Creation of functional microbial consortia

Abstract

Provided herein is technology relating to identifying and isolating microorganisms having targeted functions, particularly, but not limited to, methods, compositions and systems for screening and / or selecting individual microorganisms or microbial consortia that provide a specified function.

Description

[Technical field]

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 122,889, filed December 8, 2020, which is incorporated by reference in its entirety.

[0002] Provided herein is technology relating to identifying and isolating microorganisms having targeted functions, particularly, but not limited to, methods, compositions and systems for screening and / or selecting individual microorganisms or microbial consortia that provide a specified function. [Background technology]

[0003] Various flora and / or fauna may exist and interact in a local self-sustaining ecosystem called a biome. The activity of a biome, including the flora and / or fauna community, may affect the local environment or ecosystem. In certain environments, the flora and / or fauna may include microorganisms. Although small in physical size, the activity of microorganisms in an environment may have a significant effect. For example, yeast operating in a sugar situation in a closed environment may produce alcohol to the point where no more alcohol can be produced in the closed system. This may occur through sugar depletion or through the amount of alcohol preventing further alcohol production. On a larger scale, the activity of the flora and / or fauna in a biome may produce global effects that go beyond the local ecosystem. For example, Rothschild and Mancinelli, among others, have hypothesized that microbial mats and stromatolites contributed to CO2 fixation, greatly lowering the amount of CO2 in the Earth's atmosphere during the Cambrian period.

[0004] It is desirable to explore not only the activity of a single fauna or flora, but also the activity of fauna and / or flora in concert with each other to optimize their impact on environmental variables. However, conventional techniques for identifying and / or isolating microbial organisms focus on the specific phenotype of an individual isolated microorganism, and most conventional techniques are inefficient and slow. Therefore, there is a need to screen microbial consortia to optimize variables related to their impact on the environment or ecosystem. Summary of the Invention [Means for solving the problem]

[0005] The term "biomining" refers to the search for organisms that meet a given criteria, for example using methods that include screening and / or selection of organisms. In this context, the term "biomining" as used herein should not be confused with the use of the term in other fields that describe the use of organisms to extract metals. The traditional method of biomining is a process that starts with a set of known organisms, e.g., microorganisms, that have known desirable properties. A new set of microorganisms is identified when the new microorganisms have similarities to the known organisms, e.g., when the microorganisms have a similar phenotype to the first set of known microorganisms. The new set is then tested for a specific application. For example, in agriculture, the target species may be a legume that is used for a cover crop, and the application is the fixation of nitrogen in the stems of the legumes.

[0006] In contrast, the technology provided herein relates to "application-specific biomining" in which the biomining process is reversed from conventional biomining described above. In particular, instead of starting with a group of known microorganisms as in conventional biomining, application-specific biomining described herein identifies targets (e.g., species, environments, ecosystems, etc.) to which microorganisms should be applied, for example to functionally modify (e.g., improve) the target. The targets are then tested against a group of microbial populations that includes a large number of available microorganisms that may be known or unknown. In some examples, the group of microbial populations may undergo minimal pre-filtering or pre-selection prior to testing. Unless the context clearly indicates that the term "biomining" refers to conventional biomining, the term "biomining" as used herein refers to "application-specific biomining" as described above and herein.

[0007] For example, in various embodiments, a group of microbial populations from the entire group of microbial populations is cultured and applied to the target species for testing. The group of microbial populations applied to the test may include a portion of the entire microbial population. If one or more tests on the group of microbial populations show a trend toward a desired outcome for one or more variables being tested, the group of microbial populations is selected and subcultured, thereby focusing on growing the microbial populations that are most likely to cause the desired outcome. This process can be repeated until the desired causative organism is identified and isolated.

[0008] There are many advantages to application-centric biomining. First, traditional biomining begins with known microorganisms, and new microorganisms are added for analysis based on noticed "similarity" rather than systematic testing manipulation. In many cases, an individual microorganism may not cause a substantial desired effect on a target, but rather a group containing two or more microorganisms acting in concert, known as a "microbial consortium," causes the desired effect. Thus, by starting with a preselected group of microorganisms, researchers using traditional biomining may inadvertently exclude microorganisms that provide the desired effect in concert with other microorganisms.

[0009] In contrast, the application-centric biomining embodiments provided herein focus on the application and / or functional outcome to be achieved, e.g., the desired effect measured by observing the variable(s) being tested. Thus, the potentially flawed assumption that microorganisms with similar phenotypes cause similar desired outcomes is reduced or eliminated. Another advantage is that in application-specific biomining, the screening process can be much faster and more efficient. For example, in one comparative study, the amount of time required to discover a desired microbial consortium using application-specific biomining was cut in half (e.g., to 1 / 16th of the man-hours) using 1 / 8th of the personnel, with a corresponding reduction in costs compared to traditional biomining.

[0010] Thus, provided herein are embodiments of methods for microbial biomining, including obtaining a plurality of environmental samples including organic matter, mixing the plurality of environmental samples into a combination of mixed environmental samples, selecting a particular mixed environmental sample of the mixed environmental samples based on one or more selection criteria for testing, culturing the selected particular mixed environmental sample in an environment including one or more environmental conditions, and obtaining specific information for microorganisms present in a corresponding microbial consortium of the particular mixed environmental sample in response to determining based on one or more variable measurements obtained from the culturing that the particular mixed environmental sample produced a successful microbial biomining result. In some embodiments, the method further includes selecting an additional mixed environmental sample based on one or more selection criteria for testing in response to determining based on one or more variable measurements obtained from the culturing that the particular mixed environmental sample produced an unsuccessful microbial biomining result. In some embodiments, the method further includes obtaining additional identification information for additional microorganisms present in an additional corresponding microbial consortium of the additional mixed environmental sample in response to selecting an additional mixed environmental sample of the mixed environmental sample based on one or more selection criteria for testing, culturing the selected additional mixed environmental sample in an environment comprising one or more environmental conditions, and determining based on one or more variable measurements obtained from the culturing that the additional mixed environmental sample yielded an additional successful microbial biomining result.In some embodiments, the method further comprises culturing a corresponding microbial consortium of a particular mixed environmental sample into a microbial culture, growing a selected culture portion of the microbial culture in an environment comprising one or more environmental conditions, and obtaining additional identification information for additional microorganisms present in the additional corresponding microbial consortium of the selected culture portion in response to determining based on one or more variable measurements of the selected culture portion that the selected culture portion produced a successful microbial biomining result. In some embodiments, the method further comprises selecting an additional culture portion of the microbial culture for testing in response to determining based on one or more variable measurements of the selected culture portion that the culture portion produced an unsuccessful microbial biomining result. In some embodiments, the method further comprises growing an additional selected culture portion of the microbial culture in an environment comprising one or more environmental conditions, and obtaining additional identification information for additional microorganisms present in the additional corresponding microbial consortium of the additional selected culture portion in response to determining based on one or more variable measurements of the additional selected culture portion that the selected culture portion produced a successful microbial biomining result.

[0011] In some embodiments, the method further comprises generating a machine learning model based on the training data including the identifying information and the additional identifying information. In some embodiments, the machine learning model at least correlates one or more environmental sample variable values ​​of a plurality of environmental samples with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the plurality of environmental samples. In some embodiments, the method further comprises receiving a request for information regarding one or more variable values, and applying the machine learning model to the one or more variable values ​​to at least one of identify one or more microbial species associated with the one or more variable values, identify one or more environmental characteristics associated with the one or more variable values, or identify at least one microbial consortium associated with the one or more variable values. In some embodiments, the one or more variable values ​​may include at least one of a microbial phenotype, a desired amount of nitrogen fixation, a desired amount of carbon sequestration, one or more environmental sample characteristics, or one or more variables (e.g., one or more climate change variables). In some embodiments, the one or more environmental characteristics include an environmental source location and an environmental composition. In some embodiments, the machine learning model further correlates one or more of the environmental sample variable values ​​and the microbial variable values ​​with one or more variables (e.g., climate change variable values), including at least the absolute amount of CO sequestered by the biomass, the ratio of biomass to sequestered CO , the amount of time CO sequestered by the biomass, the absolute amount of nitrogen fixed by the biomass, the ratio of the mass of the biomass to the absolute amount of nitrogen fixed, the total benefit from CO sequestering, the ratio of the mass of food produced to the mass of CO sequestered by the biomass, or the amount of time CO sequestered by the biomass. In some embodiments, the one or more environmental conditions include at least one of a specific concentration of N gas, a specific concentration of CO gas, the availability of one or more specific nutrients, the availability of one or more specific salts, or the availability of one or more specific additives.In some embodiments, the one or more variable measurements include a variable measurement indicative of having a microorganism capable of increasing carbon sequestration, increasing nitrogen fixation, increasing biomass, or meeting a particular survival time. In some embodiments, a successful microbial biomining result is provided when each variable measurement of the one or more variable measurements at least meets a corresponding variable measurement threshold. In some embodiments, the microbial identification information includes a DNA biomarker for the microorganism.

[0012] In some embodiments, the present technology provides one or more non-transitory computer-readable media that store computer-executable instructions that, when executed, cause one or more processors to perform operations including: generating a machine learning model that at least correlates one or more environmental sample variable values ​​of a plurality of environmental samples with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the plurality of environmental samples based on training data including identification information of one or more microorganisms; receiving a request for information on one or more variable values; applying the machine learning model to the one or more variable values ​​to at least one of identify one or more microbial species associated with the one or more variable values, identify one or more environmental properties associated with the one or more variable values, or identify at least one microbial consortium associated with the one or more variable values. In some embodiments, the one or more variable values ​​may include at least one of a phenotype of a microorganism, a desired amount of nitrogen fixation, a desired amount of carbon sequestration, one or more environmental sample properties, or one or more variables (e.g., climate change variables). In some embodiments, the machine learning model further correlates one or more of the environmental sample variable values ​​and the microbial variable values ​​with one or more variable values ​​(e.g., climate change variable values) including at least the absolute amount of CO sequestered by the biomass, the ratio of biomass to sequestered CO, the amount of time CO is sequestered by the biomass, the absolute amount of nitrogen fixation by the biomass, the ratio of the mass of the biomass to the absolute amount of nitrogen fixed, the total benefit from CO sequestration, the ratio of the mass of food produced to the mass of CO sequestered by the biomass, or the amount of time CO is sequestered by the biomass.

[0013] In some embodiments, the technology provides a computing device comprising one or more processors and a memory including a plurality of computer-executable components executable by the one or more processors to perform a plurality of operations, the plurality of operations including: generating a machine learning model that at least correlates one or more environmental sample variable values ​​of a plurality of environmental samples with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the plurality of environmental samples based on training data including identification information of one or more microorganisms; receiving a request for information regarding one or more variable values; and applying the machine learning model to the one or more variable values ​​to at least one of identify one or more microbial species associated with the one or more variable values, identify one or more environmental characteristics associated with the one or more variable values, or identify at least one microbial consortium associated with the one or more variable values.

[0014] In some embodiments, the present technology provides a method for microbial biomining, comprising obtaining an environmental sample comprising organic matter, homogenizing the environmental sample to generate an input sample, culturing the input sample in an environment comprising one or more environmental conditions, and obtaining specific information for microorganisms present in the corresponding microbial consortium of the input sample in response to determining based on one or more variable measurements obtained from the culturing that the input sample produced a successful microbial biomining result. In some embodiments, the method comprises obtaining a plurality of environmental samples comprising organic matter for microbial biomining, homogenizing each environmental sample to generate a plurality of input samples, and selecting an input sample from the plurality of input samples. In some embodiments, the method further comprises generating a second input sample based on one or more selection criteria for testing in response to determining based on one or more variable measurements obtained from the culturing that the input sample produced an unsuccessful microbial biomining result.

[0015] In some embodiments, the method further comprises: generating a second input sample based on one or more selection criteria for testing; culturing the selected second input sample in an environment comprising one or more environmental conditions; and obtaining additional identification information for additional microorganisms present in a second corresponding microbial consortium of the second input sample in response to determining based on one or more variable measurements obtained from the culturing that the second input sample yielded an additional successful microbial biomining result. In some embodiments, the method further comprises culturing the corresponding microbial consortium of the input sample into a microbial culture; growing a selected culture portion of the microbial culture in an environment comprising one or more environmental conditions; and obtaining additional identification information for additional microorganisms present in the additional corresponding microbial consortium of the selected culture portion in response to determining based on one or more variable measurements of the selected culture portion that the selected culture portion yielded a successful microbial biomining result. In some embodiments, the method further comprises selecting an additional culture portion of the microbial culture for testing in response to determining based on one or more variable measurements of the selected culture portion that the culture portion yielded an unsuccessful microbial biomining result. In some embodiments, the method further includes growing an additional selected culture portion of the microbial culture in an environment comprising one or more environmental conditions, and obtaining further identifying information for additional microorganisms present in a further corresponding microbial consortium of the additional selected culture portion in response to determining based on one or more variable measurements of the additional selected culture portion that the selected culture portion yielded a successful microbial biomining result.

[0016] In some embodiments, the method further comprises generating a machine learning model based on the training data including the identifying information and the additional identifying information. In some embodiments, the machine learning model at least correlates one or more environmental sample variable values ​​of the environmental sample with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the environmental sample. In some embodiments, the method further comprises receiving a request for information about one or more variable values, and applying the machine learning model to the one or more variable values ​​to at least one of identify one or more microbial species associated with the one or more variable values, identify one or more environmental characteristics associated with the one or more variable values, and / or identify at least one microbial consortium associated with the one or more variable values. In some embodiments, the one or more variable values ​​may include at least one of a microbial phenotype, a desired amount of nitrogen fixation, a desired amount of carbon sequestration, one or more environmental sample characteristics, and / or one or more variables. In some embodiments, the one or more environmental characteristics include an environmental source location and an environmental composition. In some embodiments, the machine learning model further correlates one or more of the environmental sample variable values ​​and the microbial variable values ​​with one or more variable values, the one or more variable values ​​including at least an absolute amount of CO sequestered by the biomass, a ratio of biomass to sequestered CO, an amount of time CO is sequestered by the biomass, an absolute amount of nitrogen fixation by the biomass, a ratio of the mass of the biomass to the absolute amount of nitrogen fixed, a total benefit from CO sequestration, a ratio of the mass of food produced to the mass of CO sequestered by the biomass, or an amount of time CO is sequestered by the biomass. In some embodiments, the one or more environmental conditions include at least one of a specific concentration of N gas, a specific concentration of CO gas, the availability of one or more specific nutrients, the availability of one or more specific salts, or the availability of one or more specific additives. In some embodiments, the one or more variable measurements include a variable measurement indicative of having a microorganism capable of increasing carbon sequestration, increasing nitrogen fixation, increasing biomass, or meeting a specific survival time.In some embodiments, a successful microbial biomining result is provided when each variable measure of one or more variable measures at least meets a corresponding variable measure threshold. In some embodiments, the microbial identification information includes DNA biomarkers for said microbial organism.

