Tag: Latent ARGs

Today, I will be giving a talk at the EDAR8 conference in Brisbane, which will partially cover something that I has been cooking in the research group for some time. It started off with a recurring need to see if a particular antibiotic resistance gene (ARG) was truly new or if it had been detected in previous studies. And after having done the same procedures in several studies, we started to think that maybe it would make sense to build a resource that could be used to keep track of both established and latent ARGs (1) and that could be easily updated over time. And the result is CLEVER – a scheme for Classification of Latent and Established Variants of Extant Antibiotic Resistance Genes.

CLEVER is two things. First, it is a set of definitions for ARGs, making it easier to assign them to categories, for example for risk assessment. Second, it is a database based on these criteria, and to the best of our knowledge the first resource to organize both established and latent ARGs into the same coherent database structure, while still keeping them categorized. To achieve this, CLEVER integrates data from ResFinder (2), CARD (3), ResFinderFG (4), as well as ARGs predicted by the fARGene (5) and MUSTARD (6) tools, gathered from published literature (6-13),

Very briefly, CLEVER is built on the following definitions:

• Established ARG (E): An ARG that is experimentally verified to confer antibiotic resistance and is present in human pathogens.
• Latent ARG (L): An ARG that confers a resistance function (or is predicted to do so), but does not exist in pathogens
• Mobile ARG (M): An ARG which is present on a mobile genetic element, which could be plasmids, integrons, transposons or integrative conjugative elements.
• Chromosomal ARG (C): Any ARG that does not meet the criteria for a mobile ARG above.
• Validated ARG (V): An ARG for which the resistance function has been verified in laboratory experiments, by showing that the presence of the gene increases the MIC of the host compared to an otherwise isogenic strain that does not carry the gene, or alternatively that over-expression of the gene induces a higher MIC compared to an isogenic reference strain.
• Predicted ARG (P): An ARG for which its function has not been verified experimentally (see above), but has been predicted to be an ARG by fARGene
• Structure-predicted ARG (S): An ARG for which its function has not been verified experimentally (see above), but has been predicted to be an ARG based on 3D-structure similarity by MUSTARD

Importantly, what CLEVER also does is to give names (CLEVER IDs) to latent ARGs that currently does not have any consistent way to refer to them. This makes it possible to unambiguously reference a particular ARG family across studies, which is crucial for risk assessment and understanding the spread of AMR.

Finally, by classifying ARGs as established or latent, mobile or chromosomal and verified or predicted makes it possible to identity particular latent ARGs that until now has gone under the radar. Particularly, latent ARGs that already appear on mobile genetic elements and have several mobile variants, are likely to be imminent AMR threats to human health, making them “most-wanted” ARGs that should be targeted for phenotypic evaluation and potential inclusion into AMR surveillance efforts.

By specifically looking into potential ARGs against the last-resort antibiotics carbapenems, colistin, tigecycline and plazomicin, and pulling out latent ARGs that belong to classes potentially conferring resistance to these antibiotics that are already present on plasmids, we can already present a list of five ARGs of imminent concern, which we label the first CLEVER list of most-wanted ARGs: ~blaA-145, ~blaD2-129, ~aac6p-147, ~aph2b-14, and ~aph6-89.

Naturally, there is still a lot work to be done on CLEVER, but I am very proud of what we have already, and I think that the current version (2.0) already has potential to be very useful for AMR studies. We have already started using this internally in the lab and within SEARCHER quite extensively in the last few months, so I am happy to see the use of this resource in the larger AMR community!

References:

