Compositions, kits, and methods for assessing microbiome health

Abstract

The present disclosure provides compositions and methods related to microbiome health. In particular, the present disclosure provides novel compositions, kits, and methods for treating and / or monitoring the microbiome health of a subject using metabolic biomarkers.

Description

[0001] Atty. Docket No. UCHI-42155.601 COMPOSITIONS, KITS, AND METHODS FOR ASSESSING MICROBIOME HEALTH CROSS-REFERENCE TO RELATED APPLICATIONS This application claims the benefit of U.S. Provisional Patent Application No. 63 / 609,291, filed December 12, 2023; which is incorporated by reference herein in its entirety. FIELD The present disclosure provides compositions and methods related to microbiome health. In particular, the present disclosure provides novel compositions, kits, and methods for treating and / or monitoring the microbiome health of a subject using metabolic biomarkers. BACKGROUND There is currently no consensus on how to define the healthy gut microbiome, which in turn makes it difficult to define perturbed or imbalanced states, often referred to as dysbiosis. Without such a metric, the application of microbiome-based therapeutics and interventions is largely empirical or based on clinical symptoms. SUMMARY The present disclosure provides compositions, methods, and kits related to microbiome health. In particular, the present disclosure provides novel compositions, methods, and kits for treating and / or monitoring the microbiome health of a subject using metabolic biomarkers. Embodiments of the present disclosure include a biological sample collection kit comprising first and second labelled internal control compounds in an organic preservative solution within a resealable container. In some embodiments, the weights of the first and second labelled internal control compounds are measured and documented prior to sample collection (e.g., prior to distribution of the kit to a user or subject). In some embodiments, the weight of the preservative solution is measured and documented prior to sample collection (e.g., prior to distribution of the kit to a user or subject). In some embodiments, the combined weights of first and second labelled internal control compounds and the organic preservative solution are measured and documented prior to sample collection (e.g., prior to distribution of the kit to a user or subject). In some embodiments, the weight of the sample collection container is measured and documented prior to sample collection (e.g., prior to distribution of the kit to a user or subject). In some embodiments, the combined weights of first and second labelled Atty. Docket No. UCHI-42155.601 internal control compounds, the organic preservative solution, and the sample collection container are measured and documented prior to sample collection (e.g., prior to distribution of the kit to a user or subject). In some embodiments, the mass of biological sample collection kit comprising first and second labelled internal control compounds in an organic preservative solution within a resealable container is known. In some embodiments, the organic preservative is ethanol. In some embodiments, the organic preservative is a 95% ethanol solution. In some embodiments, the first labelled internal control compound is caffeine- (trimethyl-d9). In some embodiments, the caffeine-(trimethyl-d9) is present at a concentration of 0.1 to 2.0 mM (e.g., 0.1 mM, 0.2 mM, 0.3 mM, 0.4 mM, 0.5 mM, 0.6 mM, 0.7 mM, 0.8 mM, 0.9 mM, 1.0 mM, 1.1 mM, 1.2 mM, 1.3 mM, 1.4 mM, 1.5 mM, 1.6 mM, 1.7 mM, 1.8 mM, 1.9 mM, 2.0 mM, or ranges therebetween). In some embodiments, the second labelled internal control compound is sodium acetate-d3 (or acetate-d3). In some embodiments, the acetate-d3 is present at a concentration of 0.1 to 2.0 mM (e.g., 0.1 mM, 0.2 mM, 0.3 mM, 0.4 mM, 0.5 mM, 0.6 mM, 0.7 mM, 0.8 mM, 0.9 mM, 1.0 mM, 1.1 mM, 1.2 mM, 1.3 mM, 1.4 mM, 1.5 mM, 1.6 mM, 1.7 mM, 1.8 mM, 1.9 mM, 2.0 mM, or ranges therebetween). In some embodiments, the first labelled internal control compound is 0.1 to 2.0 mM caffeine-(trimethyl-d9) and the second labelled internal control compound is 0.1 to 2.0 mM acetate-d3 in a 95% ethanol solution. In some embodiments, the organic preservative is 1-10 mL (e.g., 1 mL, 1.5 mL, 2 mL, 2.5 mL, 3 mL, 3.5 mL, 4 mL, 4.5 mL, 5 mL, 6mL, 7mL, 8mL, 9mL 10mL, or ranges or values therebetween) of ethanol. In some embodiments, the kit further comprises one or more of a second sealable container sized to contain the resealable container, a sealable bag, a sample collection spoon, a label, instructions, sterile gloves, a commode collection device, a mailing box or envelope, and pre-paid postage. Embodiments of the present disclosure also include a method of home collection of a biological sample, comprising obtaining or receiving a biological sample collection kit; (b) placing the biological sample within the organic preservative solution in the resealable container; (c) sealing the resealable container; and (d) delivering the resealable container containing the biological sample to a testing facility. In some embodiments, the biological sample is a fecal sample. In some embodiments, the mass of biological sample collection kit comprising first and second labelled Atty. Docket No. UCHI-42155.601 internal control compounds in an organic preservative solution within a resealable container is known and the mass of the fecal sample is capable of being determined. In some embodiments, delivering the resealable container containing the biological sample to a testing facility comprises sending the resealable container by mail or courier. In some embodiments, the biological sample is not frozen for delivering the resealable container containing the biological sample to a testing facility. In some embodiments, the biological sample is delivered to the testing facility within two weeks of placing the biological sample with the organic preservative solution. In some embodiments, the mass of the resealable container is known. Embodiments of the present disclosure also includes a method of obtaining and testing a biological sample from a subject by home collection, comprising: (a) providing the subject with a biological sample collection kit, wherein the kit comprises instructions for the subject to (i) place the biological sample within the organic preservative solution in the resealable container, (ii) seal the resealable container, and (iii) deliver the resealable container containing the biological sample to a testing facility; (b) processing the biological sample; and (c) analyzing the processed biological sample by one or more biophysical techniques. In some embodiments, the biological sample is a fecal sample. In some embodiments, the subject suffers from dysbiosis of gut microbiota. In some embodiments, the subject has received treatment with broad-spectrum antibiotics. In some embodiments, providing the subject with a biological sample collection kit comprises having the kit delivered to the subject by mail or courier. In some embodiments, providing the subject with a biological sample collection kit comprises having the subject obtain the kit from a pharmacy. In some embodiments, providing the subject with a biological sample collection kit comprises having the subject receive the kit by a clinical specialist. In some embodiments, delivering the resealable container containing the biological sample to a testing facility comprises sending the resealable container by mail or courier. In some embodiments, the biological sample is not frozen for delivering the resealable container containing the biological sample to a testing facility. In some embodiments, the biological sample is delivered to the testing within two weeks of placing the biological sample with the organic preservative solution. In some embodiments, the mass of the resealable container is known. In some embodiments, processing the biological sample comprises determining the mass of the biological sample placed within the organic preservative solution. In some Atty. Docket No. UCHI-42155.601 embodiments, processing the biological sample comprises one or more of diluting the biological sample, homogenizing the biological sample, dividing the biological sample into two or more sub-samples, derivatizing the biological sample or sub-sample, and freezing biological sample or sub-sample, the drying the biological sample or sub-sample. In some embodiments, the biophysical technique comprises a mass spectrometry technique. In some embodiments, the mass spectrometry technique comprises gas chromatography-MS (GC-MS) and / or liquid chromatography-MS (LC-MS). In some embodiments, analyzing the processed biological sample by one or more biophysical techniques comprises quantitating the level of one or more metabolites in the sample. In some embodiments, one or more metabolites comprises 5-200 metabolites (e.g., 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, or ranges or values therebetween). In some embodiments, one or more metabolites are selected from butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid. Embodiments of the present disclosure also include a method of quantitating the levels of a panel of metabolites within a biological sample comprising: (a) receiving the biological sample within an organic preservative solution; (b) using a biophysical technique to quantitate a panel of metabolites within the biological sample. In some embodiments, the organic preservative solution comprises ethanol. In some embodiments, the organic preservative solution is a 95% ethanol solution. In some embodiments, the organic preservative solution comprises a first labelled internal control compound. In some embodiments, the first labelled internal control compound is caffeine-(trimethyl-d9) or sodium acetate-d3. In some embodiments, the first labelled internal control compound is present at a concentration of 0.1 to 2.0 mM (e.g., 0.1 mM, 0.2 mM, 0.3 mM, 0.4 mM, 0.5 mM, 0.6 mM, 0.7 mM, 0.8 mM, 0.9 mM, 1.0 mM, 1.1 mM, 1.2 mM, 1.3 mM, 1.4 mM, 1.5 mM, 1.6 mM, 1.7 mM, 1.8 mM, 1.9 mM, 2.0 mM, or ranges therebetween). In some embodiments, the organic preservative solution comprises a second labelled internal control compound. In some embodiments, the first and second labelled internal control compounds are caffeine-(trimethyl-d9) and acetate- d3. In some embodiments, the first and second labelled internal control compounds are present at a concentration of 0.1 to 2.0 mM (e.g., 0.1 mM, 0.2 mM, 0.3 mM, 0.4 mM, 0.5 mM, Atty. Docket No. UCHI-42155.601 0.6 mM, 0.7 mM, 0.8 mM, 0.9 mM, 1.0 mM, 1.1 mM, 1.2 mM, 1.3 mM, 1.4 mM, 1.5 mM, 1.6 mM, 1.7 mM, 1.8 mM, 1.9 mM, 2.0 mM, or ranges therebetween). In some embodiments, the biophysical technique comprises a mass spectrometry technique. In some embodiments, the mass spectrometry technique comprises GC-MS and / or LC-MS. In some embodiments, the panel of metabolites comprises 5-200 metabolites (e.g., 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 140, 160, 180, 200, or ranges or values therebetween). In some embodiments, the panel of metabolites comprises metabolites selected from butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid. In some embodiments, the biological sample is a fecal sample. In some embodiments, the fecal sample is obtained from a subject that suffers from dysbiosis of gut microbiota. In some embodiments, the subject has received treatment with broad-spectrum antibiotics. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1A shows a RF model based on selected 20 metabolites trained from 14 non- diseased subjects and used to predict healthy vs IBD subjects (ST000923, Metabolomics Workbench) AUC = 0.703 for model average across all classes. FIG. 1B shows a RF model ROC analysis distinguishes “healthy” class from IBD patients using metabolic data of 20 markers. RF model was generated from 82 non-diseased subjects and used to predict healthy vs IBD subjects (ST000923, Metabolomics Workbench). ROC was generated by repeat train and test (n=100) with 60% random selection with replacement of the train and test sets. AUC = 0.928 for class prediction of ‘healthy” AUC average for all classes (‘healthy’, ‘Crohn’s’, and ‘UC’) = 0.804. FIG.2 shows a sample processing overview (Example 1). FIG.3 shows a sample processing overview (Example 2). FIG.4 shows a healthy cohort decision tree. FIG.5A shows the age distribution of the cohort of 82. FIG.5B shows the sex distribution of the cohort of 82. FIG.5C shows demographics of the cohort of 82. FIG.6A shows the age distribution of the cohort of 71. Atty. Docket No. UCHI-42155.601 FIG.6B shows the sex distribution of the cohort of 71. FIG.6C shows demographics of the cohort of 71. FIG.7A shows the age distribution of the cohort of 62. FIG.7B shows the sex distribution of the cohort of 62. FIG.7C shows demographics of the cohort of 62. FIG. 8 show the power values & ranges, the 20 metabolite panel, the metabolite chemical structure, the n’s needed for confidence, and the population average and standard deviation. FIG.9A shows a significant association between the metabolite anthranilic acid and age. The metabolite was identified through a multivariate linear regression with age as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG. 9B shows a significant association between the metabolite cis-oleic acid and age. The metabolite was identified through a multivariate linear regression with age as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG. 9C shows a significant association between the metabolite galactose and age. The metabolite was identified through a multivariate linear regression with age as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG. 9D shows a significant association between the metabolite gamma-muricholic acid and age. The metabolite was identified through a multivariate linear regression with age as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG.9E shows a significant association between the metabolite linoleic acid and age. The metabolite was identified through a multivariate linear regression with age as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG. 10A shows a significant association between the metabolite glycolithocholic acid and gender. The metabolite was identified through a multivariate linear regression with gender as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG.10B shows a significant association between the metabolite dihydrocaffeic acid and gender. The metabolite was identified through a multivariate linear regression with gender Atty. Docket No. UCHI-42155.601 as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG. 10C shows a significant association between the metabolite glycochenodeoxycholic acid and gender. The metabolite was identified through a multivariate linear regression with gender as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). FIG. 10D shows a significant association between the metabolite cellobiose and gender. The metabolite was identified through a multivariate linear regression with gender as the independent variable and metabolite concentration as the dependent variable (MaAslin2, *FDR qvalue<0.05 considered significant). DETAILEDDESCRIPTION1. Definitions Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, the present document, including definitions, will control. Preferred methods and materials are described below, although methods and materials similar or equivalent to those described herein can be used in practice or testing of the present disclosure. All publications, patent applications, patents and other references mentioned herein are incorporated by reference in their entirety. The materials, methods, and examples disclosed herein are illustrative only and not intended to be limiting. The terms “comprise(s),” “include(s),” “having,” “has,” “can,” “contain(s),” and variants thereof, as used herein, are intended to be open-ended transitional phrases, terms, or words that do not preclude the possibility of additional acts or structures. The singular forms “a,” “and” and “the” include plural references unless the context clearly dictates otherwise. The present disclosure also contemplates other embodiments “comprising,” “consisting of” and “consisting essentially of,” the embodiments or elements presented herein, whether explicitly set forth or not. Ranges can be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, some embodiments includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms an embodiment. It will be further understood that the endpoints of each Atty. Docket No. UCHI-42155.601 of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that when a value is disclosed that “less than or equal to” the value, “greater than or equal to the value” and possible ranges between values are also disclosed, as appropriately understood by the skilled artisan. For example, if the value “10” is disclosed, then “less than or equal to 10” as well as “greater than or equal to 10” are also disclosed. For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 6- 9, the numbers 7 and 8 are contemplated in addition to 6 and 9, and for the range 6.0-7.0, the number 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly contemplated. “Correlated to” as used herein refers to compared to. The term “and / or”, when used in the context of a list of entities, refers to the entities being present singly or in combination. The terms “administration of” and “administering” a composition as used herein refers to providing a composition of the present disclosure to a subject in need of treatment (e.g., antiviral treatment). The compositions of the present disclosure may be administered by oral, parenteral (e.g., intramuscular, intraperitoneal, intravenous, ICV, intracisternal injection or infusion, subcutaneous injection, nebulization, or implant), by inhalation spray, nasal, vaginal, rectal, sublingual, or topical routes of administration and may be formulated, alone or together, in suitable dosage unit formulations containing conventional non-toxic pharmaceutically acceptable carriers, adjuvants and vehicles appropriate for each route of administration. As used herein, the term “subject” and “patient” as used herein interchangeably refers to any vertebrate, including, but not limited to, a mammal (e.g., cow, pig, camel, llama, horse, goat, rabbit, sheep, hamsters, guinea pig, cat, dog, rat, and mouse, a non-human primate (e.g., a monkey, such as a cynomolgus or rhesus monkey, chimpanzee, macaque, etc.) and a human (e.g., a human of any race). In some embodiments, the subject may be a human or a non-human. In one embodiment, the subject is a human. The subject or patient may be undergoing various forms of treatment. As used herein, the term “treat,” “treating” or “treatment” are each used interchangeably herein to describe reversing, alleviating, or inhibiting the progress of a disease Atty. Docket No. UCHI-42155.601 and / or injury, or one or more symptoms of such disease, to which such term applies. Depending on the condition of the subject, the term also refers to preventing a disease, and includes preventing the onset of a disease, or preventing the symptoms associated with a disease (e.g., viral infection). A treatment may be either performed in an acute or chronic way. The term also refers to reducing the severity of a disease or symptoms associated with such disease prior to affliction with the disease. Such prevention or reduction of the severity of a disease prior to affliction refers to administration of a treatment to a subject that is not at the time of administration afflicted with the disease. “Preventing” also refers to preventing the recurrence of a disease or of one or more symptoms associated with such disease. Unless otherwise defined herein, scientific and technical terms used in connection with the present disclosure shall have the meanings that are commonly understood by those of ordinary skill in the art. For example, any nomenclatures 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 that are well known and commonly used in the art. The meaning and scope of the terms should be clear; in the event, however of any latent ambiguity, definitions provided herein take precedent over any dictionary or extrinsic definition. Further, unless otherwise required by context, singular terms shall include pluralities and plural terms shall include the singular. 