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EP3386592A1 - Procédé et système de caractérisation d'affections associées à clostridium difficile - Google Patents

Procédé et système de caractérisation d'affections associées à clostridium difficile

Info

Publication number
EP3386592A1
EP3386592A1 EP16873997.7A EP16873997A EP3386592A1 EP 3386592 A1 EP3386592 A1 EP 3386592A1 EP 16873997 A EP16873997 A EP 16873997A EP 3386592 A1 EP3386592 A1 EP 3386592A1
Authority
EP
European Patent Office
Prior art keywords
microbiome
feature
genus
metabolism
difficile
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
EP16873997.7A
Other languages
German (de)
English (en)
Other versions
EP3386592A4 (fr
Inventor
Daniel Almonacid
Zachary APTE
Jessica RICHMAN
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Psomagen Inc
Original Assignee
uBiome Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Priority claimed from US15/097,862 external-priority patent/US9710606B2/en
Application filed by uBiome Inc filed Critical uBiome Inc
Publication of EP3386592A1 publication Critical patent/EP3386592A1/fr
Publication of EP3386592A4 publication Critical patent/EP3386592A4/fr
Pending legal-status Critical Current

Links

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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
    • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P31/00—Antiinfectives, i.e. antibiotics, antiseptics, chemotherapeutics
    • A61P31/04—Antibacterial agents
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61P—SPECIFIC THERAPEUTIC ACTIVITY OF CHEMICAL COMPOUNDS OR MEDICINAL PREPARATIONS
    • A61P43/00—Drugs for specific purposes, not provided for in groups A61P1/00-A61P41/00
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • G16B40/20—Supervised data analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G16B50/10—Ontologies; Annotations
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2333/00—Assays involving biological materials from specific organisms or of a specific nature
    • G01N2333/195—Assays involving biological materials from specific organisms or of a specific nature from bacteria
    • G01N2333/33—Assays involving biological materials from specific organisms or of a specific nature from bacteria from Clostridium (G)
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B50/00—ICT programming tools or database systems specially adapted for bioinformatics
    • G16B50/30—Data warehousing; Computing architectures
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/10—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to drugs or medications, e.g. for ensuring correct administration to patients
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H20/00—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance
    • G16H20/60—ICT specially adapted for therapies or health-improving plans, e.g. for handling prescriptions, for steering therapy or for monitoring patient compliance relating to nutrition control, e.g. diets
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

  • This invention relates generally to the field of microbiology and more specifically to a new and useful system and method for characterizing Clostridium difficile-associated conditions in the field of microbiology.
  • a microbiome is an ecological community of commensal, symbiotic, and pathogenic microorganisms that are associated with an organism.
  • the human microbiome includes over 10 times more microbial cells than human cells, but characterization of the human microbiome is still in nascent stages due to limitations in sample processing techniques, genetic analysis techniques, and resources for processing large amounts of data. Nonetheless, the microbiome is suspected to play at least a partial role in a number of health/disease-related states (e.g., preparation for childbirth, diabetes, auto-immune disorders, gastrointestinal disorders, rheumatoid disorders, neurological disorders, etc.).
  • FIGURES lA-iB are flowcharts of an embodiment of a method for microbiome characterization
  • FIGURE 2 depicts an embodiment of a system and method for microbiome characterization
  • FIGURE 3 depicts a variation of a process for generation of a model in an embodiment of a system and method for microbiome characterization
  • FIGURE 4 depicts variations of mechanisms by which probiotic-based therapies operate in an embodiment of a method for microbiome characterization
  • FIGURE 5 depicts examples of notification provision in an example of a method for microbiome characterization
  • FIGURE 6 depicts a variation of an interface for providing CZosfridium-associated condition-related information in an example of a method for microbiome characterization
  • FIGURE 7 depicts examples of notification provision in an example of a method for microbiome characterization
  • FIGURE 8 depicts examples of notification provision in an example of a method for microbiome characterization.
  • an embodiment of a system 200 for characterizing a CZosZridium-associated condition in relation to a user includes: a handling network (e.g., sample handling network) 210 operable to receive containers including material (e.g., biological samples including microorganism nucleic acid material, etc.) from a set of users (e.g., a population of users), the handling network including a sequencing system operable to determine microbiome sequences from sequencing the material; a processing system 220 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 CZosZridium-associated condition; and a therapy system 230 operable to promote
  • the system 200 can additionally or alternatively include one or more of: an interface 240 operable to present CZosZridium-associated condition-related information; a sample kit 250 functioning to provide a subject with the components and/or instructions to collect and/or process a biological sample from one or more collection sites of the subject; and/or any other suitable component.
  • the system 200 and/or method 100 function to generate and/or apply models (e.g., characterization models, therapy models, etc.) that can be used to characterize (e.g., diagnose) subjects according to at least one of their microbiome composition and functional features (e.g., as a clinical diagnostic, as a companion diagnostic, etc.), and/or provide therapeutic measures (e.g., probiotic-based therapeutic measures, phage-based therapeutic measures, small-molecule-based therapeutic measures, fecal matter transplant-based therapeutic measures, clinical measures, etc.) to subjects based upon microbiome characterization for a population of subjects.
  • models e.g., characterization models, therapy models, etc.
  • therapeutic measures e.g., probiotic-based therapeutic measures, phage-based therapeutic measures, small-molecule-based therapeutic measures, fecal matter transplant-based therapeutic measures, clinical measures, etc.
  • data from the population of subjects can be used to characterize subjects according to their microbiome composition and/or functional features, indicate states of health and areas of improvement based upon the characterization(s), and promote one or more therapies that can modulate the composition of a subject's microbiome toward one or more of a set of desired equilibrium states.
  • Variations of the method 100 can further facilitate monitoring and/or adjusting of therapies provided to a subject, for instance, through reception, processing, and analysis of additional samples from a subject throughout the course of therapy.
  • the system 200 and/or the method 100 can be used to promote targeted therapies to subjects suffering from various health conditions.
  • the system 200 and/or components of the system 200 preferably implement the method 100 and/or portions of the method 100, but any suitable components can partially and/or fully implement the method 100.
  • the method 100 can be implemented for a single subject for whom microbiome characterization and/or microbiome modulation with therapeutics is of interest, and can additionally or alternatively be implemented for a population of subjects (e.g., including the subject, excluding the subject), where the population of subjects can include patients dissimilar to and/or similar to the subject (e.g., in health condition, in dietary needs, in demographic features, etc.).
  • information derived from the population of subjects can be used to provide additional insight into connections between behaviors of a subject and effects on the subject's microbiome, due to aggregation of data from a population of subjects.
  • an aggregate set of biological samples is preferably received from a wide variety of subjects, collectively including subjects of one or more of: different demographics (e.g., genders, ages, marital statuses, ethnicities, nationalities, socioeconomic statuses, sexual orientations, etc.), different health conditions (e.g., health and disease states), different living situations (e.g., living alone, living with pets, living with a significant other, living with children, etc.), different dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, etc.), different behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled within a given time period), and/or any other suitable trait that has an effect on microbiome composition and/or functional features.
  • different demographics e.g., genders, ages, marital statuses, ethnicities, nationalities, socioeconomic statuses, sexual orientations, etc.
  • different health conditions
  • the method 100 can involve generation of diagnostic and therapies derived from biological sample data from any other suitable group of subjects.
  • the system 200 can be used to implement an embodiment of a method 100 for characterizing a Clostridium difficile (C. difficile) associated condition in relation to a user including: generating a microbiome composition dataset and a microbiome functional diversity dataset based on nucleic acid sequences derived from material (e.g., biological sample including nucleic acid material) from a set of users (e.g., population 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.
  • material e.g., biological sample including nucleic acid material
  • set of users e.g., population 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.
  • microbiome composition features 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.
  • sequencing technologies e.g., next-generation sequencing
  • technological issues e.g., data processing issues, information display issues, microbiome analysis issues, therapy prediction issues, etc.
  • system 200 and the method 100 confer technologically- rooted solutions to at least such technological issues.
  • the technology can confer improvements in computer-related technology (e.g., artificial intelligence, machine learning, etc.) by facilitating computer performance of functions not previously performable.
  • the technology can computationally generate microbiome characterizations and recommended therapies associated with CZosfn ' dium-associated conditions, based on microbiome sequence datasets and microorganism reference sequence databases (e.g., Genome Reference Consortium) that are recently viable due to advances in sample processing techniques and sequencing technology.
  • the microbial cells constituting a human microbiome can be over ten times larger than human cells, which can translate into a plethora of data giving rise to issues of processing and analysis to generate actionable microbiome insights in relation to potentially life- threatening CZosfridium-associated conditions (e.g., sepsis, colitis, etc.).
  • the technology can confer improvements in processing speed and microbiome characterization accuracy.
  • the technology can generate and apply Clostridium- associated condition feature-selection rules to select an optimized subset of features (e.g., microbiome composition features, microbiome functional diversity features, etc.) out of a vast potential pool of features (e.g., extractable from the plethora of microbiome data) for generating and applying characterization models and/or therapy models.
  • the Clostridium- associated condition feature-selection rules can thus enable shorter training and execution times (e.g., for predictive machine learning models), model simplification facilitating efficient interpretation of results, reduction in overfitting, and other suitable improvements.
  • the technology can transform entities (e.g., users, biological samples, therapy systems including medical devices, etc.) into different states or things.
