound, bodyweight of the sample source, feed intake of the sample source, weight gain of the sample source, feed efficiency of the sample source, presence or absence of one or more pathogens, physical characteristic(s) or measurement(s) of the sample source, production characteristics of the sample source, abundance of whey protein in milk produced by the sample source, abundance of casein protein produced by the sample source, and/or abundance of fats in milk produced by the sample source, or a combination thereof.
Measuring the number of unique first markers in each sample can, depending on the embodiment, comprise measuring the number of unique genomic DNA markers, measuring the number of unique RNA markers, and/or measuring the number of unique protein markers. The plurality of microorganism types can include one or more bacteria, archaea, fungi, protozoa, plant, other eukaryote, virus, viroid, or a combination thereof.
In some embodiments, determining the absolute number of each of the microorganism types in each sample includes subjecting the sample or a portion thereof to sequencing, centrifugation, optical microscopy, fluorescent microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain reaction (qPCR), gel electrophoresis and/or flow cytometry. In some embodiments, one or more active microorganism strains is a subtaxon of one or more microbe types selected from one or more bacteria, archaea, fungi, protozoa, plant, other eukaryote, virus, viroid, or a combination thereof. In some embodiments, one or more active microorganism strains is one or more bacterial strains, archaeal strains, fungal strains, protozoa strains, plant strains, other eukaryote strains, viral strains, viroid strains, or a combination thereof. In some embodiments, one or more active microorganism strains is one or more bacterial species or subspecies. In some embodiments, one or more active microorganism strains is one or more fungal species or subspecies.
In some embodiments, at least one unique first marker comprises a phylogenetic marker comprising a 5S ribosomal subunit gene, a 16S ribosomal subunit gene, a 23S ribosomal subunit gene, a 5.8S ribosomal subunit gene, a 18S ribosomal subunit gene, a 28S ribosomal subunit gene, a cytochrome c oxidase subunit gene, a beta-tubulin gene, an elongation factor gene, an RNA polymerase subunit gene, an internal transcribed spacer (ITS), or a combination thereof.
In some embodiments, measuring the number of unique first markers, and quantity thereof, comprises subjecting genomic DNA from each sample to a high throughput sequencing reaction, and/or subjecting genomic DNA from each sample to metagenome sequencing. In some implementations, unique first markers can include an mRNA marker, an siRNA marker, and/or a ribosomal RNA marker. In some implementations, unique first markers can include a sigma factor, a transcription factor, nucleoside associated protein, metabolic enzyme, or a combination thereof.
In some embodiments, measuring the level of expression of one or more unique second markers comprises subjecting mRNA in each sample to gene expression analysis, and in some implementations, gene expression analysis comprises a sequencing reaction. In some implementations, the gene expression analysis comprises a quantitative polymerase chain reaction (qPCR), metatranscriptome sequencing, and/or transcriptome sequencing.
In some embodiments, measuring the level of expression of one or more unique second markers includes subjecting each sample or a portion thereof to mass spectrometry analysis, metaribosome profiling, and/or ribosome profiling.
In some embodiments, measuring the level of expression of the at least one or more unique second markers includes subjecting each sample or a portion thereof to metaribosome profiling or ribosome profiling (Ribo-Seq) (see, e.g., Ingolia, N. T., S. Ghaemmaghami, J. R. Newman, and J. S. Weissman, 2009, “Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling” Science 324:218-223; Ingolia, N. T., 2014, “Ribosome profiling: new views of translation, from single codons to genome scale” Nat. Rev. Genet. 15:205-213; each of which is incorporated by reference in it entirety for all purposes). Ribo-seq is a molecular technique that can be used to determine in vivo protein synthesis at the genome-scale. This method directly measures which transcripts are being actively translated via footprinting ribosomes as they bind and interact with mRNA. The bound mRNA regions are then processed and subjected to high-throughput sequencing reactions. Ribo-seq has been shown to have a strong correlation with quantitative proteomics (see, e.g., Li, G. W., D. Burkhardt, C. Gross, and J. S. Weissman. 2014 “Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources” Cell 157:624-635, the entirety of which is herein expressly incorporated by reference).
The source type for the samples can be one of animal, soil, air, saltwater, freshwater, wastewater sludge, sediment, oil, plant, an agricultural product, bulk soil, soil rhizosphere, plant part, vegetable, an extreme environment, or a combination thereof. In some implementations, each sample is a digestive tract and/or ruminal sample. In some implementations, samples can be tissue samples, blood samples, tooth samples, perspiration samples, fingernail samples, skin samples, hair samples, feces samples, urine samples, semen samples, mucus samples, saliva samples, muscle samples, brain samples, tissue samples, and/or organ samples.
Depending on the implementation, a microbial ensemble of the disclosure can comprise two or more substantially pure microbes or microbe strains, a mixture of desired microbes/microbe strains, and can also include any additional components that can be administered to a target, e.g., for restoring microbiota to an animal. Microbial ensembles made according to the disclosure can be administered with an agent to allow the microbes to survive a target environment (e.g., the gastrointestinal tract of an animal, where the ensemble is configured to resist low pH and to grow in the gastrointestinal environment). In some embodiments, microbial ensembles can include one or more agents that increase the number and/or activity of one or more desired microbes or microbe strains, said strains being present or absent from the microbes/strains included in the ensemble. Non-limiting examples of such agents include fructooligosaccharides (e.g., oligofructose, inulin, inulin-type fructans), galactooligosaccharides, amino acids, alcohols, and mixtures thereof (see 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 herein incorporated by reference in their entireties for all purposes).
Microbial strains identified by the methods of the disclosure can be cultured/grown prior to inclusion in an ensemble. Media can be used for such growth, and can include any medium suitable to support growth of a microbe, including, by way of non-limiting example, natural or artificial including gastrin supplemental agar, LB media, blood serum, and/or tissue culture gels. It should be appreciated that the media can be used alone or in combination with one or more other media. It can also be used with or without the addition of exogenous nutrients. The medium can be modified or enriched with additional compounds or components, for example, a component which may assist in the interaction and/or selection of specific groups of microorganisms and/or strains thereof. For example, antibiotics (such as penicillin) or sterilants (for example, quaternary ammonium salts and oxidizing agents) could be present and/or the physical conditions (such as salinity, nutrients (for example organic and inorganic minerals (such as phosphorus, nitrogenous salts, ammonia, potassium and micronutrients such as cobalt and magnesium), pH, and/or temperature) could be modified.
As discussed above, systems and apparatuses can be configured according to the disclosure, and in some embodiments, can comprise a processor and memory, the memory storing processor-readable/issuable instructions to perform the method(s). In one embodiment, a system and/or apparatus are configured to perform the method. Also disclosed are processor-implementations of the methods, as discussed with reference for
It is intended that the systems and methods described herein can be performed by software (stored in memory and/or executed on hardware), hardware, or a combination thereof. Hardware components and/or modules can 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 modules (executed on hardware) can be expressed in a variety of software languages (e.g., computer code), including Unix utilities, C, C++, Java™, JavaScript (e.g., ECMAScript 6), Ruby, SQL, SAS®, the R programming language/software environment, Visual Basic™, and other object-oriented, procedural, or other programming language and development tools. Examples of computer code include, but are not limited to, micro-code or micro-instructions, machine instructions, such as produced by a compiler, code used to produce a web service, and files containing higher-level instructions that are executed by a computer using an interpreter. Additional examples of computer code include, but are not limited to, control signals, encrypted code, and compressed code.
Some embodiments described herein relate to devices with a non-transitory computer-readable medium (also can be referred to as a non-transitory processor-readable medium or memory) having instructions or computer code thereon for performing various computer-implemented operations. The computer-readable medium (or processor-readable medium) is non-transitory in the sense that it does not include transitory propagating signals per se (e.g., a propagating electromagnetic wave carrying information on a transmission medium such as space or a cable). The media and computer code (also can be referred to as code) may be those designed and constructed for the specific purpose or purposes. Examples of non-transitory computer-readable media include, but are not limited to: magnetic storage media such as hard disks, floppy disks, and magnetic tape; optical storage media such as Compact Disc/Digital Video Discs (CD/DVDs), Compact Disc-Read Only Memories (CD-ROMs), and holographic devices; magneto-optical storage media such as optical disks; carrier wave signal processing components and/or modules; and hardware devices that are specially configured to store and execute program code, 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 a computer program product, which can include, for example, the instructions and/or computer code discussed herein.
While various embodiments of
The present disclosure is further illustrated by reference to the following Experimental Data and Examples. However, it should be noted that these Experimental Data and Examples, like the embodiments described above, are illustrative and are not to be construed as restricting the scope of the disclosure in any way.
Reference is made to steps provided at
2000: Cells from a cow rumen sample are sheared off matrix. This can be done via blending or mixing the sample vigorously through sonication or vortexing followed by differential centrifugation for matrix removal from cells. Centrifugation can include a gradient centrifugation step using Nycodenz or Percoll.
2001: Organisms are stained using fluorescent dyes that target specific organism types. Flow cytometry is used to discriminate different populations based on staining properties and size.
2002: The absolute number of organisms in the sample is determined by, for example, flow cytometry. This step yields information about how many organism types (such as bacteria, archaea, fungi, viruses or protists) are in a given volume.
2003: A cow rumen sample is obtained and cells adhered to matrix are directly lysed via bead beating. Total nucleic acids are purified. Total purified nucleic acids are treated with RNAse to obtain purified genomic DNA (gDNA). qPCR is used to simultaneously amplify specific markers from the bulk gDNA and to attach sequencing adapters and barcodes to each marker. The qPCR reaction is stopped at the beginning of exponential amplification to minimize PCR-related bias. Samples are pooled and multiplexed sequencing is performed on the pooled samples using an Illumina Miseq.
2004: Cells from a cow rumen sample adhered to matrix are directly lysed via bead beating. Total nucleic acids are purified using a column-based approach. Total purified nucleic acids are treated with DNAse to obtain purified RNA. Total RNA is converted to cDNA using reverse transcriptase. qPCR is used to simultaneously amplify specific markers from the bulk cDNA and to attach sequencing adapters and barcodes to each marker. The qPCR reaction is stopped at the beginning of exponential amplification to minimize PCR-related bias. Samples are pooled and multiplexed sequencing is performed on the pooled samples using an Illumina Miseq.
2005: Sequencing output (fastq files) is processed by removing low quality base pairs and truncated reads. DNA-based datasets are analyzed using a customized UPARSE pipeline, and sequencing reads are matched to existing database entries to identify strains within the population. Unique sequences are added to the database. RNA-based datasets are analyzed using a customized UPARSE pipeline. Active strains are identified using an updated database.
2006: Using strain identity data obtained in the previous step (2005), the number of reads representing each strain is determined and represented as a percentage of total reads. The percentage is multiplied by the counts of cells (2002) to calculate the absolute cell count of each organism type in a sample and a given volume. Active strains are identified within absolute cell count datasets using the marker sequences present in the RNA-based datasets along with an appropriate threshold. Strains that do not meet the threshold are removed from analysis.
2007: Repeat 2003-2006 to establish time courses representing the dynamics of microbial populations within multiple cow rumens. Compile temporal data and store the number of cells of each active organism strain and metadata for each sample in a quantity or abundance matrix. Use quantity matrix to identify associations between active strains in a specific time point sample using rule mining approaches weighted with quantity data. Apply filters to remove insignificant rules.
2008: Calculate cell number changes of active strains over time, noting directionality of change (i.e., negative values denoting decreases, positive values denoting increases). Represent matrix as a network, with organism strains representing nodes and the quantity weighted rules representing edges. Leverage markov chains and random walks to determine connectivity between nodes and to define clusters. Filter clusters using metadata in order to identify clusters associated with desirable metadata (environmental parameter(s)). Rank target organism strains by integrating cell number changes over time and strains present in target clusters, with highest changes in cell number ranking the highest.
Objective:
Determine rumen microbial community constituents that impact the production of milk fat in dairy cows.
Animals:
Eight lactating, ruminally cannulated, Holstein cows were housed in individual tie-stalls for use in the experiment. Cows were fed twice daily, milked twice a day, and had continuous access to fresh water. One cow (cow 1) was removed from the study after the first dietary Milk Fat Depression due to complications arising from an abortion prior to the experiment.
Experimental Design and Treatment:
The experiment used a crossover design with 2 groups and 1 experimental period. The experimental period lasted 38 days: 10 days for the covariate/wash-out period and 28 days for data collection and sampling. The data collection period consisted of 10 days of dietary Milk Fat Depression (MFD) and 18 days of recovery. After the first experimental period, all cows underwent a 10-day wash out period prior to the beginning of period 2.
Dietary MFD was induced with a total mixed ration (TMR) low in fiber (29% NDF) with high starch degradability (70% degradable) and high polyunsaturated fatty acid levels (PUFA, 3.7%). The Recovery phase included two diets variable in starch degradability. Four cows were randomly assigned to the recovery diet high in fiber (37% NDF), low in PUFA (2.6%), and high in starch degradability (70% degradable). The remaining four cows were fed a recovery diet high in fiber (37% NDF), low in PUFA (2.6%), but low in starch degradability (35%).
During the 10-day covariate and 10-day wash out periods, cows were fed the high fiber, low PUFA, and low starch degradability diet.
Samples and Measurements:
Milk yield, dry matter intake, and feed efficiency were measured daily for each animal throughout the covariate, wash out, and sample collection periods. TMR samples were measured for nutrient composition. During the collection period, milk samples were collected and analyzed every 3 days. Samples were analyzed for milk component concentrations (milk fat, milk protein, lactose, milk urea nitrogen, somatic cell counts, and solids) and fatty acid compositions.
Rumen samples were collected and analyzed for microbial community composition and activity every 3 days during the collection period. The rumen was intensively sampled 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, and 22 hours after feeding during day 0, day 7, and day 10 of the dietary MFD. Similarly, the rumen was intensively sampled 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, and 22 hours after feeding on day 16 and day 28 during the recovery period. Rumen contents were analyzed for pH, acetate concentration, butyrate concentration, propionate concentration, isoacid concentration, and long chain and CLA isomer concentrations.
Rumen Sample Preparation and Sequencing:
After collection, rumen samples were centrifuged at 4,000 rpm in a swing bucket centrifuge for 20 minutes at 4° C. The supernatant was decanted, and an aliquot of each rumen content sample (1-2 mg) was added to a sterile 1.7 mL tube prefilled with 0.1 mm glass beads. A second aliquot was collected and stored in an empty, sterile 1.7 mL tube for cell counting.
Rumen samples with glass beads (1st aliquot) were homogenized with bead beating to lyse microorganisms. DNA and RNA was extracted and purified from each sample and prepared for sequencing on an Illumina Miseq. Samples were sequenced using paired-end chemistry, with 300 base pairs sequenced on each end of the library. Rumen samples in empty tubes (2nd aliquot) were stained and put through a flow cytometer to quantify the number of cells of each microorganism type in each sample.
Sequencing Read Processing and Data Analysis:
Sequencing reads were quality trimmed and processed to identify bacterial species present in the rumen based on a marker gene. Count datasets and activity datasets were integrated with the sequencing reads to determine the absolute cell numbers of active microbial species within the rumen microbial community. Production characteristics of the cow over time, including pounds of milk produced, were linked to the distribution of active microorganisms within each sample over the course of the experiment using mutual information. Maximal information coefficient (MIC) scores were calculated between pounds of milk fat produced and the absolute cell count of each active microorganism. Microorganisms were ranked by MIC score, and microorganisms with the highest MIC scores were selected as the target species most relevant to pounds of milk produced.
Tests cases to determine the impact of count data, activity data, and count and activity on the final output were run by omitting the appropriate datasets from the sequencing analysis. To assess the impact of using a linear correlation rather than the MIC on target selection, Pearson's coefficients were also calculated for pounds of milk fat produced as compared to the relative abundance of all microorganisms and the absolute cell count of active microorganisms.
Relative Abundances Vs. Absolute Cell Counts
The top 15 target species were identified for the dataset that included cell count data (absolute cell count, Table 2) and for the dataset that did not include cell count data (relative abundance, Table 1) based on MIC scores. Activity data was not used in this analysis in order to isolate the effect of cell count data on final target selection. Ultimately, the top 8 targets were the same between the two datasets. Of the remaining 7, 5 strains were present on both lists in varying order. Despite the differences in rank for these 5 strains, the calculated MIC score for each strain was the identical between the two lists. The two strains present on the absolute cell count list but not the relative abundance list, ascus_111 and ascus_288, were rank 91 and rank 16, respectively, on the relative abundance list. The two strains present on the relative abundance list but not the absolute cell count list, ascus_102 and ascus_252, were rank 50 and rank 19, respectively, on the absolute cell count list. These 4 strains did have different MIC scores on each list, thus explaining their shift in rank and subsequent impact on the other strains in the list.
Integration of cell count data did not always affect the final MIC score assigned to each strain. This may be attributed to the fact that although the microbial population did shift within the rumen daily and over the course of the 38-day experiment, it was always within 107-108 cells per milliliter. Much larger shifts in population numbers would undoubtedly have a broader impact on final MIC scores.
Inactive Species Vs. Active Species
In order to assess the impact of filtering strains based on activity data, target species were identified from a dataset that leveraged relative abundance with (Table 3) and without (Table 1) activity data as well as a dataset that leveraged absolute cell counts with (Table 4) and without (Table 2) activity data.
For the relative abundance case, ascus_126, ascus_1366, ascus_1780, ascus_299, ascus_1139, ascus_127, ascus_341, and ascus_252 were deemed target strains prior to applying activity data. These eight strains (53% of the initial top 15 targets) fell below rank 15 after integrating activity data. A similar trend was observed for the absolute cell count case. Ascus_126, ascus_1366, ascus_1780, ascus_299, ascus_1139, ascus_127, and ascus_341 (46% of the initial top 15 targets) fell below rank 15 after activity dataset integration.
The activity datasets had a much more severe effect on target rank and selection than the cell count datasets. When integrating these datasets together, if a sample is found to be inactive it is essentially changed to a “0” and not considered to be part of the analysis. Because of this, the distribution of points within a sample can become heavily altered or skewed after integration, which in turn greatly impacts the final MIC score and thus the rank order of target microorganisms.
Relative Abundances and Inactive Vs. Absolute Cell Counts and Active
Ultimately, the method defined here leverages both cell count data and activity data to identify microorganisms highly linked to relevant metadata characteristics. Within the top 15 targets selected using both methods (Table 4, Table 1), only 7 strains were found on both lists. Eight strains (53%) were unique to the absolute cell count and activity list. The top 3 targets on both lists matched in both strain as well as in rank. However, two of the three did not have the same MIC score on both lists, suggesting that they were influenced by activity dataset integration but not enough to upset their rank order.
Linear Correlations Vs. Nonparametric Approaches
Pearson's coefficients and MIC scores were calculated between pounds of milk fat produced and the absolute cell count of active microorganisms within each sample (Table 5). Strains were ranked either by MIC (Table 5a) or Pearson coefficient (Table 5b) to select target strains most relevant to milk fat production. Both MIC score and Pearson coefficient are reported in each case. Six strains were found on both lists, meaning nine (60%) unique strains were identified using the MIC approach. The rank order of strains between lists did not match—the top 3 target strains identified by each method were also unique.
Like Pearson coefficients, the MIC score is reported over a range of 0 to 1, with 1 suggesting a very tight relationship between the two variables. Here, the top 15 targets exhibited MIC scores ranging from 0.97 to 0.74. The Pearson coefficients for the correlation test case, however, ranged from 0.53 to 0.45—substantially lower than the mutual information test case. This discrepancy may be due to the differences inherent to each analysis method. While correlations are a linear estimate that measures the dispersion of points around a line, mutual information leverages probability distributions and measures the similarity between two distributions. Over the course of the experiment, the pounds of milk fat produced changed nonlinearly (
The Present Method in Entirety Vs. Conventional Approaches
The conventional approach of analyzing microbial communities relies on the use of relative abundance data with no incorporation of activity information, and ultimately ends with a simple correlation of microbial species to metadata (see, e.g., U.S. Pat. No. 9,206,680, which is herein incorporated by reference in its entirety for all purposes). Here, we have shown how the incorporation of each dataset incrementally influences the final list of targets. When applied in its entirety, the method described herein selected a completely different set of targets when compared to the conventional method (Tables 5a and 5c). Ascus_3038, the top target strain selected using the conventional approach, was plotted against milk fat to visualize the strength of the correlation (
Table 5: Top 15 Target Strains Using Mutual Information or Correlations
Example 3 shows a specific implementation with the aim to increase the total amount of milk fat and milk protein produced by a lactating ruminant, and the calculated ECM. As used herein, ECM represents the amount of energy in milk based upon milk volume, milk fat, and milk protein. ECM adjusts the milk components to 3.5% fat and 3.2% protein, thus equalizing animal performance and allowing for comparison of production at the individual animal and herd levels over time. An equation used to calculate ECM, as related to the present disclosure, is:
ECM=(0.327×milk pounds)+(12.95×fat pounds)+(7.2×protein pounds)
Application of the methodologies presented herein, utilizing the disclosed methods to identify active interrelated microbes/microbe strains and generating microbial ensembles therefrom, demonstrate an increase in the total amount of milk fat and milk protein produced by a lactating ruminant. These increases were realized without the need for further addition of hormones.
In this example, a microbial ensemble comprising two isolated microbes, Ascusb_X and Ascusf_Y, identified and generated according to the above disclosure, was administered to Holstein cows in mid-stage lactation over a period of five weeks. The cows were randomly assigned into 2 groups of 8, wherein one of the groups was a control group that received a buffer lacking a microbial ensemble. The second group, the experimental group, was administered a microbial ensemble comprising Ascusb_X and Ascusf_Y once per day for five weeks. Each of the cows were housed in individual pens and were given free access to feed and water. The diet was a high milk yield diet. Cows were fed ad libitum and the feed was weighed at the end of the day, and prior day refusals were weighed and discarded. Weighing was performed with a PS-2000 scale from Salter Brecknell (Fairmont, Minn.).
Cows were cannulated such that a cannula extended into the rumen of the cows. Cows were further provided at least 10 days of recovery post cannulation prior to administering control dosages or experimental dosages.
Administration to the control group consisted of 20 ml of a neutral buffered saline, while administration to the experimental group consisted of approximately 109 cells suspended in 20 mL of neutral buffered saline. The control group received 20 ml of the saline once per day, while the experimental group received 20 ml of the saline further comprising 109 microbial cells of the described microbial ensemble.
The rumen of every cow was sampled on days 0, 7, 14, 21, and 35, wherein day 0 was the day prior to microbial administration. Note that the experimental and control administrations were performed after the rumen was sampled on that day. Daily sampling of the rumen, beginning on day 0, with a pH meter from Hanna Instruments (Woonsocket, R.I.) was inserted into the collected rumen fluid for recordings. Rumen sampling included both particulate and fluid sampling from the center, dorsal, ventral, anterior, and posterior regions of the rumen through the cannula, and all five samples were pooled into 15 ml conical vials containing 1.5 ml of stop solution (95% ethanol, 5% phenol). A fecal sample was also collected on each sampling day, wherein feces were collected from the rectum with the use of a palpation sleeve. Cows were weighed at the time of each sampling.
Fecal samples were placed in a 2 ounce vial, stored frozen, and analyzed to determine values for apparent neutral detergent fibers (NDF) digestibility, apparent starch digestibility, and apparent protein digestibility. Rumen sampling consisted of sampling both fluid and particulate portions of the rumen, each of which was stored in a 15 ml conical tube. Cells were fixed with a 10% stop solution (5% phenol/95% ethanol mixture) and kept at 4° C. and shipped to Ascus Biosciences (San Diego, Calif.) on ice.
The milk yield was measured twice per day, once in the morning and once at night. Milk composition (% fats and % proteins, etc.) was measured twice per day, once in the morning and once at night. Milk samples were further analyzed with near-infrared spectroscopy for protein fats, solids, analysis for milk urea nitrogen (MUN), and somatic cell counts (SCC) at the Tulare Dairy Herd Improvement Association (DHIA) (Tulare, Calif.). Feed intake of individual cows and rumen pH were determined once per day.
A sample of the total mixed ration (TMR) was collected the final day of the adaptation period, and then successively collected once per week. Sampling was performed with the quartering method, wherein the samples were stored in vacuum sealed bags which were shipped to Cumberland Valley Analytical Services (Hagerstown, Md.) and analyzed with the NIR1 package. The final day of administration of buffer and/or microbial bioensemble was on day 35, however all other measurements and samplings continued as described until day 46.
Detection of Clostridium perfringens as Causative Agent for Lesion Formation in Broiler Chickens
160 male Cobb 500s were challenged with various levels of Clostridium perfringens (Table 6a). They were raised for 21 days, sacrificed, and lesion scored to quantify the progression of necrotic enteritis and the impact of C. perfringens.
Experimental Design
Birds were housed within an environmentally controlled facility in wooden floor pens (˜4′×4′ minus 2.25 sq. ft for feeder space) providing floor space & bird density of [˜0.69 ft2/bird], temperature, lighting, feeder and water. Birds were placed in clean pens containing an appropriate depth of wood shavings to provide a comfortable environment for the chicks. Additional shavings were added to pens if they become too damp for comfortable conditions for the test birds during the study. Lighting was via incandescent lights and a commercial lighting program was used as follows.
Environmental conditions for the birds (i.e. bird density, temperature, lighting, feeder and water space) were similar for all treatment groups. In order to prevent bird migration and bacterial spread from pen to pen, each pen had a solid (plastic) divider for approximately 24 inches in height between pens.
Vaccinations and Therapeutic Medication:
Birds were vaccinated for Mareks at the hatchery. Upon receipt (study day 0), birds were vaccinated for Newcastle and Infectious Bronchitis by spray application. Documentation of vaccine manufacturer, lot number and expiration date were provided with the final report.
Water:
Water was provided ad libitum throughout the study via one Plasson drinker per pen. Drinkers were checked twice daily and cleaned as needed to assure a clean and constant water supply to the birds.
Feed:
Feed was provided ad libitum throughout the study via one hanging, ˜17-inch diameter tube feeder per pen. A chick feeder tray was placed in each pen for approximately the first 4 days. Birds were placed on their respective treatment diets upon receipt (day 0) according to the Experimental Design. Feed added and removed from pens from day 0 to study end were weighed and recorded.
Daily Observations:
The test facility, pens and birds were observed at least twice daily for general flock condition, lighting, water, feed, ventilation and unanticipated events. If abnormal conditions or abnormal behavior was noted at any of the twice-daily observations they were documented and documentation included with the study records. The minimum-maximum temperatures of the test facility were recorded once daily.
Pen Cards:
There were 2 cards attached to each pen. One card identified the pen number and the second denoted the treatment number.
Animal Handling:
The animals were kept under ideal conditions for livability. The animals were handled in such a manner as to reduce injuries and unnecessary stress. Humane measures were strictly enforced.
Veterinary Care, Intervention and Euthanasia:
Birds that developed clinically significant concurrent disease unrelated to the test procedures were, at the discretion of the Study Investigator, or a designee, removed from the study and euthanized in accordance with site SOPs. In addition, moribund or injured birds were also euthanized upon authority of a Site Veterinarian or a qualified technician. The reasons for any withdrawal were documented. If an animal died, or was removed and euthanized for humane reasons, it was recorded on the mortality sheet for the pen and a necropsy performed and filed to document the reason for removal.
If euthanasia was deemed necessary by the Study Investigator, animals were euthanized by cervical dislocation.
Mortality and Culls:
Starting on study day 0, any bird that was found dead or was removed and sacrificed was weighed and necropsied. Cull birds that were unable to reach feed or water were sacrificed, weighed and documented. The weight and probable cause of death and necropsy findings were recorded on the pen mortality record.
Body Weights and Feed Intake:
Birds were weighed, by pen and individually, on approximately days 14 and 21. The feed remaining in each pen was weighed and recorded on study days 14 and 21. The feed intake during days 14-21 was calculated.
Weight Gains and Feed Conversion:
Average bird weight, on a pen and individual basis, on each weigh day were summarized. The average feed conversion was calculated on study day 21 (i.e. days 0-21) using the total feed consumption for the pen divided by the total weight of surviving birds. Adjusted feed conversion was calculated using the total feed consumption in a pen divided by the total weight of surviving birds and weight of birds that died or were removed from that pen.
Clostridium perfringens Challenge
Method of Administration:
Clostridium perfringens (CL-15, Type A, α and β2 toxins) cultures in this study were administered via the feed. Feed from each pen's feeder was used to mix with the culture. Prior to placing the cultures in the pens the treatment feed was removed from the birds for approximately 4-8 hours. For each pen of birds, a fixed amount based on study design of the broth culture at a concentration of approximately 2.0-9.0×108 cfu/ml was mixed with a fixed amount of feed (˜25 g/bird) in the feeder tray and all challenged pens were treated the same. Most of the culture-feed was consumed within 1-2 hours. So that birds in all treatments are treated similar, the groups that are not challenged also had the feed removed during the same time period as the challenged groups.
Clostridium Challenge:
The Clostridium perfringens culture (CL-15) was grown ˜5 hrs at ˜37° C. in Fluid Thioglycollate medium containing starch. CL-15 is a field strain of Clostridium perfringens from a broiler outbreak in Colorado. A fresh broth culture was prepared and used each day. For each pen of birds, a fixed amount of the overnight broth culture was mixed with a fixed amount of treatment feed in the feeder tray (see administration). The amount of feed, volume and quantitation of culture inoculum, and number of days dosed were documented in the final report and all pens will be treated the same. Birds received the C. perfringens culture for one day (Study day 17).
Data Collected:
Lesion Scoring:
Four days following the last C. perfringens culture administration, five birds were randomly selected from each pen by first bird caught, sacrificed and intestinal lesions scored for necrotic enteritis. Lesions scored as follows:
Results
The results were analyzed using the methods disclosed above (e.g., as discussed with reference to
37 birds were used in the individual lesion score analysis—although 40 birds were scored, only 37 had sufficient intestinal material for analysis. The same sequencing reads and same sequencing analysis pipeline was used for both the Ascus approach of the disclosure and the conventional approach. However, the Ascus approach also integrated activity information, as well as cell count information for each sample, as detailed earlier.
The Ascus mutual information approach was used to score the relationships between the abundance of the active strains and the individual lesion scores of the 37 broilers. Pearson correlations were calculated between the strains and individual lesion scores of the 37 broilers for the conventional approach. The causative strain, C. perfringens, was confirmed via global alignment search against the list of organisms identified from the pool of samples. The rank of this specific strain was then identified on the output of each analysis method. The Ascus approach identified the C. perfringens administered in the experiment as the number one strain linked to individual lesion score. The conventional approach identified this strain as the 26th highest strain linked to individual lesion score.
102 birds were used in the average lesion score analysis. As in the previous case, the same sequencing reads and same sequencing analysis pipeline was used for both the Ascus approach and the conventional approach. Again, the Ascus approach also integrated activity information, as well as cell count information for each sample.
The Ascus mutual information approach was used to score the relationships between the abundance of the active strains and the average lesion score of each pen. Pearson correlations were calculated between the strains and average lesion score of each pen for the conventional approach. The causative strain, C. perfringens, was confirmed via global alignment search against the list of organisms identified from the pool of samples. The rank of this specific strain was then identified on the output of each analysis method. The Ascus approach identified the C. perfringens administered in the experiment as the 4th highest strain linked to average lesion score of the pen. The conventional approach identified C. perfringens as the 15th highest strain linked to average lesion score of the pen. Average lesion score of the pen is a less accurate measurement than individual lesion score due to the variable levels of C. perfringens infection being masked by the bulk/average measurement. The drop in rank when comparing the individual lesion score analysis to the average pen lesion score analysis was expected. The collected metadata is provided below
A series of rumen samples were collected from three mid-lactation Holstein cows via a cannula during a milk fat depression episode. Rumen samples were collected at 4 AM on day 0, day 7, day 10, day 16, and day 28. Sequencing libraries were prepared from DNA purified from the rumen content and sequenced.
Raw sequencing reads were used to identify all microbial strains present in the pool of samples—4,729 unique strains were identified in the pool of samples. The relative abundance of each microbial strain was then calculated and used for subsequent analysis.
The measured pounds of milk fat produced by each animal at each time point is given in Table 8a. A mock strain was created for use in this analysis by taking the milk fat values and subtracting 1 to ensure that the mock strain and milk fat values trend together identically over time, i.e., a known linear trend/relationship exists between the mock strain and milk fat values. This mock strain was then added to the matrix of all strains previously identified in the community. MIC values and Pearson coefficients were simultaneously calculated between pounds of milk fat produced and all strains within the matrix for various conditions (described below) to establish the sensitivity and robustness of these measures as predictors of relationships.
To test the ability of the disclosed methods to detect relationships relative to the traditional methods, data points for the mock strain were removed one by one (relative abundance set to 0). The MIC and Pearson coefficient was recalculated after the removal of each data point, and the mock strain's rank was recorded (Table 8b). As can be seen, the MIC was a far more robust measure than the Pearson coefficient. Both methods were able to identify the mock strain as the number one strain related to pounds of milk fat produced when no points were removed. However, when one point was removed, the correlation method dropped the mock strain to rank 55, and then to rank 2142 when an additional point was removed. The MIC continued to predict the mock strain as the highest ranked strain until 6 points were removed.
One rationale behind removing points to test sensitivity is that when viewing a microbiome of a group of targets (e.g., animals), there are specific strains that are common to all of them, which can be referred to as the core microbiome. This group can represent a minority of the microbial population of a specific target (e.g., specific animal), and there can be a whole separate population of strains that are only found in a subset/small portion of targets/animals. In some embodiments, the more unique strains (i.e., those not found in all of the animals), can be the ones of particular relevance. Some embodiments of the disclosed methods were developed to address such “gaps” in the datasets and thus target particularly relevant microorganism and strains.
96 male Cobb 500s were raised for 21 days. Weight and feed intake were determined for individual birds, and cecum scrapings were collected after sacrifice. The cecum samples were processed using the methods of the present disclosure to identify an ensemble of microorganisms that will enhance feed efficiency when administered to broiler chickens in a production setting.
Experimental Design
120 Cobb 500 chicks were divided and placed into pens based on dietary treatment. The birds were placed in floor pens by treatment from 0-14 D. The test facility was divided into 1 block of 2 pens and 48 blocks of 2 individual cages each. Treatments were assigned to the pens/cages using a complete randomized block design; pens/cages retained their treatments throughout the study. The treatments were identified by numeric codes. Birds were assigned to the cages/pens randomly. Specific treatment groups were as follows in Table 9.
Housing:
Assignment of treatments to cages/pens was conducted using a computer program. The computer-generated assignment were as follows:
Birds were housed in an environmentally controlled facility in a large concrete floor pen (4′×8′) constructed of solid plastic (4′ tall) with clean litter. At day 14, 96 birds were moved into cages within the same environmentally controlled facility. Each cage was 24″×18″×24″.
Lighting was via incandescent lights and a commercial lighting program was used. Hours of continuous light for every 24-hour period were as follows in Table 10.
Environmental conditions for the birds (i.e. 0.53 ft2), temperature, lighting, feeder and water space) were similar for all treatment groups.
In order to prevent bird migration, each pen was checked to assure no openings greater than 1 inch existed for approximately 14 inches in height between pens.
Vaccinations:
Birds were vaccinated for Mareks at the hatchery. Upon receipt (study day 0), birds were vaccinated for Newcastle and Infectious Bronchitis by spray application. Documentation of vaccine manufacturer, lot number and expiration date were provided with the final report.
Water:
Water was provided ad libitum throughout the study. The floor pen water was via automatic bell drinkers. The battery cage water was via one nipple waterer. Drinkers were checked twice daily and cleaned as needed to assure a clean water supply to birds at all times.
Feed:
Feed was provided ad libitum throughout the study. The floor pen feed was via hanging, ˜17-inch diameter tube feeders. The battery cage feed was via one feeder trough, 9″×4″. A chick feeder tray was placed in each floor pen for approximately the first 4 days.
Daily Observations:
The test facility, pens and birds were observed at least twice daily for general flock condition, lighting, water, feed, ventilation and unanticipated events. The minimum-maximum temperature of the test facility was recorded once daily.
Mortality and Culls:
Starting on study day 0, any bird that was found dead or was removed and sacrificed was necropsied. Cull birds that are unable to reach feed or water were sacrificed and necropsied. The probable cause of death and necropsy findings were recorded on the pen mortality record.
Body Weights and Feed Intake:
˜96 birds were weighed individually each day. Feed remaining in each cage was weighed and recorded daily from 14-21 days. The feed intake for each cage was determined for each day.
Weight Gains and Feed Conversion:
Body weight gain on a cage basis and an average body weight gain on a treatment basis were determined from 14-21 days. Feed conversion was calculated for each day and overall for the period 14-21 D using the total feed consumption for the cage divided by bird weight. Average treatment feed conversion was determined for the period 14-21 days by averaging the individual feed conversions from each cage within the treatment.
Veterinary Care, Intervention and Euthanasia:
Animals that developed significant concurrent disease, are injured and whose condition may affect the outcome of the study were removed from the study and euthanized at the time that determination is made. Six days post challenge all birds in cages were removed and lesion scored.
Data Collected:
Bird weights and feed conversion, individually each day from days 14-21.
Feed amounts added and removed from floor pen and cage from day 0 to study end.
Mortality: probable cause of death day 0 to study end.
Removed birds: reason for culling day 0 to study end.
Daily observation of facility and birds, daily facility temperature.
Cecum content from each bird on day 21.
Results
The results were analyzed using the methods disclosed above (e.g., as discussed with reference to
The mutual information approach of the present disclosure was used to score the relationships between the absolute cell counts of the active strains and performance measurements, as well as relationships between two different active strains, for all 96 birds. After applying a threshold, 4039 metadata-strain relationships were deemed significant, and 8842 strain-strain relationships were deemed significant. These links, weighted by MIC score, were then used as edges (with the metadata and strains as nodes) to create a network for subsequent community detection analysis. A Louvain method community detection algorithm was applied to the network to categorize the nodes into subgroups.
The Louvain method optimizes network modularity by first removing a node from its current subgroup, and placing into neighboring subgroups. If modularity of the node's neighbors has improved, the node is reassigned to the new subgroup. If multiple groups have improved modularity, the subgroup with the most positive change is selected. This step is repeated for every node in the network until no new assignments are made. The next step involves the creation of a new, coarse-grained network, i.e. the discovered subgroups become the new nodes. The edges between nodes are defined by the sum of all of the lower-level nodes within each subgroup. From here, the first and second steps are repeated until no more modularity-optimizing changes can be made. Both local (i.e. groups made in the iterative steps) and global (i.e. final grouping) maximas can be investigated to resolve sub-groups that occur within the total microbial community, as well as identify potential hierarchies that may exist.
Modularity:
Where A is the matrix of metadata-strain and strain-strain relationships; ki=ΣiAij is the total link weight attached to node i; and m=½ ΣijAij. The Kronecker delta δ(ci,cj) is 1 when nodes i and j are assigned to the same community, and 0 otherwise.
Computing change in modularity when moving nodes:
ΔQ is the gain in modularity in subgroup C. Σin is the sum of the weights of the link in C, Σtot is the sum of the weights of the links incident to nodes in C, ki is the sum of weights of links incident to node i, ki,in is the sum of weights of links from I to nodes in C, and m is the sum of the weights of all links in the network.
Five different subgroups were detected in the chicken microbial community using the Louvain community detection method. Although a vast amount of microbial diversity exists in nature, there is far less functional diversity. Similarities and overlaps in metabolic capability create redundancies. Microorganism strains responding to the same environmental stimuli or nutrients are likely to trend similarly—this is captured by the methods of the present disclosure, and these microorganisms will ultimately be grouped together. The resulting categorization and hierarchy reveal predictions of the functionality of strains based on the groups they fall into after community-detection analysis.
After the categorization of strains is completed, microorganism strains are cultured from the samples. Due to the technical difficulties associated with isolating and growing axenic cultures from heterogeneous microbial communities, only a small fraction of strains passing both the activity and relationship thresholds of the methods of the present disclosure will ever be propagated axenically in a laboratory setting. After cultivation is completed, the ensemble of microorganism strains is selected based on whether or not an axenic culture exists, and which subgroups the strains were categorized into. Ensembles are created to contain as much functional diversity possible—that is, strains are selected such that a diverse range of subgroups are represented in the ensemble. These ensembles are then tested in efficacy and field studies to determine the effectiveness of the ensemble of strains as a product, and if the ensemble of strains demonstrates a contribution to production, the ensemble of strains could be produced and distributed as a product.
As detailed below, as few as two samples can be effective to identify active microorganism strains. In particular, the below experiment show that the methods of the disclosure properly identify C. perfringens as an active microorganism strain and causative agent of intestinal lesions and necrotic enteritis for all comparisons, including in a 2 sample comparison.
Experimental Design
Birds housed within an environmentally controlled facility in concrete floor pens (˜4′×4′ minus 2.25 sq ft of feeder space) providing floor space & bird density of [˜0.55 ft2/bird (day 0); ˜0.69 ft2/bird (day 21 after lesion scores)], temperature, humidity, lighting, feeder and water space will be similar for all test groups. Birds placed in clean pens containing an appropriate depth of clean wood shavings to provide a comfortable environment for the chicks. Additional shavings added to pens in order to maintain bird comfort. Lighting via incandescent lights and a commercial lighting program used as follows.
Environmental conditions for the birds (i.e., bird density, temperature, lighting, feeder and water space) were similar for all treatment groups. In order to prevent bird migration and bacterial spread from pen to pen, each pen had a solid (plastic) divider of approximately 24 inches in height between pens.
Vaccinations and Therapeutic Medication:
Birds were vaccinated for Mareks at the hatchery. Upon receipt (study day 0), birds were vaccinated for Newcastle and Infectious Bronchitis by spray application. Documentation of vaccine manufacturer, lot number and expiration date were provided with the final report.
Water:
Water was provided ad libitum throughout the study via one Plasson drinker per pen. Drinkers were checked twice daily and cleaned as needed to assure a clean and constant water supply to the birds.
Feed:
Feed was provided ad libitum throughout the study via one hanging, ˜17-inch diameter tube feeder per pen. A chick feeder tray was placed in each pen for approximately the first 4 days. Birds were placed on their respective treatment diets upon receipt (day 0) according to the Experimental Design. Feed added and removed from pens from day 0 to study end were weighed and recorded.
Daily Observations:
The test facility, pens and birds were observed at least twice daily for general flock condition, lighting, water, feed, ventilation and unanticipated events. If abnormal conditions or abnormal behavior is noted at any of the twice-daily observations they were documented, and the documentation was included with the study records. The minimum-maximum temperature of the test facility were recorded once daily.
Pen Cards:
There were 2 cards attached to each pen. One card identified the pen number and the second denoted the treatment number.
Animal Handling:
The animals were kept under ideal conditions for livability. The animals were handled in such a manner as to reduce injuries and unnecessary stress. Humane measures were strictly enforced.
Veterinary Care, Intervention and Euthanasia:
Birds that develop clinically significant concurrent disease unrelated to the test procedures may, at the discretion of the Study Investigator, or a designee, be removed from the study and euthanized in accordance with site SOPs. In addition, moribund or injured birds may also be euthanized upon authority of a Site Veterinarian or a qualified technician. The reasons for withdrawal were documented. If an animal dies, or is removed and euthanized for humane reasons, it was recorded on the mortality sheet for the pen and a necropsy was performed and filed to document the reason for removal.
If euthanasia was deemed necessary by the Study Investigator, animals were euthanized by cervical dislocation.
Mortality and Culls:
Starting on study day 0, any bird that was found dead or was removed and sacrificed was weighed and necropsied. Cull birds that were unable to reach feed or water were sacrificed, weighed and documented. The weight and probable cause of death and necropsy findings were recorded on the pen mortality record.
Clostridium perfringens Challenge
Method of Administration:
Clostridium perfringens (CL-15, Type A, α and β2 toxins) cultures in this study were administered via the feed. Feed from each pen's feeder was used to mix with the culture. Prior to placing the cultures in the pens the treatment feed was removed from the birds for approximately 4-8 hours. For each pen of birds, a fixed amount based on study design of the broth culture at a concentration of approximately 2.0-9.0×108 cfu/ml was mixed with a fixed amount of feed (˜25 g/bird) in the feeder tray and all challenged pens were treated the same. Most of the culture-feed was consumed within 1-2 hours. So that birds in all treatments were treated similarly, the groups that are not challenged also had the feed removed during the same time period as the challenged groups.
