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

• 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
• 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

 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关键问题。对DNA测序数据的最新分析表明，这种感染与广谱抗生素的预防治疗有关。这是由于它们在杀死通常拮抗病原体的天然肠道微生物群中的作用。这些数据的计算分析应准备抗生素和粪便移植策略的优化。这是不可能的，因为当前使用的方法基于相关性。该项目的主要目的是将最近开发的新型数学建模工具与代谢途径推理和实验相结合，以预测肠道疾病的风险和原型设计，理性设计的粪便移植疗法将其最小化。利用初步工作，PI和合作者建议：预测使用16S rRNA受限的数学模型，预测临床上与临床相关的肠道病原体介导定殖的所有稳定的微生物群；将危险回归模型与微生物群动力学预测相结合，以评估住院患者肠道感染的风险；决定 与天然肠道细菌与肠道病原体之间相互作用相关的微生物代谢途径；通过实验验证建模预测，基于原型建模的粪便移植策略。最小化肠道疾病事件的有理疗法的设计取决于我们对肠道微生物群调节的动力学的理解。因此，拟议的研究与NIAID的使命及时且相关。新的预测模型应用于来自大量住院患者的DNA测序数据，将允许鉴定具有益生菌（和失调）特性的微生物群，以治疗为目标。对微生物动力学的预测，结合基于风险分析的新型统计模型，将提供第一个计算 代谢重建方法在数学建模预测中的应用将提供有关调节潜在代谢机制的新见解，并负责病原体难治性和兼容稳定稳态的预测稳定性。建模预测的实验验证不仅将允许评估开发的数学框架的预测能力，而且还将提供机会测试所提出的合理设计的粪便移植策略的功效。