Developing tools to predict antimicrobial resistance gene and mobile genetic element profiles from metagenomic analysis and demographic data

我博士的目标是创建一种工具，利用从元基因组数据和其他人口统计学数据中的见解，预测人类的抗菌素耐药性(AMR)概况，如生活方式、地理位置和社会经济地位。博士学位将包括对预测工具的不同指标的多项调查，这些指标可以作为独立的项目，使这一博士战略灵活而稳健。世界卫生组织将AMR描述为“当今对全球健康、粮食安全和发展的最大威胁之一”。由于抗生素耐药性，常见的细菌感染正变得无法用抗生素治疗，特别是在印度和中国等国家，在这些国家，抗生素过度使用和误用很常见。不仅在AMR感染中发现了AMR基因，而且在健康人的唾液和粪便样本中也发现了AMR基因(Diaz-Torres等人，2006年，Pehrsson等人，2016年)。健康个体中的微生物可能通过以下途径获得AMR基因：与可移动遗传元件(MGE)结合，从环境中的微生物进行水平基因转移(HGT)，特别是来自过度拥挤的生活条件、恶劣的卫生条件和经常给动物服用抗微生物药物的养殖业；以及滥用先前感染的抗生素，即未完成疗程。量化全球个人的AMR状况可能会对最快针对AMR高危人群的政策产生重大影响，例如，更早地监测潜在的AMR感染和监管某些抗生素的使用。为了能够快速有效地治疗感染，卫生专业人员需要开出在第一个疗程后完全有效的抗生素。由于AMR正变得如此普遍，对医疗专业人员来说，了解一个人是否有可能表达特定的AMR基因，以及该基因是否会导致抗生素对感染无效，这一点很重要。如果知道这一点，可以使用替代抗生素甚至噬菌体1治疗作为第一疗程的治疗。到目前为止，还没有针对AMR的通用分析工具。然而，安德鲁·麦克阿瑟教授及其在加拿大汉密尔顿的合作者已经在研究AMR基因预测工具和AMR严重程度评分，并将在今年内创建软件草案(个人通信)。正因为如此，我觉得有必要把重点放在一种有助于未来诊断和监测的预测工具上。我计划创建一个工具，旨在基于1)微生物多样性的参数：物种丰富度、丰富度和共生情况，2)人口统计学，如职业和地点，以及3)这些信息如何应用于未来的抗菌素耐药性诊断，来预测个人的AMR图谱及其与MGES的关联。

The goal of my PhD is to create a tool to predict the profiles of antimicrobial resistance (AMR) in humans using insights from metagenomic data, and other demographics, such as lifestyle, location and socio-economic status. The PhD will include multiple investigations into different metrics for the prediction tool that can act as independent projects, making this PhD strategy flexible and robust. The World Health Organisation describes AMR as "one of the biggest threats to global health, food security, and development today". Common bacterial infections are becoming untreatable by antibiotics due to antibiotic resistance, especially in countries such as India and China, where antibiotic over-use and misuse are common. AMR genes have not only been identified from AMR infections, but also in saliva and stool samples from healthy individuals (Diaz-Torres et al., 2006, Pehrsson et al., 2016). Microbes in healthy individuals may acquire AMR genes through: association with mobile genetic elements (MGE) that undergo horizontal gene transfer (HGT) from microbes in the environment, especially from overcrowded living conditions, poor sanitation and farming where antimicrobial drugs are constantly administered to animals; and misuse of antibiotics from a previous infection i.e. not completing the course. Quantifying AMR status of individuals across the globe could have a significant impact on targeting policies most rapidly to those areas with populations at high risk of AMR, for example, earlier surveillance of potential AMR infections and regulating the use of certain antibiotics. To be able to treat infections quickly and efficiently, health professionals need to prescribe antibiotics that will be completely effective after a first course of treatment. Since AMR is becoming so prevalent, it is important for healthcare professionals to know whether an individual is likely to be at risk of expressing a certain AMR gene and whether this gene could lead to an antibiotic being ineffective against an infection. If this is known, alternative antibiotics or even bacteriophage 1 treatments could be used instead as a first course of treatment. So far, there is no universal profiling tool for AMR. However, research into AMR gene prediction tools and AMR severity scores for worldwide surveillance is already underway by Prof Andrew McArthur and collaborators in Hamilton, Canada, and draft software will be created within the year (personal correspondence). Because of this, I feel it is necessary to focus on a prediction tool that can benefit future diagnosis as well as surveillance. I plan to create a tool that will aim to predict an individual's AMR profile and association with MGEs based on 1) parameters of microbial diversity: species abundance, richness and co-occurrence, 2) demographics, such as profession and location, and 3) how this information could be applied to antimicrobial resistance diagnosis in the future.