The microbial network property as a bio-indicator of antibiotic transmission in the environment

Highlights: • Bacterial taxonomic compositions changed from the dominance of Firmicutes to Proteobacteria alongside antibiotic transmission. • Ofloxacin and tetracycline were most predictive antibiotics for bacterial and functional network properties, respectively. • Modularity was consistently much higher in functional networks than bacterial networks. • Modularity of association networks can be an indicator of system stability for microbial communities. • Microbial community assembly is one of the primary mechanisms underlying microbial interactions. Interspecies interaction is an essential mechanism for bacterial communities to develop antibiotic resistance via horizontal gene transfer. Nonetheless, how bacterial interactions vary along the environmental transmission of antibiotics and the underpinnings remain unclear. To address it, we explore potential microbial associations by analyzing bacterial networks generated from 16S rRNA gene sequences and functional networks containing a large number of antibiotic-resistance genes (ARGs). Antibiotic concentration decreased by more than 4000-fold along the environmental transmission chain from manure samples of swine farms to aerobic compost, compost-amended agricultural soils, and neighboring agricultural soils. Both bacterial and functional networks became larger in nodes and links with decreasing antibiotic concentrations, likely resulting from lower antibiotics stress. Nonetheless, bacterial networks became less clustered with decreasing antibiotic concentrations, while functional networks became more clustered. Modularity, a key topological property that enhances system resilience to antibiotic stress, remained high for functional networks, but the modularity values of bacterial networks were the lowest when antibiotic concentrations were intermediate. To explain it, we identified a clear shift from deterministic processes, particularly variable selection, to stochastic processes at intermediate antibiotic concentrations as the dominant mechanism in shaping bacterial communities. Collectively, our results revealed microbial network dynamics and suggest that the modularity value of association networks could serve as an important indicator of antibiotic concentrations in the environment.