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: 10827347
• 负责人: Sean Michael Gibbons
• 金额: $ 28.13万
• 依托单位: INSTITUTE FOR SYSTEMS BIOLOGY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10827347
• 关键词: Adult; Algorithms; Bile Acids; Biological; Biological Assay; Blood; Body Weight decreased; Communities; Complex; Data; Diet; Dietary Intervention; Dietary intake; Digestion; Ecology; Feces; Growth; Health; Heterogeneity; Human; Human Microbiome; In Vitro; Intervention; Maps; Measurement; Metabolic; Metabolism; Metagenomics; Modeling; Multiomic Data; Nutritional; Participant; Persons; Pharmaceutical Preparations; Phenotype; Physiology; Polysaccharides; Precision Health; Resources; Sampling; Structure; Testing; Tissues; Training; Validation; Variant; Vitamins; Work; absorption; blood lipid; cell type; cohort; commensal microbes; design; dietary; experimental study; fecal metabolome; fecal microbiome; gastrointestinal epithelium; gut microbiome; gut microbiota; host microbiome; improved; in silico; innovation; metabolic phenotype; metabolome; metabolomics; metagenome; microbial; microbial community; microbiome; microbiome research; novel; nutrition; personalized predictions; precision nutrition; reconstruction; response

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复杂多糖的消化、维生素的吸收和 初级胆汁酸、药物和其他生物活性化合物转化为可被吸收的代谢物 主持人。因此，肠道微生物的代谢活动与人体生理密切相关 以及我们饮食的营养影响。然而，人们对生态学中的变异如何理解有限。 我们的肠道植物群调节饮食对人类健康和营养的生物影响。最近的研究工作表明 肠道微生物组组成的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。在这份提案中，我们提出了一个创新的平台， 使用宏基因组和饮食数据作为约束条件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分辨代谢模型，包括细胞类型特异性 肠道上皮的代谢重建，到我们现有的微生物群落模型，以改善估计 肠道微生物群、饮食和宿主之间的代谢通量。我们称之为宿主饮食微生物组 代谢模型“CyberGut "使用现有的多组学数据，从一个队列的>3,000名成年人，我们将限制和 验证CyberGut与配对测量饮食，宿主血液代谢组和肠道微生物组。此外，本发明还提供了一种方法， 我们将生成由成对的血液和粪便代谢组组成的横截面训练和验证数据， 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 这些数据，我们将完善和测试两个新的和独立的饮食推理算法，利用粪便 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 在这个新的队列（N=40）中，我们将进行离体粪便培养实验，旨在直接定量 代谢通量和体外细菌生长速率。这些通量组学数据将用于直接进行计算机模拟测试。 CyberGut通量预测响应于饮食和宿主代谢物干预的不同面板。此外 为完善和测试我们的CyberGut模型、配对饮食、微生物组和 代谢组学数据，包括重复的通量组学分析，将是一个宝贵的资源 精准营养和人类微生物组研究社区。总之，我们将构建、完善和测试 一个跟踪饮食摄入量和预测个性化饮食营养反应的新平台， 有可能从根本上改变我们设计和测试饮食干预的方式。

项目成果

A changing gut virome ecological landscape with longevity.

不断变化的肠道病毒组生态景观和长寿。

• DOI: 10.1016/j.tim.2023.07.013
• 发表时间: 2023
• 期刊: Trends in microbiology
• 影响因子: 15.9
• 作者: Wilmanski,Tomasz;Gibbons,SeanM
• 通讯作者: Gibbons,SeanM

Generally-healthy individuals with aberrant bowel movement frequencies show enrichment for microbially-derived blood metabolites associated with reduced kidney function.

排便频率异常的一般健康个体表现出与肾功能下降相关的微生物来源的血液代谢物的富集。

• DOI: 10.1101/2023.03.04.531100
• 发表时间: 2024
• 期刊: bioRxiv : the preprint server for biology
• 作者: Johnson-Martínez,JohannesP;Diener,Christian;Levine,AnneE;Wilmanski,Tomasz;Suskind,DavidL;Ralevski,Alexandra;Hadlock,Jennifer;Magis,AndrewT;Hood,Leroy;Rappaport,Noa;Gibbons,SeanM
• 通讯作者: Gibbons,SeanM

Disease-specific loss of microbial cross-feeding interactions in the human gut.

• DOI: 10.1038/s41467-023-42112-w
• 发表时间: 2023-10-20
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Marcelino, Vanessa R.;Welsh, Caitlin;Diener, Christian;Gulliver, Emily L.;Rutten, Emily L.;Young, Remy B.;Giles, Edward M.;Gibbons, Sean M.;Greening, Chris;Forster, Samuel C.
• 通讯作者: Forster, Samuel C.

More is Different: Metabolic Modeling of Diverse Microbial Communities.

更多就是不同：不同微生物群落的代谢模型。

• DOI: 10.1128/msystems.01270-22
• 发表时间: 2023-04-27
• 期刊: mSystems
• 影响因子: 6.4