  • the system 200 and/or method 100 can identify therapies to promote to a patient to modify microbiome composition and/or function to prevent and/or ameliorate Clostridium- associated conditions, thereby transforming the microbiome of the patient.
  • the technology can transform biological samples received by a population of patients into microbiome datasets usable in generating characterization models and/or therapy models.
  • the technology can control therapy systems to promote therapies (e.g., by generating control instructions for the therapy system to execute), thereby transforming the therapy system.
  • the technology can, however, provide any other suitable benefit(s) in the context of using non-generalized computer systems for characterizing a microbiome and/or promoting a relevant therapy. , System.
  • the sample handling network 210 of the system 200 functions to receive and process (e.g., fragment, amplify, sequence, etc.) biological samples to transform microorganism nucleic acids of the biological samples into genetic sequences that can be subsequently aligned and analyzed to generate characterizations of and therapies for Clostridium- associated conditions.
  • the sample handling network 210 can additionally or alternatively function to provide a sample kit 250 (e.g., including sample containers, instructions, etc.) to a user (e.g., in response to a purchase order for a sample kit 250), such as through a mail delivery system.
  • the sample handling network 210 can additionally or alternatively include a library preparation system operable to automatically prepare biological samples (e.g., fragment and amplify using primers compatible with microbiome targets associated with the Clostridium- associated condition) in a multiplex manner to be sequenced by a sequencing system; and/or a sequencing system (e.g., MiSeq/NextSeq/HiSeq and/or other suitable sequencing platform) operable to sequence nucleic acids (e.g., microorganism DNA and/or RNA) derived from biological samples received at the sample handling network 210.
  • a library preparation system operable to automatically prepare biological samples (e.g., fragment and amplify using primers compatible with microbiome targets associated with the Clostridium- associated condition) in a multiplex manner to be sequenced by a sequencing system
  • a sequencing system e.g., MiSeq/NextSeq/HiSeq and/or other suitable sequencing platform
  • sequence nucleic acids e.g., micro
  • the sample handling network 210 is preferably remote from a user, such that a user can conveniently send a collected biological sample to the sample handling network 210, and digitally receive results based on the collected biological sample. Additionally or alternatively, the sample handling network 210 can include user action (e.g., a user pre-processing a sample), a user device (e.g., an application executing on a mobile device that aids in analysis of the sample), a remote server, and/or any other suitable entity. However, the sample handling network 210 can be configured in any suitable manner.
  • the processing system 220 of the system 200 functions to analyze a dataset (e.g., a microbiome sequence dataset) derived from a processed sample to generate and/or apply a characterization model for characterizing one or more CZosfridium-associated conditions.
  • the processing system 220 can additionally or alternatively function to generate and/or apply a therapy model for identifying a therapy used to treat a CZosiridium-associated condition; to promote the therapy (e.g., acting as a therapy system 230 to generate and/or output a therapy recommendation to a subject at a user device); and/or perform any suitable function (e.g., any portion of the method 100).
  • the processing system 220 can be operable to obtain a set of Clostridium feature-selection rules correlating the condition to subsets of composition features and functional diversity features; and generate a feature set (e.g., used in generating a characterization model) based on applying the rules to one or more microbiome datasets.
  • Such feature-selection rules can improve the processing system 220 by facilitating decreased processing time to transform the features and/or other suitable data (e.g., a supplementary dataset) into the characterization model (e.g., by training the model using training data labels derived from the supplementary dataset).
  • the processing system 220 and/or other components of the system 200 can additionally or alternatively include and/or communicate data to and/or from: a remote computing system (e.g., remote servers, cloud systems, etc.), a local computing system, a user database (e.g., storing user account information, characterization information such as for a CZosiridium-associated condition, user health records, user demographic information, associated care provider information, associated guardian information, user device information, etc.), an analysis database (storing models, collected data, historical data, public data, simulated data, generated datasets, generated analyses, diagnostic results, therapy recommendations, etc.), user device (e.g., a smartphone executing an application for storing and/or executing a characterization and/or therapy model, etc.), a care provider device (e.g., a device of a care provider associated with a user), a machine configured to receive a computer-readable medium storing computer-readable instructions, and/or any other suitable component.
  • a remote computing system e.g.
  • the therapy system 230 of the system 200 functions to promote one or more therapies (e.g., identified by the a therapy model generated and/or executed by the processing system 220) to a subject or a care provider to implement in ameliorating and/or preventing a CZosiridium-associated condition.
  • the therapy system 230 can additionally or alternatively function to monitor efficacy of one or more therapies to, for example, generate data that can be used in updating a model (e.g., a therapy model).
  • the therapy system 230 can include any one or more of: a communications system (e.g., to communicate therapy recommendations to a user device and/or care provider device; to enable telemedicine between a care provider and a subject in relation to a CZosZridium-associated condition; etc.), an application executable on a user device (e.g., a dietary regimen application for recommending microbiome composition modification therapies, etc.), a medical device (e.g., a central venous catheter for administering medication and/or fluids; colonoscopy devices, sigmoidoscopy devices, and/or other screening devices; a biometric sensor for monitoring biometric data related to a CZosZridium-associated condition, such as C.
  • a communications system e.g., to communicate therapy recommendations to a user device and/or care provider device; to enable telemedicine between a care provider and a subject in relation to a CZosZridium-associated condition; etc.
  • an application executable on a user device
  • the therapy system 230 is preferably controllable by the processing system 220.
  • the processing system 220 can generate control instructions to transmit to the therapy system 230 to execute to promote the therapy.
  • the processing system 220 can update and/or otherwise modify an application and/or other therapy system software of a device (e.g., user smartphone) to promote a therapy (e.g., promoting, at a to-do application, lifestyle changes for modifying microbiome functional diversity to reduce the risk of C. difficile-based colitis infection).
  • a therapy e.g., promoting, at a to-do application, lifestyle changes for modifying microbiome functional diversity to reduce the risk of C. difficile-based colitis infection.
  • the therapy system 230 can be configured in any other manner.
  • the system 200 can additionally or alternatively include an interface 240 functioning to improve the presentation of CZosZridium-associated condition- related information (e.g., characterizations, therapy recommendations, etc.) at a user device and/or care provider device (e.g., remotely accessing the interface 240 through an application, at a website, at a document, etc.).
  • the interface 240 can be a user interface (e.g., for presentation to a subject), a care provider interface, and/or any other suitable interface 240.
  • the interface 240 preferably includes a plurality of displays (e.g., a first display introducing microbiome composition and/or microbiome functional diversity information; a second display analyzing the information, etc.), but can include any number of displays configured in any manner.
  • the interface 240 can present information in a verbal, numerical, graphical, audio, and/or any suitable format of information.
  • the presented information can include and/or be associated with one or more of: microbiome composition, microbiome functional diversity, CZosfn ' dium-associated condition-related information (e.g., presence and/or risk of Clostridium microorganisms and/or infection, etc.), behavioral characteristics, demographic characteristics, individual characteristics, comparisons with other subjects and/or demographics (e.g., comparing risk of Clostridium infection between the user and a group of smokers, etc.), population characteristics, and/or any other suitable information.
  • the interface 240 can present a microbiome composition for the user relative to a user group sharing a demographic characteristic, where the microbiome composition includes taxonomic groups including at least one of C.
  • the interface 240 can present a microbiome composition detailing the relative abundance of different Clostridium strains (e.g., different C. difficile strains), which can possess varying correlations with Clostridium infections (e.g., a higher incidence of Clostridium-based sepsis from C. difficile Ribotype 027 strain compared to other C. difficile strains).
  • different Clostridium strains e.g., different C. difficile strains
  • Clostridium infections e.g., a higher incidence of Clostridium-based sepsis from C. difficile Ribotype 027 strain compared to other C. difficile strains.
  • the interface 240 can automatically highlight portions of the presented information, such as through one or more of: resizing operations (e.g., graphics, text, etc. for fitting information within the screen dimensions of a particular device, and/or other suitable purpose), color modification (e.g., using yellow highlighting for therapy recommendations, etc.), disability accommodation (e.g., translating text into audio), and/or other suitable operations. Highlighting presented information can function to guide a subject and/or care provider through an analysis of the presented information.
  • the interface 240 can facilitate user interaction with the interface 240.
  • the interface 240 can provide options for selecting different demographic groups (e.g., hospital patients, recently released hospital patients, exercisers, smokers, consumers of probiotics, antibiotic users, groups undergoing particular therapies, etc.) to compare (e.g., through charts and graphs) microbiome composition, functional diversity, and/or other CZosfridium-associated condition-related information.
  • the interface 240 can provide a log (e.g., for logging lifestyle habits, therapy regimens, etc.), digital surveys (e.g., for inquiring about symptoms associated with Clostridium infections), therapies, and/or other suitable components that can, for example, be used in updating characterizations, therapies, models, and/or other suitable data.
  • the interface 240 can be configured in any suitable manner.
  • any of the components of the system 200 can perform functions associated with other components.
  • the processing system 220 can perform sequencing functions associated with the sequencing system; generate characterizations of and therapies for a CZosfridium-associated condition; and promote the therapies (e.g., through generating and transmitting therapy-related notifications to a subject).
