Optimized identification of therapeutic bacterial strains in ulcerative colitis

项目摘要 溃疡性结肠炎（UC）是一种慢性肠道炎症性疾病，被认为是由遗传性结肠炎和结肠炎的组合引起的。 和环境因素。治疗方案在诱导和维持 缓解，并且目前没有治愈UC的方法。最近的研究发现，UC患者的微生物组 与健康对照组相比，这表明微生物组可能是一种有前途的治疗方法。 目标粪便微生物群移植（FMT）在治疗由结肠炎引起的结肠炎方面非常成功。 艰难梭菌感染，进一步证明了细菌治疗的潜力。最近 已完成的FOCUS（溃疡性结肠炎粪便微生物群移植）试验表明， 来自健康捐赠者的药物可以在27%的UC患者中诱导临床缓解，而安慰剂仅为8% 组然而，我们仍然缺乏对FMT能够调节的确切机制的理解， 发病机理该建议的总体假设是，FMT后UC缓解是 诱导的特定细菌，并准确鉴定细菌菌株将是至关重要的， 开发UC的微生物疗法。为了验证这一假设，我们将首先开发一个新的合奏 从深层宏基因组数据鉴定细菌物种和菌株的方法。该方法结合软件 使用基于每个工具的准确性进行加权的投票算法的工具。该方法的准确性 将使用UC患者的微生物培养物样本、计算机模拟和细菌模拟进行测试 作为黄金标准。然后，我们将把这种方法应用于从基因组数据生成的宏基因组数据。 FOCUS研究，以鉴定与UC缓解相关的细菌菌株。这将被执行 通过使用已建立的监督学习技术，以及通过一种新的方法， 发生网络分析，识别协同作用的细菌簇。基于这些结果， 我们将最终使用结肠炎的非细菌模型来验证我们的发现。的预防和/或治疗 候选细菌的潜力将通过将它们接种到定植有 UC患者的微生物群和通过幼稚T细胞转移诱导的结肠炎。我们的提案将是 首先在一项随机试验中解决了特定细菌如何调节疾病进展的问题， 通过开发更灵敏、更准确的菌株表征方法， 可以通过实验验证。合理设计我们的策略，以确定治疗细菌将产生 高度转化的结果，可用于指导未来的UC临床试验。

PROJECT SUMMARY Ulcerative colitis (UC) is a chronic gut inflammatory condition thought to be caused by a combination of genetic and environmental factors. Therapeutic options are only partially effective in inducing and maintaining remission, and there is currently no cure for UC. Recent studies have found the microbiome of UC patients is distinct compared to that of healthy controls, suggesting that the microbiome could be a promising therapeutic target. Fecal microbiota transplant (FMT) has been extremely successful in the treatment of colitis caused by Clostridium difficile infection, further demonstrating the potential for bacterial therapeutics. The recently completed FOCUS (Faecal Microbiota Transplantation in Ulcerative Colitis) trial has demonstrated that FMT from healthy donors can induce clinical remission in 27% of UC patients compared to only 8% in the placebo group. However, we still lack an understanding of the exact mechanisms by which FMT is able to modulate disease pathogenesis. The overarching hypothesis of this proposal is that UC remission after FMT is induced by specific bacteria, and that accurate identification of bacterial strains will be paramount to develop microbial therapeutics for UC. To test this hypothesis, we will first develop a novel ensemble method to identify bacterial species and strains from deep metagenomic data. This method combines software tools using a voting algorithm that is weighted based on the accuracy of each tool. The accuracy of this method will be tested using microbial culture collections from UC patients, in silico simulations, and a bacterial mock community as gold standards. We will then apply this approach to metagenomic data generated from the FOCUS study in order to identify bacterial strains that are associated with UC remission. This will be performed by using established supervised learning techniques, as well as through a novel method based on co- occurrence network analysis that identifies clusters of bacteria that act synergistically. Based on these results, we will finally validate our findings using a gnotobiotic model of colitis. The prophylactic and/or therapeutic potential of candidate bacteria will be tested by inoculating them to gnotobiotic mice colonized with the microbiota of UC patients and with colitis induced through the transfer of naïve T cells. Our proposal will be the first to address the question of how specific bacteria modulate disease progression in a randomized trial of FMT in UC through the development of more sensitive and accurate methods for strain characterization that can be experimentally validated. The rational design of our strategy to identify therapeutic bacteria will produce highly translational results that can be used to guide future clinical trials in UC.