2. Sample collection and processing Embodiments of the present disclosure include kits, compositions, and methods for obtaining a biological sample (e.g., fecal sample) from a subject, for processing the biological sample, for evaluating the quality of collected sample and / or sample collection methods (e.g., to assess the amount of evaporation and / or spillage of the biological sample during sample collection and / or sample storage, to determine the mass or volume of the sample collected, etc.), and for metabolomic analysis of the biological sample (e.g., quantification and / or identification of metabolites (e.g., exogenous or endogenous small molecules (e.g., small molecules found within cells, biofluids, tissues, or organisms (e.g., low-molecular weight molecules, such as amino acids, sugars, fatty acids, lipids, and steroids))) corresponding to a biological phenotype or genotype). In some embodiments, a biological sample means a gut microbiota sample (e.g., produced directly by the subject, produced by and / or derived by the subject’s microbiota (e.g., gut microbiota) and / or transformed from dietary, xenobiotic, or other exogenous sources). Atty. Docket No. UCHI-42155.601 In some embodiments, the biological sample is a fecal sample. In some embodiments, a kit is provided for the collection of a fecal sample from a subject. In some embodiments, the kit is intended for use by a clinician, technician, or other trained user to collect a fecal sample from a subject. In some embodiments, the kit is intended for use by a subject for self-collection of a fecal sample. In some embodiments, the kit contains materials, (e.g., sealable container(s), label(s), bag(s), gloves, mailing boxes / envelopes, in-toilet feces catcher, a spoon / scoop, etc.), reagents (e.g., preservative solution, internal controls, etc.), and instructions for using the kits to collect a fecal sample. In some embodiments, a kit is used to obtain the biological sample. In some embodiments, the kit contains all components for collecting and containing the biological sample including at least one (e.g., one or more) sample collection container (e.g., a container with an airtight lid to collect the sample at its point of origin) that is labeled for identification (e.g. a unique sample identifier) and that has a known weight, at least one (e.g., one or more) sample holding containers configured to contain the sample collection container (e.g., an outer container with an airtight lid to hold the sample collection container following collection), at least one (e.g., one or more) device to assist with sample collection at its point of origin (e.g., a toilet collection device (e.g., a bucket fitted to a standard size toilet seat and / or a sample scoop (e.g., a spoon that has a known volume, a spoon with demarcated lines to identify volume, a spoon labeled with various volumes)), a label or instructions for biological sample collection and / or transport, and gloves (e.g., waterproof gloves). In some embodiments, a collection kit comprises a sample collection reagent (e.g., fluid). In some embodiments, the sample collection reagent (e.g., fluid) is contained within the (resealable) sample collection container. In some embodiments, the sample collection reagent (e.g., fluid) includes a preservative (e.g., a liquid (e.g., ethanol (e.g., about 70%, 75%, 80%, 85%, 90%, or 95% ethanol))) capable of stabilizing components of the biological sample (e.g., preventing degradation of metabolites present in the sample at the time of collection) and preventing microbial growth (e.g., where the bacterial community composition is preserved sample and is comparable with a fresh frozen sample). In some embodiments, the weight of the sample collection reagent (e.g., fluid) is measured and documented prior to sample collection prior to sample collection (e.g., prior to distribution of the kit to a user or subject). In some embodiments, the combined weight of the sample collection reagent (e.g., fluid) and the sample collection container is measured and documented prior to sample collection (e.g., prior to distribution of the kit to a user or subject). In some embodiments, an amount sample Atty. Docket No. UCHI-42155.601 collection reagent (e.g., fluid) is included in the sample collection container (e.g., about 1 mL, 2 mL, 3 mL, 4 mL, 5 mL, or more, or values or ranges therebetween). In some embodiments, the sample collection reagent (e.g., fluid) includes at least one (e.g., one or more) internal standard(s) (e.g., a molecule that is stable in ethanol, compatible with mass spectrometry methods, of known structure (e.g., structurally identical to known biological metabolites) etc.). In some embodiments, an internal standard molecule comprises one or more features or characteristics that are detectable by a biophysical technique utilized in the methods described herein. For example, an internal standard molecule may comprise a label. In some embodiments, an internal standard molecule is isotopically labelled (e.g.,15N,2H,13C, etc.). In some embodiments, an internal standard molecule is deuterium labeled. In some embodiments, an internal standard molecule is provided (e.g., Caffeine-(trimethyl-d9) in a sample collection reagent (e.g., fluid) that has a molecular weight of 100 to 500 Da (e.g., 100 Da, 125 Da, 150 Da, 175 Da, 200 Da, 225 Da, 250 Da, 300 Da, 350 Da, 400 Da, 500 Da, or ranges therebetween) and is compatible with analysis by a biophysical technique, such as GC- MS (e.g., electron impact (EI) for TMS-MOX derivatization, and collision-induced (CI) with PFBBr derivatization) and LC (e.g., QQQ, and QTOF). In some embodiments, an internal standard molecule is provided (e.g., acetate-d3) in a sample collection reagent (e.g., fluid) that has a molecular weight of 50 to 150 Da (e.g., 50 Da, 60 Da, 70 Da, 80 Da, 90 Da, 100 Da, 110 Da, 120 Da, 130 Da, 140 Da, 150 Da, or ranges therebetween) and comprises a structure (e.g., contains a carboxylic acid) compatible with a GC-MS analysis of short chain fatty acid (e.g., PFBBr derivatization). In some embodiments, a sample collection reagent comprises 1-10 mL (e.g., 1 ml, 2 ml, 3 ml, 4 ml, 5 ml, 6 mL 7 mL 8 mL 9 mL 10 mL or ranges therebetween) of liquid. In some embodiments, the sample collection reagent comprises ethanol (e.g., 70%, 75%, 80%, 85%, 90%, 95%). In some embodiments, the sample collection reagent comprises an internal standard molecule at a concentration of 0.1 to 10 mM (e.g., 0.1 mM, 0.2 mM, 0.5 mM, 1 mM, 2 mM, 5 mM, 10 mM, or ranges therebetween. In some embodiments, the sample collection reagent comprises one or more of Caffeine-(trimethyl-d9), acetate-d3,15N -Vitamin B5;13C-5- hydroxyindole-3-acetic acid;13C-DL-Alanine;13C-L-kynurenine;13C-L-Phenylalanine;13C-L- Tryptophan;13C-Nicotinamide;13C-Nicotinic Acid;13C-Palmitate;13C-Tyrosine;13C,15N- Proline; D3(acetyl) - Melatonin; D3 (acetyl) - N-acetyl-5-hydroxytryptamine; D3 (ring) - DL- Norepinephrine; D3-Acetate; D4-5-Methoxytryptamine; D4-Biotin; D4-Chenodeoxycholic acid; D4-Cholic acid; D4-Deoxycholic acid; D4-Glycochenodeoxycholic acid; D4-Glycocholic acid; D4-Glycodeoxycholic acid; D4-Glycolithocholic acid; D4-Glycoursodeoxycholic acid; Atty. Docket No. UCHI-42155.601 D4-Lithocholic acid; D4-Taurochenodeoxycholic acid; D4-Taurocholic acid; D4- Taurodeoxycholic acid; D4-Taurolithocholic acid; D4-Tauroursodeoxycholic acid; D4- Tryptamine; D4-Ursodeoxycholic acid; D5(ring) - Kynurenic acid; D5-beta-Muricholic acid; D5-gamme-Muricholic acid; D5-omega-Muricholic acid; D5-Propionate; D6-Phenol; D6- Succinate; D7-Butyrate; D7,15N-Proline; D8-Valine; D9-Isovaleric acid; and D9-Pentanoic acid. In some embodiments, the sample collection reagent comprises 0.1 to 10 mM (e.g., 0.25 to 0.75 mM, 0.4 to 0.6 mM, 0.49 mM) caffeine-(trimethyl-d9). In some embodiments, the sample collection reagent comprises 0.1 to 10 mM (e.g., 0.75 to 1.5 mM 1.05 to 1.25 mM, 1.15 mM) sodium acetate-d3. In some embodiments, a sample collection reagent comprises 3 mL of a 95% ethanol liquid preservative and a final concentration of 0.49 mM caffeine-(trimethyl-d9) and 1.15 mM acetate-d3. In some embodiments, the concentration of at least one (e.g., one or more) internal standard molecule (e.g., sodium acetate-d3, caffeine-(trimethyl-d9), etc.) in the collected biological sample is measured to determine if evaporation or spillage occurred during sample collection, storage, transport, etc. In some embodiments, internal standard (ITSD) levels are measured for each sample and corrected for initial dilution based on mass of sample added to each vial. In some embodiments, samples with an ITSD accuracy error greater than an accuracy threshold (e.g., >20%, >18%, >16%, >14%, >12%, >10%, >8%, >6%, >4%, >2%, >1%) and / or samples with a precision error greater than a precision threshold (e.g., >20%, >18%, >16%, >14%, >12%, >10%, >8%, >6%, >4%, >2%, >1%) are evaluated for mishandling such as a spill or evaporation. In some experiments conducted during development of embodiments herein, a 12% accuracy threshold and 8% precision threshold were used based on Compliant Analytical Method Validation Plan and Template (CLIA) recommendations. In some embodiments, samples with values (e.g., sample volume and metabolite level) which fall outside the CLIA recommendations are recalculated using corrected quantified ITSD levels. In some embodiments, by determining the mass of the biological sample collected (e.g., by subtracting known masses of kit components from the total mass after collection, and correcting for spilled and / or evaporated material) it is ensured that the composition of the collected biological sample includes a known amount of biological sample (e.g., one measured scoop of fecal sample), a known amount of preservative (e.g., three mL of 95% ethanol) that is spiked with a known amount of internal standard (e.g., a final internal standard concentration of 0.49 mM caffeine-(trimethyl-d9) and / or 1.15 mM sodium acetate-d3). In some embodiments, the known amount of biological sample is added to the sample collection container containing the known amount of preservative spiked with the known amount of internal standard and the Atty. Docket No. UCHI-42155.601 weight of the composition and sample collection container is recorded. In some embodiments, the mass of the biological sample is calculated. Depending upon the desired sample collection protocol, one or multiple (e.g.. 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) samples may be collected from a subject. In some embodiments, a single sample is collected (e.g., using a kit described herein) from a single defecation event. In some embodiments, a two or more (e.g., 2, 3, or more) samples are collected (e.g., using a kit described herein) from a single defecation event. In some embodiments, separate samples are collected from two or more (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) defecation events. In some embodiments, samples are collected from separate defecation events on separate days (e.g., consecutive days, defecation events spaced out by 2, 3, 4, 5, 6, 7, or more days, etc.). In some embodiments, samples (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) are collected at intervals (e.g., once a week, every 2, 3, 4, 5, 6, 7, 8 weeks, etc.) spaced out over a span of weeks (e.g., 2, 3, 4, etc.), months (e.g., 2, 3, 4, 56, 8, 10, 12, etc.), or years (1, 2, 3, 4, or more). In some embodiments, the method for obtaining a biological sample includes sample collection and documentation of collection once per twenty-four hours. In some embodiments, a biological sample is collected and collection is documented once per day (e.g., once per twenty-four hours + / - about six hours). In some embodiments, a biological sample is collected and collection is documented once per day during a period equal to three days (e.g., not more than three biological samples are collected over seventy-two hours). In some embodiments, a biological sample is collected and collection is documented once per day during a period equal to four days (e.g., NMT four biological samples are collected over ninety-six hours). In some embodiments, a biological sample is collected and collection is documented once per day during a period equal to seven days (e.g., NMT seven biological samples are collected over one-hundred and seventy-six hours). In some embodiments, a biological sample is collected and collection is documented once per day during a period equal to fourteen days (e.g., NMT fourteen biological samples are collected over three-hundred and thirty-six hours). In some embodiments, the method for storing a biological sample includes documentation of the length of storage and storage for twenty-four hours. In some embodiments, a biological sample is stored for one day (e.g., twenty-four hours + / - about six hours). In some embodiments, a biological sample is stored for two days. In some embodiments, a biological sample is stored for three days. In some embodiments, a biological sample is stored for four days. In some embodiments, a biological sample is stored for five days. In some embodiments, a biological sample is stored for six days. In some embodiments, a biological sample is stored for seven days. In some embodiments, a biological sample is Atty. Docket No. UCHI-42155.601 stored for eight days. In some embodiments, a biological sample is stored for nine days. In some embodiments, a biological sample is stored for ten days. In some embodiments, a biological sample is stored for eleven days. In some embodiments, a biological sample is stored for twelve days. In some embodiments, a biological sample is stored for thirteen days. In some embodiments, a biological sample is stored for fourteen days. In some embodiments, collection of a biological sample comprises sequestering feces following a defecation event (e.g., in a device provided in a sample collection kit) and using an instrument (e.g., spoon, scoop) to obtain approximately the specified volume of fecal material into a sealable collection contained comprising the collection reagent (e.g., fluid). In some embodiments, the preservative present in the collection reagents allows samples to be stored for a period of time (e.g., up to 4 days, up to 7 days, up to 10 days, up to 14 days, up to 20 days, or more) without significant degradation of metabolites or microbial growth. In some embodiments, the method for storing a biological sample includes documentation of the temperature of storage and storage at temperatures including 0°C, 1°C, 2°C 3°C, 4°C, 5°C, 6°C, 7°C, 8°C, 9°C, 10°C, 11°C, 12°C, 13°C, 14°C, 15°C, 16°C, 17°C, 18°C, 19°C, 20°C, 21°C, 22°C, 23°C, 24°C, 25°C, 26°C, 27°C, 28°C, 29°C, 30°C, 31°C, 32°C, 33°C, 34°C, 35°C, 36°C, 37°C, 38°C, 39°C, 40°C. In some embodiments, the method for storing a biological sample includes storage at a temperatures including 20°C, 21°C, 22°C, 23°C, 24°C, 25°C, 26°C, 27°C, 28°C, 29°C, 30°C, 31°C, 32°C, 33°C, 34°C, 35°C, 36°C, 37°C, 38°C, 39°C, 40°C for a period of time ranging from one day to fourteen days. In some embodiments, the method for storing a biological sample includes documentation of the temperature of storage and storage at a temperature of less than 0°C (e.g., -20°C, -80°C). In some embodiments, sample collection is performed by the subject being analyzed by the methods herein. In some embodiments, the subject collects one or more samples, stores them for a period of time (e.g., not exceeding 14 days), and delivers (e.g., by mail or courier) the sample(s) to a testing facility (e.g., hospital, pharmacy, clinical testing facility, laboratory, etc.). In some embodiments, the collected sample is not frozen or refrigerated during storage by the subject (between initial collection and delivery to testing facility). In other embodiments, the collected sample may be frozen or refrigerated during all or a portion of storage by the subject (between initial collection and delivery to testing facility). In some embodiments, the collected sample(s) need not be refrigerated or frozen during delivery to the testing facility. In some embodiments, the method for storing a biological sample includes aliquoting an amount of the biological sample into a labeled secondary container (e.g., a centrifuge tube, a microcentrifuge tube). In some embodiments, the empty weight of the labeled secondary Atty. Docket No. UCHI-42155.601 container is recorded prior to aliquot. In some embodiments, the filled weight of the labeled secondary container is recorded following aliquot. In some embodiments, the aliquot mass is calculated and recorded. In some embodiments, the preservative mass is calculated and recorded. In some embodiments, the biological sample mass is calculated and recorded. In some embodiments, the aliquot mass is calculated and recorded. In some embodiments, the aliquot mass and the mass of the aliquot is calculated and recorded. In some embodiments, the empty weight and the filled weight of the labeled secondary container is recorded and the mass of the aliquot is calculated and recorded. In some embodiments, upon receipt of one or more samples from a subject (or a clinician or representative of a subject), the mass of the collected sample is determined. In some embodiments, the collected sample is processed via one or more processing steps. Sample processing steps may include but are not limited to diluting the biological sample, homogenizing the biological sample, dividing the biological sample into two or more sub- samples, derivatizing the biological sample or sub-sample, and freezing biological sample or sub-sample, the drying the biological sample or sub-sample. In some embodiments, although a sample need not be refrigerated or frozen when stored by the subject and / or during delivery of the sample to a testing facility (e.g., for a time span of up to 14 days), a sample (or a processed sample) may be frozen or refrigerated at the testing facility (or a third-party location) prior to analysis of the sample. In some embodiments, when multiple samples are provided by a subject (or a clinician or representative of a subject), the samples may be processed and / or analyzed separately. In other embodiments, two or more samples are combined and processed / analyzed as a single sample. In some embodiments, a biological sample is homogenized by shaking, agitation, stirring, or any suitable physical / mechanical means. In some embodiments, a biological sample is diluted (e.g., with water, buffer, etc.) before, after, or during homogenization. In some embodiments, a biological sample is divided (or aliquots are taken from the sample) into 2 or more (e.g., 2, 3, 4, 5, 6, 7, 8, or more) sub-samples. In some embodiments, the sample (or sub-sample) is further processed according to the specific requirements of the biophysical technique used to analyze the sample. In some embodiments, the sub-samples of a single sample are processed differently and / or analyzed by different techniques. In some embodiments, different techniques are utilized in order to quantify different metabolites in the biomarker panel. A sample or subsample may be dried Atty. Docket No. UCHI-42155.601 (e.g., under inert gas, lyophilized, etc.), diluted (e.g., in a solvent or buffer for analysis), reconstituted (e.g., after drying) in a solvent or buffer for analysis, or derivatized. Sample derivatization is a general term used for a chemical transformation designed to improve analytical capabilities of a sample. Derivatization reactions used in MS often overlap with those used in other venues of analytical chemistry. However, there are some derivatization reactions specifically designed for MS, such as those that enhance ionization or introduce a specific mass shift to the sample ions that becomes evident in the mass spectrum. Reviews and publications on derivatization reactions include: Zaikin and Halket, Encyclopedia of Mass Spectrometry, Vol. 6 (Ionization Methods), M. Gross, Ed. (Elsevier, New York, 2007); Halket and Zaikin, Encyclopedia of Mass Spectrometry, Vol. 8 (Hyphenated Methods), W.M.A. Niessen, Ed. (Elsevier, New York, 2006).; Knapp, Meth. Enzymol.193, 314–329 (1990).; Quirke, et al., Anal. Chem.66, 1302–1315 (1994).; Halket, et al. J. Exper. Botany56(410), 219–243 (2005).; Gao et al. J. Chromatogr., B825, 98–110 (2005).; Santa et al. Drug Discov. Ther.1(2), 108–118 (2007).; and Lin et al. J. Food Drug. Anal.16(1), 1–10 (2008).; incorporated by reference in their entireties. Any suitable methods of sample derivatization that finds use with the biophysical techniques described herein (e.g., LC-MS, GC-MS, etc.) or understood in the filed may find use in embodiments of the present technology. 