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

 描述(申请人提供)：由抗药性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)的患者特别容易受到这些感染，也是我们研究的重点人群。我们的目标是推广和进一步开发我们团队开创的系统生物学方法，以确定肠道微生物区系对万古霉素耐药肠球菌(VRE)、耐药肺炎克雷伯菌(ArKp)和艰难梭菌(C.diff)感染的抗性机制。我们项目的目标1是根据allo-HSCT患者首次住院期间的医院记录建立一个临床数据库，其中将包括所有实验室数据、生命体征、药学数据、饮食数据、症状和体检结果。目的2将扩大我们的粪便资料库，每年收集大约160名allo-HSCT患者的粪便样本，并通过培养和PCR确定VRE、arKp和C.diff的存在/缺失。我们将使用16S rRNA基因的NGS来确定每个样本上的微生物区系组成，将进行元基因组和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。目标3是扩展我们的数学模型，以确定微生物区系的特定成员、代谢途径和代谢产物，这些成员与胃肠道对VRE或arKp扩张的耐药性相关，或与对C.diff感染的耐药性相关。临床数据库将用于建立临床治疗或事件与肠道微生物区系或细菌代谢途径表达的变化之间的相关性。最终，在AIM 3中开发的计算平台将识别与耐药性有关的细菌物种或菌群 目标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种类，用这些保护性细菌定植小鼠，并测试对VRE、arKp和C.diff的耐药性。从这些实验研究中获得的样本将接受元基因组和代谢组学分析，以迭代的方式进一步完善在Aim 3中开发的计算模型。我们拟议的研究将开发新的和扩展现有的计算模型，以确定细菌种类和赋予耐药细菌感染耐药性的分子机制。

 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.