METHOD AND SYSTEM FOR MICROBIOME-DERIVED DIAGNOSTICS AND THERAPEUTICS FOR NEUROLOGICAL HEALTH ISSUES

1 . A method for characterizing a neurological health issue for a subject, the method comprising:

providing a set of sampling kits to a population of subjects, each sampling kit of the set of sampling kits comprising a sample container and a pre-process reagent component, wherein the sample container is operable to receive a biological sample from a collection site;

receiving an aggregate set of biological samples from the set of sampling kits;

identifying primers for nucleic acid sequences associated with the neurological health issue;

with a bridge amplification substrate of a next generation sequencing platform of a sample processing system, determining a set of microorganism nucleic acid sequences based on processing the aggregate set of biological samples with the primers;

generating at least one of a microbiome composition diversity dataset and a microbiome functional diversity dataset for the population of subjects based on the set of microorganism nucleic acid sequences;

generating a characterization of the neurological health issue based on the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset;

determining a therapy for the subject based on the characterization and a biological sample from the subject, wherein the therapy is operable to improve a state of the neurological health issue; and

providing the therapy to the subject with the neurological health issue, wherein the therapy is operable to modulate microbiome composition to improve the state of the neurological health issue.

2 . The method of claim 1 ,

wherein determining the set of microorganism nucleic acid sequences comprises:

fragmenting nucleic acid material from the aggregate set of biological samples; and

amplifying the fragmented nucleic acid material using the primers, and

wherein generating the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset is based on alignments between the set of microorganism nucleic acid sequences and a set of reference nucleic acid sequences associated with the neurological health issue.

3 . The method of claim 2 , wherein amplifying the fragmented nucleic acid material comprises performing, at a library preparation subsystem of the sample processing system, multiplex amplification with the fragmented nucleic acid material based on the primers.

4 . The method of claim 1 , wherein generating the characterization comprises performing a statistical analysis with at least one of a Kolmogorov-Smirnov test and a t-test to assess varying degrees of abundance of features derived from the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset for a first subset of the population of subjects exhibiting the neurological health issue and a second subset of the population of subjects not exhibiting the neurological health issue.

5 . The method of claim 1 , further comprising:

after providing the therapy, receiving a post-therapy biological sample from the subject;

with the next generation sequencing platform of the sample processing system, processing the post-therapy biological sample based on the primers;

generating a post-therapy characterization of the subject in relation to the neurological health issue and the therapy based on the processed post-therapy biological sample.

6 . The method of claim 5 , further comprising:

determining post-therapy microbiome composition features and post-therapy microbiome functional diversity features associated with the subject, based on the processed post-therapy biological sample;

determining an updated therapy for the subject based on the therapy model, the post-therapy microbiome composition features, and the post-therapy microbiome functional diversity features; and

providing the updated therapy to the subject, wherein the updated therapy is operable to modulate the microbiome composition and a microbiome functional diversity to improve the state of the neurological health issue.

7 . The method of claim 1 , further comprising receiving a supplementary dataset comprising biometric sensor data informative of the neurological health issue and sampled at a biometric sensor, wherein generating the characterization comprises correlating the neurological health issue to the at least one of the microbiome composition diversity dataset and the microbiome functional diversity dataset based on the biometric sensor data.

8 . The method of claim 1 , wherein generating the characterization of the neurological health issue comprises generating the characterization associated with diagnosis of at least one of: anxiety, attention deficit disorder (ADD), attention deficit hyperactive disorder (ADHD), an autism spectrum disorder, chronic fatigue syndrome, depression, pernicious anemia, and stroke.

