Impact of different continuous fertilizations on the antibiotic resistome associated with a subtropical...

The prevalence of antibiotic resistance genes (ARGs) in agricultural soils has garnered significant attention. However, the long-term impacts of various nitroge (N)-substitution fertilization regimes on the distribution of soil ARGs and their dominant drivers in a subtropical triple-cropping system remain largely unexplored. This study employed a metagenomic approach to analyze soil ARGs, microbial communities, mobile genetic elements (MGEs), and viruses from a maize-maize-cabbage rotation field experiment with five different fertilization regimes. Soil samples were collected in 2012 and 2021. A total of 615 unique ARG subtypes were identified, with multidrug, bacitracin, and rifamycin resistance genes being the most abundant. Notably, ARG types. the continuous application of fresh chicken manure (CM) over 10 years significantly increased both the count of unique ARG subtypes and the total ARG abundance compared to other fertilization regimes, such as inorganic fertilizer and composted chicken manure. Specifically, the abundance of genes associated with antibiotic target replacement (e.g., sul1 and sul2) in the CM-treated soil rose by 8.83-fold from 2021 to 2012. Our random forest analysis revealed that the abundance of three MGEs (QacEdelta, plasmids, and IstB), two viral families (Myoviridae and Podoviridae), two bacterial phyla (Chloroflexi and Planctomycetes), and two environmental factors (pH and soil organic matter (SOM)) significantly influenced the distribution of ARGs. Furthermore, variance decomposition analysis underscored the critical roles of the three MGEs and the two viral families in the dissemination of ARGs, suggesting that horizontal gene transfer (HGT) may play a key role in ARG spread. These findings enhance our understanding of how different fertilization practices influence ARG dissemination in subtropical triple-cropping agroecosystems over the long term and provide valuable insights for optimizing fertilization management strategies.