Predicting antibiotic resistance gene abundance in activated sludge using shotgun metagenomics and machine learning

Yuepeng Sun, Bertrand Clarke, Jennifer Clarke, Xu Li

Research output: Contribution to journalArticlepeer-review

60 Scopus citations

Abstract

While the microbiome of activated sludge (AS) in wastewater treatment plants (WWTPs) plays a vital role in shaping the resistome, identifying the potential bacterial hosts of antibiotic resistance genes (ARGs) in WWTPs remains challenging. The objective of this study is to explore the feasibility of using a machine learning approach, random forests (RF's), to identify the strength of associations between ARGs and bacterial taxa in metagenomic datasets from the activated sludge of WWTPs. Our results show that the abundance of select ARGs can be predicted by RF's using abundant genera (Candidatus Accumulibacter, Dechloromonas, Pesudomonas, and Thauera, etc.), (opportunistic) pathogens and indicators (Bacteroides, Clostridium, and Streptococcus, etc.), and nitrifiers (Nitrosomonas and Nitrospira, etc.) as explanatory variables. The correlations between predicted and observed abundance of ARGs (erm(B), tet(O), tet(Q), etc.) ranged from medium (0.400 < R2 < 0.600) to strong (R2 > 0.600) when validated on testing datasets. Compared to those belonging to the other two groups, individual genera in the group of (opportunistic) pathogens and indicator bacteria had more positive functional relationships with select ARGs, suggesting genera in this group (e.g., Bacteroides, Clostridium, and Streptococcus) may be hosts of select ARGs. Furthermore, RF's with (opportunistic) pathogens and indicators as explanatory variables were used to predict the abundance of select ARGs in a full-scale WWTP successfully. Machine learning approaches such as RF's can potentially identify bacterial hosts of ARGs and reveal possible functional relationships between the ARGs and microbial community in the AS of WWTPs.

Original languageEnglish (US)
Article number117384
JournalWater Research
Volume202
DOIs
StatePublished - Sep 1 2021

Keywords

  • Activated sludge
  • Antibiotic resistance genes
  • Machine learning
  • Random forests
  • Wastewater treatment plants

ASJC Scopus subject areas

  • Environmental Engineering
  • Civil and Structural Engineering
  • Ecological Modeling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution

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