Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning
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Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning. / Moradigaravand, Danesh; Li, Liguan; Dechesne, Arnaud; Nesme, Joseph; de la Cruz, Roberto; Ahmad, Huda; Banzhaf, Manuel; Sørensen, Søren J.; Smets, Barth F.; Kreft, Jan Ulrich.
I: Bioinformatics (Oxford, England), Bind 39, Nr. 7, 2023.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Plasmid permissiveness of wastewater microbiomes can be predicted from 16S rRNA sequences by machine learning
AU - Moradigaravand, Danesh
AU - Li, Liguan
AU - Dechesne, Arnaud
AU - Nesme, Joseph
AU - de la Cruz, Roberto
AU - Ahmad, Huda
AU - Banzhaf, Manuel
AU - Sørensen, Søren J.
AU - Smets, Barth F.
AU - Kreft, Jan Ulrich
N1 - Publisher Copyright: © The Author(s) 2023. Published by Oxford University Press.
PY - 2023
Y1 - 2023
N2 - 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. AVAILABILITY AND IMPLEMENTATION: The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.
AB - 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. AVAILABILITY AND IMPLEMENTATION: The predictive tool is available as an application at https://github.com/DaneshMoradigaravand/PlasmidPerm.
U2 - 10.1093/bioinformatics/btad400
DO - 10.1093/bioinformatics/btad400
M3 - Journal article
C2 - 37348862
AN - SCOPUS:85164208955
VL - 39
JO - Bioinformatics (Online)
JF - Bioinformatics (Online)
SN - 1367-4811
IS - 7
ER -
ID: 359723064