Diagnostic tests for autism, gut dysbiosis, gut disorders, and neurological disorders

Abstract

Metabolites and methods of using the metabolites for diagnosis of autism spectrum disorder (ASD) and gut dysbiosis are provided. The metabolites comprise microbially derived metabolites that are significantly different between the autistic and healthy typically developing groups, and one or more of the metabolites are used to screen and / or diagnose ASD and / or gut dysbiosis.

Description

DIAGNOSTIC TESTS FOR AUTISM, GUT DYSBIOSIS, GUT DISORDERS, AND NEUROLOGICAL DISORDERSCROSS REFERENCE TO RELATED APPLICATION

[0001] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 667,482, filed July 3, 2024, titled “DIAGNOSTIC TESTS FOR AUTISM, GUT DYSBIOSIS, GUT DISORDERS, AND NEUROLOGICAL DISORDERS,” the disclosure of which is incorporated by reference herein in its entirety.FIELD OF THE INVENTION

[0002] The invention relates to methods for identifying subjects with autism spectrum disorder and gut dysbiosis.BACKGROUND

[0003] Autism spectrum disorder (ASD) is a neurodevelopmental condition which is estimated to affect about 1 in 31 children in the United States. This condition is defined by difficulty in communication, social interaction, and restricted repetitive behaviors. Despite being categorized and diagnosed by a set of behavioral criteria, ASD is known to be associated with several co-occurring conditions that affect a multitude of physiological systems. As ASD etiology is understood to be a consequence of environmental and genetic factors, identifying distinctive metabolomics profiles of individuals with ASD has been a frequent subject of investigation.

[0004] ASD is a complex disorder which can be difficult to diagnose, and the current diagnosis is based on an assessment of social communication and behavior. Currently there is no widely accepted medical test for the diagnosis of autism, although several have been proposed. A number of metabolomic differences have been observed in individuals with ASD, many of which have also been examined for their potential role in this condition's clinical pathology. Differences in mitochondrial metabolism, the gastrointestinal system and redox regulation have been associated to varying degrees with ASD. Divergences in metabolite profiles between children with ASD and their healthy typically developing cohorts have been shown to exhibit significant differences up to the point where predictions about which metabolicprofiles belong to the ASD or healthy typically-developing individuals (TD) group have been made. Furthermore, modulating metabolomic pathways holds significant promise as the basis to develop therapies addressing ASD co-occurring conditions and symptoms.

[0005] There is a critical need for a method to screen young children for ASD, since the current average age of diagnosis is 47 months, and early intervention is much more effective if begun earlier in life. The lifetime cost of care for a person with ASD is estimated at $2 million, so it is imperative to screen early in life to maximize benefit.

[0006] Therefore, there is an unmet need for early diagnosis of ASD to facilitate early intervention and treatment.SUMMARY

[0007] Provided herein is a method of screening for Autism Spectrum Disorder (ASD) in a subject. The method comprises measuring or having measured a level of each of at least two microbially-derived metabolites in a biological sample obtained from the subject; comparing the measured level of each metabolite against a cut-off range for each metabolite, wherein the cut-off range is determined from a distribution of each of the metabolite level in at least one healthy typical developing (TD) subject, and is characterized by an upper-bound cut-off value and a lower-bound cut-off value; and indicating the subject as having ASD when the level of each of the at least two metabolites is outside the cut-off range obtained from the TD subjects.

[0008] The disclosure further encompasses a method of classifying a subject as a candidate for treatment of Autism Spectrum Disorder (ASD). The method comprises measuring or having measured a level of each of at least two microbially- derived metabolites in a biological sample obtained from the subject; comparing the measured level of each metabolite against a cut-off value for each metabolite, wherein the cut-off range is determined from a distribution of each of the metabolite level in at least one healthy typical developing (TD) subject, and is characterized by an upper-bound cut-off value and a lower-bound cut-off value; and classifying the subject as a candidate for treatment of ASD when the level of each of the at least two metabolites is outside the cut-off range obtained from the TD subject(s).

[0009] The upper bound cut-off value may be determined by obtaining for each of the at least two metabolites, the level of the metabolite at the 95th percentile of the TD multiplied by a factor about 1 .1 to about 5. The lower bound cut-off value may be determined by obtaining for each of the at least two metabolites, the level of metabolite at the 95th percentile of the TD multiplied by a factor about 0.9 to about 0.2.

[0010] The at least two microbially-derived metabolites may be selected from one or more tryptophan related metabolites, one or more phenylalanine related metabolites, and one or more yeast metabolites.

[0011] The method in some instances, may further comprise assigning a medical, behavioral, and / or nutritional treatment protocol to the subject identified as having ASD. The treatment protocol may comprise adjusting the level of one or a combination of two or more microbially-derived metabolites in the subject. In other aspects, the treatment protocol comprises administering a combination of prebiotics, probiotics, microbiota transplant, antibiotics, anti-fungal medications, diet modifications.

[0012] In specific aspects, the treatment comprises administering to the subject: (a) a behavioral management therapy, a cognitive behavior therapy, an early intervention, an educational and school-based therapy, a joint attention therapy, an occupational therapy, a parent-mediated therapy, a physical therapy, social skills training, or a speech-language therapy; (b) a medication selected from an antipsychotic drug, a selective serotonin re-uptake inhibitor (SSRI), a tricyclic, a psychoactive or anti-psychotic medication, a stimulant, an anti-anxiety medication, or an anticonvulsant; (c) a nutritional supplementation; or (d) a composition comprising fecal microbiota from a healthy neurotypical human donor.

[0013] In one aspect, the one or more tryptophan related metabolites are selected from a group consisting of: (a) 3-Oxindole, 2-Oxindole, 3 Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl-1 ,2,3,4 Tetrahydro-Beta- Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, lndole-3-Acetic Acid, lndole-3- acetoximide, lndol-3-one, lndol-2-one, Indole-N-Propionic Acid x 3**, lndole-3- carboxylic acid O Sulfate, lndole-3-ethanol, Indole, 1 - Benzazole 5, Methoxy IndoleAcetic Acid, methyl-3-indole acetate, 3-methylindole, 6-methylindole, dihydroxy indole glucuronide, 5-hydroxy-6-methoxyindole Glucuronide, 4-lndole carbaldehyde, 3- methyldioxyindole, Indole, 1 -Benzazole, Indoxyl Glucuronide, 5- methoxyindoleacetate, 2 -carboxy 2, 3, dihydroxyindole, 2-hydroxy 3-(1-methyo-1 H indol 3-yl) propanoic acid, 3-lndoleacetonitrile, lndole-3-Acetic acid O-Glucuronide, 5- Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3-lndoleacrylic acid, 1 ,1 ethyldienebistryptophan, and Indole Pyruvate; (b) lndolyl-3-acryloylglycine (IAG), 4-lndolecarbaldehyde, 3-lndolepropionicacid, 1 -Methyl-1 ,2,3,4-tetrahydro-p- carboline-3-carboxylic acid, 3-Methyldioxyindole; trans-3-lndoleacrylic acid, lndole-3- acetaldoxime, and 3-lndoleacetonitrile; (c) 3-lndolepropionic acid, 1 -Methyl-1 ,2,3,4- tetrahydro-[3-carboline-3-carboxylic acid, 3-Methyldioxyindole, lndole-3-acryloyl glycine, trans-3-lndoleacrylic acid, 4-lndolecarbaldehyde, 3-lndoleacetonitrile, and lndole-3-acetaldoxime; or (d) IAG, 3-indole propionic acid, indole-3-carbaldehyde, phenylacetyl-l-glutamine, 3-methyl-2 -oxindole, Indoxyl Sulfate (Indican), 2-oxindole,3-indole acetonitrile, 1 -methyl-1 ,2,3,4 Tetrahydro Beta Carboline, and 5- methoxyindole.

[0014] In another aspect, the one or more phenylalanine related metabolites are selected from the group consisting of: (a) 4-vinylphenol sulfate, p-Cresol, p- Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy-3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p-cresol gluduronide, 4-ethyl phenol, 4-ethylphenol sulfate,4-ethylphenol glucuronide, 3-hydroxy-3-hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, and p-Cresol Glucuronide; (b) Phenylacetylglutamine, p-Cresol, p-Cresol Sulphate, 4-(2-Aminopropoxy)-3,5- dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, Hippuric Acid, DHPPA, benzoic / hippuric, and Benzoic Acid; (c) 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6-Amino-m-cresol, 4-Methylphenol, Phenylacetylglutamine, and p-cresols sulfate; or (d) p-Cresol, Benzoic Acid, benzoic / hippuric, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, and Phenyl propionic Acid.

[0015] The one or more yeast metabolites is selected from the group consisting of: (a) N-Formyl Methionine, D-Arabinoate, (-) Threo-lso(homo)2citrate, D- Aspartate, D-Glutamate, and D-Arginyl-L-histidyl-D-prolyl-D-tyrosine; (b) Arabinitol, Tricarballylic Acid, Tartaric Acid, and Citramallic Acid; or (c) Arabinitol.

[0016] The biological sample may be whole blood, plasma, red blood cells (RBCs), other components of blood, urine, stool, saliva, cord blood, amniotic fluid, meconium, buccal swabs, biopsy-obtained tissue, cord blood, placental tissue, hair, deciduous teeth, or any combination thereof.

[0017] In one aspect, the subject is in utero, a newborn, a neonate, an infant, a toddler, a young child, a child, an adolescent, or an adult.

[0018] Further disclosed is a method of monitoring a therapeutic effect of an ASD treatment protocol in a subject suspected of having or at risk of having ASD. The method comprises: measuring in a first biological sample obtained from the subject a level of each of at least two microbially-derived metabolites; measuring in a second biological sample obtained from the subject at a period of time after the first biological sample is obtained the level of each of the at least two microbially-derived metabolites; and comparing the level of each of the at least two microbially-derived metabolites in the first sample and the second sample; wherein maintenance of the level of each of at least two microbially-derived metabolites or a change of the level of each of the at least two microbially-derived metabolites to a level of each of at least two microbially-derived metabolites selected from the metabolites in a control panel of metabolite levels created by measuring microbially-derived metabolites levels of the one or combination of two or more microbially-derived metabolites in control TD subjects is indicative that the treatment protocol is therapeutically effective in the subject.

[0019] Also provided is a kit for diagnosing Autism Spectrum Disorder (ASD) in a subject suspected of having or at risk of having ASD, determining a treatment protocol, monitoring a therapeutic effect of an ASD treatment protocol, or any combination thereof. The kit comprises: (a) a container for collecting a biological sample obtained from the subject; (b) reagents for preparing an extract from a biological sample obtained from the subject; and (c) instructions for (i) preparing theextract; (ii) measuring a level of each of at least two microbially-derived metabolites of any one of claims 6-8; and (iii) comparing each of the measured metabolite level against a control metabolite level obtained from TD subjects.

[0020] The method disclosed herein further comprises screening the subject for a gastrointestinal or other neurological disorder. The gastrointestinal or other neurological disorder is selected from Parkinson’s Disease, Chronic Kidney (Renal) Failure, Multiple Sclerosis, Colon Cancer, Irritable bowel syndrome, inflammatory bowel disease, Epilepsy, Generalized anxiety, and Schizophrenia.

[0021] In certain aspects, the method further comprises generating and assigning a score to the subject based on the level of each of the at least two microbially-derived metabolite. A score above or below a predetermined threshold value is indicative of the subject as having or at high risk of ASD; wherein the threshold value is determined based on the level of each of the at least two microbially-derived metabolite in TD subjects.BRIEF DESCRIPTION OF THE FIGURES

[0022] FIG. 1 is a schematic of a few examples of tryptophan derived metabolites.

[0023] FIG. 2A is a schematic showing the overview of an exemplary process to create the MDM System™.

[0024] FIG. 2B is a schematic of overall depiction of process workflow on the MDM system. Cohort 1 included 50 ASD participants and 47 healthy TD participants. Each step of development of the test is shown, as are the results when the same methodology was used on a separate cohort of ASD and TD participants (Cohort 2).

[0025] FIG. 3A is a dot graph depicting the total number of metabolites in individual subjects above the highest levels in typically developing subjects. Cumulative relative microbially-derived metabolite levels were derived through semiquantitative measurements. Each datapoint represents the total number of microbially-derived substances discovered in the individual participant that is outside of the typically developing range. By definition, all typical children receive a score of zero.

[0026] FIG. 3B is a dot plot showing MDM System™ results for 50 children with ASD and 47 TD, plotting the number of MDMs above the TD reference range. 47 of 50 ASD children have an average of 7 MDMs above that of any TD child. The TD children have scores of zero by definition. Thus, 94% sensitivity, 100% specificity.

[0027] FIGs. 4A - 4B is a series of dot plots showing the MDM System ™ scores for Semiquantitative (FIG. 4A) and Quantitative analysis (FIG. 4B). The vertical axis represents the number of microbially-derived metabolites above the TD reference range by individual while the horizontal axis represents age. Note that all 47 TD individuals (Blue Squares) have a score of zero by definition. FIG. 4A shows that semiquantitative analysis of forty-five of 50 ASD participants had at least one of the 14 microbial metabolites evaluated above the highest typically developing child (Range 1 -14, average 6.91 ). Five children with ASD scored 0. FIG. 4B shows that quantitative analysis of 40 of 52 ASD participants had at least one of the 14 microbial metabolites evaluated above the highest typically developing child (Range 1 -9, average 3.25). All TD individuals scored zero.

[0028] FIGs. 5A - 5B show the Quantitative MDM System™ scores for Cohort 1 (FIG. 5A) and Cohort 2 (FIG. 5B). The vertical axis represents the number of microbially-derived metabolites above the reference range by individual, while the horizontal axis represents age. Note that all 18 TD individuals (Blue Squares) have a score of zero by definition. FIG. 5A shows quantitative analysis-40 of 52 ASD participants had at least one of the 14 microbial metabolites evaluated above the highest typically developing child (Range 1-4, average 3.25). All TD individuals scored zero. FIG. 5B shows quantitative analysis - 38 of 42 ASD participants (90.5%) had at least one microbially derived metabolite above that of the highest TD participant.DETAILED DESCRIPTION

[0029] Autism Spectrum Disorder (ASD) is currently primarily diagnosed using cognitive and behavioral testing. Such testing is subjective, and usually not discriminative until subjects are at least 2-3 years of age. Consequently, a diagnosisof ASD is typically not made until even later around 4-5 years of age. The average age of diagnosis in the US is approximately 4.5 years. Thus, patient identification often lags when earlier intervention is crucial to mitigating the symptoms of ASD.

[0030] The present disclosure is based at least in part on the development of a comprehensive test for gut dysbiosis which occurs in autism and other disorders associated with the gut-brain axis. The disclosure analyzes the quantitative measurement of certain microbially derived metabolites in samples, e.g., urine, blood, saliva, and stool. These measurements can be done using e.g. LC-MS, GC-MS, and other methods. Combinations of two or more of the bacterial / yeast metabolites can be used to assess and screen for gut dysbiosis and for autism using methods such as Fischer Discriminant Analysis and other mathematical modeling techniques to develop models to accurately classify subjects with autism or other gastrointestinal or neurological disorders compared to healthy typically developing individuals (TD). The metabolites provided herein can be used as a medical test for identifying ASD in a subject. The test for ASD disclosed herein has high sensitivity and specificity, allowing earlier screening and identification of individuals with autism. Furthermore, the tests will provide valuable guidance for personalizing treatments and evaluating treatment efficacy. Further, the combination of checking for major metabolic abnormalities and multivariate analysis of bacterial / yeast metabolites yielded a very high accuracy in differentiating children with ASD vs. healthy typically developing children.