[0017] In some embodiments, the technology provides one or more non-transitory computer-readable media that store computer-executable instructions that, when executed, cause one or more processors to perform operations including: generating a machine learning model that at least correlates one or more environmental sample variable values ​​of an environmental sample with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the environmental sample based on training data including identification information of one or more microorganisms; receiving a request for information on one or more variable values; applying the machine learning model to the one or more variable values ​​to at least one of identify one or more microbial species associated with the one or more variable values; identify one or more environmental properties associated with the one or more variable values; and / or identify at least one microbial consortium associated with the one or more variable values. In some embodiments, the one or more variable values ​​may include at least one of a phenotype of a microorganism, a desired amount of nitrogen fixation, a desired amount of carbon sequestration, one or more environmental sample properties, or one or more variables. In some embodiments, the machine learning model further correlates one or more of the environmental sample variable values ​​and the microbial variable values ​​with one or more variable values, including at least the absolute amount of CO sequestered by the biomass, the ratio of biomass to sequestered CO, the amount of time CO is sequestered by the biomass, the absolute amount of nitrogen fixation by the biomass, the ratio of the mass of the biomass to the absolute amount of nitrogen fixed, the total benefit from CO sequestration, the ratio of the mass of food produced to the mass of CO sequestered by the biomass, or the amount of time CO is sequestered by the biomass.

[0018] In some embodiments, the technology provides a computing device comprising one or more processors and a memory including a plurality of computer-executable components executable by the one or more processors to perform a plurality of operations, the plurality of operations including: generating a machine learning model that at least correlates one or more environmental sample variable values ​​of an environmental sample with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the environmental sample based on training data including identification information of one or more microorganisms; receiving a request for information regarding one or more variable values; applying the machine learning model to the one or more variable values ​​to at least one of identify one or more microbial species associated with the one or more variable values; identify one or more environmental characteristics associated with the one or more variable values; and / or identify at least one microbial consortium associated with the one or more variable values.

[0019] Further, in some embodiments, the present technology provides a method for generating a microbial consortium that performs a specified function. For example, in some embodiments, the method includes providing a sample containing a plurality of microorganisms, inoculating a portion of the sample into a first volume of a growth medium to provide a first culture, growing the first culture under a set of selective conditions, generating a first taxonomic classification of the microorganisms in the first culture, inoculating a portion of the first culture into a second volume of the growth medium to provide a second culture, growing the second culture under the set of selective conditions, generating a second taxonomic classification of the microorganisms in the second culture, and using the second taxonomic classification and the first taxonomic classification to derive a measure of microbial community stability of the second culture relative to the first culture.

[0020] In some embodiments, the technology provides iterative and / or recursive methods in which steps are repeated until a measured property being monitored reaches a specified value and / or reaches a plateau. For example, in some embodiments, the technology provides a method for generating a microbial consortium that performs a specified function, comprising providing a sample comprising a plurality of microorganisms; inoculating an Nth volume of growth medium with a portion of the sample to provide an Nth culture; growing the Nth culture under a set of selective conditions; generating an Nth taxonomic classification of microorganisms in the Nth culture; inoculating an N+1th volume of the growth medium with a portion of the Nth culture; growing the Nth culture under the set of selective conditions; generating an Nth taxonomic classification of microorganisms in the Nth culture; repeating the steps of iteratively repeating the steps of: inoculating an N+1 volume of the growth medium with a portion of the N culture, with the N+1 culture functioning as the N culture until the measure of microbial community stability reaches a plateau value; growing the N+1 culture under the set of selective conditions; generating an N+1 taxonomic classification of microorganisms in the N+1 culture; and deriving a measure of microbial community stability of the N+1 culture relative to the N culture using the N+1 taxonomic classification and the N taxonomic classification; and providing a stable N+1 culture as comprising a microbial consortium that performs a specified function. In some embodiments, the sample is an environmental sample. In some embodiments, the environmental sample is a soil or water sample. In some embodiments, the growth medium and / or selective conditions select for the specified function. In some embodiments, generating a taxonomic classification comprises obtaining metagenomic nucleotide sequence data for a culture, and identifying taxonomic units present in the culture using analysis of the metagenomic nucleotide sequence data.In some embodiments, the microbial consortium comprises a number of taxonomic units that is at least 2, 3, 4, 5 or 6. In some embodiments, a microbial community having a number of taxonomic units that is less than the number of taxonomic units of the microbial consortium does not perform the designated function. In some embodiments, only any one of the taxonomic units does not perform the designated function. In some embodiments, the measure of microbial community stability comprises a measure of richness, diversity, abundance and / or membership. In some embodiments, growth occurs over an empirically determined time for growth to the end of the exponential phase. In some embodiments, the method further comprises measuring the growth rate of the Nth or N+1th culture. In some embodiments, the growth rate is determined by measuring cell mass as a function of time. In some embodiments, at least one of the taxonomic units does not grow as a pure culture in a culture medium under selective conditions. In some embodiments, a microbial community comprising a number of taxonomic units that is at least 2 and less than the number of taxonomic units of the microbial consortium does not grow in a culture medium under selective conditions.

[0021] Some portions of this specification describe embodiments of the technology in terms of algorithms and symbolic representations of operations on information. These algorithmic descriptions and representations are commonly used by those skilled in the data processing arts to effectively convey the substance of their work to others skilled in the art. While these operations are described functionally, computationally, or logically, it will be understood that they are implemented by computer programs or equivalent electrical circuits, microcode, or the like. Furthermore, it has proven convenient at times to represent these arrangements of operations as modules, without loss of generality. The described operations and their associated modules may be embodied in software, firmware, hardware, or any combination thereof.

[0022] Certain steps, operations or processes described herein may be executed or performed by one or more hardware or software modules, alone or in combination with other devices. In some embodiments, a software module is implemented by a computer program product that includes a computer-readable medium that includes computer program code that can be executed by a computer processor to perform any or all of the steps, operations or processes described.

[0023] In some embodiments, the system comprises computers and / or data storage provided virtually (e.g., as cloud computing resources). In certain embodiments, the technology includes the use of cloud computing to provide a virtual computer system that comprises components and / or performs computer functions as described herein. Thus, in some embodiments, cloud computing provides infrastructure, applications, and software as described herein over a network and / or via the Internet. In some embodiments, computing resources (e.g., data analysis, calculations, data storage, application programs, file storage, etc.) are provided remotely over a network (e.g., the Internet; and / or a cellular network).

[0024] Embodiments of the present technology may also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes and / or may comprise a general-purpose computing device selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a non-transitory, tangible computer-readable storage medium or any type of medium suitable for storing electronic instructions that may be coupled to a computer system bus. Furthermore, any computing system referred to herein may include a single processor or may be an architecture employing a multiple processor design for increased computing power.

[0025] Further embodiments will be apparent to those skilled in the art based on the teachings contained herein.

[0026] These and other features, aspects, and advantages of the present technology will become better understood with regard to the following drawings. [Brief description of the drawings]

[0027] [Figure 1] FIG. 1 illustrates an exemplary environment for application-centric microbial screening. [Diagram 2] FIG. 2 is a block diagram illustrating various components of one or more exemplary computing devices that support the use of machine learning techniques for application-centric microbial screening. [Figure 3a] FIG. 3a illustrates an exemplary process flow diagram for performing application-centric microbial screening. [Figure 3b] FIG. 3b illustrates an exemplary process flow diagram for performing application-centric microbial screening. [Figure 4] FIG. 4 is a flow diagram of an exemplary process for using machine learning techniques to identify microbial species and other information associated with one or more variables. [Diagram 5]FIG. 5 is a flow diagram of an exemplary process for generating a microbial consortium that performs a designated function. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] It should be understood that the drawings are not necessarily drawn to scale, and that objects within the drawings are not necessarily drawn to scale relative to each other. The drawings are representations intended to provide clarity and understanding to the various embodiments of the devices, systems and methods disclosed herein. Wherever possible, the same reference numbers are used throughout the drawings to refer to the same or similar parts. Furthermore, it should be understood that the drawings are not intended to limit the scope of the present teachings in any way.

[0029] Provided herein is technology relating to identifying and isolating microorganisms having targeted functions, particularly, but not limited to, methods, compositions and systems for screening and / or selecting individual microorganisms or microbial consortia that provide a specified function.

[0030] In this detailed description of various embodiments, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, those skilled in the art will appreciate that the various embodiments may be practiced with or without these specific details. In other instances, structures and devices are shown in block diagram form. Moreover, those skilled in the art will readily appreciate that the specific order in which the methods are presented and performed is illustrative, and it is contemplated that the order may be altered and still fall within the spirit and scope of the various embodiments disclosed herein.

[0031] All literature and similar materials cited in this application, including but not limited to patents, patent applications, papers, books, articles, and Internet web pages, are expressly incorporated by reference in their entirety for any purpose. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the various embodiments described herein belong. In the event that the definition of a term in an incorporated reference appears to differ from the definition provided in the present teachings, the definition provided in the present teachings shall prevail. The section headings used herein are for organizational purposes only and should not be construed as limiting the subject matter described in any way.

[0032] definition To facilitate the understanding of the present technology, several terms and phrases are defined below. Further definitions are set forth throughout the detailed description.

[0033] Throughout the specification and claims, the following terms adopt the meanings expressly associated therewith herein, unless the context clearly dictates otherwise. The phrase "in one embodiment" as used herein may refer to the same embodiment, but not necessarily to the same embodiment. Additionally, the phrase "in another embodiment" as used herein may refer to different embodiments, but not necessarily to different embodiments. Thus, as described below, various embodiments of the present invention can be readily combined without departing from the scope or spirit of the present invention.

[0034] Additionally, as used herein, the term "or" is an inclusive "or" operator and is equivalent to the term "and / or" unless the context clearly dictates otherwise. The term "based on" is not exclusive and allows for based on additional unrecited factors unless the context clearly dictates otherwise. Additionally, throughout this specification, the meanings of "a," "an," and "the" include plural references. The meaning of "in" includes "in" and "on."

[0035] As used herein, the terms "about," "approximately," "substantially," and "significantly" are understood by those of ordinary skill in the art and will vary to some extent with the context in which they are used. If there are uses of these terms that are not clear to persons of ordinary skill in the art given the context in which they are used, "about" and "approximately" mean plus or minus 10% or less of the term in question, and "substantially" and "significantly" mean plus or minus 10% or more of the term in question.

[0036] As used herein, the disclosure of a range includes the disclosure of all values ​​within the entire range and further divided ranges, including the endpoints and subranges given for that range.As used herein, the disclosure of a numerical range includes the endpoints and each intervening number therebetween with the same precision.For example, for the range of 6 to 9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range of 6.0 to 7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are expressly contemplated.

[0037] As used herein, the suffix "-free" refers to an embodiment of a technology that omits the feature of the root of the word to which it is added. That is, the term "X-free" as used herein means "without X", where X is the omitted feature of the technology in the "X-free" technology. For example, a "calcium-free" composition does not contain calcium, a "mixing-free" method does not include a mixing step, etc.

[0038] Terms such as "first", "second", "third" and the like may be used herein to describe various steps, elements, compositions, components, regions, layers and / or portions, but unless otherwise specified, these steps, elements, compositions, components, regions, layers and / or portions should not be limited by these terms. These terms are used to distinguish one step, element, composition, component, region, layer and / or portion from another step, element, composition, component, region, layer and / or portion. As used herein, terms such as "first", "second" and other numerical terms do not imply an order or sequence unless clearly indicated by the context. Thus, a first step, element, composition, component, region, layer or portion discussed herein can be referred to as a second step, element, composition, component, region, layer or portion without departing from the art.

[0039] As used herein, the terms "presence" or "absence" (or "present" or "absent") are used in a relative sense to describe the amount or level of a particular entity (e.g., a component, an action, an element). For example, when an entity is said to be "present", it means that the level or amount of this entity is above a predetermined threshold, and conversely, when an entity is said to be "absent", it means that the level or amount of this entity is below a predetermined threshold. The predetermined threshold may be a threshold of detectability or any other threshold associated with the particular test used to detect the entity. If the entity is "detected", the entity is "present", and if the entity is "not detected", the entity is "absent".

[0040] As used herein, "increase" or "decrease" refers to a detectable (e.g., measured) positive or negative change in the value of a variable relative to a previously measured value, relative to a pre-established value, and / or relative to a standard control value, respectively. An increase is a positive change, preferably of at least 10%, more preferably 50%, even more preferably 2-fold, even more preferably at least 5-fold, and most preferably at least 10-fold, relative to a previously measured value, a pre-established value, and / or relative to a standard control value of the variable. Similarly, a decrease is a negative change, preferably of at least 10%, more preferably 50%, even more preferably at least 80%, and most preferably at least 90%, relative to a previously measured value, a pre-established value, and / or relative to a standard control value of the variable. Other terms indicating quantitative changes or differences, such as "more" or "less," are used herein in the same manner as above.

[0041] As used herein, a "system" refers to a number of real and / or abstract components that work together for a common purpose. In some embodiments, a "system" is an integrated collection of hardware and / or software components. In some embodiments, each component of a system interacts with and / or is associated with one or more other components. In some embodiments, a system refers to a combination of components and software to control and direct a method. For example, a "system" or a "subsystem" may include one or more or any combination of the following: mechanical devices, hardware, hardware components, circuits, circuit configurations, logical designs, logical components, software, software modules, components of software or software modules, software procedures, software instructions, software routines, software objects, software functions, software classes, software programs, files containing software, etc., to perform the functions of the system or subsystem. Thus, the methods and apparatus of the embodiments, or certain aspects or portions thereof, may take the form of program code (e.g., instructions) embodied in a tangible medium, such as a floppy diskette, a CD-ROM, a hard drive, a flash memory, or any other machine-readable storage medium, which when loaded and executed by a machine, such as a computer, causes the machine to become an apparatus for implementing the embodiments. When executing the program code on a programmable computer, the computing device generally includes a processor, a storage medium readable by the processor (e.g., volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the embodiments, for example, through the use of application programming interfaces (APIs), reusable controls, or the like. Such programs are preferably implemented in a high-level procedural or object-oriented programming language to communicate with a computer system.However, the programs can be implemented in assembly or machine language, if desired. In any case, the language can be a compiled or interpreted language, and combined with hardware implementations.

