Hannah-Marie Martiny

Motivation: Antimicrobial resistance databases are central to genomic surveillance, but resistance determinants remain distributed across resources with different scopes, structures, and annotations. Results: We developed PanRes, a curated resistance database of 11 717 genes integrating acquired and latent determinants of antibiotic, biocide, and metal resistance within a unified ontology. We predicted representative protein structures and clustered them by structural similarity, grouping proteins into 505 high-confidence structurally conserved clusters coherent despite sequence divergence. Their structure-guided alignments were used to build Hidden Markov Models (HMMs) for remote homology search. In seven wastewater metagenomes from five European cities, PanRes 3D-based HMMs expanded detection beyond what was possible with BLAST, with 35.3% of retained hits identified only by the HMMs and generally showing greater divergence from known proteins. For beta-lactamases, several proteins retained beta-lactamase-like folds and catalytic geometry despite weak sequence similarity. Availability and implementation: PanRes is available through an interactive web platform (https://panres.rambio.dk/), a structure-informed resource for exploring the whole resistome. The database, HMM profiles, and ontology are available at Zenodo (10.5281/zenodo.22876216).

Antimicrobial resistance databases are central to genomic surveillance, but resistance determinants remain distributed across resources with different scopes, structures, and annotations. We developed PanRes, a curated resistance database of 11,717 genes integrating acquired and latent determinants of antibiotic, biocide, and metal resistance within a unified ontology. We predicted representative protein structures and clustered them by structural similarity, grouping proteins into 598 structurally conserved clusters coherent despite sequence divergence. Their structure-guided alignments were used to build Hidden Markov Models (HMMs) for remote homology search. In wastewater metagenomes from seven European cities, PanRes 3D-based HMMs expanded detection beyond high-confidence BLAST, with 35.2% of retained hits identified only by the HMMs and generally showing greater divergence from known proteins. For beta-lactamases, several proteins retained beta-lactamase-like folds and catalytic geometry despite weak sequence similarity. PanRes is available through an interactive web platform (https://panres.rambio.dk/), a structure-informed resource for exploring the whole resistome.

Antimicrobial resistance genes (ARGs) have rapidly emerged and spread globally, but the pathways driving their spread remain poorly understood. We analyzed 1240 sewage samples from 351 cities across 111 countries, comparing ARGs known to be mobilized with those identified through functional metagenomics (FG). FG ARGs showed stronger associations with bacterial taxa than the acquired ARGs. Network analyses further confirmed this and showed potential for source attribution of both known and novel ARGs. The FG resistome was more evenly dispersed globally, whereas the acquired resistome followed distinct geographical patterns. City-wise distance-decay analyses revealed that the FG ARGs showed significant decay within countries but not across regions or globally. In contrast, acquired ARGs showed decay at both national and regional scales. At the variant level, both ARG groups had significant national and regional distance-decay effects, but only FG ARGs at a global scale. Additionally, we observed stronger distance effects in Sub-Saharan Africa and East Asia compared to North America. Our findings suggest that differential selection and niche competition, rather than dispersal, shape the global resistome patterns. A limited number of bacterial taxa may act as reservoirs of latent FG ARGs, highlighting the need of targeted surveillance to mitigate future resistance threats.

Wastewaters are plausible arenas for antibiotic resistance evolution and transmission, yet selection for resistance by municipal wastewater has rarely been empirically demonstrated. Here, we experimentally investigate the potential of untreated municipal wastewater from 47 countries to select for antibiotic resistance and explore possible drivers. Using a functional selection assay with 340 mixed Escherichia coli strains, we find that sterile-filtered samples from 14 countries select significantly for resistance to at least one antibiotic class compared to baseline, while the majority select significantly against resistance. Similar results were generated using natural wastewater communities. Additionally, we report a comprehensive characterization of antibiotics and antibacterial biocides in the wastewaters. None of the 22 analyzed antibiotics could be assigned as key drivers for selection in E. coli , whereas e.g. folate pathway antagonists and macrolides often exceed predicted non-selective concentrations for other bacteria by >10-fold. Despite detecting 13 (out of 20 analyzed) organic antibacterial biocides, their potential for co-selection remains unclear. Measured chemical constituents correlate only weakly with observed selection, suggesting complex mixture effects and/or selection by unmeasured compounds. The clear deselection of resistance by most samples indicates that many resistant strains have impaired fitness in wastewaters with limited antibiotic selection pressure.