9 . The method of claim 8 , wherein generating the characterization associated with the diagnosis comprises generating the characterization based on a set of microbiome composition features extracted from the microbiome composition diversity dataset, and wherein the set of microbiome composition features is associated with a set of taxa comprising at least one of: Sarcina (genus), Sutterella (genus), Terrisporobacter (genus), Blautia (genus), Lactobacillus (genus), Parabacteroides (genus), Lactobacillaceae (family), Clostridiaceae (family), Flavobacteriaceae (family), Flavobacteriales (order), Rhodospirillales (order), Flavobacteriia (class), Proteobacteria (phylum), Pseudomonas (genus), Parvimonas (genus), Pseudomonadaceae (family), Bacteroidia (order), Lactococcus (genus), Pseudoclavibacter (genus), Citrobacter (genus), Fusobacterium (genus), Methanosphaera (genus), Microbacteriaceae (family), Fusobacteriaceae (family), Fusobacteriales (order), Fusobacteriia (class), Fusobacteria (phylum), Anaerosporobacter (genus), Asteroleplasma (genus), Megamonas (genus), Coriobacteriales (order), Mollicutes (class), Cronobacter (genus), Cronobacter sakazakii (species), Gammaproteobacteria (class), Clostridiales bacterium A2-162 (species), Barnesiella (genus), Erysipelotrichaceae (family), Pseudomonadales (order), Dorea (genus), Collinsella (genus), Bifidobacterium (genus), Moryella (genus), Faecalibacterium (genus), Erysipelatoclostridium (genus), Intestinimona (genus), Dialister (genus), Bacteroides (genus), Coriobacteriaceae (family), Oscillospiraceae (family), Bifidobacteriaceae (family), Ruminococcaceae (family), Prevotellaceae (family), Bacteroidaceae (family), Streptococcaceae (family), Rikenellaceae (family), Peptostreptococcaceae (family), Coriobacteriales (order), Bifidobacteriales (order), Erysipelotrichales (order), Bacteroidales (order), Clostridiales (order), Selenomonadales (order), Actinomycetales (order), Actinobacteria (class), Erysipelotrichia (class), Bacteroidia (class), Clostridia (class), Negativicutes (class), Verrucomicrobiae (class), Actinobacteria (phylum), Bacteroidetes (phylum), Firmicutes (phylum), Verrucomicrobia (phylum), Bacteroides uniformis (species), Flavonifractor (genus), unclassified Lachnospiraceae (family), Prevotella (genus), Ruminococcus (genus), Acidobacteria (phylum), Actinobacteridae (subclass), Rhodospirillaceae (family), Blautia faecis (species), Staphylococcaceae (family), Clostridiales incertae sedis (family), Clostridiales Family XI. Incertae sedis (family), Finegoldia (genus), Peptoniphilus (genus), Finegoldia magna (species), Corynebacterineae (family), Corynebacteriaceae (family), and Corynebacterium (genus).

10 . A method for characterizing a neurological health issue for a subject, the method comprising:

providing a sampling kit to the subject, wherein the sampling kit comprises a sample container operable to receive a biological sample from a collection site;

receiving the biological sample at the sampling kit from the subject;

with an amplification substrate of a next generation sequencing platform of a sample processing system, determining a microorganism nucleic acid sequence based on processing the biological sample;

generating a microbiome feature dataset associated with the neurological health issue, based on the microorganism nucleic acid sequence;

generating a characterization of the neurological health issue for the subject based on the microbiome feature dataset and a characterization model derived from a set of subjects associated with the neurological health issue; and

providing a therapy to the subject based on the characterization, wherein the therapy is operable to improve a state associated with the neurological health issue.

11 . The method of claim 10 , wherein determining the microorganism nucleic acid sequence comprises:

identifying a primer for a nucleic acid sequence associated with the neurological health issue; and

amplifying nucleic acid material derived from the biological sample, using the identified primer.

12 . The method of claim 10 , wherein the microbiome feature dataset comprises a set of microbiome composition features and a set of microbiome functional diversity features indicative of systemic functions present in microbiome components of the biological sample.

13 . The method of claim 10 , wherein providing the therapy comprises providing a bacteriophage-based therapy to the subject, the bacteriophage-based therapy operable to modulate at least one of microbiome composition and microbiome functional diversity to improve the state of the neurological health issue.