[0042] Unless otherwise defined herein, scientific and technical terms used in connection with the present technology shall have the meanings commonly understood by those skilled in the art. Furthermore, unless otherwise required by context, singular terms shall include the plural and plural terms shall include the singular. In general, the nomenclature used in connection with and techniques of cell and tissue culture, molecular biology, immunology, microbiology, genetics, and protein and nucleic acid chemistry and hybridization described herein are those well known and commonly used in the art. The methods and techniques of the present technology are generally performed according to conventional methods well known in the art and as described in the various general and more specific references cited and discussed throughout this specification, unless otherwise indicated.See, e.g., Sambrook et al., Molecular Cloning: A Laboratory Manual, 2d ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (1989); Sambrook et al., Molecular Cloning: A Laboratory Manual, 3d ed., Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (2000); Ausubel et al., Current Protocols in Molecular Biology, Greene Publishing Associates (1992 and Supplements to 2000); Ausubel et al., Short Protocols in Molecular Biology: A Compendium of Methods from Current Protocols in Molecular Biology, 4th ed., Wiley & Sons (1999); Harlow and Lane, Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (1990); Harlow and Lane, Using Antibodies: A Laboratory Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (2000); Manual, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY (1998); and T. Kieser et al., Practical Streptomyces Genetics, John Innes Foundation, Norwich (2000).

[0043] As used herein, the term "culturable organism" refers to an organism that can be maintained and grown in a laboratory. In some embodiments, a culturable organism may not be maintained and grown in a laboratory in pure culture in the absence of other organisms, and thus may be referred to as a "non-culturable organism" with respect to growing in pure culture. However, in some embodiments, such an organism may be grown in a laboratory in a microbial consortium that includes at least one other organism, and thus may be a "culturable organism" with respect to the consortium, and also a "non-culturable organism" with respect to growing in pure culture without other members of the consortium.

[0044] As used herein, the term "selected environment", "condition" or "conditions" refers to any external characteristic in which a particular organism or microbial consortium of a microbial community grows more efficiently (e.g., faster, to a higher amount or concentration, with greater survival rate, etc.) than one or more other organisms or consortium of the microbial community. Exemplary "conditions" or "environments" include, but are not limited to, a particular medium, volume, container, temperature, mixing, aeration, gravity, electromagnetic field, cell density, pH, nutrients, phosphate source, nitrogen source, symbiosis with one or more organisms, and / or interactions with a single organism or multiple organisms (e.g., mixed populations). Substances that may be toxic to one or more organisms or consortium of a microbial community, such as heavy metals, antibiotics, and chlorinated compounds, are also included as "conditions" or "environments". It should be understood that time can also be considered a "condition" since organisms are not static entities. Thus, a culture grown over an extended period of time (e.g., days, weeks, months, years) may produce a culture that contains a particular organism or consortium in a proportion of the culture that is relatively higher than the relative amount of that particular organism or consortium in the culture prior to growth over that period.

[0045] As used herein, the term "selection" refers to the increase in frequency of different "kinds" of individuals in a population by deliberately or spontaneously removing or enriching some kinds over others. The nature of a "kind" can be defined by genetic characterization (e.g., genes or nucleotide sequences); functional characterization (e.g., enzymatic, metabolic capabilities); taxonomic characterization (e.g., strains, subspecies, species, genus, family, or operational taxonomic units (OTUs) based on nucleotide sequence similarities or differences); or physical characterization. Furthermore, a kind can include one or many individuals. Typical examples of selection include, but are not limited to, growth rate selection, where individuals that grow and reproduce more quickly become more prevalent in a population. An important consideration when making a selection is to determine what "the selection is about" or what is being "selected for", i.e., the genetic, functional, and / or physical differences that are advantageous or disadvantageous in a particular environment. Growth rate selection is applied to select organisms that have a growth rate that is faster than other individuals in the population and that can be transmitted from parent cells to their progeny.

[0046] As used herein, the term "enrichment" refers to a process in which the abundance (e.g., expressed in absolute and / or relative terms) of one or more organisms, one or more functional capabilities, one or more genes or gene products, or one or more nucleotide sequences of interest is increased relative to the abundance of one or more other organisms, one or more other functional capabilities, one or more other genes or gene products, or one or more other nucleotide sequences. For example, in some embodiments, the term "enrichment" refers to a process of increasing the number (e.g., absolute number and / or relative number) of one or more microorganisms present in a culture, for example, by culturing in a suitable medium under selective conditions.

[0047] As used herein, the term "medium" or "media" refers to the chemical environment to which an organism is exposed or provided access. The organism may be immersed in the medium or may be in physical proximity to the medium (e.g., physical contact). Media typically include water with other additional nutrients and / or chemicals that may contribute to the growth or maintenance of the organism. The ingredients may be purified chemicals (e.g., "defined" media) or complex uncharacterized mixtures of chemicals, such as extracts made from milk or blood. Standardized media are widely used in laboratories. Examples of media for bacterial growth include, but are not limited to, LB and M9 minimal media. When used with respect to media, the term "minimal" refers to a medium that supports the growth of an organism but is composed of only the simplest chemical compounds possible. For example, M9 minimal medium can be composed of the following components dissolved in water and sterilized: 48 mM Na2HPO4, 22 mM KH2PO4, 9 mM NaCl, 19 mM NH4Cl, 2 mM MgSO4, 0.1 mM CaCl2, 0.2% carbon and energy source (e.g., glucose).

[0048] As used herein, the term "culture" refers to a medium in a container or enclosure having at least one cell or individual of a viable organism, usually a medium in which the organism can grow. As used herein, the term "continuous culture" is intended to mean a liquid culture into which new medium is added at a rate equal to the rate at which the medium is removed. Conversely, as used herein, "batch culture" is intended to mean a culture of fixed size or volume in which new medium is not added or removed.

[0049] As used herein, the term "genetic basis" refers to the genetic or genomic causes that underlie a particular observation.

[0050] As used herein, the term "genetic" refers to heritable information encoded in a sequence of DNA nucleotides. Thus, the term "genetic characterization" is intended to mean the sequencing, genotyping, comparison, mapping, or other assay of the information encoded in DNA.

[0051] As used herein, the term "genetic material" refers to the DNA in an organism that is transmitted from one generation to the next. Typically, genetic material refers to the genome of an organism. Extrachromosomal elements, such as organelles or plasmid DNA, may also be part of the genetic material that determines the characteristics of an organism.

[0052] As used herein, the term "genetic change" or "genetic adaptation" refers to one or more mutations in the genome of an organism. As used herein, the term "mutation" refers to differences in the sequence of DNA nucleotides of two related organisms, including, for example, substitutions, deletions, insertions and rearrangements, or the movement of mobile genetic elements.

[0053] As used herein, the term "evaluation" is intended to mean the observation or measurement of an observable phenotype of an organism. Evaluation typically includes analysis, interpretation, and / or comparison with the phenotype of another organism. It should be understood that phenotypes can be evaluated both at the gene level (e.g., with respect to nucleotide sequence) and at the level of gene products. Furthermore, phenotypes can be evaluated with respect to the behavior of the organism in the environment and / or the behavior of individual molecules or groups of molecules within the organism. Such comparisons are useful in determining the detailed function of mutated products resulting from genetic adaptations.

[0054] As used herein, the term "stepwise" is intended to mean a manner of succession of events, one following the other in time. As used herein, the term "concurrently" is intended to mean occurring at the same time.

[0055] As used herein, the terms "microbial", "microbial organism" and "microorganism" refer to organisms that exist as microscopic cells contained within the archaea, bacteria or eukaryote domains in the three-domain system (see Woese (1990) Proc Natl Acad Sci USA 87:4576-79, incorporated herein by reference), where eukaryotes include yeast and filamentous fungi, protozoa, algae or higher protists. Thus, the term is intended to encompass prokaryotic or eukaryotic cells or organisms that have a microscopic size, including all species of bacteria, archaea and eubacteria, as well as eukaryotic microorganisms such as yeast and fungi. Also included are cell cultures of any species that can be cultured for the production of chemicals.

[0056] As described herein, in some embodiments, the microorganism is a prokaryotic microorganism. In some embodiments, the prokaryotic microorganism is a bacterium. "Bacteria" or "Eubacteria" refers to the domain of prokaryotes. Bacteria include at least eleven different groups: (1) Gram-positive (Gram+) bacteria, within which there are two major subdivisions: (1) high G+C group (Actinomycetes, Mycobacteria, Micrococcus, etc.) (2) low G+C group (Bacillus, Clostridia, Lactobacillus, Staphylococci, Streptococci, Mycoplasma); (2) Proteobacteria, e.g., Proteobacteria ... Synthetic + non-photosynthetic Gram-negative bacteria (including most "common" Gram-negative bacteria);(3) Cyanobacteria, e.g., oxygen-producing phototrophs;(4) Spirochetes and related species;(5) Planctomyces;(6) Bacteroides, Flavobacteria;(7) Chlamydia;(8) Green sulfur bacteria;(9) Green non-sulfur bacteria (also anaerobic phototrophs);(10) Radioresistant micrococci and related species;(11) Thermotoga and Thermosipho thermophiles. "Gram-negative bacteria" includes cocci, non-enteric bacilli, and enteric bacilli.Examples of genera of Gram-negative bacteria include the genera Neisseria, Spirillum, Pasteurella, Brucella, Yersinia, Francisella, Haemophilus, Bordetella, Escherichia, Salmonella, Shigella, Klebsiella, Proteus, Vibrio, and Pseudomonas. The genera include Pseudomonas, Bacteroides, Acetobacter, Aerobacter, Agrobacterium, Azotobacter, Spirilla, Serratia, Vibrio, Rhizobium, Chlamydia, Rickettsia, Treponema and Fusobacterium. "Gram positive bacteria" includes cocci, non-spore forming bacilli and spore forming bacilli. Examples of genera of Gram-positive bacteria include the genera Actinomyces, Bacillus, Clostridium, Corynebacterium, Erysipelothrix, Lactobacillus, Listeria, Mycobacterium, Myxococcus, Nocardia, Staphylococcus, Streptococcus, and Streptomyces.

[0057] As used herein, the term "naturally occurring" when used in reference to a microorganism is intended to mean a microorganism found in nature. For example, a naturally occurring organism can be isolated from a natural source and has not been intentionally modified by humans in the laboratory.

[0058] As used herein, the term "non-naturally occurring" as applied to a microorganism refers to a microorganism that contains at least one genetic modification that is not normally found in naturally occurring microorganisms. Genetic modifications include, for example, modifications that introduce expressible nucleic acids encoding metabolic polypeptides, other nucleic acid additions, nucleic acid deletions, and / or other functional disruptions of the genetic material of the microorganism. Such modifications include, for example, coding regions and functional fragments thereof for heterologous, homologous, or both heterologous and homologous polypeptides to the referenced species. Further modifications include, for example, non-coding regulatory regions, where the modification alters the expression of a gene or operon.

[0059] As used herein, the term "microbial consortium" (plural "microbial consortia") refers to a set of microbial species or strains of species that can be described as performing a common function or as being responsible for, resulting in, or correlating with a recognizable parameter or phenotypic trait. A consortium may include two or more taxonomic units of microorganisms (e.g., a family, a genus, a species, or strains of a species). In some examples, microorganisms coexist symbiotically in a community. A microbial consortium may be described by describing the taxonomic units present in the consortium (e.g., multiple strains, subspecies, species, genera, families, or operational taxonomic units (OTUs) based on similarities or differences in nucleotide sequences); by describing the genes present in the consortium; by describing the nucleotide sequences present in the consortium; or by describing the functions present in and / or provided by the consortium. A microbial consortium can be a subpopulation of organisms found in a microbial community.

[0060] As used herein, the term "microbial community" refers to a group of microorganisms that includes two or more taxonomic units of microorganisms (e.g., a family, a genera, a species, or a strain of a species). Unlike a microbial consortium, a microbial community does not necessarily act in concert to perform a common function or be responsible for, result in, or correlate with a discernible parameter or phenotypic trait.

[0061] As used herein, the term "metagenome" is defined as "the collective genome of all microorganisms present in a given habitat" (Handelsman et al., (1998) Chem. Biol. 5:R245-R249). The term is also intended to include nucleic acid extracted from a microbial community or consortium (e.g., from an environmental sample) as representative of the microbial community or consortium, regardless of whether all genomic nucleic acid of the microbial community or consortium is extracted.

[0062] As used herein, the term "taxonomic unit" is a group of organisms that are considered similar enough to be treated as an independent unit. Taxonomic units may include, but are not limited to, families, genus, species, or subspecies populations (e.g., strains).

[0063] As used herein, the term "operational taxonomic unit (OTU)" refers to a group of microorganisms that are considered similar enough to be treated as an independent unit. OTUs can include, but are not limited to, taxonomic families, genuses, or species. OTUs are often defined by comparing nucleotide sequences between organisms. In some cases, OTUs can include a group of microorganisms that are treated as a unit based on 97% or more, 95% or more, 90% or more, 80% or more, or 70% or more sequence identity between at least some of the differentiation biomarkers, such as the 16S rRNA gene.

[0064] The term "genus" may be defined as a taxonomic group of related species according to the Taxonomic Outline of Bacteria and Archaea (Garrity et al. (2007) The Taxonomic Outline of Bacteria and Archaea. TOBA Release 7.7, March 2007. Michigan State University Board of Trustees). The term "species" may be defined as a collection of closely related organisms that have greater than 97% 16S ribosomal RNA sequence homology and greater than 70% genomic hybridization and are sufficiently distinct from all other organisms to be recognized as a separate taxonomic unit.

[0065] As used herein, the term "relative abundance" refers to the abundance of microorganisms of a particular taxonomic unit (e.g., OTU) in a first biological sample compared to the abundance of microorganisms of the corresponding taxonomic unit in one or another (e.g., second) sample. "Relative abundance" can be reflected, for example, in the number of isolated species corresponding to the taxonomic unit, or the extent to which biomarkers (e.g., nucleotide sequences) specific to the taxonomic unit are present or expressed in a given sample. The relative abundance of a particular taxonomic unit in a sample can be determined using culture-based or non-culture-based methods well known in the art. Non-culture-based methods include sequence analysis of amplified polynucleotides specific for the taxonomic unit, or comparison of proteomic-based profiles in the sample that reflect the number and extent of polypeptide-based, lipid-based, polysaccharide-based or carbohydrate-based biomarkers characteristic of one or more taxonomic units present in the sample. The relative abundance or abundance of a taxonomic unit or OTU can be calculated with reference to all detected taxonomic units / OTUs, or with reference to some set of invariant taxonomic units / OTUs. In some embodiments, taxonomic units are identified using sequence-based methods, such as those described in Wood (2014) "Kraken: ultrafast metagenomic sequence classification using exact alignments" Genome Biology 15:R46 and Wood (2019) "Improved metagenomic analysis with Kraken 2" Genome Biology 20:257, each of which is incorporated herein by reference.

