METHOD AND SYSTEM FOR CHARACTERIZATION OF CLOSTRIDIUM DIFFICILE ASSOCIATED CONDITIONS
An embodiment of a system and method for characterizing a Clostridium -associated condition in relation to a user includes: a handling network operable to receive containers including material from a set of users, the handling network including a sequencing system operable to determine microbiome sequences from sequencing the material; a processing system operable to generate a microbiome composition dataset and a microbiome functional diversity dataset based on the microbiome sequences, receive a supplementary dataset associated with the Clostridium -associated condition for the set of users; transform the supplementary dataset and features extracted from the microbiome composition dataset and the microbiome functional diversity dataset into a characterization model for the Clostridium -associated condition; and a therapy system operable to promote a therapy to the user based on characterizing the user in relation to the Clostridium -associated condition using the characterization model.
1 . A system for characterizing a Clostridium -associated condition in relation to a user, the system comprising:
a handling network operable to receive containers comprising material from a set of users, the handling network comprising a sequencing system operable to determine microbiome sequences from sequencing the material;
a processing system operable to:
generate a microbiome composition dataset and a microbiome functional diversity dataset based on the microbiome sequences;
receive a supplementary dataset associated with the Clostridium -associated condition for the set of users;
transform the supplementary dataset and features extracted from the microbiome composition dataset and the microbiome functional diversity dataset into a characterization model for the Clostridium -associated condition; and
a therapy system operable to promote a therapy to the user based on characterizing the user in relation to the Clostridium -associated condition using the characterization model.
2 . The system of claim 1 , wherein the processing system is further operable to:
obtain a set of Clostridium -associated feature-selection rules correlating the Clostridium -associated condition to a subset of microbiome composition features and a subset of microbiome functional diversity features; and
generate the features based on evaluating the microbiome composition dataset and the microbiome functional diversity dataset against the set of Clostridium -associated feature-selection rules.
3 . The system of claim 2 , wherein the set of Clostridium -associated feature-selection rules improve the processing system by facilitating decreased processing time to transform the supplementary dataset and the features into the characterization model.
4 . The system of claim 2 , wherein the subset of microbiome functional diversity features comprises at least one of: a cluster of orthologous group of proteins feature, a genomic functional feature, a taxonomic feature, a chemical functional feature, and a systemic functional feature.
5 . The system of claim 1 ,
wherein the Clostridium -associated condition comprises a Clostridium difficile Ribotype 027 strain infection comprising at least one of sepsis and colitis, and
wherein the associated features comprise at least one of the following: a microbiome functional diversity feature associated with bile acid metabolism, and a microbiome composition feature associated with a relative abundance of Bacteroidetes, Firmicutes, and Proteobacteria.
6 . The system of claim 1 , wherein the features comprise Kyoto Encyclopedia of Genes and Genomes (KEGG) functional features associated with at least one of: pentose phosphate pathway, gluconeogenesis, and carbon fixation.
7 . The system of claim 1 , further comprising an interface operable to improve display of Clostridium -associated condition information derived from the characterization model, wherein the Clostridium -associated condition information comprises a microbiome composition for the user relative to user group sharing a demographic characteristic, and wherein the microbiome composition comprises taxonomic groups comprising at least one of Clostridium difficile, Clostridium botulinum, and Clostridium perfringens.
8 . The system of Claim 7 , wherein the Clostridium -associated condition information comprises a risk of infection for the user relative to the user group, wherein the risk of infection is associated with at least one of: the taxonomic groups and functional features, and wherein the therapy is operable to reduce the risk of infection.
9 . The system of claim 1 , further comprising a sample kit comprising the containers, wherein the handling network is operable to deliver the containers to the set of users, and wherein the handling network further comprises a library preparation system operable to fragment and perform multiplex amplification on the material using primers compatible with microbiome targets associated with the Clostridium -associated condition.
