By linking ARGs to their wider genetic contexts and hosts, the findings shed light on the previously unrecognized carriers of resistance genes in wastewater, and provides a valuable methodology for early identification of newly arising ARGs and their hosts.
Abstract
Background
Antibiotic resistance genes (ARGs) circulating among clinically relevant bacteria pose serious challenges to public health. Given the ancient and environmental bacterial origins of ARGs, a better understanding of the carriers of ARGs beyond the clinically most relevant species is urgently needed for longer-term resistance monitoring and intervention measures. While the risks of emerging ARGs from environmental sources have been recognized, the identification bottlenecks stem from the limitations of shotgun metagenomic sequencing and bioinformatic methods.
Results
We use long-read metagenomic sequencing and bacteria-specific methylation profiles to re-establish the links between established (well-described) or latent (absent in databases) ARGs and their bacterial and genetic contexts in wastewater. We analyze base modification data produced by PacBio SMRT sequencing using an in-house pipeline utilizing position weight matrices and UMAP visualizations, which we validate by a synthetic community with known bacterial composition. Our analysis reveals several previously unreported ARGs and ARG-host linkages in wastewater. For instance, we find that Arcobacter, a key wastewater associated taxon and emerging pathogen, carries a latent beta-lactamase gene with high predicted mobility potential. Of the other understudied beta-lactamases, we describe blaMCA within pdif-modules across highly varying contexts suggesting its recent acquisition events. Additionally, we uncover the wastewater resident taxa mediated carriage of clinically important ARGs.
Conclusions
By linking ARGs to their wider genetic contexts and hosts, our findings shed light on the previously unrecognized carriers of resistance genes in wastewater. The presented approach provides a valuable methodology for early identification of newly arising ARGs and their hosts.
Wastewater systems are increasingly recognized as important environmental reservoirs of antimicrobial resistance (AMR), acting as interfaces where resistant bacteria, antimicrobial resistance genes (ARGs), and mobile genetic elements (MGEs) converge and potentially disseminate. Wastewater samples were collected from hospital and community sites, including municipal medical centers, household wastewater outlets, and open drainage systems. Genomic DNA was extracted using the ZymoBIOMICS DNA/RNA Miniprep Kit and sequenced on the Oxford Nanopore Technologies MinION MK1D platform using the Native Barcoding Kit (SQK-NBD114.24, V14). Sequencing data were processed through a custom Snakemake workflow integrating quality control, taxonomic profiling, resistome characterization, mobilome analysis, and genome-resolved metagenomics. A total of 489 unique ARGs conferring resistance to 29 antibiotic classes were identified through metagenomic analysis. The resistome was dominated by genes conferring resistance to {beta}-lactams (including cephalosporins and carbapenems), aminoglycosides, tetracyclines, macrolides, and fluoroquinolones. Clinically important resistance determinants, including blaOXA, blaTEM, blaGES, blaCARB, cfxA, tet, qnr, sul, dfrA, erm, msrE, and aminoglycoside-modifying enzyme genes such as aac(3) and ant(3'') were detected across both hospital and community wastewater samples. Resistance mechanisms were predominantly driven by antibiotic inactivation, followed by efflux and target protection. Several priority bacterial pathogens were detected, including Escherichia coli, Klebsiella pneumoniae, Enterobacter cloacae, Pseudomonas aeruginosa, and Acinetobacter baumannii. Integration/excision elements were the predominant category of MGEs, followed by transfer-associated elements and replication/recombination/repair functions. Plasmid analysis further identified diverse incompatibility groups, predominantly IncP6, IncC, IncF, and IncR replicons, supporting the widespread occurrence of plasmid-mediated horizontal gene transfer in both settings. These findings reveal a substantial burden of clinically relevant ARGs, mobile genetic elements, and potential bacterial pathogens in hospital and community wastewater in Conakry. This study provides the first metagenomic baseline for environmental AMR surveillance in Guinea and highlights the urgent need for integrated One Health strategies to mitigate the environmental dissemination of antimicrobial resistance.
T. A. C. Gnimadi, A. Keita, Y. Hounmanou et al.· medRxiv· 0 citations
The resistome, defined as the complete set of antibiotic resistance genes (ARGs) present in the microbiota of a given environment, is a critical component for understanding the evolutionary dynamics of antimicrobial resistance (AMR) and its impact on human, animal, and environmental health. This review summarizes current methods and technological advances and offers a forward-looking perspective on resistome research. A systematic literature search was conducted. References on short-read and long-read sequencing, amplicon sequencing, shotgun metagenomics, and multi-omics integration were included, as were bioinformatics tools for the detection, quantification, and annotation of ARGs. The results indicate that next-generation sequencing (NGS) technologies have significantly improved the characterization of ARGs across ecosystems, enabling high-resolution microbial profiling and the discovery of new variants. Furthermore, integrating multi-omics approaches with computational tools improves data accuracy, reduces analysis and reporting times, and facilitates the development of predictive models. However, significant challenges remain, which will be key to strengthening epidemiological surveillance under the One Health approach.