[0066] As used herein, the term "significantly altered relative abundance" refers to a statistically significant increase or decrease in the relative abundance of the number of microorganisms of a particular taxonomic unit compared to the total microorganisms in a sample or compared to the number of microorganisms of the corresponding taxonomic unit present in another sample. In some embodiments, a "significant increase" or "significant decrease" in relative abundance is defined as a statistically significant increase or a statistically significant decrease above a reference value. In some embodiments, a statistically significant increase or a statistically significant decrease is an increase or decrease that is two, three or four times the standard deviation of the relative abundance. In some embodiments, a statistically significant increase or a statistically significant decrease is an increase or decrease with a P value equal to or less than 0.1, 0.05, 0.01 or 0.005.

[0067] In some embodiments, a "significant decrease" or "significant increase" in relative abundance refers to a statistically significant difference in one or more indicator species or taxonomic units compared to each other or to a reference species or taxonomic unit using a non-parametric statistical test such as a signed-rank test. In some embodiments, a "significant decrease" or "significant increase" in relative abundance is determined using a model using Bayesian estimation and association approaches.

[0068] In certain embodiments, an increase in relative abundance reflects a 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100% or greater increase over a baseline value, hi some embodiments, an increase in relative abundance reflects a 2-fold, 3-fold, 5-fold, 10-fold, 20-fold, 50-fold or 100-fold increase over a baseline value.

[0069] As used herein, the terms "isolate", "isolated", "isolated microorganism", and the like are intended to mean that one or more microorganisms have been separated from at least one of the substances with which they are associated in a particular environment (e.g., soil, water, or higher multicellular organisms). Thus, an "isolated microorganism" is not present in its naturally occurring environment; rather, through various techniques described herein, the microorganism has been removed from its natural environment and placed in a non-naturally occurring state of existence. Thus, an isolated strain may exist, for example, as a biologically pure culture, or as a spore (or other form of the strain) associated with a carrier composition. In certain aspects of the present disclosure, an isolated microorganism exists as an isolated biologically pure culture. It will be understood by those skilled in the art that an isolated biologically pure culture of a particular microorganism means that the culture is substantially free (within scientific reasonableness) of other organisms and contains only the individual microorganism of interest. A culture may contain various concentrations of the microorganism, and an isolated biologically pure microorganism will often necessarily differ from less pure or impure material. Additionally, in some aspects, the present disclosure provides a quantitative measure of the limits of concentration or purity found within isolated, biologically pure microbial cultures, and in some embodiments, the presence of these purity values ​​is an additional attribute that distinguishes the microorganisms disclosed herein from those present in nature.

[0070] As used herein, the term "improved" refers to improving a characteristic of an environment compared to a control environment or compared to a known average amount associated with the characteristic in question. For example, an "improved" soil may refer to a soil that increases the production of plant biomass after application of a beneficial microorganism or microbial consortium to the soil compared to the plant biomass produced by soil that has not been treated with the beneficial microorganism or microbial consortium, and other soil characteristics are substantially and / or essentially the same in terms of their effect on the production of plant biomass. Alternatively, the production of plant biomass after application of a beneficial microorganism or microbial consortium to the soil can be compared to the average biomass normally produced by the soil, as shown in scientific or agricultural publications known to those skilled in the art. As used herein, "improved" does not necessarily require that the data be statistically significant (e.g., p<0.05); rather, any quantifiable difference indicating that one value (e.g., the average treatment value) is different from another value (e.g., the average control value) may reach the level of "improved."

[0071] As used herein, the term "phenotype" refers to the observable characteristics of an individual cell, cell culture, organism, or group of organisms (e.g., a microbial consortium) that result from the interaction between the genetic makeup (e.g., genotype) of the individual cell, cell culture, organism, or group of organisms and the environment.

[0072] In some embodiments, a microorganism may be "endogenous" to an environment. As used herein, a microorganism is considered "endogenous" to an environment if it originates from the environment from which it is sourced. That is, a microorganism is endogenous to an environment if it is naturally found associated with said environment. In embodiments in which endogenous microorganisms are applied to an environment, the endogenous microorganisms are applied in amounts that differ from the levels found in the natural designated environment. Thus, a microorganism endogenous to a given environment can still improve the environment if the microorganism is present in the environment at a level that does not occur naturally and / or if the microorganism is applied to the environment together with other organisms that are exogenous and / or endogenous to the environment and present at levels that do not occur naturally.

[0073] In some embodiments, a microorganism may be "exogenous" (also referred to as "heterologous") to an environment. As used herein, a microorganism is considered to be "exogenous" to an environment if it is not derived from the environment from which it is sourced. That is, a microorganism is exogenous to an environment if it is not naturally found associated with the environment. For example, a microorganism that is normally associated with a first environment may be considered to be exogenous to a second environment that lacks the microorganism in nature.

[0074] As used herein, "environmental sample" means a sample taken or obtained from any part of the environment (e.g., ecosystem, ecological niche, habitat, etc.). Environmental samples can include liquid samples from rivers, lakes, ponds, oceans, glaciers, icebergs, rain, snow, sewage, reservoirs, tap water, drinking water, etc.; solid samples from soil, compost, sand, rocks, concrete, wood, bricks, sewage, etc.; and gas samples from the atmosphere, deep sea hydrothermal vents, industrial exhaust gases, automobile exhaust gases, etc. Typically, samples that are not in liquid form are converted to liquid form before analyzing the sample with the present method.

[0075] explanation Provided herein are techniques for identifying and isolating microorganisms with targeted functions, particularly, but not limited to, techniques for screening and / or selecting individual microorganisms or microbial consortia that provide a specified function, compositions and systems.In one respect, the technique presents a problem of a desired function to a microbial community, and the survival or increased abundance of members of the community depends on one or more members of the community responding to the functional solution.The genetic basis of the solution is not important, only the relevant characteristics of one or more members in the responding organism or microbial consortium are important.Therefore, the selection is not biased toward a particular set of genes and does not depend on current knowledge.

[0076] For example, as shown in Fig. 1, an embodiment of the present technology relates to a method including microbial screening for biomining. In particular, Fig. 1 shows an exemplary environment 100 for application-centric microbial screening (e.g., for effective climate change variables and biomining). Because application-centric biomining focuses on application (e.g., functional) outcomes measured via variables being tested, rather than individual microbial phenotypes, the desired application outcomes need not be limited to local ecosystems. Thus, application-centric biomining can be used to identify microbial consortia that may result in changes in more specific and / or more general environments, including, but not limited to, microbial environments, species-associated microbiomes, ecosystems, local environments, and global environments.

[0077] One class of applications concerns influencing climate change variables. It is scientifically well known that atmospheric CO2 levels have been steadily increasing since the Industrial Revolution, contributing to global warming. Using biomes to lower atmospheric CO2 levels is well understood. As mentioned before, there is a hypothesis that stromatolites contributed significantly to lowering atmospheric CO2 during the Cambrian period. It is understood that when forest biomes such as the Amazon are cut down, the CO2 sink disappears, which then contributes to the increase in atmospheric CO2. This therefore suggests that through application-centric biomining, microbial consortia that can affect climate change may be discovered more quickly and more efficiently.

[0078] There are many ways to address global warming, each with a group of variables that can be tested for desired outcomes in application-centric biomining. Such variables related to global warming or climate change are referred to herein as climate change variables. One specific application is the use of microbial consortia to maximize the sequestration of CO2 in biomass. Thus, the absolute amount of CO2 and the mass ratio of biomass to sequestered CO2, for example, are candidates for climate change variables that provide the desired application or functional outcome. These climate change variables can be mathematically treated as statistically independent variables, as they relate to the desired end result. It has also been observed that sequestration of CO2 in soil is related to nitrogen fixation. Thus, the absolute amount of nitrogen fixation and the mass ratio of biomass to fixed nitrogen can be mathematically treated as statistically dependent variables.

[0079] Since the desired outcome is application specific, the variables being tested, e.g., variables related to climate change or not, do not have to be measures of biological or chemical factors. The desired outcome can be economic. In one example, the application can be to maximize the revenue for implementing CO2 sequestration. In particular, as awareness of global warming increases, economic markets for carbon credits as well as direct payments for CO2 sequestration are developing. Thus, the economic climate change variable can include the total benefit obtained from CO2 sequestration (e.g., revenue from CO2 sequestration minus the cost of implementing the application of the microbial consortium).

[0080] It may be desirable to measure some climate change variables to provide assistance to a comprehensive analysis of the benefits and disadvantages of CO2 sequestration. For example, CO2 sequestration may be achieved by growing forests of trees. However, the trees themselves are not edible by humans as food. Thus, climate change variables may include the ratio of mass of food produced to mass of CO2 sequestered. Similarly, CO2 sequestration in biomass is likely to be short-term (e.g., 1-2 years due to gas release during decomposition). Thus, climate change variables may include the duration of CO2 sequestration.

[0081] In various embodiments, application-centric biomining can be performed based on one or more climate change variables. For example, the climate change variables can be measurements of CO2 sequestration, nitrogen fixation, and microbial survival / persistence. Such an example is based on the understanding that cyanobacteria (photosynthetic microorganisms) are highly effective at consuming CO2, while other microorganisms, such as Azotobacter vinelandii, can fix nitrogen, aiding in carbon sequestration and increasing organic matter in biomass.

[0082] During application-centric biomining, one or more environmental samples 102 (e.g., organic-rich environmental samples) may be collected. If a single environmental sample 102 is collected, the method includes homogenizing the environmental sample to provide an input sample for application-centric biomining (see, e.g., FIG. 1). If multiple environmental samples 102 are collected, the method includes blending the multiple environmental samples to provide a blended environmental sample, and homogenizing the blended environmental sample to provide an input sample for application-centric biomining (see, e.g., FIG. 1).

[0083] In embodiments involving the use of multiple environmental samples to generate an input sample, collecting and mixing multiple environmental samples can help maximize not only the statistical sample space of microorganisms to screen therefrom, but also the combinations of microorganisms present in the microbial consortium identified and / or generated using the techniques described herein applied to the input sample. Furthermore, collecting and mixing multiple environmental samples to generate an input sample to which the techniques described herein are applied can generate novel microbial consortia not present in nature by combining microorganisms that do not normally live in the same environment in nature. In some embodiments, various environmental samples from different geographical areas can be mixed to further increase the statistical sample space of microbial consortium combinations. For example, some embodiments provide that multiple environmental samples can be obtained, each environmental sample taken from a different ecosystem, habitat, and / or ecological niche. Some embodiments further provide that multiple environmental samples can be obtained from sites separated from each other by more than 1 m, 10 m, 100 m, 1000 m, 10,000 m, or 10,000 m. In some embodiments, samples are taken from two or more points anywhere on the Earth, including above and below the Earth's land and water surfaces.

[0084] In some examples, multiple input samples 104 may be created during collection. Each input sample of the multiple input samples 104 may include a different combination of individual environmental samples mixed together. For example, environmental samples A, B, and C (from one or more different ecosystems, habitats, and / or ecological niches) may be mixed to provide an input sample that includes A and B, B and C, or A and C. As a further example, environmental samples A, B, C, and D (from one or more different ecosystems, habitats, and / or ecological niches) may be mixed to provide an input sample that includes A, B, and C; A, B, and D; A, C, and D; or B, C, and D. As another example, environmental samples A, B, C, D and E (from one or more different ecosystems, habitats and / or ecological niches) may be mixed to provide an input sample including: A and B; A and C; A and D; A and E; B and C; B and D; B and E; C and D; C and E; D and E; A, B and C; A, B and D; A, B and E; A, C and D; A, C and E; A, D and E; B, C and D; B, C and E; B, D and E; C, D and E; A, B, C and D; A, B, C and E; A, B, D and E; A, C, D and E; B, C, D and E; or A, B, C, D and E. Each input sample of the plurality of input samples 104 may include a range of proportional compositions of any two individual environmental samples of the plurality of individual samples that are mixed together to provide the input sample. For example, any two individual environmental samples can be mixed together to provide an input sample that comprises a proportional composition of the first environmental sample in the range of 0.01 to 0.99 (e.g., including 0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95 or 0.99 of the first environmental sample) and a proportional composition of the second environmental sample in the range of 0.99 to 0.01 (e.g., including 0.99, 0.95, 0.90, 0.80, 0.70, 0.60, 0.50, 0.40, 0.30, 0.20, 0.10, 0.05 or 0.01 of the second environmental sample).

[0085] The input sample 104 may be isolated and developed using a variety of amounts and types of environmental samples mixed together. This is because it is recognized that combinations of microorganisms may not only be beneficial, but may also render individual microorganisms less effective or microorganisms from foreign environmental samples may dominate. Additionally, embodiments of the present technology include the use of a single environmental sample that is homogenized to provide an input sample. Those skilled in the art will appreciate that a single environmental sample may contain multiple individual ecosystems or ecological niches that are not mixed in nature but become mixed when the single sample is homogenized. For example, an environmental sample may include multiple separate subsamples that exist as separate strata, layers or subcommunities, such as a strata of cylindrical soil core samples, a strata of microbial mat samples, a strata of water column samples, subcommunities of microbial communities including biofilms, etc.

[0086] Thus, embodiments of the methods provided herein include the use of a single environmental sample that is homogenized to provide the input sample 104, and / or the use of multiple environmental samples that are mixed and homogenized to provide the input sample 104.

[0087] In some embodiments, a selection 106 of an input sample (e.g., an environmental sample or a mixed environmental sample of multiple mixed environmental samples) 104 may be performed based on one or more criteria 108, for example as shown in Figure 1. In some examples, at least an initial focus may be on microbial consortia known to fix carbon and nitrogen in environmental samples, such as the Cyanobacteria and Azotobacter previously mentioned. The focus on a particular microbial consortium may be determined by the target species. In another example, if the target species is a legume, the focus may be on Gluconacetobacter and Herbaspirillium because they are more effective at fixing nitrogen in the leaves and stems, Azospirillum because they are also effective at fixing nitrogen in the stems and roots, and Azotobacter and Beijerinckia because they are effective at fixing nitrogen in the rhizosphere of legumes.

