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

 描述(由申请人提供)：艰难梭菌感染(CDI)经常被归因于医疗保健暴露，并与显著的发病率和死亡率有关。虽然已知某些艰难梭菌菌株和环境因素，如肠道微生物区系受损，与更强的毒力和较差的预后有关，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，但由于缺乏关于定植的无症状艰难梭菌携带者的数据，这些病例不太可能代表所有的传播事件。为 对于既往移居的患者，这在多大程度上使他们容易发生CDI尚不完全清楚。我们的目标是在西奈山医院两个高危患者队列的前瞻性研究中解决CDI传播和发病机制方面的这些和其他知识缺陷：住进重症监护病房的患者和肝/肾移植接受者。对于每个队列，我们将在入院时和在他们住院期间定期获取粪便样本。将使用高通量测序和筛选方法，对325名发生CDI的患者以及650名时间匹配的对照组的感染前和感染后样本进行前所未有的分析，以表征定植和感染性艰难梭菌分离株和相关的肠道微生物群。我们方法的关键是新的长读基因组测序技术，该技术能够从CDI患者的粪便样本中快速、经济高效地组装艰难梭菌菌株。基于这些基因组之间已识别的变异，我们将构建一个低成本的筛选程序；该分析将在我们的整个粪便样本中检测到不同的艰难梭菌菌株，即使存在非常低的丰度。这将使我们能够区分社区和医院获得性感染，并全面绘制艰难梭菌传播网络图。与此同时，我们将通过对CDI患者入院时的粪便样本进行深度元基因组测序，以及在感染前的多个时间点进行16S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区系的一个大的纵向横截面，在此基础上评估CDI进展与抗生素等治疗的反应。我们将把我们的定植和传染性艰难梭菌分离株的高分辨率图谱及其相应的微生物区系背景与患者电子健康记录中的数据相结合，以确定CDI的风险因素，并探索艰难梭菌基因组变异对疾病的影响。除了解决有关CDI在医疗环境中的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，由此产生的数据集为社区提供了一个巨大的基因组资源，以了解艰难梭菌和患者体内整个微生物组之间的动态。我们预计我们的发现将对治疗和感染控制实践产生重大影响，最终将导致CDI发生率降低。

 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.