Source tracking of antibiotic resistance genes in the environment — Challenges, progress, and prospects - Li, Li-Guan; Huang, Qi; Yin, Xiaole; Zhang, Tong (Water research (Oxford), 2020)
Antibiotic resistance has become a global public health concern, rendering common infections untreatable. Given the widespread occurrence, increasing attention is being turned toward environmental pathways that potentially contribute to antibiotic resistance gene (ARG) dissemination outside the clinical realm. Studies during the past decade have clearly proved the increased ARG pollution trend along with gradient of anthropogenic interference, mainly through marker-ARG detection by PCR-based approaches. However, accurate source-tracking has been always confounded by various factors in previous studies, such as autochthonous ARG level, spatiotemporal variability and environmental resistome complexity, as well as inherent method limitation. The rapidly developed metagenomics profiles ARG occurrence within the sample-wide genomic context, opening a new avenue for source tracking of environmental ARG pollution. Coupling with machine-learning classification, it has been demonstrated the potential of metagenomic ARG profiles in unambiguously assigning source contribution. Through identifying indicator ARG and recovering ARG-host genomes, metagenomics-based analysis will further increase the resolution and accuracy of source tracking. In this review, challenges and progresses in source-tracking studies on environmental ARG pollution will be discussed, with specific focus on recent metagenomics-guide approaches. We propose an integrative metagenomics-based framework, in which coordinated efforts on experimental design and metagenomic analysis will assist in realizing the ultimate goal of robust source-tracking in environmental ARG pollution. [Display omitted] •Environmental pollution of ARGs has been a public health concern.•Quantitative source-tracking remains as a bottleneck in marker-ARG based methods.•Metagenomics-based ARG profiling can circumvent limitations in traditional methods.•Machine-learning classification is of great potential of accurate source-tracking.•Robust source tracking can be realized by an integrative metagenomic framework.