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CyberGut: towards personalized human-microbiome metabolic modeling for precision health and nutrition

CyberGut：针对精准健康和营养的个性化人类微生物代谢模型

基本信息

批准号：
10654052
负责人：
Sean Michael Gibbons
金额：
$ 75.88万
依托单位：
INSTITUTE FOR SYSTEMS BIOLOGY
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10654052
关键词：
Adult Algorithms Amendment Bile Acids Biochemical Biochemical Pathway Biological Biological Assay Biological Markers Blood Body Weight decreased Colon Communities Complex Custom Data Data Set Diet Dietary Assessment Dietary Component Dietary Intervention Dietary intake Digestion Ecology Ecosystem Environment Feces Foundations Growth Health Heterogeneity Human Human Microbiome In Vitro Incubated Individual Intervention Knowledge Maps Measurement Measures Metabolic Metabolism Metagenomics Microbe Modeling Mucins Multiomic Data Nutritional Organ Participant Persons Pharmaceutical Preparations Phenotype Physiology Polysaccharides Precision Health Production Publishing Resources Sampling Seminal Structure Systems Biology Testing Tissues Training Validation Variant Vitamins Work absorption bioinformatics tool blood lipid cell type cohort commensal microbes design dietary experience experimental study fecal metabolome fecal microbiome gastrointestinal epithelium genome-wide gut microbiome gut microbiota host microbiome improved in silico in vivo innovation metabolic phenotype metabolome metabolomics metagenome microbial microbial community microbiome microbiome composition microbiome research model building novel nutrition personalized predictions precision nutrition predictive modeling reconstruction recruit response stem stool sample tool

项目摘要

PROJECT SUMMARY The gut microbiome aids in the digestion of complex polysaccharides, the absorption of vitamins, and the conversion of primary bile acids, drugs, and other bioactive compounds into metabolites that can be absorbed by the host. Thus, the metabolic activity of commensal microbes is closely intertwined with human physiology and the nutritional impact of our diet. However, there is limited understanding of how variation in the ecology of our intestinal flora modulates the biological impact of diet on human health and nutrition. Recent work has shown that differences in the composition of the gut microbiome can help explain person-to-person heterogeneity in glycemic responses, blood lipid profiles, and weight loss. In this proposal, we present an innovative platform for personalized metabolic modeling of the gut microbiome using metagenomic and dietary data as constraints. We propose the integration of tissue-resolved metabolic models of relevant host tissues, including a cell-type-specific metabolic reconstruction of the gut epithelium, into our existing microbial community model to improve estimates of metabolic fluxes between the gut microbiota, the diet, and the host. We will call this host-diet-microbiome metabolic model ‘CyberGut.’ Using existing multi-omic data from a cohort of >3,000 adults, we will constrain and validate CyberGut with paired measurements of diet, host blood metabolomes, and gut microbiomes. In addition, we will generate cross-sectional training and validation data consisting of paired blood and fecal metabolomes, fecal microbiomes, and detailed 3-day dietary recall data from a new cohort of 100 healthy participants. Using these data, we will refine and test two novel and independent diet-inference algorithms, which leverage stool metagenomes and stool untargeted metabolomes, respectively. Furthermore, using samples taken from a subset of this new cohort (N=40), we will perform ex vivo stool culturing experiments, designed to directly quantify metabolic fluxes and bacterial growth rates in vitro. These fluxomic data will be used to directly test in silico CyberGut flux predictions in response to a diverse panel of dietary and host metabolite interventions. In addition to contributing to the refinement and testing of our CyberGut model, the paired diet, microbiome, and metabolomic data, including replicate fluxomic assays, generated in this proposal will be an invaluable resource to the precision nutrition and human microbiome research community. In summary, we will build, refine, and test a novel platform for tracking dietary intake and predicting personalized nutritional responses to diet, which has the potential to fundamentally alter how we design and test dietary interventions.

项目总结