  • the system 200 and/or method 100 can include any suitable components and/or functions analogous to those described in U.S. App. No. 14/593,424 filed 09-JAN-
  • a method 100 for characterizing a Clostridium- associated condition in relation to a subject includes: generating at least one of a microbiome composition dataset and a microbiome function diversity dataset based on processing biological samples associated with a population of subjects S110; receiving a supplementary dataset informative of the CZosfn ' dium-associated condition for at least a subset of the population of subjects S120; and performing a characterization process derived from the supplementary dataset and features extracted from at least one of the microbiome composition dataset and microbiome functional diversity dataset S130.
  • a Clostridium- associated condition e.g., a C. difficile condition
  • the method 100 can additionally or alternatively include: determining a therapy for preventing, ameliorating, and/or otherwise modifying a CZosZridium-associated condition S140; processing a biological sample from a subject S150; determining, with the characterization process, a characterization of the subject based upon processing a microbiome dataset (e.g., microbiome composition dataset, microbiome functional diversity dataset, etc.) derived from the biological sample of the subject S160; promoting a therapy to the subject based upon the characterization and the therapy model S170; monitoring effectiveness of the therapy for the subject, based upon processing biological samples, to assess microbiome composition and/or functional features for the subject at a set of time points associated with the probiotic therapy S180; and/or any other suitable operations.
  • a microbiome dataset e.g., microbiome composition dataset, microbiome functional diversity dataset, etc.
  • Block S110 recites: generating at least one of a microbiome composition dataset and a microbiome function diversity dataset based on processing biological samples associated with a population of subjects.
  • Block S110 functions to process each of an aggregate set of biological samples, in order to determine compositional and/or functional aspects associated with the microbiome of each of a population of subjects.
  • Compositional and functional aspects can include compositional aspects at the microorganism level, including parameters related to distribution of microorganisms across different groups of kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable infraspecies taxon (e.g., as measured in total abundance of each group, relative abundance of each group, total number of groups represented, etc.).
  • compositional and functional aspects can also be represented in terms of operational taxonomic units (OTUs).
  • Compositional and functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, 16S rRNA sequences, 18S rRNA sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.).
  • Compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g. enzyme activities, transport functions, immune activities, etc.).
  • Outputs of Block S110 can thus be used to provide features of interest for the characterization process of Block S130 and/or the therapy process of Block S140, where the features can be microorganism-based (e.g., presence of a genus of bacteria), genetic-based (e.g., based upon representation of specific genetic regions and/or sequences) functional-based (e.g., presence of a specific catalytic activity), and/or otherwise configured.
  • microorganism-based e.g., presence of a genus of bacteria
  • genetic-based e.g., based upon representation of specific genetic regions and/or sequences
  • functional-based e.g., presence of a specific catalytic activity
  • Block S110 can include assessment and/or processing based upon phylogenetic markers derived from bacteria and/or archaea in relation to gene families associated with one or more of: ribosomal protein S2, ribosomal protein S3, ribosomal protein S5, ribosomal protein S7, ribosomal protein S8, ribosomal protein S9, ribosomal protein S10, ribosomal protein S11, ribosomal protein S12/S23, ribosomal protein S13, ribosomal protein SisP/Si3e, ribosomal protein S17, ribosomal protein S19, ribosomal protein Li, ribosomal protein L2, ribosomal protein L3, ribosomal protein L4/Lie, ribosomal protein L5, ribosomal protein L6, ribosomal protein L10, ribosomal protein L11, ribosomal protein L13, ribosomal
  • characterizing the microbiome composition and/or functional features for each of the aggregate set of biological samples thus preferably includes a combination of sample processing techniques (e.g., wet laboratory techniques) and computational techniques (e.g., utilizing tools of bioinformatics) to quantitatively and/or qualitatively characterize the microbiome and functional features associated with each biological sample from a subject or population of subjects.
  • sample processing techniques e.g., wet laboratory techniques
  • computational techniques e.g., utilizing tools of bioinformatics
  • sample processing in Block S110 can include any one or more of: lysing a biological sample, disrupting membranes in cells of a biological sample, separation of undesired elements (e.g., RNA, proteins) from the biological sample, purification of nucleic acids (e.g., DNA) in a biological sample, amplification (e.g., with a library preparation system) of nucleic acids from the biological sample, further purification of amplified nucleic acids of the biological sample, sequencing (e.g., with a sequencing system) of amplified nucleic acids of the biological sample, and/or other suitable sample processing operations.
  • nucleic acids e.g., DNA
  • amplification e.g., with a library preparation system
  • sequencing e.g., with a sequencing system
  • lysing a biological sample and/or disrupting membranes in cells of a biological sample preferably includes physical methods (e.g., bead beating, nitrogen decompression, homogenization, sonication), which omit certain reagents that produce bias in representation of certain bacterial groups upon sequencing.
  • lysing or disrupting in Block Siio can involve chemical methods (e.g., using a detergent, using a solvent, using a surfactant, etc.).
  • lysing or disrupting in Block Siio can involve biological methods.
  • separation of undesired elements can include removal of RNA using RNases and/or removal of proteins using proteases.
  • purification of nucleic acids can include one or more of: precipitation of nucleic acids from the biological samples (e.g., using alcohol-based precipitation methods), liquid-liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography-based purification techniques (e.g., column adsorption), purification techniques involving use of binding moiety-bound particles (e.g., magnetic beads, buoyant beads, beads with size distributions, ultrasonically responsive beads, etc.) configured to bind nucleic acids and configured to release nucleic acids in the presence of an elution environment (e.g., having an elution solution, providing a pH shift, providing a temperature shift, etc.), and any other suitable purification techniques.
  • solvent-based precipitation methods e.g., liquid-liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography-based purification techniques (e.g., column adsorption), purification techniques involving use of binding moiety-
  • amplification of purified nucleic acids preferably includes one or more of: polymerase chain reaction (PCR)-based techniques (e.g., solid-phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), nucleic acid sequence based amplification (NASBA), strand displacement amplification (SDA), rolling circle amplification (RCA), ligase chain reaction (LCR), and any other suitable amplification technique.
  • PCR polymerase chain reaction
  • HDA helicase-dependent amplification
  • LAMP loop mediated isothermal amplification
  • NASBA nucleic acid sequence based amplification
  • SDA strand displacement amplification
  • RCA rolling circle amplification
  • LCR ligase chain reaction
  • the primers used are preferably selected to prevent or minimize amplification bias, as well as configured to amplify nucleic acid regions/sequences (e.g., of the 16S rRNA region, the 18S rRNA region, the ITS region, etc.) that are informative taxonomically, phylogenetically, for diagnostics, for formulations (e.g., for probiotic formulations), and/or for any other suitable purpose.
  • universal primers e.g., a F27-R338 primer set for 16S rRNA, a F515-R806 primer set for 16S rRNA, etc.
  • amplification bias e.g., a F27-R338 primer set for 16S rRNA, a F515-R806 primer set for 16S rRNA, etc.
  • Primers used in variations of Block S110 can additionally or alternatively include incorporated barcode sequences specific to each biological sample, which can facilitate identification of biological samples post-amplification. Primers used in variations of Block S110 can additionally or alternatively include adaptor regions configured to cooperate with sequencing techniques involving complementary adaptors (e.g., Illumina Sequencing). Additionally or alternatively, Block Sno can implement any other step configured to facilitate processing (e.g., using a Nextera kit).
  • sequencing of purified nucleic acids can include methods involving targeted amplicon sequencing and/or metagenomic sequencing, implementing techniques including one or more of: sequencing-by-synthesis techniques (e.g., Illumina sequencing), capillary sequencing techniques (e.g., Sanger sequencing), pyrosequencing techniques, a nanopore sequencing techniques (e.g., using an Oxford Nanopore technique), or any other suitable sequencing technique.
  • sequencing-by-synthesis techniques e.g., Illumina sequencing
  • capillary sequencing techniques e.g., Sanger sequencing
  • pyrosequencing techniques e.g., a nanopore sequencing techniques (e.g., using an Oxford Nanopore technique), or any other suitable sequencing technique.
  • nanopore sequencing techniques e.g., using an Oxford Nanopore technique
  • amplification and sequencing of nucleic acids from biological samples of the set of biological samples includes: solid-phase PCR involving bridge amplification of DNA fragments of the biological samples on a substrate with oligo adapters, where amplification involves primers having a forward index sequence (e.g., corresponding to an Illumina forward index for MiSeq/NextSeq/HiSeq platforms), a forward barcode sequence, a transposase sequence (e.g., corresponding to a transposase binding site for MiSeq/NextSeq/HiSeq platforms), a linker (e.g., a zero, one, or two-base fragment configured to reduce homogeneity and improve sequence results), an additional random base, a sequence for targeting a specific target region (e.g., a i6S rRNA region, a i8S rRNA region, a ITS region), a reverse index sequence (e.g., corresponding to an
  • sample processing in Block Sno can include further purification of amplified nucleic acids (e.g., PCR products) prior to sequencing, which functions to remove excess amplification elements (e.g., primers, dNTPs, enzymes, salts, etc.).
  • additional purification can be facilitated using any one or more of: purification kits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acid binding filters, centrifugation, and any other suitable purification technique.
  • computational processing in Block Sno can include any one or more of: identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), alignment and mapping of microbiome-derived sequences (e.g., alignment of fragmented sequences using one or more of single-ended alignment, ungapped alignment, gapped alignment, pairing), and generating features derived from compositional and/or functional aspects of the microbiome associated with a biological sample.