Clostridium Challenge:
The Clostridium perfringens culture (CL-15) was grown ˜5 hrs at ˜37° C. in Fluid Thioglycollate medium containing starch. CL-15 is a field strain of Clostridium perfringens from a broiler outbreak in Colorado. A fresh broth culture was prepared and used each day. For each pen of birds, a fixed amount of the overnight broth culture was mixed with a fixed amount of treatment feed in the feeder tray. The amount of feed, volume and quantitation of culture inoculum, and number of days dosed were documented in the final report and all pens will be treated the same. Birds will receive the C. perfringens culture for one day (Study day 17).
Data Collected
Intestinal content for analysis with the methods of the present application
Bird weights, by pen and individually, and feed efficiency, by pen, on approximately days 14 and 21.
Feed amounts added and removed from each pen from day 0 to study end.
Mortality: sex, weight and probable cause of death day 0 to study end.
Removed birds: reason for culling, sex and weight day 0 to study end.
Daily observation of facility and birds, daily facility temperature.
Lesion score 5 birds/pen on approximate day 21
Samples collected from 48 lesion scored birds
Lesion Scoring:
Four days following the last C. perfringens culture administration, five birds were randomly selected from each pen by first bird caught, sacrificed and intestinal lesions scored for necrotic enteritis. Lesions scored as follows:
0=normal: no NE lesions, small intestine has normal elasticity (rolls back to normal position after being opened)
1=mild: small intestinal wall is thin and flaccid (remains flat when opened and doesn't roll back into normal position after being opened); excess mucus covering mucus membrane
2=moderate: noticeable reddening and swelling of the intestinal wall; minor ulceration and necrosis of the intestine membrane; excess mucus
3=severe: extensive area(s) of necrosis and ulceration of the small intestinal membrane; significant hemorrhage; layer of fibrin and necrotic debris on the mucus membrane (Turkish towel appearance)
4=dead or moribund: bird that would likely die within 24 hours and has NE lesion score of 2 or more
Results
The results were analyzed using the methods of the present application. Strain-level microbial absolute cell count and activity were determined for the small intestine content of all 48 birds. The methods of the present application integrated activity information, as well as absolute cell count information for each sample.
The mutual information approach of the present application was used to score the relationships between the absolute cell count of the active strains and the individual lesion scores of 10 randomly selected broilers. One sample was randomly removed from the dataset, and the analysis was repeated. This was repeated until only two broiler samples were compared.
The causative strain, C. perfringens, was confirmed via global alignment search against the list of organisms identified from the pool of samples. Its rank (with a rank position of 1 being the strain most implicated in causing lesion scores) against all strains analyzed are presented in Table 12:
Table 12 illustrates that C. perfringens was properly identified as an active microorganism strain and causative agent of lesion scores for all comparisons, including the 2 sample comparison, using the disclosed methods. As the sample number was reduced, the number of false positives (i.e., other strains also being identified as causative agents) increased beginning at the 7-sample comparison where two strains, including C. perfringens, tied for a rank of 1. This trend continued down to the 2 sample comparison, where 31 strains, including C. perfringens, tied for the number 1 rank.
Generally, while using additional samples can reduce the noise/number of false positives, further analysis and processing of the resulting strains can be used to identify C. perfringens as the causative strain, including from a total of 31 identified strains. Depending on the embodiment, configuration, and application, methods of the disclosure can be practiced with small numbers of samples, and the number of samples utilized can vary depending on the sample source, sample type, metadata, complexity of the target microbiome, and so forth.
Embodiment A1 is a method, comprising: 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 microorganism types in each sample; determining a number of each detected microorganism type of the one or more microorganism types in each sample; measuring a number of unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain; integrating the number of each microorganism type and the number of the first markers to yield the absolute cell count of each microorganism strain present in each sample; measuring at least one unique second marker for each microorganism strain based on a specified threshold to determine an activity level for that microorganism strain in each sample; filtering the absolute cell count by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for each of the at least two samples; comparing the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with at least one measured metadata or additional active microorganism strain for each of the at least two samples and categorizing the active microorganism strains into at least two groups based on predicted function and/or chemistry; selecting at least one microorganism strain from the at least two groups; and combining the selected at least one microorganism strain from the at least two groups to form a ensemble of microorganisms configured to alter a property corresponding to the at least one metadata.
Embodiment A2 is a method according to embodiment A1, wherein measuring the number of unique first markers includes measuring the number of unique genomic DNA markers in each sample. Embodiment A3 is a method according to embodiment A1, wherein measuring the number of unique first markers includes measuring the number of unique RNA markers in each sample. Embodiment A4 is a method according to embodiment A1, wherein measuring the number of unique first markers includes measuring the number of unique protein markers in each sample. Embodiment A5 is a method according to embodiment A1, wherein measuring the number of unique first markers includes measuring the number of exclusive intermediate markers in each sample. Embodiment A6 is a method according to embodiment A1, wherein measuring the number of unique first markers includes measuring the number of unique protein markers and measuring the number of unique genomic DNA markers in each sample. Embodiment A7 is a method according to embodiment A1, wherein measuring the number of unique first markers includes measuring the number of unique protein markers and measuring the number of unique RNA markers in each sample. Embodiment A8 is a method according to embodiment A1, wherein measuring the number of unique first markers, and quantity thereof, includes subjecting genomic DNA from each sample to a high throughput sequencing reaction. Embodiment A9 is a method according to embodiment A1, wherein measuring the number of unique first markers, and quantity thereof, includes subjecting genomic DNA from each sample to metagenome sequencing. Embodiment A10 is a method according to embodiment A1, wherein the unique first markers include at least one of an mRNA marker, an siRNA marker, and/or a ribosomal RNA marker. Embodiment A11 is a method according to embodiment A1, wherein the unique first markers include at least one of a sigma factor, a transcription factor, nucleoside associated protein, and/or metabolic enzyme.
Embodiment A12 is a method according to any one of embodiments A1-A11, wherein measuring the at least one unique second marker includes measuring a level of expression of the at least one unique second marker in each sample. Embodiment A13 is a method according to embodiment A12, wherein measuring the level of expression of the at least one unique second marker includes subjecting mRNA in the sample to gene expression analysis. Embodiment A14 is a method according to embodiment A13, wherein the gene expression analysis includes a sequencing reaction. Embodiment A15 is a method according to embodiment A13, wherein the gene expression analysis includes a quantitative polymerase chain reaction (qPCR), metatranscriptome sequencing, and/or transcriptome sequencing. Embodiment A16 is a method according to embodiment A12, wherein measuring the level of expression of the at least one unique second marker includes subjecting each sample or a portion thereof to mass spectrometry analysis. Embodiment A17 is a method according to embodiment A12, wherein measuring the level of expression of the at least one unique second marker includes subjecting each sample or a portion thereof to metaribosome profiling, or ribosome profiling.
Embodiment A18 is a method according to any one of embodiments A1-A17, wherein the one or more microorganism types includes bacteria, archaea, fungi, protozoa, plant, other eukaryote, viruses, viroids, or a combination thereof. Embodiment A19 is a method according to any one of embodiments A1-A18, wherein the one or more microorganism strains is one or more bacterial strains, archaeal strains, fungal strains, protozoa strains, plant strains, other eukaryote strains, viral strains, viroid strains, or a combination thereof. Embodiment A20 is a method according to embodiment A19, wherein the one or more microorganism strains is one or more fungal species or sub-species; and/or wherein the one or more microorganism strains is one or more bacterial species or sub-species.
Embodiment A21 is a method according to any one of embodiments A1-A20, wherein determining the number of each of the one or more microorganism types in each sample includes subjecting each sample or a portion thereof to sequencing, centrifugation, optical microscopy, fluorescent microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain reaction (qPCR), gel electrophoresis, and/or flow cytometry.
Embodiment A22 is a method according to embodiment A1, wherein the unique first markers include a phylogenetic marker comprising a 5S ribosomal subunit gene, a 16S ribosomal subunit gene, a 23S ribosomal subunit gene, a 5.8S ribosomal subunit gene, a 18S ribosomal subunit gene, a 28S ribosomal subunit gene, a cytochrome c oxidase subunit gene, a β-tubulin gene, an elongation factor gene, an RNA polymerase subunit gene, an internal transcribed spacer (ITS), or a combination thereof.
Embodiment A22a is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker. Embodiment A22b is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 5S ribosomal subunit gene. Embodiment A22c is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 16S ribosomal subunit gene. Embodiment A22d is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 23S ribosomal subunit gene. Embodiment A22e is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 5.8S ribosomal subunit gene. Embodiment A22f is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 18S ribosomal subunit gene. Embodiment A22g is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a 28S ribosomal subunit gene. Embodiment A22h is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a cytochrome c oxidase subunit gene. Embodiment A22i is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising a β-tubulin gene. Embodiment A22j is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising an elongation factor gene. Embodiment A22k is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising an RNA polymerase subunit gene. Embodiment A22l is a method according to embodiment A1, wherein the unique first marker does not include a phylogenetic marker comprising an internal transcribed spacer (ITS).
Embodiment A23 is a method according to embodiment A22, wherein measuring the number of unique markers, and quantity thereof, includes subjecting genomic DNA from each sample to a high throughput sequencing reaction. Embodiment A24 is a method according to embodiment A22, wherein measuring the number of unique markers, and quantity thereof, comprises subjecting genomic DNA to genomic sequencing. Embodiment A25 is a method according to embodiment A22, wherein measuring the number of unique markers, and quantity thereof, comprises subjecting genomic DNA to amplicon sequencing.
Embodiment A26 is a method according to any one of embodiments A1-A25, wherein the at least one different characteristic includes a collection time at which each of the at least two samples was collected, such that the collection time for a first sample is different from the collection time of a second sample.
Embodiment A27 is a method according to any one of embodiments A1-A25, wherein the at least one different characteristic includes a collection location at which each of the at least two samples was collected, such that the collection location for a first sample is different from the collection location of a second sample.
Embodiment A28 is a method according to any one of embodiments A1-A27, wherein the at least one common characteristic includes a sample source type, such that the sample source type for a first sample is the same as the sample source type of a second sample. Embodiment A29 is a method according to embodiment A28, wherein the sample source type is one of animal type, organ type, soil type, water type, sediment type, oil type, plant type, agricultural product type, bulk soil type, soil rhizosphere type, or plant part type.
Embodiment A30 is a method according to any one of embodiments A1-A27, wherein the at least one common characteristic includes that each of the at least two samples is a gastrointestinal sample.
Embodiment A31 is a method according to any one of embodiments A1-A27, wherein the at least one common characteristic includes an animal sample source type, each sample having a further common characteristic such that each sample is a tissue sample, a blood sample, a tooth sample, a perspiration sample, a fingernail sample, a skin sample, a hair sample, a feces sample, a urine sample, a semen sample, a mucus sample, a saliva sample, a muscle sample, a brain sample, or an organ sample.
Embodiment A32 is a method according to any one of embodiments A1-A31, further comprising: obtaining at least one further sample from a target, based on the at least one measured metadata, wherein the at least one further sample from the target shares at least one common characteristic with the at least two samples; and for the at least one further sample from the target, detecting the presence of one or more microorganism types, determining a number of each detected microorganism type of the one or more microorganism types, measuring a number of unique first markers and quantity thereof, integrating the number of each microorganism type and the number of the first markers to yield the absolute cell count of each microorganism strain present, measuring at least one unique second marker for each microorganism strain to determine an activity level for that microorganism strain, filtering the absolute cell count by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for the at least one further sample from the target; wherein the selection of the at least one microorganism strain from each of the at least two groups is based on the list of active microorganisms strains and their respective absolute cell counts for the at least one further sample from the target such that the formed ensemble is configured to alter a property of the target that corresponds to the at least one metadata.
Embodiment A33 is a method according to any one of embodiments A1-A32, wherein comparing the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with at least one measured metadata or additional active microorganism strain for each of the at least two samples includes determining the co-occurrence of the one or more active microorganism strains in each sample with the at least one measured metadata or additional active microorganism strain. Embodiment A34 is a method according to embodiment A33, wherein the at least one measured metadata includes one or more parameters, wherein the one or more parameters is at least one of sample pH, sample temperature, abundance of a fat, abundance of a protein, abundance of a carbohydrate, abundance of a mineral, abundance of a vitamin, abundance of a natural product, abundance of a specified compound, bodyweight of the sample source, feed intake of the sample source, weight gain of the sample source, feed efficiency of the sample source, presence or absence of one or more pathogens, physical characteristic(s) or measurement(s) of the sample source, production characteristics of the sample source, or a combination thereof. Embodiment A35 is a method according to embodiment A34, wherein the one or more parameters is at least one of abundance of whey protein, abundance of casein protein, and/or abundance of fats in milk.
Embodiment A36 is a method according to any one of embodiments A33-A35, wherein determining the co-occurrence of the one or more active microorganism strains and the at least one measured metadata in each sample includes creating matrices populated with linkages denoting metadata and microorganism strain associations, the absolute cell count of the one or more active microorganism strains and the measure of the one more unique second markers to represent one or more networks of a heterogeneous microbial community or communities. Embodiment A37 is a method according to embodiment A36, wherein the at least one measured metadata comprises a presence, activity and/or quantity of a second microorganism strain.
Embodiment A38 is a method according to any one of embodiments A33-A37, wherein determining the co-occurrence of the one or more active microorganism strains and the at least one measured metadata and categorizing the active microorganism strains includes network analysis and/or cluster analysis to measure connectivity of each microorganism strain within a network, wherein the network represents a collection of the at least two samples that share a common characteristic, measured metadata, and/or related environmental parameter. Embodiment A39 is a method according to embodiment A38, wherein the at least one measured metadata comprises a presence, activity and/or quantity of a second microorganism strain. Embodiment A40 is a method according to embodiment A38 or A39, wherein the network analysis and/or cluster analysis includes linkage analysis, modularity analysis, robustness measures, betweenness measures, connectivity measures, transitivity measures, centrality measures, or a combination thereof. Embodiment A41 is a method according to any one of embodiments A38-A40, wherein the cluster analysis includes building a connectivity model, subspace model, distribution model, density model, or a centroid model.
Embodiment A42 is a method according to embodiment A38 or embodiment A39, wherein the network analysis includes predictive modeling of network through link mining and prediction, collective classification, link-based clustering, relational similarity, or a combination thereof. Embodiment A43 is a method according to embodiment A38 or embodiment 3A9, wherein the network analysis comprises differential equation based modeling of populations. Embodiment A44 is a method according to embodiment A43, wherein the network analysis comprises Lotka-Volterra modeling. Embodiment A45 is a method according to embodiment A38 or embodiment A39, wherein the cluster analysis is a heuristic method. Embodiment A46 is a method according to embodiment A45, wherein the heuristic method is the Louvain method.
Embodiment A47 is a method according to embodiment A38 or embodiment A39, where the network analysis includes nonparametric methods to establish connectivity between variables. Embodiment A48 is a method according to embodiment A38 or embodiment A39, wherein the network analysis includes mutual information and/or maximal information coefficient calculations between variables to establish connectivity.
Embodiment A49 is a method for forming an ensemble of active microorganism strains configured to alter a property or characteristic in an environment based on two or more sample sets that share at least one common or related environmental parameter between the two or more sample sets and that have at least one different environmental parameter between the two or more sample sets, each sample set comprising at least one sample including a heterogeneous microbial community, wherein the one or more microorganism strains is a subtaxon of one or more organism types, comprising: detecting the presence of a plurality of microorganism types in each sample; determining the absolute number of cells of each of the detected microorganism types in each sample; measuring the number of unique first markers in each sample, and quantity thereof, wherein a unique first marker is a marker of a microorganism strain; at the protein or RNA level, measuring the level of expression of one or more unique second markers, wherein a unique second marker is a marker of activity of a microorganism strain; determining activity of the detected microorganism strains for each sample based on the level of expression of the one or more unique second markers exceeding a specified threshold; calculating the absolute cell count of each detected active microorganism strain in each sample based upon the quantity of the one or more first markers and the absolute number of cells of the microorganism types from which the one or more microorganism strains is a subtaxon, wherein the one or more active microorganism strains expresses the second unique marker above the specified threshold; determining the co-occurrence of the active microorganism strains in the samples with at least one environmental parameter or additional active microorganism strain based on maximal information coefficient network analysis to measure connectivity of each microorganism strain within a network, wherein the network is the collection of the at least two or more sample sets with at least one common or related environmental parameter; selecting a plurality of active microorganism strains from the one or more active microorganism strains based on the network analysis; and forming an ensemble of active microorganism strains from the selected plurality of active microorganism strains, the ensemble of active microorganism strains configured to selectively alter a property or characteristic of an environment when the ensemble of active microorganism strains is introduced into that environment.
Embodiment A50 is a method according to embodiment A49, wherein the at least one environmental parameter comprises a presence, activity and/or quantity of a second microorganism strain. Embodiment A51 is a method according to embodiment A49 or embodiment A50, wherein at least one measured indicia of at least one common or related environmental factor for a first sample set is different from a measured indicia of the at least one common or related environmental factor for a second sample set.
Embodiment A52 is a method according to embodiment A49 or embodiment A50, wherein each sample set comprises a plurality of samples, and a measured indicia of at least one common or related environmental factor for each sample within a sample set is substantially similar, and an average measured indicia for one sample set is different from the average measured indicia from another sample set. Embodiment A53 is a method according to embodiment A49 or embodiment A50, wherein each sample set comprises a plurality of samples, and a first sample set is collected from a first population and a second sample set is collected from a second population. Embodiment A54 is a method according to embodiment A49 or A50, wherein each sample set comprises a plurality of samples, and a first sample set is collected from a first population at a first time and a second sample set is collected from the first population at a second time different from the first time. Embodiment A55 is a method according to any one of embodiments A49-A54, wherein at least one common or related environmental factor includes nutrient information.
Embodiment A56 is a method according to any one of embodiments A49-A54, wherein at least one common or related environmental factor includes dietary information. Embodiment A57 is a method of any one of embodiments A49-A54, wherein at least one common or related environmental factor includes animal characteristics. Embodiment A58 is a method according to any one of embodiments A49-A54, wherein at least one common or related environmental factor includes infection information or health status.
Embodiment A59 is a method according to embodiment A51, wherein at least one measured indicia is sample pH, sample temperature, abundance of a fat, abundance of a protein, abundance of a carbohydrate, abundance of a mineral, abundance of a vitamin, abundance of a natural product, abundance of a specified compound, bodyweight of the sample source, feed intake of the sample source, weight gain of the sample source, feed efficiency of the sample source, presence or absence of one or more pathogens, physical characteristic(s) or measurement(s) of the sample source, production characteristics of the sample source, or a combination thereof.
Embodiment A60 is a method according to embodiment A49 or embodiment A50, wherein the at least one parameter is at least one of abundance of whey protein, abundance of casein protein, and/or abundance of fats in milk. Embodiment A61 is a method according to any one of embodiments A49-A60, wherein measuring the number of unique first markers in each sample comprises measuring the number of unique genomic DNA markers. Embodiment A62 is a method according to any one of embodiments A49-A60, wherein measuring the number of unique first markers in the sample comprises measuring the number of unique RNA markers. Embodiment A63 is a method according to any one of embodiments A49-A60, wherein measuring the number of unique first markers in the sample comprises measuring the number of unique protein markers.
Embodiment A64 is a method according to any one of embodiments A49-A63, wherein the plurality of microorganism types includes one or more bacteria, archaea, fungi, protozoa, plant, other eukaryote, virus, viroid, or a combination thereof. Embodiment A65 is a method according to any one of embodiments A49-A64, wherein determining the absolute cell number of each of the microorganism types in each sample includes subjecting the sample or a portion thereof to sequencing, centrifugation, optical microscopy, fluorescent microscopy, staining, mass spectrometry, microfluidics, quantitative polymerase chain reaction (qPCR), gel electrophoresis and/or flow cytometry. Embodiment A66 is a method according to any one of embodiments A49-A65, wherein one or more active microorganism strains is a subtaxon of one or more microbe types selected from one or more bacteria, archaea, fungi, protozoa, plant, other eukaryote, virus, viroid, or a combination thereof.
Embodiment A67 is a method according to any one of embodiments A49-A65, wherein one or more active microorganism strains is one or more bacterial strains, archaeal strains, fungal strains, protozoa strains, plant strains, other eukaryote strains, viral strains, viroid strains, or a combination thereof. Embodiment A68 is a method according to any one of embodiments A49-A67, wherein one or more active microorganism strains is one or more fungal species, fungal subspecies, bacterial species and/or bacterial subspecies. Embodiment A69 is a method according to any one of embodiments A49-A68, wherein at least one unique first marker comprises a phylogenetic marker comprising a 5S ribosomal subunit gene, a 16S ribosomal subunit gene, a 23S ribosomal subunit gene, a 5.8S ribosomal subunit gene, a 18S ribosomal subunit gene, a 28S ribosomal subunit gene, a cytochrome c oxidase subunit gene, a beta-tubulin gene, an elongation factor gene, an RNA polymerase subunit gene, an internal transcribed spacer (ITS), or a combination thereof.
Embodiment A70 is a method according to embodiment A49 or embodiment A50, wherein measuring the number of unique first markers, and quantity thereof, comprises subjecting genomic DNA from each sample to a high throughput sequencing reaction. Embodiment A71 is a method according to embodiment A49 or A50, wherein measuring the number of unique first markers, and quantity thereof, comprises subjecting genomic DNA from each sample to metagenome sequencing. Embodiment A72 is a method according to embodiment A49 or A50, wherein a unique first marker comprises an mRNA marker, an siRNA marker, or a ribosomal RNA marker. Embodiment A73 is a method according to embodiment A49 or embodiment A50, wherein a unique first marker comprises a sigma factor, a transcription factor, nucleoside associated protein, metabolic enzyme, or a combination thereof.
Embodiment A74 is a method according to any one of embodiments A49-A73, wherein measuring the level of expression of one or more unique second markers comprises subjecting mRNA in the sample to gene expression analysis. Embodiment A75 is a method according to embodiment A74, wherein the gene expression analysis comprises a sequencing reaction. Embodiment A76 is a method according to embodiment A74, wherein the gene expression analysis comprises a quantitative polymerase chain reaction (qPCR), metatranscriptome sequencing, and/or transcriptome sequencing.
Embodiment A77 is a method according to any one of embodiments A49-A68 and embodiments A74-A76, wherein measuring the level of expression of one or more unique second markers includes subjecting each sample or a portion thereof to mass spectrometry analysis. Embodiment A78 is a method according to any one of embodiments A49-A68 and embodiments A74-A76, wherein measuring the level of expression of one or more unique second markers comprises subjecting the sample or a portion thereof to metaribosome profiling, and/or ribosome profiling.
Embodiment A79 is a method according to any one of embodiments A49-A78, wherein the source type for the samples is one of animal, soil, air, saltwater, freshwater, wastewater sludge, sediment, oil, plant, an agricultural product, bulk soil, soil rhizosphere, plant part, vegetable, an extreme environment, or a combination thereof.
Embodiment A80 is a method according to any one of embodiments A49-A78, wherein each sample is a gastrointestinal sample. Embodiment A81 is a method according to any one of embodiments A49-A78, wherein each sample is one of a tissue sample, blood sample, tooth sample, perspiration sample, fingernail sample, skin sample, hair sample, feces sample, urine sample, semen sample, mucus sample, saliva sample, muscle sample, brain sample, or organ sample.
Embodiment A82 is a processor-implemented method, comprising: receiving sample data from at least two samples sharing at least one common characteristic and having a least one different characteristic; for each sample, determining the presence of one or more microorganism types in each sample; determining a number of each detected microorganism type of the one or more microorganism types in each sample; determining a number of unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain; integrating, via a processor, the number of each microorganism type and the number of the first markers to yield the absolute cell count of each microorganism strain present in each sample; determining an activity level for each microorganism strain in each sample based on a measure of at least one unique second marker for each microorganism strain exceeding a specified threshold, a microorganism strain being identified as active if the measure of at least one unique second marker for that strain exceeds the corresponding threshold; filtering the absolute cell count of each microorganism strain by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for each of the at least two samples; conducting a network analysis, via at least one processor, of the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with at least one measured metadata or additional active microorganism strain for each of the at least two samples, the network analysis including determining maximal information coefficient scores between each active microorganism strain and every other active microorganism strain and determining maximal information coefficient scores between each active microorganism strain and the respective at least one measured metadata or additional active microorganism strain; categorizing the active microorganism strains based on predicted function and/or chemistry; identifying a plurality of active microorganism strains based on the categorization; and outputting the identified plurality of active microorganism strains.
Embodiment A83 is the processor-implemented method of embodiment A82, further comprising: assembling an active microorganism ensemble configured to, when applied to a target, alter a property corresponding to the at least one measured metadata. Embodiment A84 is the processor-implemented method of embodiment A82, wherein the output plurality of active microorganism strains is used to assemble an active microorganism ensemble configured to, when applied to a target, alter a property corresponding to the at least one measured metadata. Embodiment A85 is the processor-implemented method of embodiment A82, further comprising: identifying at least one pathogen based on the output plurality of identified active microorganism strains. Embodiment A86 is a processor-implemented method of any one of embodiments A82-A85, wherein the output plurality of active microorganism strains is further used to assemble an active microorganism ensemble configured to, when applied to a target, target the at least one identified pathogen and treat and/or prevent a symptom associated with the at least one identified pathogen.
Embodiment A87 is a method of forming an active microorganism bioensemble of active microorganism strains configured to alter a property in a target biological environment, comprising: obtaining at least two samples sharing at least one common characteristic and having at least one different characteristic; for each sample, detecting the presence of one or more microorganism types in each sample; determining a number of each detected microorganism type of the one or more microorganism types in each sample; measuring a number of unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain; integrating the number of each microorganism type and the number of the first markers to yield the absolute cell count of each microorganism strain present in each sample; measuring at least one unique second marker for each microorganism strain based on a specified threshold to determine an activity level for that microorganism strain in each sample; filtering the absolute cell count by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for each of the at least two samples; comparing the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with at least one measured metadata for each of the at least two samples, the comparison including determining the co-occurrence of the active microorganism strains in each sample with the at least one measured metadata, determining the co-occurrence of the active microorganism strains and the at least one measured metadata in each sample including creating matrices populated with linkages denoting metadata and microorganism strain relationships, the absolute cell count of the active microorganism strains, and the measure of the unique second markers, to represent one or more heterogeneous microbial community networks; grouping the active microorganism strains into at least two groups according to predicted function and/or chemistry based on at least one of nonparametric network analysis and cluster analysis identifying connectivity of each active microorganism strain and measured metadata within an active heterogeneous microbial community network; selecting at least one microorganism strain from each of the at least two groups; and combining the selected microorganism strains and with a carrier medium to form a bioensemble of active microorganisms configured to alter a property corresponding to the at least one metadata of target biological environment when the bioensemble is introduced into that target biological environment.
Embodiment A88 is the method according to embodiment A87, further comprising: obtaining at least one further sample, based on the at least one measured metadata, wherein the at least one further sample shares at least one characteristic with the at least two samples; and for the at least one further sample, detecting the presence of one or more microorganism types, determining a number of each detected microorganism type of the one or more microorganism types, measuring a number of unique first markers and quantity thereof, integrating the number of each microorganism type and the number of the first markers to yield the absolute cell count of each microorganism strain present, measuring at least one unique second marker for each microorganism strain to determine an activity level for that microorganism strain, filtering the absolute cell count by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for the at least one further sample; wherein comparing the filtered absolute cell counts of active microorganisms strains comprises comparing the filtered absolute cell counts of active microorganism strains for each of the at least two samples and the at least one further sample with the at least one measured metadata, such that the selection of the active microorganism strains is at least partially based on the list of active microorganisms strains and their respective absolute cell counts for the at least one further sample.
Embodiment A89 is a method for forming a synthetic ensemble of active microorganism strains configured to alter a property in a biological environment, based on two or more sample sets each having a plurality of environmental parameters, at least one parameter of the plurality of environmental parameters being a common environmental parameter that is similar between the two or more sample sets and at least one environmental parameter being a different environmental parameter that is different between each of the two or more sample sets, each sample set including at least one sample comprising a heterogeneous microbial community obtained from a biological sample source, at least one of the active microorganism strains being a subtaxon of one or more organism types, the method comprising: detecting the presence of a plurality of microorganism types in each sample; determining the absolute number of cells of each of the detected microorganism types in each sample; measuring the number of unique first markers in each sample, and quantity thereof, a unique first marker being a marker of a microorganism strain; measuring the level of expression of one or more unique RNA markers, wherein a unique RNA marker is a marker of activity of a microorganism strain; determining activity of each of the detected microorganism strains for each sample based on the level of expression of the one or more unique RNA markers exceeding a specified threshold; calculating the absolute cell count of each detected active microorganism strain in each sample based upon the quantity of the one or more first markers and the absolute number of cells of the microorganism types from which the one or more microorganism strains is a subtaxon, the one or more active microorganism strains expressing one or more unique RNA markers above the specified threshold; analyzing the active microorganism strains of the two or more sample sets, the analyzing including conducting nonparametric network analysis of each of the active microorganism strains for each of the two or more sample sets, the at least one common environmental parameter, and the at least one different environmental parameter, the nonparametric network analysis including (1) determining the maximal information coefficient score between each active microorganism strain and every other active microorganism strain and (2) determining the maximal information coefficient score between each active microorganism strain and the at least one different environmental parameter; selecting a plurality of active microorganism strains from the one or more active microorganism strains based on the nonparametric network analysis; and forming a synthetic ensemble of active microorganism strains comprising the selected plurality of active microorganism strains and a microbial carrier medium, the ensemble of active microorganism strains configured to selectively alter a property of a biological environment when the synthetic ensemble of active microorganism strains is introduced into that biological environment.
Embodiment A90 is a method of forming an active microorganism bioensemble configured to alter a property in a target biological environment, comprising: obtaining at least two samples sharing at least one common environmental parameter and having at least one different environmental parameter; for each sample, detecting the presence of one or more microorganism types in each sample; determining a number of each detected microorganism type of the one or more microorganism types in each sample; measuring a number of unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain of a detected microorganism type; determining the absolute cell count of each microorganism strain present in each sample based on the number of each detected microorganism type and the proportional/relative number of the corresponding or related unique first markers for that microorganism type; measuring at least one unique second marker for each microorganism strain based on a specified threshold to determine an activity level for that microorganism strain in each sample; filtering the absolute cell count of each microorganism strain by the determined activity to provide a list of active microorganisms strains and their respective absolute cell counts for each of the at least two samples; comparing the filtered absolute cell counts of active microorganisms strains for each of the at least two samples with at least one measured metadata for each of the at least two samples, the comparison including determining the co-occurrence of the active microorganism strains in each sample with the at least one measured metadata, determining the co-occurrence of the active microorganism strains and the at least one measured metadata in each sample including creating matrices populated with linkages denoting metadata and microorganism strain relationships, the absolute cell count of the active microorganism strains, and the measure of the unique second markers, to represent one or more heterogeneous microbial community networks; grouping the active microorganism strains into at least two groups according to predicted function and/or chemistry based on at least one of nonparametric network analysis and cluster analysis identifying connectivity of each active microorganism strain and measured metadata within an active heterogeneous microbial community network; selecting at least one microorganism strain from each of the at least two groups; and combining the selected microorganism strains and with a carrier medium to form a synthetic bioensemble of active microorganisms configured to alter a property corresponding to the at least one metadata of target biological environment when the bioensemble is introduced into that target biological environment.
Embodiment A91 is a method, comprising: (1) selecting at least two microorganism strains, the selection of the at least two microorganism strains based on processing a plurality of samples collected from a sample population, the processing including: (a) for each sample of the plurality of samples: detecting the presence of one or more microorganism types and determining a number of each detected microorganism type; measuring a number of unique first markers, and quantity thereof, each unique first marker being a marker of a microorganism strain; determining the absolute cell count of each microorganism strain based on the number of each microorganism type and the number of the first markers; determining an activity level for each microorganism strain based on at least one unique second marker; generating a list of active microorganism strains and their respective absolute cell counts based on absolute cell count and determined activity; (b) analyzing the absolute cell counts of active microorganisms strains of each of the samples of the plurality of samples with at least one measured metadata and categorizing active microorganism strains according to predicted function and/or chemistry; (c) identifying at least one fungus strain and a least one bacterium strain based on the categorization; (2) preparing the at least one fungus strain and preparing the at least one bacterium strain for inclusion in a synthetic microbial ensemble configured to alter a property corresponding to the at least one metadata when in use; and (3) forming the synthetic microbial ensemble from the prepared at least one fungus strain, the prepared at least one bacterium strain, and at least one carrier.
Embodiment A92 is a method of Embodiment A91, wherein preparing the at least one fungus strain includes preservation by vaporization.
Embodiment A93 is a method of Embodiment A91 or A92, wherein preparing the at least one bacterium strain includes spray drying spores of the at least one bacterium.
Embodiment A94a is a method of any one of Embodiments A91, A92, or A93, wherein the at least one fungus strain is a Pichia fungus strain.
Embodiment A94b is a method of any one of Embodiments A91, A92, or A93, wherein the at least one fungus strain is substantially similar to a Pichia fungus strain.
Embodiment A95a is a method of any one of Embodiments A91, A92, or A93, wherein the at least one fungus strain is Pichia kudriavzevii.
Embodiment A95b is a method of any one of Embodiments A91, A92, or A93, wherein the at least one fungus strain is substantially similar to Pichia kudriavzevii.
Embodiment A96a is a method of any one of Embodiments A91-A93, wherein the at least one fungus strain includes SEQ ID NO: 32.
Embodiment A96b is a method of any one of Embodiments A91, A92, or A93, wherein the at least one fungus strain is substantially similar to SEQ ID NO: 32.
Embodiment A97a is a method of any one of Embodiments A91-A96b, wherein the at least one bacterium strain is a Clostridium bacterium strain.
Embodiment A97b is a method of any one of Embodiments A91-A96b, wherein the at least one bacterium strain is substantially similar to a Clostridium bacterium strain.
Embodiment A98a is a method of any one of Embodiments A91-A96b, wherein the at least one bacterium strain is Clostridium butyricum.
Embodiment A98a is a method of any one of Embodiments A91-A96b, wherein the at least one bacterium strain is substantially similar to Clostridium butyricum.
Embodiment A99a is a method of any one of Embodiments A91-A96b, wherein the at least one bacterium strain includes SEQ ID NO: 28.
Embodiment A99b is a method of any one of Embodiments A91-A96b, wherein the at least one bacterium strain is substantially similar to SEQ ID NO: 28.
Embodiment A100 is a method of any one of Embodiments A91-A99b, where the carrier includes calcium carbonate.
Embodiment A101 is a method of any one of Embodiments A91-A99b, where the carrier includes silicon dioxide.
Embodiment A102 is a synthetic microbial ensemble product, comprising a synthetic microbial ensemble formed from the method of any one of Embodiments A91-A101.
Embodiment A103 is the synthetic microbial ensemble product of Embodiment A102, further comprising at least one sugar.
Embodiment A104 is the synthetic microbial ensemble product of Embodiment A103, wherein the at least one sugar is a disaccharide.
Embodiment A105 is the synthetic microbial ensemble product of Embodiment A103, wherein the at least one sugar is sucrose.
Embodiment A106 is the synthetic microbial ensemble product of any one of Embodiments A102, A103, A104, or A105, further comprising at least one sugar alcohol.
Embodiment A107 is the synthetic microbial ensemble product of Embodiment A106, wherein the at least one sugar alcohol is mannitol.
According to some embodiments, synthetic ensembles/synthetic bio ensembles (also referred to herein as an endomicrobial supplement (EMS) or endomicrobial supplements (EMSs)) can be formed, selected, and/or made according to the disclosure. The following abreviations are used herein: BIC—Bayes Information Criterion; DMI—dry matter intake; ECM—energy-corrected milk; FCM—fat-corrected milk; FY—fat yield; MUN—milk urea nitrogen; PY—protein yield; RA—relative abundance; PBS—phosphate buffered saline; SCC—somatic cell count; TMR—total mixed ration; TRT—treatment; and YPD—yeast peptone digest.
The rumen microbial environment is a diverse continuous-culture system that has been studied since the 1940s and 1950s (Krause, D., and J. B. Russell. 1996. SYMPOSIUM: RUMINAL MICROBIOLOGY How Many Ruminal Bacteria Are There?. J. Dairy Sci. 79:1467-1475. doi:10.3168/jds.S0022-0302(96)76506-2, the entirety of which is incorporated by reference herein for all purposes).
The development of next-generation sequencing has enabled a better understanding of the entire rumen microbiome and allowed for the development of products targeting the microbial populations residing in animal digestive tracts. A multitude of studies investigating the influence of live fed microorganisms on dairy cow efficiencies have been reported, including: AiZahal, O., H. McGill, A. Kleinberg, J. I. Holliday, I. K. Hindrichsen, T. F. Duffield, and B. W. McBride. 2014. Use of a direct-fed microbial product as a supplement during the transition period in dairy cattle. J. Dairy Sci. 97:7102-7114. doi:10.3168/jds.2014-8248; Chiquette, J., J. Lagrost, C. L. Girard, G. Talbot, S. Li, J. C. Plaizier, and I. K. Hindrichsen. 2015. Efficacy of the direct-fed microbial Enterococcus faecium alone or in combination with Saccharomyces cerevisiae or Lactococcus lactis during induced subacute ruminal acidosis. J. Dairy Sci. 98:190-203. doi:10.3168/jds.2014-8219; Ferraretto, L. F., and R. D. Shaver. 2015. Effect of direct-fed microbial supplementation on lactation performance and total-tract starch digestibility by midlactation dairy cows. Prof. Anim. Sci. 31:63-67. doi:10.15232/pas.2014-01369; and Carpenter, A. J., C. M. Ylioja, C. F. Vargas, L. K. Mamedova, L. G. Mendonca, J. F. Coetzee, L. C. Hollis, R. Gehring, and B. J. Bradford. 2016. Hot topic: Early postpartum treatment of commercial dairy cows with nonsteroidal antiinflammatory drugs increases whole-lactation milk yield. J Dairy Sci 99:672-679. doi:10.3168/jds.2015-10048; each of which is explicitly incorporated herein by reference in its entirety for all purposes. Unfortunately, such supplementations had little or no effect on cow performance or diet digestibility. Disclosed below is a study utilizing a synthetic ensemble/EMS according to the disclosure, the EMS being a synthetic formulation of a live microbial supplement comprised of microorganisms that can be naturally-occurring in the target animal, but in a carrier/synthetic carrier and/or at ratios not found in environment (and/or free from other microorganisms that are found in the environment). The EMS used in the study below comprised two mutualistic microorganisms (Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov.) that were originally isolated from the rumen of high performing dairy cows consuming total mixed ration (TMR). These strains were selected by analyzing rumen microbiome shifts during diet-induced changes in milk production according to methods of the disclosure. Using media similar to the rumen environment (for example, as disclosed by Bryant, M. P., and I. M. Robinson. 1961. An Improved Nonselective Culture Medium for Ruminal Bacteria and Its Use in Determining Diurnal Variation in Numbers of Bacteria in the Rumen. J. Dairy Sci. 44:1446-1456. doi:10.3168/jds.S0022-0302(61)89906-2, explicitly incorporated herein by reference in its entirety for all purposes), in vitro experiments of the two strains were conducted to demonstrate digestibility of cellulose and generation of volatile fatty acids. Based on the disclosed selection method, it was hypothesized that the microbes in the EMS would colonize within the inoculated group and have a positive effect on milk composition and yield compared to the control group.
Multiparous Holstein cows (n=8 per treatment group) from a commercial dairy in second and third lactation were enrolled in this study conducted at DairyExperts (Tulare, Calif., USA). Animal selection criteria included cows between 60 and 120 days in milk (DIM), milk production of 36 kg or more, and somatic cell count (SCC) below 200,000 cells/mL in accordance with the previous DHIA monthly test. In the two d after arrival, all cows were surgically fitted with a ruminal cannula on the left flank fossa (Bar Diamond 10 cm 1 C Cannula, Parma, Id., USA). All cows underwent a 10-d surgery recovery period (pre-TRT) and adaptation to new facilities and diet. Daily health observations were conducted throughout study.
The microorganisms used in this experiment, Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov., were selected using the methods disclosed herein originally isolated from the rumen of high performing dairy cows consuming a total mixed ration diet (TMR). The EMS was prepared anaerobically, daily during TRT using fresh cultures of C. butyricum sp. nov. and P. kudriavzevii sp. nov. in 1-L Pyrex® round media storage bottles GL-45 with stoppers no. 9 and open top screw caps (Corning, N.Y., USA) containing 1 L of yeast peptone digest medium (YPD, Sigma Aldrich, St. Louis, Mo., USA). Each d of the TRT period, culture OD600 was measured using a Metash V-5000 Visible Spectrophotometer (Shanghai Metash Instruments Co. Ltd., Shanghai, P. R. China) and cells were suspended in 1× Phosphate Buffered Saline (PBS, Molecular Biologicals International, Inc., Irvine, Calif., USA) for a final cell concentration of 2×108 for C. butyricum and 5×107 for P. kudriazevii. Cows were randomly allocated into two study groups of 8 cows each; CON and INO. The CON group received 20 mL of sterile 1×PBS once a d during intervention period via cannula using a 20 cc syringe (Care Touch Medical Equipment, Lake Worth, Fla., USA), while the INO group received a 20 mL daily dose of the EMS. Animals were penned individually and fed twice daily in separate feed containers after the morning and afternoon milkings. Prior day refusals were weighed and discarded daily before the morning feeding, and feed weights were recorded twice daily at each feeding throughout the study.
All cows were weighed individually using a PS-2000 scale (Salter Brecknell, Fairmont, Minn., USA) after the morning milking, on the last day of pre-TRT period, and then on TRT days: 7, 14, 21, and 28; and post-TRT days: 1, 6 and 10. Milk weights were collected at each milking from ICAR approved Waikato MKV milk meters (Waikato, Hamilton, NZ) installed on each milking unit long milk hose. A composite milk sample per cow was collected at each milking on the last day of pre-TRT period, during the TRT and post-TRT period. Milk was analyzed using near-infrared spectroscopy (NIR) for crude protein, fat, and milk urea nitrogen (MUN) at the Tulare DHIA Laboratory (Tulare, Calif., USA). Rumen samples were collected once a day prior to TRT after the morning milking on TRT days: 1, 2, 3, 5, 8, 11, 14, 17, 20, 23, 26, 29, and 32; and post-TRT days: 1, 4, 7 and 10. For each cow, a composite sample containing fluid and particulate collected from the dorsal, central, anterior, and caudal parts of the rumen was transferred into 15-mL polypropylene conical tube (Corning®, Corning, N.Y., USA) containing 3 mL of a stop solution (95% Ethanol/5% TRIzol/phenol, Sigma Aldrich, St. Louis, Mo., USA). Samples were stored on dry ice until transferred to storage at −80° C.
