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.

项目概要 肠道微生物组有助于复杂多糖的消化、维生素的吸收以及 将初级胆汁酸、药物和其他生物活性化合物转化为可吸收的代谢物 由主机。因此，共生微生物的代谢活动与人类生理密切相关 以及我们饮食的营养影响。然而，人们对生态学变异如何发生的了解还很有限。 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差异有助于解释人与人之间的异质性 血糖反应、血脂状况和体重减轻。在本提案中，我们提出了一个创新平台 使用宏基因组和饮食数据作为约束对肠道微生物组进行个性化代谢建模。我们 提出整合相关宿主组织的组织解析代谢模型，包括细胞类型特异性 将肠道上皮的代谢重建纳入我们现有的微生物群落模型中以改进估计 肠道微生物群、饮食和宿主之间的代谢通量。我们将其称为宿主-饮食-微生物组 代谢模型“Cyber​​Gut”。使用来自超过 3,000 名成年人的队列的现有多组学数据，我们将限制和 通过饮食、宿主血液代谢组和肠道微生物组的配对测量来验证 Cyber​​Gut。此外， 我们将生成由配对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详细 3 天饮食回忆数据。使用 根据这些数据，我们将完善和测试两种新颖且独立的饮食推断算法，该算法利用粪便 分别是宏基因组和粪便非靶向代谢组。此外，使用从子集中获取的样本 对于这个新队列（N = 40），我们将进行离体粪便培养实验，旨在直接量化 体外代谢通量和细菌生长速率。这些通量组数据将用于直接进行计算机测试 Cyber​​Gut 通量预测响应多种饮食和宿主代谢物干预。此外 为我们的 Cyber​​Gut 模型、配对饮食、微生物组和 本提案中生成的代谢组数据，包括重复通量组测定，将是宝贵的资源 致精准营养和人类微生物组研究界。总之，我们将构建、完善和测试 一个用于跟踪饮食摄入量和预测对饮食的个性化营养反应的新颖平台， 有可能从根本上改变我们设计和测试饮食干预措施的方式。