  • identification of microbiome-derived sequences can include mapping of sequence data from sample processing to a subject reference genome (e.g., provided by the Genome Reference Consortium), in order to remove subject genome-derived sequences. Unidentified sequences remaining after mapping of sequence data to the subject reference genome can then be further clustered into operational taxonomic units (OTUs) based upon sequence similarity and/or reference-based approaches (e.g., using VAMPS, using MG- RAST, using QIIME databases), aligned (e.g., using a genome hashing approach, using a Needleman-Wunsch algorithm, using a Smith-Waterman algorithm), and mapped to reference bacterial genomes (e.g., provided by the National Center for Biotechnology Information), using an alignment algorithm (e.g., Basic Local Alignment Search Tool, FPGA accelerated alignment tool, BWT-indexing with BWA, BWT-indexing with SOAP, BWT- indexing with Bowtie, etc.).
  • OTUs operational taxono
  • Mapping of unidentified sequences can additionally or alternatively include mapping to reference archaeal genomes, viral genomes and/or eukaryotic genomes. Furthermore, mapping of taxa can be performed in relation to existing databases, and/or in relation to custom-generated databases.
  • Block Sno can include determining alignments between microorganism nucleic acid sequences and reference sequences associated with the C. difficile condition, where generating the microbiome composition dataset and the microbiome functional diversity dataset is based on the alignments.
  • Block Sno upon identification of represented groups of microorganisms of the microbiome associated with a biological sample, generating features derived from compositional and functional aspects of the microbiome associated with a biological sample can be performed. Additionally or alternatively, generated features can include generating features that describe the presence or absence of certain taxonomic groups of microorganisms, and/or ratios between exhibited taxonomic groups of microorganisms.
  • generating features can include generating features describing one or more of: quantities of represented taxonomic groups, networks of represented taxonomic groups, correlations in representation of different taxonomic groups, interactions between different taxonomic groups, products produced by different taxonomic groups, interactions between products produced by different taxonomic groups, ratios between dead and alive microorganisms (e.g., for different represented taxonomic groups, based upon analysis of RNAs), phylogenetic distance (e.g., in terms of Kantorovich- Rubinstein distances, Wasserstein distances etc.), any other suitable taxonomic group- related feature(s), any other suitable genetic or functional feature(s).
  • generating features can include generating features describing relative abundance of different microorganism groups, for instance, using a sparCC approach, using Genome Relative Abundance and Average size (GAAS) approach and/or using a Genome Relative Abundance using Mixture Model theory (GRAMMy) approach that uses sequence-similarity data to perform a maximum likelihood estimation of the relative abundance of one or more groups of microorganisms.
  • generating features can include generating statistical measures of taxonomic variation, as derived from abundance metrics.
  • generating features can include generating features derived from relative abundance factors (e.g., in relation to changes in abundance of a taxon, which affects abundance of other taxa).
  • generating features can include generation of qualitative features describing presence of one or more taxonomic groups, in isolation and/or in combination. Additionally or alternatively, generating features can include generation of features related to genetic markers (e.g., representative i6S rRNA, 18S rRNA, and/or ITS sequences) characterizing microorganisms of the microbiome associated with a biological sample. Additionally or alternatively, generating features can include generation of features related to functional associations of specific genes and/or organisms having the specific genes. Additionally or alternatively, generating features can include generation of features related to pathogenicity of a taxon and/or products attributed to a taxon.
  • genetic markers e.g., representative i6S rRNA, 18S rRNA, and/or ITS sequences
  • Block S120 can, however, include generation of any other suitable feature(s) derived from sequencing and mapping of nucleic acids of a biological sample.
  • the feature(s) can be combinatory (e.g. involving pairs, triplets), correlative (e.g., related to correlations between different features), and/or related to changes in features (e.g., temporal changes, changes across sample sites, spatial changes, etc.).
  • Block S110 can be performed in any suitable manner, some embodiments, variations, and examples of which are described in U.S. App. No. 14/593,424 filed 09-JAN-
  • Block S120 recites: receiving a supplementary dataset informative of the CZosfn ' dium-associated condition for at least a subset of the population of subjects.
  • Block S120 functions to acquire additional data associated with one or more subjects of the set of subjects, which can be used to train and/or validate the characterization process generated in Block S130.
  • the supplementary dataset preferably includes survey-derived data, but can additionally or alternatively include any one or more of: contextual data derived from sensors, medical data (e.g., current and historical medical data), and any other suitable type of data.
  • the survey-derived data preferably provides physiological, demographic, and behavioral information in association with a subject.
  • Physiological information can include information related to physiological features (e.g., height, weight, body mass index, body fat percent, body hair level, etc.).
  • Demographic information can include information related to demographic features (e.g., gender, age, ethnicity, marital status, number of siblings, socioeconomic status, sexual orientation, etc.).
  • Behavioral information can include information related to one or more of: health conditions (e.g., health and disease states), living situations (e.g., living alone, living with pets, living with a significant other, living with children, etc.), dietary habits (e.g., omnivorous, vegetarian, vegan, sugar consumption, acid consumption, etc.), behavioral tendencies (e.g., levels of physical activity, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled within a given time period), different levels of sexual activity (e.g., related to numbers of partners and sexual orientation), and any other suitable behavioral information.
  • Survey-derived data can include quantitative data, qualitative data that can be converted to quantitative data (e.g., using scales of severity, mapping of qualitative responses to quantified scores, etc.), and/or other suitable data.
  • Block S120 can include providing one or more surveys to a subject of the population of subjects, or to an entity associated with a subject of the population of subjects.
  • Surveys can be provided in person (e.g., in coordination with sample provision and reception from a subject), electronically (e.g., during account setup by a subject, at an application executing at an electronic device of a subject, at a web application accessible through an internet connection, etc.), and/or in any other suitable manner.
  • Block S130 can include receiving one or more of: physical activity- or physical action-related data (e.g., accelerometer and gyroscope data from a mobile device or wearable electronic device of a subject), environmental data (e.g., temperature data, elevation data, climate data, light parameter data, etc.), patient nutrition or diet-related data (e.g., data from food establishment check-ins, data from spectrophotometric analysis, etc.), biometric data (e.g., data recorded through sensors within the patient's mobile computing device, data recorded through a wearable or other peripheral device in communication with the patient's mobile computing device), location data (e.g., using GPS elements), and any other suitable data.
  • physical activity- or physical action-related data e.g., accelerometer and gyroscope data from a mobile device or wearable electronic device of a subject
  • environmental data e.g., temperature data, elevation data, climate data, light parameter data, etc.
  • patient nutrition or diet-related data e.g., data
  • portions of the supplementary dataset can be derived from medical record data and/or clinical data of the subject(s). As such, portions of the supplementary dataset can be derived from one or more electronic health records (EHRs) of the subject(s).
  • the supplementary dataset of Block S120 can include any other suitable diagnostic information (e.g., clinical diagnosis information), which can be combined with analyses derived from features to support characterization of subjects in subsequent blocks of the method 100. For instance, information derived from a colonoscopy, biopsy, blood test, diagnostic imaging, survey-related information, and any other suitable test can be used to supplement Block S120.
  • diagnostic information e.g., clinical diagnosis information
  • information derived from a colonoscopy, biopsy, blood test, diagnostic imaging, survey-related information, and any other suitable test can be used to supplement Block S120.
  • supplementary datasets and receiving supplementary datasets can be configured in any suitable manner.
  • Block S130 recites: performing a characterization process derived from the supplementary dataset and features extracted from at least one of the microbiome composition dataset and microbiome functional diversity dataset.
  • Block S130 functions to identify features and/or feature combinations that can be used to characterize subjects or groups based upon their microbiome composition and/or functional features.
  • Block S130 can additionally or alternatively function to generate a characterization model (e.g., using identified features) for determining characterizations associated with a Clostridium- associated condition.
  • the characterization process can be used as a diagnostic tool that can characterize a subject (e.g., in terms of behavioral traits, in terms of medical conditions, in terms of demographic traits, etc.) based upon their microbiome composition and/or functional features, in relation to one or more of their health condition states, behavioral traits, medical conditions, demographic traits, and any other suitable traits. Such characterization can then be used to suggest or provide personalized therapies by way of the therapy model of Block S140.
  • Block S130 can use computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) to characterize a subject as exhibiting features characteristic of a group of subjects with a health condition.
  • computational methods e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.
  • characterization can be based upon features determined in accordance with feature-selection rules (e.g., CZosfn ' dium-associated condition feature-selection rules defining correlations between microbiome features and one or more CZosZridium-associated conditions).
  • feature-selection rules e.g., CZosfn ' dium-associated condition feature-selection rules defining correlations between microbiome features and one or more CZosZridium-associated conditions.
  • Block S130 can include obtaining a set of Clostridium (e.g., C.
  • associated feature-selection rules correlating the CZosfn ' dium-associated condition to a subset of microbiome composition features and a subset of microbiome functional diversity features; and generating a feature set based on evaluating the microbiome composition dataset and the microbiome functional diversity dataset against the set of Clostridium feature-selection rules.
  • the feature-selection rules can include one or more of: application of statistical analysis operations (e.g., an analysis of probability distributions, etc.), supplementary dataset-based feature-selection rules (e.g., selecting features correlated with supplementary dataset informative of a CZosZridium-associated condition, etc.), processing-based feature-selection rules (e.g., selecting amount and/or type of features based on processing efficiency and/or other processing constraints, etc.), accuracy-based feature-selection rules (e.g., filtering irrelevant and/or redundant features in relation to the CZosfn ' dium-associated condition, etc.), user- selected feature-selection rules, and/or any other suitable feature-selection rules.