3. Sample analysis Embodiments of the present disclosure also include methods of analyzing a biological sample (e.g., fecal sample) from a subject to isolate and / or detect and / or determine (quantitatively) the levels of various biomarkers (e.g., microbiome biomarkers, metabolomic biomarkers, etc.). In some embodiments, a metabolomics screen is performed on a sample (e.g., fecal sample) from a subject identify, quantify, etc. various metabolites present. In some embodiments, a metabolomic screen is performed to detect and / or quantify as many metabolites as are detectable by the methods used. In some embodiments, one or more key metabolites (e.g., metabolites identified herein) are detected and / or quantified. In some embodiments, 2 or more metabolites are detected and / or quantified (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 15, 20, 25, 30, 35, 40, 45, 50, 75, 100, or more). In some embodiments, 100 or fewer metabolites are detected and / or quantified (e.g., 100, 80, 60, 50, 40, 30, 20, 10, 5, or fewer). In some embodiments, 1-20 (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20) of the metabolites described herein are detected and / or quantified. Atty. Docket No. UCHI-42155.601 In some embodiments, any technique and / or instrumentation suitable for detecting / quantifying small molecules in a complex environment may find use in embodiments herein. In some embodiments, analytical platforms (e.g., high-throughput platforms, automated platforms, etc.) utilizing nuclear magnetic resonance (NMR) spectroscopy, gas chromatography (GC), liquid chromatography (LC), and / or mass spectrometry (MS) are employed to measure the metabolites within a biological sample. In some embodiments, NMR, GC, and / or LC coupled to MS is utilized. In some embodiments, a point-of-care (POC) device is utilized for detecting / quantifying small molecules in a complex environment. In some embodiments, a POC device allows for detection / quantification of metabolite biomarkers at a clinic, hospital, or other testing facility accessible or near to a patient, at reduced cost compared to traditional instruments, and in a short time span. In some embodiments, a POC device utilizes biosensors comprising antibodies, antibody, fragments, aptamers, etc. for binding to metabolite biomarkers and optical, fluorescent, luminescent, electrochemical, etc. detection for quantifying the metabolite biomarkers. Embodiments herein are not limited by the technique, POC or otherwise, used for quantification of the metabolite biomarkers. Mass spectrometry can accurately identify / quantify thousands of metabolites within complex biological samples. In some embodiments, metabolites are detected / quantified in a biological sample using MS techniques, such as MALDI / TOF (matrix assisted laser desorption / ionization / time-of-flight), SELDI / TOF (surface-enhanced laser desorption / ionization / TOF), liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), high performance liquid chromatography- mass spectrometry (HPLC-MS), capillary electrophoresis-mass spectrometry (CE-MS), nuclear magnetic resonance spectrometry (NMR), tandem mass spectrometry (e.g., MS / MS, MS / MS / MS, MSnetc.), secondary ion mass spectrometry (SIMS), or ion mobility spectrometry (e.g. GC-IMS, IMS-MS, LC-IMS, LC-IMS-MS etc.). Mass spectrometry methods are well known in the art and have been used to quantify and / or identify biomolecules, such metabolites. In certain embodiments, a gas phase ion spectrophotometer is used. In other embodiments, laser-desorption / ionization mass spectrometry is used to identify metabolites. Modern laser desorption / ionization mass spectrometry ("LDI-MS") can be practiced in two main variations: matrix assisted laser desorption / ionization ("MALDI") mass spectrometry and surface-enhanced laser desorption / ionization ("SELDI"). In MALDI, the metabolite is mixed with a solution containing a matrix, and a drop of the liquid is placed Atty. Docket No. UCHI-42155.601 on the surface of a substrate. The matrix solution then co-crystallizes with the biomarkers. The substrate is inserted into the mass spectrometer. Laser energy is directed to the substrate surface where it desorbs and ionizes the proteins without significantly fragmenting them. However, MALDI has limitations as an analytical tool. It does not provide means for fractionating the biological fluid, and the matrix material can interfere with detection, especially for low molecular weight analytes. In SELDI, the substrate surface is modified so that it is an active participant in the desorption process. In one variant, the surface is derivatized with adsorbent and / or capture reagents that selectively bind the biomarker of interest. In another variant, the surface is derivatized with energy absorbing molecules that are not desorbed when struck with the laser. In another variant, the surface is derivatized with molecules that bind the biomarker of interest and that contain a photolytic bond that is broken upon application of the laser. In each of these methods, the derivatizing agent generally is localized to a specific location on the substrate surface where the sample is applied. The two methods can be combined by, for example, using a SELDI affinity surface to capture an analyte (e.g., biomarker) and adding matrix-containing liquid to the captured analyte to provide the energy absorbing material. For additional information regarding mass spectrometers, see, e.g., Principles of Instrumental Analysis, 3rd edition., Skoog, Saunders College Publishing, Philadelphia, 1985; and Kirk-Othmer Encyclopedia of Chemical Technology, 4.sup.th ed. Vol.15 (John Wiley & Sons, New York 1995), pp.1071-1094; incorporated by reference in their entireties. In some embodiments, the data from mass spectrometry is represented as a mass chromatogram. A "mass chromatogram" is a representation of mass spectrometry data as a chromatogram. Typically, the x-axis represents time and the y-axis represents signal intensity. In one aspect the mass chromatogram is a total ion current (TIC) chromatogram. In another aspect, the mass chromatogram is a base peak chromatogram. In other embodiments, the mass chromatogram is a selected ion monitoring (SIM) chromatogram. In yet another embodiment, the mass chromatogram is a selected reaction monitoring (SRM) chromatogram. In one embodiment, the mass chromatogram is an extracted ion chromatogram (EIC). In an EIC, the abundance of a single mass to charge ratio (m / z) feature is reported for the entire run. The total intensity or base peak intensity within a mass tolerance window around a particular analyte's mass to charge ratio is plotted at every point in the analysis. The size of the mass tolerance window typically depends on the mass accuracy and mass resolution of the instrument collecting the data. As used herein, the term Atty. Docket No. UCHI-42155.601 "feature" refers to a single small metabolite, or a fragment of a metabolite. In some embodiments, the term feature may also include noise upon further investigation. In some embodiments, detection of the presence of a metabolite involves detection of signal intensity. This, in turn, can reflect the quantity and character of a biomarker. For example, in certain embodiments, the signal strength of peak values from spectra of a first sample and a second sample can be compared (e.g., visually, by computer analysis etc.) to determine the relative amounts of particular metabolites. Software programs such as the Biomarker Wizard program (Ciphergen Biosystems, Inc., Fremont, Calif.) can be used to aid in analyzing mass spectra. The mass spectrometers and their techniques are well known. A person skilled in the art understands that any of the components of a mass spectrometer, e.g., desorption source, mass analyzer, detect, etc., and varied sample preparations can be combined with other suitable components or preparations described herein, or to those known in the art. For example, in some embodiments a control sample may contain heavy atoms, e.g.13C,2H, thereby permitting the test sample to be mixed with the known control sample in the same mass spectrometry run. In some embodiments, a collected biological sample contains one or more internal controls. In some embodiments, a laser desorption time-of-flight (TOF) mass spectrometer is used. In laser desorption mass spectrometry, a substrate with a bound marker is introduced into an inlet system. The marker is desorbed and ionized into the gas phase by laser from the ionization source. The ions generated are collected by an ion optic assembly, and then in a time-of-flight mass analyzer, ions are accelerated through a short high voltage field and let drift into a high vacuum chamber. At the far end of the high vacuum chamber, the accelerated ions strike a sensitive detector surface at a different time. Since the time-of-flight is a function of the mass of the ions, the elapsed time between ion formation and ion detector impact can be used to identify the presence or absence of molecules of specific mass to charge ratio. In one embodiment of the invention, levels of metabolites are detected by ESI- QTOF mass spectrometry. Methods of detecting metabolites also include the use of surface plasmon resonance (SPR). The SPR biosensing technology has been combined with MALDI- TOF mass spectrometry for the desorption and identification of metabolites. Data for statistical analysis can be extracted from chromatograms (spectra of mass signals) using software for statistical methods known in the art. Statistics is the science of making effective use of numerical data relating to groups of individuals or experiments. Methods for statistical analysis are well-known in the art. In one embodiment a computer is Atty. Docket No. UCHI-42155.601 used for statistical analysis. In one embodiment, the Agilent Mass Profiler or Mass Profiler Professional software is used for statistical analysis. In another embodiment, the Agilent Mass Hunter software Qual software is used for statistical analysis. In other embodiments, alternative statistical analysis methods can be used. Such other statistical methods include the Analysis of Variance (ANOVA) test, Chi-square test, Correlation test, Factor analysis test, Mann-Whitney U test, Mean square weighted derivation (MSWD), Pearson product-moment correlation coefficient, Regression analysis, Spearman's rank correlation coefficient, Student's T test, Welch's T-test, Tukey's test, and Time series analysis. In various embodiments, signals from mass spectrometry are transformed in different ways to improve the performance of the method. Either individual signals or summaries of the distributions of signals (such as mean, median or variance) can be so transformed. Possible transformations include taking the logarithm, taking some positive or negative power, for example the square root or inverse, or taking the arcsin (Myers, Classical and Modern Regression with Applications, 2nd edition, Duxbury Press, 1990). In some embodiments, samples are analyzed by one or more biophysical techniques in order to accurately quantitate multiple metabolites with the sample. In some embodiments, a biological sample obtained from a subject is separated into multiple sub- samples for analysis by separate techniques. In some embodiments, a sample (or sub-samples thereof) is analyzed by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) methods. More particularly, a sample (or sub- samples thereof) maybe analyzed by one or more of chemical ionization (CI) GC-MS (e.g., (- ) CI-GC-MS, Agilent 8890) for the level of volatile compounds such as short chain fatty acids (e.g., butyrate), electron ionization (EI) GC-MS (e.g., (+)EI-GC-MS, Agilent 7890B) for the level of low molecular weight compound classes such as carbohydrates and amino acids, negative mode liquid chromatography-electrospray ionization-quadrupole time-of- flight-MS (e.g., (-)LC-ESI-QTOF-MS, Agilent 6546) for the measurement of bile acids from the primary, secondary and glyco / tauro-conjugated subclasses, and LC-triple quadrupole-MS (e.g., (+)LC-ESI-QQQ-MS, Agilent 6547) to measure indole and tryptophan catabolites. 4. Biomarker selection and assessing microbiome health Embodiments of the present disclosure also include methods, compositions, and kits for identifying, selecting, and analyzing (e.g., quantifying) at least one (e.g., one or more) biomarker (e.g., a metabolite (e.g., an exogenous or endogenous small molecule (e.g., a small Atty. Docket No. UCHI-42155.601 molecule found within cells, biofluids, tissues, or organisms (e.g., a low-molecular weight molecule, such as amino acids, sugars, fatty acids, lipids, and steroids))) corresponding to a biological phenotype or genotype). In some embodiments, the level of at least one (e.g., one or more) prognostic biomarker in a sample from a subject is indicative / prognostic / diagnostic of a condition / outcome in the subject (e.g., a prognostic biomarker). Experiments were conducted during development of embodiments herein to identify a panel of biomarkers that when quantitated individually and / or collectively provide information about the health status of a subject (e.g., health and / or makeup of the subject’s gut microbiome) and / or a treatment course of action to maintain or enhance the health of the subject. In some embodiments, methods of identifying such biomarkers are within the scope herein. In some embodiments, a method for identifying a biomarker includes machine learning algorithms to select biomarkers which (i) represent multiple molecular classes (e.g., representative of multiple biological subsystems (e.g., the gut microbiome (e.g., microbes (e.g., bacteria, fungi, protists) and / or their genetic elements (e.g., genomes) in the digestive tract of a subject)); (ii) are present (e.g., quantifiable) within and among healthy individuals; and (iii) statistically powered (e.g., the probability that a test of significance will detect a deviation from the null hypothesis, the likelihood of detecting an effect when there actually is an effect). In some embodiments, a biomarker is tagged with the molecular class to which it belongs (e.g., bile acid, organic acid, fatty acid, amino acid, carbohydrate / sugar, indole). As described herein, health is defined by remaining within a reference range for a given biomarker. Thus, in some embodiments, a simulated population mean of a biomarker was calculated and a reference range (e.g., 1, 2, 3, and 4 standard deviations of the population mean ) was provided to simulate mild to severe unhealth. In some embodiments, random forests (randomForest, boruta) and neural nets (neuralnet) are used to generate models trained on the reference range for a given biomarker with repeat (nperm=100) train and test of 60% and 40% of the dataset, respectively. In some embodiments, default parameters are used for neural nets and ntree=100 and nperm=1000 used for random forests. In some embodiments, these models are used to test external datasets (e.g., from the Metabolomic Workbench (e.g., ST000923, ST000665, ST000091,ST000106,ST001515,ST000923,ST000992)) from biological samples to determine whether they can identify the “healthy” vs “unhealthy” as defined by the individual parameters (eg. IBD vs nonIBD). In some embodiments, to run performance metrics (e.g., accuracy and precision), molecular class is defined as “healthy” and “unhealthy” for each external dataset sample and class predictions generated using repeat train and test (n=50 times) and AUC are Atty. Docket No. UCHI-42155.601 calculated for the P Rate (Precision Rate ) vs TP Rate curve (True Positive Rate) for each class of as well as the combined classes (healthy, unhealthy, and healthy+unhealthy). See FIG.1. In some embodiments, the method for selecting a panel of biomarkers (e.g., a metabolite panel) includes randomly selecting two biomarkers from each molecular class, generating a model from an internal dataset, and testing the model on external datasets with performance of the two biomarkers model evaluated in the same manner as a full model. In some embodiments, evaluation of a model is performed iteratively (nperm=5000) with replacement. In some embodiments, identification of a top performing model is determined by selecting a model that has a minimal AUC > 0.8, a threshold Gini score, a threshold node purity, and / or a threshold biomarker variance. In some embodiments, biomarkers that repeatedly meet the selected criteria are then used to generate a final model and evaluated for model performance both internally in a mock training set as well as across the external studies to confirm its discriminating capacity. Thus, in some embodiments, the composition of a panel of biomarkers includes any one or more selected from the group consisting of those included in Table 1. Table 1. List of metabolites, population average, and reference range Population Average (µg / mL) and Reference range (µg / mL) Atty. Docket No. UCHI-42155.601 2-aminobutyric acid* 0.2464 0 - 1.017 * lite quantified in a fecal sample from a statistically significant population of healthy human subjects. In Table 1, the “reference range represents the range of values of the metabolite quantified in a fecal sample from a statistically significant population of healthy human subjects. In some embodiments, the metabolite biomarkers, panels thereof, levels thereof, and composite scores thereof that are provided herein correlate with normal gut microbiome levels (e.g., relative to the general healthy population). In some embodiments, metabolite biomarker levels and / or a composite of multiple metabolite biomarkers within a range relative to a population average and / or reference range indicates normal (e.g., not atypically high, not atypically low) levels of gut microflora (e.g., good / healthy / commensal bacteria, unhealthy / bad / pathogenic bacteria, etc.). In some embodiments, metabolite biomarker levels and / or a composite of multiple metabolite biomarkers that are beyond a threshold different (e.g., higher) than a population average and / or reference range indicates abnormal (e.g., atypically high, atypically low) levels of gut microflora (e.g., good / healthy / commensal bacteria, unhealthy / bad / pathogenic bacteria, etc.). In some embodiments, metabolite biomarker levels and / or a composite of multiple metabolite biomarkers that are beyond a threshold different (e.g., higher) than a population average and / or reference range are indicative of dysbiosis (e.g., low levels of good / healthy / commensal bacteria and / or high levels of Atty. Docket No. UCHI-42155.601 unhealthy / bad / pathogenic bacteria). In other embodiments, metabolite biomarker levels and / or a composite of multiple metabolite biomarkers that are beyond a threshold different (e.g., higher) than a population average and / or reference range are indicative of robustly healthy microflora (e.g., atypically high levels of good / healthy / commensal bacteria and / or low levels of unhealthy / bad / pathogenic bacteria). In some embodiments, metabolite biomarker levels and / or a composite of multiple metabolite biomarkers that are beyond a threshold different (e.g., higher) than a population average and / or reference range do not indicate the health or dysbiosis of the gut microbiota of the subject, but instead indicate whether the gut microbiota of the subject is similar to the general healthy population. Experiments conducted during development of embodiments herein demonstrate that the levels of various metabolites present in biological samples (e.g., fecal sample, rectal swab, etc.) from a subject correlate to the health of the gut microbiome of the subject and are diagnostic of the health of the subject. In some embodiments, provided herein are panels of metabolites (e.g., comprising one or more biomarkers of Table 1), the levels of which in biological samples (e.g., fecal sample) from a subject correlate with the health of the gut microbiome of the subject. In some embodiments, increased differences in the levels of one or more biomarkers of a panel (e.g., comprising metabolites of Table 1) from the population average, above / below a threshold, etc. it is indicative of an atypical gut microbiome e.g., dysbiosis (e.g., unhealthy gut microbiome) or robust microbiome (e.g., healthy gut microbiome). In some embodiments, the levels of all or a subset of the metabolites in a biomarker panel are correlated to the presence / absence of specific unhealthy (e.g., pathogenic) bacteria or groups of bacteria in the gut microflora of the subject. For example, if a metabolite or group of metabolites are quantified as, beyond a threshold from the population average, outside of a reference range, etc., this may indicate increased levels of unhealthy (e.g., pathogenic) bacteria in the gut microflora of the subject. In some embodiments, levels of specific metabolites or groups of metabolites correlate with the levels of specific unhealthy (e.g., pathogenic) bacteria. Examples of unhealthy (e.g., pathogenic) bacteria, the presence or increased level of which may be indicated by levels of the biomarkers herein, include but are not limited to Clostridium perfringens, Clostridium difficile (C. diff), Escherichia coli (E. coli), Salmonella, Shigella, Staphylococcus, etc. In some embodiments, the levels of all or a subset of the metabolites in a biomarker panel are correlated to the presence of specific good (e.g., commensal) bacteria or groups of bacteria in the gut microflora of the subject. For example, if a metabolite or group of Atty. Docket No. UCHI-42155.601 metabolites are quantified as within the reference range(s), this may indicate healthy levels of commensal bacteria in the gut microflora of the subject. In some embodiments, levels of specific metabolites or groups of metabolites correlate with the levels of specific commensal bacteria. Good (e.g., commensal) gut bacteria, the presence or increased level of which may be indicated by levels of the biomarkers herein, include but are not limited to those of the phyla Bacillota, Bacteroidota, Actinomycetota, Pseudomonadota, Fusobacteriota, and Verrucomicrobiota. Examples of good (e.g., commensal) bacteria, the presence or increased level of which may be indicated by levels of the biomarkers herein, include but are not limited to Lactobacillus, Bacillus, Clostridium, Enterococcus, and Ruminococcus, Bacteroides, and Prevotella. In some embodiments, methods are provided of assessing and / or analyzing the levels of metabolite biomarkers in a biological sample (e.g., fecal sample) from a subject. In some embodiments, the metabolomic biomarkers in the panels herein and / or assessed herein include one or more biomarkers selected from butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid. In some embodiments, the levels of one or more of butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid in a sample are quantitated. In some embodiments, an increased level (e.g., relative to a the population average, a reference range, control, or a threshold level) of one or more of butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid in a biological sample (e.g., fecal sample) from a subject is correlated with increased presence of unhealthy (e.g., pathogenic bacteria) and / or decreased presence of healthy (e.g., commensal) bacteria. In some embodiments, a decreased level (e.g., within a reference range, below a threshold, relative to control, etc.) of one or more of butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid in a biological sample (e.g., fecal sample) from a subject is correlated with decreased presence Atty. Docket No. UCHI-42155.601 of unhealthy (e.g., pathogenic bacteria) and / or increased presence of healthy (e.g., commensal) bacteria. In some embodiments, the level (e.g., concentration) of a biomarker is quantitated in a sample from the subject using the methods, kits, reagents, etc. herein. In some embodiments, the level of the biomarker is compared to a control value that is representative of a “normal” level for a healthy subject. In some embodiments, the control value is the population average or a value based upon the population average or a reference range. In some embodiments, the difference between the quantitated biomarker level and the control value is determined (e.g., quantitated biomarker level minus the control value, absolute value of the quantitated biomarker level, etc.). In some embodiments, the difference between the quantitated biomarker level and the control value is divided by the standard deviation for the population used to determine the control value. In some embodiments, the difference between the quantitated biomarker level and the control value divided by the standard deviation is the biomarker score for that given biomarker. In some embodiments, a metabolite biomarker score above a threshold value indicates an abnormal level for that metabolite. In some embodiments, a threshold for an individual biomarker score is 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or more, or ranges or values therebetween. In some embodiments, a composite metabolite biomarker score is calculated by taking the sum of individual metabolite biomarker scores for a panel of metabolite biomarkers. In some embodiments, a composite metabolite biomarker score is calculated by taking the average of individual metabolite biomarker scores for a panel of metabolite biomarkers. In some embodiments, a composite metabolite biomarker score above a threshold value indicates an abnormal level for that metabolite. In some embodiments, when a composite metabolite biomarker score is an average of individual biomarker scores, a threshold for a composite biomarker score is 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or more, or ranges or values therebetween. In some embodiments, when a composite metabolite biomarker score is a sum of individual biomarker scores, a threshold for a composite biomarker score is the number of biomarkers in the panel multiplied by 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or more, or ranges or values therebetween. In some embodiments, composite metabolite biomarker score is calculated by the formula: . Atty. Docket No. UCHI-42155.601 In some embodiments, a subject is determined to have normal gut microbiome (or a high likelihood of having a normal gut microbiome) if the composite metabolite biomarker score is below a composite threshold value. In some embodiments, the composite threshold value is n * (individual biomarker threshold), wherein n is the number of biomarkers analyzed in the panel and the individual biomarker threshold is 0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of acetate in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of acetate in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of acetate is any amount above a threshold level. In some embodiments, the population average level of acetate in a fecal sample is 0.3866 µg / mL. In some embodiments, the upper threshold for a normal acetate level is 0.5 µg / mL 1.0 µg / mL 1.5 µg / mL 2.0 µg / mL 3.0 µg / mL 4.0 µg / mL 5.0 µg / mL 6.0 µg / mL 8.0 µg / mL 10 µg / mL 15 µg / mL 20 µg / mL 30 µg / mL, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of butyrate in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of butyrate in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of butyrate is any amount above a threshold level. In some embodiments, the population average level of butyrate in a fecal sample is 0.6055 µg / mL. In some embodiments, the upper threshold for a normal butyrate level is 1.0 µg / mL, 1.5 µg / mL, 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, 15 µg / mL, 20 µg / mL, 30 µg / mL, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of cysteine in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of cysteine in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of cysteine is any amount above a threshold level. In some embodiments, the population average level of Atty. Docket No. UCHI-42155.601 cysteine in a fecal sample is 0.0092 µg / mL. In some embodiments, the upper threshold for a normal cysteine level is 0.02 µg / mL, 0.03 µg / mL, 0.04 µg / mL, 0.05 µg / mL, 0.1 µg / mL, 0.15 µg / mL, 0.2 µg / mL, 0.3 µg / mL, 0.4 µg / mL, 0.50 µg / mL, 0.6 µg / mL, 0.8 µg / mL, 1.0 µg / mL, 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of isoleucine in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of isoleucine in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of isoleucine is any amount above a threshold level. In some embodiments, the population average level of isoleucine in a fecal sample is 1.5304 µg / mL. In some embodiments, the upper threshold for a normal isoleucine level is 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, 15 µg / mL, 20 µg / mL, 30 µg / mL, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of leucine in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of leucine in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of leucine is any amount above a threshold level. In some embodiments, the population average level of leucine in a fecal sample is 1.7947 µg / mL. In some embodiments, the upper threshold for a normal leucine level is 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, 15 µg / mL, 20 µg / mL, 30 µg / mL, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of p-cresol in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of p-cresol in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of p-cresol is any amount above a threshold level. In some embodiments, the population average level of p-cresol in a fecal sample is 0.1429 µg / mL. In some embodiments, the upper threshold for a normal p-cresol level is 0.3 µg / mL, 0.4 µg / mL, 0.5 µg / mL, 1.0 µg / mL, 1.5 µg / mL, 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, or more or ranges or values therebetween. Atty. Docket No. UCHI-42155.601 In some embodiments, methods are provided herein for assessing the level of propionate in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of propionate in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of propionate is any amount above a threshold level. In some embodiments, the population average level of propionate in a fecal sample is 0.3323 µg / mL. In some embodiments, the upper threshold for a normal propionate level is 0.5 µg / mL, 1.0 µg / mL, 1.5 µg / mL, 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of valine in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of valine in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of valine is any amount above a threshold level. In some embodiments, the population average level of valine in a fecal sample is 1.7713 µg / mL. In some embodiments, the upper threshold for a normal valine level is 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, 15 µg / mL, 20 µg / mL, 30 µg / mL, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of kynurenine in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of kynurenine in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of kynurenine is any amount above a threshold level. In some embodiments, the population average level of kynurenine in a fecal sample is 0.0008 µg / mL. In some embodiments, the upper threshold for a normal kynurenine level is 0.0012µg / mL, 0.0015 µg / mL, 0.002 µg / mL, 0.0025 µg / mL, 0.003 µg / mL, 0.004 µg / mL, 0.005 µg / mL, 0.006 µg / mL, 0.008 µg / mL, 0.01 µg / mL, 0.02 µg / mL, 0.03 µg / mL, 0.04 µg / mL, 0.05 µg / mL, 0.1 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of tryptophan in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an Atty. Docket No. UCHI-42155.601 increased level (e.g., relative to the population average or a threshold level) of tryptophan in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of tryptophan is any amount above a threshold level. In some embodiments, the population average level of tryptophan in a fecal sample is 0.0029 µg / mL. In some embodiments, the upper threshold for a normal tryptophan level is 0.01 µg / mL, 0.02 µg / mL, 0.05 µg / mL, 0.08 µg / mL, 0.1 µg / mL, 0.2 µg / mL, 0.4 µg / mL, 0.5 µg / mL, 0.8 µg / mL, 1.0 µg / mL, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of 2- aminobutyric acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of 2-aminobutyric acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of 2-aminobutyric acid is any amount above a threshold level. In some embodiments, the population average level of 2-aminobutyric acid in a fecal sample is 0.2464 µg / mL. In some embodiments, the upper threshold for a normal 2-aminobutyric acid level is 0.5 µg / mL, 1.0 µg / mL, 1.5 µg / mL, 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, 20 µg / mL, 30 µg / mL, or more, or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of caffeic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of caffeic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of caffeic acid is any amount above a threshold level. In some embodiments, the population average level of caffeic acid in a fecal sample is 0.0852 µg / mL. In some embodiments, the upper threshold for a normal caffeic acid level is 0.1 µg / mL, 0.2 µg / mL, 0.5 µg / mL, 1.0 µg / mL, 1.5 µg / mL, 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of deoxycholic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) Atty. Docket No. UCHI-42155.601 of deoxycholic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of deoxycholic acid is any amount above a threshold level. In some embodiments, the population average level of deoxycholic acid in a fecal sample is 8.4980 µg / mL. In some embodiments, the upper threshold for a normal deoxycholic acid level is 12 µg / mL, 14 µg / mL, 16 µg / mL, 20 µg / mL, 25 µg / mL, 30 µg / mL, 40 µg / mL, 50 µg / mL, 60 µg / mL, 70 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of sinapic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of sinapic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of sinapic acid is any amount above a threshold level. In some embodiments, the population average level of sinapic acid in a fecal sample is 0.0077 µg / mL. In some embodiments, the upper threshold for a normal sinapic acid level is 0.01 µg / mL, 0.02 µg / mL, 0.04 µg / mL, 0.06 µg / mL, 0.08 µg / mL, 1.0 µg / mL, 2.0 µg / mL, 4.0 µg / mL, 6.0 µg / mL, 10 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the ratio of conjugated / deconjugated bile acids in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of the ratio of conjugated / deconjugated bile acids in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of the ratio of conjugated / deconjugated bile acids is any amount above a threshold level. In some embodiments, the population average level of the ratio of conjugated / deconjugated bile acids in a fecal sample is 0.3422 µg / mL. In some embodiments, the upper threshold for a normal the ratio of conjugated / deconjugated bile acids level is 0.5 µg / mL, 1.0 µg / mL, 1.5 µg / mL, 2.0 µg / mL, 3.0 µg / mL, 4.0 µg / mL, 5.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, 10 µg / mL, 15 µg / mL, 20 µg / mL, 25 µg / mL, 30 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of cholic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an Atty. Docket No. UCHI-42155.601 increased level (e.g., relative to the population average or a threshold level) of cholic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of cholic acid is any amount above a threshold level. In some embodiments, the population average level of cholic acid in a fecal sample is 3.0531 µg / mL. In some embodiments, the upper threshold for a normal cholic acid level is 4 µg / mL, 6 µg / mL, 8 µg / mL, 10 µg / mL, 15 µg / mL, 20 µg / mL, 25 µg / mL, 30 µg / mL, 40 µg / mL, 50 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of lithocholic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of lithocholic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of lithocholic acid is any amount above a threshold level. In some embodiments, the population average level of lithocholic acid in a fecal sample is 2.4438 µg / mL. In some embodiments, the upper threshold for a normal lithocholic acid level is 4 µg / mL, 5 µg / mL, 6 µg / mL, 8 µg / mL, 10 µg / mL, 12 µg / mL, 15 µg / mL, 20 µg / mL, 30 µg / mL, 40 µg / mL, 50 µg / mL or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of taurocholic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of taurocholic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of taurocholic acid is any amount above a threshold level. In some embodiments, the population average level of taurocholic acid in a fecal sample is 0.0418 µg / mL. In some embodiments, the upper threshold for a normal taurocholic acid level is 0.05 µg / mL, 0.10 µg / mL, 0.15 µg / mL, 0.20 µg / mL, 0.30 µg / mL, 0.40 µg / mL, 0.50 µg / mL, 0.60 µg / mL, 0.80 µg / mL, 1.0 µg / mL, 2.0 µg / mL, 4.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of chenodeoxycholic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) Atty. Docket No. UCHI-42155.601 of chenodeoxycholic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of chenodeoxycholic acid is any amount above a threshold level. In some embodiments, the population average level of chenodeoxycholic acid in a fecal sample is 5.3808 µg / mL. In some embodiments, the upper threshold for a normal chenodeoxycholic acid level is 10 µg / mL, 15 µg / mL, 20 µg / mL, 30 µg / mL, 40 µg / mL, 50 µg / mL, 60 µg / mL, 80 µg / mL, 100 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of allolithocholic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of allolithocholic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of allolithocholic acid is any amount above a threshold level. In some embodiments, the population average level of allolithocholic acid in a fecal sample is 0.0713 µg / mL. In some embodiments, the upper threshold for a normal allolithocholic acid level is 0.10 µg / mL, 0.15 µg / mL, 0.20 µg / mL, 0.30 µg / mL, 0.40 µg / mL, 0.50 µg / mL, 0.60 µg / mL, 0.80 µg / mL, 1.0 µg / mL, 2.0 µg / mL, 4.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, or more or ranges or values therebetween. In some embodiments, methods are provided herein for assessing the level of isolithocholic acid in a biological sample (e.g., fecal sample) from a subject. In some embodiments, an increased level (e.g., relative to the population average or a threshold level) of isolithocholic acid in a biological sample (e.g., stool sample, rectal swab, etc.) from a subject, alone, in a composite with other biomarkers in a panel, or in combination with one or more other biomarkers, is correlated with abnormal microbiota. In some embodiments, an increased level of isolithocholic acid is any amount above a threshold level. In some embodiments, the population average level of isolithocholic acid in a fecal sample is 0.1915 µg / mL. In some embodiments, the upper threshold for a normal isolithocholic acid level is 0.30 µg / mL, 0.40 µg / mL, 0.50 µg / mL, 0.60 µg / mL, 0.80 µg / mL, 1.0 µg / mL, 2.0 µg / mL, 4.0 µg / mL, 6.0 µg / mL, 8.0 µg / mL, or more or ranges or values therebetween. In some embodiments, a metabolite biomarker panel comprises 2 or more biomarkers (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 25, 30, 40, 50, 75, 100, 200, or more, or ranges or values therebetween). In some embodiments, a metabolite Atty. Docket No. UCHI-42155.601 biomarker panel comprises 2 or more biomarkers selected from Table 1 (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or ranges or values therebetween). In some embodiments, a metabolite biomarker panel comprises 100 or fewer biomarkers (e.g., 100, 90, 80, 70, 60, 50, 40, 30, 25, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, or ranges or values therebetween). In some embodiments, a metabolite biomarker panel comprises 20 or fewer biomarkers selected from Table 1 (e.g., 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or ranges or values therebetween). In some embodiments, a kit is used to analyze a biomarker contained within a biological sample. In some embodiments, the kit contains all components for quantifying a biomarker contained within a biological sample including at least one (e.g., one or more) biomarker (or a panel of biomarkers) that has a known level (e.g., a known concentration, a known weight, a known amount (e.g., a biomarker standard)) and at least one (e.g., one or more) analytical platforms (e.g., high-throughput platforms, automated platforms, etc.) utilizing a POC device, nuclear magnetic resonance (NMR) spectroscopy, gas chromatography (GC), liquid chromatography (LC), and / or mass spectrometry (MS) or NMR, GC, and / or LC coupled to MS. 5. Treatment In some embodiments, the ability to quantitate the amount of a biomarker in a biological sample from a subject allows a clinician to make assessments regarding the condition of the subject, to further provide a diagnosis or prognosis for the subject, and / or to further recommend or administer a treatment course of action. In some embodiments, quantitation of a panel of metabolite biomarkers in a sample from the subject, and comparison to a control or threshold value, provides an indication of whether the subject has a normal gut microbiome or whether the gut microbiome of the subject is atypical (e.g., beyond a threshold for a normal gut microbiome). In some embodiments, an atypical gut microbiome (as assessed by the methods herein) may contain low levels of healthy / good / commensal bacteria and / or may contain increased levels of bad / unhealthy / pathogenic bacteria. Thus, embodiments of the present disclosure also include methods of providing decision-making information regarding a treatment course of action and, in some embodiments, further comprising the step of treating a subject with a dietary and / or other intervention. In some embodiments, the method of providing decision-making information regarding a treatment course of action comprises if the presence / absence / level of at least one (e.g., one or more) biomarker in at least one (e.g., one or more) of the subject’s biological Atty. Docket No. UCHI-42155.601 sample is indicative / prognostic / diagnostic of a condition / outcome in the subject includes measurement (e.g., by the techniques described herein and / or understood in the field) of a biomarker in a subject’s biological sample and comparison of the presence / absence / level of that metabolite in a control subject or a threshold value. Various algorithms and / or means of analyzing the metabolite levels quantitated by the methods herein can be employed to provide decision-making information regarding a treatment course of action. For example, in some embodiments, if the biomarker level is above or below the control or threshold, determinations about the state or the subject are inferred or concluded. In embodiments in which