Rumen samples were centrifuged at 4,000 rpm for 15 min, the supernatant was decanted and 0.5 mL was aliquoted for DNA extraction using the PowerViral® Environmental RNA/DNA Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad, Calif., USA). The V1-V3 region of the 16S rRNA gene was amplified using 27F (see, e.g., Lane, D. J. 1991. 16S/23S rRNA Sequencing. Nucleic acid Tech. Bact. Syst. 115-175. doi:10.1007/s00227-012-2133-0, explicitly incorporated herein by reference in its entirety for all purposes) and 534R (see, e.g., Muyzer, G., E. C. de Waal, and A. G. Uitterlinden. 1993. Profiling of complex microbial populations by denaturing gradient gel electrophoresis analysis of polymerase chain reaction-amplified genes coding for 16S rRNA. Appl. Environ. Microbiol. 59:695-700. doi:0099-2240/93/030695-06$02.00/0, explicitly incorporated herein by reference in its entirety for all purposes) modified for Illumina sequencing, and the ITS region was amplified using ITS5 and ITS4 (see, e.g., White, T. J., S. B. Lee, and J. W. Taylor. 1990. Amplification and Direct Sequencing of Fungal Ribosomal RNA Genes for Phylogenetics. in PCR Protocols: A Guide to Methods and Applications. W. T. J. Innis M. A., Gelfand D. H., Sninsky J. J., ed. Academic Press, Inc. New York, N.Y., explicitly incorporated herein by reference in its entirety for all purposes) modified for Illumina sequencing following standard protocols (Q5® High-Fidelity DNA Polymerase (New England Biolabs, Inc., Ipswich, Mass., USA). Following amplification, PCR products were verified with a standard agarose gel electrophoresis and purified using AMPure XP bead (Beckman Coulter, Brea, Calif., USA). The purified amplicon library was quantified and sequenced on the MiSeq Platform (Illumina, San Diego, Calif., USA) according to standard protocols (see, e.g., Caporaso, J. G., C. L. Lauber, W. a Walters, D. Berg-Lyons, J. Huntley, N. Fierer, S. M. Owens, J. Betley, L. Fraser, M. Bauer, N. Gorrley, J. a Gilbert, G. Smith, and R. Knight, 2012. Ultra-high-throughput microbial community analysis on the Ilurnina HiSeq and MiSeq platforms. ISME J. 6:1621-1624. doi:10.1038/ismej.2012.8, explicitly incorporated herein by reference in its entirety for all purposes). Raw fastq read were de-multiplexed on the MiSeq Platform (Illumina, San Diego, Calif., USA) and processed using USEARCH (version 8.1.1756). Sequencing results were used to determine the relative abundances (RA) of C. butyricum sp. nov. and P. kudriavzevii sp. nov. and identify colonization patterns of EMS within the groups.
Dairy cow performance data was analyzed using the SAS/STAT software, Version 9.3 of the SAS System (SAS Institute Inc., Cary, N.C., USA). Daily values were originally analyzed implementing random coefficients models with linear and quadratic terms. Due to the small sample size and the model complexity, for several of the outcomes the model convergence was not obtained. However, in this embodiment, daily values were averaged to produce weekly means. Week 5 averages included only 4 days, while weeks 1 to 4 included 7 daily values. Weekly DMI, milk yield, milk composition, body weight gain, and rumen pH were analyzed as repeated measures using the MIXED procedure available within SAS/STAT software. The model included the fixed effect of treatment (CON vs. INO), time (week 1, 2, 3, 4 and 5) and their interaction. Milk yield and DMI measured the three days prior to treatment application were averaged and used as covariate for the corresponding outcome variable. Cow within treatment was the subject of the repeated statement. The covariance structure that provided the best fit according to the Bayes Information Criterion (BIC) was chosen. The covariance structure employed for this implementation was comprised of unstructured for DMI, milk protein and lactose percentages and fat yield, compound symmetry for milk urea nitrogen, and first order autoregressive for the remaining outcomes. Furthermore, where appropriate separate residual variances for each treatment were estimated as they provided a better fit according to BIC. When a significant treatment by time interaction was observed, treatment means within week were compared using the SLICE option. Significance was declared at P-value <0.05 and tendency was declared at 0.05≤P<0.10.
Treatment least square means, fixed effects and covariance parameters estimates of the analysis including all cows are shown in Table 13. An inclination for a higher milk fat percentage for INO vs. the CON was observed (P=0.0991) and averaged 4.06% and 3.87%, respectively. Although the treatment by week interaction was not significant (P=0.2677,
1DMI = Daily Mass Intake; FCM = fat-corrected milk; ECM = energy-corrected milk; MUN = milk urea nitrogen; BW = body weight
2Cov = covariate effect, TRT = treatment effect, Day = day effect; TRT*Day = treatment by day interaction.
A treatment by week interaction was observed for milk yield (P=0.0025,
Colonization of the EMS was observed using the RA data generated by Illumina sequencing (
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Referring to
Referring to
Results from this on-farm study reveal that inoculation of an EMS containing C. butyricum sp. nov and P. kudriavzevii sp. nov., via cannula had positive effect on multiparous Holstein dairy cow production and efficiency. Despite a small number of cows in each group (n=8), the effect size was substantially. Weekly means were used for statistical analysis, and therefore values at the beginning of TRT prior to dairy cow response were incorporated in the analysis. The disclosed methods expand knowledge of the rumen microbiome and enable shifts in dairy cow nutrition, including avoiding or otherwise steering away from antibiotics and chemical additives, the disclosed methods and systems, as well as synthetic ensembles generated thereby (e.g., EMSs) facilitate elucidation of the mechanisms employed by microorganisms within the rumen and the relationship between their colonization and cow performance.
In some aspects, the present disclosure provides isolated microbes, including novel strains of microbes, presented in Table 14 and/or Table 16.
In other aspects, the present disclosure provides isolated whole microbial cultures of the microbes identified in Table 14 and Table 16. These cultures may comprise microbes at various concentrations.
In some aspects, the disclosure provides for utilizing one or more microbes selected from Table 14 and/or Table 16 to increase a phenotypic trait of interest in a ruminant. Furthermore, the disclosure provides for methods of modulating the rumen microbiome by utilizing one or more microbes selected from Table 14 and/or Table 16.
In some embodiments, a microbial ensemble comprises at least two microbial strains selected from Table 14 and/or Table 16. In some embodiments, a microbial ensemble comprises at least one microbial strain selected from Table 14 and/or Table 16. In a further embodiment, a microbial ensemble comprises at least two microbial strains, wherein each microbe comprise a 16S rRNA sequence encoded by a sequence selected from SEQ ID NOs:1-30 and 2045-2103 or an ITS sequence selected from SEQ ID NOs:31-60 and 2104-2107. In an additional embodiment, a microbial ensemble comprises at least one microbial strain, wherein each microbe comprise a 16S rRNA sequence encoded by a sequence selected from SEQ ID NOs:1-30 and 2045-2103, or an ITS sequence selected from SEQ ID NOs:31-60 and 2104-2107.
In some embodiments, the microbial ensemble of the present disclosure comprise at least two microbial strains, wherein each microbe comprises a 16S rRNA sequence encoded by a sequence selected from SEQ ID NOs:1-30, SEQ ID NOs:61-1988, or SEQ ID NOs:2045-2103; or an ITS sequences selected from SEQ ID NOs:31-60, SEQ ID NOs:1989-2044, or SEQ ID NOs:2104-2107.
In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_24. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_24. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_24. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_24. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_1801, Ascusf_45, and Ascusf_24. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_7, Ascusb_1801, Ascusf_45, and Ascusf_24. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_268, Ascusf_45, and Ascusf_24. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_7, Ascusb_268, Ascusf_45, and Ascusf_24. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_232, Ascusf_45, and Ascusf_24. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_7, Ascusb_232, Ascusf_45, and Ascusf_24. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_249. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_249. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_353. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_353. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_23. In a further embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_23. In one embodiment, the microbial ensemble comprises at least two microbial strains comprising Ascusb_3138 and Ascusf_15. In a further embodiment, the microbial ensemble comprises at least one microbial strain comprising Ascusb_3138 and Ascusf_15. In one embodiment, the at least one microbial strain comprises Ascusb_3138. In another embodiment, the at least one microbial strain comprises Ascusf_15.
In one embodiment, a composition comprises a microbial ensemble of the present disclosure and an acceptable carrier. In a further embodiment, a composition comprises a microbial ensemble of the present disclosure and acceptable carrier. In a further embodiment, the microbial ensemble is encapsulated. In a further embodiment, the encapsulated microbial ensemble comprises a polymer. In a further embodiment, the polymer may be selected from a saccharide polymer, agar polymer, agarose polymer, protein polymer, sugar polymer, and lipid polymer.
In some embodiments, the acceptable carrier is selected from the group consisting of edible feed grade material, mineral mixture, water, glycol, molasses, and corn oil. In some embodiments, the at least two microbial strains forming the microbial ensemble are present in the composition at 102 to 1015 cells per gram of said composition.
In some embodiments, the composition may be mixed with livestock feed.
In some embodiments, a method of imparting at least one improved trait upon an animal comprises administering the composition to the animal. In further embodiments, the animal is a ruminant, which may further be a cow.
In some embodiments, the composition is administered at least once per day. In a further embodiment, the composition is administered at least once per month. In a further embodiment, the composition is administered at least once per week. In a further embodiment, the composition is administered at least once per hour.
In some embodiments, the administration comprises injection of the composition into the rumen. In some embodiments, the composition is administered anally. In further embodiments, anal administration comprises inserting a suppository into the rectum. In some embodiments, the composition is administered orally. In some aspects, the oral administration comprises administering the composition in combination with the animal's feed, water, medicine, or vaccination. In some aspects, the oral administration comprises applying the composition in a gel or viscous solution to a body part of the animal, wherein the animal ingests the composition by licking. In some embodiments, the administration comprises spraying the composition onto the animal, and wherein the animal ingests the composition. In some embodiments, the administration occurs each time the animal is fed. In some embodiments, the oral administration comprises administering the composition in combination with the animal feed.
In some embodiments, the at least one improved trait is selected from the group consisting of: an increase of fat in milk, an increase of carbohydrates in milk, an increase of protein in milk, an increase of vitamins in milk, an increase of minerals in milk, an increase in milk volume, an improved efficiency in feed utilization and digestibility, an increase in polysaccharide and lignin degradation, an increase in fatty acid concentration in the rumen, pH balance in the rumen, a reduction in methane emissions, a reduction in manure production, improved dry matter intake, an increase in energy corrected milk (ECM) by weight and/or volume, an improved efficiency of nitrogen utilization, and any combination thereof; wherein said increase or reduction is determined by comparing against an animal not having been administered said composition.
In some embodiments, the increase in fat in milk is an increase in triglycerides, triacylglycerides, diacylglycerides, monoacylglycerides, phospholipids, cholesterol, glycolipids, and/or fatty acids. In some embodiments, an increase of carbohydrates is an increase in oligosaccharides, lactose, glucose, and/or glucose. In some embodiments, an increase in polysaccharide degradation is an increase in the degradation of cellulose, lignin, and/or hemicellulose. In some embodiments, an increase in fatty acid concentration is an increase in acetic acid, propionic acid, and/or butyric acid.
In some embodiments, the at least two microbial strains or the at least one microbial strain present in a composition, or ensemble, of the disclosure exhibit an increased utility that is not exhibited when said strains occur alone or when said strains are present at a naturally occurring concentration. In some embodiments, compositions of the disclosure, comprising at least two microbial strains as taught herein, exhibit a synergistic effect on imparting at least one improved trait in an animal. In some embodiments, the compositions of the disclosure—comprising one or more isolated microbes as taught herein—exhibit markedly different characteristics/properties compared to their closest naturally occurring counterpart. That is, the compositions of the disclosure exhibit markedly different functional and/or structural characteristics/properties, as compared to their closest naturally occurring counterpart. For instance, the microbes of the disclosure are structurally different from a microbe as it naturally exists in a rumen, for at least the following reasons: said microbe can be isolated and purified, such that it is not found in the milieu of the rumen, said microbe can be present at concentrations that do not occur in the rumen, said microbe can be associated with acceptable carriers that do not occur in the rumen, said microbe can be formulated to be shelf-stable and exist outside the rumen environment, and said microbe can be combined with other microbes at concentrations that do not exist in the rumen. Further, the microbes of the disclosure are functionally different from a microbe as it naturally exists in a rumen, for at least the following reasons: said microbe when applied in an isolated and purified form can lead to modulation of the rumen microbiome, increased milk production, and/or improved milk compositional characteristics, said microbe can be formulated to be shelf-stable and able to exist outside the rumen environment, such that the microbe now has a new utility as a supplement capable of administration to a ruminant, wherein the microbe could not have such a utility in it's natural state in the rumen, as the microbe would be unable to survive outside the rumen without the intervention of the hand of man to formulate the microbe into a shelf-stable state and impart this new utility that has the aforementioned functional characteristics not possessed by the microbe in it's natural state of existence in the rumen.
In one embodiment, the disclosure provides for a ruminant feed supplement capable of increasing a desirable phenotypic trait in a ruminant. In a particular embodiment, the ruminant feed supplement comprises: a microbial ensemble of the present disclosure at a concentration that does not occur naturally, and an acceptable carrier. In one aspect, the microbial ensemble is encapsulated.
In one embodiment, an isolated microbial strain is selected from any one of the microbial strains in Table 14 and/or Table 16. In one embodiment, an isolated microbial strain is selected from the group consisting of: Ascusb_7 deposited as Bigelow Accession Deposit No. Patent 201612011; Ascusb_32 deposited as Bigelow Accession Deposit No. Patent 201612007; Ascusb_82 deposited as Bigelow Accession Deposit No. Patent 201612012; Ascusb_119 deposited as Bigelow Accession Deposit No. Patent 201612009; Ascusb_1801 deposited as Bigelow Accession Deposit No. Patent 201612009; Ascusf_206 deposited as Bigelow Accession Deposit No. Patent 201612003; Ascusf_23 deposited as Bigelow Accession Deposit No. Patent 201612014; Ascusf_24 deposited as Bigelow Accession Deposit No. Patent 201612004; Ascusf_45 deposited as Bigelow Accession Deposit No. Patent 201612002; Ascusf_208 deposited as Bigelow Accession Deposit No. Patent 201612003; Ascusb_3138 deposited as NRRL Accession Deposit No. B-67248; and Ascusf_15 deposited as NRRL Accession Deposit No. Y-67249. In one embodiment, an isolated microbial strain of the present disclosure comprises a polynucleotide sequence sharing at least 90% sequence identity with any one of SEQ ID NOs:1-2107. In another embodiment, an isolated microbial strain of the present disclosure comprises a polynucleotide sequence sharing at least 90% sequence identity with any one of SEQ ID NOs:1-60 and 2045-2107. In one embodiment, a substantially pure culture of an isolated microbial strain may comprise any one of the strains or microbes of the present disclosure.
In one embodiment, a method of modulating the microbiome of a ruminant comprises administering a composition of the present disclosure. In a further embodiment, the administration of the composition imparts at least one improved trait upon the ruminant. In one embodiment, the at least one improved trait is selected from one or more of: an increase of fat in milk, an increase of carbohydrates in milk, an increase of protein in milk, an increase of vitamins in milk, an increase of minerals in milk, an increase in milk volume, an improved efficiency in feed utilization and digestibility, an increase in polysaccharide and lignin degradation, an increase in fatty acid concentration in the rumen, pH balance in the rumen, a reduction in methane emissions, a reduction in manure production, improved dry matter intake, an increase in energy corrected milk (ECM) by weight and/or volume, and an improved efficiency of nitrogen utilization; wherein said increase or reduction is determined by comparing against an animal not having been administered said composition. In an additional embodiment, the modulation of the microbiome is a decrease in the proportion of the microbial strains present in the microbiome prior to the administration of the composition, wherein the decrease is measured relative to the microbiome of the ruminant prior to the administration of the composition. In one embodiment, the method of increasing fat in milk is an increase in triglycerides, triacylglycerides, diacylglycerides, monoacylglycerides, phospholipids, cholesterol, glycolipids, and/or fatty acids. In one embodiment, the method of increasing carbohydrates is an increase in oligosaccharides, lactose, glucose, and/or galactose.
In one embodiment, the method of increasing polysaccharide degradation is an increase in the degradation of lignin, cellulose, pectin and/or hemicellulose. In one embodiment, the method of increasing fatty acid concentration is an increase in acetic acid, propionic acid, and/or butyric acid. In one embodiment, the method of modulation of the microbiome is an increase in the proportion of the at least one microbial strain of the microbiome, wherein the increase is measured relative to a ruminant that did not have the at least one microbial strain administered.
In one embodiment, the method of modulation of the microbiome is a decrease in the proportion of the microbial strains present in the microbiome prior to the administration of the composition, wherein the decrease is measured relative to the microbiome of the ruminant prior to the administration of the composition.
In one embodiment, a method of increasing resistance of cows to the colonization of pathogenic microbes comprises administering a composition of the present disclosure, resulting in the pathogenic microbes being unable to colonize the gastrointestinal tract of a cow. In another embodiment, a method for treating cows for the presence of at least one pathogenic microbe comprises the administration of a microbial ensemble of the present disclosure and an acceptable carrier. In a further embodiment, the administration of the microbial ensemble or microbial composition results in the relative abundance of the at least one pathogenic microbe to decrease to less than 5% relative abundance in the gastrointestinal tract. In another embodiment, the administration of the microbial ensemble or microbial composition results in the relative abundance of the at least one pathogenic microbe to decrease to less than 1% relative abundance in the gastrointestinal tract. In another embodiment, the administration of the microbial ensemble or microbial composition results in the pathogenic microbe being undetectable in the gastrointestinal tract.
In one embodiment, the microbial compositions and/or ensemble comprise bacteria and/or fungi in spore form. In one embodiment, the microbial compositions and/or ensemble of the disclosure comprise bacteria and/or fungi in whole cell form. In one embodiment, the microbial compositions and/or ensemble of the disclosure comprise bacteria and/or fungi in lysed cell form. In some aspects of formulating microbes according to the disclosure, the microbes are: fermented→filtered→centrifuged→lyophilized or spray dried→and optionally coated (i.e. a “fluidized bed step”).
Some microorganisms described in this Application were deposited on Apr. 25, 20161, with the United States Department of Agriculture (USDA) Agricultural Research Service (ARS) Culture Collection (NRRL®), located at 1815 N. University St., Peoria, Ill. 61604, USA. Some microorganisms described in this application were deposited with the Bigelow National Center for Marine Algae and Microbiota, located at 60 Bigelow Drive, East Boothbay, Me. 04544, USA. The deposits were made under the terms of the Budapest Treaty on the International Recognition of the Deposit of Microorganisms for the Purposes of Patent Procedure. The NRRL® and/or Bigelow National Center for Marine Algae and Microbiota accession numbers for the aforementioned Budapest Treaty deposits are provided in Table 16. The accession numbers and corresponding dates of deposit for the microorganisms described in this Application are separately provided in Table 38. The strains designated in the below tables have been deposited in the labs of Ascus Biosciences, Inc. since at least Dec. 15, 2015. In Table 14, the closest predicted hits for taxonomy of the microbes are listed in columns 2, and 5. Column 2 is the top taxonomic hit predicted by BLAST, and column 5 is the top taxonomic hit for genus+species predicted by BLAST. The strains designated in the below table have been deposited in the labs of Ascus Biosciences, Inc. since at least Dec. 15, 2015. 1 ASC-01 (NRRL B-67248) and ASC-02 (NRRL Y-67249) were deposited on this date
Ruminococcus
bromii
Ruminococcus
bromii
Intestinimonas
butyriciproducens
Pseudobutyrivibrio
ruminis
Roseburia
aerobacterium
inulinivorans
Eubacterium
Eubacterium
ventriosum
ventriosum
Faecalibacterium
prausnitzii
Saccharofermentans
Saccharofermentans
acetigenes
Clostridium
Ruminococcus
flavefaciens
Clostridium
saccharolyticum
Clostridium
lentocellum
Ruminococcus
bacterium
flavefaciens
Faecalibacterium
Faecalibacterium
prausnitzii
Saccharofermentans
acetigenes
Blautia luti
Clostridium
sensu
lentocellum
stricto
Coprococcus catus
Anaeroplasma
Anaeroplasma
varium
varium
Clostridium
sensu
stercorarium
stricto
Aminiphilus
circumscriptus
Aminiphilus
circumscriptus
Bacteroides
Alistipes shahii
Alistipes
Alistipes shahii
shahii
Oscillibacter
valericigenes
Odoribacter
splanchnicus
Tannerella
forsythia
Hydrogeno-
sensu
anaerobacterium
stricto
saccharovorans
Clostridium
Clostridium
sensu
butyricum
butyricum
stricto
Faecalibacterium
prausnitzii
Roseburia
Roseburia
intestinalis
intestinalis
Succinivibrio
Succinivibrio
dextrinosolvens
dextrinosolvens
Prevotella
ruminicola
Prevotella
Prevotella
ruminicola
ruminicola
Prevotella
Prevotella
ruminicola
ruminicola
Ruminobacter
Ruminobacter
amylophilus
Blautia
Blautia producta
producta
Succinivibrio
Succinivibrio
dextrinosolvens
dextrinosolvens
Butyrivibrio
Butyrivibrio
fibrisolvens
fibrisolvens
Prevotella
Prevotella
ruminicola
ruminicola
Prevotella
Prevotella
ruminicola
ruminicola
Prevotella
Prevotella
ruminicola
ruminicola
Prevotella albensis
Roseburia
inulinivorans
Ruminococcus
Ruminococcus
gnavus
gnavus
Ruminobacter
Ruminobacter
amylophilus
Butyrivibrio
Butyrivibrio
hungatei
Eubacterium
Eubacterium
oxidoreducens
oxidoreducens
Prevotella brevis
Prevotella sp.
Prevotella copri
Eubacterium
Eubacterium
ruminantium
ruminantium
xylanovorans
Lachnospira
Lachnospira
pectinoschiza
pectinoschiza
Butyrivibrio
Butyrivibrio
fibrisolvens
fibrisolvens
Pseudobutyrivibrio
Pseudobutyrivibrio
ruminis
Sinimarinibacterium
Sinimarinibacterium
flocculans
flocculans
Butyrivibrio
fibrisolvens
Pseudobutyrivibrio
Pseudobutyrivibrio
ruminis
ruminis
Chloroflexi
Anaerolinea
thermophila
Eubacterium
rectale
Propionibacterium
Propionibacterium
acnes
acnes
Pseudobutyrivibrio
ruminis
Olsenella profusa
Streptococcus
Streptococcus
dentirousetti
dentirousetti
Butyrivibrio
Butyrivibrio
proteoclasticus
Butyrivibrio
hungatei
Eubacterium
oxidoreducens
Ruminococcus
bromii
Clostridium
algidixylanolyticum
Eubacterium
ruminantium
Catenisphaera
Catenisphaera
adipataccumulans
adipataccumulans
Solobacterium
moorei
Eubacterium
Eubacterium
ruminantium
ruminantium
Butyrivibrio
Butyrivibrio
proteoclasticus
proteoclasticus
Ralstonia sp.
Ralsonia insidiosa
Butyrivibrio
Butyrivibrio
proteoclasticus
Casaltella
massiliensis
Eubacterium
xylanophilum
Acholeplasma
Acholeplasma
brassicae
brassicae
Mitsuokella
Mitsuokella
jalaludinii
jalaludinii
Prevotella
Prevotella
ruminicola
ruminicola
Butyrivibrio
Butyrivibrio
fibrisolvens
Succinivibrio
Succinivibrio
dextrinosolvens
dextrinosolvens
Ruminobacter
Ruminobacter
amylophilus
Sharpea
azabuensis
Prevotella
Prevotella
ruminicola
ruminicola
Prevotella sp.
Prevotella
ruminicola
Prevotella
ruminicola
Prevotella
Prevotella
ruminicola
ruminicola
Prevotella
Prevotella
ruminicola
ruminicola
Piromyces sp.
Neocallimastix
frontalis
Pichia
Pichia kudriavzevii
xylopsoc
kudriavzevii
Orpinomyces
Neocallimastix
frontalis
Neocallimastix
Neocallimastix
frontalis
frontalis
Orpinomyces
Neocallimastix
frontalis
Candida
Candida apicola
apicol
apicola
Candida
Candida
rugosa
akabanensis
akabanensis
Neocallimastix
Neocallimastix
frontalis
Orpinomyces
Orpinomyces
joyonii
Neocallimastix
Neocallimastix
frontalis
frontalis
Neocallimastix
Neocallimastix
frontalis
Neocallimastix
Neocallimastix
frontalis
frontalis
Basidiomycota
Sugiyamaella
lignohabitans
Caecomyces
Cyllamyces
aberensis
Orpinomyces
Orpinomyces
joyonii
Caecomyces
Caecomyces
communis
Caecomyces
Caecomyces
communis
Cyllamyces
Cyllamyces
aberensis
Piromyces sp.
Neocallimastix
frontalis
Caecomyces
Cyllamyces
aberensis
Neocallimastix
Neocallimastix
frontalis
Piromyces sp.
Neocallimastix
frontalis
Neocallimastix
Neocallimastix
frontalis
Candida
Candida ethanolica
ethanolica
Piromyces sp.
Neocallimastix
frontalis
Neocallimastix
Neocallimastix
frontalis
Piromyces sp.
Neocallimastix
frontalis
Tremellales
Tremella giraffa
capitalensis
Neocallimastix
Neocallimastix
frontalis
frontalis
Neocallimastix
Neocallimastix
frontalis
frontalis
Fungal sp.
Orpinomyces
joyonii
Piromyces sp.
Neocallimastix
frontalis
Piromyces sp.
Neocallimastix
frontalis
Piromyces sp.
Neocallimastix
frontalis
Clostridium IV (Cluster)
Streptococcus
Ruminococcus (Genus)
Clostridium IV (Cluster)
Roseburia (Genus)
Roseburia (Genus)
Hydrogenoan-
aerobacterium (Genus)
Clostridium XIVa
Saccharofermentans
Saccharofermentans
Butyricicoccus (Genus)
Solobacterium
Papillibacter (Genus)
Ruminococcus (Genus)
Hydrogenoanaero-
Ralstonia (Genus)
bacterium (Genus)
Pelotomaculum (Genus)
Saccharofermentans
Eubacterium
Lachnobacterium
Butyricicoccus sensu
Acholeplasma
stricto (Genus)
Selenomonas
Anaeroplasma (Genus)
Prevotella (Genus)
Clostridium sensu stricto
Butyricicoccus (Genus)
Succinivibrio
Butyricicoccus (Genus)
Ruminobacter
Rikenella (Genus)
Sharpea (Genus)
Tannerella (Genus)
Prevotella (Genus)
Howardella (Genus)
Prevotella (Genus)
Prevotella (Genus)
Prevotella (Genus)
Butyricimonas (Genus)
Prevotella (Genus)
Clostridium sensu stricto
Prevotella (Genus)
Clostridium sensu stricto
Piromyces (Genus)
Saccharofermentans
Candida xylopsoc
Orpinomyces
Succinivibrio (Genus)
Orpinomycs
Prevotella (Genus)
Orpinomyces
Prevotella (Genus)
Candida apicol
Prevotella (Genus)
Candida rugosa
Ruminobacter (Genus)
Neocallimastix
Syntrophococcus
Orpinomyces
Succinivibrio (Genus)
Orpinomyces
Pseudobutyrivibrio
Neocallimastix
Prevotella (Genus)
Neocallimastix
Prevotella (Genus)
Ascomycota
Prevotella (Genus)
Piromyces (Genus)
Prevotella (Genus)
Orpinomyces
Cyllamyces
Syntrophococcus
Piromyces (Genus)
Ruminobacter (Genus)
Cyllamyces
Butyrivibrio (Genus)
Piromyces (Genus)
Piromyces (Genus)
Prevotella (Genus)
Neocallimastix
Prevotella (Genus)
Piromyces (Genus)
Neocallimastix
Roseburia (Genus)
Piromyces (Genus)
Butyrivibrio (Genus)
Orpinomyces
Pseudobutyrivibrio
Piromyces (Genus)
Turicibacter (Genus)
Phyllosticta
capitalensis (Genus +
Orpinomyces
Pseudobutyrivibrio
Orpinomyces
Anaerolinea (Genus)
Orpinomyces
Roseburia (Genus)
Piromyces (Genus)
Propionibacterium
Piromyces (Genus)
Piromyces (Genus)
Olsenella (Genus)
Clostridium IV (Cluster)
Ruminococcus (Genus)
Clostridium IV (Cluster)
Roseburia (Genus)
Hydrogenoan-
aerobacterium (Genus)
Clostridium XIVa
Saccharofermentans
Saccharofermentans
Butyricicoccus (Genus)
Papillibacter (Genus)
Ruminococcus (Genus)
Hydrogenoanaero-
bacterium (Genus)
Pelotomaculum (Genus)
Saccharofermentans
Butyricicoccus sensu
stricto (Genus)
Anaeroplasma (Genus)
Clostridium sensu stricto
Butyricicoccus (Genus)
Butyricicoccus (Genus)
Rikenella (Genus)
Tannerella (Genus)
Howardella (Genus)
Prevotella (Genus)
Butyricimonas (Genus)
Clostridium sensu stricto
Clostridium sensu stricto
Saccharofermentans
Succinivibrio (Genus)
Prevotella (Genus)
Prevotella (Genus)
Prevotella (Genus)
Ruminobacter (Genus)
Syntrophococcus
Succinivibrio (Genus)
Pseudobutyrivibrio
Prevotella (Genus)
Prevotella (Genus)
Prevotella (Genus)
Prevotella (Genus)
Syntrophococcus
Ruminobacter (Genus)
Butyrivibrio (Genus)
Prevotella (Genus)
Prevotella (Genus)
Roseburia (Genus)
Butyrivibrio (Genus)
Pseudobutyrivibrio
Turicibacter (Genus)
Pseudobutyrivibrio
Anaerolinea (Genus)
Roseburia (Genus)
Propionibacterium
Olsenella (Genus)
Corynebacterium
Prevotella
Comamonas
Hippea
Anaerovorax
Rummeliibacillus
Prevotella
Anaerovorax
Pseudoflavonifractor
Prevotella
Coprococcus
Pyramidobacter
Syntrophococcus
Prevotella
Prevotella
Roseburia
Acidothermus
Adlercreutzia
Prevotella
Proteiniclasticum
Anaerovorax
Prevotella
Bacteroides
Prevotella
Acinetobacter
Erysipelothrix
Bacteroides
Butyrivibrio
Eubacterium
Prevotella
Eubacterium
Prevotella
Coprococcus
Prevotella
Prevotella
Catonella
Methanobrevibacter
Ruminococcus
Coprococcus
Prevotella
Anaerovorax
Asteroleplasma
Caulobacter
Roseburia
Acinetobacter
Bacteroides
Erysipelothrix
Coprococcus
Bacteroides
Coprococcus
Anaerovorax
Pseudoflavonifractor
Pseudoflavonifractor
Prevotella
Roseburia
Prevotella
Coprococcus
Prevotella
Prevotella
Catonella
Methanobrevibacter
Ruminococcus
Coprococcus
Prevotella
Anaerovorax
Asteroleplasma
Caulobacter
Roseburia
Acinetobacter
Bacteroides
Erysipelothrix
Coprococcus
Bacteroides
Coprococcus
Anaerovorax
Pseudoflavonifractor
Pseudoflavonifractor
Prevotella
Roseburia
Prevotella
Ruminococcus
Atopobium
Eubacterium
Robinsoniella
Neisseria
Ruminococcus
Prevotella
Slackia
Prevotella
Bacteroides
Anaerorhabdus
Bacteroides
Prevotella
Corynebacterium
Atopobium
Streptophyta
Prevotella
Roseburia
Prevotella
Prevotella
Eubacterium
Rhodocista
Prevotella
Prevotella
Prevotella
Streptophyta
Ochrobactrum
Mogibacterium
Adlercreutzia
Prevotella
Riemerella
Prevotella
Roseburia
Slackia
Syntrophococcus
Prevotella
Treponema
Prevotella
Anaerovorax
Prevotella
Methanobrevibacter
Corynebacterium
Alkaliphilus
Ruminococcus
Eubacterium
Bacteroides
Roseburia
Lentisphaera
Eubacterium
Roseburia
Hahella
Butyricicoccus
Prevotella
Desulfovibrio
Sphingobacterium
Roseburia
Bacteroides
Ruminococcus
Prevotella
Asteroleplasma
Syntrophococcus
Victivallis
Lachnobacterium
Anaerorhabdus
Altererythrobacter
Proteiniclasticum
Bifidobacterium
Desulfovibrio
Nitrobacter
Enterorhabdus
Oscillibacter
Nautilia
Corynebacterium
Ruminococcus
Coprococcus
Eubacterium
Rikenella
Paenibacillus
Ruminococcus
Prevotella
Haematobacter
Prevotella
Enterorhabdus
Blautia
Sporobacter
Oscillibacter
Atopobium
Sporobacter
Oscillibacter
Mogibacterium
Roseburia
Pelotomaculum
Pelotomaculum
Robinsoniella
Coprococcus
Wautersiella
Planctomyces
Treponema
Coprococcus
Paracoccus
Ruminococcus
Atopobium
Prevotella
Prevotella
Dethiosulfovibrio
Saccharofermentans
Roseburia
Hydrogeno
anaerobacterium
Victivallis
Pelotomaculum
Saccharofermentans
Coprococcus
Papillibacter
Bartonella
Eubacterium
Asaccharobacter
Blautia
Prevotella
Ruminococcus
Selenomonas
Treponema
Adlercreutzia
Butyricicoccus
Pseudoflavonifractor
Corynebacterium
Adlercreutzia
Selenomonas
Coraliomargarita
Paraprevotella
Oscillibacter
Anaerovorax
Saccharofermentans
Erysipelothrix
Agaricicola
Denitrobacterium
Armatimonadetes
Asaccharobacter
Anaeroplasma
Prevotella
Streptococcus
Cellulosilyticum
Asaccharobacter
Enterorhabdus
Treponema
Roseburia
Victivallis
Prevotella
Roseburia
Ruminococcus
Mogibacterium
Prevotella
Victivallis
Cyanobacteria
Treponema
Stenotrophomonas
Sphingobium
Oscillibacter
Methylobacterium
Zhangella
Oscillibacter
Coraliomargarita
Eubacterium
Enterorhabdus
Saccharofermentans
Victivallis
Coprococcus
Pseudoflavonifractor
Anaeroplasma
Anaeroplasma
Bacteroides
Acinetobacter
Victivallis
Victivallis
Mogibacterium
Oscillibacter
Butyricimonas
Dethiosulfovibrio
Pseudoflavonifractor
Anaeroplasma
Oscillibacter
Herbiconiux
Eubacterium
Armatimonadetes
Selenomonas
Mogibacterium
Roseburia
Anaerovibrio
Saccharofermentans
Saccharofermentans
Prevotella
Robinsoniella
Brevundimonas
Anaerotruncus
Victivallis
Bacteroides
Prevotella
Ruminococcus
Pelobacter
Coprococcus
Coprococcus
Victivallis
Anaerovibrio
Anaerovorax
Proteiniclasticum
Anaerovorax
Selenomonas
Hydrogenoanaerobacterium
Acetanaerobacterium
Asaccharobacter
Saccharofermentans
Prevotella
Anaeroplasma
Spirochaeta
Alkaliphilus
Paraprevotella
Hippea
Prevotella
Prevotella
Hydrogenoanaerobacterium
Paraeggerthella
Adhaeribacter
Syntrophococcus
Saccharofermentans
Coraliomargarita
Sharpea
Anaerovorax
Blautia
Anaerovorax
Coraliomargarita
Aquiflexum
Pedobacter
Robinsoniella
Pelomonas
Saccharofermentans
Paracoccus
Enterorhabdus
Beijerinckia
Sporobacter
Bacillus
Saccharofermentans
Spirochaeta
Prevotella
Eubacterium
Herbiconiux
Brevundimonas
Mogibacterium
Anaerorhabdus
Victivallis
Prevotella
Anaerovorax
Aquiflexum
Oscillibacter
Altererythrobacter
Hydrogeno
anaerobacterium
Saccharofermentans
Roseburia
Anaeroplasma
Planctomyces
Ruminococcus
Selenomonas
Anaeroplasma
Anaerovorax
Rummeliibacillus
Anaeroplasma
Butyrivibrio
Anaerotruncus
Syntrophococcus
Paraeggerthella
Papillibacter
Prevotella
Papillibacter
Streptococcus
Methanobrevibacter
Prevotella
Prevotella
Prevotella
Coraliomargarita
Prevotella
Thermotalea
Atopobium
Prevotella
Mogibacterium
Eggerthella
Blautia
Vampirovibrio
Papillibacter
Beijerinckia
Bacteroides
Desulfotomaculum
Acidobacteria
Cryptanaerobacter
Prevotella
Syntrophomonas
Erysipelothrix
Selenomonas
Flavobacterium
Thermotalea
Mucilaginibacter
Bacteroides
Ruminococcus
Asaccharobacter
Blautia
Mucilaginibacter
Coprococcus
Butyricimonas
Treponema
Anaerovorax
Saccharofermentans
Ruminococcus
Eubacterium
Ruminococcus
Faecalibacterium
Anaerovibrio
Asaccharobacter
Pelotomaculum
Spirochaeta
Prevotella
Anaerovorax
Victivallis
Syntrophococcus
Syntrophococcus
Desulfovibrio
Prevotella
Victivallis
Selenomonas
Bacteroides
Eggerthella
Selenomonas
Mogibacterium
Armatimonadetes
Victivallis
Paraprevotella
Brevundimonas
Prevotella
Prevotella
Robinsoniella
Butyricimonas
Spirochaeta
Hydrogenoanaerobacterium
Proteiniclasticum
Roseburia
Anaerofustis
Succiniclasticum
Anaeroplasma
Oscillibacter
Escherichia/Shigella
Bacteroides
Prevotella
Coprococcus
Oscillibacter
Parabacteroides
Bacteroides
Mogibacterium
Solobacterium
Bacteroides
Victivallis
Saccharofermentans
Saccharofermentans
Olivibacter
Thermotalea
Proteiniclasticum
Anaeroplasma
Treponema
Desulfotomaculum
Bacillus
Anaerovorax
Ruminococcus
Agarivorans
Anaerotruncus
Papillibacter
Bacteroides
Ruminococcus
Oscillibacter
Nitrobacter
Limibacter
Desulfovibrio
Coprococcus
Anaerovorax
Spirochaeta
Cyanobacteria
Saccharofermentans
Anaeroplasma
Victivallis
Enterorhabdus
Erysipelothrix
Gelidibacter
Roseburia
Neisseria
Prevotella
Cyanobacteria
Oscillibacter
Prevotella
Saccharofermentans
Spirochaeta
Adlercreutzia
Adlercreutzia
Prevotella
Syntrophococcus
Treponema
Prevotella
Sharpea
Dongia
Eubacterium
Prevotella
Parabacteroides
Brevundimonas
Ruminococcus
Thermotalea
Victivallis
Anaeroplasma
Oscillibacter
Ruminococcus
Roseburia
Eggerthella
Lactobacillus
Bacteroides
Cellulosilyticum
Brevundimonas
Prevotella
Helicobacter
Proteiniclasticum
Brevundimonas
Prevotella
Desulfovibrio
Coraliomargarita
Eubacterium
Sphingomonas
Prevotella
Paraprevotella
Ruminococcus
Saccharofermentans
Turicibacter
Prevotella
Fusibacter
Rummeliibacillus
Mogibacterium
Bacteroides
Pelospora
Eggerthella
Eubacterium
Blautia
Ehrlichia
Eubacterium
Prevotella
Treponema
Hydrogenoanaerobacterium
Selenomonas
Saccharofermentans
Anaerovorax
Spirochaeta
Brevundimonas
Eubacterium
Anaerovorax
Ruminococcus
Papillibacter
Hydrogenoanaerobacterium
Asaccharobacter
Rhodocista
Beijerinckia
Lactobacillus
Cryptanaerobacter
Prevotella
Anaerovibrio
Anaerovorax
Enterorhabdus
Selenomonas
Eubacterium
Thermotalea
Enterorhabdus
Acetanaerobacterium
Treponema
Enterorhabdus
Prevotella
Desulfovibrio
Aminobacter
Rikenella
Gordonibacter
Papillibacter
Syntrophococcus
Hahella
Vampirovibrio
Coprococcus
Coraliomargarita
Desulfotomaculum
Helicobacter
Syntrophococcus
Paludibacter
Adhaeribacter
Idiomarina
Selenomonas
Acetanaerobacterium
Bifidobacterium
Asaccharobacter
Eubacterium
Anaeroplasma
Saccharofermentans
Ruminococcus
Acholeplasma
Pedobacter
Sphingomonas
Verrucomicrobia
Anaerovorax
Spirochaeta
Paraeggerthella
Bacteroides
Paenibacillus
Prevotella
Bacteroides
Roseburia
Pedobacter
Robinsoniella
Anaeroplasma
Turicibacter
Papillibacter
Saccharofermentans
Sporobacter
Asaccharobacter
Bacteroides
Anaeroplasma
Sporobacter
Streptomyces
Arcobacter
Barnesiella
Lactobacillus
Flavobacterium
Victivallis
Ureaplasma
Acetanaerobacterium
Slackia
Oscillibacter
Prevotella
Proteiniphilum
Spirochaeta
Ruminococcus
Prevotella
Butyricicoccus
Devosia
Anaeroplasma
Oscillibacter
Barnesiella
Atopobium
Methanobrevibacter
Butyricimonas
Butyricimonas
Asaccharobacter
Enhydrobacter
Treponema
Adlercreutzia
Prevotella
Pseudoflavonifractor
Syntrophococcus
Demequina
Saccharofermentans
Sphaerisporangium
Anaeroplasma
Geobacillus
Prevotella
Victivallis
Bacteroides
Demequina
Paraeggerthella
Paraprevotella
Pseudoflavonifractor
Roseburia
Gelidibacter
Rhizobium
Acholeplasma
Bacteroides
Bacteroides
Papillibacter
Fusibacter
Coraliomargarita
Papillibacter
Acholeplasma
Catenibacterium
Nitrobacter
Victivallis
Selenomonas
Enterorhabdus
Eubacterium
Roseburia
Prevotella
Asaccharobacter
Bacteroides
Gelidibacter
Prevotella
Oscillibacter
Asteroleplasma
Anaeroplasma
Oscillibacter
Bilophila
Oscillibacter
Prevotella
Geosporobacter
Butyricimonas
Pseudoflavonifractor
Barnesiella
Selenomonas
Prevotella
Enterorhabdus
Oscillibacter
Pelotomaculum
Cellulosilyticum
Parabacteroides
Papillibacter
Bacteroides
Prevotella
Hydrogeno
anaerobacterium
Prevotella
Howardella
Slackia
Methylobacter
Treponema
Devosia
Ruminococcus
Methanobrevibacter
Paraprevotella
Desulfobulbus
Butyricicoccus
Dialister
Selenomonas
Spirochaeta
Cellulosilyticum
Prevotella
Pseudoflavonifractor
Oscillibacter
Faecalibacterium
Eubacterium
Prevotella
Paenibacillus
Pedobacter
Butyricicoccus
Roseburia
Hydrogenoanaerobacterium
Adhaeribacter
Eubacterium
Bacteroides
Victivallis
Roseburia
Treponema
Prevotella
Prevotella
Hydrogenoanaerobacterium
Bacteroides
Bacteroides
Lactobacillus
Adlercreutzia
Dethiosulfovibrio
Lutispora
Turicibacter
Cyanobacteria
Cyanobacteria
Bulleidia
Aquiflexum
Roseburia
Glaciecola
Hydrogenoanaerobacterium
Sphaerobacter
Cyanobacteria
Prevotella
Turicibacter
Ruminococcus
Saccharofermentans
Ruminococcus
Fibrobacter
Proteiniclasticum
Anaeroplasma
Cyanobacteria
Algoriphagus
Howardella
Barnesiella
Prevotella
Butyricimonas
Blautia
Prevotella
Blautia
Flavobacterium
Prevotella
Eubacterium
Butyricicoccus
Fluviicola
Anaerovibrio
Blautia
Verrucomicrobia
Spirochaeta
Anaerovorax
Roseburia
Mucilaginibacter
Prevotella
Coprococcus
Acholeplasma
Lactobacillus
Prevotella
Bifidobacterium
Adhaeribacter
Hydrogenoanaerobacterium
Acetivibrio
Cyanobacteria
Flammeovirga
Dethiosulfovibrio
Hippea
Faecalibacterium
Spirochaeta
Brevundimonas
Mucilaginibacter
Hydrogeno
anaerobacterium
Asaccharobacter
Mogibacterium
Oscillibacter
Faecalibacterium
Altererythrobacter
Gelidibacter
Prevotella
Anaerovorax
Riemerella
Sphingobacterium
Syntrophococcus
Bacteroides
Papillibacter
Butyricicoccus
Hydrogeno
anaerobacterium
Marvinbryantia
Brevibacillus
Prevotella
Aminobacter
Sporotomaculum
Pedobacter
Victivallis
Gelidibacter
Prevotella
Wautersiella
Slackia
Pyramidobacter
Prevotella
Lentisphaera
Desulfoluna
Prevotella
Prevotella
Cyanobacteria
Helicobacter
Coprococcus
Bradyrhizobium
Sphingobacterium
Gelidibacter
Vasilyevaea
Eubacterium
Eubacterium
Syntrophococcus
Prevotella
Treponema
Anaerovorax
Sulfurovum
Papillibacter
Paracoccus
Hydrogeno
anaerobacterium
Adhaeribacter
Bacteroides
Hydrogeno
anaerobacterium
Telmatospirillum
Hydrogeno
anaerobacterium
Vasilyevaea
Anaeroplasma
Sporotomaculum
Enterorhabdus
Bacteroides
Anaerotruncus
Rhodopirellula
Gelidibacter
Anaerofustis
Butyricicoccus
Butyricicoccus
Cryptanaerobacter
Mogibacterium
Syntrophococcus
Bacteroides
Treponema
Coraliomargarita
Ruminococcus
Prevotella
Pseudaminobacter
Prevotella
Treponema
Syntrophococcus
Tenacibaculum
Parabacteroides
Luteimonas
Eubacterium
Roseburia
Oscillibacter
Cyanobacteria
Prevotella
Treponema
Victivallis
Oscillibacter
Papillibacter
Cellulosilyticum
Treponema
Ruminococcus
Coraliomargarita
Butyricicoccus
Blautia
Prevotella
Neptunomonas
Howardella
Roseburia
Oscillibacter
Sporobacter
Butyricicoccus
Filomicrobium
Bacteroides
Brevundimonas
Paracoccus
Schlegelella
Diaphorobacter
Saccharopolyspora
Prevotella
Eggerthella
Gelidibacter
Prevotella
Pseudomonas
Prevotella
Prevotella
Prevotella
Brevundimonas
Bacteroides
Photobacterium
Prevotella
Anaeroplasma
Caldilinea
Victivallis
Brevundimonas
Cyanobacteria
Prevotella
Slackia
Pedobacter
Prevotella
Trueperella
Oscillibacter
Cyanobacteria
Victivallis
Bacteroides
Micrococcus
Olivibacter
Anaerophaga
Selenomonas
Megasphaera
Eubacterium
Cyanobacteria
Treponema
Cryptanaerobacter
Xanthomonas
Asteroleplasma
Cyanobacteria
Sporotomaculum
Bacteroides
Asaccharobacter
Cyanobacteria
Treponema
Prevotella
Turicibacter
Oscillibacter
Deinococcus
Pedobacter
Anaerovorax
Bacteroides
Rhodococcus
Treponema
Mucilaginibacter
Olivibacter
Barnesiella
Gelidibacter
Methanobrevibacter
Anaerotruncus
Mesorhizobium
Planctomyces
Aerococcus
Victivallis
Cyanobacteria
Bacteroides
Ruminococcus
Saccharofermentans
Oscillibacter
Fibrobacter
Kiloniella
Olivibacter
Spirochaeta
Prevotella
Olivibacter
Prevotella
Parabacteroides
Prevotella
Leifsonia
Victivallis
Treponema
Cyanobacteria
Sporotomaculum
Spirochaeta
Anaerovorax
Oscillibacter
Victivallis
Spirochaeta
Oscillibacter
Prevotella
Anaeroplasma
Adlercreutzia
Beijerinckia
Prevotella
Coprococcus
Lentisphaera
Saccharofermentans
Porphyrobacter
Rhodobacter
Oscillibacter
Roseburia
Prevotella
Aquiflexum
Rhodopirellula
Bacteroides
Bacteroides
Prevotella
Mogibacterium
Prevotella
Prevotella
Capnocytophaga
Acholeplasma
Succinivibrio
Pseudonocardia
Butyricimonas
Anaerovorax
Prevotella
Butyricimonas
Parabacteroides
Bacteroides
Cyanobacteria
Riemerella
Anaeroplasma
Ruminococcus
Verrucomicrobia
Syntrophococcus
Barnesiella
Olivibacter
Cryptanaerobacter
Saccharofermentans
Coprococcus
Barnesiella
Hydrogenoanaerobacterium
Selenomonas
Prevotella
Hydrogenoanaerobacterium
Spirochaeta
Enterorhabdus
Thermoanaerobacter
Armatimonadetes
Syntrophococcus
Sphingobium
Geosporobacter
Enterorhabdus
Verrucomicrobia
Parabacteroides
Cryptanaerobacter
Anaeroplasma
Spirochaeta
Prevotella
Roseburia
Pedobacter
Pedobacter
Eggerthella
Prevotella
Rikenella
Anaerophaga
Spirochaeta
Weissella
Butyricicoccus
Hahella
Acholeplasma
Cellulosilyticum
Verrucomicrobia
Pseudoflavonifractor
Calditerricola
Adlercreutzia
Bulleidia
Mucilaginibacter
Victivallis
Anaerovorax
Prevotella
Bacteroides
Schwartzia
Pyramidobacter
Eubacterium
Roseburia
Enterorhabdus
Pedobacter
Desulfotomaculum
Proteiniclasticum
Prevotella
Faecalibacterium
Microbacterium
Leucobacter
Prevotella
Sphingobacterium
Fusibacter
Howardella
Pedobacter
Caldilinea
Turicibacter
Alistipes
Prevotella
Butyricimonas
Anaerovibrio
Prevotella
Pseudoflavonifractor
Corynebacterium
Leucobacter
Kerstersia
Slackia
Lactococcus
Prevotella
Prevotella
Bacteroides
Lactobacillus
Prevotella
Syntrophococcus
Victivallis
Bacteroides
Acidobacteria
Prevotella
Verrucomicrobia
Treponema
Pyramidobacter
Robinsoniella
Bifidobacterium
Bacteroides
Gordonibacter
Enterorhabdus
Lactobacillus
Bacteroides
Prevotella
Tannerella
Bacteroides
Prevotella
Gelidibacter
Cyanobacteria
Rhodoplanes
Selenomonas
Escherichia/
Shigella
Rikenella
Coprococcus
Hyphomicrobium
Verrucomicrobia
Staphylococcus
Verrucomicrobia
Victivallis
Selenomonas
Desulfobulbus
Spirochaeta
Kordia
Bosea
Enterococcus
Xanthobacter
Lactobacillus
Prevotella
Acidaminococcus
Eubacterium
Bacteroides
Lactobacillus
Devosia
Pedobacter
Corynebacterium
Spirochaeta
Anaeroplasma
Saccharofermentans
Slackia
Limibacter
Sphingobium
Riemerella
Saccharofermentans
Bacteroides
Prevotella
Selenomonas
Victivallis
Howardella
Pelospora
Selenomonas
Fibrobacter
Sphingomonas
Selenomonas
Eggerthella
Treponema
Mogibacterium
Adlercreutzia
Selenomonas
Methylomicrobium
Leuconostoc
Pyramidobacter
Butyrivibrio
Bacteroides
Butyricimonas
Ruminococcus
Butyrivibrio
Corynebacterium
Proteiniborus
Spirochaeta
Acetitomaculum
Selenomonas
Altererythrobacter
Atopobium
Desulfotomaculum
Pedobacter
Bacteroides
Asaccharobacter
Microbacterium
Treponema
Dethiosulfovibrio
Oscillibacter
Selenomonas
Eubacterium
Ruminococcus
Treponema
Spirochaeta
Roseburia
Ruminococcus
Butyricimonas
Pedobacter
Spirochaeta
Parabacteroides
Methylococcus
Enterorhabdus
Gelidibacter
Sporobacter
Pedobacter
Cyanobacteria
Syntrophococcus
Slackia
Mogibacterium
Prevotella
Pseudoflavonifractor
Veillonella
Bacillus
Pedobacter
Fibrobacter
Paenibacillus
Brevundimonas
Desulfovibrio
Helicobacter
Prevotella
Prevotella
Herbiconiux
Rikenella
Hippea
Lactobacillus
Eubacterium
Lactobacillus
Lactobacillus
Desulfotomaculum
Prevotella
Staphylococcus
Tenacibaculum
Parabacteroides
Pedobacter
Helicobacter
Proteiniclasticum
Anaplasma
Bacteroides
Mucilaginibacter
Verrucomicrobia
Selenomonas
Parabacteroides
Eubacterium
Coprococcus
Weissella
Pedobacter
Sphingomonas
Treponema
Geobacter
Filomicrobium
Prevotella
Pedobacter
Pedobacter
Bifidobacterium
Saccharofermentans
Ruminococcus
Flavobacterium
Rhodopirellula
Roseburia
Prevotella
Limibacter
Saccharofermentans
Prevotella
Pseudoxanthomonas
Anaerorhabdus
Streptomyces
Pedobacter
Cellulomonas
Olivibacter
Treponema
Gelidibacter
Ruminococcus
Gemmatimonas
Prevotella
Ethanoligenens
Leucobacter
Eggerthella
Prevotella
Prevotella
Solobacterium
Xanthobacter
Verrucomicrobia
Desulfovibrio
Microbacterium
Oscillibacter
Blautia
Papillibacter
Prevotella
Lentisphaera
Ruminococcus
Bacteroides
Catonella
Verrucomicrobia
Prevotella
Mogibacterium
Ruminococcus
Eubacterium
Rhodomicrobium
Butyricicoccus
Saccharofermentans
Prevotella
Mannheimia
Lactobacillus
Adlercreutzia
Selenomonas
Paenibacillus
Paenibacillus
Butyricimonas
Wandonia
Puniceicoccus
Lactonifactor
Selenomonas
Brevundimonas
Prevotella
Gelidibacter
Mogibacterium
Coprococcus
Verrucomicrobia
Barnesiella
Verrucomicrobia
Anaerovorax
Bacteroides
Parasporobacterium
Prevotella
Parapedobacter
Streptomyces
Thermotalea
Alkaliflexus
Oscillibacter
Anaerotruncus
Spirochaeta
Sporotomaculum
Sporacetigenium
Bulleidia
Syntrophomonas
Desulfatiferula
Hydrogeno
anaerobacterium
Mogibacterium
Spirochaeta
Prevotella
Treponema
Spiroplasma
Bacteroides
Treponema
Selenomonas
Butyricicoccus
Gelidibacter
Acetitomaculum
Proteiniclasticum
Papillibacter
Prevotella
Elusimicrobium
Devosia
Roseburia
Mucilaginibacter
Mogibacterium
Saccharofermentans
Paenibacillus
Anaerotruncus
Leucobacter
Eubacterium
Beijerinckia
Prevotella
Cyanobacteria
Pseudoflavonifractor
Butyrivibrio
Acholeplasma
Filomicrobium
Pseudoflavonifractor
Anaerophaga
Asaccharobacter
Kordia
Ruminococcus
Ethanoligenens
Barnesiella
Eubacterium
Prevotella
Anaerophaga
Acetitomaculum
Prevotella
Marinoscillum
Pedobacter
Prevotella
Prevotella
Anaerovorax
Lishizhenia
Pedobacter
Howardella
Roseburia
Anaerovorax
Lentisphaera
Prevotella
Saccharofermentans
Cyanobacteria
Proteiniphilum
Schwartzia
Anaerorhabdus
Robinsoniella
Flavobacterium
Pedobacter
Selenomonas
Rhizobium
Victivallis
Butyricimonas
Parabacteroides
Adhaeribacter
Eubacterium
Acidobacteria
Treponema
Schwartzia
Prevotella
Selenomonas
Beijerinckia
Eubacterium
Adhaeribacter
Verrucomicrobia
Desulfobulbus
Bacteroides
Rummeliibacillus
Agarivorans
Selenomonas
Verrucomicrobia
Prevotella
Spirochaeta
Selenomonas
Spiroplasma
Pedobacter
Cyanobacteria
Lactobacillus
Prevotella
Prevotella
Marinobacter
Butyricimonas
Prevotella
Dongia
Anaerovorax
Butyricimonas
Cryptanaerobacter
Papillibacter
Escherichia/Shigella
Butyricicoccus
Prevotella
Thermotalea
Cohaesibacter
Spirochaeta
Hydrogenoanaerobacterium
Papillibacter
Sporosarcina
Selenomonas
Papillibacter
Saccharofermentans
Desulfotomaculum
Pedobacter
Anaeroplasma
Treponema
Mogibacterium
The term “a” or “an” may refer to one or more of that entity, i.e. can refer to plural referents. As such, the terms “a” or “an”, “one or more” and “at least one” are used interchangeably herein. In addition, reference to “an element” by the indefinite article “a” or “an” does not exclude the possibility that more than one of the elements is present, unless the context clearly requires that there is one and only one of the elements.