  • application of statistical analysis operations e.g., an analysis of probability distributions, etc.
  • supplementary dataset-based feature-selection rules e.g., selecting features correlated with supplementary dataset informative of a CZosZridium-associated condition, etc.
  • feature-selection rules can include application of a statistical analysis of similarities and/or differences between a first group of subjects exhibiting a target state (e.g., a health condition state) and a second group of subjects not exhibiting the target state (e.g., a "normal” state).
  • a target state e.g., a health condition state
  • a second group of subjects not exhibiting the target state e.g., a "normal” state.
  • KS Kolmogorov- Smirnov
  • permutation test e.g., a permutation test
  • Cramer-von Mises test e.g., t-test, Welch's t-test, z-test, chi-squared test, test associated with distributions, etc.
  • any other statistical test e.g., t-test, Welch's t-test, z-test, chi-squared test, test associated with distributions, etc.
  • one or more such statistical hypothesis tests can be used to assess a set of features having varying degrees of abundance in a first group of subjects exhibiting a target state (e.g., a sick state) and a second group of subjects not exhibiting the target state (e.g., having a normal state).
  • the set of features assessed can be constrained based upon percent abundance and/or any other suitable parameter pertaining to diversity in association with the first group of subjects and the second group of subjects, in order to increase or decrease confidence in the characterization.
  • a feature can be derived from a taxon of bacteria that is abundant in a certain percentage of subjects of the first group and subjects of the second group, where a relative abundance of the taxon between the first group of subjects and the second group of subjects can be determined from the KS test, with an indication of significance (e.g., in terms of p- value).
  • an output of Block S130 can include a normalized relative abundance value (e.g., 25% greater abundance of a taxon in sick subjects vs. healthy subjects) with an indication of significance (e.g., a p-value of 0.0013).
  • Variations of feature generation can additionally or alternatively implement or be derived from functional features or metadata features (e.g., non-bacterial markers).
  • Block S130 can include applying a first set of feature-selection rules to define a first feature subset for generating a first characterization model of a first C. difficile strain, and applying a second set of feature- selection rules to define a second feature subset for generating a first characterization model of a second C. difficile strain.
  • any suitable number and/or type of feature- selection rules can be applied in any manner to define one or more feature sets.
  • Block S130 can additionally or alternatively transform input data from at least one of the microbiome composition dataset and microbiome functional diversity dataset into feature vectors that can be tested for efficacy in predicting characterizations of the population of subjects.
  • Block S130 can include generating a set of microbiome feature vectors for a set of users (e.g., a population of subjects) based on a subset of microbiome composition features and a subset of microbiome functional diversity features, and training a characterization model with the set of microbiome feature vectors.
  • Data from the supplementary dataset can be used to provide indication of one or more characterizations of a set of characterizations, where the characterization process is trained with a training dataset of candidate features and candidate classifications to identify features and/or feature combinations that have high degrees (or low degrees) of predictive power in accurately predicting a classification.
  • refinement of the characterization process with the training dataset identifies feature sets (e.g., of subject features, of combinations of features) having high correlation with specific classifications of subjects.
  • feature vectors effective in predicting classifications of the characterization process can include features related to one or more of: microbiome diversity metrics (e.g., in relation to distribution across taxonomic groups, in relation to distribution across archaeal, bacterial, viral, and/or eukaryotic groups), presence of taxonomic groups in one's microbiome, representation of specific genetic sequences (e.g., 16S rRNA sequences) in one's microbiome, relative abundance of taxonomic groups in one's microbiome, microbiome resilience metrics (e.g., in response to a perturbation determined from the supplementary dataset), abundance of genes that encode proteins or RNAs with given functions (enzymes, transporters, proteins from the immune system, hormones, interference RNAs, etc.) and any other suitable features derived from the microbiome diversity dataset, the microbiome functional diversity dataset, and/or the supplementary dataset.
  • microbiome diversity metrics e.g., in relation to distribution across taxonomic groups, in relation to distribution across arch
  • features can include a functional diversity feature associated with bile acid metabolism, and/or a composition feature associated with a relative abundance of Bacteroidetes, Firmicutes, and Proteobacteria, where the features can be used in generating and/or applying a characterization model (e.g., for characterizing a C. difficile Ribotype 027 strain infection including at least one of sepsis and colitis, etc.).
  • a characterization model e.g., for characterizing a C. difficile Ribotype 027 strain infection including at least one of sepsis and colitis, etc.
  • combinations of features can be used in a feature vector, where features can be grouped and/or weighted in providing a combined feature as part of a feature set.
  • one feature or feature set can include a weighted composite of the number of represented classes of bacteria in one's microbiome, presence of a specific genus of bacteria in one's microbiome, representation of a specific 16S rRNA sequence in one's microbiome, and relative abundance of a first phylum over a second phylum of bacteria.
  • the feature vectors can additionally or alternatively be determined in any other suitable manner.
  • Block S130 can include generating a characterization model based on one or more features (e.g., described above) and/or supplementary data, but characterization models can be generated based on any suitable data.
  • Block S130 can include applying a feature set (e.g., generated based on CZosfn ' dium-associated condition feature-selection rules) with the supplementary dataset to generate a characterization model for the C. difficile-associated condition.
  • Different characterization models can be generated for different demographic groups (e.g., a first characterization model characterizing a CZosfridium-associated condition for recently released hospital patients, a second characterization model for antibiotic users, etc.), individual subjects, supplementary data (e.g., models incorporating features derived from biometric sensor data vs. models independent of supplementary data, etc.), and/or other suitable criteria.
  • a first characterization model characterizing a CZosfridium-associated condition for recently released hospital patients e.g., a second characterization model for antibiotic users, etc.
  • supplementary data e.g., models incorporating features derived from biometric sensor data vs. models independent of supplementary data, etc.
  • the method can include generating a characterization model for a demographic group of exercisers; associating the characterization model with user accounts (e.g., at a database of the processing system) for subjects who indicate physical activity (e.g., at a digital survey presented by the interface); and retrieving the characterization model (e.g., from the database) for characterizing the subjects.
  • Generating a plurality of characterization models suited to different contexts can confer improvements to the processing system by improving characterization accuracy (e.g., by tailoring analysis to a particular subject's demographic and/or situation, etc.), retrieval of the appropriate characterization model from a database (e.g., by associating customized characterization models with particular user accounts and/or other identifiers), training and/or execution of characterization models (e.g., when the customized models are associated with a subset of a pool of potential CZosfn ' dium-associated condition features, where the remaining features are less pertinent to a particular subject), and/or other suitable aspects of the processing system.
  • characterization accuracy e.g., by tailoring analysis to a particular subject's demographic and/or situation, etc.
  • retrieval of the appropriate characterization model from a database e.g., by associating customized characterization models with particular user accounts and/or other identifiers
  • training and/or execution of characterization models e.g., when the
  • Block S130 in an example of the variation of Block S130, the characterization process can be generated and trained according to a random forest predictor (RFP) algorithm that combines bagging (e.g., bootstrap aggregation) and selection of random sets of features from a training dataset to construct a set of decision trees, T, associated with the random sets of features.
  • RFP random forest predictor
  • N cases from the set of decision trees are sampled at random with replacement to create a subset of decision trees, and for each node, m prediction features are selected from all of the prediction features for assessment.
  • the prediction feature that provides the best split at the node is used to perform the split (e.g., as a bifurcation at the node, as a trifurcation at the node).
  • the strength of the characterization process in identifying features that are strong in predicting classifications can be increased substantially.
  • measures to prevent bias e.g., sampling bias
  • account for an amount of bias can be included during processing to increase robustness of the model.
  • any number of characterization models can be generated for any suitable purpose. However, performing a characterization process can be performed in any suitable manner.
  • a characterization process of Block S130 based upon statistical analyses can identify the sets of features that have the highest correlations with a C. dij ⁇ iciZe-associated condition.
  • the characterization process of Block S130 can facilitate identification of which microorganism population(s) are upregulated or downregulated in relation to C. difficile activity, and/or which microbiome functional aspects (e.g., in relation to COG/KEGG pathways) are upregulated or downregulated in relation to C. difficile activity.
  • composition and/or functional diversity related to C. difficile can be characterized in relation to other species within the Clostridium genus.
  • the characterization processes of Block S130 can include 1) characterizing strains of C. difficile (e.g., Ribotype 027, Ribotype 002, Ribotype 106, Ribotype 017, Ribotype 078, etc.) present in a sample, and 2) characterizing, at the strain level, relationship(s) between C. difficile strains and microorganism population and/or functional aspect upregulation/downregulation (e.g., upregulation/downregulation of particular C. difficile strains in relation to upregulation/downregulation of other taxonomic groups and/or other strains).
  • the method 100 can be used to identify 98% of all C.
  • the method 100 can identify the strains(s) of C. difficile present in a sample, relationships between the strain(s) and Bifidobacterial populations of the sample, and functional aspects in relation to pH and/or butyrate modulation.
  • the method 100 can include identifying between specific C. difficile strains and microbiome composition features, functional diversity, and/or CZosfn ' dium-associated conditions.
  • characterizing aspects in relation to Clostridium strains can be performed in any suitable manner.
