Metatranscriptomic Analysis Reveals Actively Expressed Antimicrobial-Resistant Genes and Their Hosts in Hospital Wastewater

Abstract

Antimicrobial resistance is a major global concern and economic threat, necessitating a reliable monitoring approach to understand its frequency and spread via the environment. Hospital wastewater serves as a critical reservoir for antimicrobial-resistant organisms; however, its role in resistance gene distribution and dissemination remains poorly understood. This study integrates metagenomic and metatranscriptomic analyses, elucidating the dynamics of antimicrobial resistance in hospital wastewater. Integrated metagenomic and metatranscriptomic sequencing were used to identify actively expressed antimicrobial-resistant genes and antimicrobial-resistant bacteria, offering comprehensive insights into antimicrobial resistance dynamics in hospital wastewater. Liquid chromatography-tandem mass spectrometry analysis revealed the presence of ampicillin, sulbactam, levofloxacin, sulfamethoxazole, and trimethoprim in the sample, which could apply selective pressure on antimicrobial resistance gene expression. While multidrug resistance genes were the most prevalent sequences in both metagenome-assembled genomes and plasmids, plasmid-derived sequences showed a high mRNA/DNA ratio, emphasizing the presence of functionally expressed antimicrobial resistance genes on plasmids rather than on chromosomes. The metagenomic and metatranscriptomic analyses revealed Serratia nevei MAG14 with high mRNA levels of antimicrobial resistance genes; moreover, multidrug-resistant Serratia sp., genetically related to MAG14, was isolated from the wastewater, supporting the phenotypic characterization of crucial antimicrobial-resistant bacteria and validating the genome analysis results. The findings underscore key genes and bacteria as targets for antimicrobial resistance surveillance in hospital wastewater to protect public and environmental health.

Keywords: antimicrobial resistance; hospital wastewater; metatranscriptomic analysis.

Figure 1

Distribution of antibiotic compounds in hospital wastewater. *: Below the limit of quantification (LOQ).

Figure 2

16S rRNA gene metabarcoding analysis at the genus level in hospital wastewater. Others: Genera with <1% relative abundance; Unclassified: not assigned to any taxon at the genus level.

Figure 3

Profile of antimicrobial-resistant genes (ARGs) and transcriptional levels from metagenome-assembled genome sequences. Transcripts per kilobase million (TPM)-normalized values were calculated to compare ARG abundance. Normalized DNA and mRNA counts of drug class (A) and ARGs (C), along with their respective fold changes between expression level and gene abundance (B,D), are shown.

Figure 4

Profile of antimicrobial-resistant genes (ARGs) and transcriptional levels from predicted plasmid-derived sequences. Transcripts per kilobase million (TPM)-normalized values were calculated to compare ARG abundance. Normalized DNA and mRNA counts of drug class (A) and ARGs (C), along with their respective fold changes between expression level and gene abundance (B,D), are shown.

Figure 5

Correlation heatmap between metagenome-assembled genomes (MAGs) and antimicrobial-resistant genes (ARGs). Heatmaps, based on transcripts per kilobase million (TPM)-normalized values, illustrate ARG abundance (A) and fold changes between the expression level and gene abundance (B) for each MAG.

Figure 5

Correlation heatmap between metagenome-assembled genomes (MAGs) and antimicrobial-resistant genes (ARGs). Heatmaps, based on transcripts per kilobase million (TPM)-normalized values, illustrate ARG abundance (A) and fold changes between the expression level and gene abundance (B) for each MAG.

Figure 6

Phylogenetic tree of Serratia spp. genomes from the metagenome-assembled genome (MAG) and bacterial isolates. The maximum likelihood tree was constructed with 1000 bootstrap replicates. The presence of antimicrobial-resistant genes in these genomes is indicated.