10 . A method for characterizing a Clostridium difficile ( C. difficile ) associated condition in relation to a user, the method comprising:
generating a microbiome composition dataset and a microbiome functional diversity dataset based on nucleic acid sequences derived from material samples from a set of users;
receiving a supplementary dataset informative of the C. difficile associated condition for the set of users;
obtaining a set of C. difficile associated feature-selection rules correlating the C. difficile associated condition to a subset of microbiome composition features and a subset of microbiome functional diversity features;
generating a feature set based on evaluating the microbiome composition dataset and the microbiome functional diversity dataset against the set of C. difficile associated feature-selection rules;
applying the feature set with the supplementary dataset to generate a characterization model for the C. difficile associated condition;
generating a characterization of the user in relation to the C. difficile associated condition using the characterization model; and
promoting a therapy to the user based on the characterization.
11 . The method of claim 10 , wherein the therapy is operable to facilitate modification of a user microbiome composition and a user microbiome functional diversity associated with the C. difficile associated condition, wherein promoting the therapy comprises controlling a therapy system to promote the therapy.
12 . The method of claim 10 , wherein generating the associated feature set comprises generating a set of microbiome feature vectors for the set of users based on the subset of microbiome composition features and the subset of microbiome functional diversity features, and wherein applying the feature set comprises training the characterization model with the set of microbiome feature vectors.
13 . The method of claim 10 , further comprising:
fragmenting and amplifying nucleic acid material derived from microorganisms in the sample material;
sequencing, with any suitable sequencing system, the nucleic acid material to determine the nucleic acid sequences; and
determining alignments between the nucleic acid sequences and reference sequences associated with the C. difficile associated condition, wherein generating the microbiome composition dataset and the microbiome functional diversity dataset is based on the alignments.
14 . The method of claim 10 , wherein the C. difficile associated condition is a C. difficile infection comprising at least one of sepsis and colitis, and wherein the characterization of the user comprises a diagnostic analysis for the C. difficile infection.
15 . The method of claim 14 , wherein the subset of microbiome functional diversity features comprises a functional feature associated with bile acid metabolism, and wherein generating the diagnostic analysis is based on using the characterization model with the associated functional feature.
16 . The method of claim 14 , wherein the supplementary dataset comprises biometric sensor data informative of the C. difficile infection, and wherein the set of C. difficile associated feature-selection rules correlates the C. difficile infection to a biometric feature derived from the biometric sensor data.
17 . The method of claim 10 , wherein the C. difficile associated condition comprises a C. difficile infection risk, and wherein comprises a therapy operable to facilitate modification of a user microbiome composition to reduce the C. difficile infection risk.
18 . The method of claim 17 , wherein the subset of microbiome composition features comprises a composition feature associated with a relative abundance of Bacteroidetes, Firmicutes, and Proteobacteria, wherein generating the characterization comprises determining the C. difficile infection risk based on using the characterization model with the composition feature, and wherein the therapy is operable to modify the relative abundance of Bacteroidetes, Firmicutes, and Proteobacteria to reduce the C. difficile infection risk.
19 . The method of claim 17 , wherein the supplementary dataset comprises antibiotic regimen data associated with the set of users, and wherein applying the associated feature set comprises applying the associated feature set with the antibiotic regimen data to generate the characterization model.
20 . The method of claim 10 , wherein the C. difficile associated condition comprises presence of C. difficile Ribotype 027 strain, and wherein generating the characterization comprises determining the presence of the C. difficile Ribotype 027 strain in a user microbiome composition.
21 . The method of claim 19 , wherein the associated feature set comprises a composition feature associated with a set of taxa comprising at least one of: Clostridium (genus), Clostridiaceae (family), and Firmicutes (phylum), and wherein determining the presence of the C. difficile Ribotype 027 strain comprises processing the characterization model with the composition associated feature.
22 . The method of claim 20 , wherein the associated feature set comprises a composition feature associated with a set of taxa comprising at least one of: Flavonifractor plautii (species), Bifidobacterium longum (species), Bacteroides fragilis (species), Bifidobacterium bifidum (species), Erysipelatoclostridium ramosum (species), Parabacteroides distasonis (species), Bacteroides vulgatus (species), Faecalibacterium prausnitzii (species), Blautia sp. YHC-4 (species), Blautia faecis (species), Bacteroides acidifaciens (species), Collinsella aerofaciens (species), Anaerostipes caccae (species), bacterium NLAE-zl-P855 (species), Bacteroides thetaiotaomicron (species), Bacteroides vulgatus (species), Bacteroides xylanisolvens (species), Bilophila wadsworthia (species), Blautia product (species), Clostridium clostridioforme (species), Clostridium hathewayi (species), Clostridium innocuum (species), Clostridium symbiosum (species), Eggerthella lenta (species), Escherichia coli (species), Haemophilus parainfluenzae (species), Intestinibacter bartlettii (species), Ruminococcus gnavus (species) and Ruminococcus torques (species); and wherein determining the presence of the C. difficile strain comprises processing the characterization model with the composition associated feature.