  • a set of features useful for characterizations associated with C. difficile includes features derived from one or more of the following taxa: Flavonifr actor 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-ZI-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), Ruminococc
  • the set of features associated with a C. difficile-related condition can be derived from one or more of: a clusters of orthologous groups (COG) code, a cellular processes and signaling Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway derived feature, a metabolism KEGG pathway derived feature, a signaling molecules and interaction KEGG pathway derived feature, a translation KEGG pathway derived feature, an other ion-coupled transporters KEGG pathway derived feature, a bacterial toxins KEGG pathway derived feature, a caprolactam degradation KEGG pathway derived feature, an ascorbate and aldarate metabolism KEGG pathway derived feature, an inorganic ion transport and metabolism KEGG pathway derived feature, a protein SCO1/2 KEGG pathway derived feature (e.g., a K07152 KEGG code associated with protein SCO1/2), a cytochrome KEGG pathway derived feature (e.g., a K
  • characterization of the subject in Block S130 can include characterization of the subject in relation to a C. difficile-based health condition, based upon detection of one or more of the above features, in a manner that is an alternative or supplemental to typical methods of diagnosis or characterization.
  • the set of features can, however, include any other suitable features useful for diagnostics/characterization of a subject.
  • Characterization of the subject(s) in Block S130 can additionally or alternatively implement use of a high false positive test and/or a high false negative test to further analyze sensitivity of the characterization process in supporting analyses generated according to embodiments of the method 100.
  • characterizing a CZosfn ' dium-associated condition in Block S130 can include generating a diagnostic analysis of a Clostridium infection (e.g., estimating a risk of infection, diagnosing the infection, etc.) and/or associated complications including any one or more of: a C.
  • Clostridium Botulinum infection e.g., botulism, flaccid paralytic disease, etc.
  • Clostridium perfringens infection e.g., cellulitis, fascitis, gas gangrene, tissue necrosis, bacteremia, emphysematous cholecystitis, etc.
  • Clostridium tetani e.g., tetanus, etc.
  • Generating a diagnostic analysis can be based on relative abundance of taxonomic groups (e.g., diagnosing a C. difficile infection based on a high abundance of C. difficile Ribotype 027 strain; estimating an increased infection risk based on a decreased abundance of a taxonomic group correlated with defending against a Clostridium infection), functional diversity (e.g., based on bile acid metabolism; estimating a decreased infection risk based on increased production of types of bile acids such as Chenodeoxycholic acid inhibiting Clostridium spore germination; etc.), and/or any other suitable data.
  • taxonomic groups e.g., diagnosing a C. difficile infection based on a high abundance of C. difficile Ribotype 027 strain; estimating an increased infection risk based on a decreased abundance of a taxonomic group correlated with defending against a Clostridium infection
  • functional diversity e.g., based on bile acid metabolism; estimating a
  • characterizing a CZosiridium-associated condition can be based on one or more supplementary datasets.
  • the set of CZosiridium-associated feature-selection rules can correlate the Clostridium infection to a biometric feature derived from biometric sensor data informative of a Clostridium- associated condition (e.g., temperature data, cardiovascular data, blood data, stool data, etc. indicating the presence of symptoms such as fever, nausea, abdominal pain, diarrhea, etc.).
  • performing a characterization process can be based on antibiotic and/or probiotic regimen data associated with a population of users, where particular regimens can aid in illuminating microbiome compositions and/or functional diversity correlated with CZosiridium-associated conditions.
  • performing a characterization process in relation to a CZosiridium- associated condition can be performed in any suitable manner.
  • the method 100 can additionally or alternatively include Block S140, which recites: determining a therapy for preventing, ameliorating, and/or otherwise modifying a CZosiridium-associated condition.
  • Block S140 functions to identify and/or predict therapies (e.g., probiotic-based therapies, phage-based therapies, small molecule-based therapies, fecal matter transplant-based therapies, etc.) that can shift a subject's microbiome composition and/or functional features toward a desired equilibrium state in promotion of the subject's health (e.g., reduce the risk of a CZosiridium-associated condition, ameliorate a CZosfn ' dium-associated condition, etc.).
  • Block S140 can additionally or alternatively include generating and/or applying a therapy model for determining the therapy.
  • the therapies can be selected from therapies including one or more of: probiotic therapies, phage-based therapies, small molecule-based therapies, fecal matter transplant-based therapies, cognitive/behavioral therapies, physical rehabilitation therapies, clinical therapies, medication-based therapies, diet-related therapies, and/or any other suitable therapy designed to operate in any other suitable manner in promoting a user's health.
  • probiotic therapies e.g., phage-based therapies, small molecule-based therapies, fecal matter transplant-based therapies, cognitive/behavioral therapies, physical rehabilitation therapies, clinical therapies, medication-based therapies, diet-related therapies, and/or any other suitable therapy designed to operate in any other suitable manner in promoting a user's health.
  • phage-based therapies small molecule-based therapies
  • fecal matter transplant-based therapies e.g., small molecule-based therapies
  • cognitive/behavioral therapies e.g., cognitive/behavioral therapies, physical rehabilitation therapies, clinical therapies, medication-based therapies, diet-related therapies, and
  • bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the subject.
  • bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
  • candidate therapies of the therapy model can perform one or more of: blocking pathogen entry into an epithelial cell by providing a physical barrier (e.g., by way of colonization resistance), inducing formation of a mucous barrier by stimulation of goblet cells, enhance integrity of apical tight junctions between epithelial cells of a subject (e.g., by stimulating up regulation of zona-occludens 1, by preventing tight junction protein redistribution), producing antimicrobial factors, stimulating production of antiinflammatory cytokines (e.g., by signaling of dendritic cells and induction of regulatory T- cells), triggering an immune response, and performing any other suitable function that adjusts a subject's microbiome away from a state of dysbiosis.
  • a physical barrier e.g., by way of colonization resistance
  • inducing formation of a mucous barrier by stimulation of goblet cells e.g., by stimulating up regulation of zona-occludens 1, by preventing tight junction protein redistribution
  • the therapy model is preferably based upon data from a large population of subjects, which can include the population of subjects from which the microbiome diversity datasets are derived in Block S110, where microbiome composition and/or functional features or states of health, prior exposure to and post exposure to a variety of therapeutic measures, are well characterized.
  • data can be used to train and validate the therapy provision model, in identifying therapeutic measures that provide desired outcomes for subjects based upon different microbiome characterizations.
  • support vector machines as a supervised machine learning algorithm, can be used to generate the therapy provision model.
  • any other suitable machine learning algorithm described above can facilitate generation of the therapy provision model.
  • Processing of therapy models can be analogous to processing of characterization models (e.g., described for Block S130), where any number of treatment models can be generated for different purposes (e.g., different demographic groups, individuals, supplementary datasets, etc.), associated with user accounts and/or other identifiers, and/or otherwise processed for customizing therapy determination and/or promotion for different subjects.
  • characterization models e.g., described for Block S130
  • any number of treatment models can be generated for different purposes (e.g., different demographic groups, individuals, supplementary datasets, etc.), associated with user accounts and/or other identifiers, and/or otherwise processed for customizing therapy determination and/or promotion for different subjects.
  • Block S140 while some methods of statistical analyses and machine learning are described in relation to performance of the Blocks above, variations of the method 100 can additionally or alternatively utilize any other suitable algorithms in performing the characterization process.
  • the algorithm(s) can be characterized by a learning style including any one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), and any other suitable learning style.
  • supervised learning e.g., using logistic regression, using back propagation neural networks
  • unsupervised learning e.g., using an Apriori algorithm, using K-means clustering
  • semi-supervised learning e.g., using a Q-learning algorithm, using temporal difference learning
  • reinforcement learning e.g., using a Q-learning algorithm, using temporal difference learning
  • the algorithm(s) can implement any one or more of: a regression algorithm (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, etc.), an instance-based method (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, etc.), a regularization method (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, etc.), a decision tree learning method (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, chi- squared automatic interaction detection, decision stump, random forest, multivariate adaptive regression splines, gradient boosting machines, etc.), a Bayesian method (e.g., na ' ive Bayes, averaged one-dependence estimators, Bayesian belief network, etc.), a kernel method (e.g., a support vector machine, a radial basis function, a linear discriminant analysis, etc.
  • the therapy model can be derived in relation to identification of a "normal" or baseline microbiome composition and/or functional features, as assessed from subjects of a population of subjects who are identified to be in good health.
  • therapies that modulate microbiome compositions and/or functional features toward those of subjects in good health can be generated in Block S140.
  • Block S140 can thus include identification of one or more baseline microbiome compositions and/or functional features (e.g., one baseline microbiome for each of a set of demographics), and potential therapy formulations and therapy regimens that can shift microbiomes of subjects who are in a state of dysbiosis toward one of the identified baseline microbiome compositions and/or functional features.
  • the therapy model can, however, be generated and/or refined in any other suitable manner.
  • microorganism compositions associated with probiotic therapies associated with the therapy model preferably include microorganisms that are culturable (e.g., able to be expanded to provide a scalable therapy) and non-lethal (e.g., non- lethal in their desired therapeutic dosages).
  • microorganism compositions can include a single type of microorganism that has an acute or moderated effect upon a subject's microbiome.
  • microorganism compositions can include balanced combinations of multiple types of microorganisms that are configured to cooperate with each other in driving a subject's microbiome toward a desired state.
  • a combination of multiple types of bacteria in a probiotic therapy can include a first bacteria type that generates products that are used by a second bacteria type that has a strong effect in positively affecting a subject's microbiome.