[0031] Further provided herein is a comprehensive assessment of gut dysbiosis of the type associated with autism spectrum disorder or other disorders in a subject.I. Terminology

[0032] For the purposes of promoting an understanding of the principles of the present disclosure, reference will now be made to preferred aspects and specific language will be used to describe the same. It will nevertheless be understood that no limitation of the scope of the disclosure is thereby intended, such alteration andfurther modifications of the disclosure as illustrated herein, being contemplated as would normally occur to one skilled in the art to which the disclosure relates.

[0033] As used in the specification, articles “a” and “an” are used herein to refer to one or to more than one (i.e. , at least one) of the grammatical object of the article. By way of example, “an element” means at least one element and can include more than one element.

[0034] “About” is used to provide flexibility to a numerical range endpoint by providing that a given value may be “slightly above” or “slightly below” the endpoint without affecting the desired result. The term “about” in association with a numerical value means that the numerical value can vary plus or minus by 5% or less of the numerical value.

[0035] Throughout this specification, unless the context requires otherwise, the word “comprise” and “include” and variations (e.g., “comprises,” “comprising,” “includes,” “including”) will be understood to imply the inclusion of a stated component, feature, element, or step or group of components, features, elements or steps but not the exclusion of any other integer or step or group of integers or steps.

[0036] As used herein, “and / or” refers to and encompasses any and all possible combinations of one or more of the associated listed items, as well as the lack of combinations where interpreted in the alternative (“or”).

[0037] As used herein, the transitional phrase “consisting essentially of” (and grammatical variants) is to be interpreted as encompassing the recited materials or steps “and those that do not materially affect the basic and novel characteristic(s)” of the claimed invention. Thus, the term “consisting essentially of” as used herein should not be interpreted as equivalent to “comprising.”

[0038] Moreover, the present disclosure also contemplates that in some aspects, any feature or combination of features set forth herein can be excluded or omitted. To illustrate, if the specification states that a complex comprises components A, B and C, it is specifically intended that any of A, B or C, or a combination thereof, can be omitted and disclaimed singularly or in any combination.

[0039] Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within therange, unless otherwise-indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. For example, if a concentration range is stated as 1 % to 50%, it is intended that values such as 2% to 40%, 10% to 30%, or 1 % to 3%, etc., are expressly enumerated in this specification. These are only examples of what is specifically intended, and all possible combinations of numerical values between and including the lowest value and the highest value enumerated are to be considered to be expressly stated in this disclosure.

[0040] As used herein, “treatment,” “therapy” and / or “therapy regimen” refer to the clinical intervention made in response to a disease, disorder or physiological condition manifested by a patient or to which a patient may be susceptible. The aim of treatment includes alleviation or prevention of symptoms, slowing or stopping the progression or worsening of a disease, disorder, or condition and / or the remission of the disease, disorder or condition.

[0041] As used herein, the term “autism spectrum disorder” or“ASD” is a complex neurodevelopmental condition characterized by widespread abnormalities of social interactions and communication, as well as restricted interests and repetitive behaviors. ASD typically appears during the first three years of life and manifests in characteristic symptoms or behavioral traits as well as social deficits. A diagnosis of ASD now includes several conditions that used to be diagnosed separately: autistic disorder, pervasive developmental disorder not otherwise specified (PDD-NOS), and Asperger syndrome. All of these conditions are now encompassed by the diagnostic criteria for autism spectrum disorder as set forth in the American Psychiatric Association's Diagnostic & Statistical Manual of Mental Disorders, Fifth Edition (DSM- V).

[0042] In addition to the spectrum of symptoms seen within these principal diagnostic criteria, ASD individuals display a wide range of neurological comorbidities, including intellectual disability, epilepsy, A.D.H.D. and anxiety and mood disorders, as well as non-neurological comorbidities, including blood hyperserotonemia, immune dysregulation, and Gl dysfunction (e.g., chronic constipation, diarrhea, abdominal pain, and gastroesophageal reflux).

[0043] The term “effective amount” or “therapeutically effective amount” refers to an amount sufficient to affect beneficial or desirable biological and / or clinical results.

[0044] As used herein, “individual”, “subject”, “host”, and “patient” can be used interchangeably herein and refer to any mammalian subject for whom diagnosis, treatment, prophylaxis or therapy is desired, for example, humans. As used herein, the term “subject” and “patient” are used interchangeably herein and refer to human.

[0045] As used herein “amount” or “level” or “abundance” refers to the amount of a particular analyte (e.g., a metabolite) present in the sample. The amount may be a number, ratio, proportion, or a percentage of the analyte compared to the control sample or determined using a standard curve. The amount may be an absolute amount or a relative amount.

[0046] As used herein, a biological sample may be of any biological tissue, fluid, or cell from the subject. The sample can be solid or fluid. The sample can be a heterogeneous cell population. Non-limiting examples of suitable biological samples include sputum, serum, blood, blood cells (e.g., white cells), a biopsy, urine, peritoneal fluid, pleural fluid, blood, saliva, stool, placental tissue, cord blood, nasal swab, cheek swab, or cells derived therefrom. The biopsy can be a fine needle aspirate biopsy, a core needle biopsy, a vacuum assisted biopsy, an open surgical biopsy, a shave biopsy, a punch biopsy, an incisional biopsy, a curettage biopsy, or a deep shave biopsy. Biological samples may also include sections of tissues, such as frozen sections or formalin fixed sections taken for histological purposes. Methods of collecting a biological sample from a subject are well known in the art. In some aspects, the biological sample is blood, plasma, platelet, RBC, saliva, stool, or urine. In a specific aspect, the biological sample is urine.

[0047] A sample from the subject can be procured one or more times and testing performed one or more times. In some aspects, samples can be procured from the subject before, during, and / or after a treatment for ASD. In some aspects, a sample can be procured from the subject prior to the start of ASD. In some aspects, sample can be procured from the subject undergoing ASD treatment. Additionally,samples can be procured repeatedly at multiple stages after initial sample procurement, to determine and / or monitor ASD in a subject.

[0048] In some aspects, a control sample can be procured from a healthy subject. In some aspects, the control can be an average of the combination of disclosed biomarker levels from different healthy sources (e.g., more than one healthy control subject). In some aspects, the control sample can be a pooled sample. In some aspects, the control sample is procured from a healthy typically developing subject or a neurotypical subject.

[0049] As used herein, a “healthy neurotypical subject” refers to a subject determined as not having cognitive and / or behavioral disorders including ASD, ADHD, depression, and anxiety. In some aspects, a healthy neurotypical subject does not have first-degree relatives with ASD. In further aspects, a healthy neurotypical subject does not have, or is not diagnosed or suspected with gastrointestinal issues.

[0050] As used herein, a “toxic metabolite” includes compounds that may be beneficial at low concentrations, but are toxic at high levels above the threshold point.II. Microbially derived Metabolites

[0051] The present disclosure encompasses microbially derived metabolites present at abnormal levels in a subject with dysbiosis, or a subject that suffers from, or is at risk of suffering from ASD. The microbially derived metabolites fall into three categories: 1 ) Tryptophan related metabolites, 2) Phenylalanine related metabolites, and 3) other microbially-derived metabolites or yeast metabolites. In various aspects, the metabolite may be selected from one or more metabolites listed in Table 1, Table 3, or Table 4. The one or more microbially derived metabolites are evaluated in a sample obtained from a subject with dysbiosis, have, or is at risk of ASD. In one aspect, evaluation comprises determining the level of each of the one or more metabolites in a sample obtained from the subject.

[0052] Tryptophan related metabolites of the disclosure comprise one or more Tryptophan compounds, derivatives, or structurally similar compounds, non-limiting examples of which are shown in FIG. 1.

[0053] In certain aspects, Tryptophan related metabolites are selected from one or more of 3-Oxindole, 2-Oxindole, 3-Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl-1 ,2,3,4 Tetrahydro-Beta-Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, lndole-3-Acetic Acid, lndole-3-acetoximide, lndol-3-one, lndol-2- one, Indole-N-Propionic Acid x 3**, lndole-3-carboxylic acid 0 Sulfate, lndole-3- ethanol, Methoxy Indole Acetic Acid, methyl 3 indole acetate, 3-methylindole, 6- methylindole, dihydroxy indole glucuronide, 5 hydroxy 6 methoxyindole Glucuronide, 4 Indole carbaldehyde, 3-methyldioxyindole, Indoxyl Glucuronide, 5- methoxyindoleacetate, 2-carboxy-2-3-dihydroxyindole, 2-hydroxy-3 (1 -methyo-1 H indol 3-yl) propanoic acid, 3-lndoleacetonitrile, Indole 3 Acetic acid O Glucuronide, 5 Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3-lndoleacrylic acid, 1 ,1 ethyldienebistryptophan, and Indole Pyruvate.

[0054] In one aspect, the Tryptophan related metabolites are selected from the group consisting of lndolyl-3-acryloylglycine (IAG), 4-lndolecarbaldehyde, 3- Indolepropionicacid, 1 -Methyl-1 ,2,3,4-tetrahydro-|3-carboline-3-carboxylic acid, 3- Methyldioxyindole, trans-3-lndoleacrylic acid, lndole-3-acetaldoxime, 3- Indoleacetonitrile, and any combinations thereof. In another aspect, the Tryptophan related metabolites are selected from the group consisting of 3-lndolepropionic acid, 1 -Methyl-1 ,2,3,4-tetrahydro-[3-carboline-3-carboxylic acid, 3-Methyldioxyindole, lndole-3-acryloyl glycine, frans-3-lndoleacrylic acid, 4-lndolecarbaldehyde, 3- Indoleacetonitrile, and lndole-3-acetaldoxime. In yet another aspect, the Tryptophan related metabolites are selected from the group consisting of IAG, 3-indole propionic acid, indole-3-carbaldehyde, phenylacetyl-l-glutamine, 3-methyl-2-oxindole, Indoxyl Sulfate (Indican), Indoxyl Sulfate (Indican), 3-indole acetonitrile, 1-methyl-1 ,2,3,4 Tetrahydro Beta Carboline, 5-methoxyindole, and 5-methoxyindole.

[0055] The Tryptophan related metabolites may comprise at least two metabolites selected from the group consisting of lndolyl-3-acryloylglycine (IAG), 4- Indolecarbaldehyde, 3-lndolepropionicacid, 1 -Methyl-1 ,2,3,4-tetrahydro-p-carboline- 3-carboxylic acid, 3-Methyldioxyindole, trans-3-lndoleacrylic acid, lndole-3- acetaldoxime, 3-lndoleacetonitrile. In another aspect, the Tryptophan related metabolites may comprise at least two metabolites selected from the group consistingof 3-lndolepropionic acid, 1 -Methyl-1 ,2,3,4-tetrahydro-[3-carboline-3-carboxylic acid,3-Methyldioxyindole, lndole-3-acryloyl glycine, frans-3-lndoleacrylic acid, 4- Indolecarbaldehyde, 3-lndoleacetonitrile, and lndole-3-acetaldoxime. In yet another aspect, the Tryptophan related metabolites may comprise at least two metabolites selected from the group consisting of IAG, 3-indole propionic acid, indole-3- carbaldehyde, phenylacetyl-l-glutamine, 3-methyl-2 -oxindole, Indoxyl Sulfate (Indican), Indoxyl Sulfate (Indican), 3-indole acetonitrile, 1 -methyl-1 ,2,3,4 Tetrahydro Beta Carboline, 5-methoxyindole, and 5-methoxyindole.

[0056] Phenylalanine related metabolites of the disclosure comprise one or more phenylalanine compounds, derivatives, or structurally similar compounds.

[0057] The phenylalanine may be selected from one or more of 4-vinylphenol sulfate, p-Cresol, p-Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy-3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p-cresol gluduronide, 4 ethyl phenol, 4-ethylphenol sulfate,4-ethylphenol glucuronide, 3 hydroxy 3 hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, and p-Cresol Glucuronide.

[0058] In certain aspects, Phenylalanine related metabolites are selected from one or more of Phenylacetylglutamine, p-Cresol, p-Cresol Sulphate, 4-(2- Aminopropoxy)-3,5-dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, Hippuric Acid, dihydroxyphenylpropionic acid (DHPPA), benzoic / hippuric, Benzoic Acid, and any combinations thereof. In another aspect, Phenylalanine related metabolites are selected from one or more of 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6-Amino-m- cresol, 4-Methylphenol, Phenylacetylglutamine, and p-cresols sulfate. In yet another aspect, Phenylalanine related metabolites are selected from one or more of p-Cresol, Benzoic Acid, benzoic / hippuric, p-Cresol Sulphate, Dihydroxyphenyl propionic Acid, Hippuric Acid, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, and Phenyl propionic Acid.

[0059] Phenylalanine-related metabolites may comprise at least two metabolites selected from the group consisting of Phenylacetylglutamine, p-Cresol, p-Cresol Sulphate, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, Hippuric Acid, dihydroxyphenylpropionic acid (DHPPA), benzoic / hippuric, Benzoic Acid. In another aspect, at least two metabolites may be selected from the group consisting of 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6-Amino-m-cresol, 4-Methylphenol, Phenylacetylglutamine, and p-cresols sulfate. In yet another aspect, at least two metabolites may be selected from the group consisting of p-Cresol, Benzoic Acid, benzoic / hippuric, p-Cresol Sulphate, Dihydroxyphenyl propionic Acid, Hippuric Acid, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, and Phenyl propionic Acid.

[0060] In another aspect, the microbially derived metabolites present at abnormal levels in a sample from a subject with dysbiosis, have, or is at risk of ASD are one or more of yeast metabolites. Yeast metabolites is selected from one or more of Arabinitol, Tricarballylic Acid, Tartaric Acid, Citramallic Acid, and any combinations thereof. The yeast metabolites may comprise at least two metabolites selected from the group consisting of Arabinitol, Tricarballylic Acid, Tartaric Acid, and Citramallic Acid. In another aspect, the yeast metabolite is Arabinitol.

[0061] Other microbially derived metabolites disclosed herein may be selected from one or more of N-Formyl Methionine, D-Arabinoate, (-) Threo-lso(homo)2citrate, D-Aspartate, and D-Glutamate. Another example is D-Arginyl-L-histidyl-D-prolyl-D- tyrosine, which has no human pathway, is statistically much lower in ASD vs. typically developing controls.

[0062] In some aspects, the other microbially derived metabolites is indoxyl sulfate, N-methyl-1 ,2,3,4-tetrahydro-beta-carboline-3-carboxylic acid, indole-3- acetic acid, Terrequinone A, N-Formylmethionine, 3-methyloxyindole, Indole acrylic acid, 4- Vinylphenol sulfate, 2-oxindole, N-Phenylacetylglytamine, 3-oxindole, or any combination thereof.

[0063] In one aspect, the evaluation of the microbially derived metabolite comprises assessing the level of one, or a combination of two or more metabolites in a sample obtained from the subject with dysbiosis, have, or is at risk of ASD. For instance, the level of one or the levels of 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 40,or more metabolites can be measured. The metabolites and combinations of metabolites are selected from the microbially derived metabolites.