14 . The method of claim 10 , wherein providing the therapy comprises automatically initiating a signal that controls a treatment device to provide the therapy to the subject with the neurological health condition based upon the characterization.

15 . The method of claim 10 , wherein generating the characterization comprises generating the characterization based on a set of microbiome composition features for a set of taxa associated with at least one of: relative abundance, presence, and absence.

16 . The method of claim 15 , wherein generating the characterization comprises determining the set of microbiome composition features based on at least one of: normalizations, feature vectors derived at least one of linear latent variable analysis and non-linear latent variable analysis, linear regression, non-linear regression, kernel methods, feature embedding methods, machine learning, and statistical inference methods.

17 . The method of claim 10 , further comprising collecting a supplementary social and behavioral dataset from the subject, wherein generating the characterization comprises determining a cause of the neurological health issue for the subject based on the supplementary social and behavioral dataset, and wherein providing the therapy comprises providing the therapy based on the cause.

18 . The method of claim 10 , wherein the neurological health issue comprises an autism spectrum disorder, and wherein the microbiome feature dataset is associated with a set of taxa comprising at least one of: Lactococcus (genus), Pseudoclavibacter (genus), Citrobacter (genus), Fusobacterium (genus), Methanosphaera (genus), Microbacteriaceae (family), Fusobacteriaceae (family), Fusobacteriales (order), Bacteroidales (order), Flavobacteriales (order), Fusobacteriia (class), Bacteroidia (class), Flavobacteriia (class), Fusobacteria (phylum), Bacteroidetes (phylum), butyrate-producing bacterium L1-93 (species), Anaerosporobacter (genus), Finegoldia (genus), Peptoniphilus (genus), Asteroleplasma (genus), Dorea (genus), Megamonas (genus), Coriobacteriaceae (family), Coriobacteriales (order), Mollicutes (class), Bacteroides fragilis (species), Holdemania (genus), Subdoligranulum sp. 4_3_54A2FAA (species), Blautia (genus), Ruminococcus (genus), Ruminococcus obeum (species), Prevotella (genus), Coprococcus (genus), Collinsella aerofaciens (species), Marvinbryantia (genus), Clostridiales bacterium A2-162 (species), and Oscillospiraceae (family).

19 . The method of claim 10 , wherein the neurological health issue comprises chronic fatigue syndrome, and wherein the microbiome feature dataset is associated with a set of taxa comprising at least one of: Dorea (genus), Collinsella (genus), Bifidobacterium (genus), Moryella (genus), Faecalibacterium (genus), Erysipelatoclostridium (genus), Intestinimona (genus), Dialister (genus), Bacteroides (genus), Coriobacteriaceae (family), Oscillospiraceae (family), Bifidobacteriaceae (family), Ruminococcaceae (family), Erysipelotrichaceae (family), Prevotellaceae (family), Bacteroidaceae (family), Streptococcaceae (family), Rikenellaceae (family), Peptostreptococcaceae (family), Coriobacteriales (order), Bifidobacteriales (order), Erysipelotrichales (order), Bacteroidales (order), Clostridiales (order), Selenomonadales (order), Actinomycetales (order), Actinobacteria (class), Erysipelotrichia (class), Bacteroidia (class), Clostridia (class), Negativicutes (class), Verrucomicrobiae (class), Actinobacteria (phylum), Bacteroidetes (phylum), Firmicutes (phylum), Verrucomicrobia (phylum), Clostridium lavalense (species), Deltaproteobacteria (class), Holdemania (genus), Alistipes (genus), Rikenellaceae (family), Bilophila (genus), Bilophila wadsworthia (species), bacterium NLAE-zl-P827 (species), Rhodospirillales (order), Rhodospirillaceae (family), Coprococcus (genus), Actinobacteria (phylum), Odoribacter splanchnicus (species), Actinobacteridae (subclass), Clostridium leptum (species), Firmicutes (phylum), Odoribacter (genus), Actinomycetales (phylum), Roseburia (genus), bacterium NLAE-zl-H54 (species), Corynebacterineae (family), Corynebacterium (genus), Corynebacteriaceae (family), Dorea (genus), Peptostreptococcaceae (family), Roseburia inulinivorans (species), Streptococcus thermophilus (species), Actinomyces (genus), Actinomycetaceae (family), and Actinomycineae (family).