[0088] Cultivation 110 of the input sample may be performed under one or more environmental conditions. In some examples, the input sample may be stored in a column where light enters for photosynthesis. In some embodiments, the culture medium is provided without nitrogen or carbon (e.g., nitrogen-free and carbon-free medium or "C / N-free medium"), which would prevent a determination that the microbial consortium contributed to any measured nitrogen or carbon uptake. The input sample may be exposed to nitrogen for fixation by supplying nitrogen from environmental concentrations, or by bubbling in anoxic N2 and supplying salts and other nutrients known to be required by the microorganisms to perform nitrogen fixation. The input sample may also be exposed to CO2, for example, by environmental concentrations or by bubbling in CO2.

[0089] After incubation and time, the cultures may be tested 112 based on one or more variables 114. In some examples, climate variables related to the desired biomining results are tested. In this case, the input sample may be tested for increased carbon and nitrogen or the ability of the resident microbiome to fix CO2 and / or nitrogen. Measurement may be by mass. The DNA of the microorganisms constituting the candidate microbial consortium is then isolated and sequenced for identification. Within the DNA, biomarkers such as 16S rRNA or GroEL are identified for each microorganism. These biomarkers will be useful for future microbial identification. The cultures are then tested on nitrogen- and carbon-free media in culture plates to measure survival time and / or persistence. Selection 116 of one or more microbial cultures and / or a particular portion of one or more microbial cultures based at least on carbon capture, nitrogen fixation and / or persistence is performed to provide cultures for testing 118.

[0090] In some cases, additives may be applied to promote uptake of the microbial consortium by the environment (e.g., soil) or culture medium. For example, the microbial consortium may require carbon, energy, nitrogen, micronutrients, and reducing equivalents. As a specific example, water and glucose sprays can promote E. coli in the environment or culture medium to generate reducing equivalents and four-carbon skeletons used in CO2 sequestration. As another example, organisms containing high levels of cellulase and / or lignase can be added to the environment or culture medium to aid in the decomposition of crop residues. The above process can be repeated several times through multiple iterations 120 and / or 122, each iteration further isolating and generating specific information 124 for the microorganisms and specific microbial consortiums that have achieved the desired results with respect to selected variables, such as climate variables, carbon sequestration, nitrogen fixation, and survival time / persistence.

[0091] In some embodiments, the selection of microorganisms and microbial consortia for further testing is aided by statistical models and computational methods, including machine learning. Statistical models embodied in machine learning models can be used to direct the selection of microorganisms in both application-centric and traditional biomining. For example, during experiments and iterations, data on the location of a particular environmental sample source, the composition of the environmental sample, the microorganisms and their associated genetic biomarkers, the microbial consortia can be correlated with outcomes related to various variables, such as climate change or otherwise. In some instances where machine learning models complement traditional biomining, the data can also be complemented by information capturing the phenotype of the microorganisms.

[0092] Once the minimum required amount of data 126 is achieved, the data can be developed into a machine learning model 128 that correlates microorganisms and biomarkers, as well as combinations of microorganisms, to the variables being tested. For application-centric biomining, a selection of microbial consortia for initial testing 130, and / or a selection of environmental sample characteristics can be suggested by the machine learning model 128 as outcomes 132 based on the variables being tested 134. For traditional biomining, a desired phenotype can be input along with a desired outcome 132 for the variables being tested 134, and associated microorganisms can be suggested by the machine learning model for further testing.

[0093] In some embodiments, for example as shown in FIG. 2, a computing device supports machine learning techniques for microbial screening for useful variables and biomining applications. The computing device 200 may provide a communication interface 202, one or more processors 204, memory 206, and device hardware 208. The communication interface 202 may include wireless and / or wired communication components that enable the device to transmit data to and receive data from other networked devices. The device hardware 208 may include additional interface, data communication, or data storage hardware. For example, the hardware interface may include a data output device (e.g., visual display, audio speaker), and one or more data input devices. The data input devices may include, but are not limited to, one or more combinations of a keypad, keyboard, mouse device, a touch screen that accepts motions, a microphone, a voice or speech recognition device, and any other suitable device.

[0094] Memory 206 may be implemented using computer readable media such as computer storage media. Computer readable media includes at least two types of computer readable media: computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVDs), high definition multimedia / data storage disks or other optical storage devices, magnetic cassettes, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other non-transmission media that can be used to store information for access by a computing device. Computing device 200 may also be in the form of either a virtual machine or a container on a virtual machine, such as provided via Kubernetes or Docker. In this case, the virtual machine is hosted on a physical computer platform and served via a hypervisor. Colloquially, the virtual machine configuration may be referred to as a "cloud."

[0095] The processor 204 and memory 206 of the computing device 200 may execute an operating system 210. The operating system 210 may then provide an execution environment for the machine learning platform 212. The operating system 210 may include components that enable the computing device 200 to not only send and receive data via various interfaces (e.g., user controls, communication interfaces, and / or memory input / output devices), but also to process the data using the processor 204 to generate output. The operating system 210 may include a display component that presents output (e.g., displaying the data on an electronic display, storing the data in memory, transmitting the data to another electronic device, etc.). Additionally, the operating system 210 may include other components that perform various additional functions typically associated with operating systems.

[0096] The machine learning platform 212 may include a data input module 214, a model generation module 216, and a selection module 218. The modules may include routines, program instructions, objects, and / or data structures that perform particular tasks or implement particular abstract data types. The memory 206 may also include a data store 220 used by the machine learning platform 212.

[0097] The data input module 214 may receive data from a variety of sources, such as a database or data entered via a user interface. The data input module 214 may use a data adapter to read data from a database of data sources. For example, the data input module 214 may use a data-agnostic data adapter to access an unstructured database and / or a database-specific data adapter to access a structured database. The received data may include microbial specific information, such as DNA biomarkers, phenotypic information, environmental variables (e.g., nutrient type, CO2 level, amount of sunlight, etc.), environmental sample characteristics (e.g., composition, source location, etc.), associated microbial growth information, etc.

[0098] The model generation module 216 may train a machine learning model, such as the machine learning model 128, via a model training algorithm. The model training algorithm may perform a training data input stage, a feature engineering stage, and a model generation stage. In the training data input stage, the model training algorithm may receive training data, such as data received via the data input module 214. During the feature engineering stage, the model training algorithm may pinpoint features in the training data. Thus, feature engineering may be used by the model training algorithm to capture important characteristics and relationships in the training data that help the machine learning model distinguish different classes of data. During the model generation stage, the model training algorithm may select an initial type of machine learning algorithm to train the machine learning model using the training data. After application of the selected machine learning algorithm to the training data, the model training algorithm may determine a training error measure of the machine learning model. If the training error measure exceeds a training error threshold, the model training algorithm may use a rules engine to select a different type of machine learning algorithm based on the magnitude of the training error measure. Different types of machine learning algorithms may include Bayesian algorithms, decision tree algorithms, support vector machine (SVM) algorithms, combinations of trees algorithms (e.g., random forests and gradient boosted trees), artificial neural networks, etc. The training process is typically repeated until the training results fall below a training error threshold, generating a trained machine learning model. The trained machine learning model 128 may be stored in the data store 220.

[0099] The selection module 218 may apply the trained machine learning model 128 to one or more query variable values ​​to generate query results for biomining. In some examples, a selection of microbial consortia to initiate testing and / or a selection of environmental sample characteristics may be suggested by the selection module 218 applying the machine learning model 128 to the query variable values. In other examples, a desired phenotype may be input along with a desired outcome (e.g., a desired measurement of CO2 sequestration, duration of CO2 sequestration, etc.) for the variable being tested (e.g., amount of nitrogen fixation, survival time of the microorganism, etc.). The selection module 218 may then apply the machine learning model 128 to the input data to suggest relevant microorganisms for further testing.

[0100] Thus, machine learning models can be developed to aid in the selection of microorganisms and microbial consortia using statistically significant amounts of data. Machine learning models can also augment traditional biomining if they are supplemented with phenotypic data for the constituent microorganisms.

[0101] In some embodiments, for example, as shown in Figures 3a and 3b, the present technology provides a process (e.g., process 300) for performing an application-centric microbial screening method. The order in which the operations are described in the exemplary process 300 is not intended to be construed as limiting, and any number of the described blocks may be combined in any order and / or in parallel to perform the process. Furthermore, in some embodiments, the process 300 may be performed by obtaining several environmental samples 302 and combining the environmental samples into a combination of mixed samples 304 to provide several input samples (e.g., mixed samples) for selection in step 306. In some embodiments, a single environmental sample may be homogenized to provide the input sample selected for input in step 306. In some embodiments, several single environmental samples and / or several mixed environmental samples may provide multiple input samples for selection in step 306. 3a and 3b are described with respect to obtaining several environmental samples 302, blending multiple environmental samples 304, and selecting a blended environmental sample 306 for culturing in step 308, the technology is not limited to methods that include blending multiple environmental samples, and includes embodiments in which a single environmental sample is homogenized and provided as the selected sample for culturing in step 308. Additionally, embodiments include making and providing multiple homogenized single environmental samples, and selecting one homogenized single environmental sample from the multiple homogenized single environmental samples for culturing in step 308.

[0102] Thus, the steps (e.g., steps 302, 304, and 306) of a process (e.g., process 300) for performing an application-centric microbial screening method should be understood as including providing several (e.g., one or more) mixed environmental samples made by mixing and homogenizing a plurality of environmental samples, or providing several (e.g., one or more) single environmental samples that are homogenized for culturing in step 308. References throughout the method description to a mixed environmental sample should be understood to refer to a mixed environmental sample made by mixing and homogenizing a plurality of environmental samples or a homogenized single environmental sample. References throughout the method description to a plurality of mixed environmental samples should be understood to refer to a plurality of mixed environmental samples, where each mixed environmental sample of the plurality of mixed environmental samples has been made by mixing and homogenizing a plurality of environmental samples and / or is a homogenized single environmental sample.

[0103] In some embodiments, for example, in block 302, multiple environmental samples including organic matter may be obtained for biomining. In block 304, the multiple environmental samples may be mixed into a mixed environmental sample combination. For example, the multiple environmental samples may be from different geographic regions such that the environmental samples contain different consortia of microorganisms. Mixing 304 may be performed by varying the amount and type of environmental samples to help maximize the combination of microorganisms. For example, environmental samples A, B, and C (from one or more different ecosystems, habitats, and / or ecological niches) may be mixed (e.g., in block 304) to provide an input sample including A and B, B and C, or A and C. As a further example, environmental samples A, B, C, and D (from one or more different ecosystems, habitats, and / or ecological niches) may be mixed (e.g., in block 304) to provide an input sample including A, B, and C; A, B, and D; A, C, and D; or B, C, and D. As another example, environmental samples A, B, C, D, and E (from one or more different ecosystems, habitats, and / or ecological niches) may be mixed (e.g., in block 304) to provide an input sample that includes: A and B; A and C; A and D; A and E; B and C; B and D; B and E; C and D; C and E; D and E; A, B and C; A, B and D; A, B and E; A, C and D; A, C and E; A, D and E; B, C and D; B, C and E; B, D and E; C, D and E; A, B, C and D; A, B, C and E; A, B, D and E; A, C, D and E; B, C, D and E; or A, B, C, D and E. Each input sample of the plurality of input samples may include a range of percentage compositions of any two individual environmental samples of the plurality of individual samples that are mixed together to provide the input sample.For example, any two individual environmental samples can be mixed together to provide an input sample that comprises a proportional composition of the first environmental sample in the range of 0.01 to 0.99 (e.g., including 0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95 or 0.99 of the first environmental sample) and a proportional composition of the second environmental sample in the range of 0.99 to 0.01 (e.g., including 0.99, 0.95, 0.90, 0.80, 0.70, 0.60, 0.50, 0.40, 0.30, 0.20, 0.10, 0.05 or 0.01 of the second environmental sample).

[0104] At block 306, a particular one of the mixed environmental samples may be selected based on one or more selection criteria for testing. For example, the mixed environmental sample may be selected based on whether the sample contains at least a certain microbial species, in some examples, and / or whether it exhibits a certain characteristic (e.g., function), such as the ability to fix a certain amount of nitrogen or the ability to fix a certain amount of carbon. At block 308, the selected mixed environmental sample may be cultured in an environment that includes one or more environmental conditions. For example, the environmental conditions may include a particular concentration of N2 gas, a particular concentration of CO2 gas, the availability of one or more particular nutrients, one or more particular salts, one or more particular additives, etc. The selected mixed environmental sample may be cultured in the environment for a predetermined period of time, such as minutes, hours, days, weeks, months, or years.

[0105] At block 310, a determination may be made whether a particular mixed environmental sample produced a successful biomining result based on one or more variable measurements obtained from the culture of the selected mixed environmental sample. In various embodiments, the variables may include variables such as the absolute amount of CO2 sequestered by the biomass in the culture, the ratio of biomass to sequestered CO2, the amount of time CO2 is sequestered by the biomass, etc. (e.g., climate change variables). Thus, a successful biomining result may be determined when each variable measurement in the set of one or more variable measurements obtained for the culture of the selected mixed environmental sample at least meets a corresponding variable measurement threshold. For example, the satisfaction of a measurement threshold may be the result of increased carbon sequestration, increased nitrogen fixation, increased biomass, or having a microorganism capable of meeting a particular survival time. At decision block 312, if the culture of the selected mixed environmental sample produced a successful result ("Yes" at decision block 312), the process 300 may proceed to block 314.

[0106] In block 314, identification information for the microorganisms present in the corresponding microbial consortium of the selected mixed environmental sample can be obtained. For example, the DNA of the microorganisms that make up the corresponding microbial consortium can be isolated and sequenced (e.g., as described herein). Within the DNA for each microorganism, biomarkers such as 16S rRNA or GroEL can be identified.

[0107] At block 316, the corresponding microbial consortium of the selected mixed environmental sample may be cultured into a microbial culture. At block 318, a culture portion of the microbial culture may be selected for testing. In some examples, the culture portion may be a randomly selected portion of the culture. In other examples, the culture portion may be selected based on whether the culture portion contains at least a certain microbial species and / or can exhibit a certain characteristic (e.g., function), such as the ability to fix a certain amount of nitrogen, the ability to fix a certain amount of carbon, and / or the ability to have a certain survival time / persistence. At block 320, the selected culture portion of the microbial culture may be grown in an environment including one or more environmental conditions. For example, the environmental conditions may include a specific concentration of N2 gas, a specific concentration of CO2 gas, the availability of one or more specific nutrients, one or more specific salts, one or more specific additives, etc. The selected culture portion may be grown in the environment for a predetermined period of time.

