Evolutionary and Functional Significance of Novel Mutations in MDR-XDR TB

2015年，全球有1040万例活动性结核病（TB）病例，140万人死于结核病。 TB.虽然我们有有效的结核病治疗，但耐药病例的发生率正在增加， 威胁到结核病控制工作。2015年，48万例新的耐多药结核病（MDR-TB）病例 本文报告其中，近6万人是广泛耐药结核病（XDR-TB）。的 在某些地区情况更糟。在白俄罗斯，几乎每两个病例中就有一个（48%）是 耐多药结核病（新发结核病患者占35.3%，既往接受过治疗的结核病患者占76.5%）。因为全球治愈率 耐多药结核病的发病率仍远低于世卫组织为2015年设定的目标（ 由于广泛耐药结核治疗成功率甚至更低（白俄罗斯为20%）， 迫切需要更好地了解问题，并确定/评估新的 因此，我们建议使用毒品/混合毒品，因为白俄罗斯的情况可能会蔓延。 两项联合治疗试验涉及四种新的和重新用途的药物贝达喹啉， 利奈唑胺、氯法齐明和delamanid正在世界卫生组织的资助下进行 作为这两项试验的一部分，每月的痰液样本将被 从所有患者中收集6个月。不幸的是，在第一次试验中， 完成联合治疗后，6例治疗失败，1例死亡 已经被记录下来了。该项目旨在利用这两项试验所创造的资源， 揭示以前未知的耐药性机制，耐药性的进化路径， 以及对四种新的/重新利用的药物产生耐药性的时间轴。我们的方法将是使用在 计算机比较基因组和表观遗传学（甲基化组和转录组）分析， 策划治疗失败的非特征性（epi）基因组变化的综合目录 例基因和调控元件的计算机功能表征 对五种研究药物的耐药性将阐明耐药性的机制。随后的 时间进程样本的基因组分析和MIC表征将使我们能够 了解抗性的进化路径，以及每次抗性水平的变化 进化事件这将使我们能够理解，例如， 逐步增加的水平，或自发地在高水平。对于前者，我们将 能够识别与每种抗性相关的逐步遗传和甲基化标记 水平这允许临床医生决定是否增加药物剂量或改变药物浓度。 药物疗法是最好的治疗方法。

In 2015, there were 10.4 million cases of active tuberculosis (TB) and 1.4 million deaths due to TB. While we have effective TB treatment, the incidence of drug resistant cases is increasing and threatens TB control efforts. In 2015, 480,000 new cases of multi drug-resistant TB (MDR-TB) were reported. Among these, nearly 60,000 were extensively drug-resistant TB (XDR-TB). The scenario is much worse in some regions. In Belarus, close to one of each two cases (48%) are MDR-TB (35.3% in new and 76.5% in previously treated TB-patients). Since the global cure rate of MDR-TB is still well below the target set by WHO for 2015 (exceedingly low in many parts of the world), and since XDR-TB treatment success rates are even lower (20% in Belarus), there is an urgent need for improved understanding of the problem and to identify/evaluate new drugs/combinations of drugs as the situation in Belarus is likely to spread. Two trials for combinatorial treatment involving four new and repurposed drugs bedaquiline, linezolid, clofazimine, and delamanid are underway thanks to funding from the World Health Organization and the Global Fund. As part of these two trials, monthly sputum samples will be collected for six months from all patients. Unfortunately, in the first trial with 30 patients having completed the combinatorial treatment, in six the treatment has failed, and one death has been recorded. This project aims to leverage the resources created by the two trials in order to uncover previously unknown mechanisms of drug resistance, evolutionary path to resistance, and timeline to resistance to the four new/repurposed drugs. Our approach will be to use in silico comparative genomic and epigenetic (methylome and transcriptomic) analysis in order to curate a comprehensive catalog of uncharacterized (epi)genomic changes in failed treatment cases. In silico functional characterization of genes and regulatory elements associated with resistance to the five study drugs will then elucidate the mechanism of resistance. A subsequent phylogenomic analysis and MIC characterization of time-course samples will allow us to understand the evolutionary path to resistance, and the change in resistance level after each evolutionary event. This will allow us to understand for example whether resistance emerges in steps with increasing levels, or spontaneously at a high level. In the case of the former, we will be able to identify the stepwise genetic and methylation markers associated with each resistance level. This allows the clinician to decide whether increasing the drug dosage or change of the drug regimen is the best course of action.