Reference throughout this specification to “one embodiment”, “an embodiment”, “one aspect”, or “an aspect” means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics can be combined in any suitable manner in one or more embodiments.
As used herein, in particular embodiments, the terms “about” or “approximately” when preceding a numerical value indicates the value plus or minus a range of 10%.
As used herein the terms “microorganism” or “microbe” should be taken broadly. These terms are used interchangeably and include, but are not limited to, the two prokaryotic domains, Bacteria and Archaea, eukaryotic fungi and protists, as well as viruses. In some embodiments, the disclosure refers to the “microbes” of Table 14 or Table 16, or the “microbes” incorporated by reference. This characterization can refer to not only the predicted taxonomic microbial identifiers of the table, but also the identified strains of the microbes listed in the table
As used herein, “isolate,” “isolated,” “isolated microbe,” and like terms, are intended to mean that the one or more microorganisms has been separated from at least one of the materials with which it is associated in a particular environment (for example soil, water, animal tissue). Microbes of the present disclosure may include spores and/or vegetative cells. In some embodiments, microbes of the present disclosure include microbes in a viable but non-culturable (VBNC) state. See Liao and Zhao (US Publication US2015267163A1). In some embodiments, microbes of the present disclosure include microbes in a biofilm. See Merritt et al. (U.S. Pat. No. 7,427,408). Thus, an “isolated microbe” does not exist in its naturally occurring environment; rather, it is through the various techniques described herein that the microbe has been removed from its natural setting and placed into a non-naturally occurring state of existence. Thus, the isolated strain or isolated microbe may exist as, for example, a biologically pure culture, or as spores (or other forms of the strain) in association with an acceptable carrier.
As used herein, “spore” or “spores” refer to structures produced by bacteria and fungi that are adapted for survival and dispersal. Spores are generally characterized as dormant structures, however spores are capable of differentiation through the process of germination. Germination is the differentiation of spores into vegetative cells that are capable of metabolic activity, growth, and reproduction. The germination of a single spore results in a single fungal or bacterial vegetative cell. Fungal spores are units of asexual reproduction, and in some cases are necessary structures in fungal life cycles. Bacterial spores are structures for surviving conditions that may ordinarily be nonconductive to the survival or growth of vegetative cells. As used herein, “microbial composition” refers to a composition comprising one or more microbes of the present disclosure, wherein a microbial composition, in some embodiments, is administered to animals of the present disclosure. As used herein, “carrier”, “acceptable carrier”, or “pharmaceutical carrier” refers to a diluent, adjuvant, excipient, or vehicle with which the compound is administered. Such carriers can be sterile liquids, such as water and oils, including those of petroleum, animal, vegetable, or synthetic origin; such as peanut oil, soybean oil, mineral oil, sesame oil, and the like. Water or aqueous solution saline solutions and aqueous dextrose and glycerol solutions are preferably employed as carriers, in some embodiments as injectable solutions. Alternatively, the carrier can be a solid dosage form carrier, including but not limited to one or more of a binder (for compressed pills), a glidant, an encapsulating agent, a flavorant, and a colorant. The choice of carrier can be selected with regard to the intended route of administration and standard pharmaceutical practice. See Hardee and Baggo (1998. Development and Formulation of Veterinary Dosage Forms. 2nd Ed. CRC Press. 504 pg.); E. W. Martin (1970. Remington's Pharmaceutical Sciences. 17th Ed. Mack Pub. Co.); and Blaser et al. (US Publication US20110280840A1).
In certain aspects of the disclosure, the isolated microbes exist as isolated and biologically pure cultures. It will be appreciated by one of skill in the art, that an isolated and biologically pure culture of a particular microbe, denotes that said culture is substantially free (within scientific reason) of other living organisms and contains only the individual microbe in question. The culture can contain varying concentrations of said microbe. The present disclosure notes that isolated and biologically pure microbes often “necessarily differ from less pure or impure materials.” See, e.g. In re Bergstrom, 427 F.2d 1394, (CCPA 1970) (discussing purified prostaglandins), see also, In re Bergy, 596 F.2d 952 (CCPA 1979) (discussing purified microbes), see also, Parke-Davis & Co. v. H. K. Mulford & Co., 189 F. 95 (S.D.N.Y. 1911) (Learned Hand discussing purified adrenaline), aff'd in part, rev'd in part, 196 F. 496 (2d Cir. 1912), each of which are incorporated herein by reference. Furthermore, in some aspects, the disclosure provides for certain quantitative measures of the concentration, or purity limitations, that must be found within an isolated and biologically pure microbial culture. The presence of these purity values, in certain embodiments, is a further attribute that distinguishes the presently disclosed microbes from those microbes existing in a natural state. See, e.g., Merck & Co. v. Olin Mathieson Chemical Corp., 253 F.2d 156 (4th Cir. 1958) (discussing purity limitations for vitamin B12 produced by microbes), incorporated herein by reference.
As used herein, “individual isolates” should be taken to mean a composition, or culture, comprising a predominance of a single genera, species, or strain, of microorganism, following separation from one or more other microorganisms. The phrase should not be taken to indicate the extent to which the microorganism has been isolated or purified. However, “individual isolates” can comprise substantially only one genus, species, or strain, of microorganism.
As used herein, “microbiome” refers to the collection of microorganisms that inhabit the digestive tract or gastrointestinal tract of an animal (including the rumen if said animal is a ruminant) and the microorgansims' physical environment (i.e. the microbiome has a biotic and physical component). The microbiome is fluid and may be modulated by numerous naturally occurring and artificial conditions (e.g., change in diet, disease, antimicrobial agents, influx of additional microorganisms, etc.). The modulation of the microbiome of a rumen that can be achieved via administration of the compositions of the disclosure, can take the form of: (a) increasing or decreasing a particular Family, Genus, Species, or functional grouping of microbe (i.e. alteration of the biotic component of the rumen microbiome) and/or (b) increasing or decreasing volatile fatty acids in the rumen, increasing or decreasing rumen pH, increasing or decreasing any other physical parameter important for rumen health (i.e. alteration of the abiotic component of the rumen mircrobiome). As used herein, “probiotic” refers to a substantially pure microbe (i.e., a single isolate) or a mixture of desired microbes, and may also include any additional components that can be administered to a mammal for restoring microbiota. Probiotics or microbial inoculant compositions of the invention may be administered with an agent to allow the microbes to survive the environment of the gastrointestinal tract, i.e., to resist low pH and to grow in the gastrointestinal environment. In some embodiments, the present compositions (e.g., microbial compositions) are probiotics in some aspects.
As used herein, “prebiotic” refers to an agent that increases the number and/or activity of one or more desired microbes. Non-limiting examples of prebiotics that may be useful in the methods of the present disclosure include fructooligosaccharides (e.g., oligofructose, inulin, inulin-type fructans), galactooligosaccharides, amino acids, alcohols, and mixtures thereof. See Ramirez-Farias et al. (2008. Br. J. Nutr. 4:1-10) and Pool-Zobel and Sauer (2007. J. Nutr. 137:2580-2584 and supplemental).
The term “growth medium” as used herein, is any medium which is suitable to support growth of a microbe. By way of example, the media may be natural or artificial including gastrin supplemental agar, LB media, blood serum, and tissue culture gels. It should be appreciated that the media may be used alone or in combination with one or more other media. It may also be used with or without the addition of exogenous nutrients.
The medium may be amended or enriched with additional compounds or components, for example, a component which may assist in the interaction and/or selection of specific groups of microorganisms. For example, antibiotics (such as penicillin) or sterilants (for example, quaternary ammonium salts and oxidizing agents) could be present and/or the physical conditions (such as salinity, nutrients (for example organic and inorganic minerals (such as phosphorus, nitrogenous salts, ammonia, potassium and micronutrients such as cobalt and magnesium), pH, and/or temperature) could be amended.
As used herein, the term “ruminant” includes mammals that are capable of acquiring nutrients from plant-based food by fermenting it in a specialized stomach (rumen) prior to digestion, principally through microbial actions. Ruminants included cattle, goats, sheep, giraffes, yaks, deer, antelope, and others. As used herein, the term “bovid” includes any member of family Bovidae, which include hoofed mammals such as antelope, sheep, goats, and cattle, among others.
As used herein, “energy-corrected milk” or “ECM” represents the amount of energy in milk based upon milk volume, milk fat, and milk protein. ECM adjusts the milk components to 3.5% fat and 3.2% protein, thus equalizing animal performance and allowing for comparison of production at the individual animal and herd levels over time. An equation used to calculate ECM, as related to the present disclosure, is:
ECM=(0.327×milk pounds)+(12.95×fat pounds)+(7.2×protein pounds)
As used herein, “improved” should be taken broadly to encompass improvement of a characteristic of interest, as compared to a control group, or as compared to a known average quantity associated with the characteristic in question. For example, “improved” milk production associated with application of a beneficial microbe, or ensemble, of the disclosure can be demonstrated by comparing the milk produced by an ungulate treated by the microbes taught herein to the milk of an ungulate not treated. In the present disclosure, “improved” does not necessarily demand that the data be statistically significant (i.e. p<0.05); rather, any quantifiable difference demonstrating that one value (e.g. the average treatment value) is different from another (e.g. the average control value) can rise to the level of “improved.”
As used herein, “inhibiting and suppressing” and like terms should not be construed to require complete inhibition or suppression, although this may be desired in some embodiments. The term “marker” or “unique marker” as used herein is an indicator of unique microorganism type, microorganism strain or activity of a microorganism strain. A marker can be measured in biological samples and includes without limitation, a nucleic acid-based marker such as a ribosomal RNA gene, a peptide- or protein-based marker, and/or an intermediate or other small molecule marker.
A intermediate in one embodiment is a small molecule. Intermediates can have various functions, including in energy, structure, signaling, stimulatory/inhibitory and/or other enzyme effects, and in interactions with other organisms (such as pigments, odorants and pheromones). As used herein, the term “genotype” refers to the genetic makeup of an individual cell, cell culture, tissue, organism, or group of organisms.
As used herein, the term “allele(s)” means any of one or more alternative forms of a gene, all of which alleles relate to at least one trait or characteristic. In a diploid cell, the two alleles of a given gene occupy corresponding loci on a pair of homologous chromosomes. Since the present disclosure, in embodiments, relates to QTLs, i.e. genomic regions that may comprise one or more genes or regulatory sequences, it is in some instances more accurate to refer to “haplotype” (i.e. an allele of a chromosomal segment) instead of “allele”, however, in those instances, the term “allele” should be understood to comprise the term “haplotype”. Alleles are considered identical when they express a similar phenotype. Differences in sequence are possible but not important as long as they do not influence phenotype. As used herein, the term “locus” (loci plural) means a specific place or places or a site on a chromosome where for example a gene or genetic marker is found.
As used herein, the term “genetically linked” refers to two or more traits that are co-inherited at a high rate during breeding such that they are difficult to separate through crossing.
A “recombination” or “recombination event” as used herein refers to a chromosomal crossing over or independent assortment. The term “recombinant” refers to an organism having a new genetic makeup arising as a result of a recombination event.
As used herein, the term “molecular marker” or “genetic marker” refers to an indicator that is used in methods for visualizing differences in characteristics of nucleic acid sequences. Examples of such indicators are restriction fragment length polymorphism (RFLP) markers, amplified fragment length polymorphism (AFLP) markers, single nucleotide polymorphisms (SNPs), insertion mutations, microsatellite markers (SSRs), sequence-characterized amplified regions (SCARs), cleaved amplified polymorphic sequence (CAPS) markers or isozyme markers or combinations of the markers described herein which defines a specific genetic and chromosomal location. Markers further include polynucleotide sequences encoding 16S or 18S rRNA, and internal transcribed spacer (ITS) sequences, which are sequences found between small-subunit and large-subunit rRNA genes that have proven to be especially useful in elucidating relationships or distinctions among when compared against one another. Mapping of molecular markers in the vicinity of an allele is a procedure which can be performed by the average person skilled in molecular-biological techniques. The primary structure of major rRNA subunit 16S comprise a particular combination of conserved, variable, and hypervariable regions that evolve at different rates and enable the resolution of both very ancient lineages such as domains, and more modern lineages such as genera. The secondary structure of the 16S subunit include approximately 50 helices which result in base pairing of about 67% of the residues. These highly conserved secondary structural features are of great functional importance and can be used to ensure positional homology in multiple sequence alignments and phylogenetic analysis. Over the previous few decades, the 16S rRNA gene has become the most sequenced taxonomic marker and is the cornerstone for the current systematic classification of bacteria and archaea (Yarza et al. 2014. Nature Rev. Micro. 12:635-45).
In some embodiments, a sequence identity of 94.5% or lower for two 16S rRNA genes is strong evidence for distinct genera, 86.5% or lower is strong evidence for distinct families, 82% or lower is strong evidence for distinct orders, 78.5% is strong evidence for distinct classes, and 75% or lower is strong evidence for distinct phyla. The comparative analysis of 16S rRNA gene sequences enables the establishment of taxonomic thresholds that are useful not only for the classification of cultured microorganisms but also for the classification of the many environmental sequences. Yarza et al. 2014. Nature Rev. Micro. 12:635-45). As used herein, the term “trait” refers to a characteristic or phenotype. For example, in the context of some embodiments of the present disclosure, quantity of milk fat produced relates to the amount of triglycerides, triacylglycerides, diacylglycerides, monoacylglycerides, phospholipids, cholesterol, glycolipids, and fatty acids present in milk. Desirable traits may also include other milk characteristics, including but not limited to: predominance of short chain fatty acids, medium chain fatty acids, and long chain fatty acids; quantity of carbohydrates such as lactose, glucose, galactose, and other oligosaccharides; quantity of proteins such as caseins and whey; quantity of vitamins, minerals, milk yield/volume; reductions in methane emissions or manure; improved efficiency of nitrogen utilization; improved dry matter intake; improved feed efficiency and digestibility; increased degradation of cellulose, lignin, and hemicellulose; increased rumen concentrations of fatty acids such as acetic acid, propionic acid, and butyric acid; etc.
A trait may be inherited in a dominant or recessive manner, or in a partial or incomplete-dominant manner. A trait may be monogenic (i.e. determined by a single locus) or polygenic (i.e. determined by more than one locus) or may also result from the interaction of one or more genes with the environment. In the context of this disclosure, traits may also result from the interaction of one or more mammalian genes and one or more microorganism genes.
As used herein, the term “homozygous” means a genetic condition existing when two identical alleles reside at a specific locus, but are positioned individually on corresponding pairs of homologous chromosomes in the cell of a diploid organism. Conversely, as used herein, the term “heterozygous” means a genetic condition existing when two different alleles reside at a specific locus, but are positioned individually on corresponding pairs of homologous chromosomes in the cell of a diploid organism.
As used herein, the term “phenotype” refers to the observable characteristics of an individual cell, cell culture, organism (e.g., a ruminant), or group of organisms which results from the interaction between that individual's genetic makeup (i.e., genotype) and the environment.
As used herein, the term “chimeric” or “recombinant” when describing a nucleic acid sequence or a protein sequence refers to a nucleic acid, or a protein sequence, that links at least two heterologous polynucleotides, or two heterologous polypeptides, into a single macromolecule, or that re-arranges one or more elements of at least one natural nucleic acid or protein sequence. For example, the term “recombinant” can refer to an artificial combination of two otherwise separated segments of sequence, e.g., by chemical synthesis or by the manipulation of isolated segments of nucleic acids by genetic engineering techniques.
As used herein, a “synthetic nucleotide sequence” or “synthetic polynucleotide sequence” is a nucleotide sequence that is not known to occur in nature or that is not naturally occurring. Generally, such a synthetic nucleotide sequence will comprise at least one nucleotide difference when compared to any other naturally occurring nucleotide sequence.
As used herein, the term “nucleic acid” refers to a polymeric form of nucleotides of any length, either ribonucleotides or deoxyribonucleotides, or analogs thereof. This term refers to the primary structure of the molecule, and thus includes double- and single-stranded DNA, as well as double- and single-stranded RNA. It also includes modified nucleic acids such as methylated and/or capped nucleic acids, nucleic acids containing modified bases, backbone modifications, and the like. The terms “nucleic acid” and “nucleotide sequence” are used interchangeably.
As used herein, the term “gene” refers to any segment of DNA associated with a biological function. Thus, genes include, but are not limited to, coding sequences and/or the regulatory sequences required for their expression. Genes can also include non-expressed DNA segments that, for example, form recognition sequences for other proteins. Genes can be obtained from a variety of sources, including cloning from a source of interest or synthesizing from known or predicted sequence information, and may include sequences designed to have desired parameters. As used herein, the term “homologous” or “homologue” or “ortholog” is known in the art and refers to related sequences that share a common ancestor or family member and are determined based on the degree of sequence identity. The terms “homology,” “homologous,” “substantially similar” and “corresponding substantially” are used interchangeably herein. They refer to nucleic acid fragments wherein changes in one or more nucleotide bases do not affect the ability of the nucleic acid fragment to mediate gene expression or produce a certain phenotype. These terms also refer to modifications of the nucleic acid fragments of the instant disclosure such as deletion or insertion of one or more nucleotides that do not substantially alter the functional properties of the resulting nucleic acid fragment relative to the initial, unmodified fragment. It is therefore understood, as those skilled in the art will appreciate, that the disclosure encompasses more than the specific exemplary sequences. These terms describe the relationship between a gene found in one species, subspecies, variety, cultivar or strain and the corresponding or equivalent gene in another species, subspecies, variety, cultivar or strain. For purposes of this disclosure homologous sequences are compared. “Homologous sequences” or “homologues” or “orthologs” are thought, believed, or known to be functionally related. A functional relationship may be indicated in any one of a number of ways, including, but not limited to: (a) degree of sequence identity and/or (b) the same or similar biological function. Preferably, both (a) and (b) are indicated. Homology can be determined using software programs readily available in the art, such as those discussed in Current Protocols in Molecular Biology (F. M. Ausubel et al., eds., 1987) Supplement 30, section 7.718, Table 7.71. Some alignment programs are MacVector (Oxford Molecular Ltd, Oxford, U.K.), ALIGN Plus (Scientific and Educational Software, Pennsylvania) and AlignX (Vector NTI, Invitrogen, Carlsbad, Calif.). Another alignment program is Sequencher (Gene Codes, Ann Arbor, Mich.), using default parameters.
As used herein, the term “nucleotide change” refers to, e.g., nucleotide substitution, deletion, and/or insertion, as is well understood in the art. For example, mutations contain alterations that produce silent substitutions, additions, or deletions, but do not alter the properties or activities of the encoded protein or how the proteins are made.
As used herein, the term “protein modification” refers to, e.g., amino acid substitution, amino acid modification, deletion, and/or insertion, as is well understood in the art.
As used herein, the term “at least a portion” or “fragment” of a nucleic acid or polypeptide means a portion having the minimal size characteristics of such sequences, or any larger fragment of the full length molecule, up to and including the full length molecule. A fragment of a polynucleotide of the disclosure may encode a biologically active portion of a genetic regulatory element. A biologically active portion of a genetic regulatory element can be prepared by isolating a portion of one of the polynucleotides of the disclosure that comprises the genetic regulatory element and assessing activity as described herein. Similarly, a portion of a polypeptide may be 4 amino acids, 5 amino acids, 6 amino acids, 7 amino acids, and so on, going up to the full length polypeptide. The length of the portion to be used will depend on the particular application. A portion of a nucleic acid useful as a hybridization probe may be as short as 12 nucleotides; in some embodiments, it is 20 nucleotides. A portion of a polypeptide useful as an epitope may be as short as 4 amino acids. A portion of a polypeptide that performs the function of the full-length polypeptide would generally be longer than 4 amino acids.
Variant polynucleotides also encompass sequences derived from a mutagenic and recombinogenic procedure such as DNA shuffling. Strategies for such DNA shuffling are known in the art. See, for example, Stemmer (1994) PNAS 91:10747-10751; Stemmer (1994) Nature 370:389-391; Crameri et al. (1997) Nature Biotech. 15:436-438; Moore et al. (1997) J. Mol. Biol. 272:336-347; Zhang et al. (1997) PNAS 94:4504-4509; Crameri et al. (1998) Nature 391:288-291; and U.S. Pat. Nos. 5,605,793 and 5,837,458. For PCR amplifications of the polynucleotides disclosed herein, oligonucleotide primers can be designed for use in PCR reactions to amplify corresponding DNA sequences from cDNA or genomic DNA extracted from any organism of interest. Methods for designing PCR primers and PCR cloning are generally known in the art and are disclosed in Sambrook et al. (1989) Molecular Cloning: A Laboratory Manual (2nd ed., Cold Spring Harbor Laboratory Press, Plainview, N.Y.). See also Innis et al., eds. (1990) PCR Protocols: A Guide to Methods and Applications (Academic Press, New York); Innis and Gelfand, eds. (1995) PCR Strategies (Academic Press, New York); and Innis and Gelfand, eds. (1999) PCR Methods Manual (Academic Press, New York). Known methods of PCR include, but are not limited to, methods using paired primers, nested primers, single specific primers, degenerate primers, gene-specific primers, vector-specific primers, partially-mismatched primers, and the like.
The term “primer” as used herein refers to an oligonucleotide which is capable of annealing to the amplification target allowing a DNA polymerase to attach, thereby serving as a point of initiation of DNA synthesis when placed under conditions in which synthesis of primer extension product is induced, i.e., in the presence of nucleotides and an agent for polymerization such as DNA polymerase and at a suitable temperature and pH. The (amplification) primer is preferably single stranded for maximum efficiency in amplification. Preferably, the primer is an oligodeoxyribonucleotide. The primer must be sufficiently long to prime the synthesis of extension products in the presence of the agent for polymerization. The exact lengths of the primers will depend on many factors, including temperature and composition (A/T vs. G/C content) of primer. A pair of bi-directional primers consists of one forward and one reverse primer as commonly used in the art of DNA amplification such as in PCR amplification.
The terms “stringency” or “stringent hybridization conditions” refer to hybridization conditions that affect the stability of hybrids, e.g., temperature, salt concentration, pH, formamide concentration and the like. These conditions are empirically optimized to maximize specific binding and minimize non-specific binding of primer or probe to its target nucleic acid sequence. The terms as used include reference to conditions under which a probe or primer will hybridize to its target sequence, to a detectably greater degree than other sequences (e.g. at least 2-fold over background). Stringent conditions are sequence dependent and will be different in different circumstances. Longer sequences hybridize specifically at higher temperatures. Generally, stringent conditions are selected to be about 5° C. lower than the thermal melting point (Tm) for the specific sequence at a defined ionic strength and pH. The Tm is the temperature (under defined ionic strength and pH) at which 50% of a complementary target sequence hybridizes to a perfectly matched probe or primer. Typically, stringent conditions will be those in which the salt concentration is less than about 1.0 M Na+ ion, typically about 0.01 to 1.0 M Na+ion concentration (or other salts) at pH 7.0 to 8.3 and the temperature is at least about 30° C. for short probes or primers (e.g. 10 to 50 nucleotides) and at least about 60° C. for long probes or primers (e.g. greater than 50 nucleotides). Stringent conditions may also be achieved with the addition of destabilizing agents such as formamide. Exemplary low stringent conditions or “conditions of reduced stringency” include hybridization with a buffer solution of 30% formamide, 1 M NaCl, 1% SDS at 37° C. and a wash in 2×SSC at 40° C. Exemplary high stringency conditions include hybridization in 50% formamide, 1M NaCl, 1% SDS at 37° C., and a wash in 0.1×SSC at 60° C. Hybridization procedures are well known in the art and are described by e.g. Ausubel et al., 1998 and Sambrook et al., 2001. In some embodiments, stringent conditions are hybridization in 0.25 M Na2HPO4 buffer (pH 7.2) containing 1 mM Na2EDTA, 0.5-20% sodium dodecyl sulfate at 45° C., such as 0.5%, 1%, 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19% or 20%, followed by a wash in 5×SSC, containing 0.1% (w/v) sodium dodecyl sulfate, at 55° C. to 65° C.
As used herein, “promoter” refers to a DNA sequence capable of controlling the expression of a coding sequence or functional RNA. The promoter sequence consists of proximal and more distal upstream elements, the latter elements often referred to as enhancers. Accordingly, an “enhancer” is a DNA sequence that can stimulate promoter activity, and may be an innate element of the promoter or a heterologous element inserted to enhance the level or tissue specificity of a promoter. Promoters may be derived in their entirety from a native gene, or be composed of different elements derived from different promoters found in nature, or even comprise synthetic DNA segments. It is understood by those skilled in the art that different promoters may direct the expression of a gene in different tissues or cell types, or at different stages of development, or in response to different environmental conditions. It is further recognized that since in most cases the exact boundaries of regulatory sequences have not been completely defined, DNA fragments of some variation may have identical promoter activity.
As used herein, a “constitutive promoter” is a promoter which is active under most conditions and/or during most development stages. There are several advantages to using constitutive promoters in expression vectors used in biotechnology, such as: high level of production of proteins used to select transgenic cells or organisms; high level of expression of reporter proteins or scorable markers, allowing easy detection and quantification; high level of production of a transcription factor that is part of a regulatory transcription system; production of compounds that requires ubiquitous activity in the organism; and production of compounds that are required during all stages of development. Non-limiting exemplary constitutive promoters include, CaMV 35S promoter, opine promoters, ubiquitin promoter, alcohol dehydrogenase promoter, etc.
As used herein, a “non-constitutive promoter” is a promoter which is active under certain conditions, in certain types of cells, and/or during certain development stages. For example, tissue specific, tissue preferred, cell type specific, cell type preferred, inducible promoters, and promoters under development control are non-constitutive promoters. Examples of promoters under developmental control include promoters that preferentially initiate transcription in certain tissues.
As used herein, “inducible” or “repressible” promoter is a promoter which is under chemical or environmental factors control. Examples of environmental conditions that may affect transcription by inducible promoters include anaerobic conditions, certain chemicals, the presence of light, acidic or basic conditions, etc.
As used herein, a “tissue specific” promoter is a promoter that initiates transcription only in certain tissues. Unlike constitutive expression of genes, tissue-specific expression is the result of several interacting levels of gene regulation. As such, in the art sometimes it is preferable to use promoters from homologous or closely related species to achieve efficient and reliable expression of transgenes in particular tissues. This is one of the main reasons for the large amount of tissue-specific promoters isolated from particular tissues found in both scientific and patent literature.
As used herein, the term “operably linked” refers to the association of nucleic acid sequences on a single nucleic acid fragment so that the function of one is regulated by the other. For example, a promoter is operably linked with a coding sequence when it is capable of regulating the expression of that coding sequence (i.e., that the coding sequence is under the transcriptional control of the promoter). Coding sequences can be operably linked to regulatory sequences in a sense or antisense orientation. In another example, the complementary RNA regions of the disclosure can be operably linked, either directly or indirectly, 5′ to the target mRNA, or 3′ to the target mRNA, or within the target mRNA, or a first complementary region is 5′ and its complement is 3′ to the target mRNA.
As used herein, the phrases “recombinant construct”, “expression construct”, “chimeric construct”, “construct”, and “recombinant DNA construct” are used interchangeably herein. A recombinant construct comprises an artificial combination of nucleic acid fragments, e.g., regulatory and coding sequences that are not found together in nature. For example, a chimeric construct may comprise regulatory sequences and coding sequences that are derived from different sources, or regulatory sequences and coding sequences derived from the same source, but arranged in a manner different than that found in nature. Such construct may be used by itself or may be used in conjunction with a vector. If a vector is used then the choice of vector is dependent upon the method that will be used to transform host cells as is well known to those skilled in the art. For example, a plasmid vector can be used. The skilled artisan is well aware of the genetic elements that must be present on the vector in order to successfully transform, select and propagate host cells comprising any of the isolated nucleic acid fragments of the disclosure. The skilled artisan will also recognize that different independent transformation events will result in different levels and patterns of expression (Jones et al., (1985) EMBO J. 4:2411-2418; De Almeida et al., (1989) Mol. Gen. Genetics 218:78-86), and thus that multiple events must be screened in order to obtain lines displaying the desired expression level and pattern. Such screening may be accomplished by Southern analysis of DNA, Northern analysis of mRNA expression, immunoblotting analysis of protein expression, or phenotypic analysis, among others. Vectors can be plasmids, viruses, bacteriophages, pro-viruses, phagemids, transposons, artificial chromosomes, and the like, that replicate autonomously or can integrate into a chromosome of a host cell. A vector can also be a naked RNA polynucleotide, a naked DNA polynucleotide, a polynucleotide composed of both DNA and RNA within the same strand, a poly-lysine-conjugated DNA or RNA, a peptide-conjugated DNA or RNA, a liposome-conjugated DNA, or the like, that is not autonomously replicating. As used herein, the term “expression” refers to the production of a functional end-product e.g., an mRNA or a protein (precursor or mature).
In some embodiments, the cell or organism has at least one heterologous trait. As used herein, the term “heterologous trait” refers to a phenotype imparted to a transformed host cell or transgenic organism by an exogenous DNA segment, heterologous polynucleotide or heterologous nucleic acid. Various changes in phenotype are of interest to the present disclosure, including but not limited to modifying the fatty acid composition in milk, altering the carbohydrate content of milk, increasing an ungulate's yield of an economically important trait (e.g., milk, milk fat, milk proteins, etc.) and the like. These results can be achieved by providing expression of heterologous products or increased expression of endogenous products in organisms using the methods and compositions of the present disclosure.
As used herein, the term “MIC” means maximal information coefficient. MIC is a type of nonparamentric network analysis that identifies a score (MIC score) between active microbial strains of the present disclosure and at least one measured metadata (e.g., milk fat). Further, U.S. application Ser. No. 15/217,575, filed on Jul. 22, 2016 (issued as U.S. Pat. No. 9,540,676 on Jan. 10, 2017) is hereby incorporated by reference in its entirety.
The maximal information coefficient (MIC) is then calculated between strains and metadata 3021a, and between strains 3021b; as seen in
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Based on the output of the network analysis, active strains are selected 3025 for preparing products (e.g., ensembles, aggregates, and/or other synthetic groupings) containing the selected strains. The output of the network analysis can also be used to inform the selection of strains for further product composition testing.
The use of thresholds is discussed above for analyses and determinations. Thresholds can be, depending on the implementation and application: (1) empirically determined (e.g., based on distribution levels, setting a cutoff at a number that removes a specified or significant portion of low level reads); (2) any non-zero value; (3) percentage/percentile based; (4) only strains whose normalized second marker (i.e., activity) reads is greater than normalized first marker (cell count) reads; (5) log 2 fold change between activity and quantity or cell count; (6) normalized second marker (activity) reads is greater than mean second marker (activity) reads for entire sample (and/or sample set); and/or any magnitude threshold described above in addition to a statistical threshold (i.e., significance testing). The following example provides thresholding detail for distributions of RNA-based second marker measurements with respect to DNA-based first marker measurements, according to one embodiment.
As used herein “shelf-stable” refers to a functional attribute and new utility acquired by the microbes formulated according to the disclosure, which enable said microbes to exist in a useful/active state outside of their natural environment in the rumen (i.e. a markedly different characteristic). Thus, shelf-stable is a functional attribute created by the formulations/compositions of the disclosure and denoting that the microbe formulated into a shelf-stable composition can exist outside the rumen and under ambient conditions for a period of time that can be determined depending upon the particular formulation utilized, but in general means that the microbes can be formulated to exist in a composition that is stable under ambient conditions for at least a few days and generally at 1411421421431431441441451451461“shelf-stable ruminant supplement” is a composition comprising one or more microbes of the disclosure, said microbes formulated in a composition, such that the composition is stable under ambient conditions for at least one week, meaning that the microbes comprised in the composition (e.g. whole cell, spore, or lysed cell) are able to impart one or more beneficial phenotypic properties to a ruminant when administered (e.g. increased milk yield, improved milk compositional characteristics, improved rumen health, and/or modulation of the rumen microbiome).