[0108] At block 322, a determination may be made whether the selected culture portion produced a successful microbial biomining result based on one or more variable measurements of the selected culture portion. In various embodiments, the variables may include variables such as the absolute amount of CO sequestered by the biomass of the selected culture portion, the ratio of biomass to sequestered CO, the amount of time CO is sequestered by the biomass (e.g., climate change variables). Thus, a successful microbial biomining result may be determined when each variable measurement in a set of one or more variable measurements obtained for the culture portion at least meets a corresponding variable measurement threshold. For example, satisfaction of a measurement threshold may be the result of having a microorganism capable of increasing carbon sequestration, increasing nitrogen fixation, increasing biomass, or meeting a particular survival time.

[0109] At decision block 324, if the selected culture portion yielded a successful biomining result ("Yes" at decision block 324), the process 300 may proceed to block 326. At block 326, identification information for the microorganisms present in the corresponding microbial consortium of the selected culture portion may be obtained. For example, DNA of the microorganisms that make up the corresponding microbial consortium may be isolated and sequenced. Within the DNA for each microorganism, biomarkers such as 16S rRNA or GroEL may be identified.

[0110] The process 300 may then proceed to decision block 328. Returning to decision block 324, if the selected culture portion did not yield a successful biomining result ("No" at decision block 324), the process 300 may proceed directly to decision block 328. At decision block 328, if there are more culture portions of the culture to be tested ("Yes" at decision block 328), the process 300 may proceed to block 330. For example, there may be more culture portions to be tested if the number of culture portions of the microbial culture selected for testing has not yet reached the threshold test number, if the number of successful biomining results for the microbial culture has not yet reached the success threshold number, or if there are more portions of the microbial culture remaining for testing. At block 330, more culture portions of the microbial culture may be selected for testing. The process 300 may then return to block 320.

[0111] However, if there are no more culture portions of the culture to be tested ("No" at decision block 328), the process 300 may proceed to decision block 332. At decision block 332, if there are more mixed environmental samples to be tested ("Yes" at decision block 332), the process 300 may proceed to block 334. At block 334, additional mixed environmental samples may be selected based on one or more selection criteria for testing. However, if there are no more mixed environmental samples to be tested ("No" at decision block 332), the process 300 may end at block 334 such that testing is terminated. For example, there may be more mixed environmental samples to be tested if the number of mixed environmental samples selected for testing has not yet reached the threshold test number, if the number of successful biomining results for the combination of mixed environmental samples has not yet reached the success threshold number, or if there are more mixed environmental samples remaining for testing.

[0112] Returning to decision block 312, if the culture of the selected mixed environmental sample did not yield successful biomining results ("No" at decision block 312), process 300 may proceed directly to decision block 332. In some alternative embodiments, process 300 may proceed directly to decision block 332 from block 314 instead of proceeding through blocks 316-330 before proceeding to decision block 332.

[0113] In some embodiments, as shown, for example, in FIG. 4, the technology provides machine learning techniques to identify microbial species and other information associated with one or more variables. The exemplary process 400 is illustrated as a collection of blocks in a logical flow chart that represents a sequence of operations that can be performed in hardware, software, or a combination thereof. In the context of software, the blocks represent computer-executable instructions that, when executed by one or more processors, perform the described operations. Generally, computer-executable instructions may include routines, code segments, programs, objects, components, data structures, etc. that perform a particular function or implement a particular abstract data type. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described blocks may be combined in any order and / or in parallel to perform the process.

[0114] In block 402, the machine learning platform may generate a machine learning model that at least correlates one or more environmental sample variable values ​​of the environmental sample with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the one or more environmental samples. In various embodiments, the machine learning model may further correlate such variable values ​​with values ​​of environmental variables (e.g., climate change variables). For example, during experiments and iterations of a microbial identification process such as process 300, data regarding the location of a particular environmental sample source, the composition of the environmental sample, the microorganisms and their associated genetic biomarkers, the microbial consortium, may be correlated with results regarding various variables (e.g., climate change or otherwise) and used as training data to generate the machine learning model. Other correlated variables may include the ratio of biomass mass to absolute amount of nitrogen fixed, the total benefit from CO2 sequestration by biomass, the ratio of food mass produced to mass of CO2 sequestered by biomass, the amount of time CO2 is sequestered by biomass, etc.

[0115] At block 404, the machine learning platform may receive an input of one or more variable values. For example, the variable values ​​may include a phenotype of a microorganism, a desired amount of nitrogen fixation, a desired amount of carbon sequestration, an environmental sample characteristic, one or more climate change variables, etc. At block 406, the machine learning platform may include a request for information related to the one or more variable values. At decision block 408, if the requested information includes a microbial species associated with the one or more variable values, the process 400 may proceed to block 410. At block 410, the machine learning platform may apply a machine learning model to the one or more variables to identify one or more microbial species associated with the one or more variable values. In some examples, the microbial species suggested by the machine learning model may be used for further testing for microbial biomining.

[0116] Returning to decision block 408, if the requested information includes environmental characteristics associated with one or more variables, process 400 may proceed to block 412. At block 412, the machine learning platform may apply a machine learning model to the one or more variables to identify one or more environmental characteristics associated with the one or more variable values.

[0117] Returning to decision block 408, if the requested information includes a microbial consortium associated with one or more variables, process 400 may proceed to block 414. In block 414, the machine learning platform may apply a machine learning module to the one or more variable values ​​to identify at least one microbial consortium associated with the one or more variable values. In some examples, the microbial consortium suggested by the machine learning model may be used for further testing for microbial biomining.

[0118] In contrast to traditional biomining, the application-centric biomining techniques described herein start with large samples of microorganisms (e.g., from one or more environmental samples) and microbial consortia (e.g., including one or more microorganisms from a natural consortium and / or one or more microorganisms from different environments, ecosystems, habitats, and / or ecological niches) to generate new consortia that include new combinations of microorganisms acting in concert. Microorganisms and microbial consortia that provide the desired results by testing for application-specific variables can then be sequenced and then subcultured until the desired microorganisms and microbial consortia are identified and / or isolated. With a statistically significant amount of data, machine learning models can be developed to aid in the selection of microorganisms and microbial consortia. Machine learning models can also augment traditional biomining if the machine learning models are supplemented with phenotypic data for the constituent microorganisms.

[0119] As described herein, application-centric biomining focuses on the variables being tested, rather than the underlying phenotype of the microorganism. The variables being tested can be direct variables of interest, such as a measure of carbon sequestration, and related dependent variables, such as nitrogen fixation. The variables can be biological in nature, such as survival time and / or persistence of the microorganism. Because the variables being tested are not phenotypically or otherwise directly linked to the microorganism, the variables being tested can be global, such as effects on global climate change and / or global food production. Furthermore, the variables being tested need not be biological or chemical in nature. The variables being tested can be economic, such as benefits from carbon sequestration and comparison of food production versus carbon sequestration.

[0120] In the specific example of agriculture and soil, this separation of the variables being tested allows for a finer analysis of a broad range of agricultural practices. In the past, the impact of one farm could be differentiated by soil management techniques and crop management techniques. However, application-specific biomining offers the ability to select variables and find microbial consortia to maximize desired outcomes. Because these outcomes do not have to be agricultural variables, application-specific biomining increases the ability to link agricultural performance and production to arbitrary variables such as agronomics and climate change.

[0121] In some embodiments, the present technology provides additional methods for selecting microbial consortia that provide a specified function. In some embodiments, the present technology provides methods for screening microbial communities, microbial consortia, and / or multiple microorganisms to create and / or identify microbial consortia that provide a specified function. In some embodiments, the present technology creates microbial consortia that are not found in nature by combining microorganisms from different environments, ecological niches, and / or habitats (e.g., microorganisms that are not found together in nature).

[0122] In some embodiments, the technology provides a method for generating a microbial consortium that provides a specified function, as shown, for example, in Figure 5. The method includes providing a sample containing a plurality of microorganisms (501), inoculating an Nth volume of growth medium with a portion of the sample to provide an Nth culture (502), growing the Nth culture under a set of selective conditions (503), generating an Nth taxonomic classification of microorganisms in the Nth culture (504), inoculating an N+1th volume of the growth medium with a portion of the Nth culture (505), growing the N+1th culture under a set of selective conditions (506), generating an N+1th taxonomic classification of microorganisms in the N+1th culture (507), and deriving a measure of microbial community stability of the N+1th culture relative to the Nth culture using the N+1th taxonomic classification and the Nth taxonomic classification (508). The measure of microbial community stability is monitored to identify when the measure of microbial community stability has reached a plateau value. If the measure of microbial community stability has not reached a plateau value (509), steps 505-508 are performed again by providing the N+1th sample in step 505 as an Nth sample (510). If the measure of microbial community stability has reached a plateau value (509), the method includes providing the N+1th stable culture as a culture comprising a microbial consortium that performs a designated function (511). In some embodiments, steps 505-508 are repeated 2, 3, 4, 5, 6, 7, 8, 9, or 10 or more times.

[0123] In some embodiments, the method further comprises isolating each of the microorganisms of the stable microbial consortium in pure culture. In some embodiments, the method further comprises obtaining a genomic sequence of each of the microorganisms of the stable microbial consortium in pure culture. In some embodiments, the method further comprises storing (e.g., by freezing (e.g., at -80°C)) the stable microbial consortium and / or each of the microorganisms of the stable microbial consortium. In some embodiments, the method further comprises measuring a designated function of the stable microbial consortium using a test substrate and a method for measuring an output of the function.

[0124] The present technology is not limited in the type of sample (e.g., environmental sample) containing microorganisms used as starting material (e.g., input sample) on which the methods described herein (e.g., methods for selecting microbial consortia and / or methods for screening to identify microbial consortia) are performed. In some embodiments, the input sample used can be an environmental sample from any source, such as naturally occurring or manmade air, water systems and water sources, soil, or any other sample of interest. In some embodiments, environmental samples can be obtained, for example, from indoor or outdoor air or air particle collection systems; indoor surfaces, and surfaces of machines, devices, or instruments. In some embodiments, the sample is taken from an ecosystem (e.g., in some embodiments, the sample is an environmental sample taken from an ecosystem). The ecosystem can be terrestrial, including all known terrestrial environments, including but not limited to soil, surfaces, and above-surface environments. Ecosystems include those classified in the Land Cover Classification System (LCCS) of the Food and Agriculture Organization and the Forest-Range Environmental Study Ecosystems (FRES) developed by the United States Forest Service. Exemplary ecosystems include forests, such as tropical rainforests, temperate rainforests, temperate broadleaf forests, boreal forests, taiga and montane coniferous forests; grasslands, including savannas and steppes; deserts; wetlands, including swamps, marshes, swamps, estuaries and morasses; riverine ecosystems, highlands and tundra ecosystems. Ecosystems further include those associated with aquatic environments, such as lakes, rivers, springs, coral reefs, coasts, estuaries, seamounts, ocean trenches and intertidal zones. Ecosystems also include soils, humus soils, mineral soils and aquifers. Ecosystems further encompass subterranean environments, such as mines, oil fields, caves, faults and fracture zones, geothermal fields and aquifers. Ecosystems further include microbiomes associated with plants, animals and humans. Exemplary plant-associated microbiomes include those found in or near roots, bark, stems, leaves and flowers. Animal and human associated microbiomes include those found in the gastrointestinal tract, respiratory system, nasal passages, urogenital tract, mammary glands, oral cavity, ear canal, feces, urine and skin.In some embodiments, the sample can be any type of clinical or medical sample, for example, the sample can be from the blood, urine, feces, nasal passages, lungs, or intestines of a mammal.

[0125] For example, in some embodiments, one or more environmental samples are collected. If a single environmental sample is collected, the method includes homogenizing the environmental sample to provide an input sample (e.g., at block 501). If multiple environmental samples are collected, the method includes blending the multiple environmental samples to provide a blended environmental sample, and homogenizing the blended environmental sample to provide the input sample (e.g., at block 501).

[0126] In embodiments involving the use of multiple environmental samples to generate an input sample, collecting and mixing multiple environmental samples can help maximize not only the statistical sample space of microorganisms to screen therefrom, but also the combinations of microorganisms present in the microbial consortium identified and / or generated using the techniques described herein applied to the input sample. Furthermore, collecting and mixing multiple environmental samples to generate an input sample to which the techniques described herein are applied can generate novel microbial consortia not present in nature by combining microorganisms that do not normally live in the same environment in nature. In some embodiments, various environmental samples from different geographical areas can be mixed to further increase the statistical sample space of microbial consortium combinations. For example, some embodiments provide that multiple environmental samples can be obtained, each environmental sample taken from a different ecosystem, habitat, and / or ecological niche. Some embodiments further provide that multiple environmental samples can be obtained from sites separated from each other by more than 1 m, 10 m, 100 m, 1000 m, 10,000 m, or 10,000 m. In some embodiments, samples are taken from two or more points anywhere on the Earth, including above and below the Earth's land and water surfaces.

[0127] In some examples, multiple input samples may be generated during collection. Each input sample of the multiple input samples may include a different combination of individual environmental samples mixed together. For example, environmental samples A, B, and C (from one or more different ecosystems, habitats, and / or ecological niches) may be mixed to provide an input sample that includes A and B, B and C, or A and C. As a further example, environmental samples A, B, C, and D (from one or more different ecosystems, habitats, and / or ecological niches) may be mixed to provide an input sample that includes A, B, and C; A, B, and D; A, C, and D; or B, C, and D. As another example, environmental samples A, B, C, D and E (from one or more different ecosystems, habitats and / or ecological niches) may be mixed to provide an input sample that includes: A and B; A and C; A and D; A and E; B and C; B and D; B and E; C and D; C and E; D and E; A, B and C; A, B and D; A, B and E; A, C and D; A, C and E; A, D and E; B, C and D; B, C and E; B, D and E; C, D and E; A, B, C and D; A, B, C and E; A, B, D and E; A, C, D and E; B, C, D and E; or A, B, C, D and E. Each input sample of the plurality of input samples may include a range of proportional compositions of any two individual environmental samples of the plurality of individual samples that are mixed together to provide the input sample. For example, any two individual environmental samples can be mixed together to provide an input sample that comprises a proportional composition of the first environmental sample in the range of 0.01 to 0.99 (e.g., including 0.01, 0.05, 0.10, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80, 0.90, 0.95 or 0.99 of the first environmental sample) and a proportional composition of the second environmental sample in the range of 0.99 to 0.01 (e.g., including 0.99, 0.95, 0.90, 0.80, 0.70, 0.60, 0.50, 0.40, 0.30, 0.20, 0.10, 0.05 or 0.01 of the second environmental sample).