[0064] One, or a combination of two or more microbially-derived metabolites is one or more of the Tryptophan related metabolites, Phenylalanine related metabolites, and yeast metabolites, disclosed herein. In some instances, at least two microbially-derived metabolites are evaluated and are selected from one or more tryptophan related metabolites, one or more phenylalanine related metabolites, and one or more yeast metabolites.

[0065] In certain aspects, at least two microbially-derived metabolites evaluated are selected from the group consisting of 3-Oxindole, 2-Oxindole, 3 Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl-1 ,2,3,4 Tetrahydro- Beta-Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, lndole-3-Acetic Acid, lndole-3-acetoximide, lndol-3-one, lndol-2-one, Indole-N-Propionic Acid x 3**, Indole- 3-carboxylic acid 0 Sulfate, lndole-3-ethanol, Indole, 1 - Benzazole 5, Methoxy Indole Acetic Acid, methyl 3 indole acetate, 3 methylindole, 6 methylindole, dihydroxy indole glucuronide, 5 hydroxy 6 methoxyindole Glucuronide, 4 Indole carbaldehyde, 3 methyldioxyindole, Indole, 1-Benzazole5, Indoxyl Glucuronide, 5methoxyindoleacetate, 2 carboxy 2 3 dihydroxyindole, 2 hydroxy 3 (1 -methyo-1 H indol 3 yl) propanoic acid, 3 Indoleacetonitrile, Indole 3 Acetic acid 0 Glucuronide, 5 Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3-lndoleacrylic acid, 1 ,1 ethyldienebistryptophan, and Indole Pyruvate. In an alternative aspect, metabolites evaluated comprise 3-Oxindole, 2-Oxindole, 3 Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl-1 , 2, 3, 4 Tetrahydro-Beta- Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, lndole-3-Acetic Acid, lndole-3- acetoximide, lndol-3-one, lndol-2-one, Indole-N-Propionic Acid x 3**, lndole-3- carboxylic acid 0 Sulfate, lndole-3-ethanol, Indole, 1 - Benzazole 5, Methoxy Indole Acetic Acid, methyl 3 indole acetate, 3 methylindole, 6 methylindole, dihydroxy indole glucuronide, 5 hydroxy 6 methoxyindole Glucuronide, 4 Indole carbaldehyde, 3 methyldioxyindole, Indole, 1-Benzazole5, Indoxyl Glucuronide, 5methoxyindoleacetate, 2 carboxy 2 3 dihydroxyindole, 2 hydroxy 3 (1 -methyo-1 H indol 3 yl) propanoic acid, 3 Indoleacetonitrile, Indole 3 Acetic acid 0 Glucuronide, 5Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3-lndoleacrylic acid, 1 ,1 ethyldienebistryptophan, and Indole Pyruvate.

[0066] In certain aspects, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from the group consisting of lndolyl-3-acryloylglycine (IAG), 4-lndolecarbaldehyde, 3-lndolepropionicacid, 1- Methyl-1 ,2,3,4-tetrahydro-[3-carboline-3-carboxylic acid, 3-Methyldioxyindole, trans-3- Indoleacrylic acid, lndole-3-acetaldoxime, 3-lndoleacetonitrile, Phenylacetylglutamine, p-Cresol, p-Cresol Sulphate, 4-(2-Aminopropoxy)-3,5- dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, Hippuric Acid, DHPPA, benzoic / hippuric, Benzoic Acid, Arabinitol, Tricarballylic Acid, Tartaric Acid, and Citramallic Acid.

[0067] In another instance, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from the group consisting of 3-lndolepropionic acid, 1 -Methyl-1 ,2,3,4-tetrahydro-[3-carboline-3-carboxylic acid, 3-Methyldioxyindole, lndole-3-acryloyl glycine, frans-3-lndoleacrylic acid, 4- Indolecarbaldehyde, 3-lndoleacetonitrile, lndole-3-acetaldoxime, 4-(2-Aminopropoxy)- 3,5-dimethylphenol, 6-Amino-m -cresol, 4-Methylphenol, Phenylacetylglutamine, p- cresols sulfate, and Arabinitol.

[0068] In yet another instance, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from the group consisting of IAG, 3-indole propionic acid, indole-3-carbaldehyde, phenylacetyl-l-glutamine, 3- methyl-2 -oxindole, Indoxyl Sulfate (Indican), 2-oxindole, 3-indole acetonitrile, 1 - methyl-1 ,2,3,4 Tetrahydro Beta Carboline, 5-methoxyindole, p-Cresol, Benzoic Acid, benzoic / hippuric, p-Cresol Sulphate, Dihydroxyphenyl propionic Acid, Hippuric Acid, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, Phenyl propionic Acid, Arabinitol, Citramalic Acid, Tartaric Acid, and Tricarballylic Acid.

[0069] In some aspects, one, or a combination of two or more microbially- derived metabolites is one or more of the 4-vinylphenol sulfate, p-Cresol, p-Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy-3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4- (2-Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p-cresol gluduronide, 4 ethyl phenol, 4-ethylphenol sulfate, 4-ethylphenol glucuronide, 3 hydroxy 3 hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, and p-Cresol Glucuronide. In certain aspects, measuring metabolites in a sample comprises measuring the levels of 4-vinylphenol sulfate, p- Cresol, p-Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy-3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p-cresol gluduronide, 4 ethyl phenol, 4-ethylphenol sulfate, 4-ethylphenol glucuronide, 3 hydroxy 3 hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, and p-Cresol Glucuronide.

[0070] In some aspects, one, or a combination of two or more microbially- derived metabolites is one or more of the N-Formyl Methionine, D-Arabinoate, (-) Threo-lso(homo)2citrate, D-Aspartate, and D-Glutamate. In certain aspects, measuring metabolites in a sample comprises measuring the levels of N-Formyl Methionine, D-Arabinoate, (-) Threo-lso(homo)2citrate, D-Aspartate, D-Glutamate, and D-Arginyl-L-histidyl-D-prolyl-D-tyrosine.

[0071] In one aspect, one, or a combination of two or more microbially-derived metabolites is 3-Oxindole, 2-Oxindole, 3 Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl-1 ,2,3,4 Tetrahydro-Beta-Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, lndole-3-Acetic Acid, lndole-3-acetoximide, lndol-3-one, lndol-2- one, Indole-N-Propionic Acid x 3**, lndole-3-carboxylic acid O Sulfate, lndole-3- ethanol, Indole, 1 - Benzazole 5, Methoxy Indole Acetic Acid, methyl 3 indole acetate, 3 methylindole, 6 methylindole, dihydroxy indole glucuronide, 5 hydroxy 6 methoxyindole Glucuronide, 4 Indole carbaldehyde, 3 methyldioxyindole, Indole, 1- Benzazole5, Indoxyl Glucuronide, 5methoxyindoleacetate, 2 carboxy 2 3 dihydroxyindole, 2 hydroxy 3 (1 -methyo-1 H indol 3 yl) propanoic acid, 3 Indoleacetonitrile, Indole 3 Acetic acid O Glucuronide, 5 Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3-lndoleacrylic acid, 1 ,1 ethyldienebistryptophan, Indole Pyruvate, 4-vinylphenol sulfate, p-Cresol, p-Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy-3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p- cresol gluduronide, 4 ethyl phenol, 4-ethylphenol sulfate, 4-ethylphenol glucuronide, 3 hydroxy 3 hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, p-Cresol Glucuronide, N-Formyl Methionine, D-Arabinoate, (-) Threo-lso(homo)2citrate, D-Aspartate, and D-Glutamate.

[0072] In certain aspects, the metabolites evaluated comprise 3-Oxindole, 2- Oxindole, 3 Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl- 1 ,2, 3, 4 Tetrahydro-Beta-Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, Indole-3-Acetic Acid, lndole-3-acetoximide, lndol-3-one, lndol-2-one, Indole-N-Propionic Acid x 3**, lndole-3-carboxylic acid 0 Sulfate, lndole-3-ethanol, Indole, 1 - Benzazole 5, Methoxy Indole Acetic Acid, methyl 3 indole acetate, 3 methylindole, 6 methylindole, dihydroxy indole glucuronide, 5 hydroxy 6 methoxyindole Glucuronide, 4 Indole carbaldehyde, 3 methyldioxyindole, Indole, 1 -Benzazole5, Indoxyl Glucuronide, 5methoxyindoleacetate, 2 carboxy 2 3 dihydroxyindole, 2 hydroxy 3 (1 - methyo-1 H indol 3 yl) propanoic acid, 3 Indoleacetonitrile, Indole 3 Acetic acid 0 Glucuronide, 5 Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3- Indoleacrylic acid, 1 ,1 ethyldienebistryptophan, Indole Pyruvate, 4-vinylphenol sulfate, p-Cresol, p-Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy-3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p-cresol gluduronide, 4 ethyl phenol, 4-ethylphenol sulfate,4-ethylphenol glucuronide, 3 hydroxy 3 hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, p-Cresol Glucuronide, N-Formyl Methionine, D-Arabinoate, (-) Threo-lso(homo)2citrate, D-Aspartate, D-Glutamate, and D-Arginyl-L-histidyl-D-prolyl-D-tyrosine.

[0073] In certain aspects, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination a first group consisting of lndolyl-3-acryloylglycine (IAG), 4-lndolecarbaldehyde, 3-lndolepropionicacid, 1 - Methyl-1 ,2,3,4-tetrahydro-[3-carboline-3-carboxylic acid, 3-Methyldioxyindole, trans-3- Indoleacrylic acid, lndole-3-acetaldoxime, and 3-lndoleacetonitrile; and a second group consisting of Phenylacetylglutamine, p-Cresol, p-Cresol Sulphate, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, Hippuric Acid, DHPPA, benzoic / hippuric, and Benzoic Acid.

[0074] In certain aspects, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from a first group consisting of lndolyl-3-acryloylglycine (IAG), 4-lndolecarbaldehyde, 3- Indolepropionicacid, 1 -Methyl-1 , 2, 3, 4-tetrahydro-|3-carboline-3-carboxylic acid, 3- Methyldioxyindole, trans-3-lndoleacrylic acid, lndole-3-acetaldoxime, and 3- Indoleacetonitrile, and a second group consisting of Arabinitol, Tricarballylic Acid, Tartaric Acid, and Citramallic Acid.

[0075] In certain aspects, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from a first group consisting of Phenylacetylglutamine, p-Cresol, p-Cresol Sulphate, 4-(2- Aminopropoxy)-3,5-dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, Hippuric Acid, DHPPA, benzoic / hippuric, and Benzoic Acid; and a second group consisting of Arabinitol, Tricarballylic Acid, Tartaric Acid, and Citramallic Acid.

[0076] In another instance, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from a first group consisting of 3-lndolepropionic acid, 1 -Methyl-1 ,2,3,4-tetrahydro-[3-carboline-3- carboxylic acid, 3-Methyldioxyindole, lndole-3-acryloyl glycine, trans-3-lndoleacrylic acid, 4-lndolecarbaldehyde, 3-lndoleacetonitrile, and lndole-3-acetaldoxime, and a second group consisting of Arabinitol.

[0077] In another instance, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from a first group consisting of 3-lndolepropionic acid, 1 -Methyl-1 ,2,3,4-tetrahydro-|3-carboline-3- carboxylic acid, 3-Methyldioxyindole, lndole-3-acryloyl glycine, trans-3-lndoleacrylic acid, 4-lndolecarbaldehyde, 3-lndoleacetonitrile, and lndole-3-acetaldoxime, and a second group consisting of 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6-Amino-m- cresol, 4-Methylphenol, Phenylacetylglutamine, and p-cresols sulfate.

[0078] In another instance, at least two microbially-derived metabolites are evaluated and are selected in a two-metabolite combination from a first group consisting of 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6-Amino-m-cresol, 4- Methylphenol, Phenylacetylglutamine, and p-cresols sulfate and a second group consisting of Arabinitol.

[0079] In yet another instance, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from a first group consisting of IAG, 3-indole propionic acid, indole-3-carbaldehyde, phenylacetyl-l- glutamine, 3-methyl-2-oxindole, Indoxyl Sulfate (Indican), 2-oxindole, 3-indole acetonitrile, 1 -methyl-1 ,2,3,4 Tetrahydro Beta Carboline, and 5-methoxyindol and a second group consisting of p-Cresol, Benzoic Acid, benzoic / hippuric, p-Cresol Sulphate, Dihydroxyphenyl propionic Acid, Hippuric Acid, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, and Phenyl propionic Acid.

[0080] In yet another instance, at least two microbially-derived metabolites are evaluated and are selected in a two metabolite combination from a first group consisting of IAG, 3-indole propionic acid, indole-3-carbaldehyde, phenylacetyl-l- glutamine, 3-methyl-2-oxindole, Indoxyl Sulfate (Indican), 2-oxindole, 3-indole acetonitrile, 1 -methyl-1 ,2,3,4 Tetrahydro Beta Carboline, and 5-methoxyindole and a second group consisting of Arabinitol, Citramalic Acid, Tartaric Acid, and Tricarballylic Acid.

[0081] In yet another instance, at least two microbially-derived metabolites evaluated in a sample are selected in a two metabolites combination from a first group consisting of p-Cresol, Benzoic Acid, benzoic / hippuric, p-Cresol Sulphate, Dihydroxyphenyl propionic Acid, Hippuric Acid, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, and Phenyl propionic Acid and a second group consisting of Arabinitol, Citramalic Acid, Tartaric Acid, and Tricarballylic Acid.

[0082] The at least two microbially-derived metabolites may be selected from the group consisting of p-Cresol, p-cresol sulfate, 1-methyl-1 ,2,3,4 tetrahydro beta carboline, indole-3-propionic acid, arabinitol, and phenylacetylglutamine. The at least two microbially-derived metabolites in the sample may be selected from the groupconsisting of P-cresol, p-cresol sulfate, indoxyl sulfate phenylacetylglutamine, indole- 3-acetonitrile acetylnitrile, and indole 3-acetoxidoximide.III. Evaluation of microbially derived metabolites

[0083] In some aspects, the metabolites are evaluated by measuring the level of each of the metabolite in a biological sample obtained from the subject. The sample may be selected from blood, plasma, red blood cells (RBC), saliva, stool, and urine. Multiple different samples may be obtained from a subject and analyzed.

[0084] Metabolites can be detected in a variety of ways, including assays based on chromatography and / or mass spectrometry, fluorimetry, electrophoresis, immune-affinity, hybridization, immunochemistry, ultra-violet spectroscopy (UV), fluorescence analysis, radiochemical analysis, near-infrared spectroscopy (nearIR), nuclear magnetic resonance spectroscopy (NMR), light scattering analysis (LS), and nephelometry.One of the possible methods to measure metabolites is LC-MS. The method used for preparing and measuring the level of each of the metabolite in various samples are described in further detail in: Kang et al., mSphere 2020, Vol. 5, Issue 5, the disclosure of which is incorporated herein by reference in its entirety; however the proposed analysis method is not limited by which method is used to measure the metabolites

[0085] The subject can be, without limitation, a human, a non-human primate, a mouse, a rat, a guinea pig, and a dog. In some aspects, the subject is a human subject. The subject may be in utero, a newborn, a neonate, an infant, a toddler, a young child, a child, an adolescent, or an adult. For example, the subject can be a premature newborn, a term newborn, a neonate, an infant, a toddler, a young child, a child, an adolescent, a pediatric patient, a geriatric patient. In one aspect, the subject is a child patient below about 18, 15, 12, 10, 8, 6, 4, 3, 2, or 1 year old. In another aspect, the subject is an adult patient. In another aspect, the subject is an elderly patient. In another aspect, the subject is about between 1 and 2, between 1 and 3, between 1 and 4, between 1 and 5, between 2 and 10, between 3 and 18, between 21 and 50, between 21 and 40, between 21 and 30, between 50 and 90, between 60 and 90, between 70 and 90, between 60 and 80, or between 65 and 75 years old.

