Abstract
Antibiotics resistance genes (ARGs) are mainly caused by the extensive use and abuse of antibiotics and have become a global public health concern. Owing to the development of high-throughput sequencing, metagenomic sequencing has been widely applied to profile the composition of ARGs, investigate their distribution pattern, and track their sources in diverse environments. However, the lack of a detailed transmission mechanism of ARGs limits the management of its pollution. Hence, it’s essential to introduce how to utilize the metagenomic data to obtain an in-depth understanding of the distribution pattern and transmission of ARGs. This review provides an assessment of metagenomic data utilization in ARG studies and summarizes current bioinformatic tools and databases, including ARGs-OAP, ARG analyzer, DeepARG, CARD, and SARG, for profiling the composition of ARGs and tracking the source of ARGs. Several bioinformatic tools and databases were then benchmarked. Our results showed that although SARG is a good database, the application of two or more bioinformatic tools and databases could provide a comprehensive view of ARG profiles in diverse environmental samples. Finally, several perspectives were proposed for future studies to obtain an in-depth understanding of ARGs based on metagenomic data. Our review of the utilization of metagenomic data together with bioinformatic tools and databases in ARG studies could provide insights on exploring the profiles and transmission mechanism of ARG in different environments that mitigate the spread of ARGs and manage the ARGs pollution.
Highlights
Since the discovery of penicillin, researchers have opened the modern era of the innovation, development, and application of antibiotics in human society
With the growing number of microbiome studies focusing on Antibiotics resistance genes (ARGs), many metagenomic datasets, bioinformatic tools, and associated ARG databases have been generated for ARG analysis
This review summarized current bioinformatic approaches and databases for identifying potential ARGs in metagenomic data
Summary
Since the discovery of penicillin, researchers have opened the modern era of the innovation, development, and application of antibiotics in human society. A total of 139, 442, and 491 ARG subtypes were identified in sediments from the Yamuna River, sediments from an urban river in Beijing (Chaobai River), and activated sludge reactors, respectively (Chen et al, 2019a; Zhao et al, 2019; Das et al, 2020) Based on these published studies, we found that many studies have focused on the composition of ARGs and their dynamics; only a few studies investigated the transmission of ARGs; the transmission route for ARGs is poorly characterized (Zhou et al, 2018; Chao et al, 2019; Vrancianu et al, 2020). With the growing number of microbiome studies focusing on ARGs, many metagenomic datasets, bioinformatic tools, and associated ARG databases have been generated for ARG analysis By using these tools and databases, researchers have profiled ARG composition in different environments and deepened the understanding of ARGs. some urgent scientific questions remain unanswered, such as which bioinformatic tools and ARG databases are suitable for detecting potential ARGs? Several critical comments and perspectives were proposed for future ARG studies to obtain an in-depth understanding of ARGs based on metagenomic data
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