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of next-generation sequencing data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic data sets. Using more than 6.76∙10 ⁸ read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials (CIAs). Using these profiles, we found evidence of several ARGs conferring resistance for CIAs being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs. IMPORTANCE: Understanding the collateral effects happening in a resistome can reveal previously unknown links between antimicrobial resistance genes (ARGs). Through the analysis of pairwise ARG abundances in 214K metagenomic samples, we observed that the co-abundance is highly dependent on the environmental context and argue that these correlations can be used to show the risk of co-selection occurring in different settings.

Analyzing metagenomic data can be highly valuable for understanding the function and distribution of antimicrobial resistance genes (ARGs). However, there is a need for standardized and reproducible workflows to ensure the comparability of studies, as the current options involve various tools and reference databases, each designed with a specific purpose in mind. In this work, we have created the workflow ARGprofiler to process large amounts of raw sequencing reads for studying the composition, distribution, and function of ARGs. ARGprofiler tackles the challenge of deciding which reference database to use by providing the PanRes database of 14 078 unique ARGs that combines several existing collections into one. Our pipeline is designed to not only produce abundance tables of genes and microbes but also to reconstruct the flanking regions of ARGs with ARGextender. ARGextender is a bioinformatic approach combining KMA and SPAdes to recruit reads for a targeted de novo assembly. While our aim is on ARGs, the pipeline also creates Mash sketches for fast searching and comparisons of sequencing runs. The ARGprofiler pipeline is a Snakemake workflow that supports the reuse of metagenomic sequencing data and is easily installable and maintained at https://github.com/genomicepidemiology/ARGprofiler.

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of NGS data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic datasets. Using more than 6.76·10 ⁸ read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials. Using these profiles, we found evidence of several ARGs conferring resistance for critically important antimicrobials (CIA) being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs.

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods, as well as a deeper understanding of how antimicrobial resistance genes (ARGs) have been transmitted around the world. The large pool of sequencing data available in public repositories provides an excellent resource for monitoring the temporal and spatial dissemination of AMR in different ecological settings. However, only a limited number of research groups globally have the computational resources to analyze such data. We retrieved 442 Tbp of sequencing reads from 214,095 metagenomic samples from the European Nucleotide Archive (ENA) and aligned them using a uniform approach against ARGs and 16S/18S rRNA genes. Here, we present the results of this extensive computational analysis and share the counts of reads aligned. Over 6.76∙10⁸read fragments were assigned to ARGs and 3.21∙10⁹to rRNA genes, where we observed distinct differences in both the abundance of ARGs and the link between microbiome and resistome compositions across various sampling types. This collection is another step towards establishing global surveillance of AMR and can serve as a resource for further research into the environmental spread and dynamic changes of ARGs.

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods, as well as a deeper understanding of how antimicrobial resistance genes (ARGs) have transmitted around the world. The large pool of sequencing data available in public repositories provides an excellent resource for monitoring the temporal and spatial dissemination of AMR in different ecological settings. However, only a limited number of research groups globally have the computational resources allowing analyses of such data. We retrieved 442 Tbp of sequencing reads from 214,095 metagenomic samples from the European Nucleotide Archive (ENA) and aligned them using a uniform approach against ARGs and 16S/18S rRNA genes. Here, we present the results of this extensive computational analysis and share the counts of reads aligned. Over 6.76 · 10 ⁸ read fragments were assigned to ARGs and 3.21 · 10 ⁹ to rRNA genes, where we observed distinct differences in both the abundance of ARGs and the link between microbiome and resistome compositions across various sampling types. This collection is another step towards establishing a global surveillance of AMR and can serve as a resource for further research into the environmental spread and dynamic changes of ARGs.