[0086] In one aspect, the subject may present with symptoms of impaired language, behavioral, or social development, may be assessed by clinicians. Alternatively, the subject may not present any developmental disorders or conditions.

[0087] Biological samples for use in measuring the microbially derived metabolites can be obtained using any known methods in the art. In some aspects, a blood sample may comprise or consist of plasma, red blood cells (RBC), platelets, whole blood. In some aspects, the same metabolite(s) can be measured in one or more of samples comprising or consisting of plasma, RBC, platelets, whole blood, stool, saliva, and urine. In one aspect, the biological sample is a urine sample. In one instance, the biological sample is whole blood, plasma, red blood cells (RBCs), other components of blood, urine, stool, saliva, cord blood, amniotic fluid, meconium, buccal swabs, biopsy-obtained tissue, cord blood, placental tissue, hair, deciduous teeth, or any combination thereof.

[0088] The sample for determining the level of metabolite can be obtained one time or multiple times from the subject. Sample, for example urine, can be obtained from the subject, using any known method. The urine can be obtained using a clean catch method, obtained by 24-hour urine collection, or using a catheter. The determination of level of one or more metabolites can be using quantitative or semi- quantitative evaluations.

[0089] In some aspects, mass spectrometry (MS) can be used to evaluate metabolites in a sample, for e.g., plasma, urine, RBC, saliva, stool, and whole blood. Non-limiting examples of MS that can be used to measure metabolites include Gas chromatography-mass spectrometry GC-MS, Liquid chromatography-mass spectrometry (LC-MS), LCT-MS, Inductively Coupled Plasma-Mass Spectrometry (ICP-MS), and liquid chromatography-tandem mass spectroscopy (LCT-MS).

[0090] In some aspects, provided in the disclosure is a method of evaluating metabolites using spectrophotometry. In some aspects, urine samples procured from a subject can be evaluated using spectrophotometry. A standard curve for each metabolite can be prepared using standard solutions prepared in a buffer for e.g., phosphate buffer, PBS pH=7.0. In some aspects, buffers are treated with chelating resin to remove impurities for e.g., metals. Absorbance measurements can be madeusing various concentrations of the standard solutions, and the standard curve can be prepared using a median absorbance measurement obtained for each standard solution. The samples can be analyzed using a spectrophotometer using an appropriate absorbance measurement for each metabolite to be analyzed, and comparing the measurement to the standard curve to obtain the amount of metabolite in the sample.

[0091] In some aspects, evaluation of the metabolites can be conducted using microbiological assays. Microbiological assays involve measuring specific growth response to presence of metabolite of a microorganism which estimate the amount of metabolite in a sample. Briefly, microbiological assays determine metabolite levels in an extract of a sample (for e.g., urine) obtained from the subject, by measuring the growth of the metabolite sensitive microorganism in a metabolite free medium to which a known amount of the sample extract had been added. Since the growth of the sensitive microorganism is, proportional to the metabolite concentration in the medium, measuring a growth parameter, for e.g., optical density after incubation for a selected period of time and at a certain selected temperature, and comparing this parameter to values observed by running the same test with samples containing different, known concentrations of metabolite determine the concentration of metabolite in the sample extract. In some aspects, measuring the metabolite involves titratable acidity produced by microorganism as a function of the quantity of metabolite present in the sample. In some aspects, a predetermined standard curve can be used to evaluate the amount of metabolite in a sample. Non-limiting examples of metabolite sensitive microorganism include, Lactobacillus casei (sensitive to biotin and folic acid), Saccharomyces cerevisiae (sensitive to biotin), Lactobacillus arabinosus (sensitive to biotin), Tetrahymena thermophila (sensitive to lipoate), Torulopsis pintolopessi (sensitive to choline), carnitine-specific mutant of the enteric yeast Torulopsis bovina (sensitive to carnitine) and Ochromonas Danica (sensitive to biotin).

[0092] In some aspects, metabolites can be evaluated using colorimetric or fluorometric assays.

[0093] In some aspects, a significant difference in the level of one or a combination of metabolite can be an increase or a decrease in the level of the metabolite in the sample when compared to the level of the metabolite in the control panel of metabolite levels. A significantly different level of the one or combination of metabolites can be determined by applying each of the measured levels of the metabolites against a database of metabolite control measured levels created by measuring metabolite levels of the one or more metabolite in control subjects. The database can be stored on a computer system. In one aspect, the measured levels of each metabolite can be applied against a reference range of the metabolite obtained by measuring a lowest and a highest metabolite level of each of the one or more microbially-derived metabolites in healthy typically developing (TD) subjects, and indicating the subject as having or at risk of ASD when the level of each of the metabolite is outside the reference range of the metabolite. The method may further comprise generating and assigning a score to the sample based each of the microbially-derived metabolite above or below the reference range. In such instances, the subject is indicated as having or at risk of ASD when the assigned score is above or below a predetermined threshold value. The threshold value may be determined based on the level of each of the microbially-derived metabolite in TD subjects. The score may be generated based on number of metabolites outside the reference range. Averages, means, or medians may be used to define the reference range.

[0094] In another aspect, the measured levels of each metabolite can be applied against a reference range of the metabolite obtained by measuring a lowest and a highest metabolite level of each of the one or more microbially-derived metabolites in one or more non-treated previously diagnosed ASD subjects. The method may further comprise assigning a score to the sample based on the number of the microbially-derived metabolites above or below the identified range and indicating the subject as having or at risk of ASD when the level of each of the metabolite is within the range of the level of metabolite in the non-treated previously diagnosed ASD. The subject is indicated as having or at risk of ASD when the assigned score is same as the non-treated previously diagnosed ASD. The scoremay be generated based on number of metabolites outside the reference range. Averages, means, or medians may be used to define the reference range.

[0095] In some aspects, measured metabolites have an elevated level compared to control sample, such as for e.g., a sample from a healthy TD subject. In such aspects, the metabolite have an elevated level of at least about .01 %, at least about 0.05%, at least about 0.1 %, at least about 0.2%, at least about 0.3%, at least about 0.4%, at least about 0.5%, at least about 1 %, at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 99%, or at least about 100% greater as compared to a control sample.

[0096] In some aspects, measured metabolites have decreased level compared to control sample, for e.g., a sample from healthy TD subject. In such aspects, the metabolite have a decreased level of at least about 0.01 %, at least about 0.05%, at least about 0.1 %, at least about 0.2%, at least about 0.3%, at least about 0.4%, at least about 0.5%, at least about 1 %, at least about 5%, at least about 10%, at least about 15%, at least about 20%, at least about 25%, at least about 30%, at least about 35%, at least about 40%, at least about 45%, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 99%, or at least about 100% greater as compared to a control sample.

[0097] After obtaining an assessment of metabolites, a computational modelling system may be applied for the diagnosis of dysbiosis-associated neurological and gastrointestinal disorders with the microbially-derived metabolites, as well as administering therapeutics.

[0098] Combinations of any of the above microbially-derived metabolites can be analyzed by a variety of statistical methods to train classification learner models in machine learning software programs. Examples of statistical methods able to predictdysbiosis include Fischer Discriminant Analysis, Neural Network Classification, Naive Gaussian Distribution, k-nearest neighbor, boosted or bagged trees logistical regression, subspace KNN, Linear or quadratic or cubic discriminant analysis, Support Vector Machine, and others. These models can be trained on metabolite datasets from various diseases to determine the optimal metabolite levels threshold cutoffs specific to each disease and can then be validated using test sets or crossfold validation.

[0099] The metabolites with levels different in the sample from the subject compared to a sample from TD can be identified by comparing the measured level of the metabolite in the biological sample to the level of the metabolite in the control panel of microbially-derived metabolite levels using a univariate statistical analysis method selected from hypothesis testing, evaluating the area under the receiver operator curve (AUROC) values, or a combination of both.

[0100] Hypotheses testing can be performed by evaluating the type of distribution for each of the ASD and TD groups’ measurements and selecting an appropriate parametric or non-parametric test. A parametric or non-parametric test can be selected by determining the normality and variance of each individual clinical measurement variable for both the ASD and TD groups separately. Further, a parametric test can be performed if a normality assumption holds true for the ASD and TD groups. An equal variance t-test or Welch’s test (unequal variance t-test) can be performed if the observed variance is significantly different between the ASD and TD groups. In some aspects, a Mann-Whitney test is performed if the ASD and TD groups follow the same non-parametric distribution. In some aspects, the ASD and TD groups are adjusted by their means and subjected to the Kolmogorov-Smirnov test where different distributions are observed. In some aspects, the method further comprises determining the false discovery rate (FDR) for each measurement using a leave-one-out (L-1 -O) approach.

[0101] The combination of two or more metabolites can be identified by performing multivariate analysis using Fisher Discriminant Analysis (FDA) and support vector machines (SVM) and selecting the combinations of two or moremetabolites comprising the best FDA or SVM measure for each combination of elements.

[0102] Performing FDA can comprise the steps of (a) evaluating all possible combinations of two, three, four, or more microbially-derived metabolite from among the microbially-derived metabolits disclosed herein; (b) examining the fitted AUROC and performance when subjected to cross-validation using leave-one-out cross- validation; and (c) calculating the area under the AUROC value for each combination of two, three, four, or more microbially-derived metabolites. The combination of two or more metabolites can comprise the combinations of two, three, four, or more microbially-derived metabolites comprising the highest 1000 AUROC values following leave-one-out cross-validation. The method can further comprise using a greedy algorithm to identify combinations of microbially-derived metabolites comprising five or more metabolites.

[0103] In some aspects, the control panel of metabolite levels is stored on a computer system. The method can screen and / or diagnose ASD at birth or pre-birth.

[0104] Fisher Discriminant Analysis (FDA) is a dimensionality reduction technique and classifier designed to maximize class separability in datasets. It achieves this by projecting high-dimensional data onto a lower-dimensional space where the between-class distance is maximized and within-class variance is minimized. FDA assumes linear separability and normal distribution within classes, making it effective for classification tasks when these assumptions hold, though it may struggle with highly nonlinear or imbalanced datasets.

[0105] Artificial neural networks are models consisting of interconnected layers of nodes that process input data through weighted connections and nonlinear activation functions. This architecture enables the network to learn complex, hierarchical patterns in data, making it effective for classification problems where the classes are not linearly separable. Neural networks approximate nonlinear decision boundaries by adjusting internal parameters through training using algorithms such as backpropagation, allowing them to outperform linear methods in many classification tasks.

[0106] In MATLAB, Naive Bayes classification for categorical data assumes that predictor variables are conditionally independent given the class label. Using the built-in Classification Learner App, this method applies Bayes’ Theorem to estimate class posteriors from training data. The model is efficient for high-dimensional categorical datasets and is well-suited for applications where quick, interpretable classification is needed. MATLAB automates the training, cross-validation, and prediction steps, making it easy to deploy Naive Bayes models without manual coding, while still allowing visualization and performance evaluation through confusion matrices, ROC curves, and loss plots.

[0107] In one aspect, Microbial Metabolite Counting Method (MMCM) is used for statistical analysis. MMCM utilizes combinations of abnormal levels of individual metabolites named in the above sections as a potential indicator of gut dysbiosis and as an indicator for autism. The metabolite levels are determined in urine or other bodily fluids like saliva, blood, or stool. The test utilizes a reference range for each metabolite as determined by the levels found in healthy individuals. The test can further comprise awarding a score to the sample based upon how many metabolites are above that range. A cut-off is then established for the number of elevated metabolites vs. healthy typically-developing individuals. In some aspects, the method comprises utilizing a reference range for each metabolite as determined by the levels found in healthy individuals, and then awarding a score to the sample based upon how many microbially-derived metabolitess are above the reference range. A cut-off is then established for the number of elevated microbially-derived metabolites vs. healthy typically developing individuals. In another aspect, the method comprises utilizing a reference range for each metabolite as determined by the levels found in a previously diagnosed non-treated ASD subject.

[0108] A classification test, assay, or method may have an associated ROC curve (Receiver Operating Characteristic curve) that plots false positive rate (1 - specificity) against true positive rate (sensitivity). The area under the ROC curve (AUC) is a measure of how well the classifier can distinguish between two diagnostic groups. The maximum AUC is 1 .0 (a perfect test) and the minimum area is 0.5 (e.g.the area where there is no discrimination of normal versus ASD). It is appreciated that as an AUC approaches one, the accuracy of a test increases.

[0109] In some aspects, a high degree of risk prediction accuracy is a test or assay wherein the AUC is at least 0.60. In another aspect, a high degree of risk prediction accuracy is a test or assay wherein the AUC at least 0.65, at least 0.70, at least 0.75, at least 0.80, at least 0.85, at least 0.90, or at least 0.95.

[0110] In another aspect, a mean difference of metabolite levels is assessed among or between populations, e.g., between an ASD population and a TD population. Each of the metabolites from samples may be assessed for enrichment in a tail of a distribution curve. The assessment comprises, for example, determining whether a greater proportion of samples from a population ( for e.g., ASD) as compared to a second population (for e.g., TD) reside in a tail of the distribution curve. In some aspects, both mean differences and tail effects may be identified and utilized. A tail is determined by a predetermined threshold value. For example, a sample is assigned to be within a tail if its measurement for a metabolite is higher than the value corresponding to a 90thpercentile in a population for that metabolite (right tail, or upper tail), or is lower than the value corresponding to a 15thpercentile (left tail, or lower tail). In some aspects, the threshold for a right (upper) tail for a given metabolite is the value corresponding to the 80th, 81st, 82nd, 83rd, 84th, 85th, 86th, 87th, 88th, 89th, 90th, 91st, 92nd, 93rd, 94th, 95th, 96th, 97th, 98th, or 99thpercentile (e.g., where a sample is designated to be within a right tail if its measurement for the given metabolite is higher than the value associated with this percentile). In some aspects, the threshold for a left (lower) tail for a given metabolite is the value corresponding to the 25th, 24th, 23rd, 22nd, 21st, 20th, 19th, 18th, 17th, 16th, 15th, 14th, 13th, 12th, 1 1th, 10th, 9th, 8th, 7th, 6th, 5th, 4th, 3rd, 2nd, or 1stpercentile (e.g., where a sample is designated to be within a left tail if its measurement for the given metabolite is lower than the value associated with this percentile). Percentile values shown are inclusive of fractional values.

[0111] In some aspects, a distribution curve may be generated from a plot of level of each of the metabolite. The distribution curve may be associated with a metabolite level from a subject, TD subjects, or non-treated previously diagnosedASD subjects. Such metabolite distribution curves may be used to evaluate the risk of ASD.

[0112] In one instance, evaluation comprises comparing a measured level of each metabolite against a cut-off range for each metabolite. In such instances, the cut-off range is determined from a distribution of each of the metabolite level in healthy typical developing (TD) subjects. The cut-off range may also comprise an upper bound cut off value and a lower bound cut off value.

