Elucidating genetic mechanisms of Clostridioides difficile pathogenesis and patient immune manipulation

项目摘要 在他们的2019年抗生素耐药性威胁报告中，疾病控制和预防中心列出了 艰难梭菌(前称梭状芽孢杆菌)是一种紧迫的威胁。作为最常见的医疗保健相关 感染，它对个人的生活和整个医疗系统都有巨大的影响。 艰难梭菌感染(Cdi)最常与最近抗生素的使用有关。 光谱抗生素可导致正常肠道微生物区系的破坏，进而使艰难梭菌孢子 以萌发并压倒正常情况下将艰难梭菌挡在门外的剩余微生物群。 虽然CDI的患者风险因素已被很好地理解，但遗传变异在 感染菌株对严重CDI进展的影响较小。考虑到这两个领域的广泛多样性 常见艰难梭菌核心基因的核苷酸序列和基因含量的变异，很可能是 不同的艰难梭菌菌株与宿主的相互作用方式存在显著差异。事实上，在那里 已经有许多关于某些序列类型导致严重疾病的倾向的变化的报告， 尽管调节菌株水平差异的遗传变异在很大程度上是未知的。 在这项建议中，我采用数据驱动的方法来识别影响患者免疫的遗传变异 反应和临床轨迹。为此，我将利用通过以下方式创建的海量数据存储库 对密歇根医学院所有艰难梭菌阳性病例进行全面抽样。此存储库中包含以下内容 1,678株艰难梭菌全基因组测序，相关处理的电子健康记录数据来自 1516例患者，CDI时保存血清1178例。血清细胞因子水平 已经对其中220名患者进行了检测。为支持这项建议而进行的初步研究 证明感染菌株基因组中编码的变异对两个初始患者都有预测作用 免疫反应和随后的严重感染，支持菌株遗传背景的贡献 到病人的临床轨迹。我将在这些研究的基础上，尝试识别特定的变种，基因 以及调节临床结果差异的途径。为此，我将联合使用 机器学习和细菌全基因组关联研究(BGwas)以深入了解细菌遗传 影响患者免疫反应的特征，如通过血清细胞因子测量以及 细菌遗传变异与严重后果相关。然后我将通过以下方式验证这些生物信息学发现 通过比较预测的和实际的1)细胞因子测量来评估模型预测的准确性 被扣留的血清样本，以及2)CDI小鼠模型体内严重程度的结果。由此产生的理解 艰难梭菌影响患者细胞因子反应和严重预后的遗传因素中 用来改进当前的治疗策略，并指出针对CDI的新的治疗目标。

Project Summary In their 2019 Antibiotic Resistance Threats Report, the Centers for Disease Control and Prevention listed Clostridioides (formerly Clostridium) difficile as an urgent threat. As the most common healthcare-associated infection, it has an enormous impact on both the lives of individuals and the healthcare system at large. Developing a C. difficile infection (CDI) is most often associated with the recent use of antibiotics, as broad spectrum antibiotics can lead to a disruption of the normal gut microbiota, which in turn allows C. difficile spores to germinate and overwhelm the remaining microbiome that normally keeps the vegetative C. difficile at bay. Although the patient risk factors for CDI are fairly well understood, the potential roles of genetic variation in the infecting strain in influencing the progression to severe CDI are less so. Given the extensive diversity in both the nucleotide sequences of core genes and variation in gene content among common C. difficile strains, it is likely that there are significant differences in how different strains of C. difficile interact with the host. Indeed, there have been numerous reports of variation in the propensity for certain sequence types to cause severe disease, although the genetic variation mediating strain-level differences is largely unknown. In this proposal I take a data driven approach to identify genetic variants influencing patient immune responses and clinical trajectories. To accomplish this, I will leverage a massive data repository created through comprehensive sampling of all C. difficile positive cases at Michigan Medicine. Included in this repository are 1,678 C. difficile whole genome sequenced isolates, associated processed electronic health record data from 1,516 patients, and banked serum during the instance of CDI for 1178 patients. Serum cytokine levels have already been determined for 220 of these patients. Preliminary studies conducted in support of this proposal demonstrate that variation encoded in the genomes of infecting strains are predictive of both initial patient immune responses and subsequent severe infections, supporting the contribution of strain genetic background to patient clinical trajectories. I will build upon these studies and attempt to identify the specific variants, genes and pathways that are mediating variation in clinical outcomes. To this end I will employ a combination of machine learning and bacterial genome-wide association studies (bGWAS) to gain insight into bacterial genetic features that influence patient immune response as quantified by serum cytokine measurements, as well as bacterial genetic variation associated with severe outcomes. I will then validate these bioinformatic findings by evaluating the accuracy of model predictions by comparison of predicted and actual 1) cytokine measures on withheld serum samples, and 2) in vivo severity outcome in a mouse model of CDI. The resulting understanding of the genetic factors of C. difficile that impact patient cytokine response and severity outcome can then be leveraged to improve current treatment strategies, as well as indicate novel targets for therapy against CDI.