the levels of multiple metabolites are measured to provide a prognosis or to determine a treatment course of action, an algorithm may be employed to combine the level of the multiple biomarkers, and / or their levels relative to individual thresholds, into a single prognosis or treatment course of action. In some embodiments, a score is provided for each biomarker, based on the presence / absence / level of the biomarker in the sample relative to a control or threshold value. In some embodiments, the combination of scores from multiple biomarkers is used to provide a prognosis and / or determine a treatment course of action. In some embodiments, if a biomarker presence / absence / level is above a threshold it is given a first score (e.g., 1 or 0) and if the biomarker level is below a threshold it is given a second score (e.g., 0 or 1). In some embodiments, a biomarker presence / absence / level is scored based on how far above or below the biomarker level is relative to a threshold (e.g., scored 0-100). In some embodiments, the scores of multiple biomarkers are combined to provide a prognosis (e.g., likelihood of normal / atypical gut microbiome (e.g., qualitative (e.g., high, intermediate, or low), percentage (e.g., 50%, 60%, 70%, 80%, 90%, etc.), etc.)). In some embodiments, the combined score of multiple biomarkers (e.g., based on comparison to thresholds) is compared to a ‘panel threshold’ or multiple panel thresholds to classify the subject and the likelihood of health (e.g., normal / atypical gut microbiome). In some embodiments, comparison to a panel threshold or multiple panel thresholds allows for stratification (e.g., qualitative or quantitative) of the likelihood of normal / atypical gut microbiome of a subject. In some embodiments, examples of a qualitative likelihood of normal / atypical gut microbiome are low risk, intermediate risk, high risk, severe risk, etc. In some embodiments, examples of a quantitative likelihood of normal / atypical gut microbiome are 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 95%, etc. Individual and / or combined biomarker scores, based on comparison to controls or threshold values, can be determined and combined in any suitable manner to achieve a statistically relevant prognosis. The scope herein is not limited by the various ways of Atty. Docket No. UCHI-42155.601 comparing biomarker levels to thresholds and combining them to produce a prognosis and / or treatment course of action. In some embodiments, the method of treating a subject with a treatment if the presence / absence / level of at least one (e.g., one or more) biomarker in the subject’s biological sample is indicative / prognostic / diagnostic of a condition / outcome in the subject includes a dietary intervention, microbiome augmentation (e.g., prebiotics, probiotics (i.e., live biotherapeutic products)) and / or microbiota-derived metabolites. In some embodiments, the methods may further employ pharmacological and / or chemical agents, independently, or in conjunction, with the disclosed invention to treat the subject. In some embodiments, drugs are used in conjunction with the disclosed compositions and methods. In some embodiments, a pain-relieving drug is used in conjunction with the disclosed compositions and methods. In some embodiments, a microbiome augmentation (e.g., prebiotics, probiotics (i.e., live biotherapeutic products) and / or microbiota-derived metabolites) is used in conjunction with the disclosed compositions and methods. In some embodiments, the methods may further employ administration to a subject of metabolites and / or beneficial bacteria based on the presence / absence / level of at least one (e.g., one or more) biomarker in the subject’s biological sample. In some embodiments, the kits, reagents, and methods herein find use in monitoring the effectiveness of treatments and / or the health of the gut microbiome over time. In some embodiments, one or more metabolite biomarkers (e.g., a panel herein) are quantitated is a sample form a subject at a first timepoint (e.g., pre-treatment, pre-antibiotics, post-antibiotics, etc.). In some embodiments, the biomarker levels are compared to a threshold or control as described herein. In some embodiments, a treatment course of action is determined based on the assessment of the gut microbiome using the methods herein and / or factors (e.g., symptoms, other diagnostic / prognostic tests, etc.). In some embodiments, the one or more metabolite biomarkers (e.g., a panel herein) are quantitated is a sample form a subject at a second timepoint (e.g., post-treatment, after a timespan (e.g., 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 1 week, 2 weeks, 3 weeks, 1 month, 2 months, 4 months, 6 months, 1 year, or more or ranges therebetween, etc.). In some embodiments, comparison of the biomarker levels at the first and second timepoints provides a measure of the change in the health of the subject’s microbiome. In some embodiments, biomarker levels are quantitated and analyzed at multiple timepoints (e.g., 2, 3, 4, 5, 6, 7, 8, 9, 10, 20 , or more) over the course of weeks, months, years, etc. to monitor the health of a subject’s microbiome and / or the effectiveness of a treatment course of action in correcting an atypical microbiome. Atty. Docket No. UCHI-42155.601 6. Examples It will be readily apparent to those skilled in the art that other suitable modifications and adaptations of the methods of the present disclosure described herein are readily applicable and appreciable, and may be made using suitable equivalents without departing from the scope of the present disclosure or the aspects and embodiments disclosed herein. Having now described the present disclosure in detail, the same will be more clearly understood by reference to the following examples, which are merely intended only to illustrate some aspects and embodiments of the disclosure, and should not be viewed as limiting to the scope of the disclosure. The disclosures of all journal references, U.S. patents, and publications referred to herein are hereby incorporated by reference in their entireties. The present disclosure has multiple aspects, illustrated by the following non-limiting examples. Example 1 Preservative Study Evaluating Different Preservatives & Timeframes for Stabilizing DNA and Metabolites in Home Stool Collection Kits. Study Design and Rationale. The study design (FIG. 2) followed the general framework described herein in Example 1. Immediate freezing of samples at -20°C or -80°C is the “gold standard” of bacterial community profiling and metabolite analysis. However, is not always practically feasible. Study compliance and patient amenability to providing stool specimens is hampered by either a requirement for the patient to return to the medical facility to provide a specimen “on-demand” or the costs associated with courier services / overnight frozen shipping to properly preserve a home collected specimen. Additionally, home stool collections work best when the amount of effort, including stool manipulation, asked of volunteers / patients is at a minimum. Ideally, this would entail asking patients to prepare a single sample tube that can be utilized by the downstream processing lab for both DNA analysis (e.g., 16S / metagenomics) and metabolomics. Samples destined for nucleic acid isolation and analysis (e.g., sequencing) are particularly sensitive to nucleic acid degradation resulting from enzymatic activity (ex., nucleases) and environmental factors (ex., time and temperature). Atty. Docket No. UCHI-42155.601 Thus, to effectively accomplish home stool collections, identification of a single preservative with the ability to co-stabilize DNA and metabolites is needed. Specimen stabilization using such a preservative would be substantiated if the bacterial community composition and metabolite quantification were comparable with the gold standard methodology (i.e., collection within 4 hours of immediate freezing at -80°C, hereinafter referred to as “fresh frozen samples”). Preservative Options. DESS (DMSO-EDTA-salt solution, 20% DMSO (v / v), 250 mM EDTA, saturated with sodium chloride) is a non-proprietary, non-hazardous storage medium that shows strong potential for use as a preservative in a wide variety of microbial ecology studies with varying specimen types. DMSO permeates cells and facilitates the rapid entry of EDTA that suppresses nuclease activity by chelating divalent cations. Sodium chloride salt further suppresses enzymatic activity and contributes sodium ions that stabilize the negatively charged phosphate backbone of DNA. Ethanol (95% ethanol) does not directly affect DNA integrity and, in high enough concentrations (e.g., ≥95%), ethanol can rapidly penetrate and disrupt cellular membrane function (halting DNase activity and killing micro- organisms). The analysis described herein used DESS and ethanol to determine if either preservative possessed the ability to co-stabilize DNA & fecal metabolites. Study Design. On-demand collection of stool presents many more challenges than the on-demand collection of other clinical samples (e.g., urine or saliva) especially as the timing of stool production is very specific to an individual. As a result, a maximum flexibility in sample collection timing was desired. Additionally, return of specimens to the laboratory using the United States Postal Service (USPS) reduced the hassle to the volunteers while also minimizing shipping costs. To accommodate these considerations, a preservative was needed that would stabilize DNA / metabolites over a wide timeframe and a range of temperatures. The study design incorporated both components by comparing sample stability at 0- hr to sample stability at 96-hours (four days) at approximately 22°C (room temperature) to simulate extended post office shipping transit times and at 37°C (98.6°F) to simulate the elevated temperatures of the summer months or during transport in enclosed metal cargo trucks. Additionally, as metabolomics assessments rely on high performance liquid chromatography (HPLC), HPLC-grade chemicals were tested to determine whether they would be required to make an accurate comparison to fresh frozen samples or whether use of standard grade chemicals would be sufficient. Atty. Docket No. UCHI-42155.601 Overview. The sample processing overview (FIG.1) resulted in preparation of 36 aliquots from each donor sample. Each condition set (i.e., EtOH [standard], 22°C, 96hrs) was tested in triplicate to evaluate technical variability. The amount of stool aliquoted into each was between 1.79 – 3.22 grams. Planned Consumables. Homogenization and transferring of the stool sample was performed using a Corning Lab Cell culture RNAse / DNase free spoon (Corning, Cat no.3004). For initial aliquoting of the samples, stool was placed into Sarstedt Faeces tube, (Sarstedt, cat# 80.623 transparent, screw cap, 101 x 16.5 mm, sterile). Documented variables. The following metrics were documented for each aliquoted specimen: Post-defecation stool aliquoting (i) tube weight (dry) (in grams); (ii) tube weight (w / 3-mL preservative) (in grams); (iii) tube weight (post-inoculation) (in grams); (iv) mass of stool (in grams). Chemical Components and Preservative Preparation Protocols. For preparation of preservatives with HPLC-grade chemicals, glassware (graduated cylinders and storage bottles) specifically designated for HPLC grade chemicals were used to ensure no occurrence of extraneous contamination. For preparation of preservatives with standard grade chemicals, common glassware (graduated cylinders & storage bottles) used in the laboratory for a variety of experimental purposes was utilized. Specimens Used in Analysis. Fecal samples were collected from three volunteers (two women and one man). The volunteers were chosen because 16S rRNA and metagenomic analysis of fecal specimens, provided at earlier dates, had demonstrated that their gut microbiome profiles were diverse, both in general bacterial community composition and in organism abundance. This allowed performance of the study on assorted bacterial communities. Two volunteer samples were collected in the laboratory and processed immediately after defecation, while the third was collected at home and returned to our laboratory within three hours post-defecation. No medical records were collected. Gender and the sampling date / time were the only information provided by each volunteer. All subjects provided written informed consent prior to participating in the study. These samples were anonymized and treated according to medical ethical guidelines. Specimens were collected under University of Chicago Institutional Review Board, IRB12- 2122. Atty. Docket No. UCHI-42155.601 Because of the stool mass required to prepare all the various experimental permutations, volunteers were provided with a Commode Specimen Collection System (Fisher Scientific, cat no.02-544-208) to allow for large volume collection. While typical laboratory methodology would incorporate vortexing to mix the samples in the preservative, this was not done. Patients at home performed end-over-end rotations or vigorous shaking for a period of 20-sec to mix the samples in the preservative. The time elements of (1) sample collection time, (2) processing start times, (3) time of placement at given storage temps (immediately frozen, 22°C or 37°C) were recorded. Additionally, the time elements of (1) total processing time and (2) time duration at storage condition (until frozen) were calculated. The summarized information is outlined in Table 2. Table 2. Summary of Documented Time Elements Associated with the Specimens Used for Analysis. Confirmation of Storage Temperatures. Storage at room temperature (22°C) was performed by leaving the sample tubes on the laboratory bench top. Storage at elevated temperatures (37°C) was performed by placing the sample tubes inside a 37°C ambient air incubator. To verify the storage temperatures were those desired and no atypical fluctuations occurred during the storage timeframe, a cold chain temperature monitoring device (Traceable Logger-Trac, VWR, cat no. 10154-370) was attached to the storage rack holding the tubes. Temperature readings were taken every 5-min for the entire duration of the storage period. Samples 2 and 3 overlapped in dates so the same temperature logging device was used to record both samples and the associated references hereafter denote “sample 2 / 3” in the summaries. Atty. Docket No. UCHI-42155.601 Table 3. Summary of Compiled Temperature Data Statistics obtained for each sample. Room temperature Elevated temperature Sample 1average: 21.1°C; median: 20.7°C; range: average: 37.87°C; median: 38.1°C; range: 35.6 - C; of samples for submission for 16S and metabolomics was performed in a manner mimicking a real-world workflow. After the 96-hour timeframe was completed, the samples were placed at -80°C for a minimum of 48-hours to simulate receipt and storage by the laboratory. When convenient, then samples were subsequently thawed, and then sub-aliquotted to prepare the samples accordingly. Documenting of tube weights with and without preservative. During this post- freezing sub-aliquotting process (i.e., preparation of 1.5-mL microfuge tube samples used in the submission for 16S analysis or metabolomics), the following metrics were documented (i) tube weight (dry) (in grams); (ii) tube weight (filled; raw stool) (in grams); (iii) tube weight (filled; preservative slurry) (in grams); (iv) tube weight (filled; preservative removed) (in grams); (v) mass of stool (in grams). Analysis Notes. All 36 aliquots (i) were analyzed for volunteer 1 (i.e., sample 1); (ii) encompassed all tested preservatives (No preservative, DESS, EtOH-HPLC, EtOH- STANDARD); and (iii) encompassed all timeframes (flash frozen – 0hrs, 22°C – 96 hrs and 37°C – 96 hrs). Based on the results for volunteer 1, only a subset of samples were analyzed for volunteer 2 (i.e., sample 2) and volunteer 3 (i.e., sample 3). Analysis was limited to the “No preservative” and “EtOH-standard grade” groups and included all timeframes (flash frozen – 0hrs, 22°C – 96 hrs and 37°C – 96 hrs). Broad Strokes Experimental Result Summary. DESS was found to stabilize only DNA (as demonstrated by 16S rRNA analysis) and not fecal metabolites. 95% EtOH co- stabilized both DNA (as demonstrated by 16S rRNA analysis) and fecal metabolites at both 22°C and 37°C through 96 hours. For the 16S sequence analysis, an increase in the relative abundance of the genus Bacteroides in the EtOH preserved samples was identified as compared Atty. Docket No. UCHI-42155.601 to the fresh frozen samples, but this was independent of timeframe (T0 vs T96). It was hypothesized that this is perhaps due to enhanced bacterial lysis in the presence of the ethanol. As long as all samples were collected and processed in a similar manner, this difference in fresh frozen vs preservative stabilized was not concerning. The comparison of HPLC-grade chemicals to standard grade chemicals demonstrated that use of standard-grade chemicals was sufficient for preparing the preservative. Example 2 Preservative Study Expanded evaluation of preservative capabilities through additional samples and increasing preservation timeframes. Study Rationale for Validation Cohort. To (i) expand upon the number of study participants used in evaluating our proposed sample preservative as doing so would account for a greater level of individual-to-individual variability and ensure the stabilization results obtained in study#1 are reproducible and (ii) expand out further on the timeframes evaluated to include 2-weeks and 4-weeks. Expansion would allow for greater flexibility during home stool collections and any required sample return shipping. The desired evaluation includes assessing if all metabolites stabilize out to the same timepoint or whether the results are unique to a given metabolite. Study Design. The study design (FIG.3) followed the general framework of that was used in Example 1 with the following exceptions (i) due to the number of storage times being assessed, and increased number of aliquots needed to be prepared from each collected sample; (ii) the preservative was spiked with two internal controls to align with actual home stool collection methodology (i.e., caffeine-(trimethyl-d9), Sigma-Aldrich, cat#725625-100mg – final concentration: 0.49 mM and acetate-d3, Cambridge Isotope Laboratories, Cat no. DLM- 3126-25 - final concentration: 1.15 mM; presence of both chemicals allowed for identification of preservative evaporation or spillage during sample collection or processing during the analysis phase). Study Volunteers. Six stool samples were collected using a convenience sample of volunteers – no demographic selection or other criteria were applied. Participants included four women and two men. Volunteers were asked to collect stool samples either at home or at work and return as soon as possible to the laboratory (maximum time from defecation to start of processing was 2 hours). No temperature requirements were requested for sample transit to the lab. Atty. Docket No. UCHI-42155.601 Experimental Conditions. Six stool samples were collected (labeled P4, P5, P6, P7, P8 and P9)(“Biological Replicates”). Samples P4, P6 and P7 were sent for analysis. The remaining three samples (P5, P8 and P9) were continuously held at -80°C. P5 caveat: Samples 10-12 were pulled at 96-hrs instead of samples 24-26. Therefore, only the 4-week timepoints were analyzed appropriately. P9 caveat: 4-week / room temperature tubes were found to be missing once the storage period ended (and were never located). No problems identified with the 0-hr, 96-hr, and 336-hr tubes. Three aliquots were prepared for each sample storage condition / temp combination (“Technical Replicates”). The rationale being that thoroughness of sample homogenization alone can lead to sample-to-sample variation. Analyzing each combination in triplicate would assist in identifying the impact of technical effects versus biological ones. Storage conditions (n=2). Sample stabilization in 95% ethanol (standard grade reagents) was compared to using no preservative. Storage temps (n=3). The temperatures evaluated included : -80°C (considered the gold standard), room temperature (22°C), and high temperature (37°C). Room temperature (~22°C) was achieved by leaving the aliquots out on the lab bench. High temperature (37°C) was achieved by incubating the aliquots in a designated 37°C ambient air incubator. Storage time. Timeframes of 96-hours, 2 weeks (336-hours) and 4 weeks (672 hours) would be compared to direct freezing (with or without preservative). Combining these different parameters together resulted in 14 different experimental conditions (Table 3) with three replicates / condition for a total of 42 aliquots required from the primary sample. Table 3. List of experimental parameters (consisting of preservative use, storage temperature and storage time) evaluated in Example 2.

