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

Cyber​​Gut：针对精准健康和营养的个性化人类微生物代谢模型

• 批准号: 10502912
• 负责人: Sean Michael Gibbons
• 金额: $ 71.87万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 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/10502912
• 关键词: Adult; Algorithms; Amendment; Anaerobic Bacteria; Bile Acids; Biochemical; Biochemical Pathway; Biological; Biological Assay; Biological Markers; Blood; Body Weight decreased; 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; Individual; Intervention; Knowledge; 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; base; 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

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.

项目摘要 肠道微生物组有助于消化复杂多糖，维生素的抽象和 原代胆汁酸，药物和其他生物活性化合物转化为可以吸收的代谢产物 由主机。这就是共生微生物的代谢活性与人类生理学紧密相互交织 以及我们饮食的营养影响。但是，人们对如何在生态学上的变化有限 我们的肠道菌群调节饮食对人类健康和营养的生物学影响。最近的工作表明 肠道微生物组组成的这种差异可以帮助解释人与人之间的异质性 血糖反应，血脂谱和体重减轻。在此提案中，我们提出了一个创新平台 使用元基因组和饮食数据作为约束，对肠道微生物组的个性化代谢建模。我们 建议相关宿主组织的组织分辨代谢模型的整合，包括细胞类型 肠道上皮的代谢重建为我们现有的微生物社区模型，以改善估计值 肠道菌群，饮食和宿主之间的代谢通量。我们将这个宿主 - 摩克生物组称为 代谢模型“ cybergut”。使用来自> 3,000名成年人的队列中现有的多OMIC数据，我们将约束和 用饮食，宿主血液代谢组和肠道微生物组的配对测量结果来验证网络凝。此外， 我们将生成由配对的血液和粪便代谢组组成的横断面训练和验证数据， 粪便微生物组，以及来自100名健康参与者的新队列的3天饮食召回数据。使用 这些数据，我们将完善并测试两种新型和独立的饮食推动算法，这些算法利用粪便 宏基因组和粪便不靶向代谢组。此外，使用从子集中取的样品 在这个新的队列（n = 40）中，我们将进行离体粪便培养实验，旨在直接量化 代谢通量和细菌的体外生长速率。这些通量数据将用于直接在计算机中测试 网络通量预测响应于饮食和宿主代谢物干预措施的潜水员面板。此外 为我们的Cyber​​gut模型，配对饮食，微生物组和 代谢组数据，包括复制通量测定，在此提案中生成的数据将是宝贵的资源 到精确的营养和人类微生物组研究界。总而言之，我们将建立，完善和测试 一个新颖的平台，用于跟踪饮食摄入量并预测对饮食的个性化营养反应，这具有 从根本上改变我们设计和测试饮食干预措施的潜力。