23 . The method of claim 20 , wherein the associated feature set comprises a composition feature associated with a set of taxa comprising at least one of: Roseburia (genus), Veillonella (genus), Kluyvera (genus), Sarcina (genus), Subdoligranulum (genus), Bifidobacterium (genus), Faecalibacterium (genus), Bilophila (genus), Lactobacillus (genus), Eubacterium (genus), Parabacteroides (genus), Akkermansia (genus), Dorea (genus), Bacteroides (genus), Moryella (genus), Anaerotruncus (genus), Enterococcus (genus), Eggerthella (genus), Collinsella (genus), Anaerobacter (genus), Megasphaera (genus), Alistipes (genus), Intestinimonas (genus), Streptococcus (genus), Flavonifractor (genus), Clostridium (genus), Peptoclostridium (genus), Pseudobutyrivibrio (genus), Erysipelatoclostridium (genus), Anaerostipes (genus), Blautia (genus), Escherichia - Shigella (genus), Haemophilus (genus), Hungatella (genus), Intestinibacter (genus) and Lachnoclostridium (genus); and wherein determining the presence of the C. difficile strain comprises processing the characterization model with the composition associated feature.
24 . The method of claim 20 , wherein the associated feature set comprises a composition feature associated with a set of taxa comprising at least one of: Ruminococcaceae (family), Enterobacteriaceae (family), Coriobacteriaceae (family), Lactobacillaceae (family), Lachnospiraceae (family), Bifidobacteriaceae (family), Eubacteriaceae (family), Verrucomicrobiaceae (family), Bacteroidaceae (family), Oscillospiraceae (family), Enterococcaceae (family), Rikenellaceae (family), Bradyrhizobiaceae (family), Clostridiaceae (family), Peptostreptococcaceae (family), Veillonellaceae (family), Christensenellaceae (family), Erysipelotrichaceae (family) and Streptococcaceae (family); and wherein determining the presence of the C. difficile strain comprises processing the characterization model with the composition associated feature.
25 . The method of claim 20 , wherein the associated feature set comprises a composition feature associated with a set of taxa comprising at least one of: Enterobacteriales (order), Clostridiales (order), Coriobacteriales (order), Bifidobacteriales (order), Verrucomicrobiales (order), Selenomonadales (order), Erysipelotrichales (order), Lactobacillales (order); and wherein determining the presence of the C. difficile strain comprises processing the characterization model with the composition associated feature.
26 . The method of claim 10 , wherein the associated feature set comprises a composition feature associated with a set of taxa comprising at least one of: Clostridia (class), Actinobacteria (class), Verrucomicrobiae (class), Alphaproteobacteria (class), Deltaproteobacteria (class), Negativicutes (class), Erysipelotrichia (class), Gammaproteobacteria (class), Bacilli (class); and wherein determining the presence of the C. difficile strain comprises processing the characterization model with the composition associated feature.
27 . The method of claim 10 , wherein the associated feature set comprises a composition feature associated with a set of taxa comprising at least one of: Proteobacteria (phylum), Actinobacteria (phylum), Verrucomicrobia (phylum) and Firmicutes (phylum); and wherein determining the presence of the C. difficile strain comprises processing the characterization model with the composition associated feature.
28 . The method of claim 10 , wherein the feature set comprises Kyoto Encyclopedia of Genes and Genomes (KEGG) functional features associated with at least one of: pentose phosphate pathway, gluconeogenesis, and carbon fixation, and wherein generating the characterization comprises processing the characterization model with the KEGG functional features.