[0002] Atty. Docket No. UCHI-42155.601 Planned Consuma bles. Due to the mass of stool required, participants were asked to collect stool using the Fisherbrand™ Commode Specimen Collection System (Fisher Scientific, cat no. 02-544-208). Homogenization and transferring of the stool sample was performed using a Corning Lab Cell culture RNAse / DNase free spoon (Corning, Cat no.3004). For initial aliquoting of the samples, stool was placed into Sarstedt Faeces tube, (Sarstedt, cat# 80.623 transparent, screw cap, 101 x 16.5 mm, sterile. Confirmation of Storage Temperatures. To verify the storage temperatures were those desired and no atypical fluctuations occurred during the storage timeframe, a cold chain temperature monitoring device (Traceable Logger-Trac, VWR, cat no. 10154-370) was attached to the storage rack holding the tubes. Temperature readings were taken every 5-min for the entire duration of the storage period. Collection Tube Setup and Sample Processing. All pre-labeled empty collection (Sarstedt) tubes were weighed & weight was documented. 3-mL preservative (with internal controls added) was added to the empty tube, the tube weighed & weight was documented. After a thorough homogenization, stool was added to each Sarstedt / preservative tube and tube weights were documented. After completion of designated storage time, samples were immediately frozen at -80°C, to align with the envisioned workflow for actual volunteer / patient samples. After a minimum freeze time of 48-hrs (time solely dictated by convenience), the samples were thawed and sub-aliquoted to prepare samples for 16S rRNA amplicon sequence analysis and untargeted metabolomics. T=0 and T=96-hrs were prepared first.2-weeks and 4- weeks were processed subsequently and followed the same set of instructions. Other documented variables included (i) date / time of sample collection; (ii) date / time sample processing started; (iii) date / time samples were placed under storage Atty. Docket No. UCHI-42155.601 condition; (iv) date / time samples were placed at -80°C; and (v) date / time samples of any subsequent aliquoting. Analysis Plan. The plan was to prepare all samples (n=4 timepoints) at the same time but only to analyze subsets (due to funding constraints) with the remaining timepoint(s) remaining frozen but available for analysis when needed. Samples submitted to Argonne National Laboratory for 16S rRNA amplicon sequencing. Samples submitted to Duchossois Family Institute Host-Microbe Metabolomics Facility (DFI-HMMF) in two batches for untargeted metabolomics analysis: (i) the first batch included flash frozen, 96-hour and 2-week samples of P4, P6 & P7 [tubes 1 to 15 and 22 to 36] and (ii) the second batch included 4- week samples of P4, P6 & P7 [tubes 16 to 21 and 37 to 42]. Broad Strokes Experimental Result Summary. Metabolite stabilization out to 96- hours was confirmed. Metabolite stabilization out to 2-weeks (under both 22°C and 37°C conditions) was apparent for many, but not all, 300+ metabolites tested. Thus, an evaluation would need to be made on a metabolite-by-metabolite basis regarding their most appropriate storage timeframe. Some metabolites were capable of stabilization out to 4-weeks (under both 22°C and 37°C conditions). Example 3 Exemplary Mass Spectrometry Methods Metabolite Selection Criteria. The preserved fecal metabolome was analyzed across four mass spectrometry platforms in the Duchossois Family Institute Host-Microbe Metabolomics Facility (DFI-HMMF) to capture 330 metabolites that reflect host-microbe functions. All 330 compounds were analyzed to calculate the set of metabolites that accurately quantified microbiome health. The criteria for metabolite inclusion are as follows: (i) compound represents a key functional subsystem of gut microbiota (fermentation, bile acid conversion, tryptophan metabolism, essential amino acid synthesis, fatty acid processing, and disease associated markers, and others); (ii) compound has uniform levels among healthy adult populations to establish reference ranges. Healthy is defined as the absence of disease; (iii) compounds are stable at 14 days in the preservative; (iv) each metabolite performs well to define gut microbiome health alone but is essential in the group (20 metabolite) calculation; (v) analysis of each metabolite must be possible on the same chromatography column and mass spectrometry method to allow for a single injection of each donor sample. A single injection will streamline run, analysis, and quality control (QC) time. Atty. Docket No. UCHI-42155.601 Preservative and Quality Control Standard in Kit. Ethanol Preservative. Ethanol (95%) is an effective bactericidal and metabolite extraction solvent and is necessary to preserve the metabolome for 14 days at room temperature. Compounds were evaluated over time to determine stability. Not all 330 compounds were stable in the preservative. Quality Control Standard Addition to Ethanol Preservative. Prior to fecal sample collection, two internal standards (acetate-d3: 1.15 mM, Cambridge Isotope Laboratories #DLM-3126-25) and caffeine-(trimethyl-d9): 0.49 mM, Sigma-Aldrich #725625) were added to the 95% ethanol in the collection vessel to evaluate collection (evaporation, spillage) and analysis methods. Compounds were chosen for their long-term stability in ethanol and compatibility with the four mass spectrometry platforms: (i) the small molecular weight (80.10 Da) and structure (contains a carboxylic acid) of acetate-d3 is compatible with the GC-MS panel required for short chain fatty acid analyses (PFBBr derivatization); (ii) the larger molecular weight (203.25 Da) of caffeine-(trimethyl-d9) is compatible with the GC-MS panel (TMS- MOX) and the LC-based panels (QQQ, and QTOF). These compounds are structurally identical to known biological metabolites (acetate and caffeine). However, they contain deuterium, a stable isotope of hydrogen, in the place of hydrogens. Deuterated compounds that have the deuterium covalently bound to carbon and not nitrogen or oxygen are stable and nearly identical in all physiochemical properties compared to the endogenous compound which makes them ideal for use as an internal standard. Mass Spectrometry Methods. Performance metrics such as accuracy, precision, inter / intrarun accuracy and precision were previously validated by the DFI-HMMF and are used in routine quality control calculations. The methods, in brief, are as follows. After the spiked ethanol was added to the collection vial, the vessel was weighed for an initial mass. When the vessel returned to the laboratory, it was weighed, and the deposited sample mass calculated. The samples were homogenized into a slurry, diluted to 100 mg / mL, and split into four aliquots for downstream gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) methods. Sample analysis required multiple MS instruments to cover a range of physiochemical properties in microbiome associated metabolites including molecular weight, solubility, and ionization efficiency. Following sample aliquoting, samples were either directly derivatized (pentafluorobenzyl bromide, PFBBr, method), dried down under nitrogen and then derivatized for GC methods (trimethylsilane – methoxyamine, TMS-MOX, method), or reconstituted in organic solvent for LC methods. Following PFB derivatization, the aliquot was analyzed by GC-MS ((-) CI-GC- MS, Agilent 8890) for the presence of volatile compounds such as short chain fatty acids Atty. Docket No. UCHI-42155.601 (SCFAs, ex. butyrate). The second aliquot was derivatized with TMS-MOX and analyzed by GC-MS ((+)EI-GC-MS, Agilent 7890B) for the study of low molecular weight compound classes such as carbohydrates and amino acids. A third fraction was analyzed with the use of negative mode liquid chromatography-electrospray ionization-quadrupole time-of-flight-MS ((-)LC-ESI-QTOF-MS, Agilent 6546) for the measurement of 49 bile acids from the primary, secondary and glyco / tauro-conjugated subclasses. The fourth aliquot was analyzed by positive mode LC-triple quadrupole-MS ((+)LC-ESI-QQQ-MS, Agilent 6547) to measure 34 indole and tryptophan catabolites. Calculation of Sample Mishandling. Across all platforms, the concentration of acetate-d3and caffeine-(trimethyl-d9) were compared to determine if evaporation or spillage had occurred. From each sample, ITSD levels were measured and corrected for initial dilution based on mass of sample added to each vial. Samples with ITSDs that quantified >12% maximum accuracy error and / or >8% maximum precision error were evaluated for donor mishandling such as a spill or evaporation. Percentage cutoffs were chosen based on CLIA recommendations. Individual samples falling outside of these QC ranges, the original sample volumes and metabolite levels were recalculated using corrected quantified ITSD levels. Example 4 Exemplary Mass Spectrometry Methods Summary. Using supervised machine learning algorithms to select among known health-related metabolites, a mass spectrometry (MS)-based functional marker panel was developed to quantitatively profile the fecal microbiota of 14 healthy adult individuals, each who provided 8 samples over a period of one month. Normal reference ranges were established for the ~20 top performing fecal metabolite markers, selected on the criteria of (i) representing different functional subsystems of the gut microbiome; (ii) uniformity among cohort individuals; and (iii) performance individually and as a whole – the latter serving to develop a composite index for the health of the adult gut microbiome. The panel and index were then tested in a patient with a ten-year history of prolonged antibiotic induced functional bowel disorder (FBD) and an independent cohort of 21 healthy adult individuals. The metabolomic panel for assessing gut microbiota health, generated by analysis of our initial 14 healthy adult subject cohort, established tight reference ranges that held up in 20 / 21 adult subjects of an independent healthy cohort. Additionally, the metabolomic panel identified disturbances in the gut microbiota of the FBD patient that guided the management Atty. Docket No. UCHI-42155.601 of his condition. Stepwise dietary intervention restored metabolic imbalances of the gut microbiota, while his alpha diversity remained largely unchanged. The metabolomic panel identifies specific functional imbalances in gut microbiota and, through a composite index, distinguishes between gut eubiosis and dysbiosis. While additional validation is needed, this study provides proof of concept and merit for a quantitative, function-based marker panel capable of establishing normal reference ranges for the healthy adult gut microbiome. Introduction. Although an altered gut microbiome is believed to contribute to the pathogenesis of many common diseases, no clinical tool or metric exists to assess overall health of the gut microbiome. While parameters such as microbial diversity, presence of certain “good” microbial taxa, and / or gut community stability may be associated with a healthy microbiome, each of these is fraught with exceptions. An ecosystem can be stable and diverse yet still cause problems. Additionally, studies that have sequenced the microbiome in patients report conflicting results on microbial taxa that characterize health versus disease conditions. The most common microbiome profiling method for profiling the microbiome in the literature is through 16S rRNA amplicon sequencing, which uses primers for a stable region of the bacterial genome. These profiles can broadly identify the presence of certain groups of microbial taxa which provides general information on their role within the gastrointestinal (GI) tract. However, 16S rRNA profiles do not provide information on the current function of a microbe such as which metabolites are produced or transformed, which is arguably more relevant to understanding the microbiome’s impact on the host. Moreover, there is enormous variability in 16S rRNA amplicon bacterial profiles, even among healthy individuals, making it challenging to define the state of health of the gut microbiome. Additionally, 16S rRNA profiles are represented as relative abundance and have, at best, genus level resolution, making it ill-suited as a quantitative metric to define a healthy gut microbiome. Other methods for profiling microbiota include metagenomic sequencing, which provides strain- and gene-level information about bacteria and other microbes including fungi, viruses, and protists; and metabolomic profiling, which uses techniques such as mass spectrometry (MS) to analyze the presence of all (untargeted) or a defined (targeted) set of metabolites. Metagenomic and metabolomics methods can cost orders of magnitude more than 16S rRNA sequencing and result in large datasets that demand a higher level of computational resources and skill to analyze. However, these strategies are useful for the development of targeted assays and therapies. Atty. Docket No. UCHI-42155.601 Given the lack of understanding for how to connect the presence of GI microbes and metabolite levels to states of microbiome health, scalable quantitative metrics have not been previously developed and current treatment methods and selection of microbiome-based therapeutics are largely empirical. Patients are frequently advised to try probiotics (i.e., live biotherapeutic products)), but receive limited guidance on which probiotics would be most useful to them. Additionally, the lack of such a metric precludes an understanding of which patients may benefit from microbiome-based therapies (e.g., a dietary intervention, microbiome augmentation (e.g., prebiotics, probiotics (i.e., live biotherapeutic products)) and / or microbiota-derived metabolites). This is problematic as only a subset of patients respond to such therapies in several GI diseases. A clinical metric of microbiome health would also facilitate the monitoring of microbiome improvements over time in response to treatment. One of the clearest examples of diseases precipitated by microbiome alterations are those that develop after prolonged antibiotic treatment and / or acute gastrointestinal infections, such as in post-infectious FBD. One common microbiome-targeted treatment for FBD patients is a diet that is low in fermentable oligosaccharides, disaccharides, monosaccharides, and polyols (FODMAPs). FODMAPs are a group of fermentable carbohydrates that precipitate and / or exacerbate symptoms in many FBD patients. Because gastrointestinal microbes are normally required for the fermentation of these carbohydrates, the benefits observed in these patients upon reduction of FODMAPs suggest a role of certain gut microbes in the disease pathology. However, not all FBD patients respond to microbiome-based therapies, reflecting heterogeneity among patients in the extent of microbiome involvement. A screening tool for specific microbiome deficiencies could aid in stratifying patients for appropriate therapeutic approaches. As proof of concept for a clinically useful metric of microbiome health, the development of a quantitative metabolomic marker panel to define the state of health of the gut microbiome is reported. The panel comprises targeted metabolomics of ~20 fecal metabolite markers representing different functional subsystems of the gut microbiome that are relatively homogeneous among healthy adult subjects. These features are important, as then healthy reference ranges, akin to blood chemistries, were established. When the quantitative MS assay values for each of these metabolites are combined together, a composite index for the health of the adult gut microbiome is produced. This metabolomic panel identified specific metabolite imbalances in a patient with a ten-year history of FBD that began during an 18-month course of antibiotics following two bouts of foodborne illness. The marker panel proved useful for Atty. Docket No. UCHI-42155.601 managing the patient with stepwise dietary intervention, eventually resulting in correction of several metabolomic imbalances. A quantitative metabolomic panel that provides a composite score to indicate microbiome health was created. Stool samples from a pilot cohort of 14 healthy individuals over a time course of approximately 4 weeks were profiled to calculate an index for the Normal Ranges of the values of several key metabolites, each representing a molecular class or subsystem that has been reported as related to human health. Using machine learning algorithms, ~20 top performing fecal metabolite markers were identified, selected on the criteria of (i) representing different functional subsystems of the gut microbiome and (ii) uniformity among cohort individuals (i.., minimizing variability within and among healthy individuals). Reference ranges were established for these markers. The values of these metabolites were then combined to create a composite score of microbiome health. The panel was then validated with an independent cohort of 21 adults. It was found that the metric performed well, with 20 / 21 adults falling within the normal reference ranges created from the 14 individuals. The index was then tested of a patient with a 10-year history of chronic constipation, diarrhea, intestinal discomfort, and food intolerances which had developed after two episodes of acute gastroenteritis, followed by 18 months of antibiotic treatment. The panel identified several metabolites outside of the normal ranges in this patient. This information guided management of the patient with a dietary intervention, and his microbiome was profiled for 1 year as he underwent the dietary intervention. His scores on the index gradually resolved, eventually reaching the normal ranges, with the dietary intervention that was targeted toward his individual microbiome metabolic deficiencies. Thus, the panel, which was created based on existing research and knowledge about metabolites that are important for health, performed well in two populations and informed management of the patient’s condition, ultimately correcting several metabolic imbalances in his gut. The panel provided reference ranges for Normal Ranges of key metabolites that may aid in treatment selection across a variety of health and disease states. As such, this addresses a major unmet need in personalized medicine and may facilitate a more scientific approach to prescribing microbiome-based therapeutics to patients. Use Case: Utilization of the index in a patient with FBD. In early 2021, a 69-year- old non-Hispanic White male (“Patient Q”) discussed his chronic constipation, excessive flatulence, and food intolerances, which developed around 2013. He had developed the symptoms during the 12thmonth of an 18-month course of doxycycline for eye irritation. He reported having experienced two instances of food borne illness in 2013 as well (one from a Atty. Docket No. UCHI-42155.601 seafood restaurant and the other from a Mediterranean restaurant). He found that his symptoms were related to consumption of certain foods, including garlic, onions, table sugar, milk, potatoes, and bread, among others. The symptoms generally began several hours after ingesting the problematic foods. The symptoms described were increased excessive flatulence approximately one hour following trigger foods with resultant pressure in his bladder and the sensation to urinate even when there is little or nothing present to urinate. After consuming a trigger food, the lingering effects would persist for several days even with avoidance of further trigger foods and consumption of other non-trigger foods. The symptoms he experienced had significant impact on his quality of life to the point that it made travel or eating at a restaurant difficult as he was unable to control for the available foods or ingredients. When bloating and gas occurred, he had associated constipation and could go two to three days without a bowel movement. At baseline, he had regular daily bowel movements that he described when looking at a Bristol stool chart as type 4 in appearance. However, the consistency tends to fall apart in keeping with something more akin to a Bristol type 5 stool. Prior to his symptoms beginning in 2013, his stools were typically a Bristol type 2. He currently denies straining with bowel movements of blood in his stool. He denies any abdominal distention or pain, nausea or associated vomiting, dysphagia, and odynophagia. He has minimal heartburn only associated with one of his medications. He had no histological or anatomical abnormalities upon endoscopy and colonoscopy with biopsies. Baseline stool samples were collected from Patient Q (see Methods) and he began working with a registered dietitian. Briefly, the dietitian’s approach to Patient Q’s dietary intervention was as follows. First, the patient provided details of his habitual diet including lists of foods frequently eaten and tolerated, foods that are known to trigger his GI symptoms, and foods that he would like to eat but is afraid may elicit GI symptoms. Additionally, all medications and supplements were considered including twice a day polyethylene glycol, a fiber supplement taken twice daily, and gabapentin taken three times daily. Patient Q was advised to continue his habitual diet and consume designated foods from one fermentable fiber group per week. Specifically, he would consume one serving on Day 1, two servings on Day 2, and three servings on Days 3-5. On Days 6 and 7, it was the patient’s choice whether to continue foods from the week or go back to his habitual diet. If he tolerated a certain food that was introduced, he did not abstain from eating it during other FODMAP group weeks - he could continue eating tolerated foods as an effort to diversify his overall diet. Likewise, if a particular food or fermentable carbohydrate was introduced that Atty. Docket No. UCHI-42155.601 provoked significant symptoms, then the food(s) were immediately eliminated from the interventions. Stool samples were collected weekly and subsequently analyzed for microbiome composition and metabolome. This provided a record of how his microbiome changed in response to the continued dietary intervention. Discussion. There is currently no clinical metric for the overall health of the gut microbiome. As such, clinicians cannot diagnose specific microbiome deficiencies nor monitor beneficial changes in patients’ microbiomes in