In some aspects, the present disclosure provides isolated microbes, including novel strains of microbes, presented in Table 14 and Table 16. In other aspects, the present disclosure provides isolated whole microbial cultures of the microbes identified in Table 14 and Table 16. These cultures may comprise microbes at various concentrations. In some aspects, the disclosure provides for utilizing one or more microbes selected from Table 14 and Table 16 to increase a phenotypic trait of interest in a ruminant.
In some embodiments, the disclosure provides isolated microbial species belonging to taxonomic families of Clostridiaceae, Ruminococcaceae, Lachnospiraceae, Acidaminococcaceae, Peptococcaceae, Porphyromonadaceae, Prevotellaceae, Neocallimastigaceae, Saccharomycetaceae, Phaeosphaeriaceae, Erysipelotrichia, Anaerolinaeceae, Atopobiaceae, Botryosphaeriaceae, Eubacteriaceae, Acholeplasmataceae, Succinivibrionaceae, Lactobacillaceae, Selenomonadaceae, Burkholderiaceae, and Streptococcaceae.
In further embodiments, isolated microbial species may be selected from genera of family Clostridiaceae, including Acetanaerobacterium, Acetivibrio, Acidaminobacter, Alkaliphilus, Anaerobacter, Anaerostipes, Anaerotruncus, Anoxynatronum, Bryantella, Butyricicoccus, Caldanaerocella, Caloramator, Caloranaerobacter, Caminicella, Candidatus Arthromitus, Clostridium, Coprobacillus, Dorea, Ethanologenbacterium, Faecalibacterium, Garciella, Guggenheimella, Hespellia, Linmingia, Natronincola, Oxobacter, Parasporobacterium, Sarcina, Soehngenia, Sporobacter, Subdoligranulum, Tepidibacter, Tepidimicrobium, Thermobrachium, Thermohalobacter, and Tindallia.
In further embodiments, isolated microbial species may be selected from genera of family Ruminococcaceae, including Ruminococcus, Acetivibrio, Sporobacter, Anaerofilium, Papillibacter, Oscillospira, Gemmiger, Faecalibacterium, Fastidiosipila, Anaerotruncus, Ethanolingenens, Acetanaerobacterium, Subdoligranulum, Hydrogenoanaerobacterium, and Candidadus Soleaferrea.
In further embodiments, isolated microbial species may be selected from genera of family Lachnospiraceae, including Butyrivibrio, Roseburia, Lachnospira, Acetitomaculum, Coprococcus, Johnsonella, Catonella, Pseudobutyrivibrio, Syntrophococcus, Sporobacterium, Parasporobacterium, Lachnobacterium, Shuttleworthia, Dorea, Anaerostipes, Hespellia, Marvinbryantia, Oribacterium, Moryella, Blautia, Robinsoniella, Cellulosilyticum, Lachnoanaerobaculum, Stomatobaculum, Fusicatenibacter, Acetatifactor, and Eisenbergiella.
In further embodiments, isolated microbial species may be selected from genera of family Acidaminococcaceae, including Acidaminococcus, Phascolarctobacterium, Succiniclasticum, and Succinispira.
In further embodiments, isolated microbial species may be selected from genera of family Peptococcaceae, including Desulfotomaculum, Peptococcus, Desulfitobacterium, Syntrophobotulus, Dehalobacter, Sporotomaculum, Desulfosporosinus, Desulfonispora, Pelotomaculum, Thermincola, Cryptanaerobacter, Desulfitibacter, Candidatus Desulforudis, Desulfurispora, and Desulfitospora.
In further embodiments, isolated microbial species may be selected from genera of family Porphyromonadaceae, including Porphyromonas, Dysgonomonas, Tannerella, Odoribacter, Proteiniphilum, Petrimonas, Paludibacter, Parabacteroides, Barnesiella, Candidatus Vestibaculum, Butyricimonas, Macellibacteroides, and Coprobacter.
In further embodiments, isolated microbial species may be selected from genera of family Anaerolinaeceae including Anaerolinea, Bellilinea, Leptolinea, Levilinea, Longilinea, Ornatilinea, and Pelolinea.
In further embodiments, isolated microbial species may be selected from genera of family Atopobiaceae including Atopbium and Olsenella.
In further embodiments, isolated microbial species may be selected from genera of family Eubacteriaceae including Acetobacterium, Alkalibacter, Alkalibaculum, Aminicella, Anaerofustis, Eubacterium, Garciella, and Pseudoramibacter.
In further embodiments, isolated microbial species may be selected from genera of family Acholeplasmataceae including Acholeplasma.
In further embodiments, isolated microbial species may be selected from genera of family Succinivibrionaceae including Anaerobiospirillum, Ruminobacter, Succinatimonas, Succinimonas, and Succinivibrio.
In further embodiments, isolated microbial species may be selected from genera of family Lactobacillaceae including Lactobacillus, Paralactobacillus, Pediococcus, and Sharpea.
In further embodiments, isolated microbial species may be selected from genera of family Selenomonadaceae including Anaerovibrio, Centipeda, Megamonas, Mitsuokella, Pectinatus, Propionispira, Schwartzia, Selenomonas, and Zymophilus.
In further embodiments, isolated microbial species may be selected from genera of family Burkholderiaceae including Burkholderia, Chitinimonas, Cupriavidus, Lautropia, Limnobacter, Pandoraea, Paraburkholderia, Paucimonas, Polynucleobacter, Ralstonia, Thermothrix, and Wautersia.
In further embodiments, isolated microbial species may be selected from genera of family Streptococcaceae including Lactococcus, Lactovum, and Streptococcus.
In further embodiments, isolated microbial species may be selected from genera of family Anaerolinaeceae including Aestuariimicrobium, Arachnia, Auraticoccus, Brooklawnia, Friedmanniella, Granulicoccus, Luteococcus, Mariniluteicoccus, Microlunatus, Micropruina, Naumannella, Propionibacterium, Propionicicella, Propioniciclava, Propioniferax, Propionimicrobium, and Tessaracoccus.
In further embodiments, isolated microbial species may be selected from genera of family Prevotellaceae, including Paraprevotella, Prevotella, Hallella, Xylanibacter, and Alloprevotella.
In further embodiments, isolated microbial species can be selected from genera of family Neocallimastigaceae, including Anaeromyces, Caecomyces, Cyllamyces, Neocallimastix, Orpinomyces, and Piromyces.
In further embodiments, isolated microbial species may be selected from genera of family Saccharomycetaceae, including Brettanomyces, Candida, Citeromyces, Cyniclomyces, Debaryomyces, Issatchenkia, Kazachstania (syn. Arxiozyma), Kluyveromyces, Komagataella, Kuraishia, Lachancea, Lodderomyces, Nakaseomyces, Pachysolen, Pichia, Saccharomyces, Spathaspora, Tetrapisispora, Vanderwaltozyma, Torulaspora, Williopsis, Zygosaccharomyces, and Zygotorulaspora.
In further embodiments, isolated microbial species may be selected from genera of family Erysipelotrichaceae, including Erysipelothrix, Solobacterium, Turicibacter, Faecalibaculum, Faecalicoccus, Faecalitalea, Holdemanella, Holdemania, Dielma, Eggerthia, Erysipelatoclostridium, Allobacterium, Breznakia, Bulleidia, Catenibacterium, Catenisphaera, and Coprobacillus.
In further embodiments, isolated microbial species may be selected from genera of family Phaeosphaeriaceae, including Barria, Bricookea, Carinispora, Chaetoplea, Eudarluca, Hadrospora, Isthmosporella, Katumotoa, Lautitia, Metameris, Mixtura, Neophaeosphaeria, Nodulosphaeria, Ophiosphaerella, Phaeosphaeris, Phaeosphaeriopsis, Setomelanomma, Stagonospora, Teratosphaeria, and Wilmia.
In further embodiments, isolated microbial species may be selected from genera of family Botryosphaeriaceae, including Amarenomyces, Aplosporella, Auerswaldiella, Botryosphaeria, Dichomera, Diplodia, Discochora, Dothidothia, Dothiorella, Fusicoccum, Granulodiplodia, Guignardia, Lasiodiplodia, Leptodothiorella, Leptodothiorella, Leptoguignardia, Macrophoma, Macrophomina, Nattrassia, Neodeightonia, Neofusicocum, Neoscytalidium, Otthia, Phaeobotryosphaeria, Phomatosphaeropsis, Phyllosticta, Pseudofusicoccum, Saccharata, Sivanesania, and Thyrostroma.
In some embodiments, the disclosure provides isolated microbial species belonging to genera of: Clostridium, Ruminococcus, Roseburia, Hydrogenoanaerobacterium, Saccharofermentans, Papillibacter, Pelotomaculum, Butyricicoccus, Tannerella, Prevotella, Butyricimonas, Piromyces, Candida, Vrystaatia, Orpinomyces, Neocallimastix, and Phyllosticta. In further embodiments, the disclosure provides isolated microbial species belonging to the family of Lachnospiraceae, and the order of Saccharomycetales. In further embodiments, the disclosure provides isolated microbial species of Candida xylopsoci, Vrystaatia aloeicola, and Phyllosticta capitalensis.
In some embodiments, a microbe from the taxa disclosed herein are utilized to impart one or more beneficial properties or improved traits to milk in ruminants.
In some embodiments, the disclosure provides isolated microbial species, selected from the group consisting of: Clostridium, Ruminococcus, Roseburia, Hydrogenoanaerobacterium, Saccharofermentans, Papillibacter, Pelotomaculum, Butyricicoccus, Tannerella, Prevotella, Butyricimonas, Piromyces, Pichia, Candida, Vrystaatia, Orpinomyces, Neocallimastix, and Phyllosticta.
In some embodiments, the disclosure provides novel isolated microbial strains of species, selected from the group consisting of: Clostridium, Ruminococcus, Roseburia, Hydrogenoanaerobacterium, Saccharofermentans, Papillibacter, Pelotomaculum, Butyricicoccus, Tannerella, Prevotella, Butyricimonas, Piromyces, Pichia, Candida, Vrystaatia, Orpinomyces, Neocallimastix, and Phyllosticta. Particular novel strains of these aforementioned taxonomic groups can be found in Table 14 and/or Table 16. Furthermore, the disclosure relates to microbes having characteristics substantially similar to that of a microbe identified in Table 14 or Table 16. The isolated microbial species, and novel strains of said species, identified in the present disclosure, are able to impart beneficial properties or traits to ruminant milk production. For instance, the isolated microbes described in Table 14 and Table 16, or ensemble of said microbes, are able to increase total milk fat in ruminant milk. The increase can be quantitatively measured, for example, by measuring the effect that said microbial application has upon the modulation of total milk fat.
In some embodiments, the isolated microbial strains are microbes of the present disclosure that have been genetically modified. In some embodiments, the genetically modified or recombinant microbes comprise polynucleotide sequences which do not naturally occur in said microbes. In some embodiments, the microbes may comprise heterologous polynucleotides. In further embodiments, the heterologous polynucleotides may be operably linked to one or more polynucleotides native to the microbes.
In some embodiments, the heterologous polynucleotides may be reporter genes or selectable markers. In some embodiments, reporter genes may be selected from any of the family of fluorescence proteins (e.g., GFP, RFP, YFP, and the like), β-galactosidase, luciferase. In some embodiments, selectable markers may be selected from neomycin phosphotransferase, hygromycin phosphotransferase, aminoglycoside adenyltransferase, dihydrofolate reductase, acetolactase synthase, bromoxynil nitrilase, β-glucuronidase, dihydrogolate reductase, and chloramphenicol acetyltransferase. In some embodiments, the heterologous polynucleotide may be operably linked to one or more promoter.
Intestinimonas
Anaerolinea
Pseudobutyrivibrio
Olsenella
Eubacterium
Catenisphaera
Faecalibacterium
Solobacterium
Blautia
Ralsonia
Coprococcus
Casaltella
Anaeroplasma
Acholeplasma
Aminiphilus
Mitsuokella
Alistipes
Sharpea
Oscillibacter
Neocallimastix
Odoribacter
Pichia
Candida
Hydrogenoanaerobacterium
Orpinomyces
Succinivibrio
Sugiyamaella
Ruminobacter
Cyllamyces
Lachnospira
Caecomyces
Sinimarinibacterium
Tremella
Hydrogenoanaerobacterium
Turicibacter
Clostridium XIVa
Anaerolinea
Saccharofermentans
Piromyces
Butyricicoccus
Olsenella
Papillibacter
Clostridium XICa
Pelotomaculum
Erysipelotrichaceae
Lachnospiracea
Solobacterium
Anaeroplasma
Ralstonia
Clostridium
Eubacterium
Rikenella
Lachnobacterium
Tannerella
Acholeplasma
Howardella
Selenomonas
Butyricimonas
Sharpea
Succinivibrio
Phyllosticta
Ruminobacter
Candida xylopsoc
Syntrophococcus
Candida apicol
Pseudobutyrivibrio
Saccharomycetales
Ascomycota
Candida rugos
In some aspects, the disclosure provides microbial ensembles comprising a combination of at least any two microbes selected from amongst the microbes identified in Table 14 and/or Table 16. In certain embodiments, the ensembles of the present disclosure comprise two microbes, or three microbes, or four microbes, or five microbes, or six microbes, or seven microbes, or eight microbes, or nine microbes, or ten or more microbes. Said microbes of the ensembles are different microbial species, or different strains of a microbial species.
In some embodiments, the disclosure provides ensembles, comprising: at least two isolated microbial species belonging to genera of: Clostridium, Ruminococcus, Roseburia, Hydrogenoanaerobacterium, Saccharofermentans, Papillibacter, Pelotomaculum, Butyricicoccus, Tannerella, Prevotella, Butyricimonas, Piromyces, Pichia, Candida, Vrystaatia, Orpinomyces, Neocallimastix, and Phyllosticta. Particular novel strains of species of these aforementioned genera can be found in Table 14 and/or Table 16.
In some embodiments, the disclosure provides ensembles, comprising: at least two isolated microbial species, selected from the group consisting of species of the family of Lachnospiraceae, and the order of Saccharomycetales. In particular aspects, the disclosure provides microbial ensembles, comprising species as grouped in Table 18-Table 24. With respect to Table 18-Table 24, the letters A through I represent a non-limiting selection of microbes of the present disclosure, defined as:
A=Strain designation Ascusb_7 identified in Table 14;
B=Strain designation Ascusb_3138 identified in Table 14;
C=Strain designation Ascusb_82 identified in Table 14;
D=Strain designation Ascusb_119 identified in Table 14;
E=Strain designation Ascusb_1801 identified in Table 14;
F=Strain designation Ascusf_23 identified in Table 14;
G=Strain designation Ascusf_24 identified in Table 14;
H=Strain designation Ascusf_45 identified in Table 14; and
I=Strain designation Ascusf_15 identified in Table 14.
In some embodiments, the microbial ensembles can be selected from any member group from Table 18-Table 24.
Isolated Microbes—Source Material
The microbes of the present disclosure were obtained, among other places, at various locales in the United States from the gastrointestinal tract of cows.
Isolated Microbes—Microbial Culture Techniques
The microbes of Table 14 and Table 16 were matched to their nearest taxonomic groups by utilizing classification tools of the Ribosomal Database Project (RDP) for 16s rRNA sequences and the User-friendly Nordic ITS Ectomycorrhiza (UNITE) database for ITS rRNA sequences. Examples of matching microbes to their nearest taxa may be found in Lan et al. (2012. PLOS one. 7(3):e32491), Schloss and Westcott (2011. Appl. Environ. Microbiol. 77(10):3219-3226), and Koljalg et al. (2005. New Phytologist. 166(3):1063-1068).
The isolation, identification, and culturing of the microbes of the present disclosure can be effected using standard microbiological techniques. Examples of such techniques may be found in Gerhardt, P. (ed.) Methods for General and Molecular Microbiology. American Society for Microbiology, Washington, D.C. (1994) and Lennette, E. H. (ed.) Manual of Clinical Microbiology, Third Edition. American Society for Microbiology, Washington, D.C. (1980), each of which is incorporated by reference.
Isolation can be effected by streaking the specimen on a solid medium (e.g., nutrient agar plates) to obtain a single colony, which is characterized by the phenotypic traits described hereinabove (e.g., Gram positive/negative, capable of forming spores aerobically/anaerobically, cellular morphology, carbon source metabolism, acid/base production, enzyme secretion, metabolic secretions, etc.) and to reduce the likelihood of working with a culture which has become contaminated.
For example, for microbes of the disclosure, biologically pure isolates can be obtained through repeated subculture of biological samples, each subculture followed by streaking onto solid media to obtain individual colonies or colony forming units. Methods of preparing, thawing, and growing lyophilized bacteria are commonly known, for example, Gherna, R. L. and C. A. Reddy. 2007. Culture Preservation, p 1019-1033. In C. A. Reddy, T. J. Beveridge, J. A. Breznak, G. A. Marzluf, T. M. Schmidt, and L. R. Snyder, eds. American Society for Microbiology, Washington, D.C., 1033 pages; herein incorporated by reference. Thus freeze dried liquid formulations and cultures stored long term at −70° C. in solutions containing glycerol are contemplated for use in providing formulations of the present disclosure.
The microbes of the disclosure can be propagated in a liquid medium under aerobic conditions, or alternatively anaerobic conditions. Medium for growing the bacterial strains of the present disclosure includes a carbon source, a nitrogen source, and inorganic salts, as well as specially required substances such as vitamins, amino acids, nucleic acids and the like. Examples of suitable carbon sources which can be used for growing the microbes include, but are not limited to, starch, peptone, yeast extract, amino acids, sugars such as glucose, arabinose, mannose, glucosamine, maltose, and the like; salts of organic acids such as acetic acid, fumaric acid, adipic acid, propionic acid, citric acid, gluconic acid, malic acid, pyruvic acid, malonic acid and the like; alcohols such as ethanol and glycerol and the like; oil or fat such as soybean oil, rice bran oil, olive oil, corn oil, sesame oil. The amount of the carbon source added varies according to the kind of carbon source and is typically between 1 to 100 gram(s) per liter of medium. Preferably, glucose, starch, and/or peptone is contained in the medium as a major carbon source, at a concentration of 0.1-5% (W/V). Examples of suitable nitrogen sources which can be used for growing the bacterial strains of the present disclosure include, but are not limited to, amino acids, yeast extract, tryptone, beef extract, peptone, potassium nitrate, ammonium nitrate, ammonium chloride, ammonium sulfate, ammonium phosphate, ammonia or combinations thereof. The amount of nitrogen source varies according to the type of nitrogen source, typically between 0.1 to 30 gram per liter of medium. The inorganic salts, potassium dihydrogen phosphate, dipotassium hydrogen phosphate, disodium hydrogen phosphate, magnesium sulfate, magnesium chloride, ferric sulfate, ferrous sulfate, ferric chloride, ferrous chloride, manganous sulfate, manganous chloride, zinc sulfate, zinc chloride, cupric sulfate, calcium chloride, sodium chloride, calcium carbonate, sodium carbonate can be used alone or in combination. The amount of inorganic acid varies according to the kind of the inorganic salt, typically between 0.001 to 10 gram per liter of medium. Examples of specially required substances include, but are not limited to, vitamins, nucleic acids, yeast extract, peptone, meat extract, malt extract, dried yeast and combinations thereof. Cultivation can be effected at a temperature, which allows the growth of the microbial strains, essentially, between 20° C. and 46° C. In some aspects, a temperature range is 30° C.-39° C. For optimal growth, in some embodiments, the medium can be adjusted to pH 6.0-7.4. It will be appreciated that commercially available media may also be used to culture the microbial strains, such as Nutrient Broth or Nutrient Agar available from Difco, Detroit, Mich. It will be appreciated that cultivation time may differ depending on the type of culture medium used and the concentration of sugar as a major carbon source.
In some aspects, cultivation lasts between 24-96 hours. Microbial cells thus obtained are isolated using methods, which are well known in the art. Examples include, but are not limited to, membrane filtration and centrifugal separation. The pH may be adjusted using sodium hydroxide and the like and the culture may be dried using a freeze dryer, until the water content becomes equal to 4% or less. Microbial co-cultures may be obtained by propagating each strain as described hereinabove. In some aspects, microbial multi-strain cultures may be obtained by propagating two or more of the strains described hereinabove. It will be appreciated that the microbial strains may be cultured together when compatible culture conditions can be employed.
Isolated Microbes—Microbial Strains
Microbes can be distinguished into a genus based on polyphasic taxonomy, which incorporates all available phenotypic and genotypic data into a consensus classification (Vandamme et al. 1996. Polyphasic taxonomy, a consensus approach to bacterial systematics. Microbiol Rev 1996, 60:407-438). One accepted genotypic method for defining species is based on overall genomic relatedness, such that strains which share approximately 70% or more relatedness using DNA-DNA hybridization, with 5° C. or less ΔTm (the difference in the melting temperature between homologous and heterologous hybrids), under standard conditions, are considered to be members of the same species. Thus, populations that share greater than the aforementioned 70% threshold can be considered to be variants of the same species. Another accepted genotypic method for defining species is to isolate marker genes of the present disclosure, sequence these genes, and align these sequenced genes from multiple isolates or variants. The microbes are interpreted as belonging to the same species if one or more of the sequenced genes share at least 97% sequence identity.
The 16S or 18S rRNA sequences or ITS sequences are often used for making distinctions between species and strains, in that if one of the aforementioned sequences share less than a specified percent sequence identity from a reference sequence, then the two organisms from which the sequences were obtained are said to be of different species or strains.
Thus, one could consider microbes to be of the same species, if they share at least 80%, 85%, 90%, 95%, 97%, 98%, or 99% sequence identity across the 16S or 18S rRNA sequence, or the ITS 1 or ITS2 sequence.
Further, one could define microbial strains of a species, as those that share at least 80%, 85%, 90%, 95%, 97%, 98%, or 99% sequence identity across the 16S or 18S rRNA sequence, or the ITS1 or ITS2 sequence.
In one embodiment, microbial strains of the present disclosure include those that comprise polynucleotide sequences that share at least 70%, 75%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity with any one of SEQ ID NOs:1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 39, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 2045, 2046, 2047, 2048, 2049, 2050, 2051, 2052, 2053, 2054, 2055, 2056, 2057, 2058, 2059, 2060, 2061, 2062, 2063, 2064, 2065, 2066, 2067, 2068, 2069, 2070, 2071, 2072, 2073, 2074, 2075, 2076, 2077, 2078, 2079, 2080, 2081, 2082, 2083, 2084, 2085, 2086, 2087, 2088, 2089, 2090, 2091, 2092, 2093, 2094, 2095, 2096, 2097, 2098, 2099, 2100, 2101, 2102, 2103, 2104, 2105, 2106, and 2107. In a further embodiment, microbial strains of the present disclosure include those that comprise polynucleotide sequences that share at least 70%, 75%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99% or 100% sequence identity with any one of SEQ ID NOs:1-2107.
Comparisons can also be made with 23S rRNA sequences against reference sequences.
Unculturable microbes often cannot be assigned to a definite species in the absence of a phenotype determination, the microbes can be given a Candidatus designation within a genus provided their 16S or 18S rRNA sequences or ITS sequences subscribes to the principles of identity with known species.
One approach is to observe the distribution of a large number of strains of closely related species in sequence space and to identify clusters of strains that are well resolved from other clusters. This approach has been developed by using the concatenated sequences of multiple core (house-keeping) genes to assess clustering patterns, and has been called multilocus sequence analysis (MLSA) or multilocus sequence phylogenetic analysis. MLSA has been used successfully to explore clustering patterns among large numbers of strains assigned to very closely related species by current taxonomic methods, to look at the relationships between small numbers of strains within a genus, or within a broader taxonomic grouping, and to address specific taxonomic questions. More generally, the method can be used to ask whether bacterial species exist—that is, to observe whether large populations of similar strains invariably fall into well-resolved clusters, or whether in some cases there is a genetic continuum in which clear separation into clusters is not observed.
In some embodiments, in order to more accurately make a determination of genera, a determination of phenotypic traits, such as morphological, biochemical, and physiological characteristics can be made for comparison with a reference genus archetype. The colony morphology can include color, shape, pigmentation, production of slime, etc. Features of the cell are described as to shape, size, Gram reaction, extracellular material, presence of endospores, flagella presence and location, motility, and inclusion bodies. Biochemical and physiological features describe growth of the organism at different ranges of temperature, pH, salinity and atmospheric conditions, growth in presence of different sole carbon and nitrogen sources. One of skill should be reasonably apprised as to the phenotypic traits that define the genera of the present disclosure.
In one embodiment, the microbes taught herein were identified utilizing 16S rRNA gene sequences and ITS sequences. It is known in the art that 16S rRNA contains hypervariable regions that can provide species/strain-specific signature sequences useful for bacterial identification, and that ITS sequences can also provide species/strain-specific signature sequences useful for fungal identification.
Phylogenetic analysis using the rRNA genes and/or ITS sequences are used to define “substantially similar” species belonging to common genera and also to define “substantially similar” strains of a given taxonomic species. Furthermore, physiological and/or biochemical properties of the isolates can be utilized to highlight both minor and significant differences between strains that could lead to advantageous behavior in ruminants.
Compositions of the present disclosure may include combinations of fungal spores and bacterial spores, fungal spores and bacterial vegetative cells, fungal vegetative cells and bacterial spores, fungal vegetative cells and bacterial vegetative cells. In some embodiments, compositions of the present disclosure comprise bacteria only in the form of spores. In some embodiments, compositions of the present disclosure comprise bacteria only in the form of vegetative cells. In some embodiments, compositions of the present disclosure comprise bacteria in the absence of fungi. In some embodiments, compositions of the present disclosure comprise fungi in the absence of bacteria.
Bacterial spores may include endospores and akinetes. Fungal spores may include statismospores, ballistospores, autospores, aplanospores, zoospores, mitospores, megaspores, microspores, meiospores, chlamydospores, urediniospores, teliospores, oospores, carpospores, tetraspores, sporangiospores, zygospores, ascospores, basidiospores, ascospores, and asciospores.
In some embodiments, spores of the composition germinate upon administration to animals of the present disclosure. In some embodiments, spores of the composition germinate only upon administration to animals of the present disclosure.
In some embodiments, the microbes of the disclosure are combined into synthetic microbial compositions or ensembles. In some embodiments, the microbial compositions include ruminant feed, such as cereals (barley, maize, oats, and the like); starches (tapioca and the like); oilseed cakes; and vegetable wastes. In some embodiments, the microbial compositions include vitamins, minerals, trace elements, emulsifiers, aromatizing products, binders, colorants, odorants, thickening agents, and the like.
In some embodiments, the microbial compositions of the present disclosure are solid. Where solid compositions are used, it may be desired to include one or more carrier materials including, but not limited to, one or more of: mineral earths such as silicas, talc, kaolin, limestone, chalk, clay, dolomite, diatomaceous earth; calcium sulfate; magnesium sulfate; magnesium oxide; calcium carbonate; silicon dioxide; products of vegetable origin such as cereal meals, tree bark meal, wood meal, and nutshell meal; and/or the like.
In some embodiments, the microbial compositions of the present disclosure are liquid. In further embodiments, the liquid comprises a solvent that may include water or an alcohol, and other animal-safe solvents. In some embodiments, the microbial compositions of the present disclosure include binders such as animal-safe polymers, carboxymethylcellulose, starch, polyvinyl alcohol, and the like.
In some embodiments, the microbial compositions of the present disclosure comprise thickening agents such as silica, clay, natural extracts of seeds or seaweed, synthetic derivatives of cellulose, guar gum, locust bean gum, alginates, and methylcelluloses. In some embodiments, the microbial compositions comprise anti-settling agents such as modified starches, polyvinyl alcohol, xanthan gum, and the like.
In some embodiments, the microbial compositions of the present disclosure comprise colorants including organic chromophores classified as nitroso; nitro; azo, including monoazo, bisazo and polyazo; acridine, anthraquinone, azine, diphenylmethane, indamine, indophenol, methine, oxazine, phthalocyanine, thiazine, thiazole, triarylmethane, xanthene. In some embodiments, the microbial compositions of the present disclosure comprise trace nutrients such as salts of iron, manganese, boron, copper, cobalt, molybdenum and zinc.
In some embodiments, the microbial compositions of the present disclosure comprise an animal-safe virucide or nematicide. In some embodiments, microbial compositions of the present disclosure comprise saccharides (e.g., monosaccharides, disaccharides, trisaccharides, polysaccharides, oligosaccharides, and the like), polymeric saccharides, lipids, polymeric lipids, lipopolysaccharides, proteins, polymeric proteins, lipoproteins, nucleic acids, nucleic acid polymers, silica, inorganic salts and combinations thereof. In a further embodiment, microbial compositions comprise polymers of agar, agarose, gelrite, gellan gumand the like. In some embodiments, microbial compositions comprise plastic capsules, emulsions (e.g., water and oil), membranes, and artificial membranes. In some embodiments, emulsions or linked polymer solutions may comprise microbial compositions of the present disclosure. See, e.g., Harel and Bennett U.S. Pat. No. 8,460,726B2, the entirety of which is herein explicitly incorporated by reference for all purposes.
In some embodiments, synthetic microbial compositions of the present disclosure are configured in a solid form (e.g., dispersed lyophilized spores) or a liquid form (microbes interspersed in a storage medium).
In some embodiments, synthetic microbial compositions of the present disclosure comprise one or more preservatives. The preservatives may be in liquid or gas formulations. The preservatives may be selected from one or more of monosaccharide, disaccharide, trisaccharide, polysaccharide, acetic acid, ascorbic acid, calcium ascorbate, erythorbic acid, iso-ascorbic acid, erythrobic acid, potassium nitrate, sodium ascorbate, sodium erythorbate, sodium iso-ascorbate, sodium nitrate, sodium nitrite, nitrogen, benzoic acid, calcium sorbate, ethyl lauroyl arginate, methyl-p-hydroxy benzoate, methyl paraben, potassium acetate, potassium benzoiate, potassium bisulphite, potassium diacetate, potassium lactate, potassium metabisulphite, potassium sorbate, propyl-p-hydroxy benzoate, propyl paraben, sodium acetate, sodium benzoate, sodium bisulphite, sodium nitrite, sodium diacetate, sodium lactate, sodium metabisulphite, sodium salt of methyl-p-hydroxy benzoic acid, sodium salt of propyl-p-hydroxy benzoic acid, sodium sulphate, sodium sulfite, sodium dithionite, sulphurous acid, calcium propionate, dimethyl dicarbonate, natamycin, potassium sorbate, potassium bisulfite, potassium metabisulfite, propionic acid, sodium diacetate, sodium propionate, sodium sorbate, sorbic acid, ascorbic acid, ascorbyl palmitate, ascorbyl stearate, butylated hydro-xyanisole, butylated hydroxytoluene (BHT), butylated hydroxyl anisole (BHA), citric acid, citric acid esters of mono- and/or diglycerides, L-cysteine, L-cysteine hydrochloride, gum guaiacum, gum guaiac, lecithin, lecithin citrate, monoglyceride citrate, monoisopropyl citrate, propyl gallate, sodium metabisulphite, tartaric acid, tertiary butyl hydroquinone, stannous chloride, thiodipropionic acid, dilauryl thiodipropionate, distearyl thiodipropionate, ethoxyquin, sulfur dioxide, formic acid, or tocopherol(s).
In some embodiments, microbial compositions of the present disclosure include bacterial and/or fungal cells in spore form, vegetative cell form, and/or lysed cell form. In one embodiment, the lysed cell form acts as a mycotoxin binder, e.g. mycotoxins binding to dead cells.
In some embodiments, the microbial compositions are shelf stable in a refrigerator (35-40° F.) for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 days. In some embodiments, the microbial compositions are shelf stable in a refrigerator (35-40° F.) for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 weeks.
In some embodiments, the microbial compositions are shelf stable at room temperature (68-72° F.) or between 50-77° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 days. In some embodiments, the microbial compositions are shelf stable at room temperature (68-72° F.) or between 50-77° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 weeks.
In some embodiments, the microbial compositions are shelf stable at −23-35° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 days. In some embodiments, the microbial compositions are shelf stable at −23-35° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 weeks.
In some embodiments, the microbial compositions are shelf stable at 77-100° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 days. In some embodiments, the microbial compositions are shelf stable at 77-100° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 weeks.
In some embodiments, the microbial compositions are shelf stable at 101-213° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 days. In some embodiments, the microbial compositions are shelf stable at 101-213° F. for a period of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 weeks.
In some embodiments, the microbial compositions of the present disclosure are shelf stable at refrigeration temperatures (35-40° F.), at room temperature (68-72° F.), between 50-77° F., between −23-35° F., between 70-100° F., or between 101-213° F. for a period of about 1 to 100, about 1 to 95, about 1 to 90, about 1 to 85, about 1 to 80, about 1 to 75, about 1 to 70, about 1 to 65, about 1 to 60, about 1 to 55, about 1 to 50, about 1 to 45, about 1 to 40, about 1 to 35, about 1 to 30, about 1 to 25, about 1 to 20, about 1 to 15, about 1 to 10, about 1 to 5, about 5 to 100, about 5 to 95, about 5 to 90, about 5 to 85, about 5 to 80, about 5 to 75, about 5 to 70, about 5 to 65, about 5 to 60, about 5 to 55, about 5 to 50, about 5 to 45, about 5 to 40, about 5 to 35, about 5 to 30, about 5 to 25, about 5 to 20, about 5 to 15, about 5 to 10, about 10 to 100, about 10 to 95, about 10 to 90, about 10 to 85, about 10 to 80, about 10 to 75, about 10 to 70, about 10 to 65, about 10 to 60, about 10 to 55, about 10 to 50, about 10 to 45, about 10 to 40, about 10 to 35, about 10 to 30, about 10 to 25, about 10 to 20, about 10 to 15, about 15 to 100, about 15 to 95, about 15 to 90, about 15 to 85, about 15 to 80, about 15 to 75, about 15 to 70, about 15 to 65, about 15 to 60, about 15 to 55, about 15 to 50, about 15 to 45, about 15 to 40, about 15 to 35, about 15 to 30, about 15 to 25, about 15 to 20, about 20 to 100, about 20 to 95, about 20 to 90, about 20 to 85, about 20 to 80, about 20 to 75, about 20 to 70, about 20 to 65, about 20 to 60, about 20 to 55, about 20 to 50, about 20 to 45, about 20 to 40, about 20 to 35, about 20 to 30, about 20 to 25, about 25 to 100, about 25 to 95, about 25 to 90, about 25 to 85, about 25 to 80, about 25 to 75, about 25 to 70, about 25 to 65, about 25 to 60, about 25 to 55, about 25 to 50, about 25 to 45, about 25 to 40, about 25 to 35, about 25 to 30, about 30 to 100, about 30 to 95, about 30 to 90, about 30 to 85, about 30 to 80, about 30 to 75, about 30 to 70, about 30 to 65, about 30 to 60, about 30 to 55, about 30 to 50, about 30 to 45, about 30 to 40, about 30 to 35, about 35 to 100, about 35 to 95, about 35 to 90, about 35 to 85, about 35 to 80, about 35 to 75, about 35 to 70, about 35 to 65, about 35 to 60, about 35 to 55, about 35 to 50, about 35 to 45, about 35 to 40, about 40 to 100, about 40 to 95, about 40 to 90, about 40 to 85, about 40 to 80, about 40 to 75, about 40 to 70, about 40 to 65, about 40 to 60, about 40 to 55, about 40 to 50, about 40 to 45, about 45 to 100, about 45 to 95, about 45 to 90, about 45 to 85, about 45 to 80, about 45 to 75, about 45 to 70, about 45 to 65, about 45 to 60, about 45 to 55, about 45 to 50, about 50 to 100, about 50 to 95, about 50 to 90, about 50 to 85, about 50 to 80, about 50 to 75, about 50 to 70, about 50 to 65, about 50 to 60, about 50 to 55, about 55 to 100, about 55 to 95, about 55 to 90, about 55 to 85, about 55 to 80, about 55 to 75, about 55 to 70, about 55 to 65, about 55 to 60, about 60 to 100, about 60 to 95, about 60 to 90, about 60 to 85, about 60 to 80, about 60 to 75, about 60 to 70, about 60 to 65, about 65 to 100, about 65 to 95, about 65 to 90, about 65 to 85, about 65 to 80, about 65 to 75, about 65 to 70, about 70 to 100, about 70 to 95, about 70 to 90, about 70 to 85, about 70 to 80, about 70 to 75, about 75 to 100, about 75 to 95, about 75 to 90, about 75 to 85, about 75 to 80, about 80 to 100, about 80 to 95, about 80 to 90, about 80 to 85, about 85 to 100, about 85 to 95, about 85 to 90, about 90 to 100, about 90 to 95, or 95 to 100 weeks
In some embodiments, the microbial compositions of the present disclosure are shelf stable at refrigeration temperatures (35-40° F.), at room temperature (68-72° F.), between 50-77° F., between −23-35° F., between 70-100° F., or between 101-213° F. for a period of 1 to 100, 1 to 95, 1 to 90, 1 to 85, 1 to 80, 1 to 75, 1 to 70, 1 to 65, 1 to 60, 1 to 55, 1 to 50, 1 to 45, 1 to 40, 1 to 35, 1 to 30, 1 to 25, 1 to 20, 1 to 15, 1 to 10, 1 to 5, 5 to 100, 5 to 95, 5 to 90, 5 to 85, 5 to 80, 5 to 75, 5 to 70, 5 to 65, 5 to 60, 5 to 55, 5 to 50, 5 to 45, 5 to 40, 5 to 35, 5 to 30, 5 to 25, 5 to 20, 5 to 15, 5 to 10, 10 to 100, 10 to 95, 10 to 90, 10 to 85, 10 to 80, 10 to 75, 10 to 70, 10 to 65, 10 to 60, 10 to 55, 10 to 50, 10 to 45, 10 to 40, 10 to 35, 10 to 30, 10 to 25, 10 to 20, 10 to 15, 15 to 100, 15 to 95, 15 to 90, 15 to 85, 15 to 80, 15 to 75, 15 to 70, 15 to 65, 15 to 60, 15 to 55, 15 to 50, 15 to 45, 15 to 40, 15 to 35, 15 to 30, 15 to 25, 15 to 20, 20 to 100, 20 to 95, 20 to 90, 20 to 85, 20 to 80, 20 to 75, 20 to 70, 20 to 65, 20 to 60, 20 to 55, 20 to 50, 20 to 45, 20 to 40, 20 to 35, 20 to 30, 20 to 25, 25 to 100, 25 to 95, 25 to 90, 25 to 85, 25 to 80, 25 to 75, 25 to 70, 25 to 65, 25 to 60, 25 to 55, 25 to 50, 25 to 45, 25 to 40, 25 to 35, 25 to 30, 30 to 100, 30 to 95, 30 to 90, 30 to 85, 30 to 80, 30 to 75, 30 to 70, 30 to 65, 30 to 60, 30 to 55, 30 to 50, 30 to 45, 30 to 40, 30 to 35, 35 to 100, 35 to 95, 35 to 90, 35 to 85, 35 to 80, 35 to 75, 35 to 70, 35 to 65, 35 to 60, 35 to 55, 35 to 50, 35 to 45, 35 to 40, 40 to 100, 40 to 95, 40 to 90, 40 to 85, 40 to 80, 40 to 75, 40 to 70, 40 to 65, 40 to 60, 40 to 55, 40 to 50, 40 to 45, 45 to 100, 45 to 95, 45 to 90, 45 to 85, 45 to 80, 45 to 75, 45 to 70, 45 to 65, 45 to 60, 45 to 55, 45 to 50, 50 to 100, 50 to 95, 50 to 90, 50 to 85, 50 to 80, 50 to 75, 50 to 70, 50 to 65, 50 to 60, 50 to 55, 55 to 100, 55 to 95, 55 to 90, 55 to 85, 55 to 80, 55 to 75, 55 to 70, 55 to 65, 55 to 60, 60 to 100, 60 to 95, 60 to 90, 60 to 85, 60 to 80, 60 to 75, 60 to 70, 60 to 65, 65 to 100, 65 to 95, 65 to 90, 65 to 85, 65 to 80, 65 to 75, 65 to 70, 70 to 100, 70 to 95, 70 to 90, 70 to 85, 70 to 80, 70 to 75, 75 to 100, 75 to 95, 75 to 90, 75 to 85, 75 to 80, 80 to 100, 80 to 95, 80 to 90, 80 to 85, 85 to 100, 85 to 95, 85 to 90, 90 to 100, 90 to 95, or 95 to 100 weeks.
In some embodiments, the microbial compositions of the present disclosure are shelf stable at refrigeration temperatures (35-40° F.), at room temperature (68-72° F.), between 50-77° F., between −23-35° F., between 70-100° F., or between 101-213° F. for a period of about 1 to 36, about 1 to 34, about 1 to 32, about 1 to 30, about 1 to 28, about 1 to 26, about 1 to 24, about 1 to 22, about 1 to 20, about 1 to 18, about 1 to 16, about 1 to 14, about 1 to 12, about 1 to 10, about 1 to 8, about 1 to 6, about 1 one 4, about 1 to 2, about 4 to 36, about 4 to 34, about 4 to 32, about 4 to 30, about 4 to 28, about 4 to 26, about 4 to 24, about 4 to 22, about 4 to 20, about 4 to 18, about 4 to 16, about 4 to 14, about 4 to 12, about 4 to 10, about 4 to 8, about 4 to 6, about 6 to 36, about 6 to 34, about 6 to 32, about 6 to 30, about 6 to 28, about 6 to 26, about 6 to 24, about 6 to 22, about 6 to 20, about 6 to 18, about 6 to 16, about 6 to 14, about 6 to 12, about 6 to 10, about 6 to 8, about 8 to 36, about 8 to 34, about 8 to 32, about 8 to 30, about 8 to 28, about 8 to 26, about 8 to 24, about 8 to 22, about 8 to 20, about 8 to 18, about 8 to 16, about 8 to 14, about 8 to 12, about 8 to 10, about 10 to 36, about 10 to 34, about 10 to 32, about 10 to 30, about 10 to 28, about 10 to 26, about 10 to 24, about 10 to 22, about 10 to 20, about 10 to 18, about 10 to 16, about 10 to 14, about 10 to 12, about 12 to 36, about 12 to 34, about 12 to 32, about 12 to 30, about 12 to 28, about 12 to 26, about 12 to 24, about 12 to 22, about 12 to 20, about 12 to 18, about 12 to 16, about 12 to 14, about 14 to 36, about 14 to 34, about 14 to 32, about 14 to 30, about 14 to 28, about 14 to 26, about 14 to 24, about 14 to 22, about 14 to 20, about 14 to 18, about 14 to 16, about 16 to 36, about 16 to 34, about 16 to 32, about 16 to 30, about 16 to 28, about 16 to 26, about 16 to 24, about 16 to 22, about 16 to 20, about 16 to 18, about 18 to 36, about 18 to 34, about 18 to 32, about 18 to 30, about 18 to 28, about 18 to 26, about 18 to 24, about 18 to 22, about 18 to 20, about 20 to 36, about 20 to 34, about 20 to 32, about 20 to 30, about 20 to 28, about 20 to 26, about 20 to 24, about 20 to 22, about 22 to 36, about 22 to 34, about 22 to 32, about 22 to 30, about 22 to 28, about 22 to 26, about 22 to 24, about 24 to 36, about 24 to 34, about 24 to 32, about 24 to 30, about 24 to 28, about 24 to 26, about 26 to 36, about 26 to 34, about 26 to 32, about 26 to 30, about 26 to 28, about 28 to 36, about 28 to 34, about 28 to 32, about 28 to 30, about 30 to 36, about 30 to 34, about 30 to 32, about 32 to 36, about 32 to 34, or about 34 to 36 months.
