Mathematical modeling from metagenomics - minimizing risk of enteric infections

 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，这种感染与广谱抗生素的预防性治疗有关。这是由于它们在杀死天然肠道微生物群中的作用，通常拮抗病原体。这些数据的计算分析应有助于优化抗生素和粪便移植策略。这是不可能的，因为目前使用的方法是基于相关性。该项目的主要目标是将联合收割机与最新开发的新型数学建模工具结合代谢途径推断和实验，以预测肠道疾病的风险，并设计合理的粪便移植疗法原型，以最大限度地减少肠道疾病的风险。利用前期工作，PI和合作者建议：使用16 S rRNA约束数学模型预测介导临床相关肠道病原体定殖的所有稳定微生物群谱;将联合收割机风险回归建模与微生物群动力学预测相结合，以评估住院患者的肠道感染风险;确定 与天然肠道细菌和肠道病原体之间的相互作用相关的微生物代谢途径;通过对建模预测的实验验证，基于原型建模的粪便移植策略。设计合理的治疗方法，最大限度地减少肠道疾病的发病率取决于我们对肠道微生物群调节动力学的理解。因此，拟议的研究是及时的，与国家影响和发展研究所的使命相关。将新的预测模型应用于来自大量住院患者的DNA测序数据，将允许识别具有益生菌（和微生态失调）特性的微生物群状态，以通过治疗进行靶向。微生物动力学的预测，结合基于风险分析的新统计模型，将提供第一个计算 用于准实时监测肠道疾病风险的工具。代谢重建方法在数学建模预测中的应用将为调节和负责病原体难治性和相容性稳定稳态的预测稳定性的潜在代谢机制提供新的见解。模型预测的实验验证不仅将允许评估所开发的数学框架的预测能力，而且还将提供测试所提出的合理设计的粪便移植策略的功效的机会。

 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.