[0113] In certain aspects, the upper bound cut-off value may be determined by obtaining for each of the metabolite, the level of the metabolite at for example, 80th, 85th, 90thor 95thpercentile of the TD multiplied by a factor. The factor may be about 1 , 1.1 , 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9. 2.0, 2.1 , 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9. 3.0, 3.1 , 3.2, 3.3, 3.4, 3.5, 3.6, 3.7, 3.8, 3.9. 4.0, 4.1 , 4.2, 4.3, 4.4, 4.5, 4.6, 4.7, 4.8, 4.9. or 5.0. In one aspect, the factor is about 1 .1 to about 5.

[0114] The lower bound cut-off value may be determined by obtaining for each of the metabolite, the level of metabolite for example, at 80th, 85th, 90thor 95thpercentile of the TD multiplied by a factor. The factor may be 3.0, 2.9, 2.8, 2.7, 2.6, 2.5, 2.4, 2.3, 2.2, 2.1 , 2.0, 1.9, 1.8, 1.7, 1.6, 1.5, 1.4, 1.3, 1.2, 1.1 , 1.0, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4. 0.3, 0.2, or 0.1 . In a specific aspect, the factor is about 0.9 to about 0.2.

[0115] In such instances, the subject is indicated or classified as having or at risk of ASD when each of the level of the microbially derived metabolite is outside the cut-off range obtained from the TD subjects.

[0116] In some aspects, evaluation may further comprise generating and assigning a score to the subject based on the level of each of the microbially-derived metabolite. A score above or below a predetermined threshold value may be indicative of the subject as having or at high risk of ASD. The threshold value may be determined based on the level of each of the microbially-derived metabolite in TD subjects. In some instances, the score may be assigned by determining the number of microbially derived metabolite above a highest level of the respective metabolite in the TD subjects.III. Methods of Screening and Treatment

[0117] One aspect of the present disclosure encompasses a method of screening and / or diagnosing Autism Spectrum Disorder (ASD) in a subject suspected of having or at risk of having ASD. The method comprises the steps of measuring or having measured levels of one or more microbially-derived metabolites in a biological sample obtained from the subject; and comparing the measured levels of each metabolite against a reference range of metabolite obtained by measuring a lowest and a highest metabolite levels for each of the one or more of the microbially-derived metabolites in healthy typically developing (TD) subjects. The subject is indicated as having ASD when the each of the level of the one or more metabolites are outside the reference range of level of one or more metabolite from the TD subjects.

[0118] The method may comprise measuring or having measured a level of each of one or more microbially-derived metabolites in a biological sample obtained from the subject; and comparing the measured level of each metabolite against a cutoff range for each metabolite. The cut-off range may be determined from a distribution of each of the metabolite level in healthy typical developing (TD) subjects. Further, the cut-off range may comprise an upper bound cut off value and a lower bound cut off value. The subject is indicated as having ( / .e., suffering from) or at risk of having ASD when the level of each metabolite is outside a predetermined cut-off range obtained from the TD subjects.

[0119] In a specific aspect, provided herein is method of screening for ASD in a subject, comprises measuring or having measured a level of each of at least two microbially-derived metabolites in a biological sample obtained from the subject. The method further comprises, comparing the measured level of each metabolite against a cut-off range for each metabolite, wherein the cut-off range is determined from a distribution of each of the metabolite level in healthy typical developing (TD) subjects. The cut-off range comprises an upper bound cut off value and a lower bound cut off value. The subject is indicated as having ASD when the level of each of the at least two metabolites is outside a predetermined cut-off value obtained from the TD subjects.

[0120] In further aspects, the method of screening / diagnosing comprises assigning a score to the subject based on the number of microbially-derivedmetabolite above the identified range; and indicating the subject as having or at risk of ASD, when the assigned score is different from the score assigned to TD subjects. In one specific aspect, the score assigned to the TD subjects may be zero. In another aspect, the method comprises generating and assigning a score to the subject based on the level of each of the microbially-derived metabolite. In such aspects, a score above or below a predetermined threshold value is indicative of the subject as having, or at high risk of having ASD. The threshold value may be determined based on the level of each of the at least two microbially-derived metabolite in TD subjects.

[0121] The disclosure further encompasses a method of classifying a subject as a candidate for treatment of ASD. The method comprises the steps of measuring or having measured levels of one or more microbially-derived metabolites in a biological sample obtained from the subject; and comparing the measured levels of each metabolite against a reference range of metabolite obtained by measuring a lowest and a highest metabolite levels for each of the one or more of the microbially- derived metabolites in healthy typically developing (TD) subjects. The subject is classified as a candidate for treatment of ASD, when the each of the level of the one or more metabolites are outside the reference range of level of one or more metabolite from the TD subjects

[0122] Further provided is a method of classifying a subject as a candidate for treatment of ASD. The method comprises measuring or having measured a level of each of one or more microbially-derived metabolites in a biological sample obtained from the subject. The method further comprises comparing the measured level of each metabolite against a cut-off range for each metabolite. The cut-off range is determined from a distribution of each of the metabolite level in healthy typical developing (TD) subjects. The cut-off range can comprise an upper bound cut off value and a lower bound cut-off value. The subject is classified as a candidate for treatment of ASD when each of the level of the one or more metabolites are outside the cut-off range obtained from the TD subjects.

[0123] In a specific aspect, the method of classifying a subject as a candidate for treatment of ASD comprises measuring or having measured a level of each of at least two microbially-derived metabolites in a biological sample obtained from thesubject, and comparing the measured level of each metabolite against a cut-off value for each metabolite. The cut-off range is determined from a distribution of each of the metabolite level in healthy typical developing (TD) subjects; and wherein the cut-off range comprises an upper bound cut off value and a lower bound cut off value. The subject is classified as a candidate for treatment of ASD when the level of each of the at least two metabolites are outside a predetermined cut-off range obtained from the TD subjects.

[0124] The upper bound cut-off value may be determined by obtaining, for each of the at least two metabolites, the level of the metabolite at the 95thpercentile of the TD multiplied by a factor about 1 .1 to about 5. The lower bound cut-off value is determined by obtaining, for each of the at least two metabolites, the level of metabolite at the 95thpercentile of the TD multiplied by a factor about 0.9 to about 0.2.

[0125] The biological sample for use in the method can be whole blood, plasma, red blood cells (RBCs), urine, stool, saliva, serum, cord blood, placental tissue, hair, deciduous teeth, or any combination thereof.

[0126] The microbially-derived metabolite can be a metabolite associated with the one or combination of two or more microbially-derived metabolites identified as having a level in the biological sample significantly different from the level of the one or combination of microbially-derived metabolites in the control sample.

[0127] In one aspect, the at least two microbially-derived metabolites are selected from one or more tryptophan related metabolites, one or more phenylalanine related metabolites, and one or more yeast metabolites.

[0128] In some aspects, the method comprises measuring the level of one or more tryptophan related metabolites selected from the group consisting of 3- Oxindole, 2-Oxindole, 3 Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl-1 ,2,3,4 Tetrahydro-Beta-Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, lndole-3-Acetic Acid, lndole-3-acetoximide, lndol-3-one, lndol-2-one, Indole-N- Propionic Acid x 3**, lndole-3-carboxylic acid O Sulfate, lndole-3-ethanol, Indole, 1 - Benzazole 5, Methoxy Indole Acetic Acid, methyl-3-indole acetate, 3-methylindole, 6- methylindole, dihydroxy indole glucuronide, 5-hydroxy-6-methoxyindole Glucuronide,4-lndole carbaldehyde, 3-methyldioxyindole, Indole, 1 -Benzazole, Indoxyl Glucuronide, 5-methoxyindoleacetate, 2-carboxy 2, 3, dihydroxyindole, 2-hydroxy 3- (1 -methyo-1 H indol 3-yl) propanoic acid, 3-lndoleacetonitrile, lndole-3-Acetic acid 0- Glucuronide, 5-Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3- Indoleacrylic acid, 1 ,1 ethyldienebistryptophan, and Indole Pyruvate. In another aspect, the method comprises measuring the level of one or more tryptophan related metabolites selected from the group consisting of lndolyl-3-acryloylglycine (IAG), 4- Indolecarbaldehyde, 3-lndolepropionicacid, 1 -Methyl-1 ,2,3,4-tetrahydro-[3-carboline-3-carboxylic acid, 3-Methyldioxyindole; trans-3-lndoleacrylic acid, lndole-3- acetaldoxime, and 3-lndoleacetonitrile. In yet another aspect, the method comprises measuring the level of one or more tryptophan related metabolites selected from the group consisting of 3-lndolepropionic acid, 1-Methyl-1 ,2,3,4-tetrahydro-|3-carboline-3- carboxylic acid, 3-Methyldioxyindole, lndole-3-acryloyl glycine, frans-3-lndoleacrylic acid, 4-lndolecarbaldehyde, 3-lndoleacetonitrile, and lndole-3-acetaldoxime.Alternatively, the method comprises measuring the level of one or more tryptophan related metabolites selected from the group consisting of IAG, 3-indole propionic acid, indole-3-carbaldehyde, phenylacetyl-l-glutamine, 3-methyl-2-oxindole, Indoxyl Sulfate (Indican), 2-oxindole, 3-indole acetonitrile, 1 -methyl-1 ,2,3,4 Tetrahydro Beta Carboline, and 5-methoxyindole.

[0129] In further aspects, the method comprises measuring one or more phenylalanine related metabolites selected from the group consisting of 4-vinylphenol sulfate, p-Cresol, p-Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy-3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p-cresol gluduronide, 4-ethyl phenol, 4-ethylphenol sulfate,4-ethylphenol glucuronide, 3-hydroxy-3-hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, and p-Cresol Glucuronide. In another aspect, the method comprises measuring one or more phenylalanine related metabolites selected from the group consisting of Phenylacetylglutamine, p-Cresol, p- Cresol Sulphate, 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, HippuricAcid, DHPPA, benzoic / hippuric, and Benzoic Acid. In yet another aspect, the method comprises measuring one or more phenylalanine related metabolites selected from the group consisting of 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6-Amino-m-cresol, 4- Methylphenol, Phenylacetylglutamine, and p-cresols sulfate. Alternatively, the method comprises measuring one or more phenylalanine related metabolites selected from the group p-Cresol, Benzoic Acid, benzoic / hippuric, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, and Phenyl propionic Acid.

[0130] In another aspect, the method comprises measuring one or more yeast metabolites is selected from the group consisting of N-Formyl Methionine, D- Arabinoate, (-) Threo-lso(homo)2-citrate, D-Aspartate, D-Glutamate, and D-Arginyl-L- histidyl-D-prolyl-D-tyrosine. In other aspects, the method comprises measuring one or more yeast metabolites is selected from the group consisting of Arabinitol, Tricarballylic Acid, Tartaric Acid, and Citramallic Acid. In certain aspects, the method comprises measuring the metabolite Arabinitol.

[0131] In some aspects, the method can screen, diagnose, or classify ASD with high level of sensitivity and specificity. In some aspects, the method can indicate a subject as having or at risk of ASD with a sensitivity greater than or equal to 90%, greater than or equal to 91 %, greater than or equal to 92%, greater than or equal to 93%, greater than or equal to 94%, greater than or equal to 95%, greater than or equal to 96%, greater than or equal to 97%, greater than or equal to 98%, greater than or equal to 99%, or even with a 100% sensitivity. In some aspects, the method can indicate a subject as having or at risk of ASD with a specificity greater than or equal to 90%, greater than or equal to 91 %, greater than or equal to 92%, greater than or equal to 93%, greater than or equal to 94%, greater than or equal to 95%, greater than or equal to 96%, greater than or equal to 97%, greater than or equal to 98%, greater than or equal to 99%, or even with a 100% specificity. In some aspects, the method can screen and / or diagnose ASD with a specificity of at least about 80% to 90%, a specificity of at least about 80% to 90%, or both. In one aspect, the method can indicate a subject as having or at risk of ASD with a specificity and sensitivity of 100%.

[0132] In some aspects, methods disclosed herein can indicate a subject as having or at risk of ASD with a low misclassification error, such as a misclassification error of about 10, 8, 9, 7, 6, 5, 4, 3, 2, or about 1 % or even with no misclassification error. In some aspects, the method can indicate a subject as having or at risk of ASD with a misclassification error of about 5% or less, or about 3% or less. In further aspects, the method can diagnose ASD with an accuracy of about 75, 80, 85, 90, 95% or with about 100% accuracy. In some aspects, the method can indicate a subject as having or at risk of ASD with an accuracy of about 95% or higher, such as with an accuracy of about 97% or with about 100% accuracy.

[0133] The method can further comprise assigning a medical, behavioral, and / or nutritional treatment to the subject identified as having or at risk of ASD. In some aspects, the treatment protocol can comprise adjusting the level of one or a combination of two or more microbially-derived metabolite in the subject. The treatment can also comprise administering a combination of gastrointestinal treatments, including prebiotics, probiotics, antibiotics, antifungal medications, nutritional supplements, microbiota transplant (also called fecal microbiota transplant), and dietary modifications. The treatment, in some aspects, comprise administering a combination of prebiotics, probiotics, microbiota transplant, antibiotics, anti-fungal medications, diet modifications. The treatment may further include adjusting the level of one or a combination of two or more microbially-derived metabolites in the subject identified as having ASD.

[0134] In some aspects, the treatment protocols can comprise restoring the level of one or more microbially-derived metabolites identified as significantly different in the biological sample obtained from the subject to a level of the one or more microbially-derived metabolites in the control panel of metabolite levels obtained from healthy TD individuals with no Gl problems. Similarly, when a microbially-derived metabolite represents a group of microbially-derived metabolites correlated with the microbially-derived metabolites, the treatment protocol can comprise restoring the level of one or more of the group of microbially-derived metabolites associated with the identified microbially-derived metabolite. The microbially-derived metabolites can be supplemented by nutritional means, or by oral or parenteral administration ofcompositions comprising the metabolite. The level of a microbially-derived metabolite can be restored by about 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90% or more.

[0135] In some aspects, treatment comprises administering to the subject indicated as having or at risk of ASD, a behavioral management therapy, a cognitive behavior therapy, an early intervention, an educational and school-based therapy, a joint attention therapy, an occupational therapy, a parent-mediated therapy, a physical therapy, social skills training, a speech-language therapy, or any combination thereof. The treatment may comprise administering a medication selected from an antipsychotic drug, a selective serotonin re-uptake inhibitor (SSRI), a tricyclic, a psychoactive or anti-psychotic medication, a stimulant, an anti-anxiety medication, an anticonvulsant, or any combination thereof. The treatment may also comprise a nutritional supplementation.

[0136] In certain aspects, a composition comprising a fecal microbiota from a healthy neurotypical human donor is administered to the subject indicated as having or at risk of having ASD.

[0137] Another aspect of the instant disclosure encompasses a method of determining a personalized treatment protocol for a subject suspected of having or at risk of having ASD. The method comprises diagnosing ASD in a subject suspected of having or at risk of having ASD, and assigning a personalized medical, behavioral, or nutritional treatment protocol to the subject if the subject is diagnosed with ASD. Diagnosing ASD can be as described above. The treatment protocol can comprise administering a combination of gastrointestinal treatments, including prebiotics, probiotics, antibiotics, antifungals, nutritional supplements, microbiota transplant (also called fecal microbiota transplant), and dietary changes.

