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

项目总结/摘要 目前尚不存在监测结核病（TB）治疗反应的快速准确方法。努力 结果改善主要集中在早期识别利福平敏感性，然后及时治疗 启动和依从性监测。最常用的快速分子药敏试验给出了二分的 截止值。然而，最近的研究表明，最低抑菌浓度（MIC）仅低于这些 断点也预示着不良结果。即使患者接受了大部分治疗， 差异很大。痰液清除延迟（培养物从生长转化为无生长）的范围可从 几天到5个月，在药物敏感的结核病中，失败或复发率可高达20%。期间 在人类感染和抗生素治疗的数周至数月中，宿主Mtb群体经历了大量的 可测量的基因变化。这些变化可能是中性的，或允许病原体适应免疫、抗生素 或代谢压力，例如可能导致遗传性药物耐受性的低铁或钴胺素水平， 抗性表型在这里，我们建议研究宿主纵向病原体动态，包括 种群多样性随着时间的推移，并确定基因的选择，揭示宿主-病原体相互作用。 宿主病原菌动态的研究可以提高我们对感染治愈的认识，为临床治疗提供基础。 使用全基因组测序监测治疗反应的方法，避免延误， 传统的基于文化的方法的生物危害。我们还建议开发一个基因组- 基于MIC的预测因子，并评估MIC预测是否与培养物转化延迟相关， 临床反应差。我们将系统地研究结核病治疗的病原体样本 患者队列（南非伍斯特的NIAID TRUST TB队列-PI Dr. Jacobson），结合长和深 短读段测序以解析全基因组组装和低等位基因频率的变体。我们有强大 初步数据表明，长读测序比短读测序更能揭示结核分枝杆菌的遗传多样性。 单独测序，并且先前已经表征了基因子集中的定向选择，包括 抗性基因座、B12生物合成途径和已知与宿主先天防御相互作用的PPE基因。的 拟议的工作是通过我们在群体遗传学，机器学习和 耐药性预测的克隆细菌，如结核分枝杆菌，并将允许，第一次，研究方向和 对结核分枝杆菌遗传变异的全部谱进行多样化选择。它还将允许培训中等收入国家 基于跨研究和地理区域管理的约17，000个隔离数据集的预测模型。两项研究的目的 承诺告知我们病原体遗传变异如何影响宿主中Mtb的存活， 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.