In some embodiments, the microbial compositions of the present disclosure are shelf stable at refrigeration temperatures (35-40° F.), at room temperature (68-72° F.), between 50-77° F., between −23-35° F., between 70-100° F., or between 101-213° F. for a period of 1 to 36 1 to 34 1 to 32 1 to 30 1 to 28 1 to 26 1 to 24 1 to 22 1 to 20 to 18 1 to 16 1 to 14 1 to 12 1 to 10 1 to 8 1 to 6 1 one 4 1 to 2 4 to 36 4 to 34 4 to 32 4 to 30 4 to 28 4 to 26 4 to 24 4 to 22 4 to 20 4 to 184 to 164 to 144 to 124 to 104 to 8 4 to 6 6 to 36 6 to 34 6 to 326 to 30 6 to 28 6 to 26 6 to 24 6 to 22 6 to 20 6 to 18 6 to 16 6 to 14 6 to 12 6 to 10 6 to 8 8 to 36 8 to 34 8 to 32 8 to 30 8 to 28 8 to 26 8 to 24 8 to 22 8 to 20 8 to 18 8 to 16 8 to 14 8 to 12 8 to 10 10 to 36 10 to 34 10 to 32 10 to 30 10 to 28 10 to 26 10 to 24 10 to 22 10 to 20 10 to 18 10 to 16 10 to 14 10 to 12 12 to 36 12 to 34 12 to 32 12 to 30 12 to 28 12 to 26 12 to 24 12 to 22 12 to 20 12 to 18 12 to 16 12 to 14 14 to 36 14 to 34 14 to 32 14 to 30 14 to 28 14 to 26 14 to 24 14 to 22 14 to 20 14 to 18 14 to 16 16 to 36 16 to 34 16 to 32 16 to 30 16 to 28 16 to 26 16 to 24 16 to 22 16 to 20 16 to 18 18 to 36 18 to 34 18 to 32 18 to 30 18 to 28 18 to 26 18 to 24 18 to 22 18 to 20 20 to 36 20 to 34 20 to 32 20 to 30 20 to 28 20 to 26 20 to 24 20 to 22 22 to 36 22 to 34 22 to 32 22 to 30 22 to 28 22 to 26 22 to 24 24 to 36 24 to 34 24 to 32 24 to 30 24 to 28 24 to 26 26 to 36 26 to 34 26 to 32 26 to 30 26 to 28 28 to 36 28 to 34 28 to 32 28 to 30 30 to 36 30 to 34 30 to 32 32 to 36 32 to 34, or about 34 to 36.
In some embodiments, the microbial compositions of the present disclosure are shelf stable at any of the disclosed temperatures and/or temperature ranges and spans of time at a relative humidity of at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, or 98%.
Synthetic Ensemble Compositions
In some embodiments, ensembles (e.g., the microbes and/or synthetic microbial compositions) of the disclosure are encapsulated in an encapsulating composition. An encapsulating composition protects the microbes from external stressors prior to entering the gastrointestinal tract of ungulates. Encapsulating compositions further create an environment that may be beneficial to the microbes, such as minimizing the oxidative stresses of an aerobic environment on anaerobic microbes. See Kalsta et al. (U.S. Pat. No. 5,104,662A), Ford (U.S. Pat. No. 5,733,568A), and Mosbach and Nilsson (U.S. Pat. No. 4,647,536A) for encapsulation compositions of microbes, and methods of encapsulating microbes.
In one embodiment, the encapsulating composition comprises microcapsules having a multiplicity of liquid cores encapsulated in a solid shell material. For purposes of the disclosure, a “multiplicity” of cores is defined as two or more.
A first category of useful fusible shell materials is that of normally solid fats, including fats which are already of suitable hardness and animal or vegetable fats and oils which are hydrogenated until their melting points are sufficiently high to serve the purposes of the present disclosure. Depending on the desired process and storage temperatures and the specific material selected, a particular fat can be either a normally solid or normally liquid material. The terms “normally solid” and “normally liquid” as used herein refer to the state of a material at desired temperatures for storing the resulting microcapsules. Since fats and hydrogenated oils do not, strictly speaking, have melting points, the term “melting point” is used herein to describe the minimum temperature at which the fusible material becomes sufficiently softened or liquid to be successfully emulsified and spray cooled, thus roughly corresponding to the maximum temperature at which the shell material has sufficient integrity to prevent release of the choline cores. “Melting point” is similarly defined herein for other materials which do not have a sharp melting point.
Specific examples of fats and oils useful herein (some of which require hardening) are as follows: animal oils and fats, such as beef tallow, mutton tallow, lamb tallow, lard or pork fat, fish oil, and sperm oil; vegetable oils, such as canola oil, cottonseed oil, peanut oil, corn oil, olive oil, soybean oil, sunflower oil, safflower oil, coconut oil, palm oil, linseed oil, tung oil, and castor oil; fatty acid monoglycerides and diglycerides; free fatty acids, such as stearic acid, palmitic acid, and oleic acid; and mixtures thereof. The above listing of oils and fats is not meant to be exhaustive, but only exemplary. Specific examples of fatty acids include linoleic acid, γ-linoleic acid, dihomo-γ-linolenic acid, arachidonic acid, docosatetraenoic acid, vaccenic acid, nervonic acid, mead acid, erucic acid, gondoic acid, elaidic acid, oleic acid, palitoleic acid, stearidonic acid, eicosapentaenoic acid, valeric acid, caproic acid, enanthic acid, caprylic acid, pelargonic acid, capric acid, undecylic acid, lauric acid, tridecylic acid, myristic acid, pentadecylic acid, palmitic acid, margaric acid, stearic acid, nonadecyclic acid, arachidic acid, heneicosylic acid, behenic acid, tricosylic acid, lignoceric acid, pentacosylic acid, cerotic acid, heptacosylic acid, montanic acid, nonacosylic acid, melissic acid, henatriacontylic acid, lacceroic acid, psyllic acid, geddic acid, ceroplastic acid, hexatriacontylic acid, heptatriacontanoic acid, and octatriacontanoic acid.
Another category of fusible materials useful as encapsulating shell materials is that of waxes. Representative waxes contemplated for use herein are as follows: animal waxes, such as beeswax, lanolin, shell wax, and Chinese insect wax; vegetable waxes, such as carnauba, candelilla, bayberry, and sugar cane; mineral waxes, such as paraffin, microcrystalline petroleum, ozocerite, ceresin, and montan; synthetic waxes, such as low molecular weight polyolefin (e.g., CARBOWAX), and polyol ether-esters (e.g., sorbitol); Fischer-Tropsch process synthetic waxes; and mixtures thereof. Water-soluble waxes, such as CARBOWAX and sorbitol, are not contemplated herein if the core is aqueous.
Still other fusible compounds useful herein are fusible natural resins, such as rosin, balsam, shellac, and mixtures thereof. Various adjunct materials are contemplated for incorporation in fusible materials according to the present disclosure. For example, antioxidants, light stabilizers, dyes and lakes, flavors, essential oils, anti-caking agents, fillers, pH stabilizers, sugars (monosaccharides, disaccharides, trisaccharides, and polysaccharides) and the like can be incorporated in the fusible material in amounts which do not diminish its utility for the present disclosure. The core material contemplated according to some embodiments herein constitutes from about 0.1% to about 50%, about 1% to about 35%, or about 5% to about 30% by weight of the microcapsules. In some embodiments, the core material contemplated herein constitutes no more than about 30% by weight of the microcapsules. In some embodiments, the core material contemplated herein constitutes about 5% by weight of the microcapsules. Depending on the implementation, the core material can be a liquid or solid at contemplated storage temperatures of the microcapsules.
The cores can include other additives, including edible sugars, such as sucrose, glucose, maltose, fructose, lactose, cellobiose, monosaccharides, disaccharides, trisaccharides, polysaccharides, and mixtures thereof; artificial sweeteners, such as aspartame, saccharin, cyclamate salts, and mixtures thereof; edible acids, such as acetic acid (vinegar), citric acid, ascorbic acid, tartaric acid, and mixtures thereof; edible starches, such as corn starch; hydrolyzed vegetable protein; water-soluble vitamins, such as Vitamin C; water-soluble medicaments; water-soluble nutritional materials, such as ferrous sulfate; flavors; salts; monosodium glutamate; antimicrobial agents, such as sorbic acid; antimycotic agents, such as potassium sorbate, sorbic acid, sodium benzoate, and benzoic acid; food grade pigments and dyes; and mixtures thereof. Other potentially useful supplemental core materials are also contemplated, depending on the implementation.
Emulsifying agents can be utilized in some embodiments to assist in the formation of stable emulsions. Representative emulsifying agents include glyceryl monostearate, polysorbate esters, ethoxylated mono- and diglycerides, and mixtures thereof.
For ease of processing, and particularly to enable the successful formation of a reasonably stable emulsion, the viscosities of the core material and the shell material should be similar at the temperature at which the emulsion is formed. In some embodiments, the ratio of the viscosity of the shell to the viscosity of the core, expressed in centipoise or comparable units, and both measured at the temperature of the emulsion, can be from about 22:1 to about 1:1, from about 8:1 to about 1:1, or from about 3:1 to about 1:1. A ratio of 1:1 can be utilized in some embodiments, and other viscosities can be employed for various applications where a viscosity ratio within the recited ranges is useful.
Encapsulating compositions are not limited to microcapsule compositions as disclosed above. In some embodiments encapsulating compositions encapsulate the microbial compositions in an adhesive polymer that can be natural or synthetic without toxic effect. In some embodiments, the encapsulating composition may be a matrix selected from sugar matrix, gelatin matrix, polymer matrix, silica matrix, starch matrix, foam matrix, etc. In some embodiments, the encapsulating composition may be selected from polyvinyl acetates; polyvinyl acetate copolymers; ethylene vinyl acetate (EVA) copolymers; polyvinyl alcohols; polyvinyl alcohol copolymers; celluloses, including ethylcelluloses, methylcelluloses, hydroxymethylcelluloses, hydroxypropylcelluloses and carboxymethylcellulose; polyvinylpyrolidones; polysaccharides, including starch, modified starch, dextrins, maltodextrins, alginate and chitosans; monosaccharides; fats; fatty acids, including oils; proteins, including gelatin and zeins; gum arabics; shellacs; vinylidene chloride and vinylidene chloride copolymers; calcium lignosulfonates; acrylic copolymers; polyvinylacrylates; polyethylene oxide; acrylamide polymers and copolymers; polyhydroxyethyl acrylate, methylacrylamide monomers; and polychloroprene.
In some embodiments, the encapsulating shell of the present disclosure can be up to 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, 390 μm, 400 μm, 410 μm, 420 μm, 430 μm, 440 μm, 450 μm, 460 μm, 470 μm, 480 μm, 490 μm, 500 μm, 510 μm, 520 μm, 530 μm, 540 μm, 550 μm, 560 μm, 570 μm, 580 μm, 590 μm, 600 μm, 610 μm, 620 μm, 630 μm, 640 μm, 650 μm, 660 μm, 670 μm, 680 μm, 690 μm, 700 μm, 710 μm, 720 μm, 730 μm, 740 μm, 750 μm, 760 μm, 770 μm, 780 μm, 790 μm, 800 μm, 810 μm, 820 μm, 830 μm, 840 μm, 850 μm, 860 μm, 870 μm, 880 μm, 890 μm, 900 μm, 910 μm, 920 μm, 930 μm, 940 μm, 950 μm, 960 μm, 970 μm, 980 μm, 990 μm, 1000 μm, 1010 μm, 1020 μm, 1030 μm, 1040 μm, 1050 μm, 1060 μm, 1070 μm, 1080 μm, 1090 μm, 1100 μm, 1110 μm, 1120 μm, 1130 μm, 1140 μm, 1150 μm, 1160 μm, 1170 μm, 1180 μm, 1190 μm, 1200 μm, 1210 μm, 1220 μm, 1230 μm, 1240 μm, 1250 μm, 1260 μm, 1270 μm, 1280 μm, 1290 μm, 1300 μm, 1310 μm, 1320 μm, 1330 μm, 1340 μm, 1350 μm, 1360 μm, 1370 μm, 1380 μm, 1390 μm, 1400 μm, 1410 μm, 1420 μm, 1430 μm, 1440 μm, 1450 μm, 1460 μm, 1470 μm, 1480 μm, 1490 μm, 1500 μm, 1510 μm, 1520 μm, 1530 μm, 1540 μm, 1550 μm, 1560 μm, 1570 μm, 1580 μm, 1590 μm, 1600 μm, 1610 μm, 1620 μm, 1630 μm, 1640 μm, 1650 μm, 1660 μm, 1670 μm, 1680 μm, 1690 μm, 1700 μm, 1710 μm, 1720 μm, 1730 μm, 1740 μm, 1750 μm, 1760 μm, 1770 μm, 1780 μm, 1790 μm, 1800 μm, 1810 μm, 1820 μm, 1830 μm, 1840 μm, 1850 μm, 1860 μm, 1870 μm, 1880 μm, 1890 μm, 1900 μm, 1910 μm, 1920 μm, 1930 μm, 1940 μm, 1950 μm, 1960 μm, 1970 μm, 1980 μm, 1990 μm, 2000 μm, 2010 μm, 2020 μm, 2030 μm, 2040 μm, 2050 μm, 2060 μm, 2070 μm, 2080 μm, 2090 μm, 2100 μm, 2110 μm, 2120 μm, 2130 μm, 2140 μm, 2150 μm, 2160 μm, 2170 μm, 2180 μm, 2190 μm, 2200 μm, 2210 μm, 2220 μm, 2230 μm, 2240 μm, 2250 μm, 2260 μm, 2270 μm, 2280 μm, 2290 μm, 2300 μm, 2310 μm, 2320 μm, 2330 μm, 2340 μm, 2350 μm, 2360 μm, 2370 μm, 2380 μm, 2390 μm, 2400 μm, 2410 μm, 2420 μm, 2430 μm, 2440 μm, 2450 μm, 2460 μm, 2470 μm, 2480 μm, 2490 μm, 2500 μm, 2510 μm, 2520 μm, 2530 μm, 2540 μm, 2550 μm, 2560 μm, 2570 μm, 2580 μm, 2590 μm, 2600 μm, 2610 μm, 2620 μm, 2630 μm, 2640 μm, 2650 μm, 2660 μm, 2670 μm, 2680 μm, 2690 μm, 2700 μm, 2710 μm, 2720 μm, 2730 μm, 2740 μm, 2750 μm, 2760 μm, 2770 μm, 2780 μm, 2790 μm, 2800 μm, 2810 μm, 2820 μm, 2830 μm, 2840 μm, 2850 μm, 2860 μm, 2870 μm, 2880 μm, 2890 μm, 2900 μm, 2910 μm, 2920 μm, 2930 μm, 2940 μm, 2950 μm, 2960 μm, 2970 μm, 2980 μm, 2990 μm, or 3000 μm thick.
Additional method and formulations of synthetic ensembles can include formulations and methods as disclosed in one or more of the following U.S. Pat. Nos. 6,537,666, 6,306,345, 5,766,520, 6,509,146, 6,884,866, 7,153,472, 6,692,695, 6,872,357, 7,074,431, and/or 6534087, each of which is herein expressly incorporated by reference in its entirety.
Animal Feed
In some embodiments, compositions of the present disclosure are mixed with animal feed. In some embodiments, animal feed may be present in various forms such as pellets, capsules, granulated, powdered, liquid, or semi-liquid.
In some embodiments, compositions of the present disclosure are mixed into the premix at at the feed mill (e.g., Carghill or Western Millin), alone as a standalone premix, and/or alongside other feed additives such as MONENSIN, vitamins, etc. In one embodiment, the compositions of the present disclosure are mixed into the feed at the feed mill. In another embodiment, compositions of the present disclosure are mixed into the feed itself.
In some embodiments, feed of the present disclosure may be supplemented with water, premix or premixes, forage, fodder, beans (e.g., whole, cracked, or ground), grains (e.g., whole, cracked, or ground), bean- or grain-based oils, bean- or grain-based meals, bean- or grain-based haylage or silage, bean- or grain-based syrups, fatty acids, sugar alcohols (e.g., polyhydric alcohols), commercially available formula feeds, and mixtures thereof.
In some embodiments, forage encompasses hay, haylage, and silage. In some embodiments, hays include grass hays (e.g., sudangrass, orchardgrass, or the like), alfalfa hay, and clover hay. In some embodiments, haylages include grass haylages, sorghum haylage, and alfalfa haylage. In some embodiments, silages include maize, oat, wheat, alfalfa, clover, and the like.
In some embodiments, premix or premixes can be utilized in the feed. Premixes may comprise micro-ingredients such as vitamins, minerals, amino acids; chemical preservatives; pharmaceutical compositions such as antibiotics and other medicaments; fermentation products, and other ingredients. In some embodiments, premixes are blended into the feed.
In some embodiments, the feed may include feed concentrates such as soybean hulls, sugar beet pulp, molasses, high protein soybean meal, ground corn, shelled corn, wheat midds, distiller grain, cottonseed hulls, rumen-bypass protein, rumen-bypass fat, and grease. See Luhman (U.S. Publication US20150216817A1), Anderson et al. (U.S. Pat. No. 3,484,243) and Porter and Luhman (U.S. Pat. No. 9,179,694B2) for animal feed and animal feed supplements capable of use in the present compositions and methods.
In some embodiments, feed occurs as a compound, which includes, in a mixed composition capable of meeting the basic dietary needs, the feed itself, vitamins, minerals, amino acids, and other necessary components. Compound feed may further comprise premixes.
In some embodiments, microbial compositions of the present disclosure may be mixed with animal feed, premix, and/or compound feed. Individual components of the animal feed may be mixed with the microbial compositions prior to feeding to ruminants. The microbial compositions of the present disclosure may be applied into or on a premix, into or on a feed, and/or into or on a compound feed.
Administration of Synthetic Ensembles
In some embodiments, the synthetic microbial compositions of the present disclosure are administered to ruminants via the oral route. In some embodiments the microbial compositions are administered via a direct injection route into the gastrointestinal tract. In further embodiments, the direct injection administration delivers the microbial compositions directly to the rumen. In some embodiments, the microbial compositions of the present disclosure are administered to animals anally. In further embodiments, anal administration is in the form of an inserted suppository.
In some embodiments, the microbial composition is administered in a dose comprise a total of, or at least, 1 ml, 2 ml, 3 ml, 4 ml, 5 ml, 6 ml, 7 ml, 8 ml, 9 ml, 10 ml, 11 ml, 12 ml, 13 ml, 14 ml, 15 ml, 16 ml, 17 ml, 18 ml, 19 ml, 20 ml, 21 ml, 22 ml, 23 ml, 24 ml, 25 ml, 26 ml, 27 ml, 28 ml, 29 ml, 30 ml, 31 ml, 32 ml, 33 ml, 34 ml, 35 ml, 36 ml, 37 ml, 38 ml, 39 ml, 40 ml, 41 m, 42 ml, 43 ml, 44 ml, 45 ml, 46 ml, 47 ml, 48 ml, 49 ml, 50 ml, 60 ml, 70 ml, 80 ml, 90 ml, 100 ml, 200 ml, 300 ml, 400 ml, 500 ml, 600 ml, 700 ml, 800 ml, 900 ml, or 1,000 ml.
In some embodiments, the microbial composition is administered in a dose comprising a total of, or at least, 1018, 1017, 1016, 1015, 1014, 1013, 1012, 1011, 1010, 109, 108, 107, 106, 105, 104, 103, or 102 microbial cells.
In some embodiments, the microbial compositions are mixed with feed, and the administration occurs through the ingestion of the microbial compositions along with the feed. In some embodiments, the dose of the microbial composition is administered such that there exists 102 to 1012, 103 to 1012, 104 to 1012, 105 to 1012, 106 to 1012, 107 to 1012, 108 to 1012, 109 to 1012, 1010 to 1012, 1011 to 1012, 102 to 1011, 103 to 1011, 104 to 1011, 105 to 1011, 106 to 1011, 107 to 1011, 108 to 1011, 109 to 1011, 1010 to 1011, 102 to 1010, 103 to 1010, 104 to 1010, 105 to 1010, 106 to 1010, 107 to 1010, 108 to 1010, 109 to 1010, 102 to 109, 103 to 109, 104 to 109, 105 to 109, 106 to 109, 107 to 109, 108 to 109, 102 to 108, 103 to 108, 104 to 108, 105 to 108, 106 to 108, 107 to 108, 102 to 107, 103 to 107, 104 to 107, 105 to 107, 106 to 107, 102 to 106, 103 to 106, 104 to 106, 105 to 106, 102 to 105, 103 to 105, 104 to 105, 102 to 104, 103 to 104, 102 to 103, 1012, 1011, 1010, 109, 108, 107, 106, 105, 104, 103, or 102 total microbial cells per gram or milliliter of the composition.
In some embodiments, the administered dose of the microbial composition comprises 102 to 1018, 103 to 1018, 104 to 1018, 105 to 1018, 106 to 1018, 107 to 1018, 108 to 1018, 109 to 1018, 1010 to 1018, 1011 to 1018, 1012 to 1018, 1013 to 1018, 1014 to 1018, 1015 to 1018, 1016 to 1018, 1017 to 1018, 102 to 1012, 103 to 1012, 104 to 1012, 105 to 1012, 106 to 1012, 107 to 1012, 108 to 1012, 109 to 1012, 1010 to 1012, 1011 to 1012, 102 to 1011, 103 to 1011, 104 to 1011, 105 to 1011, 106 to 1011, 107 to 1011, 108 to 1011, 109 to 1011, 1010 to 1011, 102 to 1010, 103 to 1010, 104 to 1010, 105 to 1010, 106 to 1010, 107 to 1010, 108 to 1010, 109 to 1010, 102 to 109, 103 to 109, 104 to 109, 105 to 109, 106 to 109, 107 to 109, 108 to 109, 102 to 108, 103 to 108, 104 to 108, 105 to 108, 106 to 108, 107 to 108, 102 to 107, 103 to 107, 104 to 107, 105 to 107, 106 to 107, 102 to 106, 103 to 106, 104 to 106, 105 to 106, 102 to 105, 103 to 105, 104 to 105, 102 to 104, 103 to 104, 102 to 103, 1018, 1017, 1016, 1015, 1014, 1013, 1012, 1011, 1010, 109, 108, 107, 106, 105, 104, 103, or 102 total microbial cells.
In some embodiments, the composition is administered 1 or more times per day. In some aspects, the composition is administered with food each time the animal is fed. In some embodiments, the composition is administered 1 to 10, 1 to 9, 1 to 8, 1 to 7, 1 to 6, 1 to 5, 1 to 4, 1 to 3, 1 to 2, 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 2 to 3, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 3 to 4, 4 to 10, 4 to 9, 4 to 8, 4 to 7, 4 to 6, 4 to 5, 5 to 10, 5 to 9, 5 to 8, 5 to 7, 5 to 6, 6 to 10, 6 to 9, 6 to 8, 6 to 7, 7 to 10, 7 to 9, 7 to 8, 8 to 10, 8 to 9, 9 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 times per day.
In some embodiments, the microbial composition is administered 1 to 10, 1 to 9, 1 to 8, 1 to 7, 1 to 6, 1 to 5, 1 to 4, 1 to 3, 1 to 2, 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 2 to 3, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 3 to 4, 4 to 10, 4 to 9, 4 to 8, 4 to 7, 4 to 6, 4 to 5, 5 to 10, 5 to 9, 5 to 8, 5 to 7, 5 to 6, 6 to 10, 6 to 9, 6 to 8, 6 to 7, 7 to 10, 7 to 9, 7 to 8, 8 to 10, 8 to 9, 9 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 times per week.
In some embodiments, the microbial composition is administered 1 to 10, 1 to 9, 1 to 8, 1 to 7, 1 to 6, 1 to 5, 1 to 4, 1 to 3, 1 to 2, 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 2 to 3, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 3 to 4, 4 to 10, 4 to 9, 4 to 8, 4 to 7, 4 to 6, 4 to 5, 5 to 10, 5 to 9, 5 to 8, 5 to 7, 5 to 6, 6 to 10, 6 to 9, 6 to 8, 6 to 7, 7 to 10, 7 to 9, 7 to 8, 8 to 10, 8 to 9, 9 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 times per month.
In some embodiments, the microbial composition is administered 1 to 10, 1 to 9, 1 to 8, 1 to 7, 1 to 6, 1 to 5, 1 to 4, 1 to 3, 1 to 2, 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 2 to 3, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 3 to 4, 4 to 10, 4 to 9, 4 to 8, 4 to 7, 4 to 6, 4 to 5, 5 to 10, 5 to 9, 5 to 8, 5 to 7, 5 to 6, 6 to 10, 6 to 9, 6 to 8, 6 to 7, 7 to 10, 7 to 9, 7 to 8, 8 to 10, 8 to 9, 9 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 times per year.
In some embodiments, the feed can be uniformly coated with one or more layers of the microbes and/or microbial compositions disclosed herein, using conventional methods of mixing, spraying, or a combination thereof through the use of treatment application equipment that is specifically designed and manufactured to accurately, safely, and efficiently apply coatings. Such equipment uses various types of coating technology such as rotary coaters, drum coaters, fluidized bed techniques, spouted beds, rotary mists, or a combination thereof. Liquid treatments such as those of the present disclosure can be applied via either a spinning “atomizer” disk or a spray nozzle, which evenly distributes the microbial composition onto the feed as it moves though the spray pattern. In some aspects, the feed is then mixed or tumbled for an additional period of time to achieve additional treatment distribution and drying.
In some embodiments, the feed coats of the present disclosure can be up to 10 μm, 20 μm, 30 μm, 40 μm, 50 μm, 60 μm, 70 μm, 80 μm, 90 μm, 100 μm, 110 μm, 120 μm, 130 μm, 140 μm, 150 μm, 160 μm, 170 μm, 180 μm, 190 μm, 200 μm, 210 μm, 220 μm, 230 μm, 240 μm, 250 μm, 260 μm, 270 μm, 280 μm, 290 μm, 300 μm, 310 μm, 320 μm, 330 μm, 340 μm, 350 μm, 360 μm, 370 μm, 380 μm, 390 μm, 400 μm, 410 μm, 420 μm, 430 μm, 440 μm, 450 μm, 460 μm, 470 μm, 4805 μm, 490 μm, 500 μm, 510 μm, 520 μm, 530 μm, 540 μm, 550 μm, 560 μm, 570 μm, 580 μm, 590 μm, 600 μm, 610 μm, 620 μm, 630 μm, 640 μm, 650 μm, 660 μm, 670 μm, 680 μm, 690 μm, 700 μm, 710 μm, 720 μm, 730 μm, 740 μm, 750 μm, 760 μm, 770 μm, 780 μm, 790 μm, 800 μm, 810 μm, 820 μm, 830 μm, 840 μm, 850 μm, 860 μm, 870 μm, 880 μm, 890 μm, 900 μm, 910 μm, 920 μm, 930 μm, 940 μm, 950 μm, 960 μm, 970 μm, 980 μm, 990 μm, 1000 μm, 1010 μm, 1020 μm, 1030 μm, 1040 μm, 1050 μm, 1060 μm, 1070 μm, 1080 μm, 1090 μm, 1100 μm, 1110 μm, 1120 μm, 1130 μm, 1140 μm, 1150 μm, 1160 μm, 1170 μm, 1180 μm, 1190 μm, 1200 μm, 1210 μm, 1220 μm, 1230 μm, 1240 μm, 1250 μm, 1260 μm, 1270 μm, 1280 μm, 1290 μm, 1300 μm, 1310 μm, 1320 μm, 1330 μm, 1340 μm, 1350 μm, 1360 μm, 1370 μm, 1380 μm, 1390 μm, 1400 μm, 1410 μm, 1420 μm, 1430 μm, 1440 μm, 1450 μm, 1460 μm, 1470 μm, 1480 μm, 1490 μm, 1500 μm, 1510 μm, 1520 μm, 1530 μm, 1540 μm, 1550 μm, 1560 μm, 1570 μm, 1580 μm, 1590 μm, 1600 μm, 1610 μm, 1620 μm, 1630 μm, 1640 μm, 1650 μm, 1660 μm, 1670 μm, 1680 μm, 1690 μm, 1700 μm, 1710 μm, 1720 μm, 1730 μm, 1740 μm, 1750 μm, 1760 μm, 1770 μm, 1780 μm, 1790 μm, 1800 μm, 1810 μm, 1820 μm, 1830 μm, 1840 μm, 1850 μm, 1860 μm, 1870 μm, 1880 μm, 1890 μm, 1900 μm, 1910 μm, 1920 μm, 1930 μm, 1940 μm, 1950 μm, 1960 μm, 1970 μm, 1980 μm, 1990 μm, 2000 μm, 2010 μm, 2020 μm, 2030 μm, 2040 μm, 2050 μm, 2060 μm, 2070 μm, 2080 μm, 2090 μm, 2100 μm, 2110 μm, 2120 μm, 2130 μm, 2140 μm, 2150 μm, 2160 μm, 2170 μm, 2180 μm, 2190 μm, 2200 μm, 2210 μm, 2220 μm, 2230 μm, 2240 μm, 2250 μm, 2260 μm, 2270 μm, 2280 μm, 2290 μm, 2300 μm, 2310 μm, 2320 μm, 2330 μm, 2340 μm, 2350 μm, 2360 μm, 2370 μm, 2380 μm, 2390 μm, 2400 μm, 2410 μm, 2420 μm, 2430 μm, 2440 μm, 2450 μm, 2460 μm, 2470 μm, 2480 μm, 2490 μm, 2500 μm, 2510 μm, 2520 μm, 2530 μm, 2540 μm, 2550 μm, 2560 μm, 2570 μm, 2580 μm, 2590 μm, 2600 μm, 2610 μm, 2620 μm, 2630 μm, 2640 μm, 2650 μm, 2660 μm, 2670 μm, 2680 μm, 2690 μm, 2700 μm, 2710 μm, 2720 μm, 2730 μm, 2740 μm, 2750 μm, 2760 μm, 2770 μm, 2780 μm, 2790 μm, 2800 μm, 2810 μm, 2820 μm, 2830 μm, 2840 μm, 2850 μm, 2860 μm, 2870 μm, 2880 μm, 2890 μm, 2900 μm, 2910 μm, 2920 μm, 2930 μm, 2940 μm, 2950 μm, 2960 μm, 2970 μm, 2980 μm, 2990 μm, or 3000 μm thick.
In some embodiments, the microbial cells can be coated freely onto any number of compositions or they can be formulated in a liquid or solid composition before being coated onto a composition. For example, a solid composition comprising the microorganisms can be prepared by mixing a solid carrier with a suspension of the spores until the solid carriers are impregnated with the spore or cell suspension. This mixture can then be dried to obtain the desired particles.
In some other embodiments, the solid or liquid microbial compositions of the present disclosure further contain functional agents e.g., activated carbon, minerals, vitamins, and other agents capable of improving the quality of the products or a combination thereof.
Methods of coating and compositions in use of said methods can be particularly useful when they are modified by the addition of one of the embodiments of the present disclosure. Such coating methods and apparatus for their application are disclosed in, for example: U.S. Pat. Nos. 8,097,245, and 7,998,502; and PCT Pat. App. Publication Nos. WO 2008/076975, WO 2010/138522, WO 2011/094469, WO 2010/111347, and WO 2010/111565 each of which is incorporated by reference herein.
In some embodiments, the microbes or microbial ensembles of the present disclosure exhibit a synergistic effect, on one or more of the traits described herein, in the presence of one or more of the microbes or ensembles coming into contact with one another. The synergistic effect obtained by the taught methods can be quantified, for example, according to Colby's formula (i.e., (E)=X+Y−(X*Y/100)). See Colby, R. S., “Calculating Synergistic and Antagonistic Responses of Herbicide Combinations,” 1967. Weeds. Vol. 15, pp. 20-22, incorporated herein by reference in its entirety. Thus, “synergistic” is intended to reflect an outcome/parameter/effect that has been increased by more than an additive amount.
In some embodiments, the microbes or microbial ensembles of the present disclosure may be administered via bolus. In one embodiment, a bolus (e.g., capsule containing the composition) is inserted into a bolus gun, and the bolus gun is inserted into the buccal cavity and/or esophagus of the animal, followed by the release/injection of the bolus into the animal's digestive tract. In one embodiment, the bolus gun/applicator is a BOVIKALC bolus gun/applicator. In another embodiment, the bolus gun/applicator is a QUADRICAL gun/applicator.
In some embodiments, the microbes or microbial ensembles of the present disclosure may be administered via drench. In one embodiment, the drench is an oral drench. A drench administration comprises utilizing a drench kit/applicator/syringe that injects/releases a liquid comprising the microbes or microbial ensembles into the buccal cavity and/or esophagus of the animal.
In some embodiments, the microbes or microbial ensembles of the present disclosure may be administered in a time-released fashion. The composition may be coated in a chemical composition, or may be contained in a mechanical device or capsule that releases the microbes or microbial ensembles over a period of time instead all at once. In one embodiment, the microbes or microbial ensembles are administered to an animal in a time-release capsule. In one embodiment, the composition may be coated in a chemical composition, or may be contained in a mechanical device or capsule that releases the microbes or microbial ensembles all at once a period of time hours post ingestion.
In some embodiments, the microbes or microbial ensembles are administered in a time-released fashion between 1 to 5, 1 to 10, 1 to 15, 1 to 20, 1 to 24, 1 to 25, 1 to 30, 1 to 35, 1 to 40, 1 to 45, 1 to 50, 1 to 55, 1 to 60, 1 to 65, 1 to 70, 1 to 75, 1 to 80, 1 to 85, 1 to 90, 1 to 95, or 1 to 100 hours.
In some embodiments, the microbes or microbial ensembles are administered in a time-released fashion between 1 to 2, 1 to 3, 1 to 4, 1 to 5, 1 to 6, 1 to 7, 1 to 8, 1 to 9, 1 to 10, 1 to 11, 1 to 12, 1 to 13, 1 to 14, 1 to 15, 1 to 16, 1 to 17, 1 to 18, 1 to 19, 1 to 20, 1 to 21, 1 to 22, 1 to 23, 1 to 24, 1 to 25, 1 to 26, 1 to 27, 1 to 28, 1 to 29, or 1 to 30 days.
As used herein the term “microorganism” should be taken broadly. It includes, but is not limited to, the two prokaryotic domains, Bacteria and Archaea, as well as eukaryotic fungi, protists, and viruses. By way of example, the microorganisms may include species of the genera of: Clostridium, Ruminococcus, Roseburia, Hydrogenoanaerobacterium, Saccharofermentans, Papillibacter, Pelotomaculum, Butyricicoccus, Tannerella, Prevotella, Butyricimonas, Piromyces, Pichia, Candida, Vrystaatia, Orpinomyces, Neocallimastix, and Phyllosticta. The microorganisms may further include species belonging to the family of Lachnospiraceae, and the order of Saccharomycetales. In some embodiments, the microorganisms may include species of any genera disclosed herein.
In certain embodiments, the microorganism is unculturable. This should be taken to mean that the microorganism is not known to be culturable or is difficult to culture using methods known to one skilled in the art. In one embodiment, the microbes are obtained from animals (e.g., mammals, reptiles, birds, and the like), soil (e.g., rhizosphere), air, water (e.g., marine, freshwater, wastewater sludge), sediment, oil, plants (e.g., roots, leaves, stems), agricultural products, and extreme environments (e.g., acid mine drainage or hydrothermal systems). In a further embodiment, microbes obtained from marine or freshwater environments such as an ocean, river, or lake. In a further embodiment, the microbes can be from the surface of the body of water, or any depth of the body of water (e.g., a deep sea sample).
The microorganisms of the disclosure may be isolated in substantially pure or mixed cultures. They may be concentrated, diluted, or provided in the natural concentrations in which they are found in the source material. For example, microorganisms from saline sediments may be isolated for use in this disclosure by suspending the sediment in fresh water and allowing the sediment to fall to the bottom. The water containing the bulk of the microorganisms may be removed by decantation after a suitable period of settling and either administered to the GI tract of an ungulate, or concentrated by filtering or centrifugation, diluted to an appropriate concentration and administered to the GI tract of an ungulate with the bulk of the salt removed. By way of further example, microorganisms from mineralized or toxic sources may be similarly treated to recover the microbes for application to the ungulate to minimize the potential for damage to the animal.
In another embodiment, the microorganisms are used in a crude form, in which they are not isolated from the source material in which they naturally reside. For example, the microorganisms are provided in combination with the source material in which they reside; for example, fecal matter, cud, or other composition found in the gastrointestinal tract. In this embodiment, the source material may include one or more species of microorganisms.
In some embodiments, a mixed population of microorganisms is used in the methods of the disclosure. In embodiments of the disclosure where the microorganisms are isolated from a source material (for example, the material in which they naturally reside), any one or a combination of a number of standard techniques which will be readily known to skilled persons may be used. However, by way of example, these in general employ processes by which a solid or liquid culture of a single microorganism can be obtained in a substantially pure form, usually by physical separation on the surface of a solid microbial growth medium or by volumetric dilutive isolation into a liquid microbial growth medium. These processes may include isolation from dry material, liquid suspension, slurries or homogenates in which the material is spread in a thin layer over an appropriate solid gel growth medium, or serial dilutions of the material made into a sterile medium and inoculated into liquid or solid culture media.
In some embodiments, the material containing the microorganisms may be pre-treated prior to the isolation process in order to either multiply all microorganisms in the material. Microorganisms can then be isolated from the enriched materials as disclosed above.
In certain embodiments, as mentioned herein before, the microorganism(s) may be used in crude form and need not be isolated from an animal or a media. For example, cud, feces, or growth media which includes the microorganisms identified to be of benefit to increased milk production in ungulates may be obtained and used as a crude source of microorganisms for the next round of the method or as a crude source of microorganisms at the conclusion of the method. For example, fresh feces could be obtained and optionally processed.
In some embodiments, the microbiome of a ruminant, including the rumen microbiome, comprises a diverse arrive of microbes with a wide variety of metabolic capabilities. The microbiome is influenced by a range of factors including diet, variations in animal metabolism, and breed, among others. Most bovine diets are plant-based and rich in complex polysaccharides that enrich the gastrointestinal microbial community for microbes capable of breaking down specific polymeric components in the diet. The end products of primary degradation sustains a chain of microbes that ultimately produce a range of organic acids together with hydrogen and carbon dioxide. Because of the complex and interlinked nature of the microbiome, changing the diet and thus substrates for primary degradation may have a cascading effect on rumen microbial metabolism, with changes in both the organic acid profiles and the methane levels produced, thus impacting the quality and quantity of animal production and or the products produced by the animal. See Menezes et al. (2011. FEMS Microbiol. Ecol. 78(2):256-265.)
In some aspects, the present disclosure is drawn to administering microbial compositions described herein to modulate or shift the microbiome of a ruminant. In some embodiments, the microbiome is shifted through the administration of one or more microbes to the gastrointestinal tract. In further embodiments, the one or more microbes are those selected from Table 14 or Table 16. In some embodiments, the microbiome shift or modulation includes a decrease or loss of specific microbes that were present prior to the administration of one or more microbes of the present disclosure. In some embodiments, the microbiome shift or modulation includes an increase in microbes that were present prior to the administration of one or more microbes of the present disclosure. In some embodiments, the microbiome shift or modulation includes a gain of one or more microbes that were not present prior to the administration of one or more microbes of the present disclosure. In a further embodiment, the gain of one or more microbes is a microbe that was not specifically included in the administered microbial ensemble.
In some embodiments, the administration of microbes of the present disclosure results in a sustained modulation of the microbiome such that the administered microbes are present in the microbiome for a period of at least 1 to 10, 1 to 9, 1 to 8, 1 to 7, 1 to 6, 1 to 5, 1 to 4, 1 to 3, 1 to 2, 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 2 to 3, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 3 to 4, 4 to 10, 4 to 9, 4 to 8, 4 to 7, 4 to 6, 4 to 5, 5 to 10, 5 to 9, 5 to 8, 5 to 7, 5 to 6, 6 to 10, 6 to 9, 6 to 8, 6 to 7, 7 to 10, 7 to 9, 7 to 8, 8 to 10, 8 to 9, 9 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 days.
In some embodiments, the administration of microbes of the present disclosure results in a sustained modulation of the microbiome such that the administered microbes are present in the microbiome for a period of at least 1 to 10, 1 to 9, 1 to 8, 1 to 7, 1 to 6, 1 to 5, 1 to 4, 1 to 3, 1 to 2, 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 2 to 3, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 3 to 4, 4 to 10, 4 to 9, 4 to 8, 4 to 7, 4 to 6, 4 to 5, 5 to 10, 5 to 9, 5 to 8, 5 to 7, 5 to 6, 6 to 10, 6 to 9, 6 to 8, 6 to 7, 7 to 10, 7 to 9, 7 to 8, 8 to 10, 8 to 9, 9 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 weeks.
In some embodiments, the administration of microbes of the present disclosure results in a sustained modulation of the microbiome such that the administered microbes are present in the microbiome for a period of at least 1 to 10, 1 to 9, 1 to 8, 1 to 7, 1 to 6, 1 to 5, 1 to 4, 1 to 3, 1 to 2, 2 to 10, 2 to 9, 2 to 8, 2 to 7, 2 to 6, 2 to 5, 2 to 4, 2 to 3, 3 to 10, 3 to 9, 3 to 8, 3 to 7, 3 to 6, 3 to 5, 3 to 4, 4 to 10, 4 to 9, 4 to 8, 4 to 7, 4 to 6, 4 to 5, 5 to 10, 5 to 9, 5 to 8, 5 to 7, 5 to 6, 6 to 10, 6 to 9, 6 to 8, 6 to 7, 7 to 10, 7 to 9, 7 to 8, 8 to 10, 8 to 9, 9 to 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 months.
In some embodiments, the presence of the administered microbes are detected by sampling the gastrointestinal tract and using primers to amplify the 16S or 18S rDNA sequences, or the ITS rDNA sequences of the administered microbes. In some embodiments, the administered microbes are one or more of those selected from Table 14 or Table 16, and the corresponding rDNA sequences are those selected from SEQ ID NOs:1-60, SEQ ID NOs:2045-2107 and the SEQ ID NOs identified in Table 16.
In some embodiments, the microbiome of a ruminant is measured by amplifying polynucleotides collected from gastrointestinal samples, wherein the polynucleotides may be 16S or 18S rDNA fragments, or ITS rDNA fragments of microbial rDNA. In one embodiment, the microbiome is fingerprinted by a method of denaturing gradient gel electrophoresis (DGGE) wherein the amplified rDNA fragments are sorted by where they denature, and form a unique banding pattern in a gel that may be used for comparing the microbiome of the same ruminant over time or the microbiomes of multiple ruminants. In another embodiment, the microbiome is fingerprinted by a method of terminal restriction fragment length polymorphism (T-RFLP), wherein labelled PCR fragments are digested using a restriction enzyme and then sorted by size. In a further embodiment, the data collected from the T-RFLP method is evaluated by nonmetric multidimensional scaling (nMDS) ordination and PERMANOVA statistics identify differences in microbiomes, thus allowing for the identification and measurement of shifts in the microbiome. See also Shanks et al. (2011. Appl. Environ. Microbiol. 77(9):2992-3001), Petri et al. (2013. PLOS one. 8(12):e83424), and Menezes et al. (2011. FEMS Microbiol. Ecol. 78(2):256-265.)
In some embodiments, the administration of microbes of the present disclosure results in a modulation or shift of the microbiome which further results in a desired phenotype or improved trait.
According to the methods provided herein, a sample is processed to detect the presence of one or more microorganism types in the sample (
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 microorganism types (
In one embodiment, a sample is stained with one or more fluorescent dyes wherein a fluorescent dye is specific to a particular microorganism type, to enable detection via a flow cytometer or some other detection and quantification method that harnesses fluorescence, such as fluorescence microscopy. The method can provide quantification of the number of cells and/or cell volume of a given organism type in a sample. In a further embodiment, as described herein, flow cytometry is harnessed to determine the presence and quantity of a unique first marker and/or unique second marker of the organism type, such as enzyme expression, cell surface protein expression, etc. Two- or three-variable histograms or contour plots of, for example, light scattering versus fluorescence from a cell membrane stain (versus fluorescence from a protein stain or DNA stain) may also be generated, and thus an impression may be gained of the distribution of a variety of properties of interest among the cells in the population as a whole. A number of displays of such multiparameter flow cytometric data are in common use and are amenable for use with the methods described herein.
