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

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*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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 (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.

Original languageEnglish (US)
Article numberbtad400
JournalBioinformatics
Volume39
Issue number7
DOIs
StatePublished - Jul 1 2023

Bibliographical note

Funding Information:
This work was supported by funding from the Joint Programming Initiative on Antimicrobial Resistance (JPI-AMR) via the Danish Innovation Foundation (DARWIN Project 7044-00004B). D.M. was supported by the KAUST baseline fund [BAS/1/1108-01-01]. M.B. was supported by a UKRI Future Leaders Fellowship [MR/V027204/1].

Publisher Copyright:
© The Author(s) 2023.

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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