[0128] Input samples can be isolated and developed using a variety of amounts and types of environmental samples mixed together. This is because it is recognized that combinations of microorganisms can be beneficial, but can also make individual microorganisms less effective or microorganisms from foreign environmental samples can dominate. Furthermore, embodiments of the present technology include the use of a single environmental sample that is homogenized to provide an input sample. Those skilled in the art will appreciate that a single environmental sample can contain multiple individual ecosystems or ecological niches that are not mixed in nature but become mixed when the single sample is homogenized. For example, an environmental sample can include multiple separate subsamples that exist as separate strata, layers or subcommunities, such as a strata of cylindrical soil core samples, a strata of microbial mat samples, a strata of water column samples, subcommunities of microbial communities including biofilms, etc.

[0129] Thus, embodiments of the methods provided herein include the use of a single environmental sample that is homogenized to provide an input sample in step 501, and / or the use of multiple environmental samples that are mixed and homogenized to provide an input sample in step 501.

[0130] The present technology provides methods for reducing the complexity of a microbial community (e.g., present in an environmental sample) while selecting and / or identifying microbial consortia that perform a specified function. Exemplary functions for which microbial consortia may be selected and / or identified include, for example, biodegradation, fermentation, production of chemical precursors, biosensing, nitrogen fixation, and carbon fixation.

[0131] In some embodiments, the environmental sample is used to inoculate a culture medium, and the inoculated culture medium is grown under selective conditions provided by the culture medium (e.g., presence, absence, or type of carbon source; presence, absence, or type of nitrogen source; presence, absence, or type of cofactors, minerals, vitamins, or other nutrients; presence, absence, or type of cations and / or anions; presence, absence, or type of trace minerals, cations and / or anions; presence, absence, or type of solid growth substrate, such as sand or other solid substrate) or with selective conditions provided externally to the growth medium (e.g., temperature; humidity; presence, absence, wavelength and / or intensity of light; light / dark cycle; pressure; culture volume; culture volume material, size or geometry; presence, absence, type or intensity of culture agitation; presence, absence and / or type of gas provided).

[0132] In some embodiments, the culture is inoculated (e.g., in step 502 and / or step 505) and incubated (e.g., in step 503 and / or step 506) for a period of time, e.g., 30-60 minutes (e.g., 30.0, 30.5, 31.0, 31.5, 32.0, 32.5, 33.0, 33.5, 34.0, 34.5, 35.0, 35.5, 36.0, 36.5, 37.0, 37.5, 38.0, 38.5, 39.0, 39.5, 40.0, 40.5, 41.0, 41.5, 42.0, 42.5, 43.0, 43.5, 44.0, 44.5, 45.0, 45.5, 46.0, 46.5, 47.0, 47.5, 48.0, 48.5, 49.0, 50.0, 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0, 54.5, 55.0, 55.5, 56.0, 56.5, 57.0, 57.5, 58.0, 58.5, 59.0, 59.5, 60.0, 60.5, 61.0, 61.5, 62.0, 62.5, 63.0, 63.5, 64.0, 64.5, 65.0, 65.5, 66.0, 5.5, 46.0, 46.5, 47.0, 47.5, 48.0, 48.5, 49.0, 49.5, 50.0, 50.5, 51.0, 51.5, 52.0, 52.5, 53.0, 53.5, 54.0, 54.5, 55.0, 55.5, 56.0, 56.5, 57.0, 57.5, 5 8.0, 58.5, 59.0, 59.5 or 60.0 minutes; 1 to 24 hours (e.g., 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13.0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0, 22.5, 23.0, 23.5 or 24.0 hours; 1 to 30 days (e.g., 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.0, 8.5, 9.0, 9.5, 10.0, 10.5, 11.0, 11.5, 12.0, 12.5, 13. 0, 13.5, 14.0, 14.5, 15.0, 15.5, 16.0, 16.5, 17.0, 17.5, 18.0, 18.5, 19.0, 19.5, 20.0, 20.5, 21.0, 21.5, 22.0, 22.5, 23.0, 23.5, 24.0, 24.5, 25.0, 25.5, 26.0, 26.5, 27.0, 27.5, 28.0, 28.5, 29.0, 29.5 or 30.0 days; and / or 1 to 10 weeks (e.g., 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 6.5, 7.0, 7.5, 8.The cells are grown for 0, 8.5, 9.0, 9.5 or 10.0 weeks.

[0133] In some embodiments, empirical measurements of growth rate, time to exponential growth phase, time to culture saturation, or other culture growth characteristics are measured to identify the length of time for culture growth. In some embodiments, a growth time is selected that provides a culture at or near the end of the exponential growth phase to provide a culture with a robust variety and number of microorganisms for further characterization and / or selection. In some embodiments, growth is measured quantitatively and / or qualitatively using measurements of absolute or relative numbers of microorganisms in a given volume of culture. In some embodiments, absolute or relative numbers of microorganisms in a given volume of culture are measured using light scattering, measuring the dry or wet mass of solids (e.g., cells) isolated from the culture, counting colonies grown on solid media using a portion of the culture, or measuring some other characteristic of the culture or a portion thereof that correlates or causally relates to the number of microorganisms in the culture. In some embodiments, growth is characterized by determining a growth curve, and in some embodiments, growth is characterized by determining the doubling time and / or the time to half-saturation. In some embodiments, the growth rate is modeled using empirical data (eg, using a logarithmic model of growth).

[0134] In some embodiments, the microorganisms in the culture are characterized by shotgun metagenomic sequencing (e.g., in step 507). Techniques and systems for obtaining gene sequences from multiple organisms in a sample, such as an environmental sample or a clinical sample, are well known to those of skill in the art. For example, Zhou et al. (Appl. Environ. Microbiol. (1996) 62:316-322) provide a robust nucleic acid extraction and purification. This protocol can also be modified depending on the experimental goal and the type of environmental sample, such as soil, sediment, and groundwater. Many commercially available DNA extraction and purification kits can also be used. Samples with less than 2 pg of purified DNA may require amplification, which can be performed using conventional techniques known in the art, such as the whole community genome amplification (WCGA) method (Wu et al., Appl. Environ. Microbiol. (2006) 72, 4931-4941). Techniques and systems for obtaining purified RNA from environmental samples are also well known to those of skill in the art. For example, the approach described by Hurt et al. (Appl. Environ. Microbiol. (2001) 67:4495-4503) can be used. This method can simultaneously isolate DNA and RNA in the same sample. Gel electrophoresis can also be used to isolate community RNA (McGrath et al., J. Microbiol. Methods (2008) 75:172-176). Samples with less than 5 pg of purified RNA may require amplification, which can be performed using conventional techniques known in the art, such as the whole community RNA amplification approach (WCRA) to obtain cDNA (Gao et al., Appl. Environ. Microbiol. (2007) 73:563-571). In some embodiments, environmental sampling and DNA extraction are performed as previously described (DeSantis et al., Microbial Ecology, 53(3):371-383, 2007).

[0135] To obtain metagenomic sequencing data, the isolated nucleic acid (e.g., metagenomic DNA) can be subjected to a sequencing method. Sequencing methods can be broadly divided into those that typically use template amplification and those that do not. Methods that require amplification include pyrosequencing, marketed by Roche as the 454 technology platform (e.g., GS20 and GS FLX), the Solexa platform marketed by Life Technologies / Ion Torrent, Illumina, GnuBio, and the Supported Oligonucleotide Ligation and Detection (SOLiD) platform marketed by Applied Biosystems. Non-amplification approaches, also known as single molecule sequencing, are exemplified by the HeliScope platform marketed by Helicos BioSciences and the emerging platforms marketed by VisiGen, Oxford Nanopore Technologies Ltd., and Pacific Biosciences, respectively. Thus, metagenomic shotgun sequencing, in some embodiments, includes pyrosequencing, sequencing by ligation, single molecule sequencing, sequencing by synthesis (SBS), semiconductor sequencing, nanopore sequencing, massively parallel clonal, massively parallel single molecule SBS, massively parallel single molecule real-time, massively parallel single molecule real-time nanopore technologies, etc. Morozova and Marra provide a review of some such techniques in Genomics, 92:255 (2008), which is incorporated herein by reference in its entirety. Those skilled in the art will recognize that because RNA is less stable in cells and more susceptible to nuclease attack, experimentally RNA is usually reverse transcribed into DNA before sequencing.

[0136] Specific descriptions of several DNA sequencing techniques include fluorescence-based sequencing (see, e.g., Birren et al., Genome Analysis: Analyzing DNA, 1, Cold Spring Harbor, NY, which is incorporated by reference in its entirety); automated sequencing techniques; parallel sequencing of partitioned amplicons (WO 2006084132 to Kevin McKernan et al., which is incorporated by reference in its entirety); and sequencing by parallel oligonucleotide extension (see, e.g., U.S. Pat. No. 5,750,341 to Macevicz et al. and U.S. Pat. No. 6,306,597 to Macevicz et al., both of which are incorporated by reference in their entirety). Further descriptions of sequencing techniques include the Church polony technique (Mitra et al., 2003, Analytical Biochemistry 320, 55-65; Shendure et al., 2005, Science, 309, 1728-1732; U.S. Patent No. 6,432,360; U.S. Patent No. 6,485,944; U.S. Patent No. 6,511,803; which are incorporated by reference in their entireties), the 454 picotiter pyrosequencing technique (Margulies et al., 2005, Nature 437, 376-380; U.S. Patent Application Publication No. 20050130173; which are incorporated by reference in their entireties), the Solexa single base addition technique (Bennett et al., 2005, Nature 437, 376-380; U.S. Patent Application Publication No. 20050130173; which are incorporated by reference in their entireties), the 454 picotiter pyrosequencing technique (Margulies ... al., 2005, Pharmacogenomics, 6,373-382; U.S. Patent No. 6,787,308; U.S. Patent No. 6,833,246; incorporated herein by reference in their entireties), Lynx massively parallel signature sequencing technology (Brenner et al. (2000). Nat. Biotechnol. 18:630-634; U.S. Patent No. 5,695,934; U.S. Patent No. 5,714,330; incorporated herein by reference in their entireties), and Adessi PCR colony technology (Adessi et al. (2000). Nucleic Acid Res. 28, E87; WO 00018957; incorporated herein by reference in their entireties).See also, e.g., Voelkerding et al., Clinical Chem., 55:641-658, 2009; MacLean et al., Nature Rev. Microbiol., 7:287-296; each of which is incorporated by reference in its entirety.

[0137] The metagenomic nucleotide sequence data can be analyzed to characterize the microbial community (e.g., microbial consortium) from which the metagenomic nucleic acid was obtained (e.g., in step 507). For example, in some embodiments, taxonomic units in the microbial community are taxonomically classified and / or identified by obtaining metagenomic nucleotide sequence data from the microbial community and using an algorithm that associates short genomic subsequences (k-mers) in the metagenomic nucleotide sequence data with least common ancestor (LCA) taxa (e.g., using a curated database). See, e.g., Wood (2014) "Kraken: ultrafast metagenomic sequence classification using exact alignments" Genome Biology 15:R46 and Wood (2019) "Improved metagenomic analysis with Kraken 2" Genome Biology 20:257, each of which is incorporated herein by reference. In some embodiments, BLAST is used to identify microbial taxonomic units present in the microbial community (e.g., microbial consortium). See, e.g., Altschul (1990) "Basic local alignment search tool" J Mol Biol 215:403-410, incorporated herein by reference.Other tools for identifying taxonomic units in microbial communities using metagenomic sequence data from microbial communities include, for example, MEGAN (see, e.g., Huson (2007) "MEGAN analysis of metagenomic data" Genome Res 17:377-386, which is incorporated herein by reference); PhymmBL (see, e.g., Brady (2009) "Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models" Nat Methods 6:673-676; and Brady (2011) "PhymmBL expanded: confidence scores, custom databases, parallelization and more" Nat Methods 8:367, each of which is incorporated herein by reference); and the Naive Bayes Classifier (NBC) (see, e.g., Rosen (2008) "Metagenome fragment classification using N-mer frequency profiles" Adv Bioinformatics 2008:1-12, which is incorporated herein by reference).

[0138] In some embodiments, characterizing a microbial community includes identifying taxonomic units (e.g., strains, subspecies, species, genera, families) of organisms present in the microbial community in absolute and / or relative terms. In some embodiments, characterizing a microbial community includes identifying taxonomic units (e.g., strains, subspecies, species, genera, families) of organisms that are enriched in a particular passage relative to a previous passage or the original environmental sample, for example in relative terms.

[0139] In some embodiments, the technology provides an iterative method (e.g., method 500 including iterations of steps 505-510) in which a portion of the first culture is used to inoculate a second volume of fresh medium. Thus, in some embodiments, a portion of the first culture (e.g., a culture created by inoculating an environmental sample in a selective growth medium) is used to inoculate a second culture (e.g., comprising the same or different growth medium as the first sample). In some embodiments, a portion of the second culture is used to inoculate a third culture. In some embodiments, a portion of the third culture is used to inoculate a fourth culture. In some embodiments, a portion of the fourth culture is used to inoculate a fifth culture. In some embodiments, a portion of the fifth culture is used to inoculate a sixth culture. In some embodiments, a portion of the sixth culture is used to inoculate a seventh culture. In some embodiments, a portion of the seventh culture is used to inoculate an eighth culture. In some embodiments, a portion of the Nth culture is used to inoculate an N+1th culture. In some embodiments, the Nth culture is a first culture inoculated with at least a portion of the environmental sample. In some embodiments, the Nth culture is a second, third, fourth, fifth, sixth, seventh, eighth, etc. culture inoculated with at least a portion of a culture inoculated with a preceding culture (e.g., the first, second, third, fourth, fifth, sixth, or seventh culture, respectively). As used herein, the process of repeated culturing by using a portion of the Nth culture to inoculate the N+1th culture is referred to as "passaging" the culture.

[0140] Additionally, a culture inoculated directly from an environmental sample may be referred to herein as a P0 (zero) culture; the first passage involves using a portion of the P0 culture to inoculate a fresh culture medium to create a P1 culture; the second passage involves using a portion of the P1 culture to inoculate a fresh culture medium to create a P2 culture; the third passage involves using a portion of the P2 culture to inoculate a fresh culture medium to create a P3 culture; and the fourth passage involves using a portion of the P3 culture to inoculate a fresh culture medium to create a P4 culture. the fifth passage involves using a portion of the P4 culture to inoculate a fresh culture medium to create a P5 culture; the sixth passage involves using a portion of the P5 culture to inoculate a fresh culture medium to create a P6 culture; the seventh passage involves using a portion of the P6 culture to inoculate a fresh culture medium to create a P7 culture; the eighth passage involves using a portion of the P7 culture to inoculate a fresh culture medium to create a P8 culture; and the Nth passage involves using a portion of the P(N-1) culture to create a PN culture. As used herein, the term "passage number" refers to a particular passage designated by a number, e.g., passage number 1 refers to the first passage, passage number 2 refers to the second passage, etc.