[0138] In some aspects, a treatment protocol can be personalized based on the microbially-derived metabolites found to be significantly different in a sample obtained from the subject when compared to a control and identified using the method described herein. Such a personalized treatment protocol can include adjusting in the subject the level of the one or combination of microbially-derived metabolites found to be identified as being significantly different in the biological sample obtained from the subject to a level of the one or more microbially-derivedmetabolites in the control panel of metabolite levels obtained from healthy TD individuals. The treatment protocol can also include adjusting the levels of one or more metabolite associated with the one or combination of two or more microbially- derived metabolites identified as having a level in the biological sample significantly different from the level of the one or combination of microbially-derived metabolites in the control sample.

[0139] Yet another aspect of the instant disclosure encompasses a method of monitoring the therapeutic effect of an ASD treatment protocol in a subject suspected of having or at risk of having ASD. The method comprises the steps of measuring in a first biological sample obtained from the subject a level of each of one or more or at least two microbially-derived metabolites; measuring in a second biological sample obtained from the subject at a period of time after the first biological sample is obtained the level of each of the one or more or at least two microbially-derived metabolites; and comparing the level of each of the one or more or at least two microbially-derived metabolites in the first sample and the second sample. Maintenance of the level of each of the one or more or at least microbially-derived metabolites or a change of the level to a level of the one or more or at least two microbially-derived metabolites in a control panel of microbially-derived metabolites levels created by measuring microbially-derived metabolite levels of the one or more or at least two microbially-derived metabolites in healthy control TD subjects is indicative that the treatment protocol is therapeutically effective in the subject.

[0140] In some aspects, the method of monitoring a therapeutic effect of an ASD treatment protocol comprises modifying the treatment protocol. For example, in an aspect, a treatment protocol can be altered by changing the amount of one or more of the therapeutic compounds, or by changing the treatment protocol, or changing the frequency of administration of therapeutic compounds, or by changing the duration of time one or more of the therapeutic protocol administered to a subject.

[0141] Another aspect of the instant disclosure encompasses a method of assessing the behavioral severity of ASD in a subject. The method comprises the steps of (a) measuring level of one or more or at least two microbially-derived metabolites disclosed herein in a biological sample obtained from the subject;comparing the measured level of each of the microbially-derived metabolite against a control panel of metabolite level created by measuring microbially-derived metabolite levels of the one or more, or at least two microbially-derived metabolite in healthy typically developing (TD) subjects. The subject is indicated as having high behavioral severity, or at risk for having behavioral severity when the level of each of the one or more or at least two microbially-derived metabolites in the biological sample are significantly different from the levels of the one or more or at least two microbially- derived metabolites in the control panel of microbially-derived metabolite levels.

[0142] In further aspects, the method of assessing the behavioral severity of ASD in a subject, further comprise determining a personalized treatment protocol for the subject having behavioral severity, or at risk of having behavioral severity. The method comprises assigning a personalized medical, behavioral, or nutritional treatment protocol to the subject. Diagnosing ASD can be as described above. In some aspects, high behavioral severity comprise inability to use spoken language, extreme sensitivity to crowds, bright lights, or loud noise, lower IQ, repetitive behaviors, and physical symptoms like sleeplessness and epilepsy. The treatment protocol can comprise administering a combination of gastrointestinal treatments, including prebiotics, probiotics, antibiotics, antifungals, nutritional supplements, microbiota transplant (also called fecal microbiota transplant), and dietary changes.

[0143] In some aspects, the method further comprises screening for a gastrointestinal or other neurological disorder. The gastrointestinal or other neurological disorder is selected from Parkinson’s Disease, Chronic Kidney (Renal) Failure, Multiple Sclerosis, Colon Cancer, Irritable bowel syndrome, inflammatory bowel disease, Epilepsy, Generalized anxiety, and Schizophrenia.IV. Kit

[0144] An additional aspect of the instant disclosure encompasses a kit for diagnosing Autism Spectrum Disorder (ASD) in a subject suspected of having or at risk of having ASD, determining a personalized treatment protocol, monitoring a therapeutic effect of an ASD treatment protocol, or any combination thereof. The kit comprises: (a) a container for collecting a biological sample obtained from thesubject; (b) reagents for preparing an extract from a biological sample obtained from the subject; and (c) instructions for (i) preparing the extract; (ii) measuring a level of each of one or more or at least two microbially-derived metabolites; and (iii) comparing each of the measured metabolite level against a control metabolite level obtained from TD subjects.

[0145] In some aspects, the disclosed kit comprise a collection of elements including at least one non-standard laboratory reagent for use in the disclosed methods, in appropriate packaging, optionally containing instructions for use. A kit may further include any other components required to practice the methods, such as dry powders, concentrated solutions, or ready-to-use solutions. In some aspects, a kit comprises one or more containers that contain reagents for use in the methods. Containers can be boxes, ampules, bottles, vials, tubes, bags, pouches, blisterpacks, or other suitable container forms known in the art. Such containers can be made of plastic, glass, laminated paper, metal foil, or other materials suitable for holding reagents.

[0146] In some aspects, a kit further comprises instructions for testing a biological sample of a subject having or at risk of having ASD. The instructions will generally include information about the use of the kit in the disclosed methods. In other aspects, the instructions may include at least one of the following: description of possible therapies including therapeutic agents; clinical studies; and / or references. The instructions may be printed directly on the container (when present), or as a label applied to the container, or as a separate sheet, pamphlet, card, or folder supplied in or with the container.EXAMPLES

[0147] The disclosure includes measuring the concentrations of many microbially-derived metabolites (MDMs) in the urine of 52 children with ASD and 47 healthy typically developing (TD) children, all aged 2 to 11 years, with untargeted and then targeted Liquid Chromatography and Mass Spectrometry (LC-MS).MethodsF01481 Participants'. This study, approved by the Institutional Review Board of Arizona State University (STUDY00008750), and the Institutional Review Board of Harvard Medical School, was advertised to autism families and parents of TD children at four locations: Arizona (Arizona State University), Massachusetts (Harvard Medical School), Tennessee (Cool Springs Family Medicine), and Texas (The Johnson Medical Center for Child Health and Development). All participants met with a study coordinator to review and sign the consent form. Children over the age of 7 years also signed an assent form if they were developmentally able. The overall design of the study is shown in FIGs. 2A-2B. All 52 children affected by ASD and 47 children who were healthy and typically developing (TD) included in the study were between 2-11 years old.10^49] Autism Spectrum Disorder (ASD) Inclusion Criteria: Diagnosis of ASD by a psychiatrist, developmental pediatrician, or similar professional, and verification by the Childhood Autism Rating Scale (CARS-2); Age 2-11 years; and Parents are fluent in English. ro 150] ASD Exclusion criteria: Major single-gene disorder such as Fragile X; an identified major brain abnormality; current participation in a treatment study, or major change in medications, supplements, diet, or therapies in the last 2 months; and bacterial or viral illness at time of urine sample collection (temporary exclusion; delay until healthy).F01511 Typically Developing Inclusion criteria: Age 2-11 years; good physical and mental health; SRS score in the typical range (SRS raw score < 60); and parents are fluent in English, and the child speaks English.F01521 Typically Developing Exclusion Criteria: Sibling or parent with ASD; requires special services in school, or academic ability well below grade concentration; major single-gene disorder such as Fragile X; major brain abnormality; major physical or mental health problem; current participation in a treatment study, or major change in medications, supplements, diet or therapies in the last 2 months; bacterial or viral illness at time of urine sample collection (temporary exclusion; delay until healthy).F0153] Questionnaires'. Parents of ASD children were required to provide evidence of a previous diagnosis of ASD for their child, and that was confirmed by our expert autism evaluator conducting an evaluation of the child and a discussion with their parent using the Childhood Autism Rating Scale (CARS). Parents of the ASD and TD children also completed a medical history form, the Social Responsiveness Scale-2 (SRS-2) and the Parental Global Impressions of Autism (PGIA). The SRS form was also used to confirm ASD diagnosis (raw SRS score above 68), and to ensure that the TD children did not have clinically relevant autistic features.

[0154] Samp / e Collection'. Urine samples were collected from study participants. Most samples were a first morning sample, but some children were not dry overnight, so the urine sample collected was a “spot” urine sample in the early morning. Urine samples were stored in the family’s freezer and transferred for storage in -80 °C freezers until prepared for analysis.

[0155] Five participants did not overlap between the semiquantitative and quantitative analysis due to several factors including lost sample collections, shipping errors, and sample arrival dates. For the semiquantitative analysis (using 50 ASDs, 47 TDs) and quantitative analyses (using 52 ASDs, 47 TDs), the two datasets are slightly different because there were different participants included.

[0156] Metabolomics Measurements'. In order to develop a diagnostic screening test for ASD this project was divided into two investigations: an initial untargeted analysis followed by targeted analysis followed by targeted analysis. Untargeted semiquantitative metabolomics was first conducted by the Translational Research Institute of Genomics (TGEN, USA). Potential metabolites of interest were identified and a separate independent laboratory (Analutos Ltd, UK) validated the analytes using quantitative measurements with commercial standards (Table 1).Table 1: List of metabolites measured, arranged into three groups: phenylalanine- related metabolites, tryptophan-related metabolites, and yeast-related metabolites, with which metabolites were used in which analysis (Semiquantitative untargeted analysis undertaken by TGEN or quantitative targeted analysis by Analutos or both).Phenylalanine related Metabolites:: Phenylacetylglutamine Semiquantitative & Quantitative p-CresolSemiquantitative & Quantitative: p-Cresol Sulphate Semiquantitative & Quantitative4-(2-Aminopropoxy)-3,5-dimethylphenol Semiquantitative6 amino-m-cresol Semiquantitative: Phenylacetic Acid QuantitativePhenyl propionic Acid QuantitativePhe / Tyr QuantitativeHydroxybenzoic acid QuantitativeHippuricAcidQuantitativeDHPPA Quantitative benzoic / hippuric Quantitative: Benzoic Acid QuantitativeTryptophan Related Metabolites: lndolyl-3-acryloylglycine (IAG) Semiquantitative & Quantitative: 4-lndolecarbaldehyde Semiquantitative & Quantitative; 3-lndolepropionicacid Semiquantitative & Quantitative1-M . ,ethyl-1 ,2,3,4-tetrahydro-l2-carboline-3-carboxylic i S _em .iquant ..it.at ..io*■: acid ve & Quantitative: 3-Methyldioxyindole Semiquantitative i trans-3-lndoleacrylic acid Semiquantitative i lndole-3-acetaldoxime Semiquantitative: 3-lndoleacetonitrile QuantitativeesArabinitol ntitative & Quantitative: Tricarballylic Acid QuantitativeTartaric Acid Quantitative; Citramallic AcidQuantitative

[0157] For untargeted metabolomics, metabolite extraction was performed by adding 3-fold excess of acetonitrile: methanol (3:1 , v / v) to the urine for HILIC and for reversed phase chromatography, urine was diluted with equal volume of water. The metabolite extracts were clarified by centrifugation at 13,226 xg at 4°C for 15 min. The resulting supernatant were subjected to liquid chromatography and mass spectrometry analysis using Orbitrap Fusion Lumos Tribrid mass spectrometer and raw data were analyzed on Compound Discoverer 3.2 (Thermo Fisher Scientific, Ca)for compound annotation and relative quantitation as described previously (PMID: 38315779, PMID: 37658085). The MS / MS spectrum for each annotated compounds were reviewed for verifying compound identity. Relative abundances of metabolites were normalized using urinary creatinine to account for differences in hydration. Specific gravity was measured using hand-held refractometer and creatinine was measured using Jaffe method.

[0158] Relative peak intensities were determ ined for each participant for each metabolite. Creatinine was also measured to use for normalization of metabolite concentrations to account for differences in hydration. The metabolites chosen for evaluation were mostly formed by microbial modification of tryptophan, phenylalanine, or tyrosine, and then, in some cases, also later modified by human metabolism. The remaining compounds were a) generated from yeasts and b) had been previously associated with dysbiosis. One such compound is Arabinitol, as high concentrations in urine indicate overgrowth of yeast within the gut. Table 2 lists the participant characteristics that were evaluated in the semiquantitative analysis. For each metabolite, a relative reference range was created by determining 0%-100% of the TD range. There is no known pathway for humans to produce any of these metabolites, and there is no significant dietary source known for them.

[0159] Statistical Analysis. Univariate analyses were performed on both investigative datasets using Welch’s t-test. Since most metabolites had a subset of ASD participants with very high concentrations, the data were square root- transformed to improve normality, and the t-test was conducted on the transformed data. False discovery rate adjustment was also performed on the semiquantitative analysis to minimize false positives (q<0.05) while investigating biochemical differences between ASD participants vs. TD controls. The Semiquantitative nature of untargeted LC / MS measurements reports peak intensities rather than definitive concentrations, so peak intensity averages for each group, percent difference between the two groups, individual AUROC, and p-value for each metabolite are listed in the tables below. One significant limitation of untargeted LC / MS measurements is that metabolites can be misidentified. This does not apply toquantitative results, as their experiment utilized commercially available standards to determine concentrations and identities of metabolites.F01601 Multivariate Analysis -The MDM System™-. A novel multivariate scoring method called the Microbially-Derived Metabolite System (MDM System™) was developed for analyzing the metabolite data, based on the hypothesis that an extremely high peak intensity of one or more MDMs might be sufficient to classify a person with gut dysbiosis associated with ASD. For each participant, the relative peak intensity of each metabolite was compared to the TD reference range. If higher, a point was added to that participant’s individual MDM Score, and if not, no point was awarded. By definition, all TD participants received a score of zero, since all were within the TD reference range. Thus, the total MDM score ranges from 0 to 23 MDMs, representing the number of MDMs that were higher (above that of any TD child). Wll Additional Multivariate Analysis: Standard multivariate statistical analysis was performed in MATLAB, using Fisher Discriminant analysis (FDA), neural networks, and Naive Bayes methods, which also found high sensitivity and specificity - see the Supplement for details.

[0162] To protect against overfitting the data, validation was further conducted in the classification learner application of MATLAB using multiple cross-fold validation (Range 2-50), and by holding back various percentages of ASD and TD participants at random as a test set.

[0163] Quantitative Methodology: As a follow-up to the semiquantitative untargeted metabolomics approach, an independent commercial research laboratory in the United Kingdom named Analutos (analutos.com) performed targeted quantitative metabolomics on the same urine samples using commercial standards to quantitatively determine concentrations of metabolites.Example 1: Microbial metabolite counting method (MMCM)

[0164] A new statistical method was developed, referred herein as Microbial Metabolite Counting Method (MMCM). MMCM utilizes combinations of abnormal levels of individual metabolites named in the above sections as a potential indicator of gut dysbiosis and as an indicator for autism. The metabolite levels are determinedin urine or other bodily fluids like saliva, blood, or stool. The test utilizes a reference range for each metabolite as determined by the levels found in healthy typically developing individuals, and then awards a score to the sample based upon how many metabolites are above that range. A cut-off is then established for the number of elevated metabolites vs. healthy typically developing individuals.

[0165] An example is shown below for a group of 50 children with autism spectrum disorder and 48 healthy typically developing children, all ages 2-11 years. The reference range for this example was set to be the lowest and the highest value discovered in the healthy TD children. The y-axis is the number of abnormal metabolites found in urine (using creatinine correction to correct for variations in dilution). By definition, all of the healthy TD children have a score of zero. 48 of the 50 autistic children have a score from 1-37 microbially derived metabolites that were present at levels above any healthy neurotypical child, out of a total of 62 metabolites that were measured. The two ASD children who had zero microbially-derived metabolites were found to have other major errors of metabolism that were likely due to a genetic defect. A score above zero indicates a likely gut dysbiosis, is strongly associated with autism and possibly other gut-brain disorders. Higher scores and / or unusually high levels of these metabolites indicate a greater dysbiosis. No single metabolite was found to be sufficient to diagnose autism nor any other condition described herein; the entire combination of 62 metabolites may be needed because different children with ASD have different elevated microbial metabolites, presumably because there are a wide variety of possible bacterial / yeast infections in those children.

