Abstract
The overuse of antibiotics has promoted the propagation and dissemination of antibiotic resistance genes (ARGs) in environment. Due to the dense human population and intensive activities in coastal areas, the health risk of ARGs in coastal environment is becoming a severe problem. To date, there still lacks of a quantitative method to assess properly the gross antibiotic resistance at microbial community level. Here, we collected sediment samples from Hangzhou Bay (HB), Taizhou Bay (TB), and Xiangshan Bay (XB) of the East China Sea for community-level ARGs analysis. Based on the 16S rRNA genes and predictive metagenomics, we predicted the composition of intrinsic ARGs (piARGs) and some related functional groups. Firstly, a total of 40 piARG subtypes, belonging to nine drug classes and five resistance mechanisms, were obtained, among which the piARGs encoding multidrug efflux pumps were the most dominant in the three bays. Secondly, XB had higher relative abundances of piARGs and pathogens than the other two bays, which posed higher potential health risk and implied the heavier impact of long-term maricultural activities in this bay. Thirdly, the co-occurrence network analysis identified that there were more connections between piARGs and some potential pathogenic bacteria. Several piARG subtypes (e.g., tetA, aacA, aacC, and aadK) distributed widely in the microbial communities. And finally, the microbial diversity correlated negatively with the relative abundance of piARGs. Oil, salinity, and arsenic had significant effects on the variations of piARGs and potential pathogenic bacteria. The abundance-weighted average ribosomal RNA operon (rrn) copy number of microbial communities could be regarded as an indicator to evaluate the antibiotic resistance status. In conclusion, this study provides a new insight on how to evaluate antibiotic resistance status and their potential risk in environment based on a quantitative analysis of microbial communities.
Highlights
In the coastal environment all over the world, the emergence of antibiotics and the spread of antibiotic resistance genes (ARGs) is believed to be promoted under anthropogenic impacts, such as wastewater disposal and mariculture (Niu et al, 2016; Zhang et al, 2018; Lu et al, 2020)
Based on high-throughput sequencing of 16S rRNA gene and predictive metagenomic methods (Tax4Fun, FAPROTAX, and BugBase), we comprehensively investigated the compositions of microbial communities and predicted the Abbreviations: ARGs, Antibiotic resistance genes; predicted the composition of intrinsic ARGs (piARGs), Predicted intrinsic antibiotic resistance genes: mobile genetic elements (MGEs), Mobile genetic elements; rrn, Ribosomal RNA operon; aromatic compound degradation genes (ADGs), Aromatic compound degradation genes; PCR, Polymerase chain reaction; quantitative PCR (qPCR), Real-time quantitative polymerase chain reaction; OTU, Operational taxonomic unit; KEGG Orthologs (KOs), KEGG orthologs; RDA, Redundancy analysis; variance partition analysis (VPA), Variance partition analysis; ALR, Aminoglycoside resistance genes; BLR, Beta-lactam resistance genes; CAMPR, Cationic antimicrobial peptide resistance genes; MLR, Macrolide resistance genes; MDR, Multidrug resistance genes; PNR, Phenicol resistance genes; QNR, Quinolone resistance genes; TCR, Tetracycline resistance genes; VCR, Vancomycin resistance genes; HB, Hangzhou Bay; TB, Taizhou Bay; XB, Xiangshan Bay
In this study, based on predictive metagenomics, the in-depth analysis of microbial communities was conducted to explore the antibiotic resistance status in the coastal environment influenced by long-term human activities
Summary
In the coastal environment all over the world, the emergence of antibiotics and the spread of antibiotic resistance genes (ARGs) is believed to be promoted under anthropogenic impacts, such as wastewater disposal and mariculture (Niu et al, 2016; Zhang et al, 2018; Lu et al, 2020). As sediment harbors a large number of various microbes, the diversity and abundance of ARGs in sediment could provide us a high-resolution vision to evaluate the long-term anthropogenic impacts. Many previous studies suggested that the compositions of microbial community were strongly correlated with the diversity and abundance of ARGs in the environments (Su et al, 2015; Fan et al, 2018). Multiple intrinsic ARGs represent native resistance form in the bacterial genomes and contribute an important fraction of environmental antibiotic resistome (Vaz-Moreira et al, 2014). In-depth analysis of microbial community compositions might be another route to evaluate the potential antibiotic resistance status and its risk. There still lacks of a proper quantitative evaluation method at the level of microbial community
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
