Using whole genome sequencing to characterise drug resistant Mycobacterium tuberculosis in Thailand

结核病是一种传染病，对公共卫生造成沉重负担。世界卫生组织估计，每年有1000万新病例，近200万人死亡。泰国被世卫组织列为世界上结核病负担最高的22个国家之一，每年有9.3万新病例，结核病总流行率估计接近13万例，其中16%也是艾滋病毒阳性。确定谁向谁传播以及在何处传播对疾病控制至关重要。通过比较基于大规模人群的研究中引起结核病的细菌的遗传谱，我们可以根据菌株的相似性确定可能的传播。我们的工作将集中在泰国北碧府，那里曾暴发过耐多药结核病菌株。与泰国其他地区相比，这是一个结核病患病率较高的省份。将对2003年至2019年期间在最大的医院（Makarak医院）就诊的患者（n= 2000）的结核病样本进行遗传表征，并整理重要的元数据（如艾滋病毒状况）。推断出的遗传图谱将比以前使用的基因组比例更高，从而可以更准确地重建传播链。然后，将通过查看每个传播链内的数据模式，直接评估影响传播性的因素。这些因素可能是细菌本身的遗传变异，我们将寻求识别那些与耐药性和传播性相关的因素。将调查影响传播的非遗传因素，例如人类宿主的差异，包括年龄、性别、糖尿病、艾滋病毒感染和治疗以及接触模式。我们将尝试使用从泰国和全球其他人群收集和生成的数据来确认对这些因素的任何见解。最终，对遗传和其他潜在的传播过程的更好理解可能导致改进控制措施的发展。这些措施可包括新的药物或疫苗目标，或确定地理或社会确定的传播热点；从而协助泰国公共卫生部制定控制战略，并使北碧府和整个泰国受益。

Tuberculosis is an infectious disease that causes a high public health burden. The World Health Organisation estimates that there are ten million new cases and nearly two million deaths each year. Thailand is classified by WHO as one of the 22 countries in the world with the highest TB burden, with 93,000 new cases each year and an overall estimated TB prevalence of nearly 130,000 cases, 16 percent of whom are also HIV positive. Establishing who transmits to whom and where is fundamental to disease control. By comparing the genetic profiles of tuberculosis-causing bacteria within large population-based studies we can identify likely transmissions based on the similarity of the strains. Our work will focus on the Kanchanaburi province of Thailand, where there have been outbreaks of multi-drug resistant TB bacterial strains. This is a province where tuberculosis disease prevalence has been high compared other to regions in Thailand. Tuberculosis samples from patients (n=2,000) attending the largest hospital (Makarak Hospital), spanning a period from 2003 to 2019, will be genetically characterized, and important meta data (e.g. HIV status) curated. The inferred genetic profiles will use a higher proportion of the genome than used previously, allowing more accurate reconstructions of transmission chains. Factors influencing transmissibility will then be assessed directly by looking at the patterns of data within each transmission chain. These factors may be genetic variations within the bacteria themselves, and we will seek to identify those associated with drug resistance and transmissibility. Non-genetic factors affecting transmission, such as differences in the human hosts, including age, sex, diabetes, HIV infection and treatment, and contact patterns, will be investigated. We will attempt to confirm any insights into these factors using data collected and generated from other populations in Thailand and globally. Ultimately, an improved understanding of the genetic and other processes underlying transmissibility could lead to the development of improved control measures. These measures could include novel drug or vaccine targets, or identification of geographical or socially determined hotspots of transmission; thereby assisting the Thailand Ministry of Public Health with control strategies, as well as benefiting the Kanchanaburi province and wider Thailand.