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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

基本信息

批准号：
10251270
负责人：
Cesar de la Fuente
金额：
$ 34.03万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
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/10251270
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

项目摘要

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