Zinc co-exposure with different antibiotic classes drives distinct microbial community restructuring in activated...

Antibiotics and heavy metals frequently co-occur in wastewater treatment systems, yet their combined effects on activated sludge microbiomes remain insufficiently understood beyond changes in diversity and taxonomic composition. Here, lab-scale activated sludge reactors were used to investigate microbial responses to zinc (Zn), ciprofloxacin (CIP), sulfamethazine (SMZ), and their combined exposures across four stages of increasing antibiotic concentration under a constant Zn background. Microbial communities were characterized using 16S rRNA gene amplicon sequencing, differential taxon analysis, co-occurrence network analysis, and iCAMP-based assembly inference. Compared with single-contaminant treatments, combined exposure caused stronger alpha-diversity loss and clearer Bray-Curtis community separation, with Zn + CIP showing the strongest shift. Shared amplicon sequence variants (ASVs) declined with increasing stress intensity, and structural-zero analysis indicated that sensitive genera became systematically undetected, especially at higher concentration stages. The two combined treatments showed distinct restructuring patterns. Zn + CIP was associated with repeated changes in a limited set of genera and a marked enrichment of Dechloromonas at the highest concentration stage, whereas Zn + SMZ led to a broader redistribution of responsive genera without a comparable dominant replacement. Co-occurrence network analysis further revealed distinct association patterns, with Zn + CIP forming a comparatively sparse network and Zn + SMZ displaying higher density and lower modularity. Moreover, community assembly analysis indicated a shift from stronger homogeneous selection toward increased stochastic contributions under combined exposure, with dispersal limitation dominating in Zn + CIP and drift contributing more strongly in Zn + SMZ. These findings indicate that Zn combined with different antibiotic classes can drive distinct ecological trajectories in activated sludge microbiomes, providing an ecological basis for predicting how mixed contaminants may affect microbial stability, resilience, and treatment performance in wastewater treatment systems.