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

微生物组介导的对抗生素耐药病原体的恢复力的系统生物学

• 批准号: 9234463
• 负责人: Eric G. Pamer
• 金额: $ 169.71万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-03-01 至 2021-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9234463
• 关键词: Allogenic; Antibiotic Resistance; Antibiotic Therapy; Antibiotics; Bacteria; Bacterial Antibiotic Resistance; Bacterial Infections; Categories; Clinical; Clinical Treatment; Clostridium difficile; Collection; Computer Simulation; Data; Databases; Deposition; Development; Diagnostic radiologic examination; Diet; Dietary intake; Enrollment; Event; Feces; Gastrointestinal tract structure; Germ-Free; Gnotobiotic; Goals; Growth; Hematopoietic Stem Cell Transplantation; Hospitalization; Hospitals; Human; Immune; Immunocompromised Host; Infection; Integration Host Factors; Intestinal Content; Intestines; Klebsiella pneumonia bacterium; Laboratories; Lead; Machine Learning; Measurable; Mediating; Medical Records; Memorial Sloan-Kettering Cancer Center; Metabolic; Metabolic Pathway; Metagenomics; Modeling; Modification; Molecular; Mus; Patient risk; Patients; Pharmaceutical Preparations; Pharmacy facility; Population; Predisposition; Resistance; Resistance to infection; Risk; Sampling; Symbiosis; Symptoms; Systems Biology; Testing; Toxic effect; Transplantation; Uncertainty; Vancomycin Resistance; Vancomycin resistant enterococcus; antimicrobial drug; bacterial resistance; clinically relevant; commensal microbes; computer studies; design; experimental study; falls; gut microbiota; immune activation; mathematical model; member; metabolome; metabolomics; metagenome; metagenomic sequencing; microbiome; microbiota; microorganism interaction; mouse model; network models; novel; novel strategies; parallel computer; pathogen; patient population; prevent; public health relevance; rRNA Genes; resilience; resistance mechanism; text searching; transcriptome; transcriptome sequencing; transcriptomics

 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.

 描述（由申请人提供）：由抗生素耐药性细菌病原体引起的感染在免疫功能低下的宿主中极为常见，接受同种异体造血干细胞移植（allo-HSCT）的患者特别容易受到这些感染，也是我们研究的重点患者群体。我们的目标是扩展和进一步开发我们小组首创的系统生物学方法，以确定肠道微生物群对万古霉素耐药肠球菌感染的抵抗力的机制我们项目的目标 1 是根据异基因 HSCT 患者初次住院期间的医院记录建立一个临床数据库，其中包括所有实验室。目标 2 将通过每年收集约 160 名异基因 HSCT 患者的粪便样本来扩大我们的粪便库。通过培养和 PCR 确定是否存在 VRE、arKp 和 C. diff。我们将使用 16S rRNA 基因的 NGS 来确定每个样本的微生物群组成，将进行宏基因组和 RNA 测序以确定细菌转录组并进行代谢组学分析。目标 3 是扩展我们的数学模型，以确定与 VRE 或 arKp 扩展抵抗相关的微生物群、代谢途径和代谢产物的特定成员。胃肠道或与艰难梭菌感染抵抗相关。临床数据库将用于建立临床治疗或事件与肠道微生物群或细菌代谢途径表达的变化之间的相关性。最终，目标 3 中开发的计算平台。将识别与耐药性相关的细菌种类或菌群 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，并测试从这些实验研究中获得的样本对 VRE、arKp 和艰难梭菌的耐药性。将进行宏基因组和代谢组学分析，以迭代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并扩展现有的计算模型，以识别细菌种类和赋予抗生素抗性细菌耐药性的分子机制感染。