Plasmid Permissiveness of Wastewater Microbiomes can be Predicted from 16S rRNA Sequences by Machine Learning

by Danesh Moradigaravand, Liguan Li, Arnaud Dechesne, Joseph Nesme, Roberto de la Cruz, Huda Ahmad, Manuel Banzhaf, Søren J Sørensen, Barth F Smets, Jan-Ulrich Kreft
Research Article Year: 2023

Extra Information

Bioinformatics, 39 (7)

Abstract

Motivation

Wastewater Treatment Plants (WWTPs) harbor a dense and diverse microbial community. They constantly receive antimicrobial residues and resistant strains, and therefore provide conditions for Horizontal Gene Transfer (HGT) of antimicrobial resistance determinants. This facilitates the transmission of clinically important genes between, e.g., enteric and environmental bacteria, and vice versa. Despite the clinical importance, tools for predicting HGT remain underdeveloped.

Results

In this study, we examined to which extent water cycle 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, based on 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, representing the range of hosts a plasmid is able to transfer to, for three broad host-range resistance IncP 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 0.49 for pB10 (95% CI: 0.44, 0.55), 0.43 for pKJK5 (0.95% CI: 0.41, 0.49) and 0.53 for RP4 (0.95% CI: 0.48, 0.57) with the experimental permissiveness in the unseen test dataset. Predictive phylogenetic signals occurred despite the broad host-range nature of these plasmids. Our results provide a framework that contributes to the assessment of the risk of antimicrobial resistance (AMR) pollution in wastewater systems.