Thomas Nordahl Petersen

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.

Background: In Denmark, antimicrobial resistance (AMR) in pigs has been monitored since 1995 by phenotypic approaches using the same indicator bacteria. Emerging methodologies, such as metagenomics, may allow novel surveillance ways. Aim: This study aimed to assess the relevance of indicator bacteria (Escherichia coli and Enterococcus faecalis) for AMR surveillance in pigs, and the utility of metagenomics. Methods: We collated existing data on AMR and antimicrobial use (AMU) from the Danish surveillance programme and performed metagenomics sequencing on caecal samples that had been collected/stored through the programme during 1999–2004 and 2015–2018. We compared phenotypic and metagenomics results regarding AMR, and the correlation of both with AMU. Results: Via the relative abundance of AMR genes, metagenomics allowed to rank these genes as well as the AMRs they contributed to, by their level of occurrence. Across the two study periods, resistance to aminoglycosides, macrolides, tetracycline, and beta-lactams appeared prominent, while resistance to fosfomycin and quinolones appeared low. In 2015–2018 sulfonamide resistance shifted from a low occurrence category to an intermediate one. Resistance to glycopeptides consistently decreased during the entire study period. Outcomes of both phenotypic and metagenomics approaches appeared to positively correlate with AMU. Metagenomics further allowed to identify multiple time-lagged correlations between AMU and AMR, the most evident being that increased macrolide use in sow/piglets or fatteners led to increased macrolide resistance with a lag of 3–6 months. Conclusion: We validated the long-term usefulness of indicator bacteria and showed that metagenomics is a promising approach for AMR surveillance.

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.

The ever-growing collection of metagenomic samples available in public data repositories has the potential to reveal new details on the emergence and dissemination of mobilized colistin resistance genes. Our analysis of metagenomes deposited online in the last 10 years shows that the environmental distribution of mcr gene variants depends on sampling source and location, possibly leading to the emergence of new variants, although the contig on which the mcr genes were found remained consistent.

An inter-laboratory proficiency test was organized to assess the ability of participants to perform shotgun metagenomic sequencing of cold smoked salmon, experimentally spiked with a mock community composed of six bacteria, one parasite, one yeast, one DNA, and two RNA viruses. Each participant applied its in-house wet-lab workflow(s) to obtain the metagenomic dataset(s), which were then collected and analyzed using MG-RAST. A total of 27 datasets were analyzed. Sample pre-processing, DNA extraction protocol, library preparation kit, and sequencing platform, influenced the abundance of specific microorganisms of the mock community. Our results highlight that despite differences in wet-lab protocols, the reads corresponding to the mock community members spiked in the cold smoked salmon, were both detected and quantified in terms of relative abundance, in the metagenomic datasets, proving the suitability of shotgun metagenomic sequencing as a genomic tool to detect microorganisms belonging to different domains in the same food matrix. The implementation of standardized wet-lab protocols would highly facilitate the comparability of shotgun metagenomic sequencing dataset across laboratories and sectors. Moreover, there is a need for clearly defining a sequencing reads threshold, to consider pathogens as detected or undetected in a food sample.

Helminth parasites have been shown to have systemic effects in the host. Using shotgun metagenomic sequencing, we characterise the gut microbiome and resistome of 113 Zimbabwean preschool-aged children (1–5 years). We test the hypothesis that infection with the human helminth parasite,Schistosoma haematobium, is associated with changes in gut microbial and antimicrobial resistance gene abundance/diversity. Here, we show that bacteria phylaBacteroidetes,Firmicutes, Proteobacteria, and fungi phylaAscomycota, Microsporidia, Zoopagomycotadominate the microbiome. The abundance ofProteobacteria,Ascomycota, andBasidiomycotadiffer between schistosome-infected versus uninfected children. Specifically, infection is associated with increases inPseudomonas, Stenotrophomonas, Derxia, Thalassospira,Aspergillus, Tricholoma, andPeriglandula, with a decrease inAzospirillum. We find 262 AMR genes, from 12 functional drug classes, but no association with individual-specific data. To our knowledge, we describe a novel metagenomic dataset of Zimbabwean preschool-aged children, indicating an association between urogenital schistosome infection and changes in the gut microbiome.