Lenin García Gutiérrez, A. Méndez-Tenorio, M. A. López-Luis et al.· Antibiotics· 0 citations
Antimicrobial exposure can alter gut resistance reservoirs, but bulk metagenomics alone often cannot distinguish whether observed changes reflect expansion of bacterial hosts, altered abundance of plasmid-derived sequences, or redistribution of mobile elements across host backgrounds. Here, we combined longitudinal bulk short-read metagenomics with selected bulk long-read and single-cell shotgun metagenomic sequencing to analyse faecal samples from six Danish pigs over 11 weeks, including an unplanned tiamulin exposure affecting the three pigs housed on the right side of the stable. We constructed a catalogue of 885 plasmid-derived sequences collapsed into 195 bins. Twenty-eight bins and 212 contigs carried resistance annotations, including ribosomal-target markers relevant to pleuromutilin exposure. Single-cell evidence linked subsets of plasmid-derived bins and contigs to bacterial host taxa, enabling host-resolved inspection of resistance-associated plasmid-derived features in longitudinal bulk metagenomes. The microbiome-wide plasmid-derived-sequence prevalence screen identified two bins with post-event associations, whereas resistance-gene abundance and host-attributed plasmid-derived-sequence abundance screens identified no significant host-resolved associations. Because exposure was unplanned and confounded with pen side and disease signs, treatment-response results are exploratory. The main contribution is a single-cell-informed microbial ecology workflow for linking plasmid-derived resistance features to host backgrounds and longitudinal abundance patterns in complex gut
Alexander Zubov, H. Vigre, Saria Otani et al.· bioRxiv· 0 citations
Background/Objectives: Antimicrobial resistance in microbial communities is a global health concern that leads to millions of deaths each year. Many bacterial pathogens have resistance to multiple antibiotics. Domestic wastewater treatment facilities are reservoirs for antibiotic-resistant bacteria and resistance genes. Wastewater-based epidemiology surveillance is crucial for monitoring antibiotic resistance genes (ARGs). Türkiye has one of the highest levels of antibiotic resistance with a lack of research on resistomes. This study is a focused reanalysis of publicly available wastewater metagenomes from Türkiye, comparing them to global and other country’s results. Methods: Ten metagenomic data of wastewater treatment from Türkiye were downloaded from NCBI-SRA database. Metagenome assemblies were performed and high-quality metagenome-assembled genomes (HQ-MAGs) were included in the study. Taxonomic annotations and antibiotic resistance profiles were identified in both the metagenome assemblies and HQ-MAGs. Results: A total of 401 different ARGs in 25 antibiotic classes have been identified, including Mcr (including mcr-1, mcr-2, mcr-3 and mcr-5 variants) and optrA. The vanR two-component regulatory system genes for controlling vancomycin antibiotic resistance were one of the most dominant along with other vancomycin resistance genes such as vanA and vanB. A total of 115 HQ-MAGs were obtained with at least eight ARGs. The HQ-MAG with the highest number of resistance genes (58) was found to belong to E. coli. The most frequently encountered resistance genes in HQ-MAGs were the multidrug ABC transporter, vanR, bacA and patA which confer resistance to multidrug, glycopeptide, bacitracin and fluoroquinolone antibiotic groups, respectively. Conclusions: To effectively address the problems of antibiotic resistance outbreaks, comparable AMR surveillance at national and global levels is required for the identification and prioritization of ARGs and resistance genes. This is the first report conducted in Türkiye.
Applications across healthcare, environmental science, agriculture, biotechnology, and industry are reviewed with particular emphasis on clinical metagenomic next-generation sequencing (mNGS) for infectious disease diagnostics, antimicrobial resistance (AMR) surveillance, gut microbiome research, and precision medicine.
Ahmed Alsharksi· Razi Medical Journal· 0 citations
Antimicrobial resistance poses a growing global health threat, yet large-scale surveillance and risk evaluation remain constrained by the cost and accessibility of metagenomic sequencing. Here, we demonstrate that antibiotic resistance risk, integrating gene mobility, human accessibility, clinical relevance, and host pathogenicity, can be quantitatively inferred from microbial taxonomic composition through its ecological coupling with microbial hosts. By integrating 177,134 metagenome-assembled genomes, 3,058 metagenomes, and 31,216 16S rRNA profiles, we defined a comprehensive ARG host catalogue and conserved core taxa across sequencing platforms. A machine learning model built on this framework achieved high predictive accuracy in held-out test data (R2 > 0.96) and retained strong performance in an independent dataset with paired 16S rRNA and metagenomic profiles (Pearson r = 0.74; Lin's CCC = 0.73), supporting its robustness and cross-platform transferability. Applying this tool on a global scale, we demonstrate that resistance risk exhibits consistent structure across populations, with host-associated ecological factors explaining more variation than socioeconomic conditions, supporting the feasibility of translating taxonomic profiles into quantitative estimates of functional risk. This work establishes a scalable framework for inferring antibiotic resistance risk from 16S data, enabling equitable, large-scale surveillance of antimicrobial resistance while positioning microbiome composition as a predictive basis for functional risk and advancing a general paradigm for inferring microbial traits from community structure.
Qi Zhang, Zeling Wang, Chaotang Lei et al.· Environmental Pollution· 0 citations