Large-scale profiling of microbial and metabolic interactions between C. difficile and gut microbiota using ultrahigh-throughput droplet microfluidics

项目摘要/摘要 艰难梭菌(简称艰难梭菌)是一种主要的耐药肠道病原菌，是引起肺炎的主要原因。 抗生素相关性腹泻和结肠炎，严重时可导致死亡。使用抗生素治疗 经常导致感染复发，然后进行粪便微生物区系移植(FMT)治疗。 在这种情况下，来自健康捐赠者的粪便样本被移植到感染艰难梭菌的患者体内，这是 已被证明可以防止感染复发。值得注意的是，FMT可能会导致负面的健康后果 包括因病原体的潜在传播和样本中的不明因素造成的死亡。这个 FMT的观察效果表明，共生肠道细菌在抑制感染方面发挥了关键作用 由艰难梭菌引起。以前的研究已经阐明了影响C. 艰难杆菌的定植，包括利用发炎的肠道和次级胆汁酸中的关键营养物质。 我们推测，有不同类别的生态和分子机制调节对 艰难梭菌取决于生态和环境背景。事实上，艰难梭菌感染的治疗 基于已定义的微生物区系，都没有被证明是成功的。为了阐明不同类别的机制，我们 提议开发一种液滴微流控工作流程，以构建数百万个合成的人类肠道群落， 根据艰难梭菌的丰度筛选这些联合体，并确定分选的物种组成 “点击率。”将这种方法与外代谢组学和机器学习技术相结合，我们将推断微生物 影响艰难梭菌生长的相互作用和代谢产物效应物。对多样性的详细了解 抑制艰难梭菌生长的群落类型和代谢特性将是迈向 针对这一主要肠道病原体设计安全有效的治疗方法。

PROJECT SUMMARY/ABSTRACT Clostridium difficile (C. difficile) is a major antibiotic resistant intestinal pathogen that is a leading cause of antibiotic associated diarrhea and colitis which, in severe cases, can lead to death. Treatment with antibiotics frequently leads to recurrence of the infection, which is then treated with fecal microbiota transplantation (FMT). In this case, a fecal sample from a healthy donor is transplanted into a patient with C. difficile infection, which has been shown to prevent recurrence of infections. Notably, FMT can lead to negative health outcomes including death due to the potential transmission of pathogens and uncharacterized factors in the samples. The observed efficacy of FMT suggests that commensal gut bacteria play a critical role in suppressing infection caused by C. difficile. Previous studies have elucidated specific molecular mechanisms that can influence C. difficile colonization including exploitation of key nutrients available in the inflamed gut and secondary bile acids. We postulate that there are diverse classes of ecological and molecular mechanisms mediating protection from C. difficile depending on the ecological and environmental context. Indeed, treatments of C. difficile infection based on defined microbiota have not proven successful. To elucidate the diverse classes of mechanisms, we propose to develop a droplet microfluidic workflow to construct millions of synthetic human gut communities, screen these consortia based on the abundance of C. difficile and determine species composition of the sorted “hits.” Combining this method with exo-metabolomics and machine learning techniques, we will infer microbial interactions and metabolite effectors impacting C. difficile growth. A detailed understanding of the diverse community types and metabolic properties that suppress C. difficile growth will be a major advance towards designing safe and effective treatments for this major intestinal pathogen.