Mathematical modeling from metagenomics - minimizing risk of enteric infections

宏基因组学的数学模型 - 最大限度地降低肠道感染的风险

基本信息

批准号：
8879331
负责人：
Vanni Bucci
金额：
$ 46.47万
依托单位：
UNIVERSITY OF MASSACHUSETTS DARTMOUTH
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-02-05 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8879331
关键词：
Adoption Antibiotic Therapy Antibiotics Bacteremia Bacteria Biological Assay Bone Marrow Transplantation Calculi Clinic Collaborations Communities Computer Analysis Computing Methodologies Coupling DNA Sequence DNA Sequence Analysis Data Data Set Development Diagnostic Disease Ecology Enteral Enterobacteriaceae Enterococcus Goals Healthcare In Vitro Incidence Infection Intestines Invaded Knowledge Malignant Neoplasms Mediating Metabolic Metabolic Pathway Metagenomics Methods Microbe Mission Modeling Monitor Mus National Institute of Allergy and Infectious Disease Pathway interactions Patients Population Probiotics Property Prophylactic treatment Publishing Refractory Research Resistance Ribosomal RNA Risk Role Statistical Models Taxon Time Transplantation Universities Validation Work base clinically relevant combinatorial computer framework computerized tools cost design efficacy testing enteric pathogen gut microbiota hazard insight killings mathematical methods mathematical model microbial microbiome novel pathogen predictive modeling prophylactic prototype public health relevance reconstruction research study risk minimization targeted treatment theories tool

项目摘要

DESCRIPTION: Enteric infections represent a critical issue in today's healthcare. Recent analysis of DNA sequencing data has demonstrated that such infections are associated with the prophylactic treatment with broad-spectrum antibiotics. This is due to their role in killing the native intestinal microbiota, which normally antagonizes pathogens. Computational analysis of these data should facilitate the optimization of antibiotic and fecal transplantation strategies. This is not yet possible because the currently used methods are based on correlations. The main goal of this project is to combine recently developed and novel mathematical modeling tools with metabolic pathways inference and experimentation to predict the risk of enteric diseases and to prototype rationally designed fecal transplantation therapies to minimize it. Leveraging on preliminary work, the PI and collaborators propose to: predict all the stable microbiota profiles mediating colonization by clinically-relevant enteric pathogens using 16S rRNA-constrained mathematical models; combine hazard regression modeling with microbiota dynamics predictions to evaluate the risk of enteric infections in hospitalized patients; determine microbial metabolic pathways associated with the interactions between native intestinal bacteria and enteric pathogens; prototype modeling-based fecal transplantation strategies by experimental validation of modeling predictions. The design of rational therapies minimizing the incidence of enteric diseases depends on our understanding of the dynamics regulating the intestinal microbiota. For this reason, the proposed research is timely and relevant to the mission of the NIAID. The application of new predictive models to DNA sequencing data from a large population of hospitalized patients will allow identifying microbiota states with probiotic (and dysbiotic) properties to be targeted by therapies. The forecasting of microbial dynamics, combined with novel statistical models based on risk analysis, will deliver the first computational tool for monitoring the risk of enteric diseases in quasi-real time. The application of metabolic reconstruction methods to the mathematical modeling predictions will provide new insights about potential metabolic mechanisms regulating and responsible for the predicted stability of pathogen-refractory and compatible stable steady states. The experimental validation of the modeling predictions, not only will allow evaluating the predictive power of the developed mathematical frameworks, but also will provide the opportunity to test the efficacy of the proposed rationally designed fecal transplantation strategies.

描述：肠道感染是当今医疗保健中的一个关键问题，最近对 DNA 测序数据的分析表明，此类感染与广谱抗生素的预防性治疗有关，这是因为它们在杀死通常情况下的天然肠道微生物群中发挥着作用。对这些数据的计算分析应该有助于优化抗生素和粪便移植策略，因为目前使用的方法是基于相关性的。最近开发的新型数学建模工具，通过代谢途径推断和实验来预测肠道疾病的风险，并设计合理设计的粪便移植疗法以将其最小化。利用前期工作，PI 和合作者建议：预测所有稳定的微生物群概况。使用 16S rRNA 约束的数学模型介导临床相关肠道病原体的定植；将风险回归模型与微生物群动态预测相结合，评估住院患者肠道感染的风险；与天然肠道细菌和肠道病原体之间相互作用相关的微生物代谢途径；通过模型预测的实验验证基于原型模型的粪便移植策略，最大限度地减少肠道疾病的发生率取决于我们对肠道调节动力学的理解。因此，拟议的研究是及时的，并且与 NIAID 的使命相关。对大量住院患者的 DNA 测序数据应用新的预测模型将有助于识别具有益生菌（和失调）特性的微生物群状态。微生物动力学的预测与基于风险分析的新型统计模型相结合，将提供第一个计算。准实时监测肠道疾病风险的工具将代谢重建方法应用于数学模型预测将为调节和负责病原体难治性和相容稳定状态的预测稳定性提供新的见解。模型预测的实验验证不仅可以评估所开发的数学框架的预测能力，而且还提供了测试所提出的合理设计的粪便移植策略的有效性的机会。