response to treatment. To address this unmet need in microbiome medicine, a panel for assessing microbiome health was created. The panel was based on the field’s current understanding about metabolites that are important for health combined with an unsupervised machine learning approach. This approach enabled identification of a subset of metabolites reflecting major microbial subsystems. Because internal standards for each marker were incorporated in the MS runs, precise metabolite concentrations could be determined. This quantitative approach revealed high uniformity of markers among individuals in the initial training cohort whereby tight references ranges for “normal” could be established. Additionally, this targeted approach performed as well as an untargeted approach in representing different functional subsystems of the gut microbiome, while significantly reducing cost and turnaround time. In support of the panel’s validity, the panel and index held up for 20 of 21 healthy adult individuals of an independent cohort and was able to identify and define specific imbalances in a patient with a ten-year history of FBD that began during 18 months of antibiotic treatment. The marker panel was then used to manage the patient with stepwise dietary intervention, eventually resulting in correction of his metabolomic imbalances and improvement of clinical symptoms. The panel has several advantages over shotgun metagenomic sequencing, which is neither quantitative nor a direct measure of gut microbiome function (beyond functional capacity). In contrast, gut microbiota produce a known and unknown number of metabolites that directly reflect functional states and impact host health. Arguably, it does not matter who makes a specific metabolite, just that it is made to the level that is beneficial for the host or microbial community. The panel prototype represents a precision medicine approach to diseases involving the microbiome. Methods. The study was performed at the University of Chicago Medical Center. Metabolite Panel Selection. The selection process for the 20-metabolite panel involved a combination of statistical and supervised AI methods. Of the several hundred Atty. Docket No. UCHI-42155.601 targeted metabolite signals, data were first normalized between runs. Briefly, baseline values for metabolites below the baseline cutoff threshold of detection was imputed from the datasets. To correct between metabolomic runs, a generalized linear model (GLM, R package ‘combat’) batch correction was performed. Metabolites were then segregated into molecular classes / biological subsystems representative of various, microbiome dependent functions including short chain fatty acids (SCFAs), bile acids (BAs), indoles, phenyl & organic acids, and amino acids. Within these molecular classes, both population variation and intra patient variation was calculated across a sample set of 14 individuals (and subsequently 46 individuals) each with 3 repeat samples over the course of 7 days (collected irrespective of time or prandial status) with high variation metabolites excluded from down-stream analysis. Performance and final panel selection were achieved through an AI approach. Random forests (python, ‘sklearn’, ntrees=1000, features per level =10) and neural networks (python, sklearn, Scikit-Neural Network, 100 hidden layers, ReLu activation, with an Adam or L-BFGS-B solver, and regularization=0.0001) were implemented. Gini scores, precision, and recall metrics were calculated for the ability of each feature to discriminate the clinical metadata parameters (BMI, age, weight, similarity to the population average for that score, sex, and ethnicity as covariates), with top scoring features selected for downstream model evaluation and cooperative performance. Briefly, two evaluations were performed using either ‘in-house data’ only or comparisons to external datasets. In-house validation was executed using repeat random sampling (n=100 train / test permutations) using 80% and 20% of the data for the train and test sets, respectively. External data was used to evaluate the ability for the panel to correctly identify healthy vs non-healthy patients within other studies from publicly available data (Metabolomics Workbench). These included type-I diabetes (ST00091), metabolic syndrome (ST000663,ST000992), non-alcoholic fatty liver disease (ST000665), obesity (ST000106, ST001515), and IBS (ST000923). The final 20 metabolites were those that consistently and together distinguished health vs non-health in external studies and discriminated the combined clinical metadata parameters. Scoring System. 20 metabolites were used in the panel that represented multiple subsystems of the microbiome. These included SCFAs (acetate, butyrate, propionate), BAs (taurocholic acid, cholic acid, chenodeoxycholic acid, deoxycholic acid, lithocholic acid, allolithocholic acid, taurodeoxycholic acid), indoles (indole-3-acetate, tryptamine, indole-3- propionate, gamma-aminobutyric acid), phenyl and organic acids (caffeic acid, sinapic acid), and amino acids (leucine, isoleucine, valine, & tryptophan). The deviation score was calculated Atty. Docket No. UCHI-42155.601 as summation of the absolute value of the difference between an individual metabolite concentration and the mean of the healthy cohort comparator (HCS average) divided by the standard deviation of the metabolite, whereby a score of 0 would represent a perfect average for all 20 metabolites used. Across a healthy population sampled, scores ranging from 0-20 + / - 5, representing the normal variation observed across this group of healthy individuals aged 21- 55. Dietary was to sources of fermentable fibers that seem to be tolerated versus those that seem to cause GI upset. The experimental diet was then designed to introduce plant-based foods in a stepwise fashion, based on each food's main fiber source. Each week of the trial had a similar pattern – for the first five days of the week, foods with one predominant FODMAP were slowly introduced, starting with one serving the first day, two servings the second day, and three servings the third, fourth, and fifth days of the week. On Days 6 and 7 the subject was allowed to go back to his habitual diet or continue with the newly introduced foods as desired. The initial sequence of fiber introduction was foods with polyols, then fructans, then galacto-oligosaccharides (GOS). Week four it was attempted to introduce foods with fructose, but the patient developed unpleasant symptoms and therefore the introduction was backed-off and the initial three-week sequence was repeated. After results of initial stool analysis, lactose was introduced in hopes of promoting growth of Bifidobacteria, which Patient Q appeared to lack. Lactose was started with multiple small doses (1.5g lactose / dose) four times daily, building over seven weeks to 6 g lactose / dose four times daily from a variety of dairy sources. Fructose and sucrose were tried again, but again Patient Q experienced severe symptoms after a very small dose. At this point the patient was able to tolerate a substantially more varied diet and was overall satisfied with his progress. Over time, foods with more than one FODMAP fiber, such as legumes, were introduced. Sample Collection / Storage / Processing. Stool samples were collected from Patient Q using the EasySampler Stool Collection Kit (Alpco, cat #58-EZSAMPLER). Between August 2021 and August 2022, Patient Q collected 3 samples per week (consistently on Wednesdays, Saturdays, and Sundays). Samples were collected using the kit, and then immediately frozen Atty. Docket No. UCHI-42155.601 in Patient Q’s home freezer until he could arrange delivery to University of Chicago. Patient Q would usually drop off samples every 2-3 months and samples would be processed on that basis. Once samples arrived, they were frozen at -80°C until further processing could be completed. For processing, samples were thawed from -80°C and approximately 300 mg of stool was aliquoted into two microcentrifuge tubes (one for 16S rRNA sequencing, and one for metabolomic profiling). Samples were submitted to the Duchossois Family Institute (DFI) core facilities for downstream processing. Samples for the 14 healthy individuals of the training cohort were collected using the same kits. Participants collected 8 samples over a 1-month period (twice a week) and froze their samples at home until they could be brought into the lab. The training cohort samples were collected from June 2021 – September 2021, and were processed through the DFI in the same manner as Patient Q. Mass Spectrometry (MS). Development of workflow from quantitative MS measurements of the panel markers to derivation of an overall clinical index score: The preserved fecal metabolome is analyzed across four MS platforms to capture a panel of metabolites reflecting the health of the gut microbiome. Following organic solvent extraction, compound classes such as indoles and bile acids are reported as quantitative and normalized relative abundance with two liquid chromatography-MS systems (triple quadrupole-MS (QQQ, Agilent 1290 / 6470), quadrupole time-of-flight-MS (QTOF, Agilent 1290 / 6546). Additionally, compound classes such as short chain fatty acids and amino acids are derivatized and analyzed by gas chromatography-MS techniques (GC-MS) ((-) chemical ionization-GC-MS, Agilent 8890; electron impact-GC-MS, Agilent 7890B). All targeted metabolites are compared to the intact and fragmentation m / z values and corresponding retention time of authentic standards. Known concentrations of stable isotope ISs are evaluated to calculate percent of coefficient of variation across samples and batches. Separate heavy ISs are analyzed to normalize for sample evaporation and solvent spills. Data analyses were performed using Mass Hunter Quantitative Analysis software (version B.10, Agilent Technologies). Study Approval. The study was approved by the University of Chicago IRB, with written informed consent from the patient and healthy donors before beginning in the study. Raw mass spectrometry data (.d, Agilent) was converted to open-source format (.mzML) using ProteoWizard MSConvert and the files have been deposited onto the mass spectrometry data repository, MetaboLights (Study ID, MTBLS7965). Atty. Docket No. UCHI-42155.601 Example 5 Development of metabolite profile The study Rationale for the validation cohort is (i) to implement the knowledge obtained from our pilot studies and transition to home stool collections from “healthy” volunteers and (ii) to establish the typical range of levels of individual compounds metabolites in a group of healthy people. It was hypothesized that, by comparing "healthy" and "diseased" individuals, it would be possible to characterize panels of metabolites that could produce a profile or predict the state of one's health. Study Design. Elements of sample kits & collection instructions were under constant examination, and as a result, changes were made along the way to better achieve the desired mass from these collections. Additionally, results from Example 2 were not available at initiation of the study. The acceptable storage time was modified as the data became available. Dates or sample IDs associated with when changes were implemented have been notated and described below. Study Participants. This was an initial evaluation and samples were collected from anyone willing to enroll without designating exclusion criteria at the outset. In Sept 2022, only a 96-hr hold period was validated. To meet that timeframe, the personnel in laboratories of KCBD9 and adjacent floors formed the initial subject pool. On November 21, 2022, stabilization of metabolites out to two weeks was validated, allowing for subject pool expansion to friends and family of personnel. Health Designation. Designation of healthy, for the purposes of the initial decision of whether to collect or not, was based primarily on self-description of “healthy” by the volunteer. Participants were asked to fill out a questionnaire regarding use of over-the-counter medications and antibiotics in the prior 6-months, prescribed medications and diagnosis of any key conditions (Crohn’s disease, UC, Irritable Bowel Disease, Functional Bowel Disease, gluten or glucose sensitivity, lactose intolerance, etc. All study participants were assigned a de- identified code in the format of GBVC_XXX where XXX=unique numerical identifier. Sample Collection Kits. GBVC_001 to GBVC_133 utilized a modified ALPCO EasySampler collection kit. (ALPCO, Cat. No. 58-EZSAMPLER, Salem, NH). The ALPCO kits included: (i) pre-labeled brown topped sample collection tube; (ii) pre-labeled Outer Collection Tube; (iii) toilet collection device; and (iv) gloves. Three ALPCO collection kits were provided to each participant and termed a “kit set”. The kits were “modified” by addition of 3 mL of preservative to each collection tube. The tubes were weighed after the addition of preservative and documented. The preservative Atty. Docket No. UCHI-42155.601 was spiked with two heavy molecules (to serve as internal controls): (i) caffeine-(trimethyl-d9), Sigma-Aldrich, cat#725625-100mg – final concentration: 0.49 mM and (ii) sodium acetate-d3, Cambridge Isotope Laboratories, Cat no. DLM-3126-25 - final concentration: 1.15 mM. Purpose of the preservative. During the metabolomics analysis, the calculated levels of these internal controls would allow for identification of any preservative evaporation or spillage that might have occurred during sample collection or processing. Further collection kit adjustments: (i) between kits GBVC_001 to GBVC_084, participants were requested to provide four scoops of stool within 96-hours; (ii) beginning with GBVC_085, the requested amount was reduced to three scoops of stool as a result of many overloaded (>7 grams) sample submissions; (iii) for kits sets distributed after November 21, 2022, it was requested that kits be returned within 14 days of each sample collection; (iv) starting with GBVC_137, the ‘home brew” collection kit was used which had the same collection tube setup (inner and outer tubes) but the feces collection device was the ZymoResearch version; and (v) instructions were modified to request “Two (pea to blueberry sized) scoops of stool.” These collection kit adjustments facilitated a much more consistent submission of stool mass by participants. Experimental Condition Descriptions. Three stool samples were collected from three different days within a 7 day period (“biological replicates”). Microbes and metabolite levels can vary on a day-to-day basis based on diet. In prior studies, sample-to-sample (i.e. day-to- day) within patient variation was noticed. Thus, there was concern that analysis of a single sample would not give a representative result. By analyzing three samples, a statistical average per patient was calculated. No requirement for whether participants should collect on days that were sequential or intermittent. Replicates designated as GBVC_XXX_T1, GBVC_XXX_T2, and GBVC_XXX_T3. Some volunteers did mix up the order of the tubes and in any instance of discrepancy, the sample collection dates determined sample order. Storage temps. As a result of the equivalent results between 22°C and 37°C in the preservative pilot analysis, no storage temperature criteria was applied. Volunteers stored samples as was convenient and did not report conditions under which samples were held to study personnel. Storage time. For the GBVC cohort sample collection, the storage times applied varied from 96-hours to 14-days. As noted above, applied changes, based either on date of collection or sample ID, were documented. Atty. Docket No. UCHI-42155.601 Study personnel confirmed the storage times aligned with the desired by calculating the time difference between the “time of sample collection” and the “Date / Time of Sample Arrival [24-hr clock] “. The date / time of sample arrival amounted to the time that samples were placed at -80°C. Other. Participants were asked (i) to collect stool at roughly the same time each day (+ / - 6 hours) to account for microbial circadian rhythms and (ii) to record the date and time of each sample collection. Collection Tube Setup and Sample Processing. Pre-labeled empty collection tubes were prepared. Then, 3 mL preservative (with internal controls added) was added to the empty tube, the tube weighed and weight was documented in a spreadsheet. Upon sample return, the tubes were weighed and the mass documented in the same spreadsheet. By weighing the collection tubes (i) after preservative addition and (ii) again after sample collection, quantitation of the amount of stool submitted for metabolomic analysis was ensured, which would translate to the ability to quantitate metabolite levels. This quantitation would be possible provided that the heavy molecule internal controls were within the appropriate range indicating no spillage or evaporation had occurred during collection. Other documented variables included (either directly recorded or calculated from the directly recorded variables): (i) date / time of sample collection (24-hr clock); (ii) date / time of sample arrival (24-hr clock) = time placed at -80°C; (iii) total storage time (hr:min) = total time from collection to storage at -80°C; and (iv) mass of stool submitted (in grams). Samples were frozen at -80°C until aliquoting. For analysis batch 1, samples were aliquoted into microfuge tubes for 16S analysis and metabolomics analysis and then the following metrics were recorded or calculated: (i) empty tube weight (g); (ii) filled tube weight (g) (stool slurry); (iii) stool slurry mass (g); (iv) stool / tube mass (preservative removed) (g); (v) mass of preservative alone (g); (vi) mass of stool alone (g). For analysis batch 2 and 3, sample collection tubes were submitted directly to the DFI’s host microbe metabolomics facility (HMMF) after validation by the HMMF of a workflow that would align direct receipt of Gateway Biome samples (i.e., no sample aliquoting). Analysis Plan. A total of 121 volunteers submitted samples (FIG.4). Samples from 82 volunteers were submitted for untargeted metabolomics, which is a group of panels comprising 300+ metabolomic targets. Samples were chosen for submission based on having none of the following exclusion criteria: (i) an incomplete set of samples collected; (ii) sample return outside of the designated timeframe; (iii) incomplete questionnaire and / or failure to Atty. Docket No. UCHI-42155.601 submit paperwork; (iv) inappropriate sample mass submitted (i.e., tubes overloaded); and (v) any indication of spilled preservative or sample. Study personnel further refined the cohort for analysis based on the following exclusion criteria (FIG. 4): (i) age range outside of 18 – 70; (ii) a body mass index (BMI) outside the range of 18 to 35 kg / m2; (iii) a history of specified GI diseases (ex., having a diagnosed GI disease were excluded outright and GI issues, like lactose or gluten sensitivity, were placed in a sub-cohort as many volunteers reported excluding those trigger foods from their diets; thereby experiencing no GI issues); (iv) exposure to some medications within the last 6 months (ex., antibiotics, immunosuppressive agents; did not want to limit medication exposure to “None” otherwise a large proportion of young females who are on birth control would be lost and limiting medication exposure to “None” in volunteers greater than age 40 would exclude 14 (54%) of the 26 volunteers). Using interpretations of the criteria outlined above, study personnel independently coded the 82 volunteers, those submitted for metabolomics analysis, on their appropriateness for inclusion into the “healthy cohort”. Agreement for inclusion on 62 volunteers (i.e. cohort of 62). Agreement for exclusion on 11 volunteers. Discrepancy in the decision to include / exclude for 9 volunteers. These nine volunteers, when combined with the “cohort of 62”, became the “cohort of 71.” Demographics of Cohorts. The information in FIGS. 5A-5C outline the key demographic information on the cohort of 82. The information in FIGS.6A-6C outline the key demographic information on the cohort of 71. The information in FIGS.7A-7C outline the key demographic information on the cohort of 62. Bottom line: The cohort of 62 appears to be slanted towards (i) younger age demographic (23–30-year-olds) with 66% representation; (ii) female, with 58% representation; and (iii) white / Caucasian with 65% representation. Example 6 The number of subjects included in the study were increased from eighty-two subjects (n=82) to one hundred and seventy-six subjects (n=176). See Table 4, below. Three stool samples were obtained per healthy subject (healthy subjects are those with no unmanaged disease(s)) for a total of five hundred and twenty-eight samples. Targeted metabolomics for 300+ validated compounds were performed on the five hundred and twenty-eight samples. Table 4. Demographic and clinical characteristics of N=176 Discovery Cohort N Percent (%) Gender Atty. Docket No. UCHI-42155.601 74 42.05% Male 102 57.95% Female Age (Average 38 (+ / -13)) 29.55% 18-30 52 26.14% 31-40 46 17.61% 41-50 31 17.05% 51-60 30 61-80 17 9.66% BMI (Average 24.69 (+ / - 4.32)) <18.5 (underweight) 6 3.41% 97 55.11% 18.5-24.9 (healthy) 25.0-29.9 (overweight) 48 27.27% >29.9 (obese) 23 13.07% unlisted 2 1.14% Race Asian 25 14.20% 8 4.55% Black or African American 11 6.25% Hispanic or Latinx Multi- or Bi-Racial 13 7.39% White or Caucasian 111 63.07% 8 4.55% Other The datasets obtained from Example 5 (i.e., from n=82 subjects) and from Example 6 (i.e., from n=176 subjects) were integrated together and the calculations for the reference ranges and for the power calculations were recalculated. The power calculations were used to demonstrate confidence in the reported ranges. The power values & ranges, the 20 metabolite panel, the metabolite chemical structure, the n’s needed for confidence, and the population average and standard deviation are detailed in FIG.8. Age, Race, Gender, and BMI were examined to determine whether these aspects affected the metabolite ranges. With the current sampling, no significant trend was identified Atty. Docket No. UCHI-42155.601 with BMI or Race (data not shown). The identified metabolites affected by age and gender and shown in FIG.9 and FIG.10.

