Abstract：
Antimicrobial resistance (AMR) is a major global public health concern mainly affecting low- and middle-income countries (LMICs) due to lack of awareness, inadequate healthcare and sanitation infrastructure, and other environmental factors. In this study, we aimed to link microbial assembly and covariates (body mass index, smoking, and use of antibiotics) to gut microbiome structure and correlate the predictive antimicrobial gene prevalence (piARG) using PICRUSt2. We examined the gastrointestinal and oral microbial profiles of healthy adults in Pakistan through 16S rRNA gene sequencing with a focus on different ethnicities, antibiotic usage, drinking water type, smoking, and other demographic measures. We then utilised a suite of innovative statistical tools, driven by numerical ecology and machine learning, to address the above aims. We observed that drinking tap water was the main contributor to increased potential AMR signatures in the Pakistani cohort compared to other factors considered. Microbial niche breadth analysis highlighted an aberrant gut microbial signature of smokers with increased age. Moreover, covariates such as smoking and age impact the human microbial community structure in this Pakistani cohort.
摘要：
抗菌素耐药性（AMR）是一个主要的全球公共卫生问题，主要影响低收入和中等收入国家（LMICs）由于缺乏认识，医疗保健和卫生基础设施不足，和其他环境因素。在这项研究中,我们的目标是将微生物组装和协变量(体重指数，吸烟,和抗生素的使用）与肠道微生物组结构，并使用PICRUSt2将预测性抗菌基因患病率（piARG）相关联。我们通过16SrRNA基因测序检查了巴基斯坦健康成年人的胃肠道和口腔微生物谱，重点是不同种族，抗生素的使用，饮用水类型,吸烟,和其他人口测量。然后我们利用了一套创新的统计工具，在数值生态学和机器学习的驱动下，为了实现上述目标。我们观察到，与其他因素相比，饮用自来水是巴基斯坦队列中潜在AMR特征增加的主要原因。微生物生态位宽度分析强调了吸烟者随年龄增长的异常肠道微生物特征。此外，吸烟和年龄等协变量会影响该巴基斯坦队列中的人类微生物群落结构。