In one embodiment of processing the sample to detect the presence and number of one or more microorganism types, a microscopy assay is employed (
In another embodiment of in order to detect the presence and number of one or more microorganism types, the sample, or a portion thereof is subjected to fluorescence microscopy. Different fluorescent dyes can be used to directly stain cells in samples and to quantify total cell counts using an epifluorescence microscope as well as flow cytometry, described above. Useful dyes to quantify microorganisms include but are not limited to acridine orange (AO), 4,6-di-amino-2 phenylindole (DAPI) and 5-cyano-2,3 Dytolyl Tetrazolium Chloride (CTC). Viable cells can be estimated by a viability staining method such as the LIVE/DEAD® Bacterial Viability Kit (Bac-Light™) which contains two nucleic acid stains: the green-fluorescent SYTO 9™ dye penetrates all membranes and the red-fluorescent propidium iodide (PI) dye penetrates cells with damaged membranes. Therefore, cells with compromised membranes will stain red, whereas cells with undamaged membranes will stain green. Fluorescent in situ hybridization (FISH) extends epifluorescence microscopy, allowing for the fast detection and enumeration of specific organisms. FISH uses fluorescent labelled oligonucleotides probes (usually 15-25 basepairs) which bind specifically to organism DNA in the sample, allowing the visualization of the cells using an epifluorescence or confocal laser scanning microscope (CLSM). Catalyzed reporter deposition fluorescence in situ hybridization (CARD-FISH) improves upon the FISH method by using oligonucleotide probes labelled with a horse radish peroxidase (HRP) to amplify the intensity of the signal obtained from the microorganisms being studied. FISH can be combined with other techniques to characterize microorganism communities. One combined technique is high affinity peptide nucleic acid (PNA)-FISH, where the probe has an enhanced capability to penetrate through the Extracellular Polymeric Substance (EPS) matrix. Another example is LIVE/DEAD-FISH which combines the cell viability kit with FISH and has been used to assess the efficiency of disinfection in drinking water distribution systems.
In another embodiment, the sample, or a portion thereof is subjected to Raman micro-spectroscopy in order to determine the presence of a microorganism type and the absolute number of at least one microorganism type (
In yet another embodiment, the sample, or a portion thereof is subjected to centrifugation in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In another embodiment, the sample, or a portion thereof is subjected to staining in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In another embodiment, the sample, or a portion thereof is subjected to mass spectrometry (MS) in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In another embodiment, the sample, or a portion thereof is subjected to lipid analysis in order to determine the presence of a microorganism type and the number of at least one microorganism type (
In the aspects of the methods provided herein, the number of unique first makers in the sample, or portion thereof (e.g., sample aliquot) is measured, as well as the abundance of each of the unique first markers (
Any marker that is unique to an organism strain can be employed herein. For example, markers can include, but are not limited to, small subunit ribosomal RNA genes (16S/18S rDNA), large subunit ribosomal RNA genes (23S/25S/28S rDNA), intercalary 5.8S gene, cytochrome c oxidase, beta-tubulin, elongation factor, RNA polymerase and internal transcribed spacer (ITS).
Ribosomal RNA genes (rDNA), especially the small subunit ribosomal RNA genes, i.e., 18S rRNA genes (18S rDNA) in the case of eukaryotes and 16S rRNA (16S rDNA) in the case of prokaryotes, have been the predominant target for the assessment of organism types and strains in a microbial community. However, the large subunit ribosomal RNA genes, 28S rDNAs, have been also targeted. rDNAs are suitable for taxonomic identification because: (i) they are ubiquitous in all known organisms; (ii) they possess both conserved and variable regions; (iii) there is an exponentially expanding database of their sequences available for comparison. In community analysis of samples, the conserved regions serve as annealing sites for the corresponding universal PCR and/or sequencing primers, whereas the variable regions can be used for phylogenetic differentiation. In addition, the high copy number of rDNA in the cells facilitates detection from environmental samples.
The internal transcribed spacer (ITS), located between the 18S rDNA and 28S rDNA, has also been targeted. The ITS is transcribed but spliced away before assembly of the ribosomes The ITS region is composed of two highly variable spacers, ITS1 and ITS2, and the intercalary 5.8S gene. This rDNA operon occurs in multiple copies in genomes. Because the ITS region does not code for ribosome components, it is highly variable.
In one embodiment, the unique RNA marker can be an mRNA marker, an siRNA marker or a ribosomal RNA marker.
Protein-coding functional genes 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, archaea RadA and RadB, eukaryotic Rad51 and Rad57, phage UvsX); RNA polymerase β subunit (RpoB) gene, which is responsible for transcription initiation and elongation; chaperonins. Candidate marker genes have also been identified for bacteria plus archaea: ribosomal protein S2 (rpsB), ribosomal protein S10 (rpsJ), ribosomal protein L1 rplA), 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 S7, 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 S 12/S23, ribosomal protein L29, ribosomal protein S3 (rpsC), ribosomal protein S11 (rpsK), ribosomal protein L10, ribosomal protein S8, tRNA pseudouridine synthase B, ribosomal protein L18P/L5E, ribosomal protein S15P/S13e, Porphobilinogen deaminase, ribosomal protein S17, ribosomal protein L13 (rplM), phosphoribosylformylglycinamidine cyclo-ligase (rpsE), ribonuclease HII and ribosomal protein L24. Other candidate marker genes for bacteria include: transcription elongation protein NusA (nusA), rpoB DNA-directed RNA polymerase subunit beta (rpoB), GTP-binding protein EngA, rpoC DNA-directed RNA polymerase subunit beta′, priA primosome assembly protein, transcription-repair coupling factor, CTP synthase (pyrG), secY preprotein translocase subunit SecY, GTP-binding protein Obg/CgtA, DNA polymerase I, rpsF 30S ribosomal protein S6, poA DNA-directed RNA polymerase subunit alpha, peptide chain release factor 1, rplI 50S ribosomal protein L9, polyribonucleotide nucleotidyltransferase, tsf elongation factor Ts (tsf), rplQ 50S ribosomal protein L17, tRNA (guanine-N(1)-)-methyltransferase (rplS), rplY probable 50S ribosomal protein L25, DNA repair protein RadA, glucose-inhibited division protein A, ribosome-binding factor A, DNA mismatch repair protein MutL, smpB SsrA-binding protein (smpB), N-acetylglucosaminyl transferase, S-adenosyl-methyltransferase MraW, UDP-N-acetylmuramoylalanine-D-glutamate ligase, rplS 50S ribosomal protein L19, rplT 50S ribosomal protein L20 (rplT), ruvA Holliday junction DNA helicase, ruvB Holliday 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), recombination protein RecR, protein of unknown function UPF0054, miaA tRNA isopentenyltransferase, GTP-binding protein YchF, chromosomal replication initiator protein DnaA, dephospho-CoA kinase, 16S rRNA processing protein RimM, ATP-cone domain protein, 1-deoxy-D-xylulose 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 Holliday junction resolvase, rpsP 30S ribosomal protein S16, Recombinase A recA, riboflavin biosynthesis protein RibF, glycyl-tRNA synthetase beta subunit, trmU tRNA (5-methylaminomethyl-2-thiouridylate)-methyltransferase, rpml 50S ribosomal protein L35, hemE uroporphyrinogen decarboxylase, Rod shape-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.
Phospholipid fatty acids (PLFAs) may also be used as unique first markers according to the methods described herein. Because PLFAs are rapidly synthesized during microbial growth, are not found in storage molecules and degrade rapidly during cell death, it provides an accurate census of the current living community. All cells contain fatty acids (FAs) that can be extracted and esterified to form fatty acid methyl esters (FAMEs). When the FAMEs are analyzed using gas chromatography-mass spectrometry, the resulting profile constitutes a ‘fingerprint’ of the microorganisms in the sample. The chemical compositions of membranes for organisms in the domains Bacteria and Eukarya are comprised of fatty acids linked to the glycerol by an ester-type bond (phospholipid fatty acids (PLFAs)). In contrast, the membrane lipids of Archaea are composed of long and branched hydrocarbons that are joined to glycerol by an ether-type bond (phospholipid ether lipids (PLELs)). This is one of the most widely used non-genetic criteria to distinguish the three domains. In this context, the phospholipids derived from microbial cell membranes, characterized by different acyl chains, are excellent signature molecules, because such lipid structural diversity can be linked to specific microbial taxa.
As provided herein, in order to determine whether an organism strain is active, the level of expression of one or more unique second markers, which can be the same or different as the first marker, is measured (
In one embodiment, if the level of expression of the second marker is above a threshold level (e.g., a control level) or at a threshold level, the microorganism is considered to be active (
Second unique markers are measured, in one embodiment, at the protein, RNA or intermediate level. A unique second marker is the same or different as the first unique marker.
As provided above, a number of unique first markers and unique second markers can be detected according to the methods described herein. Moreover, the detection and quantification of a unique first marker is carried out according to methods known to those of ordinary skill in the art (
Nucleic acid sequencing (e.g., gDNA, cDNA, rRNA, mRNA) in one embodiment is used to determine absolute cell count of a unique first marker and/or unique second marker. Sequencing platforms include, but are not limited to, Sanger sequencing and high-throughput sequencing methods available from Roche/454 Life Sciences, Illumina/Solexa, Pacific Biosciences, Ion Torrent and Nanopore. The sequencing can be amplicon sequencing of particular DNA or RNA sequences or whole metagenome/transcriptome shotgun sequencing.
Traditional Sanger sequencing (Sanger et al. (1977) DNA sequencing with chain-terminating inhibitors. Proc Natl. Acad. Sci. USA, 74, pp. 5463-5467, incorporated by reference herein in its entirety) relies on the selective incorporation of chain-terminating dideoxynucleotides by DNA polymerase during in vitro DNA replication and is amenable for use with the methods described herein.
In another embodiment, the sample, or a portion thereof is subjected to extraction of nucleic acids, amplification of DNA of interest (such as the rRNA gene) with suitable primers and the construction of clone libraries using sequencing vectors. Selected clones are then sequenced by Sanger sequencing and the nucleotide sequence of the DNA of interest is retrieved, allowing calculation of the number of unique microorganism strains in a sample.
454 pyrosequencing from Roche/454 Life Sciences yields long reads and can be harnessed in the methods described herein (Margulies et al. (2005) Nature, 437, pp. 376-380; U.S. Pat. Nos. 6,274,320; 6,258,568; 6,210,891, each of which is herein incorporated in its entirety for all purposes). Nucleic acid to be sequenced (e.g., amplicons or nebulized genomic/metagenomic DNA) have specific adapters affixed on either end by PCR or by ligation. The DNA with adapters is fixed to tiny beads (ideally, one bead will have one DNA fragment) that are suspended in a water-in-oil emulsion. An emulsion PCR step is then performed to make multiple copies of each DNA fragment, resulting in a set of beads in which each bead contains many cloned copies of the same DNA fragment. Each bead is then placed into a well of a fiber-optic chip that also contains enzymes necessary for the sequencing-by-synthesis reactions. The addition of bases (such as A, C, G, or T) trigger pyrophosphate release, which produces flashes of light that are recorded to infer the sequence of the DNA fragments in each well. About 1 million reads per run with reads up to 1,000 bases in length can be achieved. Paired-end sequencing can be done, which produces pairs of reads, each of which begins at one end of a given DNA fragment. A molecular barcode can be created and placed between the adapter sequence and the sequence of interest in multiplex reactions, allowing each sequence to be assigned to a sample bioinformatically.
Illumina/Solexa sequencing produces average read lengths of about 25 basepairs (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 sequencing using reversible terminator chemistry. Nature, 456:53-59). This sequencing technology is also sequencing-by-synthesis but employs reversible dye terminators and a flow cell with a field of oligos attached. DNA fragments to be sequenced have specific adapters on either end and are washed over a flow cell filled with specific oligonucleotides that hybridize to the ends of the fragments. Each fragment is then replicated to make a cluster of identical fragments. Reversible dye-terminator nucleotides are then washed over the flow cell and given time to attach. The excess nucleotides are washed away, the flow cell is imaged, and the reversible terminators can be removed so that the process can repeat and nucleotides can continue to be added in subsequent cycles. Paired-end reads that are 300 bases in length each can be achieved. An Illumina platform can produce 4 billion fragments in a paired-end fashion with 125 bases for each read in a single run. Barcodes can also be used for sample multiplexing, but indexing primers are used.
The SOLiD (Sequencing by Oligonucleotide Ligation and Detection, Life Technologies) process is a “sequencing-by-ligation” approach, and can be used with the methods described herein for detecting the presence and abundance of a first marker and/or a second marker (
The Ion Torrent system, like 454 sequencing, is amenable for use with the methods described herein for detecting the presence and abundance of a first marker and/or a second marker (
Pacific Biosciences (PacBio) SMRT sequencing uses a single-molecule, real-time sequencing approach and in one embodiment, is used with the methods described herein for detecting the presence and abundance of a first marker and/or a second marker (
In one embodiment, where the first unique marker is the ITS genomic region, automated ribosomal intergenic spacer analysis (ARISA) is used in one embodiment to determine the number and identity of microorganism strains in a sample (
In another embodiment, fragment length polymorphism (RFLP) of PCR-amplified rDNA fragments, otherwise known as amplified ribosomal DNA restriction analysis (ARDRA), is used to characterize unique first markers and the abundance of the same in samples (
One fingerprinting technique used in detecting the presence and abundance of a unique first marker is single-stranded-conformation polymorphism (SSCP) (Lee et al. (1996). Appl Environ Microbiol 62, pp. 3112-3120; Scheinert et al. (1996). J. Microbiol. Methods 26, pp. 103-117; Schwieger and Tebbe (1998). Appl. Environ. Microbiol. 64, pp. 4870-4876, each of which is incorporated by reference herein in its entirety). In this technique, DNA fragments such as PCR products obtained with primers specific for the 16S rRNA gene, are denatured and directly electrophoresed on a non-denaturing gel. Separation is based on differences in size and in the folded conformation of single-stranded DNA, which influences the electrophoretic mobility. Reannealing of DNA strands during electrophoresis can be prevented by a number of strategies, including the use of one phosphorylated primer in the PCR followed by specific digestion of the phosphorylated strands with lambda exonuclease and the use of one biotinylated primer to perform magnetic separation of one single strand after denaturation. To assess the identity of the predominant populations in a given ensemble, in one embodiment, bands are excised and sequenced, or SSCP-patterns can be hybridized with specific probes. Electrophoretic conditions, such as gel matrix, temperature, and addition of glycerol to the gel, can influence the separation.
In addition to sequencing based methods, other methods for quantifying expression (e.g., gene, protein expression) of a second marker are amenable for use with the methods provided herein for determining the level of expression of one or more second markers (
In another embodiment, the sample, or a portion thereof is subjected to a quantitative polymerase chain reaction (PCR) for detecting the presence and abundance of a first marker and/or a second marker (
In another embodiment, the sample, or a portion thereof is subjected to PCR-based fingerprinting techniques to detect the presence and abundance of a first marker and/or a second marker (
In another embodiment, the sample, or a portion thereof is subjected to a chip-based platform such as microarray or microfluidics to determine the abundance of a unique first marker and/or presence/abundance of a unique second marker (
A protein expression assay, in one embodiment, is used with the methods described herein for determining the level of expression of one or more second markers (
In one embodiment, the sample, or a portion thereof is subjected to Bromodeoxyuridine (BrdU) incorporation to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to microautoradiography (MAR) combined with FISH to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to stable isotope Raman spectroscopy combined with FISH (Raman-FISH) to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to DNA/RNA stable isotope probing (SIP) to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to isotope array to determine the level of a second unique marker (
In one embodiment, the sample, or a portion thereof is subjected to a metabolomics assay to determine the level of a second unique marker (
According to the embodiments described herein, the presence and respective number of one or more active microorganism strains in a sample are determined (
Fecal samples were placed in a 2 ounce vial, stored frozen, and analyzed to determine values for apparent neutral detergent fibers (NDF) digestibility, apparent starch digestibility, and apparent protein digestibility. Rumen sampling consisted of sampling both fluid and particulate portions of the rumen, each of which was stored in a 15 ml conical tube. Cells were fixed with a 10% stop solution (5% phenol/95% ethanol mixture) and kept at 4° C. and shipped to Ascus Biosciences (Vista, Calif.) on ice.
The milk yield was measured twice per day, once in the morning and once at night. Milk composition (% fats and % proteins, etc.) was measured twice per day, once in the morning and once at night. Milk samples were further analyzed with near-infrared spectroscopy for protein fats, solids, analysis for milk urea nitrogen (MUN), and somatic cell counts (SCC) at the Tulare Dairy Herd Improvement Association (DHIA) (Tulare, Calif.). Feed intake of individual cows and rumen pH were determined once per day.
A sample of the total mixed ration (TMR) was collected the final day of the adaptation period, and then successively collected once per week. Sampling was performed with the quartering method, wherein the samples were stored in vacuum sealed bags which were shipped to Cumberland Valley Analytical Services (Hagerstown, Md.) and analyzed with the NIR1 package.
The final day of administration of buffer and/or microbial bioensembles was on day 35, however all other measurements and samplings continued as described until day 46.
Table 25 reveals the effects of daily administration of an Ascus microbial ensemble on the performance of multiparous Holstein cows (between 60 and 120 days in milk). Marked differences between the control and inoculated treatments were observed. The inoculated group experienced increases in all parameters except FCM/DMI and rumen pH. The weekly values at the beginning of the intervention period when cows were still adapting to the treatment are included in the calculations.
In certain embodiments of the disclosure, the present methods aim to increase the total amount of milk fat and milk protein produced by a lactating ruminant.
The methodologies presented herein—based upon utilizing the disclosed isolated microbes, ensembles, and compositions comprising the same—have the potential to increase the total amount of milk fat and milk protein produced by a lactating ruminant. These increases can be realized without the need for further addition of hormones.
In this example, seven microbial ensembles comprising isolated microbes from Table 14 are administered to Holstein cows in mid-stage lactation over a period of six weeks. The ensembles are as follows:
Ensemble 1—Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_24;
Ensemble 2—Ascusb_7, Ascusb_1801, Ascusf_45, and Ascusf_24;
Ensemble 3—Ascusb_7, Ascusb_268, Ascusf_45, and Ascusf_24;
Ensemble 4—Ascusb_7, Ascusb_232, Ascusf_45, and Ascusf_24;
Ensemble 5—Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_249;
Ensemble 6—Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_353; and
Ensemble 7—Ascusb_7, Ascusb_32, Ascusf_45, and Ascusf_23.
Ensemble 8—Ascusb_3138, Ascusb_1801, Ascusf_45, and Ascusf_15.
Ensemble 9—Ascusb_3138, Ascusb_268, Ascusf_45, and Ascusf_15.
Ensemble 10—Ascusb_3138, Ascusb_232, Ascusf_23, and Ascusf_15.
Ensemble 11—Ascusb_7, Ascusb_3138, Ascusf_15, and Ascusf_249.
Ensemble 12—Ascusb_7, Ascusb_3138, Ascusf_45, and Ascusf_15.
Ensemble 13—Ascusb_3138, Ascusb_32, Ascusf_15, and Ascusf_23.
Ensemble 14—Ascusb_3138 and Ascusf_15.
The cows are randomly assigned into 15 groups of 8, wherein one of the groups is a control group that receives a buffer lacking a microbial ensemble. The remaining seven groups are experimental groups and will each be administered one of the thirteen microbial bioensembles once per day for six weeks. Each of the cows are held in individual pens to mitigate cross-contamination and are given free access to feed and water. The diet is a high milk yield diet. Cows are fed twice per day and the feed will be weighed at each feeding, and prior day refusals will be weighed and discarded. Weighing is performed with a PS-2000 scale from Salter Brecknell (Fairmont, Minn.).
Cows are cannulated such that a cannula extends into the rumen of the cows. Cows are further provided at least 10 days of recovery post cannulation prior to administering control dosages or experimental dosages.
Each administration consists of 5 ml of a neutral buffered saline, and each administration consists of approximately 109 cells suspended in the saline. The control group receives 5 ml of the saline once per day, while the experimental groups receive 5 ml of the saline further comprising 109 microbial cells of the described ensembles.
The rumen of every cow is sampled on days 0, 7, 14, 21, and 35, wherein day 0 is the day prior to microbial administration. Note that the experimental and control administrations are performed after the rumen has been sampled on that day. Daily sampling of the rumen, beginning on day 0, with a pH meter from Hanna Instruments (Woonsocket, R.I.) is inserted into the collected rumen fluid for recordings. Rumen sampling included both particulate and fluid sampling from the center, dorsal, ventral, anterior, and posterior regions of the rumen through the cannula, and all five samples were pooled into 15 ml conical vials containing 1.5 ml of stop solution (95% ethanol, 5% phenol). A fecal sample is also collected on each sampling day, wherein feces are collected from the rectum with the use of a palpation sleeve. Cows are weighed at the time of each sampling.
Fecal samples are placed in a 2 ounce vial, stored frozen, and analyzed to determine values for apparent NDF digestibility, apparent starch digestibility, and apparent protein digestibility. Rumen sampling consists of sampling both fluid and particulate portions of the rumen, each of which is stored in a 15 ml conical tube. Cells are fixed with a 10% stop solution (5% phenol/95% ethanol mixture) and kept at 4° C. and shipped to Ascus Biosciences (Vista, Calif.) on ice.
The milk yield is measured twice per day, once in the morning and once at night. Milk composition (% fats and % proteins, etc.) is measured twice per day, once in the morning and once at night. Milk samples are further analyzed with near-infrared spectroscopy for protein fats, solids, analysis for milk urea nitrogen (MUN), and somatic cell counts (SCC) at the Tulare Dairy Herd Improvement Association (DHIA) (Tulare, Calif.). Feed intake of individual cows and rumen pH are determined once per day.
A sample of the total mixed ration (TMR) is collected the final day of the adaptation period, and then successively collected once per week. Sampling is performed with the quartering method, wherein the samples are stored in vacuum sealed bags which are shipped to Cumberland Valley Analytical Services (Hagerstown, Md.) and analyzed with the NIR1 package.
In some embodiments, the percent fats and percent protein of milk in each of the experimental cow groups is expected to demonstrate a statistically significant increase over the percent fats and percent protein of milk in the control cow group. In other embodiments, the increase is not expected to be statistically significant, but it is expected to be still quantifiable.
In certain embodiments of the disclosure, the present methods aim to modulate the microbiome of ruminants through the administration of one or more microbes to the gastrointestinal tract of ruminants.
The methodologies presented herein—based upon utilizing the disclosed isolated microbes, ensembles, and compositions comprising the same—have the potential to modulate the microbiome of ruminants. The modulation of a ruminant's gastrointestinal microbiome may lead to an increase of desirable traits of the present disclosure.
In this example, the microbial ensembles of Table 18 are administered to Holstein cows over a period of six weeks.
The cows are randomly assigned into 37 groups of 8, wherein one of the groups is a control group that receives a buffer lacking a microbial ensemble. The remaining thirty-six groups are experimental groups and will each be administered one of the thirty-six microbial ensembles once per day for six weeks. Each of the cows are held in individual pens to mitigate cross-contamination and are given free access to feed and water. The diet is a high milk yield diet. Cows are fed twice per day and the feed will be weighed at each feeding, and prior day refusals will be weighed and discarded. Weighing is performed with a PS-2000 scale from Salter Brecknell (Fairmont, Minn.).
Cows are cannulated such that a cannula extends into the rumen of the cows. Cows are further provided at least 10 days of recovery post cannulation prior to administering control dosages or experimental dosages.
Each administration consists of 5 ml of a neutral buffered saline, and each administration consists of approximately 109 cells suspended in the saline. The control group receives 5 ml of the saline once per day, while the experimental groups receive 5 ml of the saline further comprising 109 microbial cells of the described ensembles.
The rumen of every cow is sampled on days 0, 7, 14, 21, and 35, wherein day 0 is the day prior to administration. Note that the experimental and control administrations are performed after the rumen has been sampled on that day. Daily sampling of the rumen, beginning on day 0, with a pH meter from Hanna Instruments (Woonsocket, R.I.) is inserted into the collected rumen fluid for recordings. Rumen sampling included both particulate and fluid sampling from the center, dorsal, ventral, anterior, and posterior regions of the rumen through the cannula, and all five samples were pooled into 15 ml conical vials containing 1.5 ml of stop solution (95% ethanol, 5% phenol). A fecal sample is also collected on each sampling day, wherein feces are collected from the rectum with the use of a palpation sleeve. Cows are weighed at the time of each sampling.
Fecal samples are placed in a 2 ounce vial, stored frozen, and analyzed to determine values for apparent NDF digestibility, apparent starch digestibility, and apparent protein digestibility. Rumen sampling consists of sampling both fluid and particulate portions of the rumen, each of which is stored in a 15 ml conical tube. Cells are fixed with a 10% stop solution (5% phenol/95% ethanol mixture) and kept at 4° C. and shipped to Ascus Biosciences (Vista, Calif.) on ice.
The samples of fluid and particulate portions of the rumen, as well as the fecal samples are each evaluated for microbiome fingerprinting utilizing the T-RFLP method combined with nMDS ordination and PERMANOVA statistics.
In some embodiments, the ruminal and fecal microbiome in each of the experimental cow groups is expected to demonstrate a statistically significant change in the microbiomes over the microbiomes in the control cow group as well as the 0 day microbiome samples, wherein the change is a significant increase in the proportion of microbes administered in the experimental administrations. In other embodiments, the increase is not expected to be statistically significant, but it is expected to be still quantifiable.
Determine rumen microbial community constituents that impact the production of milk fat in dairy cows.
Eight lactating, ruminally cannulated, Holstein cows were housed in individual tie-stalls for use in the experiment. Cows were fed twice daily, milked twice a day, and had continuous access to fresh water. One cow (cow 4201) was removed from the study after the first dietary Milk Fat Depression due to complications arising from an abortion prior to the experiment.
Experimental Design and Treatment: The experiment used a crossover design with 2 groups and 1 experimental period. The experimental period lasted 38 days: 10 days for the covariate/wash-out period and 28 days for data collection and sampling. The data collection period consisted of 10 days of dietary Milk Fat Depression (MFD) and 18 days of recovery. After the first experimental period, all cows underwent a 10-day wash out period prior to the beginning of period 2.
Dietary MFD was induced with a total mixed ration (TMR) low in fiber (29% NDF) with high starch degradability (70% degradable) and high polyunsaturated fatty acid levels (PUFA, 3.7%). The Recovery phase included two diets variable in starch degradability. Four cows were randomly assigned to the recovery diet high in fiber (37% NDF), low in PUFA (2.6%), and high in starch degradability (70% degradable). The remaining four cows were fed a recovery diet high in fiber (37% NDF), low in PUFA (2.6%), but low in starch degradability (35%).
During the 10-day covariate and 10-day wash out periods, cows were fed the high fiber, low PUFA, and low starch degradability diet.
Samples and Measurements: Milk yield, dry matter intake, and feed efficiency were measured daily for each animal throughout the covariate, wash out, and sample collection periods. TMR samples were measured for nutrient composition. During the collection period, milk samples were collected and analyzed every 3 days. Samples were analyzed for milk component concentrations (milk fat, milk protein, lactose, milk urea nitrogen, somatic cell counts, and solids) and fatty acid compositions.
Rumen samples were collected and analyzed for microbial community composition and activity every 3 days during the collection period. The rumen was intensively sampled 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, and 22 hours after feeding during day 0, day 7, and day 10 of the dietary MFD. Similarly, the rumen was intensively sampled 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, and 22 hours after feeding on day 16 and day 28 of the sample collection period. Rumen contents were analyzed for pH, acetate concentration, butyrate concentration, propionate concentration, isoacid concentration, and long chain and CLA isomer concentrations. Rumen sampling included both particulate and fluid sampling from the center, dorsal, ventral, anterior, and posterior regions of the rumen through the cannula, and all five samples were pooled into 15 ml conical vials.
Rumen Sample Preparation and Sequencing: After collection, rumen samples were centrifuged at 4,000 rpm in a swing bucket centrifuge for 20 minutes at 4° C. The supernatant was decanted, and an aliquot of each rumen content sample (1-2 mg) was added to a sterile 1.7 mL tube prefilled with 0.1 mm glass beads. A second aliquot was collected and stored in an empty, sterile 1.7 mL tube for cell counting.
Rumen samples in empty tubes were stained and put through a flow cytometer to quantify the number of cells of each microorganism type in each sample. Rumen samples with glass beads were homogenized with bead beating to lyse microorganisms. DNA and RNA was extracted and purified from each sample and prepared for sequencing on an Illumina Miseq. Samples were sequenced using paired-end chemistry, with 300 base pairs sequenced on each end of the library.
Sequencing Read Processing and Data Analysis: Sequencing reads were quality trimmed and processed to identify bacterial species present in the rumen based on a marker gene, 16S rDNA, or ITS1 and/or ITS2. Count datasets and activity datasets were integrated with the sequencing reads to determine the absolute cell numbers of active microbial species within the rumen microbial community. Production characteristics of the cow over time, including pounds of milk produced, were linked to the distribution of active microorganisms within each sample over the course of the experiment using mutual information.
Tests cases to determine the impact of count data, activity data, and count and activity on the final output were run by omitting the appropriate datasets from the sequencing analysis. To assess the impact of using a linear correlation rather than the MIC on target selection, Pearson's coefficients were also calculated for pounds of milk fat produced as compared to the relative abundance of all microorganisms and the absolute cell count of active microorganisms.
One component of the Ascus Biosciences technology utilized in this application leverages mutual information to rank the importance of native microbial strains residing in the gastrointestinal tract of the animal to specific animal traits. The maximal information coefficient (MIC) scores are calculated for all microorganisms and the desired animal trait. Relationships were scored on a scale of 0 to 1, with 1 representing a strong relationship between the microbial strain and the animal trait, and 0 representing no relationship. A cut-off based on this score is used to define useful and non-useful microorganisms with respect to the improvement of specific traits.
The MICs were calculated between pounds of milk fat produced and the absolute cell count of each active microorganism. Microorganisms were ranked by MIC score, and microorganisms with the highest MIC scores were selected as the target species most relevant to pounds of milk produced. MIC scores of the microbes of the present disclosure are recited in Table 14. The greater the MIC score, the greater the ability of the microbe to confer an increase in the weight of milk fat produced by a cow.
Utilizing Ascus Biosciences' technology, the performance of currently available microbial feed additive products was predicted.
Direct-fed microbial products that claim to enhance dairy performance are openly available on the market. Some of these products contain microorganism strains that are native rumen microorganisms (Megasphaera elsdenii), or are within 97% sequence similarity of native rumen microorganisms. We have identified the species of microbes utilized in these products, and calculated their MIC score with respect to milk fat efficiency (
Lactobacillus plantarum: MIC 0.28402
The calculated MIC predicts that Lactobacillus plantarum is poorly associated with milk fat efficiency, and the art discloses that an inoculation of L. plantarum yields no increase in milk fat product, and at least one study discloses that some strains of L. plantarum create molecules that cause milk fat depression. See Lee et al. 2007. J. Appl. Microbiol. 103(4):1140-1146 and Mohammed et al. 2012. J. Dairy Sci. 95(1):328-339.
Lactobacillus acidophilus: MIC 0.30048
The calculated MIC predicts that Lactobacillus acidophilus is poorly associated with milk fat efficiency, and the art discloses that the administration of L. acidophilus to dairy cows/calves had no effect of various aspects of milk yield/milk component yield. See Higginbotham and Bath. 1993. J. Dairy Sci. 76(2):615-620; Abu-Tarboush et al. 1996. Animal Feed Sci. Tech. 57(1-2):39-49; McGilliard and Stallings. 1998. J. Dairy Sci. 81(5):1353-1357; and Raeth-Knight et al. 2007. J. Dairy Sci. 90(4):1802-1809; But see Boyd et al. 2011. 94(9):4616-4622 (discloses an increase in milk yield and milk protein yield). While Boyd et al. does disclose an increase in milk and milk protein yield, the controls of this single study do not appear to adequately isolate the the presence of L. acidophilus as the cause of the increase. The body of prior art contradicts the finding of Boyd et al.
Megasphaera elsdenii: MIC 0.32548
The calculated MIC predicts that Megasphaera elsdenii is poorly associated with milk fat efficiency, and the art provides substantial evidence to suggest that Megasphaera elsdenii has no positive effect upon milk fat efficiency, but multiple references provide evidence to suggest that it has a negative effect on milk fat efficiency. See Kim et al. 2002. J. Appl. Micro. 92(5):976-982; Hagg. 2008. Dissertation, University of Pretoria. 1-72; Hagg et al. 2010. S. African. J. Animal Sci. 40(2):101-112; Zebeli et al. 2011. J. Dairy Res. 79(1):16-25; Aikman et al. 2011. J. Dairy Sci. 94(6):2840-2849; Mohammed et al. 2012. J. Dairy Sci. 95(1):328-339; and Cacite and Weimer. 2016. J. Animal Sci. Poster Abstract. 94(sup. 5):784.
Prevotella bryantii: MIC 0.40161
The calculated MIC predicts that Prevotella bryantii is not highly associated with milk fat efficiency, and the art provides evidence that P. bryantii administered during subacute acidosis challenge in midlactation dairy cows has no apparent effect on milk yield, whereas administration of the microbe to dairy cows in early lactation yields improved milk fat concentrations. See Chiquette et al. 2012. J. Dairy Sci. 95(10):5985-5995, but see Chiquette et al. 2008. 91(9):3536-3543; respectively.
The methods of the instant example aim to increase the total amount of milk fat and milk protein produced by a lactating ruminant, and the calculated energy corrected milk (ECM).
The methodologies presented herein-based upon utilizing the disclosed isolated microbes, ensembles, and compositions comprising the same-demonstrate an increase in the total amount of milk fat and milk protein produced by a lactating ruminant. These increases were realized without the need for further addition of hormones.
In this example, a microbial ensemble comprising two isolated microbes, Ascusb_3138 (SEQ ID NO:28) and Ascusf_15 (SEQ ID NO:32), was administered to Holstein cows in mid-stage lactation over a period of five weeks.
The cows were randomly assigned into 2 groups of 8, in which one of the groups was a control group that received a buffer lacking a microbial ensemble. The second group, the experimental group, was administered a microbial ensemble comprising Ascusb_3138 (SEQ ID NO:28) and Ascusf_15 (SEQ ID NO:32) once per day for five weeks. Each cow was housed in an individual pen and was given free access to feed and water. The diet was a high milk yield diet. Cows were fed ad libitum and the feed was weighed at the end of each day, and prior day refusals were weighed and discarded. Weighing was performed with a PS-2000 scale from Salter Brecknell (Fairmont, Minn.).
Cows were cannulated such that a cannula extended into the rumen of the cows. Cows were further provided at least 10 days of recovery post cannulation prior to administering control dosages or experimental dosages.
Each administration consisted of 20 ml of a neutral buffered saline, and each administration consisted of approximately 109 cells suspended in the saline. The control group received 20 ml of the saline once per day, while the experimental group received 20 ml of the saline further comprising 109 microbial cells of the described microbial ensemble.
The rumen of every cow was sampled on days 0, 7, 14, 21, and 35, wherein day 0 was the day prior to microbial administration. Note that the experimental and control administrations were performed after the rumen was sampled on that day. Daily sampling of the rumen, beginning on day 0, with a pH meter from Hanna Instruments (Woonsocket, R.I.) was inserted into the collected rumen fluid for recordings. Rumen sampling included both particulate and fluid sampling from the center, dorsal, ventral, anterior, and posterior regions of the rumen through the cannula, and all five samples were pooled into 15 ml conical vials containing 1.5 ml of stop solution (95% ethanol, 5% phenol) and stored at 4° C. and shipped to Ascus Biosciences (Vista, Calif.) on ice.
A portion of each rumen sample was stained and put through a flow cytometer to quantify the number of cells of each microorganism type in each sample. A separate portion of the same rumen sample was homogenized with bead beating to lyse microorganisms. DNA and RNA was extracted and purified from each sample and prepared for sequencing on an Illumina Miseq. Samples were sequenced using paired-end chemistry, with 300 base pairs sequenced on each end of the library. The sequencing reads were used to quantify the number of cells of each active, microbial member present in each animal rumen in the control and experimental groups over the course of the experiment.
Ascusb_3138 and Ascusf_15 both colonized, and were active in the rumen after ˜3-5 days of daily administration, depending on the animal. This colonization was observed in the experimental group, but not in the control group. The rumen is a dynamic environment, where the chemistry of the cumulative rumen microbial population is highly intertwined. The artificial addition of Ascusb_3138 and Ascuf_15 could have effects on the overall structure of the community. To assess this potential impact, the entire microbial community was analyzed over the course of the experiment to identify higher level taxonomic shifts in microbial community population.
Distinct trends were not observed in the fungal populations over time, aside from the higher cell numbers of Ascusf_15 in the experimental animals. The bacterial populations, however, did change more predictably. To assess high level trends across individual animals over time, percent compositions of the microbial populations were calculated and compared. See Table 26. Only genera composing greater than 1% of the community were analyzed. The percent composition of genera containing known fiber-degrading bacteria, including Ruminococcus, was found to increase in experimental animals as compared to control animals. Volatile fatty acid-producing genera, including Clostridial cluster XIVa, Clostridium, Pseudobutyrivibrio, Butyricimonas, and Lachnospira were also found at higher levels in the experimental animals. The greatest shift was observed in the genera Prevotella. Members of this genus have been shown to be involved in the digestion of cellobiose, pectin, and various other structural carbohydrates within the rumen. Prevotella sp. have further been implicated in the conversion of plant lignins into beneficial antioxidants (Schogor et al. PLOS One. 9(4):e87949 (10 p.)).
To more directly measure quantitative changes in the rumen over time, cell count data was integrated with sequencing data to identify bulk changes in the population at the cell level. Fold changes in cell numbers were determined by dividing the average number of cells of each genera in the experimental group by the average number of cells of each genera in the control group. See Table 26. The cell count analysis captured many genera that fell under the threshold in the previous analysis Promicromonospora, Rhodopirellula, Olivibacter, Victivallis, Nocardia, Lentisphaera, Eubacteiru, Pedobacter, Butyricimonas, Mogibacterium, and Desulfovibrio were all found to be at least 10 fold higher on average in the experimental animals. Prevotella, Lachnospira, Butyricicoccus, Clostridium XIVa, Roseburia, Clostridium_sensu_stricto, and Pseudobutyrivibrio were found to be ˜1.5 times higher in the experimental animals.
Prevotella
Clostridium_XIVa
Lachnospiracea_
incertae_sedis
Ruminococcus
Clostridium_IV
Butyricimonas
Clostridium_
sensu_stricto
Pseudobutyrivibrio
Citrobacter
Selenomonas
Hydrogeno
anaerobacterium
Promicromonospora
Rhodopirellula
Olivibacter
Victivallis
Nocardia
Lentisphaera
Eubacterium
Pedobacter
Butyricimonas
Mogibacterium
Desulfovibrio
Anaeroplasma
Sharpea
Erysipelotrichaceae_incertae_sedis
Saccharofermentans
Parabacteroides
Papillibacter
Citrobacter
Lachnospiracea_incertae_sedis
Prevotella
Butyricicoccus
Clostridium_XlVa
Roseburia
Pseudobutyrivibrio
Clostridium_sensu_stricto
Selenomonas
Olsenella
To assess the ability of the strains to produce volatile fatty acids, High Performance Liquid Chromatography (HPLC) was utilized to measure the concentrations of acetate, butyrate, and propionate in spent media. M2GSC media was used in an assay mimicking rumen conditions as closely as possible.
For pure cultures, a single colony from each of the desired strains (from anaerobic agar plates) was inoculated into M2GSC media. A medium blank (control) was also prepared. Cultures and the medium blank were incubated at 37° C. until significant growth was visible. An optical density (OD600) was determined for each culture, and the strain ID was confirmed with Illumina sequencing. An aliquot of culture was subjected to sterile filtration into a washed glass 15 ml sample vial and analyzed by HPLC; HPLC assays were performed at Michigan State University. Enrichments that exhibited growth were also stained and cell counted to confirm that the individual strains within each enrichment grew. Strains often appeared in multiple enrichments, so the enrichment with the highest amount of growth for the strain (i.e. the highest increase in cell number of that strain) is reported in Table 28.
Due to the vast complexity of metabolisms and microbial lifestyles present in the rumen, many rumen microorganisms are incapable of axenic growth. In order to assay these organisms for desirable characteristics, enrichments cultures were established under a variety of conditions that mimicked particular features of the rumen environment. Diluted rumen fluid (1/100 dilution) was inoculated into M2GSC or M2 media supplemented with a variety of carbon sources including xylose (4 g/L), mannitol (4 g/L), glycerol (4 g/L), xylan (2 g/L), cellobiose (2 g/L), arabinose (4 g/L), mannose (4 g/L), rhaminose (2 g/L), maltose (2 g/L), maltose (2 g/L), and molasses. Rumen fluid was also sometimes omitted from the recipe. Additions including amino acids, volatile fatty acids, and antibiotics, were also varied across the enrichments. A medium blank (control) was also prepared. Cultures and the medium blank were incubated at 37° C. until significant growth was visible. An optical density (OD600) was determined for each culture, and the strain IDs were confirmed with Illumina sequencing. An aliquot of culture was subjected to sterile filtration into a washed glass 15 ml sample vial and analyzed by HPLC; HPLC assays were performed at Michigan State University. Enrichments that exhibited growth were also stained and cell counted to confirm that the individual strains within each enrichment grew. Strains often appeared in multiple enrichments, so the enrichment with the highest amount of growth for the strain (i.e, the highest increase in cell number of that strain) is reported in Table 28.
Concentrations of acetate, butyrate, and propionate were quantified for the medium blanks as well as the sterile filtered culture samples for both pure strain and enrichment experiments. HPLC parameters were as follows: Biorad Aminex HPX-87H column, 60° C., 0.5 ml/minute mobile phase 0.00325 N H2SO4, 500 psi, 35C RI detector, 45 minute run time, and 5 μL injection volume. Concentrations of acetate, butyrate, and propionate for both pure cultures and enrichments are reported in Table 28.
To assess the ability of the strains to degrade various carbon sources, an optical density (OD600) was used to measure growth of strains on multiple carbon sources over time.
For pure isolates, a single colony from each of the desired strains (from anaerobic agar plates) was inoculated into M2GSC media. A medium blank (control) was also prepared. Strains were inoculated into a carbon source assay anaerobically, wherein the assay was set up in a 2 mL sterile 96-well plate, with each well containing RAMM salts, vitamins, minerals, cysteine, and a single carbon source. Carbon sources included glucose, xylan, lactate, xylose, mannose, glycerol, pectin, molasses, and cellobiose. Cells were inoculated such that each well started at an OD600 of 0.01. Optican densities were read at 600 nm with the Synergy H4 hybrid plate reader. The strain IDs were confirmed with Illumina sequencing after all wells were in stationary phase.
As in the volatile fatty acid assay above, enrichments were also used to assay carbon source degradation. Diluted rumen fluid (1/100 dilution) was inoculated into M2GSC or M2 media supplemented with a variety of carbon sources including xylose (4 g/L), mannitol (4 g/L), glycerol (4 g/L), xylan (2 g/L), cellobiose (2 g/L), arabinose (4 g/L), mannose (4 g/L), rhaminose (2 g/L), maltose (2 g/L), maltose (2 g/L), and molasses. Rumen fluid was also sometimes omitted from the recipe. Additions including amino acids, volatile fatty acids, and antibiotics, were also varied across the enrichments. A medium blank (control) was also prepared. Cultures and the medium blank were incubated at 37° C. until significant growth was visible. An optical density (OD600) was determined for each culture, and the strain IDs were confirmed with Illumina sequencing. Enrichments that exhibited growth were also stained and cell counted to confirm that the individual strains within each enrichment grew.
To assess the ability of the strains to degrade insoluble carbon sources, visual inspection was leveraged to qualitatively determine a strain's degradation capabilities.
For pure cultures, a single colony from each of the desired strains (from anaerobic agar plates) was inoculated into anaerobic Hungate tubes containing Lowe's semi defined media with cellulose paper, starch, or grass as the sole carbon source. (Lowe et al. 1985. J. Gen. Microbiol. 131:2225-2229). Enrichment cultures using a 1/100 dilution of rumen fluid were also set up using the same medium conditions. Cultures were checked visually for degradation of insoluble carbon sources (See
All media was prepared with anaerobic water (boiled DI H2O for 15 minutes then cooled to room temperature in a water bath while sparging with N2. All media was adjusted to a pH of 6.8 with 2M HCl. 10 mL of media was then aliquoted into 15 mL hungate tubs, and the tubes were then sparged with 80% N2 20% CO2 for 3 minutes.