1. Inda-Díaz JS, Lund D, Parras-Moltó M, Johnning A, Bengtsson-Palme J, Kristiansson E. Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome 2023;11:44. https://doi.org/10.1186/s40168-023-01479-0.
2. Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. Journal of Antimicrobial Chemotherapy 2020;75:3491–500. https://doi.org/10.1093/jac/dkaa345.
3. Jia B, Raphenya AR, Alcock B, Waglechner N, Guo P, Tsang KK, et al. CARD 2017: expansion and model-centric curation of the comprehensive antibiotic resistance database. Nucleic Acids Research 2016:gkw1004. https://doi.org/10.1093/nar/gkw1004.
4. Gschwind R, Ugarcina Perovic S, Weiss M, Petitjean M, Lao J, Coelho LP, et al. ResFinderFG v2.0: a database of antibiotic resistance genes obtained by functional metagenomics. Nucleic Acids Research 2023:gkad384. https://doi.org/10.1093/nar/gkad384.
5. Berglund F, Österlund T, Boulund F, Marathe NP, Larsson DGJ, Kristiansson E. Identification and reconstruction of novel antibiotic resistance genes from metagenomes. Microbiome 2019;7, 52. https://doi.org/10.1186/s40168-019-0670-1
6. Ruppé E, Ghozlane A, Tap J, Pons N, Alvarez A-S, Maziers N, et al. Prediction of the intestinal resistome by a three-dimensional structure-based method. Nature Microbiology 2019;4:112–23. https://doi.org/10.1038/s41564-018-0292-6.
7. Inda-Díaz JS, Lund D, Parras-Moltó M, Johnning A, Bengtsson-Palme J, Kristiansson E. Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome 2023;11:44. https://doi.org/10.1186/s40168-023-01479-0.
8. Victor MP, Radisic V, Grevskott DH, Marathe NP. Hospital effluent in a low-resistance setting is responsible for dissemination of novel antibiotic resistance genes into the marine environment. Ecotoxicology and Environmental Safety 2025;301:118390. https://doi.org/10.1016/j.ecoenv.2025.118390.
9. Victor MP, Øvreås L, Marathe NP. Characterization of known and novel clinically important antibiotic resistance genes and novel microbes from wastewater-impacted high Arctic fjord sediments. Science of The Total Environment 2025;985:179699. https://doi.org/10.1016/j.scitotenv.2025.179699.
10. Li B, Jiang L, Johnson T, Wang G, Sun W, Wei G, et al. Global health risks lurking in livestock resistome. Sci Adv 2025;11:eadt8073. https://doi.org/10.1126/sciadv.adt8073.
11. Somerville V, Meola M, Nunes-Richards A, Bengtsson-Palme J, Neukamm J, Majander K, et al. Microbial community dynamics in a traditional Swiss mountain cheese over 142 years of cheesemaking 2026. https://doi.org/10.64898/2026.02.26.708305.
12. Coche‐Miranda J, Arros P, Canales N, Berríos‐Pastén C, Azziz G, Lagos R, et al. Antarctic soil microbiomes encode structurally conserved and phylogenetically diverse beta‐lactamases. iMetaOmics 2026;e70118. https://doi.org/10.1002/imo2.70118.
13. Wang K, Xu J, Li X, Zhu P, Suo R, Lu X, et al. Evolutionary selection of trimethoprim-resistant dfrA genes in lytic phages affects phage and host fitness during infection. Sci Adv 2025;11:eadt4817. https://doi.org/10.1126/sciadv.adt4817.

As part of the SEARCHER program, we are now hiring a two-year postdoc to work with innovative approaches to antibiotic resistance monitoring. You can read more about the position here and at Chalmers’ job portal, but in short we are after a wet-lab postdoc who are willing to do field work and laboratory studies to identify novel antibiotic resistance genes.

Please do not send me your CV and application letter via e-mail, but apply through the Chalmers application portal. Sending your CV to me will not increase your changes. Only contact me about this position if you have actual, relevant questions about the position (as I will otherwise get lots of unwanted e-mails…) Those questions, I am happy to answer!

What is the latent resistome? This is a term we coin in a new paper published yesterday in Microbiome. In the paper, we distinguish between the small number antibiotic resistance genes (ARGs) that are established, well-characterized, and available in existing resistance gene databases (what we refer to as “established ARGs”). These are typically ARGs encountered in clinical pathogens and are often already causing problems in human and animal infections. The remaining latently present ARGs, which we denote “latent ARGs”, are less or not at all studied, and are therefore much harder to detect (1). These latent ARGs are typically unknown and generally overlooked in most studies of resistance. They are also seldom accounted for in risk assessments of antibiotic resistance (2-4). This means that our view of the resistome and its diversity is incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants.

In our new study, we try to alleviate this issue by analyzing more than 10,000 metagenomic samples. We show that the latent ARGs are more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The total pan-resistomes, i.e., all ARGs present in an environment (including the latent ARGs), are heavily dominated by these latent ARGs. In contrast, the core resistome (the ARGs that are commonly encountered) comprise both latent and established ARGs.

In the study, we identified several latent ARGs that were shared between environments or that are already present in human pathogens. These are often located on mobile genetic elements that can be transferred between bacteria. Finally, we also show that wastewater microbiomes have surprisingly large pan- and core-resistomes, which makes this environment a potent high-risk environment for mobilization and promotion of latent ARGs, which may make it into pathogens in the future.

It is also interesting to note that this new study echoes the results of my own study from 2018, showing that soil and water environments contain a high diversity of latent ARGs (or ARGs not found in pathogens as I put it in the 2018 study), despite being almost devoid of established ARGs (5).

This project has been a collaboration with Erik Kristiansson’s research group, and particularly with Juan Inda-Diáz. It has been great fun to work with them and I hope that we will keep this collaboration going into the future! The study can be read in its entirety here.

References

1. Inda-Díaz JS, Lund D, Parras-Moltó M, Johnning A, Bengtsson-Palme J, Kristiansson E: Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome, 11, 44 (2023). doi: 10.1186/s40168-023-01479-0 [Paper link]
2. Martinez JL, Coque TM, Baquero F: What is a resistance gene? Ranking risk in resistomes. Nature Reviews Microbiology 2015, 13:116–123. doi:10.1038/nrmicro3399
3. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
4. Bengtsson-Palme J: Assessment and management of risks associated with antibiotic resistance in the environment. In: Roig B, Weiss K, Thoreau V (Eds.) Management of Emerging Public Health Issues and Risks: Multidisciplinary Approaches to the Changing Environment, 243–263. Elsevier, UK (2019). doi: 10.1016/B978-0-12-813290-6.00010-X
5. Bengtsson-Palme J: The diversity of uncharacterized antibiotic resistance genes can be predicted from known gene variants – but not always. Microbiome, 6, 125 (2018). doi: 10.1186/s40168-018-0508-2