[0141] In some embodiments, the volume of the portion of the Nth (e.g., first) culture used to inoculate the N+1th (e.g., second) culture ranges from 100 μl to 100 L or more, depending on the scale of the culture process (e.g., from research scale to pilot scale to commercial production scale). Thus, embodiments provide for removing a volume of between 100 μl and 100 L (e.g., 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, or 1000 μl; 1, 2, 5, 10, 20, 50, 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, or 1000 mL; or 1, 2, 5, 10, 20, 50, or 100 L) from a culture and adding that volume to fresh culture medium. In some embodiments, the ratio of inoculum volume to volume of fresh culture medium is about 1:10 to 1:1000. Thus, in some embodiments, the volume of fresh culture medium is 1 ml to 100,000 L (e.g., 1; 2; 5; 10; 20; 50; 100; 200; 500; or 1000 mL; 1; 2; 5; 10; 20; 50; 100; 200; 500; 1000; 2000; 5000; 10,000; 20,000; 50,000; or 100,000 L).

[0142] In some embodiments, the stability of a microbial community and / or microbial consortium is measured (e.g., in step 508), for example, by deriving a measure of similarity (or dissimilarity) between a first culture and a second culture inoculated with a portion of the first culture, and optionally tracking the measure of similarity as a function of subsequent inoculations. In some embodiments, the taxonomic classification and / or identification of the organisms of the microbial community (e.g., as provided by a taxonomic classifier (e.g., Kraken2) described above) can provide input to such a measure of stability. In some embodiments, functional capabilities or functions (e.g., genes, gene products, functional capabilities and / or activities) provided by and / or present in the microbial community provide input to the measure of stability.

[0143] To compare the similarity (or dissimilarity) of microbial communities, various measures can be used, including estimates of microbial community richness and diversity (see, e.g., Hughes (2001) "Counting the uncountable: statistical approaches to estimating microbial diversity" Appl. Environ. Microbiol. 67:4399-4406; and Ley (2005) "Obesity alters gut microbial ecology" Proc. Natl. Acad. Sci. USA 102:11070-11075, each of which is incorporated herein by reference) and estimates of alpha or beta diversity, such as the Bray-Curtis dissimilarity index (Bray and Curtis (1957) "An Ordination of the Upland Forest Communities of Southern Wisconsin" Ecol. Monogr. 27:325-349, incorporated herein by reference). Bray-Curtis distances can be calculated using the bcdist function in the ecodist package (Goslee (2007) "The ecodist package for dissimilarity-based analysis of ecological data" J Stat Softw 22:1-19, incorporated herein by reference). Bray-Curtis distance matrices for community data, geographic distances and correlations between environmental variables can be calculated using the Mantel function in the vegan package (Oksanen, vegan: Community Ecology Package for R; see, e.g., Legendre, P. and Legendre, L. (2012) Numerical Ecology. 3rd English Edition Elsevier, incorporated herein by reference).

[0144] Several tools are available that provide these and other estimates of microbial community structure (e.g., describing the abundance of community members), such as LIBSHUFF (Schloss (2004) "Integration of microbial ecology and statistics: a test to compare gene libraries" Appl. Environ. Microbiol. 70:5485-5492; and Singleton (2001) "Quantitative comparisons of 16S rRNA gene sequence libraries from environmental samples" Appl. Environ. Microbiol. 67:4374-4376, each of which is incorporated herein by reference), TreeClimber (Martin (2002) "Phylogenetic approaches for describing and comparing the diversity of microbial communities" Appl. Environ. Microbiol. 68:3673-3682; and Schloss (2006) "Introducing TreeClimber, a test to compare microbial community microbial structures” Appl. Environ. Microbiol. 72:2379-2384, each of which is incorporated herein by reference), UniFrac (Lozupone (2005) “UniFrac: a new phylogenetic method for comparing microbial communities” Appl. Environ. Microbiol.71:8228-8235, each of which is incorporated herein by reference), and analysis of molecular variance (AMOVA) (Excoffier (1992) "Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data" Genetics 131:479-491; and Martin (2002) "Phylogenetic approaches for describing and comparing the diversity of microbial communities" Appl. Environ Microbiol. 68:3673-3682, each of which is incorporated herein by reference); DOTUR (Schloss (2005) "Introducing DOTUR, a computer program for defining operational taxonomic units and estimating species richness” Appl. Environ. Microbiol. 71:1501-1506, incorporated herein by reference); and community structure using membership (e.g., the incidence-based Sorenson similarity index), abundance (e.g., Clayton θ (see, e.g., Yue (2001) “A nonparametric estimator of species overlap” Biometrics 57:743-9, incorporated herein by reference), and community richness (see, e.g., Chao (1984) “Non-parametric estimation of the number of classes in a population” Scand. J. Stat. 11:265-270; Chao (2005) “A new statistical approach for assessing similarity of species composition with incidence and abundance data” Ecol. Lett.8:148-159; Chao (2000) "Estimating the number of shared species in two communities" Stat. Sinica 10:227-246; Chao (1992) "Estimating the number of classes via sample coverage" J. Am. Stat. Assoc. 87:210-217; and Chao (2006) "The applications of Laplace's boundary-mode approximations to estimate species richness and shared species richness" Aust. NZ J Stat. 48:117-128, each of which is incorporated herein by reference), which provides several measures of shared species richness, including those of SONS (Schloss (2006) "Introducing SONS, a Tool for Operational Taxonomic Unit-Based Comparisons of Microbial Community Memberships and Structures" Appl Environ. Microbiol. 72:6773-6779, incorporated herein by reference.

[0145] As used herein, the term "stable" when used in reference to a microbial community (e.g., a microbial community, microbial consortium, microbial culture, or other group, set, or collection of microorganisms) refers to a microbial community that does not change significantly from a first culture to a second culture (e.g., as measured by the similarity measurements described above) when a portion of the first culture is used to inoculate a culture medium to create a second culture, when culture conditions, including external factors (light, nutrients, temperature, aeration, etc.) are the same for the first and second cultures. Thus, as used herein, the term "stability," when used in reference to a microbial community (e.g., "microbial community stability"), refers to a qualitative or quantitative indicator or measurement of the change in a microbial community (e.g., a microbial community, microbial consortium, microbial culture, or other group, set, or collection of microorganisms) from a first culture to a second culture (e.g., as measured by a similarity measurement as described above) when a portion of the first culture is used to inoculate a culture medium to create a second culture, when culture conditions, including external factors (light, nutrients, temperature, aeration, etc.) are the same for the first and second cultures.

[0146] Thus, monitoring the similarity measure of the culture, microbial community and / or microbial consortium as a function of passage number (e.g., in steps 508, 509, and 511) provides a measure of the stability of the culture, microbial community and / or microbial consortium in the culture from the passaging process. A decrease in the rate of change of the similarity measure as a function of passage number indicates an increase in the stability of the culture, microbial community and / or microbial consortium. A plateau or stabilization of the similarity measure as a function of passage number indicates that the culture, microbial community and / or microbial consortium is at or approaching maximum stability (e.g., in steps 509 and 511). For example, in some embodiments, a plateau of the stability measure is reached when the stability measure is within 10-20% (e.g., 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20%) of the previous stability measure. In some embodiments, a plateau of the stability measure is reached when the stability measure is within 10-20% (e.g., 10, 11, 12, 13, 14, 15, 16, 17, 18, 19 or 20%) of the previous stability measure for multiple passages (e.g., for 2, 3, 4, 5, 6, 7 or 8 passages). In some embodiments, a plateau is reached when the slope of a line fitted to the stability measure as a function of passage number is zero, substantially zero or effectively zero.

[0147] Furthermore, as used herein, the term "stable," when used with reference to one or more functions provided and / or performed by a microbial community (e.g., a microbial community, microbial consortium, microbial culture, or other group, set, or collection of microorganisms), refers to one or more functions that do not change significantly from a first culture to a second culture (e.g., as measured by examination of metagenomic sequences and / or by inferring function therefrom) when a portion of a first culture is used to inoculate a culture medium to generate a second culture, when culture conditions, including external factors (light, nutrients, temperature, aeration, etc.), are the same for the first and second cultures. Thus, as used herein, the term "stability", when used in reference to one or more functions provided by a microbial community (e.g., "functional stability"), refers to a qualitative or quantitative indicator or measurement of the change in one or more functions provided by a microbial community (e.g., a microbial community, microbial consortium, microbial culture, or other group, set, or collection of microorganisms) from a first culture to a second culture (e.g., as measured by a similarity measurement as described above) when a portion of the first culture is used to inoculate a culture medium to create a second culture, when the culture conditions, including external factors (light, nutrients, temperature, aeration, etc.), are the same for the first and second cultures. Thus, functional stability and microbial stability may be independent, such that a microbial community may be functionally stable, but the member composition and / or member abundance may change such that the microbial community does not have microbial community stability. Thus, a microbial community may have both functional stability and microbial community stability. A microbial community may have neither functional stability nor microbial community stability. A microbial community can have functional stability (e.g., regardless of the state of microbial community stability). A microbial community can have microbial community stability (e.g., regardless of the state of functional stability).

[0148] Although the disclosure herein refers to certain exemplified embodiments, it should be understood that these embodiments are presented by way of example and not by way of limitation. All publications and patents mentioned in the above specification are incorporated herein by reference in their entirety for all purposes. Various modifications and variations of the described compositions, methods and uses of the technology will be apparent to those skilled in the art without departing from the scope and spirit of the described technology. Although the technology has been described in connection with certain exemplary embodiments, it should be understood that the invention as claimed should not be unduly limited to such specific embodiments. Indeed, various modifications of the described modes for carrying out the invention that are apparent to those skilled in the art are intended to be within the scope of the following claims.

Claims

1. Obtaining multiple environmental samples containing organic matter for microbial biomining; mixing the plurality of environmental samples to form a combination of mixed environmental samples; selecting particular mixed environmental samples of said mixed environmental samples based on one or more selection criteria for testing; Incubating the selected particular mixed environmental sample in an environment comprising one or more environmental conditions; and obtaining specific information for microorganisms present in a corresponding microbial consortium of the particular mixed environmental sample in response to determining, based on one or more variable measurements obtained from the culturing, that the particular mixed environmental sample produced a successful microbial biomining result; A method comprising:

2. The method of claim 1, wherein one or more steps of the method are performed iteratively.

3. The method comprising: Cultivating the corresponding microbial consortium of said particular mixed environmental sample into a microbial culture; growing a selected culture portion of said microbial culture in an environment comprising one or more environmental conditions; and obtaining additional identification information for additional microorganisms present in additional corresponding microbial consortia of the selected culture portion in response to determining based on the one or more variable measurements of the selected culture portion that the selected culture portion produced a successful microbial biomining result; Further comprising: The method further includes generating a machine learning model based on training data including the specific information and the additional specific information; the machine learning model at least correlates one or more environmental sample variable values ​​of the plurality of environmental samples with microbial variable values ​​of one or more microbial species and one or more microbial consortia present in the plurality of environmental samples; the method further comprising receiving a request for information regarding one or more variable values, and applying the machine learning model to the one or more variable values ​​to at least one of identify one or more microbial species associated with the one or more variable values, identify one or more environmental characteristics associated with the one or more variable values, and / or identify at least one microbial consortium associated with the one or more variable values; 2. The method of claim 1, wherein the one or more variable values ​​may include at least one of a microbial phenotype, a desired amount of nitrogen fixation, a desired amount of carbon sequestration, one or more environmental sample characteristics, or one or more variables.

4. The machine learning model further correlates one or more of the environmental sample variable values ​​and the microbial variable values ​​with one or more variable values, the one or more variable values ​​being at least a function of the amount of CO sequestered by the biomass. ２ Absolute amount of sequestered CO ２ Ratio of biomass to CO ２ the amount of time that CO is sequestered by the biomass, the absolute amount of nitrogen fixed by the biomass, the mass ratio of the biomass to the absolute amount of fixed nitrogen, ２ Total benefit from sequestration, CO sequestered by the biomass ２ The ratio of the mass of food produced to the mass of CO produced by the biomass ２ The method of claim 3 , wherein comprises an amount of time to be quarantined.

5. The one or more environmental conditions include a specific concentration of N ２ Gas, specific concentration of CO ２ 2. The method of claim 1, comprising at least one of the following: availability of gas, availability of one or more specific nutrients, availability of one or more specific salts, or availability of one or more specific additives.

6. 2. The method of claim 1, wherein the one or more variable measurements include a variable measurement indicative of having increased carbon sequestration, increased nitrogen fixation, increased biomass, or a microorganism capable of meeting a particular life span.

7. 1. A method for generating a microbial consortium that performs a designated function, comprising: a) providing a sample containing a plurality of microorganisms; b) inoculating an Nth volume of growth medium with a portion of the sample to provide an Nth culture; c) growing said N cultures under a series of selective conditions; d) generating an Nth taxonomic classification of microorganisms in said Nth culture; e) inoculating an N+1 volume of the growth medium with a portion of the Nth culture; f) growing the N+1 culture under said set of selective conditions; g) generating an N+1 taxonomic classification of the microorganisms in said N+1 culture; h) using the N+1 taxonomic classification and the N taxonomic classification to derive a measure of microbial community stability of the N+1 culture relative to the N culture; i) iteratively repeating steps (e) through (h), with the N+1 culture serving as the N culture, until the measure of microbial community stability reaches a plateau value; and j) providing a stable N+1 culture as comprising a microbial consortium that performs a designated function; A method comprising:

8. The method of claim 7 , wherein the sample is an environmental sample.

9. The method of claim 8, wherein the environmental sample is a soil or water sample.

10. The method of claim 7 , wherein the growth medium and / or selective conditions select for the specified function.

11. 8. The method of claim 7, wherein generating the taxonomic classification comprises obtaining metagenomic nucleotide sequence data for a culture and identifying taxonomic units present in the culture using analysis of the metagenomic nucleotide sequence data.

12. The method of claim 7, wherein a microbial community having a number of taxonomic units less than said number of taxonomic units of a microbial consortium does not perform said specified function.

13. 8. The method of claim 7, wherein the measures of microbial community stability include measures of richness, diversity, abundance and / or membership.

14. 9. The method of claim 8, further comprising measuring the growth rate of the N or N+1 culture, wherein the growth rate is determined by measuring cell mass as a function of time.

15. The method of claim 7, wherein a microbial community comprising a number of taxonomic units that is at least two and less than the number of taxonomic units of the microbial consortium does not grow in the culture medium under the selective conditions.