20 . The method of claim 19 , wherein the microbiome feature dataset comprises a set of microbiome functional diversity features comprising at least one of: a carbohydrate metabolism KEGG L2 derived feature; a metabolism KEGG L2 derived feature; a translation KEGG L2 derived feature; a genetic information processing KEGG L2 derived feature; a transport and catabolism KEGG L2 derived feature; an enzyme families KEGG L2 derived feature; a lipid metabolism KEGG L2 derived feature; a metabolism of cofactors and vitamins KEGG L2 derived feature; a nucleotide metabolism KEGG L2 derived feature; a cell growth and death KEGG L2 derived feature; a replication and repair KEGG L2 derived feature; an environmental adaptation KEGG L2 derived feature; a signaling molecules and interaction KEGG L2 derived feature; a biosynthesis of other secondary metabolites KEGG L2 derived feature; a glycan biosynthesis and metabolism KEGG L2 derived feature; a neurodegenerative diseases KEGG L2 derived feature; a ribosome biogenesis KEGG L3 derived feature; a pentose and glucuronate interconversions KEGG L3 derived feature; peptidoglycan biosynthesis KEGG L3 derived feature; a translation proteins KEGG L3 derived feature; a fructose and mannose metabolism KEGG L3 derived feature; a naphthalene degradation KEGG L3 derived feature; an amino acid related enzymes KEGG L3 derived feature; an inorganic ion transport and metabolism KEGG L3 derived feature; a carbohydrate metabolism KEGG L3 derived feature; an aminoacyl-tRNA biosynthesis KEGG L3 derived feature; an other glycan degradation KEGG L3 derived feature; an inositol phosphate metabolism KEGG L3 derived feature; an RNA polymerase KEGG L3 derived feature; a ribosome KEGG L3 derived feature; a chromosome KEGG L3 derived feature; a sphingolipid metabolism KEGG L3 derived feature; a galactose metabolism KEGG L3 derived feature; a cell motility and secretion KEGG L3 derived feature; a thiamine metabolism KEGG L3 derived feature; a DNA repair and recombination proteins KEGG L3 derived feature; a terpenoid backbone biosynthesis KEGG L3 derived feature; a photosynthesis proteins KEGG L3 derived feature; a biosynthesis and biodegradation of secondary metabolites KEGG L3 derived feature; a photosynthesis; an other transporters KEGG L3 derived feature; a ribosome biogenesis in eukaryotes KEGG L3 derived feature; a cysteine and methionine metabolism KEGG L3 derived feature; a citrate cycle (TCA cycle) KEGG L3 derived feature; a cell cycle— Caulobacter KEGG L3 derived feature; an amino sugar and nucleotide sugar metabolism KEGG L3 derived feature; a Pentose phosphate pathway KEGG L3 derived feature; a Plant-pathogen interaction KEGG L3 derived feature; an Ethylbenzene degradation KEGG L3 derived feature; a Nicotinate and nicotinamide metabolism KEGG L3 derived feature; a translation factors KEGG L3 derived feature; a pyruvate metabolism KEGG L3 derived feature; a replication, recombination, and repair proteins KEGG L3 derived feature; a D-Alanine metabolism KEGG L3 derived feature; a Pyrimidine metabolism KEGG L3 derived feature; a nucleotide excision repair KEGG L3 derived feature; an amino acid metabolism KEGG L3 derived feature; a purine metabolism KEGG L3 derived feature; a peptidases KEGG L3 derived feature; a glyoxylate and dicarboxylate metabolism KEGG L3 derived feature; a homologous recombination KEGG L3 derived feature; and a butirosin and neomycin biosynthesis KEGG L3 derived feature.