Combining chemical and computational tools for predictive models of microbiome communities

结合化学和计算工具来构建微生物群落的预测模型

• 批准号: 10029354
• 负责人: Cesar de la Fuente
• 金额: $ 34.27万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-05 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10029354
• 关键词: Address; Anaerobic Bacteria; Antibiotics; Bacteria; Biochemical Pathway; Biological Markers; Bioreactors; Biosensing Techniques; Biosensor; Cardiovascular Diseases; Cells; Chemicals; Communities; Devices; Diabetes Mellitus; Disease; Electrical Engineering; Encyclopedias; Engineering; Etiology; Foundations; Goals; Health; Human; Human Microbiome; Hybrids; Immunity; Individual; Inflammatory Bowel Diseases; Lead; Machine Learning; Mechanics; Mediating; Metabolism; Methodology; Microbiology; Mission; Molecular; Molecular Computations; Monitor; Obesity; Peptides; Play; Population; Property; Public Health; Research; Role; Source; Systems Biology; Testing; Therapeutic Intervention; United States National Institutes of Health; Work; antimicrobial peptide; base; chemical synthesis; computer science; computerized tools; design; experimental study; fecal transplantation; fundamental research; gut bacteria; gut microbes; gut microbiome; gut microbiota; in vivo evaluation; microbial community; microbiome; microbiome composition; microbiota; molecular dynamics; nervous system disorder; network models; novel therapeutics; nutrition; pathogenic bacteria; predictive modeling; quantum; real time monitoring; scaffold; screening; synthetic biology; targeted agent; temporal measurement; tool

ABSTRACT The gut microbiome has a tremendous impact on health and disease, actively contributing to obesity, diabetes, inflammatory bowel disease, cardiovascular diseases, and several poorly understood neurological disorders. We do not yet have the necessary tools to precisely probe these microbial communities, though such tools could unlock extensive benefits to human health. Elucidating the contributions of individual species or consortia of bacteria would provide a rational basis for understanding microbiota-controlled disease and lead to novel therapies. To carry out the fundamental research planned in this proposal, we will tackle three major problems: First, we will build the first set of molecular tools that effectively and precisely modulate the microbiome bacteria; second, we will analyze the multiscale dynamics of microbial communities; and third, we will construct an ingestible biosensor for real-time monitoring of microbiome populations. Although antibiotics and fecal transplants can reconfigure microbial consortia, they do not precisely target individual bacteria. Conversely, antimicrobial peptides (AMPs) have evolved to selectively attack pathogenic bacteria but do not target microbiome bacteria, constituting desirable scaffolds for molecular engineering and potential sources of microbiome-targeting agents. We will develop a new computational peptide design methodology, based on classical and hybrid-quantum mechanical molecular dynamics (MD) simulations, to create a groundbreaking assessment of the dynamical and emergent properties of AMPs. Chemical synthesis and large-scale screening will confirm predicted selectivity against microbiome species, and a machine learning workflow will connect sequences of individual peptides to their dynamics and activity. We will then apply the synthetic AMPs to interrogate the human microbiome by selectively removing species during bacterial consortia experiments, to be carried out in bioreactors, under regular or anaerobic conditions. We will pair our experiments with whole-cell metabolic network models, providing a systems biology perspective to the analysis of inter-species interactions. An integrated ingestible biosensing device will be developed to monitor the microbiome by electrochemically sensing unique biomarkers from gut microbes. This will provide the first real-time measurements of microbiome composition and will be integrated to our bioreactors for testing, to ultimately be used for in vivo tests. This work will build the first set of molecular and computational tools for microbiome engineering and will lay the foundation to address critical gaps in our understanding of the gut micro-environment, and of the contributions of gut bacteria to the etiology of disease. Grounded in our demonstrated expertise in synthetic biology, computer science, microbiology, and electrical engineering, this project will provide a computational- experimental framework for developing a peptide encyclopedia for the gut microbiome, in line with NIH's public health mission and goals.

摘要 肠道微生物群对健康和疾病有巨大的影响，积极地促进肥胖、糖尿病、 炎症性肠病、心血管疾病和几种鲜为人知的神经疾病。 我们还没有必要的工具来准确探测这些微生物群落，尽管这样的工具 可以为人类健康带来广泛的好处。阐明个别物种或联合体的贡献 将为理解微生物区系控制的疾病提供合理的基础，并导致新的 治疗。为了开展本方案规划的基础研究，我们将解决三大问题： 首先，我们将建立第一套有效和精确地调节微生物组的分子工具 第二，我们将分析微生物群落的多尺度动态；第三，我们将构建 一种用于实时监测微生物群落的可食用生物传感器。虽然抗生素和粪便 移植可以重新配置微生物群体，但它们并不精确地针对单个细菌。相反， 抗菌肽(AMP)已经进化为选择性地攻击致病菌，但不具有靶向性 微生物组细菌，构成分子工程的理想支架和潜在的来源 微生物群靶向制剂。我们将开发一种新的计算多肽设计方法，基于 经典和混合-量子力学分子动力学(MD)模拟，创造了突破性的 AMP的动力特性和紧急特性的评估。化学合成与大规模筛选 将确认对微生物组物种的预测选择性，并将连接机器学习工作流程 单个多肽的序列与其动力学和活性的关系。然后，我们将把合成AMP应用于 在细菌联合实验期间，通过选择性地去除物种来询问人类微生物组，以 在生物反应器中，在常规或厌氧条件下进行。我们将把我们的实验与全细胞配对 代谢网络模型，为分析物种间的相互作用提供了系统生物学的视角。 将开发一种集成的可摄取生物传感装置，用于电化学监测微生物群 从肠道微生物中感知独特的生物标记物。这将提供对微生物组的第一次实时测量 组成，并将被集成到我们的生物反应器进行测试，最终用于体内测试。这 工作将为微生物组工程建立第一套分子和计算工具，并将为 基金会，以解决我们对肠道微环境和贡献的理解中的关键差距 肠道细菌对疾病病因学的影响。基于我们在合成生物学方面的专业知识， 计算机科学、微生物学和电气工程，该项目将提供一种计算- 为肠道微生物组开发多肽百科全书的实验框架，符合NIH的公共标准 健康使命和目标。