  • a combination of multiple types of bacteria in a probiotic therapy can include several bacteria types that produce proteins with the same functions that positively affect a subject's microbiome.
  • probiotic compositions can be naturally or synthetically derived.
  • a probiotic composition can be naturally derived from fecal matter or other biological matter (e.g., of one or more subjects having a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model).
  • probiotic compositions can be synthetically derived (e.g., derived using a benchtop method) based upon a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model.
  • microorganism agents that can be used in probiotic therapies can include one or more of: yeast (e.g., Saccharomyces boulardii), gram-negative bacteria (e.g., E. coli Nissle), gram-positive bacteria (e.g., Lactobacillus rhamnosus, Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus helveticus, Lactobacillus plantarum, Lactobacillus fermentum, Lactobacillus salivarius, Lactobacillus delbrueckii (Including subsp.
  • yeast e.g., Saccharomyces boulardii
  • gram-negative bacteria e.g., E. coli Nissle
  • gram-positive bacteria e.g., Lactobacillus rhamnosus, Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus helveticus, Lactobacillus plantarum, Lac
  • Lactobacillus johnsonii Lactobacillus reuteri
  • Lactobacillus gasseri Lactobacillus brevis
  • Bifidobacterium animalis including subsp. lactis
  • Bifidobacterium longum including subsp.
  • probiotic compositions can include components of one or more of the identified taxa of microorganisms (e.g., as described in Section 4.1 above, provided at dosages of 1 million to 10 billion CFUs, as determined from a therapy model that predicts positive adjustment of a subject's microbiome in response to the therapy. Additionally or alternatively, the therapy can include dosages of proteins resulting from functional presence in the microbiome compositions of subjects without a specific condition.
  • a subject can be instructed to ingest capsules including the probiotic formulation according to a regimen tailored to one or more of his/her: physiology (e.g., body mass index, weight, height), demographics (e.g., gender, age), severity of dysbiosis, sensitivity to medications, and any other suitable factor.
  • physiology e.g., body mass index, weight, height
  • demographics e.g., gender, age
  • Block S140 can include determination, based upon one or more analyses, of therapies (e.g., probiotic therapies, bacteriophage-based therapies, antibiotic therapies, fecal matter transplant therapies, etc.) that can be used to positively modulate a subject's microbiome composition and/or functional aspects in relation to improving the subject's C. difficile-associated condition.
  • therapies e.g., probiotic therapies, bacteriophage-based therapies, antibiotic therapies, fecal matter transplant therapies, etc.
  • Block S140 can include identifying, prescribing, and/or providing therapies for downregulation and/or entirely eliminating C. difficile populations in the subject, while not adversely affecting the microbiome of the subject in any other manner (e.g., in relation to microorganism populations, in relation to functional aspects, etc.).
  • the therapy can include recommending and/or controlling a central venous catheter for administering medications (e.g., antibiotics, steroids, blood pressure support, etc.) and/or fluids for ameliorating symptoms of sepsis, but any suitable therapy can be promoted in relation to treating Clostridiwn-based sepsis.
  • the therapy can include recommending and/or otherwise facilitating a medication regimen, surgical procedures (e.g., colectomy, etc.), and/or other suitable therapies for treating Clos tridium -based colitis.
  • the therapy can include an antibiotic and/or probiotic regimen to facilitate a microbiome composition suitable for defending against or preventing Clostridium infection, such as a microbiome composition including a smaller proportion of Bacteroidetes and Firmicutes, and a higher proportion of Proteobacteria (e.g., relative other users, relative other user groups, relative averages and/or other statistics, etc.).
  • a therapy can be operable to facilitate any suitable relative abundance of particular taxonomic groups, and/or any suitable microbiome composition.
  • a therapy can include scheduling an appointment with care provider (e.g., in response to a Clostridium infection risk exceeding a threshold, such as based on microbiome functional diversity and supplementary datasets indicating lifestyle choices correlated with increased risk; in response to diagnosing a Clostridium infection; etc.).
  • a threshold such as based on microbiome functional diversity and supplementary datasets indicating lifestyle choices correlated with increased risk; in response to diagnosing a Clostridium infection; etc.
  • Block S140 can be performed in any suitable manner.
  • the method can additionally or alternatively include Block S150, which recites: processing a biological sample from a subject, which functions to receive and process a biological sample to facilitate generation of a microbiome dataset for the subject that can be used to derive inputs for the characterization process.
  • receiving, processing, and analyzing the biological sample preferably facilitates generation of a microbiome dataset for the subject, which can be used to provide inputs for a characterization process.
  • the biological sample is preferably generated from the subject and/or an environment of the subject in a non-invasive manner.
  • non-invasive manners of sample reception can use any one or more of: a permeable substrate (e.g., a swab configured to wipe a region of a subject's body, toilet paper, a sponge, etc.), a non-permeable substrate (e.g., a slide, tape, etc.) a container (e.g., vial, tube, bag, etc.) configured to receive a sample from a region of a subject's body, and any other suitable sample-reception element.
  • a permeable substrate e.g., a swab configured to wipe a region of a subject's body, toilet paper, a sponge, etc.
  • a non-permeable substrate e.g., a slide, tape, etc.
  • a container e.g., vial, tube, bag, etc.
  • the biological sample can be collected from one or more of the subject's nose, skin, genitals, mouth, and gut in a non-invasive manner (e.g., using a swab and a vial).
  • the biological sample can additionally or alternatively be received in a semi-invasive manner or an invasive manner.
  • invasive manners of sample reception can use any one or more of: a needle, a syringe, a biopsy element, a lance, and any other suitable instrument for collection of a sample in a semi-invasive or invasive manner.
  • samples can include blood samples, plasma/serum samples (e.g., to enable extraction of cell-free DNA), tissue samples, and/or any other suitable sample.
  • the biological sample can be taken from the body of the subject without facilitation by another entity (e.g., a caretaker associated with a subject, a healthcare professional, an automated or semi- automated sample collection apparatus, etc.), or can alternatively be taken from the body of the subject with the assistance of another entity.
  • a sample kit can be provided to the subject.
  • the sample kit can include one or more swabs for sample acquisition, one or more containers configured to receive the swab(s) and/or other biological sampling mediums for storage, instructions for sample provision and setup of a user account, elements configured to associate the sample(s) with the subject (e.g., barcode identifiers, tags, etc.), and a receptacle that allows the sample(s) from the subject to be delivered to a sample processing operation (e.g., by a mail delivery system).
  • a sample processing operation e.g., by a mail delivery system
  • the biological sample is extracted from the subject with the help of another entity
  • one or more samples can be collected in a clinical or research setting from the subject (e.g., during a clinical appointment). The biological sample can, however, be received from the subject in any other suitable manner.
  • Block S150 processing and analyzing the biological sample from the subject is preferably performed in a manner similar to that of one of the embodiments, variations, and/or examples of sample reception described in relation to Block S110 above.
  • reception and processing of the biological sample in Block S150 can be performed for the subject using similar processes as those for receiving and processing biological samples used to generate the characterization process and/or the therapy model of the method 100, in order to provide consistency of process.
  • biological sample reception and processing in Block S150 can alternatively be performed in any other suitable manner.
  • the method 100 can additionally or alternatively include Block S160, which recites: determining, with the characterization process, a characterization of the subject based upon processing a microbiome dataset (e.g., microbiome composition dataset, microbiome functional diversity dataset, etc.) derived from the biological sample of the subject.
  • Block S160 functions to extract features from microbiome-derived data of the subject, and use the features as inputs into an embodiment, variation, or example of the characterization process (e.g., a characterization model) described in Block S130 above.
  • Determining the characterization in Block S160 thus preferably includes identifying features and/or combinations of features associated with the microbiome composition and/or functional features of the subject, inputting the features into the characterization process, and receiving an output that characterizes the subject as belonging to one or more of: a behavioral group, a gender group, a dietary group, a disease-state group, and any other suitable group capable of being identified by the characterization process.
  • Block S160 can further include generation of and/or output of a confidence metric associated with the characterization of the subject. For instance, a confidence metric can be derived from the number of features used to generate the characterization, relative weights or rankings of features used to generate the characterization, measures of bias in the characterization process, and/or any other suitable parameter associated with aspects of the characterization process.
  • Block S160 features extracted from the microbiome dataset of the subject can be supplemented with survey-derived and/or medical history-derived features from the subject, which can be used to further refine the characterization process of Block S130.
  • the microbiome dataset of the subject can additionally or alternatively be used in any other suitable manner to enhance the models of the method 100.
  • Block S160 can include generating values for features selected based on feature- selection rules (e.g., CZosfn ' dium-associated condition feature-selection rules), and using the values to characterize the subject.
  • Such processes can confer improvements in the processing system by improving feature extraction processing speed by extracting only a subset of a set of features (e.g., microbiome composition features, microbiome functional diversity features, etc.) based on feature-selection rules (e.g., used in determining the subset of features used in training the corresponding characterization model), rather than generating each feature of the set of features.
  • determining a characterization of the subject can be performed in any suitable manner.
  • the method 100 can additionally or alternatively include Block S170, which recites: promoting a therapy (e.g., determined in Block S140) to the subject based upon the characterization and the therapy model, which functions to recommend or provide a personalized therapy to the subject, in order to shift the microbiome composition and/or functional features of the subject toward a desired equilibrium state.
