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: 10100014
• 负责人: Maha Farhat
• 金额: $ 78.19万
• 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/10100014
• 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; ELF3 gene; 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; Light; Link; Machine Learning; Measurable; Metabolic; Methodology; Methods; Minimum Inhibitory Concentration measurement; Minor; 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; Race; Recurrent disease; Regulator Genes; Relapse; Resistance; Resistance development; Resolution; Rifampin; Role; Sampling; Source; South Africa; South African; Specific qualifier value; Specimen; Speed; Sputum; Structure; Technology; Test Result; Testing; Time; Time Study; Training; Treatment Failure; Treatment Protocols; Treatment outcome; Tuberculosis; Variant; Virulent; Work; arm; base; clinical predictors; cohort; experience; genetic variant; genome sequencing; genomic variation; high risk; immunological status; immunoregulation; improved; improved outcome; in silico; individual patient; innovation; medication compliance; novel; pathogen; pathogen genomics; predictive modeling; pressure; prevent; prospective; real time monitoring; recruit; resistance gene; resistant strain; response; statistical and machine 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.

项目概要/摘要 目前尚不存在监测结核病 (TB) 治疗反应的快速、准确的方法。努力 改善结果的重点是早期识别利福平敏感性，然后及时治疗 启动和依从性监测。最常用的快速分子敏感性测试给出了二分法 截止。但最近的研究表明，最低抑菌浓度 (MIC) 略低于这些值 断点也预示着不良结果。即使患者接受了大部分治疗，临床反应仍然可以 差异很大。痰液清除延迟（培养物从生长到不生长的转变）的范围可以从 几天到 5 个月，药物敏感结核病的失败或复发率可能高达 20%。期间 经过数周至数月的人类感染和抗生素治疗，结核分枝杆菌宿主群体经历了大量的 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，例如铁或钴胺素水平低可能导致遗传性药物耐受性和 耐药表型。在这里，我们建议研究宿主纵向病原体动态，包括 随时间变化的种群多样性，并识别选择下的基因，以揭示宿主与病原体的相互作用。 宿主病原体动力学的研究可以提高我们对感染治愈的认识，并为治疗疾病奠定基础。 使用全基因组测序来监测治疗反应、避免延误和 基于传统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 的预测器并评估 MIC 预测是否与文化转换的延迟相关 临床反应不佳。我们将系统地研究来自特征明确的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）结合了长和深 短读长测序可解析低等位基因频率下的全基因组组装和变异。我们有强大的 初步数据表明，长读长测序比短读长测序揭示了更多的结核分枝杆菌遗传多样性 单独测序，并且之前已经描述了基因子集中的定向选择，包括 抗性位点、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 拟议的工作是由我们在群体遗传学、机器学习和 对 Mtb 等克隆细菌的耐药性预测，将首次允许定向和耐药性研究 对 Mtb 遗传变异的全部内容进行多样化选择。它还将允许培训 MIC 基于跨研究和地域策划的约 17,000 个大型孤立数据集的预测模型。两个研究目标 有望让我们了解病原体遗传变异如何影响结核分枝杆菌在宿主和环境中的生存 对治疗的反应。