[0166] Thus, the MMCM yielded 96% sensitivity and 100% specificity without including analysis for other major errors of metabolism. Including the errors of metabolism yielded 100% sensitivity and 100% specificity.

[0167] Measurement of other metabolites may be applicable in the search for inborn or induced errors of metabolism. One example of an error of metabolism discovered during this investigation is arginosuccinate lyase deficiency. Arginosuccinate Lyase deficiency is an error of metabolism that leads to large amounts of arginosuccinate being excreted in the urine. This error likely stems from agenetic defect, is classified as a Urea cycle disorder, and has a known association with autism. Other examples include medium chain fatty acyl disorders (MCAD), which lead to levels of suberic acid or other dicarboxylic acids in the urine that are hundreds or thousands times higher than average, and if left untreated can lead to autism. Accounting for errors of metabolism as a pathophysiological cause of autism, the computational methods described herein can be optimized to elucidate further information into the subset of autism without known origin. In summary, biological markers can be used to enhance the accuracy of the MMCM and other computational methods.

[0168] The statistical models can be enhanced by including gender and age in the multivariate analysis. One way to achieve that is by rescaling the data for one gender to the other, or to a gender-neutral scale, using the average values for each gender of healthy typically-developing controls. Alternatively, models for males and females can be developed separately. Similarly, the average level of healthy typically developing controls can be used to rescale the data to an age-neutral scale, or to develop different models for different ages. The effects of age and gender could be combined together.Example 2: Participant Characteristics

[0169] 52 children with ASD and 47 TD children were recruited. 51 of the 52 children with ASD had CARS scores that met the criteria; one child was not evaluated by CARS but had an SRS raw score of 100 and PGIA of 2.8, both values are above the cut-offs of 68 and 1 .5, respectively, for ASD, so they were also included.Participant characteristics are listed in Table 2 below. Four separate sites in four US states were chosen to provide geographic diversity. Age ranges of ASD and TD participants were similar; however, the sex / gender was intentionally not matched to enable higher enrollment of TD females to establish a more reliable reference range.Table 2: Characteristics of Study Participants. CARS-(Childhood Autism Rating Scale); SRS-2-(Social Responsiveness Scale 2ndedition); PGIA-(Parental Global Index); n.s.- Not Significant; n / a-not applicableExample 3: Sem / Quantitative Un targeted ResultsF01701 Univariate Analysis'. Results of the MDM’s untargeted analysis are shown in Table 3. Five phenylalanine-derived microbial metabolites were significantly higher in the ASD group than the TD group, with the average difference ranging from 29% higher to 228% higher, and the percentage of individuals in the ASD group above the highest TD peak intensity ranging from 10% to 32%. Eight tryptophan- derived microbial metabolites were significantly higher in the ASD group than the TD group, with the difference ranging from 38% higher to 4918% higher, and the percentage of individuals in the ASD group above the highest TD peak intensity ranged from 10% to 41 %. Concentrations of arabinitol, a yeast metabolite was significantly higher in the ASD group (89% higher, p<0.001 ), with 40% of children with ASD having concentrations above that of the highest TD case.Table 3: Phenylalanine / Tyrosine, Tryptophan, and Yeast-related microbial metabolites of importance. The average percent difference between ASD and TD, the AUROC of each metabolite, the p-value, and the q-value are displayed for each metabolite. Semiquantitative Univariate AnalysisPhenylalanine Related MetabolitesAverage Raw p-Val q-Val ALIROC Above TDDifference ReferenceASD vs. TD Range4-(2-Aminopropoxy)- 229% 0.02 0.04 0.70 10%3,5-dimethylphenol6-Amino-m-cresol 219% 0.01 0.01 0.66 12%4-Methylphenol 159% 0.00 0.03 0.65 32%Phenylacetylglutami 64% 0.0001 0.04 0.68 32% ne p-cresols sulfate 54% 0.002 0.04 0.67 30%Tryptophan Related MetabolitesAverage Raw p-Val q-Val AUROC Above TDDifference ReferenceASD vs. TD Range3-lndolepropionic 561 % 0.0001 0.04 0.71 34% acid1 -Methyl-1 ,2,3,4- 537% 0.0009 0.03 0.77 44% tetrahydro-[3- carboline-3- carboxylic acid3-Methyldioxyindole 125% 0.002 0.00 0.64 22% lndole-3-acryloyl 98% 0.02 0.046 0.63 10% glycine frans-3-lndoleacrylic 98% 0.004 0.05 0.61 22% acid4- 57% 0.03 0.049 0.60 22%Indolecarbaldehyde3-lndoleacetonitrile 46% 0.006 0.049 0.65 14% lndole-3- 36% 0.005 0.03 0.64 14% acetaldoximeYeast MetaboliteAverage Raw p- q-Val ALIROC AboveDifference Vai TDASD vs. TD Referenc e RangeArabinitol 89% >0.001 0.001 0.89 40%101711 Semiquantitative Multivariate Results MDM Total Score was calculated. The MDM total score is the number of MDMs higher than the concentration of any of the TD children. The ASD group had an average MDM Total Score of 3.3 MDMs, ranging from zero to nine, whereas the TD group all had an MDM Total Score of zero MDMs by definition. Ninety percent of the ASD participants (45 of 50) had one or more extremely elevated MDMs. By setting a cut-off for ASD of one or more extremely elevated MDMs, then the MDM test yields a sensitivity of 90%, and a specificity of 100%. All TD subjects are correctly classified, and 45 of the 50 ASD children are correctly classified.

[0172] As shown in FIG. 4A-4B, no significant correlation between the number of MDMs and age was found.

[0173] Of the fifty ASD participants, five did not show elevated peak intensities of MDMs. Metabolomic analyses revealed that three of these participants likely have significant metabolic errors, potentially inborn, that may have underlain their ASD symptoms. One participant was suspected of having arginosuccinate lyase deficiency due to a large presence of arginosuccinate in the participant’s urine. Arginosuccinate lyase deficiency is a urea cycle disorder characterized by high concentrations of arginosuccinate and citrulline in blood or urine. Another participant presented a metabolomic profile consistent with glutaric aciduria type II, evidenced by high urinary concentrations of glutaric acid, lactate, hippuric acid, suberic acid, and adipic acid. A third participant exhibited a 100-fold increase in peak intensity of urinary propionic acid, suggesting a possible diagnosis of propionic or methylmalonic acidemia. Each of these metabolic disorders has been associated with ASD or severe cognitive impairments and may present as either severe infantile forms or milder forms with later onset, which could potentially bypass newborn screening.F0174] Fisher Discriminant Analysis'. As the MDM score is a new evaluation approach, more traditional AI / ML techniques have also been used for the classification. Using Fisher Discriminant Analysis (FDA), combinations of 5 metabolites produced 1 ,014,572 unique combinations with AUROC > 0.8, with the highest reaching 0.86 after leave-one-out cross-validation (LOOCV). Neural Networks and Naive Bayes approaches with cross validation resulted in accuracies to 83% and 82%, respectively. Accuracies exceeding 80% are generally considered the target for these types of tests, and the approaches exceeded them. See the supplement for full details on multivariate analysis techniques and results.Example 4: Quantitative-Targeted Metabolomics Analysis10 75] Univariate Metabolite Results: Nine metabolites were significantly higher in the ASD group compared to the TD group, including 5 MDMs related to phenylalanine, four related to tryptophan, and one related to yeast. (P-value between groups was less than 0.05; no correction was performed for multiple hypothesis testing). The highest difference in average value between groups was 74,750% for tricarballylic acid, although it did not reach statistical significance, likely due to the limit of quantification faults in many samples.

[0176] For most metabolites, there was a small subgroup with concentrations of the MDM above that of any of the TD group, ranging from 0% to 21 % (Table 4). The highest value was 21 % for p-cresol sulfate, a harmful metabolite that has been found to be elevated in 17 of 17 studies of ASD vs controls. The distribution of high metabolite peak intensities is highly heterogeneous, with some children being unusually high in one metabolite and other children being unusually high in others. Several metabolites had a substantial number of participants below the limit of detection (Table 4).Table 4: Quantitative Results. Statistically significant results are noted in BOLD font related to phenylalanine metabolism, tryptophan metabolism, or another microbial metabolism. Phe / Tyr- Ratio of Phenylalanine divided by Tyrosine. All values have units of micromoles per gram creatinine.Phenylalanine Based MetabolitesPercent of ASD aboveMean Mean p- TD BelowASD TD % Change value Range LOD p-Cresol 0.44 0.25 76% 0.003 19% 0%Benzoic Acid 1.26 0.71 77% 0.04 13% 8% benzoic / hippuric 0.02 0.01 22% 0.83 8% 3% p-Cresol Sulphate 2564 1072 139% 0.02 21 % 0%Dihydroxyphenyl propionic Acid 0.64 0.56 14% 0.66 4% 8%HippuricAcid 84 54 55% 0.04 8% 0%Hydroxybenzoic acid 4.73 1.03 360% 0.01 17% 31 %Phe / Tyr 1.65 1.22 36% 0.31 10%0%Phenylacetic Acid 1.50 1.17 28% 0.26 4% 15%Phenyl propionicAcid 0.01 0.03 -47% 0.68 0% 76%Tryptophan Based MetabolitesPercent of ASD aboveMean Mean p- TD BelowASD TD % Change value Range LODIAG 7.95 3.77 111 % 0.03 17% 0% -indole propionic acid 0.02 0.01 78% 0.61 15% 0% indole-3-carbaldehyde 0.02 0.01 319% 0.48 13% 0% phenylacetyl-l- glutamine 0.80 0.44 80% 0.002 13% 0%3-methyl-2 -oxindole 0.00 0.00 497% 0.32 12%70%Indoxyl Sulfate (Indican) 43 16 171 % 0.03 10% 0%2 -oxindole 0.00 0.00 3015% 0.32 8% 94%3-indole acetonitrile 0.00 0.00 103% 0.33 6% 8%1-methyl-1 ,2,3,4Tetrahydro BetaCarboline 0.022 0.008 183% 0.57 6% 0%5-methoxyindole 0.000 0.000 -95% 0.33 0% 95%Yeast Based MetabolitesPercent of ASD aboveMean Mean p- TD BelowASD TD % Change value Range LODArabinitol 37 27 41 % 0.01 10% 0%CitramalicAcid 0.11 0.06 85% 0.47 4% 0%Tartaric Acid 0.274 0.053 413% 0.69 2% 36%Tricarballylic Acid 0.845 0.001 74570% 0.35 0% 30%

[0177] Fisher’s discriminant analysis found 399 combinations of 3 metabolites with an AUROC of 0.7 or higher, with a maximum AUROC of 0.78, and combinations of 4, 5, or 6 metabolites only slightly increased the maximum AUROC to 0.80 (data not shown) without cross validation.

[0178] Using, the MDM System™, 78% sensitivity and 100% specificity in the quantitative analysis was achieved with the initial cohort, again demonstrating its value as a potential promising tool for early identification of ASD. The untargeted analysis by Semiquantitative and the targeted analysis by Quantitative of the same samples yielded generally similar results, with most of the same compounds being significantly elevated.

[0179] Table 5 shows the exemplary data used for calculating positive predictive value, negative predictive value, sensitivity, specificity, and overall accuracy of the MDM System™ for the untargeted and targeted data. The number of participants classified by the MDM System as either positive or negative compared tothe diagnostic criteria established in the methodology section, including a physician’s diagnosis, CARS score, SRS-2 score, and PGIA score.Table 5: Performance characteristics of the MDM system for the untargeted and targeted results. The MDM predictions of ASD vs TD diagnosis are generally in good agreement with actual diagnoses, with some differences. TP-True Positive, FP-FalseSUMMARY OF EXAMPLES

[0180] Provided herein is a comparative study contrasting microbially derived metabolites measurements of children with ASD against their healthy TD peers. Further, a method of interpreting the results of metabolomics measurements to screen for who is at high risk of gut dysbiosis and who is at high risk of autism spectrum disorder has been developed. The classification system utilizes combinations of bacterial / yeast-derived metabolites that represent a set of bacterial and yeast derived metabolites. This data analysis method provides a way to screen for gut dysbiosis, and to screen for people with autism vs. healthy typically developing individuals. The accuracy can be further enhanced by searching for major errors of metabolism and separately assessing those individuals, including gender and age in the analysis.

[0181] The abnormalities in microbially-derived metabolites identified while creating the MMCM extend beyond autism. Other neurological and gastrointestinalconditions have been linked with gut dysbiosis in the past. The MMCM and the other statistical methods listed here for the analysis of the 25 bacterial / yeast metabolites as useful tools for assessing risk for other gut, gut-brain disorders and other disorders affected by abnormal gut metabolites. Examples of such conditions include but are not limited to: Parkinson’s Disease, Chronic Kidney (Renal) Failure, Multiple Sclerosis, Colon Cancer, Irritable bowel syndrome, inflammatory bowel disease, Epilepsy, Schizophrenia, Mood Disorders (Anxiety, Depression, Bipolar Disorder, Obsessive-Compulsive Disorder, ADHD), Gender Dysphoria.[01821 Semiauantitative Analysis-Univariate: Univariate analysis showed that participants with autism have significantly higher peak intensities of many microbial metabolites compared to TD controls. Twenty-three microbially derived metabolites were statistically significantly higher in ASD than in TD participants. The metabolites were heterogeneously distributed, so that only a subset of ASD individuals had high concentrations of any individual MDMs, but most children with ASD had higher concentrations of one or more MDMs. The MDMs fell into of three categories: phenylalanine-derived, tryptophan-derived, and yeast-derived. Within each category, the compounds shared structural similarity suggests some degree of similar functionality. Some MDMs have a known toxic effect at high concentrations, and for others, their toxicity is unclear, but due to their close structural similarity to p-cresol and indoxyl sulfate, they are potentially also harmful at high concentrations and may also contribute to ASD-related symptoms. It is likely that the tryptophan-derived metabolites affect serotonin and melatonin-related neurotransmitter functions, and that the phenylalanine-related metabolites affect dopamine-related neurotransmitter functions.

[0183] P-C resol, p-cresol sulfate, 1 -methyl-1 ,2,3,4 tetrahydro beta carboline, indole-3-propionic acid, arabinitol, and phenylacetylglutamine showed the best inherent separation characteristics between groups.

[0184] To illustrate the potential neurological impact of these metabolites, we draw a parallel to ethanol, a well-known yeast-derived neurotoxin, which is commonly consumed by humans to affect their neurological function. Ethanol’s effects at high concentrations — altered motor coordination, emotional dysregulation, slurred speech,and cognitive impairment — highlight how a single microbial metabolite produced by yeast can profoundly alter brain function. Similarly, other microbial metabolites found elevated in ASD may contribute to neurological alterations such as sensory dysregulation, mood instability, and impaired language development.