  • Block S170 can include provision of a customized therapy to the subject according to their microbiome composition and functional features, as shown in FIGURE 5, where the customized therapy is a formulation of microorganisms configured to correct dysbiosis characteristic of subjects having the identified characterization.
  • outputs of Block S140 can be used to directly promote a customized therapy formulation and regimen (e.g., dosage, usage instructions) to the subject based upon a trained therapy model.
  • therapy provision can include recommendation of available therapeutic measures configured to shift microbiome composition and/or functional features toward a desired state.
  • available therapeutic measures can include one or more of: consumables (e.g., food items, beverage items, etc.), topical therapies (e.g., lotions, ointments, antiseptics, etc.), nutritional supplements (e.g., vitamins, minerals, fiber, fatty acids, amino acids, prebiotics, etc.), medications, antibiotics, bacteriophages, fecal matter transplant, and any other suitable therapeutic measure.
  • a combination of commercially available probiotic supplements can include a suitable probiotic therapy for the subject according to an output of the therapy model.
  • the therapy of Block S170 can include a bacteriophage-based therapy.
  • bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the subject.
  • bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
  • Therapy provision in Block S170 can include provision of notifications to a subject regarding the recommended therapy and/or other forms of therapy. Notifications can be provided to a subject by way of an electronic device (e.g., personal computer, mobile device, tablet, wearable, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.) that executes an application, web interface, and/or messaging client configured for notification provision.
  • an electronic device e.g., personal computer, mobile device, tablet, wearable, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.
  • a web interface of a personal computer or laptop associated with a subject can provide access, by the subject, to a user account of the subject, where the user account includes information regarding the user's characterization, detailed characterization of aspects of the user's microbiome, and notifications regarding suggested therapeutic measures generated in Blocks S140 and/or S170.
  • an application executing at a personal electronic device can be configured to provide notifications (e.g., at a display, haptically, in an auditory manner, etc.) regarding therapy suggestions generated by the therapy model of Block S170.
  • Notifications and/or probiotic therapies can additionally or alternatively be provided directly through an entity associated with a subject (e.g., a caretaker, a spouse, a significant other, a healthcare professional, etc.).
  • notifications can additionally or alternatively be provided to an entity (e.g., healthcare professional) associated with a subject, where the entity is able to administer the therapy measure (e.g., by way of prescription, by way of conducting a therapeutic session, etc.).
  • entity e.g., healthcare professional
  • Notifications can, however, be provided for therapy administration to a subject in any other suitable manner.
  • Promoting a therapy in Block S170 can include controlling a therapy system (e.g., a communications system, an application executable on a user device, a medical device, a user device, etc.) to facilitate promotion of the therapy.
  • Controlling a therapy system can include generating control instructions (e.g., at a processing system) for the therapy system, and operating the therapy system based on the control instructions (e.g., through transmitting the control instructions to the therapy system to execute).
  • promoting a therapy can include controlling an administration system for consumables (e.g., an automated medication pillbox, a probiotic administration system) to distribute the consumable according to a regimen (e.g., by scheduling regimen reminders at the administration system; prompting the subject to take particular consumables; etc.) specified by a therapy.
  • a regimen e.g., by scheduling regimen reminders at the administration system; prompting the subject to take particular consumables; etc.
  • promoting a therapy can be performed in any suitable manner.
  • the method 100 can additionally or alternatively include Block S180, which recites: monitoring effectiveness of the therapy for the subject, based upon processing biological samples, to assess microbiome composition and/or functional features for the subject at a set of time points associated with the probiotic therapy.
  • Block S180 functions to gather additional data regarding positive effects, negative effects, and/or lack of effectiveness of a probiotic therapy suggested by the therapy model for subjects of a given characterization, where the additional data can be used, for example, to generate and/or update one or more characterization models, therapy models, and/or other suitable models.
  • Monitoring of a subject during the course of a therapy promoted by the therapy model can thus be used to generate a therapy-effectiveness model for each characterization provided by the characterization process of Block S130, and each recommended therapy measure provided in Blocks S140 and S170.
  • Block S180 the subject can be prompted to provide additional biological samples at one or more key time points of a therapy regimen that incorporates the therapy, and the additional biological sample(s) can be processed and analyzed (e.g., in a manner similar to that described in relation to Block S120) to generate metrics characterizing modulation of the subject's microbiome composition and/or functional features.
  • metrics related to one or more of: a change in relative abundance of one or more taxonomic groups represented in the subject's microbiome at an earlier time point, a change in representation of a specific taxonomic group of the subject's microbiome, a ratio between abundance of a first taxonomic group of bacteria and abundance of a second taxonomic group of bacteria of the subject's microbiome, a change in relative abundance of one or more functional families in a subject's microbiome, and any other suitable metrics can be used to assess therapy effectiveness from changes in microbiome composition and/or functional features.
  • survey-derived data from the subject can be used to determine effectiveness of the therapy in Block S180.
  • monitoring effectiveness of one or more therapies can be performed in any suitable manner.
  • the method 100 can, however, include any other suitable blocks or steps configured to facilitate reception of biological samples from subjects, processing of biological samples from subjects, analyzing data derived from biological samples, and generating models that can be used to provide customized diagnostics and/or probiotic-based therapeutics according to specific microbiome compositions and/or functional features of subjects.
  • the method 100 and/or system of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
  • the instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof.
  • Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
  • the instructions can be executed by computer-executable components integrated by computer-executable components integrated with apparatuses and networks of the type described above.
  • the computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device.
  • the computer-executable component can be a processor, though any suitable dedicated hardware device can (alternatively or additionally) execute the instructions.
  • each block in the flowchart or block diagrams may represent a module, segment, step, or portion of code, which includes one or more executable instructions for implementing the specified logical function(s).
  • the functions noted in the block can occur out of the order noted in the FIGURES. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
  • the embodiments include every combination and permutation of the various system components and the various method processes, including any variations, examples, and specific examples.

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Abstract

Un mode de réalisation de l'invention concerne un système et un procédé permettant de caractériser une affection associée à Clostridium en relation avec un utilisateur qui comprend : un réseau de gestion utilisable pour recevoir des récipients comprenant un matériau provenant d'un ensemble d'utilisateurs, le réseau de gestion comprenant un système de séquençage utilisable pour déterminer des séquences du microbiome par séquençage du matériau ; un système de traitement utilisable pour créer un ensemble de données sur la composition du microbiome et un ensemble de données sur la diversité fonctionnelle du microbiome basés sur les séquences du microbiome, de recevoir un ensemble de données supplémentaires associé à l'affection associée à Clostridium pour l'ensemble d'utilisateurs ; la transformation de l'ensemble de données supplémentaires et des caractéristiques extraites de l'ensemble de données sur la composition du microbiome et l'ensemble de données sur la diversité fonctionnelle du microbiome en un modèle de caractérisation de l'affection associée à Clostridium ; et un système de traitement permettant de promouvoir une thérapie auprès de l'utilisateur basée sur la caractérisation de l'utilisateur en relation avec l'affection associée à Clostridium au moyen du modèle de caractérisation.
EP16873997.7A 2015-12-09 2016-12-09 Procédé et système de caractérisation d'affections associées à clostridium difficile Pending EP3386592A4 (fr)

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US201562265077P 2015-12-09 2015-12-09
US15/097,862 US9710606B2 (en) 2014-10-21 2016-04-13 Method and system for microbiome-derived diagnostics and therapeutics for neurological health issues
PCT/US2016/065993 WO2017100688A1 (fr) 2015-12-09 2016-12-09 Procédé et système de caractérisation d'affections associées à clostridium difficile

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CA3076359A1 (fr) * 2017-10-13 2019-04-18 Rebiotix, Inc. Indice de sante de microbiome
CN108486228B (zh) * 2018-05-25 2021-07-09 上海交通大学医学院附属上海儿童医学中心 预判断脓毒症感染情况的荧光定量pcr检测试剂盒及其方法
WO2020012467A2 (fr) * 2018-07-08 2020-01-16 Yeda Research And Development Co. Ltd. Évaluation spécifique à une personne de la sensibilité aux probiotiques
WO2020051559A1 (fr) * 2018-09-06 2020-03-12 Viome, Inc. Systèmes et méthodes d'analyse de microbiome
WO2020076874A1 (fr) 2018-10-08 2020-04-16 Viome, Inc. Procédés et compositions permettant de déterminer des recommandations de produits alimentaires
EP3924953A4 (fr) 2019-02-12 2023-02-08 Viome Life Sciences, Inc. Personnalisation de recommandations alimentaires pour réduire la réponse glycémique
CN112481395B (zh) * 2019-09-12 2024-10-18 深圳华大生命科学研究院 艰难梭菌耐药/低敏感进化分支snp标记及菌株类别鉴定方法和应用
CN113555059B (zh) * 2021-06-25 2024-02-02 中国科学院南京地理与湖泊研究所 环境变化下有机碳和微生物耦合关系的定量方法

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WO2008076696A2 (fr) * 2006-12-18 2008-06-26 Washington University In St. Louis Microbiome intestinal en tant que biomarqueur et cible thérapeutique pour traiter l'obésité ou un trouble lié à l'obésité
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CN108472506B (zh) 2022-03-29
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WO2017100688A1 (fr) 2017-06-15
CA3007503A1 (fr) 2017-06-15
AU2016366656A1 (en) 2018-06-28

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