29 . The method of claim 10 , wherein the feature set comprises Kyoto Encyclopedia of Genes and Genomes (KEGG) functional features associated with at least one of: Translation; Metabolism; Environmental Adaptation; Replication and Repair; Signaling Molecules and Interaction; Cellular Processes and Signaling; Energy Metabolism; Cell Growth and Death; Amino Acid Metabolism; Nucleotide Metabolism; Infectious Diseases; Nervous System; Signal Transduction; Endocrine System; Metabolism of Other Amino Acids; Carbohydrate Metabolism; Metabolism of Cofactors and Vitamins; Folding, sorting and Degradation; Membrane Transport; Metabolism of Terpenoids and Polyketides; Xenobiotics Biodegradation and Metabolism; Cell Motility; Metabolic Disease; Enzyme families and Biosynthesis of Other Secondary Metabolites; and wherein generating the characterization comprises processing the characterization model with the KEGG functional features.
30 . The method of claim 10 , wherein the feature set comprises Kyoto Encyclopedia of Genes and Genomes (KEGG) functional features associated with at least one of: Ribosome Biogenesis; Peptidoglycan biosynthesis; Chromosome; Inorganic ion transport and metabolism; Amino acid related enzymes; Amino acid metabolism; Ribosome; Aminoacyl-tRNA biosynthesis; Other ion-coupled transporters; Nitrogen metabolism; Photosynthesis; Translation factors; Photosynthesis proteins; Pantothenate and CoA biosynthesis, Plant-pathogen interaction, Homologous recombination, Terpenoid backbone biosynthesis, Phosphotransferase system (PTS); Bacterial toxins; Glyoxylate and dicarboxylate metabolism; DNA repair and recombination proteins; Translation proteins; Polycyclic aromatic hydrocarbon degradation; Biosynthesis and biodegradation of secondary metabolites; Tuberculosis; Pyrimidine metabolism; Cytoskeleton proteins; Protein export; Carbohydrate metabolism; One carbon pool by folate; RNA polymerase; Thiamine metabolism; Phenylalanine; tyrosine and tryptophan biosynthesis; Valine, leucine and isoleucine biosynthesis, Pentose and glucuronate interconversions; Cell cycle— Caulobacter; Butirosin and neomycin biosynthesis; DNA replication proteins; Base excision repair; Cell motility and secretion; Nucleotide excision repair; Nicotinate and nicotinamide metabolism; Glutathione metabolism; Zeatin biosynthesis; Vibrio cholerae pathogenic cycle; Alzheimer's disease; Mismatch repair; Protein folding and associated processing; Lysine biosynthesis; Fatty acid biosynthesis; Other transporters; Limonene and pinene degradation; Sulfur relay system; Glutamatergic synapse; Methane metabolism; Lipid biosynthesis proteins; Cs-Branched dibasic acid metabolism; Lysine degradation; Prenyltransferases; Ribosome biogenesis in eukaryotes; Lipopolysaccharide biosynthesis proteins; Chaperones and folding catalysts; Tryptophan metabolism; Vitamin metabolism; D-Glutamine and D-Glutamate metabolism; Bacterial chemotaxis; Transcription machinery; Two-component system; Sporulation; Restriction enzyme; Carbon fixation in photosynthetic organisms; Drug metabolism—other enzymes; Alanine, aspartate and glutamate metabolism; Pores ion channels; Histidine metabolism; Arginine and proline metabolism; Peptidases; Riboflavin metabolism; Starch and sucrose metabolism; Primary immunodeficiency; Oxidative phosphorylation; Lipid metabolism; Transcription factors; D-Alanine metabolism; Streptomycin biosynthesis; Taurine and hypotaurine metabolism; DNA replication; ABC transporters; Glycerophospholipid metabolism; Valine, leucine and isoleucine degradation; beta-Alanine metabolism; Carbon fixation pathways in prokaryotes; Polyketide sugar unit biosynthesis; Naphthalene degradation; Glycerolipid metabolism; General function prediction only; Protein kinases; Pentose phosphate pathway; Vitamin B6 metabolism; Glycosyltransferases; Phosphatidylinositol signaling system; Fructose and mannose metabolism; Membrane and intracellular structural molecules; Fatty acid metabolism and Type I diabetes mellitus; and wherein generating the characterization comprises processing the characterization model with the KEGG functional features.