Microbiome in TB treatment response and disease resolution

结核病的治疗由两个因素决定：需要联合化疗， 延长治疗时间。对于抗生素敏感性疾病， 将治愈>95%的治疗受试者的方法是2个月的INH/RIF/PZA/ETH，然后是4个月的 INH/RIF（2HRZE/4HR）。然而，大量的临床试验证据表明，大多数 治疗的受试者在较短的治疗时间内治愈，但我们缺乏任何临床或实验室 生物标志物，可以识别这些候选人的治疗缩短。使用的抗菌剂 治疗结核病的药物，包括药物敏感性和耐药性，主要是分枝杆菌特异性的， 直到最近，它们对肠道微生物组的影响还是未知的。在第一次获奖期间，这三个- I TBRU率先分析了TB感染中的肠道微生物组，从而推进了 肠道微生物组是结核病易感性和反应的一个未探索的辅助因素 接受治疗我们发现，在对结核病患者的横向和纵向研究中， HRZE疗法对肠道微生物组组成具有快速但持久的影响： 梭菌目已枯竭，其他分类群相对保存。梭菌是关键 微生物群的组分通过产生 多种化学介质，包括短链脂肪酸以及其他代谢物。 因此，我们还发现，使用新的统计建模技术， 前奖励期，结核病的解决可以建模为以下因素的综合影响： 病原体（Mtb）灭菌和抗分枝杆菌诱导的微生物组的免疫效应 扰动我们最重要的假设是微生物组组成的个体差异 和功能，无论是治疗前还是治疗期间由抗分枝杆菌药物诱导， 与不同的病原体清除率相关并可预测， 活动性TB的炎症标志物，以及最终的治疗成功（早期绝育和 没有复发）。我们提出的研究将扩展这一概念，以1）验证微生物组 衍生结核病治疗成功的生物标志物; 2）开发预测计算模型， 整合微生物组、转录组和微生物数据以预测治疗成功; 3）产量 对微生物组驱动的免疫调节和TB疾病的相互作用的机制性见解。 再加上这个TBRU的其他项目和核心，这个项目将推动我们的 了解控制少杆菌结核病。

Treatment of TB is defined by two factors: the requirement for combination chemotherapy and extended duration of therapy. For antibiotic sensitive disease, the shortest duration of therapy that will cure >95% of treated subjects is 2 months of INH/RIF/PZA/ETH, followed by 4 months of INH/RIF (2HRZE/4HR). However, abundant clinical trial evidence indicates that the majority of treated subjects are cured with shorter durations of treatment, yet we lack any clinical or laboratory biomarkers that can identify these candidates for treatment shortening. The antimicrobials used to treat TB, both drug-sensitive and drug-resistant, are predominantly mycobacterial-specific and until recently, their effects on the gut microbiome were unknown. In the first award period, this Tri- I TBRU pioneered the analysis of the intestinal microbiome in TB infection and thereby advanced the idea that the intestinal microbiome is an unexplored cofactor in TB susceptibility and response to therapy. We found, in both cross sectional and longitudinal studies of human subjects with TB, that HRZE therapy has rapid, but long-lasting, effects on intestinal microbiome composition: Clostridiales are depleted, with relative preservation of other taxons. Clostridiales are critical components of the microbiota that interact with the host immune system through production of diverse chemical mediators including short chain fatty acids as well as other metabolites. Accordingly, we have also found, using new statistical modeling techniques developed during the prior award period, that the resolution of TB disease can be modeled as a combined effect of pathogen (Mtb) sterilization and the immune effects of antimycobacterial-induced microbiome perturbation. Our overriding hypothesis is that individual differences in microbiome composition and function, either pretreatment or induced by antimycobacterials during treatment, are associated with, and predictive of, different rates of pathogen clearance, resolution of inflammatory markers of active TB, and ultimately treatment success (both early sterilization and lack of relapse). We propose studies that will expand this concept to 1) validate microbiome derived biomarkers of TB treatment success; 2) develop predictive computational models that integrate microbiome, transcriptomic, and microbiologic data to predict treatment success; 3) yield mechanistic insight into the interaction of microbiome driven immunomodulation and TB disease. Coupled with the other projects and cores of this TBRU, this project will advance our understanding of the control of paucibacillary TB.