After sterilization (autoclave) added: 2 mL of 250× modified Wolfe's vitamin mix, 10 mL of 50× modified Wolfe's mineral mix, 5 mL of 100 mM cysteine.
Experimental Design and Materials and Methods
Objective:
Determine rumen microbial community constituents that impact the production of milk fat in dairy cows.
Animals:
Eight lactating, ruminally cannulated, Holstein cows were housed in individual tie-stalls for use in the experiment. Cows were fed twice daily, milked twice a day, and had continuous access to fresh water. One cow (cow 1) was removed from the study after the first dietary Milk Fat Depression due to complications arising from an abortion prior to the experiment.
Experimental Design and Treatment:
The experiment used a crossover design with 2 groups and 1 experimental period. The experimental period lasted 38 days: 10 days for the covariate/wash-out period and 28 days for data collection and sampling. The data collection period consisted of 10 days of dietary Milk Fat Depression (MFD) and 18 days of recovery. After the first experimental period, all cows underwent a 10-day wash out period prior to the beginning of period 2.
Dietary MFD was induced with a total mixed ration (TMR) low in fiber (29% NDF) with high starch degradability (70% degradable) and high polyunsaturated fatty acid levels (PUFA, 3.7%). The Recovery phase included two diets variable in starch degradability. Four cows were randomly assigned to the recovery diet high in fiber (37% NDF), low in PUFA (2.6%), and high in starch degradability (70% degradable). The remaining four cows were fed a recovery diet high in fiber (37% NDF), low in PUFA (2.6%), but low in starch degradability (35%).
During the 10-day covariate and 10-day wash out periods, cows were fed the high fiber, low PUFA, and low starch degradability diet.
Samples and Measurements:
Milk yield, dry matter intake, and feed efficiency were measured daily for each animal throughout the covariate, wash out, and sample collection periods. TMR samples were measured for nutrient composition. During the collection period, milk samples were collected and analyzed every 3 days. Samples were analyzed for milk component concentrations (milk fat, milk protein, lactose, milk urea nitrogen, somatic cell counts, and solids) and fatty acid compositions.
Rumen samples were collected and analyzed for microbial community composition and activity every 3 days during the collection period. The rumen was intensively sampled 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, and 22 hours after feeding during day 0, day 7, and day 10 of the dietary MFD. Similarly, the rumen was intensively sampled 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, and 22 hours after feeding on day 16 and day 28 during the recovery period. Rumen contents were analyzed for pH, acetate concentration, butyrate concentration, propionate concentration, isoacid concentration, and long chain and CLA isomer concentrations.
Rumen Sample Preparation and Sequencing:
After collection, rumen samples were centrifuged at 4,000 rpm in a swing bucket centrifuge for 20 minutes at 4° C. The supernatant was decanted, and an aliquot of each rumen content sample (1-2 mg) was added to a sterile 1.7 mL tube prefilled with 0.1 mm glass beads. A second aliquot was collected and stored in an empty, sterile 1.7 mL tube for cell counting.
Rumen samples with glass beads (1st aliquot) were homogenized with bead beating to lyse microorganisms. DNA and RNA was extracted and purified from each sample and prepared for sequencing on an Illumina Miseq. Samples were sequenced using paired-end chemistry, with 300 base pairs sequenced on each end of the library. Rumen samples in empty tubes (2nd aliquot) were stained and put through a flow cytometer to quantify the number of cells of each microorganism type in each sample.
Sequencing Read Processing and Data Analysis:
Sequencing reads were quality trimmed and processed to identify bacterial species present in the rumen based on a marker gene. Count datasets and activity datasets were integrated with the sequencing reads to determine the absolute cell numbers of active microbial species within the rumen microbial community. Production characteristics of the cow over time, including pounds of milk produced, were linked to the distribution of active microorganisms within each sample over the course of the experiment using mutual information. Maximal information coefficient (MIC) scores were calculated between pounds of milk fat produced and the absolute cell count of each active microorganism. Microorganisms were ranked by MIC score, and microorganisms with the highest MIC scores were selected as the target species most relevant to pounds of milk produced.
Tests cases to determine the impact of count data, activity data, and count and activity on the final output were run by omitting the appropriate datasets from the sequencing analysis. To assess the impact of using a linear correlation rather than the MIC on target selection, Pearson's coefficients were also calculated for pounds of milk fat produced as compared to the relative abundance of all microorganisms and the absolute cell count of active microorganisms.
Relative Abundances Vs. Absolute Cell Counts
The top 15 target species were identified for the dataset that included cell count data (absolute cell count, Table 34) and for the dataset that did not include cell count data (relative abundance, Table 33) based on MIC scores. Activity data was not used in this analysis in order to isolate the effect of cell count data on final target selection. Ultimately, the top 8 targets were the same between the two datasets. Of the remaining 7, 5 strains were present on both lists in varying order. Despite the differences in rank for these 5 strains, the calculated MIC score for each strain was the identical between the two lists. The two strains present on the absolute cell count list but not the relative abundance list, ascus_111 and ascus_288, were rank 91 and rank 16, respectively, on the relative abundance list. The two strains present on the relative abundance list but not the absolute cell count list, ascus_102 and ascus_252, were rank 50 and rank 19, respectively, on the absolute cell count list. These 4 strains did have different MIC scores on each list, thus explaining their shift in rank and subsequent impact on the other strains in the list.
Integration of cell count data did not always affect the final MIC score assigned to each strain. This may be attributed to the fact that although the microbial population did shift within the rumen daily and over the course of the 38-day experiment, it was always within 107-108 cells per milliliter. Much larger shifts in population numbers would undoubtedly have a broader impact on final MIC scores.
Inactive Species Vs. Active Species
In order to assess the impact of filtering strains based on activity data, target species were identified from a dataset that leveraged relative abundance with (Table 35) and without (Table 33) activity data as well as a dataset that leveraged absolute cell counts with (Table 36) and without (Table 34) activity data.
For the relative abundance case, ascus_126, ascus_1366, ascus_1780, ascus_299, ascus_1139, ascus_127, ascus_341, and ascus_252 were deemed target strains prior to applying activity data. These eight strains (53% of the initial top 15 targets) fell below rank 15 after integrating activity data. A similar trend was observed for the absolute cell count case. Ascus_126, ascus_1366, ascus_1780, ascus_299, ascus_1139, ascus_127, and ascus_341 (46% of the initial top 15 targets) fell below rank 15 after activity dataset integration.
The activity datasets had a much more severe effect on target rank and selection than the cell count datasets. When integrating these datasets together, if a sample is found to be inactive it is essentially changed to a “0” and not considered to be part of the analysis. Because of this, the distribution of points within a sample can become heavily altered or skewed after integration, which in turn greatly impacts the final MIC score and thus the rank order of target microorganisms.
Relative Abundances and Inactive Vs. Absolute Cell Counts and Active
Ultimately, the method defined here leverages both cell count data and activity data to identify microorganisms highly linked to relevant metadata characteristics. Within the top 15 targets selected using both methods (Table 36, Table 33), only 7 strains were found on both lists. Eight strains (53%) were unique to the absolute cell count and activity list. The top 3 targets on both lists matched in both strain as well as in rank. However, two of the three did not have the same MIC score on both lists, suggesting that they were influenced by activity dataset integration but not enough to upset their rank order.
Linear Correlations Vs. Nonparametric Approaches
Pearson's coefficients and MIC scores were calculated between pounds of milk fat produced and the absolute cell count of active microorganisms within each sample (Table 37). Strains were ranked either by MIC (Table 37a) or Pearson coefficient (Table 37b) to select target strains most relevant to milk fat production. Both MIC score and Pearson coefficient are reported in each case. Six strains were found on both lists, meaning nine (60%) unique strains were identified using the MIC approach. The rank order of strains between lists did not match—the top 3 target strains identified by each method were also unique.
Like Pearson coefficients, the MIC score is reported over a range of 0 to 1, with 1 suggesting a very tight relationship between the two variables. Here, the top 15 targets exhibited MIC scores ranging from 0.97 to 0.74. The Pearson coefficients for the correlation test case, however, ranged from 0.53 to 0.45—substantially lower than the mutual information test case. This discrepancy may be due to the differences inherent to each analysis method. While correlations are a linear estimate that measures the dispersion of points around a line, mutual information leverages probability distributions and measures the similarity between two distributions. Over the course of the experiment, the pounds of milk fat produced changed nonlinearly (
The Present Method in Entirety Vs. Conventional Approaches
The conventional approach of analyzing microbial communities relies on the use of relative abundance data with no incorporation of activity information, and ultimately ends with a simple correlation of microbial species to metadata (see, e.g., U.S. Pat. No. 9,206,680, which is herein incorporated by reference in its entirety for all purposes). Here, we have shown how the incorporation of each dataset incrementally influences the final list of targets. When applied in its entirety, the method described herein selected a completely different set of targets when compared to the conventional method (Table 37a and Table 37c). Ascus_3038, the top target strain selected using the conventional approach, was plotted against milk fat to visualize the strength of the correlation (
Subject matter contemplated by the present disclosure is set out in the following numbered embodiments:
The aforementioned compositions have markedly different characteristics and/or properties not possessed by any individual bacteria or fungi as they naturally exist in the rumen. The markedly different characteristics and/or properties possessed by the aforementioned compositions can be structural, functional, or both. For example, the compositions possess the markedly different functional property of being able to increase milk production or improve milk compositional characteristics, when administered to a ruminant, as taught herein. Furthermore, the compositions possess the markedly different functional property of being shelf-stable.
Subject matter contemplated by the present disclosure is set out in the following numbered embodiments:
The aforementioned compositions have markedly different characteristics and/or properties not possessed by any individual bacteria or fungi as they naturally exist in the rumen. The markedly different characteristics and/or properties possessed by the aforementioned compositions can be structural, functional, or both. For example, the compositions possess the markedly different functional property of being able to modulate the rumen microbiome, when administered to a ruminant, as taught herein.
Subject matter contemplated by the present disclosure is set out in the following numbered embodiments:
The aforementioned compositions, utilized in the described methods, have markedly different characteristics and/or properties not possessed by any individual bacteria or fungi as they naturally exist in the rumen. The markedly different characteristics and/or properties possessed by the aforementioned compositions, utilized in the described methods, can be structural, functional, or both. For example, the compositions, utilized in the described methods, possess the markedly different functional property of being able to increase milk production or improve milk compositional characteristics, when administered to a ruminant, as taught herein. Furthermore, the compositions, utilized in the described methods, possess the markedly different functional property of being shelf-stable.
In aspects, the aforementioned microbial species—that is, a purified microbial population that comprises a bacteria with a 16S nucleic acid sequence, and/or a fungi with an ITS nucleic acid sequence, which is at least about 97% identical to a nucleic acid sequence selected from the group consisting of: SEQ ID NOs: 1-60 and 2045-2107—are members of a Markush group, as the present disclosure illustrates that the members belong to a class of microbes characterized by various physical and functional attributes, which can include any of the following: a) the ability to convert a carbon source into a volatile fatty acid such as acetate, butyrate, propionate, or combinations thereof; b) the ability to degrade a soluble or insoluble carbon source; c) the ability to impart an increase in milk production or improved milk compositional characteristics to a ruminant administered the microbe; d) the ability to modulate the microbiome of the rumen of a ruminant administered the microbe; e) the ability to be formulated into a shelf-stable composition; and/or f) possessing a MIC score of at least about 0.4 if a bacteria and possessing a MIC score of at least about 0.2 if a fungi. Thus, the members of the Markush group possess at least one property in common, which can be responsible for their function in the disclosed relationship.
All references, articles, publications, patents, patent publications, and patent applications cited herein are incorporated by reference in their entireties for all purposes. Additionally, applicant hereby incorporates the entirety of each of PCT Pub. Nos. WO 2017/120495 and WO 2016/210251, as well as US Pat. App. Pub. No. 2017/0107557 by reference herein for all purposes.
However, mention of any reference, article, publication, patent, patent publication, and patent application cited herein is not, and should not be taken as, an acknowledgment or any form of suggestion that they constitute valid prior art or form part of the common general knowledge in any country in the world.
wherein the ruminant administered the effective amount of the composition exhibits an increase in milk production or improved milk compositional characteristics, as compared to a ruminant not administered the composition.
(a) a microbial ensemble of any one of embodiments 31-43, and
(b) an acceptable carrier.
While the disclosure has been communicated with reference to the specific embodiments thereof it should be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the true spirit and scope of the disclosure. In addition, many modifications may be made to adopt a particular situation, material, composition of matter, process, process step or steps, to the objective spirit and scope of the described embodiments and disclosure. All such modifications are intended to be within the scope of the disclosure. Patents, patent applications, patent application publications, journal articles and protocols referenced herein are incorporated by reference in their entireties, for all purposes.
While various embodiments have been described and illustrated herein, those of skill in the art will readily envision a variety of other ways and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the disclosure. More generally, those skilled in the art will readily appreciate that parameters, dimensions, materials, and configurations described herein are provided as illustrative examples, and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application(s) or implementation(s) for which the disclosed teachings is/are 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. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended embodiments/claims and equivalents thereto; embodiments can be practiced otherwise than as specifically disclosed. Embodiments of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.
The above-described embodiments can be implemented in any of numerous ways. For example, the embodiments can be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can be executed on any suitable processor or collection of processors, whether provided in a single computer or distributed among multiple computers.
Further, it should be appreciated that the disclosed methods can be used in conjunction with a computer, which can be embodied in any of a number of forms, such as a rack-mounted computer, a desktop computer, a laptop computer, or a tablet computer. Additionally, a computer can be embedded in a device not generally regarded as a computer but with suitable processing capabilities, including a tablet, Personal Digital Assistant (PDA), a smart phone or any other suitable portable or fixed electronic device.
Also, a computer can have one or more input and output devices, including one or more displays. These devices can be used, among other things, to present a user interface. Examples of output devices that can be used to provide a user interface include printers or display screens for visual presentation of output and speakers or other sound generating devices for audible presentation of output. Examples of input devices that can be used for a user interface include keyboards, and pointing devices, such as mice, touch pads, and digitizing tablets. As another example, a computer can receive input information through speech recognition or in other audible format.
Such computers can be interconnected by one or more networks in any suitable form, including a local area network or a wide area network, such as an enterprise network, and intelligent network (IN) or the Internet. Such networks can be based on any suitable technology and can operate according to any suitable protocol and can include wireless networks, wired networks or fiber optic networks.
Various methods and processes outlined herein (and/or portions thereof) can be coded as software that is executable on one or more processors that employ any one of a variety of operating systems or platforms. Additionally, such software can be written using any of a number of suitable programming languages and/or programming or scripting tools, and also can be compiled as executable machine language code or intermediate code that is executed on a framework or virtual machine.
In this respect, various disclosed concepts can be embodied as a computer readable storage medium (or multiple computer readable storage media) (e.g., a computer memory, one or more floppy discs, compact discs, optical discs, magnetic tapes, flash memories, circuit configurations in Field Programmable Gate Arrays or other semiconductor devices, or other non-transitory medium or tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement the various embodiments of the disclosure discussed above. The computer readable medium or media can be transportable, such that the program or programs stored thereon can be loaded onto one or more different computers or other processors to implement various aspects of the present disclosure as discussed above.
The terms “program” or “software” are used herein in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects of embodiments as discussed above. Additionally, it should be appreciated that according to one aspect, one or more computer programs that when executed perform methods of the present disclosure need not reside on a single computer or processor, but can be distributed in a modular fashion amongst a number of different computers or processors to implement various aspects of the present disclosure.
Computer-executable instructions can be in many forms, such as program modules, 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 combined or distributed as desired in various embodiments.
Also, data structures can be stored in computer-readable media in any suitable form. For simplicity of illustration, data structures can be shown to have fields that are related through location in the data structure. Such relationships can likewise be achieved by assigning storage for the fields with locations in a computer-readable medium that convey relationship between the fields. However, any suitable mechanism can be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags or other mechanisms that establish relationship between data elements.
Also, various disclosed concepts can be embodied as one or more methods, of which examples have been provided. The acts performed as part of the method can be ordered in any suitable way. Accordingly, embodiments can be constructed in which acts are performed in an order different than illustrated, which can include performing some acts simultaneously, even though shown as sequential acts in illustrative embodiments.
All definitions, as defined and used herein, should be understood to control over dictionary definitions, definitions in documents incorporated by reference, and/or ordinary meanings of the defined terms.
Flow diagrams are used herein. The use of flow diagrams is not meant to be limiting with respect to the order of operations performed. The herein described subject matter sometimes illustrates different components contained within, or connected with, different other components. It is to be understood that such depicted architectures are merely exemplary, and that in fact many other architectures can be implemented which achieve the same functionality. In a conceptual sense, any arrangement of components to achieve the same functionality is effectively “associated” such that the desired functionality is achieved. Hence, any two components herein combined to achieve a particular functionality can be seen as “associated with” each other such that the desired functionality is achieved, irrespective of architectures or intermedia components. Likewise, any two components so associated can also be viewed as being “operably connected,” or “operably coupled,” to each other to achieve the desired functionality, and any two components capable of being so associated can also be viewed as being “operably couplable,” to each other to achieve the desired functionality. Specific examples of operably couplable include but are not limited to physically mateable and/or physically interacting components and/or wirelessly interactable and/or wirelessly interacting components and/or logically interacting and/or logically interactable components.
The indefinite articles “a” and “an,” as used herein, unless clearly indicated to the contrary, should be understood to mean “at least one.”
The phrase “and/or,” as used herein, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Multiple elements listed with “and/or” should be construed in the same fashion, i.e., “one or more” of the elements so conjoined. Other elements can optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, a reference to “A and/or B”, when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A only (optionally including elements other than B); in another embodiment, to B only (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.
As used herein “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one, of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of” or “exactly one of,” or, when used in claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in claims, shall have its ordinary meaning as used in the field of patent law.
As used herein, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements can optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a non-limiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.
1. A method, comprising: forming a bioensemble of active microorganism strains configured to alter a property in a target biological environment, the forming including: obtaining at least two sample sets, each sample set including at least one sample, an each sample sharing at least one common environmental parameter; detecting a plurality of microorganism types in each sample; determining an absolute number of cells of each detected microorganism type of the plurality of microorganism types in each sample; measuring unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain of a detected microorganism type; determining the absolute cell count of each microorganism strain present in each sample based on the number of each detected microorganism types in that sample and the number of unique first markers and quantity thereof in that sample; measuring at least one unique second marker for each microorganism strain to determine active microorganism strains in each sample; generating a set of active microorganisms strains and their respective absolute cell counts for each of the at least two samples; analyzing the active microorganisms strains and respective absolute cell counts for each sample of the at least two sample sets with at least one measured metadata for each of the at least two sample sets to identify relationships between each active microorganism strain and measured metadata; selecting a plurality of active microorganism strains from the set of active microorganism strains based on the analysis; and combining the selected plurality of active microorganism strains with a carrier medium to form a bioensemble of active microorganisms configured to alter a property of a target biological environment, corresponding to the or each measured metadata, when the bioensemble is introduced into that target biological environment.
2. The method of embodiment 1, wherein analyzing the active microorganisms strains and respective absolute cell counts for each sample of the at least two sample sets with at least one measured metadata is based on maximal information coefficient network analysis to measure connectivity of each active microorganism strain within a network and the at least one measured metadata.
3. The method of embodiment 1, wherein measuring unique first markers, and quantity thereof, includes at least one of: subjecting genomic DNA from each sample to a high throughput sequencing reaction; and/or subjecting genomic DNA from each sample to metagenome sequencing.
4. The method of embodiment 1, wherein the unique first markers include at least one of an mRNA marker, an siRNA marker, and/or a ribosomal RNA marker.
5. The method of embodiment 1, wherein the unique first markers include at least one of a sigma factor, a transcription factor, nucleoside associated protein, and/or metabolic enzyme.
6. The method of embodiment 1, wherein measuring unique first markers includes at least one of measuring unique genomic DNA markers in each sample, measuring unique RNA markers in each sample, and/or measuring unique protein markers in each sample.
7. The method of embodiment 1, wherein the unique first markers include at least one of a sigma factor and/or a transcription factor.
8. The method of embodiment 1, wherein the unique first markers include at least one of a nucleoside associated protein and/or metabolic enzyme.
9. The method of embodiment 1, wherein measuring at least one unique second marker for each microorganism strain includes measuring a level of expression of the at least one unique second marker.
10. The method of embodiment 9, wherein measuring the level of expression of the at least one unique second marker includes at least one of: subjecting sample mRNA to gene expression analysis; subjecting each sample or a portion thereof to mass spectrometry analysis; and/or subjecting each sample or a portion thereof to metaribosome profiling or ribosome profiling.
11. A processor-implemented method, comprising: obtaining at least two samples sharing at least one common environmental parameter, each sample including a heterogeneous microbial community; detecting the presence of a plurality of microorganism types in each sample; determining an absolute number of cells of each detected microorganism type of the plurality of microorganism types in each sample; measuring unique first markers in each sample, and quantity thereof, each unique first marker being a marker of a microorganism strain of a detected microorganism type; measuring a value of one or more unique second markers, a unique second marker indicative of metabolic activity of a particular microorganism strain of a detected microorganism type; determining the activity of each detected microorganism strain based on the measured value of the one or more unique second markers exceeding a specified threshold; determining the respective ratios of each active detected microorganism strain in the sample; analyzing each of the active detected microorganism strains of the at least two samples via a processor, the analysis including identifying relationships and the strengths thereof between each active detected microorganism strain and every other active detected microorganism strain, and each active detected microorganism strain and at least one measured metadata; displaying, on a graphical interface, identified relationships between active detected microorganism strains and the at least one measured metadata; and formulating a bioensemble comprising a carrier and at least two active detected microorganism strains based on identified relationships.
12. The processor-implemented method of embodiment 11, wherein the analysis is based on maximal information coefficient network analysis that measures connectivity of each active microorganism strain to every other active microorganism strain and the at least one measured metadata.
13. The processor-implemented method of embodiment 11, where an identified relationship is not displayed if the strength thereof does not exceed a specified threshold.
14. The processor-implemented method of embodiment 11, further comprising assigning each active detected microorganism strains to one of at least two groups based on predicted function thereof.
15. The processor-implemented method of embodiment 11, further comprising assigning each active detected microorganism strains to one of at least two groups based on chemistry thereof.
16. The processor-implemented method of embodiment 11, further comprising assigning each active detected microorganism strains to one at least three groups based on predicted function and/or chemistry thereof.
17. The processor-implemented method of embodiment 11, wherein the analysis includes generating matrices populated with linkages denoting metadata and microorganism strain associations.
18. The processor-implemented method of embodiment 11, wherein the analysis includes determining co-occurrence of at least one active microorganism strain and another active microorganism strain and/or the at least one measured metadata.
19. A synthetic ensemble formed using the method of any one of the preceding embodiments.
20. The synthetic ensemble of embodiment 19, wherein the synthetic ensemble comprises Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov.
Effect of Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov. on Holstein Milk Composition and Yield.
Effect of an endomicrobial supplement (EMS) on dairy cow milk composition and yield was assessed. The EMS consisted of Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov., injected at a total of 4×109 and 1×109 cells/day. Observations were collected from 16 multiparous, ruminally cannulated Holstein cows that were randomly split into a control (CON) and inoculated (INO) group. Study consisted of 3 periods: 10 day pre-treatment, 32 day treatment, and 10 day post-treatment. Cows were individually penned and fed a common TMR (17% CP, 27.1% NDF) twice daily. During morning feedings of TRT, INO received the EMS and CON received sterile PBS via rumen cannula. A composite milk sample per cow was collected at each milking on day 10 pre-TRT, and daily during the TRT and post-TRT periods. Milk composition was analyzed using near-infrared spectroscopy for crude protein, fat, and milk urea nitrogen (MUN) at the Tulare DHIA Laboratory. Data were analyzed by averaging daily values to produce weekly means for conducting repeated measures using the MIXED procedure of SAS. A composite rumen fluid sample was collected 18 times throughout the 52 day study to determine EMS colonization by sequencing the ITS and 16S rRNA V1-V3 hypervariable regions on the Illumina MiSeq Platform. EMS abundance of INO, compared with the CON, had increased on day 2 of TRT. Peak abundance of C. butyricum sp. nov. (1.4%) and P. kudriavzevii sp. nov. (5%) occurred at day 19 in INO. A tendency for a higher milk fat percentage for INO vs. CON group was observed (P=0.0991). A treatment by week interaction was observed for milk yield (P=0.0025), fat-corrected milk (FCM, P=0.0026), energy-corrected milk (ECM, P=0.0019), protein yield (PY, P=0.0012), fat yield (FY, P=0.0880), feed efficiency (FE, P=0.0671) and rumen pH (P=0.0741). Results indicate that under the conditions of this study, EMS containing Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov., have a positive effect on cow performance.
Effects of Two Endomicrobial Supplement Combinations on Holstein Heifers Milk Composition and Yield
This study evaluated the response to 2 ruminally-injected endomicrobial supplement (EMS1 and EMS2) combinations on milk composition and yield in lactating Holstein cows. The 38-d study (7 d baseline, 28 d treatment, and 10 d post-treatment period) involved 24 Holstein cows randomly allocated to 3 treatments. Animals were fed a common TMR (16.3% CP, 37.3% NDF, 0.67 Mcal of NEI/Ib). Throughout the treatment period, the EMS and control treatments were directly administered to the rumen via daily injection behind the last rib in the paralumbar fossa during morning feedings. Group 1 (G1) received EMS1 containing Clostridium butyricum sp. nov. and Pichia kudriavzevii sp. nov. injected at a total of 1×109 and 1×109 cells/d; Group 2 (G2) received EMS2 containing C. butyricum sp. nov., P. kudriavzevii sp. nov., and Ruminococcus spp sp. nov. injected at a total of 1×109, 1×109, and 1×108 cells/d; and Group 3 (G3) the control, received a basal medium suspension. Cows were milked twice daily, and milk production measurements were collected daily. Rumen tube samplings of each cow were collected on d 1, 8, 16, 24, 28, 35, and 38 to determine colonization patterns of the administered microbes via Illumina sequencing of the ITS and 16S rRNA V1-V3 hypervariable regions. All statistical comparisons of treatment main effect and two-way interactions with treatment main effect were performed at the 0.10 level of significance using the R package “nlme” and lme function for linear mixed models. Treatment by week interactions were observed to be significantly different for milk production (G2 vs. G3*wk2, P=0.0185; G2 vs. G3*wk3, P=0.0754), milk protein yield (G1 vs. G2*wk2, P=0.0302), energy-corrected milk yield (G1 vs. G3*wk2, P=0.0942; G2 vs. G3*wk2, P=0.0303), and milk protein % (G1 vs. G2*wk5+2d, P=0.0001; G1 vs. G3*wk5+2d, P=0.0009). Sequencing results were integrated with rumen content cell count data, performed using a fluorescent-activated cell sorter Sony SH800 Cell Sorter, and colonization of EMS1 and EMS2 were confirmed. These data indicate that either effective combination of EMS containing these ruminally-associated microorganisms have a positive effect on milk production and performance of Holstein cows.
Towards the Compositional Prediction of the Ruminal Microbial Community Using Temporal Modeling in Healthy and Milk Depressed States.
Sixteen ruminally cannulated cows, 8 Holsteins and 8 Jerseys, were used in a milk fat depression (MFD) model to characterize the temporal changes of rumen bacterial populations in cows shifting between a healthy, MFD, and recovery state. The experiment consisted of a 10-d covariate period (Cov) followed by a 10-d MFD induction (Ind), and an 18-d MFD recovery (Rec). Animals were fed a common TMR (16.3% CP, 37.3% NDF, 0.67 Mcal of NEI/Ib) during the Cov and Rec. During the Ind, animals were fed a low-fiber, high-starch diet that caused a 0.6% and 1.5% mean decrease in milk fat in Jersey and Holstein cows, respectively. All animals were milked and fed twice a day in addition to daily rumen sampling. Bacterial populations were characterized via 16S rRNA gene amplicon sequencing of rumen samples. MFD induced substantial transformations in the rumen bacterial populations (Cov vs. Ind vs. Rec, P=0.001) and increased alpha diversity during Ind (P<0.01). The resulting operational taxonomic unit (OTU) table was centered-log ratio (clr) transformed and bi-clustered to reveal two unsupervised naturally underlying group fluctuations amplified during MFD induction. Of the 2 groupings, 4 of the most universally fluctuating bacterial classes showed significant linear correlation between abundance and milk fat percentage during Ind. The 4 classes were Fibrobacterales (group 1, R2=0.64, P=0.0072), Clostridiales (group 1, R2=0.57, P=0.022), Bacteroidales (group 2, R2=−0.66, P=0.0056), and Selemonadales (group 2, R2=−0.16, P=0.55). The 2 groups' respective combined abundance plotted over time revealed an oscillatory nature and fit well to generative Lotka-Volterra models. The dynamics of the resulting model exhibited stable oscillatory behaviors (λ=−0.44, 0.44) with a cyclic periodicity of 6 days. Ordinary least squares regression on compositional balances were applied to the dataset and results indicate that the composition of microbial communities can be accurately predicted (R=0.81 MSE=4.0) from daily environmental and milk composition data.
Genome Sequencing of Native Rumen Microorganisms from Holstein Cows Reveals Diverse Range of Functional Capabilities.
Traditionally, 16S data have been used to profile ruminal microbial communities and functionality has been inferred based on broad level taxonomic classifications. However, the accuracy of taxonomic calls is often lacking due to the poor resolution from decreased discrimination and phylogenetic power at species and genus level. The study objective was to profile the metabolic capabilities of 20 native rumen microorganisms via in-depth analysis of their genomes coupled with metabolic modeling and flux balance analysis (FBA). For this study, 16 novel rumen bacteria and 4 novel rumen fungi from a variety of taxa were isolated from the rumen content of healthy, lactating Holsteins. Strains were whole genome sequenced (WGS) using Illumina Miseq and Oxford Nanopore sequencing platforms. Reads were assembled, annotated, and analyzed using metabolic modeling. Subsequent analysis revealed the pivotal roles that these 20 microorganisms contribute to feed digestibility and milk production. A great deal of diversity was identified in functional pathways between members of the same family or genera. For instance, each of the 8 isolates sequenced from the family Lachnospiraceae possessed a unique spectrum of genes associated with biohydrogenation and acetate production, which are commonly associated functions of Lachnospiraceae in the rumen. The family Lachnospiraceae includes the genus Butyrivibrio, which are identified for xylan degradation and butyrate production in the rumen. Two isolates sequenced from the genus Butyrivibrio displayed distinct metabolic profiles, particularly with respect to amino acid metabolism, antibiotic production, and carbon source utilization. Additionally, 3 fungi sequenced were from the family Neocallimastigaceae which are known for their cellulolytic capabilities. These Neocallimastigaceae isolates had unique polysaccharide metabolisms and docking mechanisms, suggesting that each fungal species may employ unique mechanisms to drive cellulolytic activity.
Effect of Bacillus sp. nov Endomicrobial Supplement on Growth Performance and Lesion Score of Broilers Challenged with Clostridium perfringens
As the poultry industry moves towards antibiotic free systems, Clostridium perfringens induced necrotic enteritis (NE) requires alternative solutions to decrease NE mortality in broilers and increase overall performance. The following provides a summary of an embodiment of the disclosure including data from an experiment to evaluate C. perfringens-induced necrotic enteritis (NE), growth performance, feed conversion, and mortality of broilers that were administered an endomicrobial supplement (EMS) discovered and developed via the disclosed platform, the EMS comprised of a native Bacillus sp. nov isolated from the small intestine of a healthy chicken. In the study, all treatment diets were a standard formula representative of commercial broiler diet and feed were provided ad libitum. Feed conversion ratio (FCR), body weight gain (BWG), necrotic enteritis (NE) lesion scores, and mortality were evaluated at d 0-17, d 0-28, d 0-35, d 0-42, d 17-28, d 17-35, d 28-35, and d 35-42
Experimental Design:
As illustrated in
Strain Characterization:
the whole genome of Bacillus sp. nov. was processed, sequenced and annotated. When compared to conventional Bacillus subtilis strains (see
Performance Data:
There was a consistently significant increase in feed conversion ratio (FCR) in treatment group 7 at all measured time intervals (
Clostridium Challenge:
At 21 days, group 7 had the lowest mean lesion score of any treatment group with a mean of 1.31 (
As illustrated above, Bacillus sp. nov. appears to have an effect on host physiology, leading to increased feed conversion efficiency, indicating this strain could be an effective growth promoter and antibiotic replacement. This is further corroborated by the observed increase in pen gain with increasing concentration of the EMS. Furthermore, Bacillus sp. nov can provide enhanced protection against Clostridium infection, which was reflected in the reduction in mortality in treatment 6 and 7.
Effect of Clostridium sp. nov. and Lactobacillus sp. nov. on Broilers Challenged with Clostridium perfringens
Clostridium perfringens-induced necrotic enteritis (NE) is of great economic importance to the poultry industry due to its effects on growth performance and mortality. As discussed above, teachings of the disclosure provide insight into the broiler microbiome and the relationship it has with gut health and NE infections. The disclosed discovery platform was used to survey the gastrointestinal (GI) microbiome of >6,000 broiler chickens to identify native microbes that are beneficial to broilers during a C. perfringens infection. These microbes were then isolated from the GI content of healthy birds. Three strains were selected for further testing in vivo. The efficacy of the identified strains during C. perfringens induced necrotic enteritis is illustrated by the results discussed below.
The study objective was to evaluate growth performance, feed conversion, and mortality in broilers during a Clostridium perfringens-induced necrotic enteritis challenge when supplemented with an endomicrobial supplement (EMS) consisting of native bacteria isolated from the chicken gut microbiome.
Sample Collection:
Feed conversion ratio (FCR), Body weight gain (BWG), necrotic enteritis (NE) lesion scores, and mortality were evaluated. NE lesion scores were evaluated on days 21 and 28. On days 16, 21 and 42, 2 birds from each TRT were removed, weighed, and euthanized for collection of ileal and cecal contents for general microbiome analysis via sequencing the 16S rRNA V1-V3 hypervariable regions on the Illumina MiSeq Platform.
Statistical Analysis:
Performance data between groups were analyzed and compared using one-way ANOVA.
Microbiome Analysis
Alpha Diversity (Species Diversity within Samples):
Prior studies have indicated that animals whose GI microbiome exhibits lower alpha diversity tend to be more efficient. This study investigated alpha diversity and its relationship to lesion scores—see
Beta Diversity (Difference in Diversity Between Samples):
As illustrated in
On day 21 TRT 4, 5, and 8 had lower mean NE lesion scores compared to TRT 2 (NE lesion score<1.0 vs. score>1.5); on day 28, TRT 4, 6, 8, and 9 had significantly reduced NE lesion scores. Although the mean NE lesion scores for TRT 7 and 10 was not significant on day 28, there was a trend of lowered lesion scores (score<1.0) (
As illustrated, this study demonstrates a daily-administered endomicrobial supplement containing combinations of two native Clostridium and one native Lactobacillus can help prevent the development of necrotic enteritis in birds challenged with C. perfringens
All transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” “composed of,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of” and “consisting essentially of” shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03.
This application claims the benefit of U.S. Provisional App. No. 62/633,362, filed Feb. 21, 2018; This application is also a continuation-in-part of PCT App. No. PCT/US2017/068740, filed Dec. 28, 2017, which in turn claims priority to and benefit of U.S. Provisional Patent Application No. 62/439,800, filed on Dec. 28, 2016, and also claims priority to and benefit of U.S. Provisional Patent Application No. 62/560,174, filed on Sep. 18, 2017; This application is also a continuation-in-part of PCT App. No. PCT/US2017/068753, filed Dec. 28, 2017, and which claims the benefit of U.S. Provisional Patent Application No. 62/439,804, filed on Dec. 28, 2016, and also claims priority the benefit of U.S. Provisional Patent Application No. 62/560,174, filed on Sep. 18, 2017; This application is also a continuation-in-part of PCT App. No. PCT/US2018/056563, filed Oct. 18, 2018, which in turn claims the benefit of U.S. Provisional Application No. 62/574,031, filed on Oct. 18, 2017; This application is also a continuation-in-part of U.S. patent application Ser. No. 16/042,369, filed Jul. 23, 2018, which claims the benefit of U.S. Provisional Application No. 62/574,031, filed on Oct. 18, 2017, and is also a continuation-in-part of PCT App. No. PCT/US2017/028015, filed on Apr. 17, 2017, which itself claims the benefit of and priority to U.S. Provisional Application No. 62/323,305, filed on Apr. 15, 2016, U.S. Provisional Application No. 62/335,559, filed on May 12, 2016, and U.S. Provisional Application No. 62/425,480, filed on Nov. 22, 2016; This application is also a continuation-in-part of U.S. patent application Ser. No. 16/093,923, filed Oct. 15, 2018, which is the national stage of PCT App. No. PCT/US2017/028015, filed on Apr. 17, 2017, which itself claims the benefit of and priority to U.S. Provisional Application No. 62/323,305, filed on Apr. 15, 2016, U.S. Provisional Application No. 62/335,559, filed on May 12, 2016, and U.S. Provisional Application No. 62/425,480, filed on Nov. 22, 2016; This application is also a continuation-in-part of U.S. patent application Ser. No. 16/029,398, filed Jul. 6, 2018, which is a continuation of PCT App. No. PCT/US2017/012573, filed on Jan. 6, 2017, which itself claims the benefit of U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016, U.S. Provisional Application No. 62/276,531, filed Jan. 8, 2016, U.S. Provisional Application No. 62/334,816, filed May 11, 2016, and U.S. Provisional Application No. 62/415,908, filed Nov. 1, 2016; This application is also a continuation-in-part of U.S. patent application Ser. No. 15/948,965, filed Apr. 9, 2018, which is a continuation of U.S. patent application Ser. No. 15/791,391, filed Oct. 23, 2017, which in turn is: (I) a continuation-in-part of International PCT Application No. PCT/US16/39221, filed Jun. 24, 2016, which in turn claims the benefit of: U.S. Provisional Application No. 62/184,650, filed Jun. 25, 2015, and U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016; (II) a continuation-in-part of U.S. patent application Ser. No. 15/349,829, filed on Nov. 11, 2016, which is a continuation of U.S. patent application Ser. No. 15/217,575, filed Jul. 22, 2016, issued as U.S. Pat. No. 9,540,676, which claims the benefit of U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016, and is a continuation of International PCT Application No. PCT/US16/39221, filed Jun. 24, 2016, which in turn claims the benefit of U.S. Provisional Application No. 62/184,650, filed Jun. 25, 2015, and U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016; (III) a continuation-in-part of International PCT Application No. PCT/US17/12573, filed on Jan. 6, 2017, which in turn claims the benefit of: U.S. Provisional Application No. 62/415,908, filed on Nov. 1, 2016, U.S. Provisional Application No. 62/334,816, filed on May 11, 2016, U.S. Provisional Application No. 62/276,531, filed on Jan. 8, 2016, and U.S. Provisional Application No. 62/276,142, filed on Jan. 7, 2016; (IV) claims the benefit of: U.S. Provisional Application No. 62/560,174, filed Sep. 18, 2017, and U.S. Provisional Application No. 62/415,908, filed on Nov. 1, 2016; and (V) a continuation-in-part of U.S. patent application Ser. No. 15/392,913, filed Dec. 28, 2016, now pending, which is: (i) a continuation-in-part of International PCT Application No. PCT/US16/39221, filed Jun. 24, 2016, which in turn claims the benefit of: U.S. Provisional Application No. 62/184,650, filed Jun. 25, 2015, and U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016; (ii) a continuation-in-part of U.S. patent application Ser. No. 15/349,829, filed on Nov. 11, 2016, which is a continuation of U.S. patent application Ser. No. 15/217,575, filed Jul. 22, 2016, issued as U.S. Pat. No. 9,540,676, which claims the benefit of U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016, and which is a continuation of International PCT Application No. PCT/US16/39221, filed Jun. 24, 2016, which in turn claims the benefit of U.S. Provisional Application No. 62/184,650, filed Jun. 25, 2015, and U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016; (iii) a continuation-in-part of U.S. patent application Ser. No. 15/217,575, filed Jul. 22, 2016, issued as U.S. Pat. No. 9,540,676, which claims the benefit of U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016, and which is a continuation of International PCT Application No. PCT/US16/39221, filed Jun. 24, 2016, which in turn claims the benefit of U.S. Provisional Application No. 62/184,650, filed Jun. 25, 2015, and U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016; and (iv) claims the benefit of U.S. Provisional Application No. 62/276,142, filed Jan. 7, 2016; U.S. patent application Ser. No. 15/791,391 also claims priority to and benefit of U.S. Provisional Application No. 62/560,174, filed Sep. 18, 2017; the entirety of each and every one of the aforementioned applications are herein expressly incorporated by reference in their entireties for all purposes.
| Number | Date | Country | |
|---|---|---|---|
| 62633362 | Feb 2018 | US | |
| 62439800 | Dec 2016 | US | |
| 62560174 | Sep 2017 | US | |
| 62439804 | Dec 2016 | US | |
| 62560174 | Sep 2017 | US | |
| 62574031 | Oct 2017 | US | |
| 62574031 | Oct 2017 | US | |
| 62323305 | Apr 2016 | US | |
| 62335559 | May 2016 | US | |
| 62425480 | Nov 2016 | US | |
| 62323305 | Apr 2016 | US | |
| 62335559 | May 2016 | US | |
| 62425480 | Nov 2016 | US | |
| 62276142 | Jan 2016 | US | |
| 62276531 | Jan 2016 | US | |
| 62334816 | May 2016 | US | |
| 62415908 | Nov 2016 | US | |
| 62184650 | Jun 2015 | US | |
| 62276142 | Jan 2016 | US | |
| 62276142 | Jan 2016 | US | |
| 62184650 | Jun 2015 | US | |
| 62276142 | Jan 2016 | US | |
| 62415908 | Nov 2016 | US | |
| 62334816 | May 2016 | US | |
| 62276531 | Jan 2016 | US | |
| 62276142 | Jan 2016 | US | |
| 62560174 | Sep 2017 | US | |
| 62415908 | Nov 2016 | US | |
| 62184650 | Jun 2015 | US | |
| 62276142 | Jan 2016 | US | |
| 62276142 | Jan 2016 | US | |
| 62184650 | Jun 2015 | US | |
| 62276142 | Jan 2016 | US | |
| 62276142 | Jan 2016 | US | |
| 62184650 | Jun 2015 | US | |
| 62276142 | Jan 2016 | US | |
| 62276142 | Jan 2016 | US | |
| 62560174 | Sep 2017 | US |
| Number | Date | Country | |
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| Parent | PCT/US2017/012573 | Jan 2017 | US |
| Child | 16029398 | US | |
| Parent | 15791391 | Oct 2017 | US |
| Child | 15948965 | US | |
| Parent | 15217575 | Jul 2016 | US |
| Child | 15349829 | US | |
| Parent | PCT/US2016/039221 | Jun 2016 | US |
| Child | 15217575 | US | |
| Parent | 15217575 | Jul 2016 | US |
| Child | 15349829 | US | |
| Parent | PCT/US2016/039221 | Jun 2016 | US |
| Child | 15217575 | US | |
| Parent | PCT/US2016/039221 | Jun 2016 | US |
| Child | 15217575 | US |
| Number | Date | Country | |
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| Child | 16282266 | US | |
| Parent | PCT/US2017/068740 | Dec 2017 | US |
| Child | 15948965 | US | |
| Parent | PCT/US2017/068753 | Dec 2017 | US |
| Child | PCT/US2017/068740 | US | |
| Parent | PCT/US2018/056583 | Oct 2018 | US |
| Child | PCT/US2017/068753 | US | |
| Parent | 16042369 | Jul 2018 | US |
| Child | PCT/US2018/056583 | US | |
| Parent | PCT/US2017/028015 | Apr 2017 | US |
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| Child | PCT/US2017/028015 | US | |
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| Parent | PCT/US2016/039221 | Jun 2016 | US |
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| Parent | PCT/US2017/012573 | Jan 2017 | US |
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