Systems Biology of Microbiome-mediated Resilience to Antibiotic-resistant Pathogens

 描述（由申请人提供）：由耐药细菌病原体引起的感染在免疫功能低下的宿主中非常常见。接受异基因造血干细胞移植（allo-HSCT）的患者对这些感染特别敏感，是我们研究的重点人群。我们的目标是扩展和进一步开发我们小组率先采用的系统生物学方法，以确定肠道微生物群对万古霉素耐药肠球菌（VRE）、肺炎克雷伯菌（arKp）和艰难梭菌（C. diff）。我们项目的目标1是从allo-HSCT患者的医院病历中建立一个临床数据库，其中包括所有实验室值、生命体征、药房数据、饮食数据、症状和体格检查结果。目标2将通过每年收集约160例allo-HSCT患者的粪便样本并确定VRE、arKp和C的存在/不存在来扩大我们的粪便库。通过培养和PCR鉴定。我们将使用16 S rRNA基因的NGS来确定每个样本上的微生物群组成，将进行宏基因组和RNA测序来确定细菌转录组，并对选定的粪便样本子集进行代谢组学分析。目的3是扩展我们的数学建模，以识别与胃肠道中对VRE或arKp扩增的抗性相关或与对C的抗性相关的微生物群、代谢途径和代谢产物的特定成员。diff感染临床数据库将用于建立临床治疗或事件与肠道微生物群变化或细菌代谢途径表达之间的相关性。最终，在aim 3中开发的计算平台将识别与耐药性相关的细菌物种或菌群 Aim 4将在无菌小鼠模型中测试这些关联。我们将培养与耐药性相关的细菌物种，用这些保护性细菌定殖小鼠，并测试对VRE、arKp和C. diff.从这些实验研究中获得的样本将进行宏基因组学和代谢组学分析，以迭代的方式进一步完善目标3中开发的计算模型。我们提出的研究将开发新的和扩展现有的计算模型，以确定细菌物种和分子机制，赋予耐药性细菌感染的抗性。

 DESCRIPTION (provided by applicant): Infections caused by antibiotic-resistant bacterial pathogens are exceedingly common in immunocompromised hosts. Patients undergoing allogeneic hematopoietic stem cell transplantation (allo-HSCT) are particularly susceptible to these infections and are the patient population our studies will focus upon. Our goal is to extend and further develop systems biology approaches that our group has pioneered to identify mechanisms by which the intestinal microbiota confers resistance to infection by Vancomycin-resistant Enterococcus (VRE), antibiotic-resistant Klebsiella pneumoniae (arKp) and Clostridium difficile (C. diff). Aim 1 of our project is to establish a clinical database from the hospital recrds of allo-HSCT patients during their initial hospitalization that will include all laboratory values,vital signs, pharmacy data, dietary data, symptoms and physical exam findings. Aim 2 will expand our fecal bank by collecting fecal samples from approximately 160 allo-HSCT patients per year and determining the presence/absence of VRE, arKp and C. diff by culture and PCR. We will use NGS of 16S rRNA genes to determine microbiota composition on each sample, will perform metagenomic and RNA sequencing to determine the bacterial transcriptome and perform metabolomic analyses on a selected subset of fecal samples. Aim 3 is to extend our mathematical modeling to identify specific members of the microbiota, metabolic pathways and metabolic products that correlate with resistance to VRE or arKp expansion in the GI tract or are associated with resistance to C. diff infection. The clinical database will be used to establish correlations between clinical treatments or events and changes in the intestinal microbiota or the expression of bacterial metabolic pathways. Ultimately, the computational platforms developed in aim 3 will identify bacterial species or consortia that are associated with resistance to infection and Aim 4 will test these associations in germ-free mouse models. We will culture bacterial species associated with resistance, colonize mice with these protective bacteria and test for resistance against VRE, arKp and C. diff. Samples obtained from these experimental studies will be subjected to metagenomic and metabolomic analyses to further refine, in an iterative fashion, computational models developed in aim 3. Our proposed studies will develop new and extend existing computational models to identify bacterial species and molecular mechanisms that confer resistance to antibiotic-resistant bacterial infections.