The dynamics of antimicrobial resistance genes in the pig and human gut microbiome in Uganda

拟议的研究旨在从根本上了解在代表不同抗生素使用/滥用水平的自然环境下，人与猪之间和人体内抗菌素耐药性（AMR）基因的产生、共享和转移。确定这些动态为制定有效的控制措施提供了最好的机会，并保证了在人类和动物卫生保健中限制不合理抗生素使用的努力。本研究结合了当前分子技术的使用、数据处理的效率和流行病学相关性的设置，以检测抗生素驱动的肠道细菌变化。对每一种变化进行法医调查，将揭示肠道中抗菌素耐药性基因的基本特征及其在猪-人界面上的转移潜力。待验证的假设是，在城郊环境中与猪接触的人比在农村环境中接触的人获得抗菌素耐药性基因的风险更高。具体目标是：1。利用16SrRNA、AMR基因探针和实时PCR技术，在近一年的时间里，在城市和农村地区的人和他们的猪的肠道微生物群中，发现分类和AMR基因的显著变化。2. 利用基于序列的宏基因组学对目标1中检测到的显著变化进行法医学调查，同时对前哨细菌进行全基因组测序和表型AMR检测，以了解潜在的动力学。3. 利用生物信息学、统计学和支持向量机方法对1和2中产生的数据进行预测、估计和推断AMR基因的发生、宿主交换内部和宿主交换之间的类群连锁及其潜在的表型效应。

The proposed research aims to gain fundamental insight into the generation, sharing and transfer of antimicrobial resistance (AMR) genes within and between humans and their pigs under natural settings that represent different levels of antibiotic use/misuse. Defining these dynamics offers the best chance of developing effective control measures and underwrites the efforts toward limiting irrational antibiotic use in both human and animal health care.This study combines the use of current molecular techniques, efficiency in data processing and a setting of epidemiological relevance to detect antibiotic driven changes in the gut bacteria. A forensic investigation of each of these changes would then reveal the fundamental characteristics of AMR genes in the gut and their transfer potential across the pig-human interface. The hypothesis to be tested is that people in contact with pigs in peri-urban settings are at higher risk of acquiring AMR genes across this interface than people in rural settings.The specific objectives are:1. To identify significant changes in taxonomic and AMR genes in gut microbiomes of people and their pigs in peri urban and rural settings over a one-year period using 16SrRNA, AMR gene probes and real time PCR. 2. To conduct forensic investigations of the significant changes detected in objective 1 using sequence based metagenomics on the corresponding gut microbiomes in parallel with whole genome sequencing and phenotypic AMR testing of sentinel bacteria to understand the underlying dynamics. 3. To predict, estimate and make inferences on AMR gene occurrence, taxa-linked within and between host exchange and their potential phenotypic effects using bioinformatic, statistical and support vector machine approaches on the data generated in 1&2.