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

研究细菌对结核病治疗反应的贡献：关注宿主内病原体动态

• 批准号: 10772431
• 负责人: Maha Farhat
• 金额: $ 9.43万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-22 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10772431
• 关键词: Adherence; Adopted; Affect; Aftercare; Alcohol abuse; Alcohol consumption; Anabolism; Antibiotic Therapy; Antibiotics; Attenuated; Bacteria; Biohazardous Substance; Bioinformatics; Biological; Biological Response Modifiers; Clinical; Clinical Treatment; Cobalamin; Communicable Diseases; Complex; DNA; DNA sequencing; Data; Data Set; Development; Diagnosis; Drug Kinetics; Drug Tolerance; Drug resistance; Early Intervention; Early identification; Evaluation; Evolution; Failure; Family; Foundations; Gene Frequency; Genes; Genetic; Genetic Recombination; Genetic Variation; Genome; Genomic approach; Genomics; Genotype; Geography; Growth; HIV; Heritability; Human; Immune; Immune response; Immune system; Immunocompetence; Immunotherapeutic agent; Individual; Infection; Integration Host Factors; Intervention; Iron; Isoniazid resistance; Knowledge; Link; Machine Learning; Measurable; Metabolic; Methodology; Methods; Minimum Inhibitory Concentration measurement; Minor; Modeling; Molecular; Monitor; Mutation; Mycobacterium tuberculosis; National Institute of Allergy and Infectious Disease; Outcome; Pathway interactions; Patients; Pattern; Performance; Persons; Pharmaceutical Preparations; Phenotype; Population; Population Genetics; Population Heterogeneity; Prediction of Response to Therapy; Predisposition; Recurrent disease; Regulator Genes; Relapse; Resistance; Resistance development; Resolution; Rifampin; Role; Sampling; Source; South Africa; South African; Specific qualifier value; Specimen; Speed; Sputum; Sterilization; Technology; Test Result; Testing; Time; Training; Treatment Failure; Treatment Protocols; Treatment outcome; Tuberculosis; Variant; Virulent; Work; arms race; clinical predictors; cohort; experience; genetic variant; genome sequencing; genomic variation; high risk; immunological status; improved; improved outcome; in silico; individual patient; innovation; medication compliance; novel; pathogen; pathogen genomics; predictive modeling; pressure; prevent; prospective; real time monitoring; recruit; relapse risk; resistance gene; resistant strain; response; statistical learning; treatment response; tuberculosis treatment; whole genome

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.

项目摘要/摘要 目前不存在监测结核病（TB）治疗反应的快速准确方法。努力 改进的结果集中于早期鉴定利福平易感性，然后进行及时治疗 启动和依从性监测。最常使用的快速分子敏感性测试可提供二分法 截止。尽管最近的研究表明，最低抑制浓度（MIC） 断点还可以预测结果不佳。即使患者大部分疗法都可以接受，临床反应仍然可以 差异很大。痰清除的延迟（培养从增长到没有生长）的范围从 几天到5个月，在药物敏感的结核病中，失败或复发率可能高达20％。在 在宿主MTB种群中，人类感染和抗生素治疗的数周到几个月 可测量的遗传变化。这些变化可能是中性的，也可以使病原体适应免疫，抗生素 或代谢压力，例如铁或钴胺素水平低，可能导致可遗传的药物耐受性和 阻力表型。在这里，我们建议在宿主纵向病原体动力学中研究，包括变化 随着时间的流逝，人口多样性，并确定在选择下的基因，以阐明宿主病原体相互作用。 宿主病原体动力学中的研究可以提高我们对感染的治疗的理解，并铺平 使用整个基因组测序来监测治疗反应，规避延迟和 传统基于文化的方法的生物危害。我们还提出了基因组的发展 基于MIC的预测指标，并评估MIC预测是否与培养conversion依的延迟有关 临床反应不佳。我们将系统地研究TB处理良好的病原体样本 患者队列（南非伍斯特的Niaid Trust TB队列-PI Jacobson博士）结合了长而深的 短阅读测序以在低等位基因频率下解决完整的基因组组件和变体。我们有强大 长阅读测序揭示的初步数据比短阅读可检测到的MTB遗传多样性更多 单独进行测序，并以前在基因子集中表征了定向选择 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。这 我们在人口遗传学，机器学习和 克隆细菌等克隆细菌的抗性预测，并将首次允许研究方向和研究 在MTB遗传变异的完整曲目上进行多样化的选择。它还将允许培训麦克风 大约17,000个分离株数据集的预测模型在研究和地理位置进行了策划。两个研究的目的 有望告知我们对病原体遗传变异如何影响宿主中MTB生存的理解 对治疗的反应。