Claims

Atty. Docket No. UCHI-42155.601 CLAIMSWhat is claimed is:

1. A biological sample collection kit comprising first and second labelled internal control compounds in an organic preservative solution within a resealable container.

2. The kit of claim 1, wherein the organic preservative is ethanol.

3. The kit of claim 2, wherein the organic preservative is a 95% ethanol solution.

4. The kit of any one of claims 1 - 3, wherein the first labelled internal control compound is caffeine-(trimethyl-d9).

5. The kit of claim 4, wherein the caffeine-(trimethyl-d9) is present at a concentration of 0.1 to 2.0 mM.

6. The kit of any one of claims 1 - 5, wherein the second labelled internal control compound is sodium D3 acetate.

7. The kit of claim 6, wherein the sodium acetate-d3 is present at a concentration of 0.1 to 2.0 mM.

8. The kit of claim 1, comprising 0.1 to 2.0 mM caffeine-(trimethyl-d9) and 0.1 to 2.0 mM sodium acetate-d3 in a 95% ethanol solution.

9. The kit of claim 8, comprising 1-10 mL of ethanol.

10. The kit of any one of claims 1 - 9, further comprising one or more of a second sealable container sized to contain the resealable container, a sealable bag, a sample collection spoon, a label, instructions, sterile gloves, a commode collection device, a mailing box or envelope, and pre-paid postage.Atty. Docket No. UCHI-42155.601 11. A method of home collection of a biological sample, comprising: (a) obtaining or receiving a biological sample collection kit of one of claims 1-10; (b) placing the biological sample within the organic preservative solution in the resealable container; (c) sealing the resealable container; and (d) delivering the resealable container containing the biological sample to a testing facility.

12. The method of claim 11, wherein the biological sample is a fecal sample.

13. The method of any one of claims 11 - 12, wherein delivering the resealable container containing the biological sample to a testing facility comprises sending the resealable container by mail or courier.

14. The method of any one of claims 11 - 13, wherein the biological sample is not frozen for delivering the resealable container containing the biological sample to a testing facility.

15. The method of any one of claims 11 - 14, wherein the biological sample is delivered to the testing within two weeks of placing the biological sample with the organic preservative solution.

16. A method of obtaining and testing a biological sample from a subject by home collection, comprising: (a) providing the subject with a biological sample collection kit of one of claims 1-10, wherein the kit comprises instructions for the subject to (i) place the biological sample within the organic preservative solution in the resealable container, (ii) seal the resealable container, and (iii) deliver the resealable container containing the biological sample to a testing facility; (b) processing the biological sample; and (c) analyzing the processed biological sample by one or more biophysical techniques.

17. The method of claim 16, wherein the biological sample is a fecal sample.Atty. Docket No. UCHI-42155.601 18. The method of any one of claims 16 - 17, wherein the subject suffers from dysbiosis of gut microbiota.

19. The method of any one of claims 16 - 18, wherein the subject has received treatment with broad-spectrum antibiotics.

20. The method of any one of claims 16 - 19, wherein providing the subject with a biological sample collection kit comprises having the kit delivered to the subject by mail or courier.

21. The method of any one of claims 16 - 19, wherein providing the subject with a biological sample collection kit comprises having the subject obtain the kit from a pharmacy.

22. The method of any one of claims 16 - 21, wherein delivering the resealable container containing the biological sample to a testing facility comprises sending the resealable container by mail or courier.

23. The method of any one of claims 16 - 22, wherein the biological sample is not frozen for delivering the resealable container containing the biological sample to a testing facility.

24. The method of any one of claims 16 - 23, wherein the biological sample is delivered to the testing within two weeks of placing the biological sample with the organic preservative solution.

25. The method of any one of claims 16 - 24, wherein processing the biological sample comprises determining the mass of the biological sample placed within the organic preservative solution.

26. The method of any one of claims 16 - 25, wherein processing the biological sample comprises one or more of diluting the biological sample, homogenizing the biological sample, dividing the biological sample into two or more sub-samples, derivatizing the biological sample or sub-sample, and freezing biological sample or sub-sample, the drying the biological sample or sub-sample.Atty. Docket No. UCHI-42155.601 27. The method of any one of claims 16 -26, wherein the biophysical technique comprises a mass spectrometry technique.

28. The method of claim 27, wherein the mass spectrometry technique comprises GC-MS and / or LC-MS.

29. The method of any one of claims 16 - 26, wherein the biophysical technique comprises utilizing a point-of-care device.

30. Th method of claim 29, wherein the point-of-care device utilizes metabolite binding agents and a detection / quantification methodology.

31. The method of claim 30, wherein the metabolite binding agents are antibodies, antibody fragments, or aptamers.

32. The method of any one of claims 30 - 31, wherein the detection / quantification methodology is fluorescence, luminescence, or electrochemical detection.

33. The method of any one of claims 16 - 32, wherein analyzing the processed biological sample by one or more biophysical techniques comprises quantitating the level of one or more metabolites in the sample.

34. The method of claim 33, wherein one or more metabolites comprises 5-200 metabolites.

35. The method of any one of claims 33 - 34, wherein one or more metabolites are selected from butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid.

36. A method of quantitating the levels of a panel of metabolites within a biological sample comprising: (a) receiving the biological sample within an organic preservative solution;Atty. Docket No. UCHI-42155.601 (b) using a biophysical technique to quantitate a panel of metabolites within the biological sample.

37. The method of claim 36, wherein the organic preservative solution comprises ethanol.

38. The method of claim 37, wherein the organic preservative solution is a 95% ethanol solution.

39. The method of any one of claims 36 - 38, wherein the organic preservative solution comprises a first labelled internal control compound.

40. The method of claim 39, wherein the first labelled internal control compound is caffeine-(trimethyl-d9) or sodium acetate-d3.

41. The method of any one of claims 39 - 40, wherein the first labelled internal control compound is present at a concentration of 0.1 to 2.0 mM.

42. The method of any one of claims 39 - 41, wherein the organic preservative solution comprises a second labelled internal control compound.

43. The method of claim 42, wherein the first and second labelled internal control compounds are caffeine-(trimethyl-d9) or sodium acetate-d3.

44. The method of claim 43, wherein the first and second labelled internal control compounds are present at a concentration of 0.1 to 2.0 mM.

45. The method of any one of claims 36 - 44, wherein the biophysical technique comprises a mass spectrometry technique.

46. The method of claim 45, wherein the mass spectrometry technique comprises GC-MS and / or LC-MS.

47. The method of any one of claims 36 - 44, wherein the biophysical technique comprises utilizing a point-of-care device.Atty. Docket No. UCHI-42155.601 48. The method of claim 47, wherein the point-of-care device utilizes metabolite binding agents and a detection / quantification methodology.

49. The method of claim 48, wherein the metabolite binding agents are antibodies, antibody fragments, or aptamers.

50. The method of any one of claims 48 - 49, wherein the detection / quantification methodology is fluorescence, luminescence, or electrochemical detection.

51. The method of any one of claims 36 - 50, wherein the panel of metabolites comprises 5-200 metabolites.

52. The method of claim 51, wherein the panel of metabolites comprises metabolites selected from butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid.

53. The method of any one of claims 36 - 52, wherein the biological sample is a fecal sample.

54. The method of claim 53, wherein the fecal sample is obtained from a subject that suffers from dysbiosis of gut microbiota.

55. The method of any one of claims 36 - 54, wherein the subject has received treatment with broad-spectrum antibiotics.

56. The method of claim 55, wherein the antibiotics are broad-spectrum antibiotics.

57. A method of assessing the gut microbiome of a subject comprising: (a) quantitating levels of a panel of metabolite biomarkers in a fecal sample from the subject;Atty. Docket No. UCHI-42155.601 (b) comparing the level of each metabolite biomarker in the panel to a control or threshold value for each, wherein a significant difference between the level of one or more of the metabolite biomarkers from the control or threshold value is indicative of an atypical gut microbiome.

58. The method of claim 57, wherein the panel of metabolite biomarkers comprises two or more metabolites selected from butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid.

59. The method of claim 58, wherein the panel of metabolite biomarkers comprises 50 or fewer metabolite biomarkers.

60. The method of claim 59, wherein the panel of metabolite biomarkers comprises 20 or fewer metabolite biomarkers.

61. The method of any one of claims 57 - 60, wherein the control or threshold value is based on a population average for healthy subjects.

62. The method of any one of claims 57 - 61, wherein comparing the level of each metabolite biomarker in the panel to a control or threshold value for each comprises calculating the difference between the level of each metabolite biomarker in the panel to a control or threshold value for each.

63. The method of claim 62, wherein comparing the level of each metabolite biomarker in the panel to a control or threshold value for each further comprises, dividing the difference by the standard deviation for the population of healthy subjects to generate a metabolite score for each metabolite biomarker.

64. A method of assessing the gut microbiome of a subject comprising: (a) quantitating levels of a panel of metabolite biomarkers in a fecal sample from the subject;Atty. Docket No. UCHI-42155.601 (b) generating a composite score for the panel of metabolite biomarkers by comparing the level of each metabolite biomarker in the panel to a control or threshold value for each to generate a metabolite score for each metabolite biomarker, and combining the metabolite scores to generate the composite score; (c) comparing the composite score to a composite threshold value, wherein a significant difference between the composite score to a composite threshold value is indicative of an atypical gut microbiome.

65. The method of claim 64, wherein the panel of metabolite biomarkers comprises two or more metabolites selected from butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid.

66. The method of claim 65, wherein the panel of metabolite biomarkers comprises 50 or fewer metabolite biomarkers.

67. The method of claim 66, wherein the panel of metabolite biomarkers comprises 10 or fewer metabolite biomarkers.

68. The method of any one of claims 64 - 67, wherein the control or threshold value for each metabolite biomarker is based on a population average for healthy subjects.

69. The method of any one of claims 64 - 68, wherein the metabolite score for each metabolite biomarker in the panel is calculated by calculating the difference between the level of each metabolite biomarker in the panel to a control or threshold value for each, and dividing the difference by the standard deviation for the population of healthy subjects.

70. The method of any one of claims 64 - 69, wherein the composite score is the sum or the average of the metabolite scores for each metabolite biomarker in the panel.

71. A method of monitoring the gut microbiome health of a subject over time, comprising:Atty. Docket No. UCHI-42155.601 (a) assessing the gut microbiome of the subject at a first timepoint by quantitating the levels of a panel of metabolite biomarkers in a fecal sample from the subject; (b) assessing the gut microbiome of the subject at a second timepoint by quantitating the levels of a panel of metabolite biomarkers in a fecal sample from the subject; (c) comparing the levels of the panel of metabolite biomarkers in the fecal sample from the subject at the first timepoint to the second timepoint; wherein, if a difference between the levels of the panel of metabolite biomarkers and control values for a healthy population at the second timepoint is less than a difference between the levels of the panel of metabolite biomarkers and control values for a healthy population at the first timepoint, then it indicates that the gut microbiome of the subject has normalized over the timespan.

72. The method of claim 71, wherein the subject has received treatment for dysbiosis over the timespan.

73. A method of home collection of a fecal sample, comprising: (a) obtaining or receiving a biological sample collection kit of one of claims 1-10; (b) placing the fecal sample within the organic preservative solution in the resealable container; (c) sealing the resealable container; and (d) delivering, by mail or courier, within two weeks of placing the fecal sample with the organic preservative solution, the resealable container containing the non-frozen fecal sample to a testing facility.

74. A method of obtaining and testing a fecal sample collected by home collection from a subject who suffers from dysbiosis of gut microbiota and who has received treatment with broad-spectrum antibiotics, the method comprising: (a) providing, by mail, courier, or by having the subject obtain the kit from a pharmacy, the subject with a biological sample collection kit of one of claims 1-10, wherein the kit comprises instructions for the subject to (i) place the fecal sample within the organic preservative solution in the resealable container, (ii) seal the resealable container, and (iii) deliver, in person, by mail or courier, the resealable container containing the non-frozen fecal sample to a testing facility;Atty. Docket No. UCHI-42155.601 (b) processing the fecal sample, wherein processing the fecal sample comprises one or more of diluting the biological sample, homogenizing the fecal sample, dividing the fecal sample into two or more sub-samples, derivatizing the fecal sample or sub-sample, and freezing fecal sample or sub-sample, the drying the fecal sample or sub-sample; and (c) quantitating the level of one or more metabolites in the fecal sample, wherein the one or more metabolites are selected from the group consisting of: butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid.

75. A method of quantitating the levels of a panel of metabolites within a fecal sample collected by home collection from a subject who suffers from dysbiosis of gut microbiota and who has received treatment with broad-spectrum antibiotics biological sample, the method comprising: (a) receiving the fecal sample within a 95% ethanol solution comprising a first labelled internal control compound and a second labelled internal control compound; (b) quantitating the level of one or more metabolites in the fecal sample, wherein the one or more metabolites are selected from the group consisting of: butyrate, cysteine, isoleucine, leucine, para-cresol, propionate, valine, kynurenine, tryptophan, 2-aminobutyric acid, caffeic acid, deoxycholic acid, sinapic acid, cholic acid, lithocholic acid, taurocholic acid, chenodeoxycholic acid, allolithocholic acid, and isolithocholic acid.

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