Investigating genomic factors and microbiome features that impact CDI transmission and prognosis

研究影响 CDI 传播和预后的基因组因素和微生物组特征

• 批准号: 8946067
• 负责人: Ali M Bashir
• 金额: $ 84.75万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-15 至 2020-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8946067
• 关键词: Address; Admission activity; Affect; Antibiotic Therapy; Antibiotics; Bacteria; Bacterial Genome; Biological Assay; Centers for Disease Control and Prevention (U.S.); Clinical; Clostridium difficile; Communicable Diseases; Communities; Community Hospitals; Complement; Computerized Medical Record; Data; Data Set; Data Sources; Development; Diagnosis; Disease; Disease Outbreaks; Electronic Health Record; Eligibility Determination; Environmental Risk Factor; Epigenetic Process; Equipment; Event; Exposure to; Feces; Gene Transfer; Generic Drugs; Genetic; Genetic Variation; Genome; Genomics; Genotype; Goals; Healthcare; High-Throughput Nucleotide Sequencing; Hospitalization; Hospitals; In Situ; Individual; Infection; Infection Control; Intensive Care Units; Kidney Transplantation; Knowledge; Length; Link; Liver; Location; Maps; Methods; Mining; Modeling; Morbidity - disease rate; Nosocomial Infections; Outcome; Pathogenesis; Pathogenicity; Patient Care; Patients; Plasmids; Predisposing Factor; Prophages; Prospective Studies; Reading; Resolution; Resources; Ribosomal RNA; Risk; Risk Factors; Role; Sampling; Source; Surveys; System; Technology; Testing; Time; Transplant Recipients; Treatment Protocols; Variant; Virulence; bacterial genetics; base; biobank; causal model; cohort; cost; cost efficient; epigenomics; genome sequencing; genomic variation; gut microbiota; health record; high risk; improved; interest; longitudinal analysis; metagenomic sequencing; microbiome; mortality; next generation sequencing; novel; novel strategies; outcome forecast; pathogen; predictive modeling; public health relevance; radiofrequency; response; screening; tool; transmission process

 DESCRIPTION (provided by applicant): Clostridium difficile infection (CDI) is frequently attributed to healthcare exposure and is associated with significant morbidity and mortality. While it is known that certain C. difficile strains and environmental factors such as a compromised gut microbiota are associated with greater virulence and poor prognosis, the full spectrum of CDI risk factors remains elusive. It is estimated that between 20-30% of CDI cases result from transmission between infected patients but given the paucity of data on colonized asymptomatic C. difficile carriers it is unlikely that these represent all transmission events. For patients with prior colonization, the extent to which this predisposes them to develop CDI is not fully understood. Our objective is to address these and other knowledge deficits in CDI transmission and pathogenesis in a prospective study of two high-risk patient cohorts at the Mount Sinai Hospital: patients admitted to intensive care units and liver/kidney transplant recipients. For each cohort we will obtain fecal samples on admission and at regular intervals during their hospitalization. Pre- and post-infection samples of 325 patients that develop CDI, as well as 650 time-matched controls, will be analyzed to an unprecedented level using high-throughput sequencing and screening approaches to characterize colonizing and infectious C. difficile isolates and the associated gut microbiome. Key to our approach are novel long-read genome sequencing technologies that enable rapid, cost efficient whole-genome assembly of C. difficile strains from fecal samples of CDI patients. Based on identified variants between these genomes, we will construct a low-cost screening protocol; the assay will detect distinct C. difficile strains, even when present at very low abundances, across our entire set of fecal samples. This will allow us to differentiate community from hospital-acquired infections and comprehensively map C. difficile transmission networks. Concomitantly, we will evaluate changes in the microbiome by performing deep metagenomic sequencing of fecal samples from CDI patients at the time of admission, as well as 16S sequencing from multiple time points prior to infection. The resulting data constitutes a large, longitudinal cross section of the microbiota on which to evaluate CDI progression with response to treatments such as antibiotics. We will integrate our high-resolution map of colonizing and infectious C. difficile isolates, and their corresponding microbiota background, with data from patient electronic health records to identify CDI risk factors and probe the impact of C. difficile genomic variation on disease. In addition to addressing major outstanding questions regarding the onset of CDI in healthcare settings, our project will deliver the most accurate predictive modeling of CDI to date. Moreover, the resulting dataset represents a tremendous genomic resource to the community in understanding the dynamic between C. difficile and the overall microbiome within patients. We anticipate our findings to have a major impact on treatment and infection control practices which will ultimately result in reduced CDI rates.

 描述（由申请方提供）：艰难梭菌感染（CDI）通常归因于医疗暴露，并与显著的发病率和死亡率相关。虽然已知某些C.尽管艰难梭菌菌株和环境因素如受损的肠道微生物群与更大的毒力和不良预后相关，但CDI风险因素的全谱仍然难以捉摸。据估计，20-30%的CDI病例是由感染患者之间的传播引起的，但由于缺乏关于定殖的无症状C。困难的载波，这些不太可能代表所有的传输事件。为 对于既往定植的患者，尚不完全清楚这在多大程度上使其易于发生CDI。我们的目标是在西奈山医院的两个高风险患者队列的前瞻性研究中解决CDI传播和发病机制中的这些和其他知识缺陷：入住重症监护病房的患者和肝/肾移植受者。对于每个队列，我们将在入院时和住院期间定期采集粪便样本。将使用高通量测序和筛选方法对325例发生CDI的患者的感染前和感染后样本以及650例时间匹配的对照进行前所未有的分析，以表征定植和感染性C。艰难梭菌分离株和相关的肠道微生物组。 我们的方法的关键是新的长读基因组测序技术，使快速，成本效益的全基因组组装的C。来自CDI患者粪便样品的艰难梭菌菌株。基于这些基因组之间已确定的变异，我们将构建一个低成本的筛选方案;该检测方法将检测不同的C。艰难菌株，即使在非常低的丰度，在我们的整个粪便样本集。这将使我们能够区分社区和医院获得性感染，并全面绘制C。艰难的传输网络。同时，我们将通过对CDI患者入院时的粪便样本进行深度宏基因组测序以及感染前多个时间点的16 S测序来评估微生物组的变化。由此产生的数据构成了微生物群的大的纵向横截面，在其上评估CDI进展与对诸如抗生素的治疗的响应。我们将整合我们的高分辨率地图的殖民和传染性C。艰难梭菌分离株及其相应的微生物群背景，与来自患者电子健康记录的数据，以确定CDI风险因素和探测C.基因组变异对疾病的影响。 除了解决医疗环境中关于CDI发病的主要悬而未决的问题外，我们的项目还将提供迄今为止最准确的CDI预测模型。此外，由此产生的数据集代表了一个巨大的基因组资源的社区在理解C。艰难梭菌和患者体内的整体微生物组。我们预计我们的研究结果将对治疗和感染控制实践产生重大影响，最终导致CDI率降低。