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

我的博士学位的目标是创建一种工具，利用宏基因组数据和其他人口统计数据（如生活方式，位置和社会经济地位）的见解来预测人类的抗菌素耐药性（AMR）概况。博士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，使这个博士策略灵活而强大。世界卫生组织将抗生素耐药性描述为“当今全球健康、粮食安全和发展的最大威胁之一”。由于抗生素耐药性，常见的细菌感染变得无法用抗生素治疗，特别是在印度和中国等国家，抗生素过度使用和滥用很常见。不仅从AMR感染中鉴定出AMR基因，而且在来自健康个体的唾液和粪便样品中也鉴定出AMR基因（Diaz-Torres et al.，2006年，Pehrsson等人，2016）。健康个体中的微生物可能通过以下方式获得AMR基因：与环境中微生物进行水平基因转移（HGT）的移动的遗传元件（MGE）相关联，特别是过度拥挤的生活条件，卫生条件差和不断向动物施用抗菌药物的农业;以及滥用既往感染的抗生素，即未完成疗程。量化地球仪个体的AMR状态可能会对最快针对AMR高风险人群地区的政策产生重大影响，例如早期监测潜在的AMR感染和规范某些抗生素的使用。为了能够快速有效地治疗感染，卫生专业人员需要在第一个疗程后开出完全有效的抗生素。由于AMR变得如此普遍，医疗保健专业人员必须了解个体是否可能有表达某种AMR基因的风险，以及该基因是否会导致抗生素对感染无效。如果这是已知的，替代抗生素甚至噬菌体1治疗可以作为第一个疗程。到目前为止，还没有通用的AMR分析工具。然而，Andrew麦克阿瑟教授和加拿大汉密尔顿的合作者已经在进行AMR基因预测工具和AMR严重程度评分的研究，并将在年内创建软件草案（个人通信）。正因为如此，我觉得有必要专注于一个预测工具，可以有利于未来的诊断和监测。我计划创建一个工具，旨在预测个人的AMR概况和与MGE的关联，基于1）微生物多样性参数：物种丰度，丰富度和共生，2）人口统计学，如职业和位置，以及3）这些信息如何应用于未来的抗菌素耐药性诊断。

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.