[0185] P -cresol, p-cresol sulfate, indoxyl sulfate and phenylacetylglutamine were significantly higher in the ASD group than in the TD group, and contribute to the overall ability of The MDM System™ to distinguish between ASD and controls. Some microbial metabolites like indole-3-acetonitrile acetylnitrile and indole 3- acetoxidoximide was found to be significantly different in ASD. A total of 23 MDMs were statistically significantly higher in ASD during this investigation.WS ] Semici uantitative Multivariate Analysis - The MDM System'. The MDM System™ successfully classified children with ASD from TD children with very high sensitivity and specificity (FIGs. 3A-3B). The total score on the MDM system™ did not correlate significantly with age, suggesting it may also be valid at younger and older ages, but more data with those populations is needed. For younger ages, the microbiome changes substantially when infants transition from breastmilk or formula to solid foods, so that may limit how early the MDM System™ can be used. New reference ranges may be needed for ages below 2 years. For ages above 11 years, the microbiome is similar to that of younger children, but a reference range would need to be determined if the MDM System™ is likely to be used for teens and adults.

[0187] Accuracy with cross validation of multivariate statistical techniques utilizing semi-quantitative analysis is shown in Table 6.Table 6: Multivariate statistical techniques utilizing semiquantitative analysis

[0188] Quantitative Analysis - Univariate Analysis'. 19 of the 23 putatively identified metabolites discovered during the semiquantitative analysis were confirmed as significant in differentiating between ASD and TD participants aged 2-11 years old. The other four were below the limit of detection in significant numbers of individuals, and thus were not able to be validated (Table 3).F01891 Quantitative Analysis - Multivariate Analysis: The MDM System™ resulted in 78% sensitivity and 100% specificity in the targeted analysis (FIGs. 4A- 4B, FIGs. 5A-5B, and Table 5), demonstrating our novel algorithm’s value as a promising tool for early identification of ASD. When evaluating the overall accuracy of the MDM system™, it should be noted that some, but not all, of the participants overlapped between the quantitative and semiquantitative analyses.

[0190] Table 7 shows AUROC of 3, 4, 5, or 6 metabolites using Fisher discriminant analysis.Table 7: Fisher discriminant analysis results using targeted the targeted quantitative technique

[0191] More than 4 out of 5 young children with ASD in this cohort had high concentrations of one or more microbially-derived metabolites. The MDMs investigated fall into three categories: tryptophan related, phenylalanine (or tyrosine) related, and yeast / fungi related. The former two result in MDMs that are likely to disrupt important neurotransmitter metabolism (serotonin, melatonin, and dopamine), which could adversely affect many ASD-related symptoms.

[0192] Table 8 shows average difference in percentage between subjects with ASD and TD.Table 8: Average difference in percentage and AUROC

[0193] Table 9 shows various percentiles of ASD and TD group.Table 9: Percentiles of various metabolites in ASD and TD group0.27 0 05 36Tartaric Acid 4 0.000 0.000 0.001 3 0.000 0.000 0.003 413% 0.69 2% %Tricarballylic 0.84 0 00 74570 30Acid 5 0.000 0.000 0.000 1 0.000 0.000 0.000 % 0.35 0% %

[0194] Table 10 shows performance characteristics of the MDM system for the untargeted and targeted results. The MDM predictions of ASD vs TD diagnosis are generally in good agreement with actual diagnoses, with some differences.Table 10: Performance characteristics of the MDM system for the untargeted and targeted results

[0195] A major question is whether or not high concentrations of the MDMs contribute to ASD symptoms. Some of the metabolites like p-cresol and indoxyl sulfate are known to cause ASD symptoms when administered to animals, and some studies have found that p-cresol concentration correlates with worse ASD symptoms. The other phenylalanine-derived and tryptophan-derived MDMs are similar in chemical structure to p-cresol and indoxyl sulfate, respectively, and thus, it is possible that they may have similar adverse effects.

[0196] The ASD group had significantly higher concentrations of 23 Microbial- Derived Metabolites (MDMs) compared to the TD group in this multicenter casecontrol investigation. The MDMs included phenylalanine-derived, tryptophan-derived, and yeast-derived MDMs. A Microbial-Based Metabolite total score was developed for each participant, defined as the number of MDMs exceeding the highest value of the TD children. The mean MDM Total Score was 6.91 MDMs for the ASD group, ranging from zero to nine, compared to zero by definition for the TD group. Classification using the MDM Total Score yielded a sensitivity of 90% and a specificity of 100%.

[0197] Based on the findings a large subset of children with ASD have elevated concentrations of one or more MDMs, an ASD phenotype is proposed, referred herein as “ASD-Microbial Metabolite Associated" (ASD-MMA) phenotype, which is defined by laboratory measurements of MDMs in urine samples in young children. The development of the MDM System™ as a screening tool for ASD is very promising. Further validation of the MDM System™ in another cohort is being conducted (data not shown).

[0198] In conclusion, there are no existing alternative products, processes or services that provide a comprehensive assessment of risk for ASD. The potential applications of the classification system screening extend far beyond autism, as similar empirical modeling techniques of measurements of these bacterially and yeast derived metabolites and analysis of the data obtained through these measurements can be used to screen for a wide variety of other gastrointestinal and neurological conditions, including Parkinson’s Disease, Multiple Sclerosis, schizophrenia, colon Cancer, Irritable Bowel Syndrome, depression, Mood Disorders (Anxiety, Depression, Bipolar Disorder, Obsessive-Compulsive Disorder, ADHD), Gender Dysphoria. The MDM System™, based on a simple urine collection, is a laboratory test that can provide screening for the ASD-MDM phenotype.

Claims

CLAIMSWhat is claimed is:1 . A method of screening for Autism Spectrum Disorder (ASD) in a subject, the method comprising: a) measuring or having measured a level of each of at least two microbially- derived metabolites in a biological sample obtained from the subject; b) comparing the measured level of each metabolite against a cut-off range for each metabolite, wherein the cut-off range is determined from a distribution of each of the metabolite level in at least one healthy typical developing (TD) subject, and is characterized by an upper-bound cut-off value and a lower-bound cut-off value; and c) indicating the subject as having ASD when the level of each of the at least two metabolites is outside the cut-off range obtained from the TD subjects.

2. A method of classifying a subject as a candidate for treatment of Autism Spectrum Disorder (ASD), the method comprising: a) measuring or having measured a level of each of at least two microbially- derived metabolites in a biological sample obtained from the subject; b) comparing the measured level of each metabolite against a cut-off value for each metabolite, wherein the cut-off range is determined from a distribution of each of the metabolite level in at least one healthy typical developing (TD) subject, and is characterized by an upper-bound cut-off value and a lower-bound cut-off value; and c) classifying the subject as a candidate for treatment of ASD when the level of each of the at least two metabolites is outside the cut-off range obtained from the TD subject(s).

3. The method of claim 1 or 2, wherein the upper bound cut-off value is determined by obtaining for each of the at least two metabolites, the level of the metabolite at the 95thpercentile of the TD multiplied by a factor about 1 .1 to about 5.

4. The method of claim 1 or 2, wherein the lower bound cut-off value is determined by obtaining for each of the at least two metabolites, the level of metabolite at the 95thpercentile of the TD multiplied by a factor about 0.9 to about 0.2.

5. The method of claim 1 or 2, wherein the at least two microbially-derived metabolites are selected from one or more tryptophan related metabolites, one or more phenylalanine related metabolites, and one or more yeast metabolites.

6. The method of claim 5, wherein the one or more tryptophan related metabolites are selected from a group consisting of: a) 3-Oxindole, 2-Oxindole, 3 Methyl oxindole, Indolacrylolglycine, Indole, Indoxyl sulfate, N-methyl-1 ,2,3,4 Tetrahydro-Beta-Carboline-3-Carboxylic Acid x 4**, Indole acrylic acid, lndole-3-Acetic Acid, lndole-3-acetoximide, lndol-3-one, lndol-2-one, Indole-N-Propionic Acid x 3**, lndole-3- carboxylic acid 0 Sulfate, lndole-3-ethanol, Indole, 1 - Benzazole 5, Methoxy Indole Acetic Acid, methyl-3-indole acetate, 3-methylindole, 6- methylindole, dihydroxy indole glucuronide, 5-hydroxy-6-methoxyindole Glucuronide, 4-lndole carbaldehyde, 3-methyldioxyindole, Indole, 1 - Benzazole, Indoxyl Glucuronide, 5-methoxyindoleacetate, 2-carboxy 2, 3, dihydroxyindole, 2-hydroxy 3-(1-methyo-1 H indol 3-yl) propanoic acid, 3- Indoleacetonitrile, lndole-3-Acetic acid O-Glucuronide, 5-Hydroxyindole pyruvate, Dihydroxy-1 H-lndole Glucuronide, Trans-3-lndoleacrylic acid, 1 ,1 ethyldienebistryptophan, and Indole Pyruvate; b) lndolyl-3-acryloylglycine (IAG), 4-lndolecarbaldehyde, 3- Indolepropionicacid, 1 -Methyl-1 ,2,3,4-tetrahydro-|3-carboline-3-carboxylic acid, 3-Methyldioxyindole; trans-3-lndoleacrylic acid, lndole-3- acetaldoxime, and 3-lndoleacetonitrile; c) 3-lndolepropionic acid, 1 -Methyl-1 ,2, 3, 4-tetrahydro-[3-carboline-3- carboxylic acid, 3-Methyldioxyindole, lndole-3-acryloyl glycine, trans-3-Indoleacrylic acid, 4-lndolecarbaldehyde, 3-lndoleacetonitrile, and Indole- 3-acetaldoxime; or d) IAG, 3-indole propionic acid, indole-3-carbaldehyde, phenylacetyl-l- glutamine, 3-methyl-2-oxindole, Indoxyl Sulfate (Indican), 2-oxindole, 3- indole acetonitrile, 1-methyl-1 ,2,3,4 Tetrahydro Beta Carboline, and 5- methoxyindole.

7. The method of claim 5, wherein the one or more phenylalanine related metabolites are selected from the group consisting of: a) 4-vinylphenol sulfate, p-Cresol, p-Cresol Sulfate, Phenol, 4 Phenyl hydrogen sulfate, 4 phenol sulfonic acid, Terrequinone A (2,5-Dihydroxy- 3,6-di(1 H-indol-3-yl)-1 ,4 benzoquinone), didemethylasterriquinone, 4-(2- Aminopropoxy)-3,5-dimethylphenol, 2-Hydroxy-5 Vinyl phenol hydrogen sulfate, p-cresol gluduronide, 4-ethyl phenol, 4-ethylphenol sulfate, 4- ethylphenol glucuronide, 3-hydroxy-3-hydroxyphenol propionic acid, Phenylacetylglutamine, Phenylacetylaldehyde, and p-Cresol Glucuronide; b) Phenylacetylglutamine, p-Cresol, p-Cresol Sulphate, 4-(2-Aminopropoxy)- 3,5-dimethylphenol, 6 amino-m-cresol, Phenylacetic Acid, Phenyl propionic Acid, Phe / Tyr, Hydroxybenzoic acid, Hippuric Acid, DHPPA, benzoic / hippuric, and Benzoic Acid; c) 4-(2-Aminopropoxy)-3,5-dimethylphenol, 6-Amino-m-cresol, 4- Methylphenol, Phenylacetylglutamine, and p-cresols sulfate; or d) p-Cresol, Benzoic Acid, benzoic / hippuric, Hydroxybenzoic acid, Phe / Tyr, Phenylacetic Acid, and Phenyl propionic Acid.

8. The method of claim 5, wherein the one or more yeast metabolites is selected from the group consisting of: a) N-Formyl Methionine, D-Arabinoate, (-) Threo-lso(homo)2citrate, D- Aspartate, D-Glutamate, and D-Arginyl-L-histidyl-D-prolyl-D-tyrosine; b) Arabinitol, Tricarballylic Acid, Tartaric Acid, and Citramallic Acid; or c) Arabinitol.

9. The method of claim 1 or 2, wherein the biological sample is whole blood, plasma, red blood cells (RBCs), other components of blood, urine, stool, saliva, cord blood, amniotic fluid, meconium, buccal swabs, biopsy-obtained tissue, cord blood, placental tissue, hair, deciduous teeth, or any combination thereof.

10. The method of claim 1 or 2, wherein the subject is in utero, a newborn, a neonate, an infant, a toddler, a young child, a child, an adolescent, or an adult.

11. The method of claim 1 or 2, further comprising assigning a medical, behavioral, and / or nutritional treatment protocol to the subject identified as having ASD.

12. The method of claim 11 , wherein the treatment protocol comprises adjusting the level of one or a combination of two or more microbially-derived metabolites in the subject.

13. The method of claim 11 , wherein the treatment protocol comprises administering a combination of prebiotics, probiotics, microbiota transplant, antibiotics, antifungal medications, diet modifications.

14. The method of claim 11 , wherein the treatment comprises administering to the subject: a) a behavioral management therapy, a cognitive behavior therapy, an early intervention, an educational and school-based therapy, a joint attention therapy, an occupational therapy, a parent-mediated therapy, a physical therapy, social skills training, or a speech-language therapy; b) a medication selected from an antipsychotic drug, a selective serotonin reuptake inhibitor (SSRI), a tricyclic, a psychoactive or anti-psychotic medication, a stimulant, an anti-anxiety medication, or an anticonvulsant; c) a nutritional supplementation; ord) a composition comprising fecal microbiota from a healthy neurotypical human donor.

15. A method of monitoring a therapeutic effect of an ASD treatment protocol in a subject suspected of having or at risk of having ASD, the method comprising: a) measuring in a first biological sample obtained from the subject a level of each of at least two microbially-derived metabolites selected from the metabolites of any one of claims 6-8; b) measuring in a second biological sample obtained from the subject at a period of time after the first biological sample is obtained the level of each of the at least two microbially-derived metabolites; and c) comparing the level of each of the at least two microbially-derived metabolites in the first sample and the second sample; wherein maintenance of the level of each of at least two microbially-derived metabolites or a change of the level of each of the at least two microbially- derived metabolites to a level of each of at least two microbially-derived metabolites selected from the metabolites in a control panel of metabolite levels created by measuring microbially-derived metabolites levels of the one or combination of two or more microbially-derived metabolites in control TD subjects is indicative that the treatment protocol is therapeutically effective in the subject.

16. A kit for diagnosing Autism Spectrum Disorder (ASD) in a subject suspected of having or at risk of having ASD, determining a treatment protocol, monitoring a therapeutic effect of an ASD treatment protocol, or any combination thereof, the kit comprising: (a) a container for collecting a biological sample obtained from the subject; (b) reagents for preparing an extract from a biological sample obtained from the subject; and (c) instructions for (i) preparing the extract; (ii) measuring a level of each of at least two microbially-derived metabolites of any one of claims 6-8; and (iii) comparing each of the measured metabolite level against a control metabolite level obtained from TD subjects.

17. The method of claim 1 , further comprising screening the subject for a gastrointestinal or other neurological disorder.

18. The method of claim 17, wherein the gastrointestinal or other neurological disorder is selected from Parkinson’s Disease, Chronic Kidney (Renal) Failure, Multiple Sclerosis, Colon Cancer, Irritable bowel syndrome, inflammatory bowel disease, Epilepsy, Generalized anxiety, and Schizophrenia.

19. The method of claims 1 or 2, wherein the method further comprises generating and assigning a score to the subject based on the level of each of the at least two microbially-derived metabolite.

20. The method of claim 19, wherein a score above or below a predetermined threshold value is indicative of the subject as having or at high risk of ASD; wherein the threshold value is determined based on the level of each of the at least two microbially-derived metabolite in TD subjects.

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