摘要

       抗生素耐药性的蔓延是一个日益增长的公共卫生问题。虽然许多研究都强调了环境来源和抗生素耐药性传播途径的重要性，但缺乏一种系统的方法来比较和优先考虑由不同环境分区代表的风险。在这里，我们介绍了元比较，这是一个公开可用的评估“抗药性风险”的工具，我们将其定义为抗生素抗性基因（ARGs）与可移动基因元件（mge）相关并基于亚基因组数据向病原体动员的潜力。开发了一个计算管道，其中每个ARG根据相对丰度、迁移率和病原体内的存在进行评估。这是通过收集散弹枪测序数据和分析含有ARGs的contigs来确定它们是否含有与MGEs或人类病原体相似的序列。基于集合的亚基因组，样本被投射到一个三维危险空间，并被分配抵抗风险得分。为了验证这一点，我们对之前发表的来自不同水生环境的亚基因组数据进行了测试。基于无监督机器学习，测试样本以与其来源一致的方式聚集在危险空间中。得到的分数产生了一个很好的解决上升抵抗风险排序：废水处理厂废水，乳制品泻湖，医院污水。

       The spread of antibiotic resistance is a growing public health concern. While numerous studies have highlighted the importance of environmental sources and pathways of the spread of antibiotic resistance, a systematic means of comparing and prioritizing risks represented by various environmental compartments is lacking. Here, we introduce MetaCompare, a publicly available tool for ranking 'resistome risk', which we define as the potential for antibiotic resistance genes (ARGs) to be associated with mobile genetic elements (MGEs) and mobilize to pathogens based on metagenomic data. A computational pipeline was developed in which each ARG is evaluated based on relative abundance, mobility, and presence within a pathogen. This is determined through the assembly of shotgun sequencing data and analysis of contigs containing ARGs to determine if they contain sequence similarity to MGEs or human pathogens. Based on the assembled metagenomes, samples are projected into a 3-dimensionalhazard space and assigned resistome risk scores. To validate, we tested previously published metagenomic data derived from distinct aquatic environments. Based on unsupervised machine learning, the test samples clustered in the hazard space in a manner consistent with their origin. The derived scores produced a well-resolved ascending resistome risk ranking of: wastewater treatment plant effluent, dairy lagoon, and hospital sewage.

       https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5995210/