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Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning

Lookup NU author(s): Manuel Banzhaf

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© The Author(s) 2023. 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 (AMR) 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% confidence interval (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 AMR pollution in wastewater systems.

Publication metadata

Author(s): Moradigaravand D, Li L, Dechesne A, Nesme J, de la Cruz R, Ahmad H, Banzhaf M, Sorensen SJ, Smets BF, Kreft J-U

Publication type: Article

Publication status: Published

Journal: Bioinformatics

Year: 2023

Volume: 39

Issue: 7

Print publication date: 01/07/2023

Online publication date: 22/06/2023

Acceptance date: 21/06/2023

Date deposited: 09/08/2023

ISSN (print): 1367-4803

ISSN (electronic): 1367-4811

Publisher: Oxford University Press

DOI: 10.1093/bioinformatics/btad400

PubMed id: 37348862

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