Abstract Wastewater Treatment Plants (WWTPs) contain a diverse microbial community with high cell density. They constantly receive antimicrobial residues and resistant strains and, therefore, may offer conditions for the Horizontal Gene Transfer (HGT) of antimicrobial resistance determinants, transmitting clinically important genes between, e.g., enteric and environmental bacteria and vice versa . Despite the clinical importance, tools for predicting HGT are still under-developed. In this study, we examined to which extent microbial community composition, as inferred by partial 16S rRNA gene sequences, can predict plasmid permissiveness, i.e., the ability of cells to receive a plasmid through conjugation, for microbial communities in the water cycle, using data from standardized filter mating assays using fluorescent bio-reporter plasmids. We leveraged a range of machine learning models for predicting the permissiveness for each taxon in the community, translating to the range of hosts a plasmid is able to transfer to, for three broad host-range resistance plasmids (pKJK5, pB10, and RP4). Our results indicate that the predicted permissiveness from the best performing model (random forest) showed a moderate-to-strong average correlation of 0.45 for pB10 (95% CI: 0.42-0.52), 0.42 for pKJK5 (0.95% CI: 0.38-0.45) and 0.52 for RP4 (0.95% CI:0.45-0.55) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite these being broad host-range plasmids. Our results provide a framework that contributes to assessing the risk of AMR pollution in wastewater systems. The predictive tool is available as a an application under https://github.com/DaneshMoradigaravand/PlasmidPerm .

Plasmid Permissiveness of Wastewater Microbiomes can be Predicted from 16S rDNA sequences by Machine Learning