Investigating bacterial contributions to TB treatment response: a focus on in-host pathogen dynamics

项目摘要/摘要 目前尚不存在监测结核病(TB)治疗反应的快速和准确方法。努力实现 改善结果的重点是及早确定利福平的敏感性，然后立即进行治疗 启动和遵守监测。最常用的快速分子药敏试验给出了二分性 截止日期。然而，最近的研究表明，最低抑菌浓度(MIC)略低于这些 断点也预示着糟糕的结果。即使患者接受了大部分治疗，临床反应仍然可以 差别很大。痰清除的延迟(培养从有生长到无生长)的范围从 在对药物敏感的结核病中，几天到5个月的失败或复发率可能高达20%。在.期间 数周至数月的人类感染和抗生素治疗，在宿主结核分枝杆菌人群中经历了大量的 可测量的基因变化。这些变化可能是中性的，或者允许病原体适应免疫、抗生素 或代谢压力，例如铁或钴胺水平低，可能导致遗传性药物耐受和 耐药表型。在这里，我们建议研究寄主纵向病原体动态，包括变化 随着时间的推移种群多样性，并确定被选择的基因，以阐明宿主与病原体的相互作用。 研究宿主内病原菌的动态变化可以提高我们对感染治疗的认识，为预防和治疗感染奠定基础。 使用全基因组测序来监测治疗反应的方法，绕过了延迟和 基于传统文化的方法的生物危害。我们还建议开发一种基因组-- 基于MIC的预测，并评估MIC预测是否与文化转换和 临床反应差。我们将系统地研究结核病治疗中的病原体样本。 患者队列(南非伍斯特的NIAID信任结核病队列--派·雅各布森博士)结合了长和深 短读测序，以低等位基因频率解析全基因组组装和变异。我们有很强的 初步数据显示，长读测序揭示了比短读测序更多的结核分枝杆菌遗传多样性 仅测序，并且之前已经表征了包括以下基因子集的定向选择 抗性基因、B12生物合成途径和已知与寄主天然防御相互作用的PPE基因。这个 拟议的工作是由我们在种群遗传学、机器学习和 对像结核分枝杆菌这样的克隆细菌的耐药性预测，将首次允许对定向和 对结核分枝杆菌遗传变异全系的多样化选择。它还将允许对MIC进行培训 在一个跨越研究和地理的约17,000个孤立的大型数据集上建立预测模型。这两项研究的目的都是 承诺告知我们对病原体遗传变异如何影响结核分枝杆菌在宿主中的存活和 对治疗的反应。

Project Summary/Abstract Rapid and accurate methods to monitor tuberculosis (TB) treatment response do not currently exist. Efforts to improve outcomes have focused on early identification of rifampicin susceptibility followed by prompt treatment initiation and adherence monitoring. The rapid molecular susceptibility tests most often used give dichotomous cutoffs. Recent studies though show that minimum inhibitory concentrations (MICs) just below these breakpoints also predict poor outcomes. Even if a patient takes most of their therapy, clinical response can still vary substantially. Delays in sputum clearance (culture conversion from growth to no growth) can range from a few days to 5 months and failure or relapse rates can be as high as 20% in drug-susceptible TB. During the weeks to months of human infection and antibiotic treatment, in host Mtb populations experience substantial measurable genetic changes. These changes may be neutral or allow pathogen adaption to immune, antibiotic or metabolic pressure, e.g. low iron or cobalamin levels that may result in heritable drug tolerance and resistance phenotypes. Here we propose to study in host longitudinal pathogen dynamics including changes in population diversity over time and identify genes under selection to shed light on host-pathogen interactions. The study of in host pathogen dynamics can improve our understanding of cure from infection and pave the way for the use of whole genome sequencing for monitoring treatment response, circumventing the delays and biohazards of traditional culture-based approaches. We additionally propose the development of a genome- based predictor of MIC and to assess if MIC predictions are associated with delays in culture conversion and poor clinical response. We will systematically study pathogen samples from a well characterized TB treatment patient cohort (NIAID TRUST TB cohort in Worcester, South Africa -PI Dr. Jacobson) combining long and deep short-read sequencing to resolve full genome assemblies and variants at low allele frequency. We have strong preliminary data that long-read sequencing unmasks more Mtb genetic diversity than detectable by short-read sequencing alone and have previously characterized directional selection in a subset of genes including resistance loci, the B12 biosynthesis pathway, and PPE genes known to interact with host innate defense. The proposed work is enabled by our methodological expertise in population genetics, machine learning and resistance prediction for clonal bacteria like Mtb and will allow, for the first time, the study of directional and diversifying selection on the full repertoire of Mtb genetic variation. It will also allow the training of an MIC prediction model on a large ~17,000 isolate dataset curated across studies and geographies. Both study aims promise to inform our understanding of how pathogen genetic variation affects Mtb survival in host and the response to treatment.