Category: DDLS

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.

I have written an editorial piece for the Swedish Pathogens Portal in which I reflect a bit on the upcoming EU legislation requiring monitoring of AMR in major wastewater treatment plants (1). I also veer a bit into where environmental monitoring outside of sewage may play a role, using our review paper resulting from EMBARK as the starting point (2).

This is timed to coincide with the registration deadlines for two upcoming workshops on AMR surveillance in the environment; the first being the DDLS Symposium on Data-Driven Environmental Monitoring of Infectious Disease on 7th-8th October in Uppsala, which I have been part of organising. The second is a workshop organised by CARe in Gothenburg on 28th October on the theme of sewage surveillance of antibiotic resistance, focusing on the new EU requirements.

My hope is that you will be a bit provoked by this and come to one of these workshops to discuss AMR surveillance and where to go next!

1. Bengtsson-Palme J: Surveillance of antimicrobial resistance – flying blind or flying behind? The Swedish Pathogens Portal, Editorial (2024). doi: 10.17044/scilifelab.27045433 [Link]
2. Bengtsson-Palme J, Abramova A, Berendonk TU, Coelho LP, Forslund SK, Gschwind R, Heikinheimo A, Jarquin-Diaz VH, Khan AA, Klümper U, Löber U, Nekoro M, Osińska AD, Ugarcina Perovic S, Pitkänen T, Rødland EK, Ruppé E, Wasteson Y, Wester AL, Zahra R: Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs? Environment International, 178, 108089 (2023). doi: 10.1016/j.envint.2023.108089 [Paper link]

Together with Anna Székely, I have been working on the organization of a DDLS Symposium on Data-Driven Environmental Monitoring of Infectious Diseases on October 7 – 8, in Uppsala.

The symposium will focus on promoting and enhancing data-driven environmental assessment for infectious diseases (including antibiotic-resistant bacteria) across various settings using diverse approaches. We now invite submission of abstracts for short talks.

Deadline for abstract submission: 18 September 
Deadline to register to attend: 25 September –> REGISTER HERE! <– This includes abstract submission.

Link to more information and the PROGRAM

I hope to see all of you working with AMR in the environment in Uppsala in October!

First of all, I just want to do a last reminder of PhD student position in bioinformatics and artificial intelligence applied to antibiotic resistance with Erik Kristiansson as main supervisor that closes tomorrow. More info here!

Second, two of my best and dearest colleagues at University of Gothenburg – Kaisa Thorell and Åsa Sjöling – have open postdoc positions in molecular microbiology (with Åsa) and bacterial proteomics (with Kaisa). Both of these are great opportunities to work with fantastic people on exciting subjects, so you should check these out if you are looking for postdoc positions in microbiology, molecular biology or bioinformatics! There are only a few days left to apply for these positions, so go ahead and do it now!

Finally, I am again tooting our own horn with the postdoc in innovative approaches to antibiotic resistance monitoring (within the SEARCHER program) in my own group. More info here, deadline is on July 31 with interviews to take place in August.

Erik Kristiansson, who was co-supervisor for my PhD thesis, has an opening for a PhD student funded by the DDLS program. The project is combining bioinformatics and artificial intelligence with a focus on large-scale data analysis to better understand antibiotic resistance and the emergence of novel resistance genes. The research will be centered on DNA sequence analysis, inference in biological networks, and modelling of evolution. The primary applications will be related to antibiotic resistance and bacterial genomics.

I am particularly excited about this position because I will have the benefit of co-supervising the student. The student will also be part of the DDLS research school which is now being launched, which is also super-exciting for Swedish data driven life science.

The candidate is expected to have a degree in bioinformatics, mathematical statistics, mathematics, computer science, physics, molecular biology, or any equivalent topic. Previous experience in analysis of large-scale biological data is desirable. It is important to have good computing and programming skills (e.g. in Python and R), experience with the Linux/UNIX computer environment, and, to the extent possible, previous experience in working with machine learning and/or artificial intelligence.

I had such a good time with Erik as my co-supervisor, and he has put together a truly amazing supervision team with Joakim Larsson, Anna Johnning and myself. I could not imagine a better place to apply bioinformatics and ML/AI on antibiotic resistance! Deadline is June 7! Application link here: https://www.chalmers.se/om-chalmers/arbeta-hos-oss/lediga-tjanster/?rmpage=job&rmjob=12840&rmlang=SE

My colleague and friend Clemens Wittenbecher has an open doctoral student position at Chalmers in Data-Driven Precision Health Research. Clemens works with developing novel biomarker panels to quantify individual disease risk. The project itself will focus on innovative machine learning and artificial intelligence approaches to integrate multi-layer -omics data with bioimages of cardiovascular and metabolic tissues (computer tomography, ultrasound and magnetic resonance imaging).

Clemens is a fantastic person and a great supervisor so if your interests is in line with the position, I strongly suggest you take a look at it! Application link here:

https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job&rmjob=11810&rmlang=UK

Time is passing quickly, and I have not appropriately acknowledged the many newcomers we’ve had to the lab in the past couple of months. With this post I would like to say welcome to the lab to Máté Vass and Dani Jáen Luchoro (both postdocs), Jorge Agramont and Josue Mamani Jarro (doctoral students), as well as Nathália Abichabki (visiting doctoral student from Brazil)! Some of you have already spent a couple of months in the group and we very much enjoy having you here!

A week or so ago, we took this new lab picture with everyone (except for Lisa, who is in Amsterdam). I am very proud to be working with group of extremely talented, smart, funny and goodhearted people!

Very briefly, Dani will be working on updating the BacMet database as part of the BIOCIDE project, and shares his time between my group, Joakim Larsson‘s group and the Sahlgrenska hospital. Máté was recruited within the DDLS program and will work on inferring the metacommunity ecology of antibiotic resistance based on analysis of large-scale datasets. Jorge and Josue are part of the same SIDA-funded doctoral student exchange program with Bolivia and will work on different aspects of environmental antibiotic resistance and the spread of diarrheal pathogens through the environmental matrix. Nathália, finally, is working on understanding the tolerance mechanisms to antibiotics in Klebsiella pneumoniae.

All of you are very welcome to the group!

I am happy to announce that I am joining the editorial committee of the Swedish Pandemic Preparedness Data Portal (formerly the Swedish COVID-19 portal). I will join five other researchers associated with SciLifeLab and will work together with the portal team to maximise the utility of the Portal for researchers, expand its content beyond SARS-CoV-2, and increase engagement with the research community. My main responsibility areas will be antibiotic resistance and emerging pathogens.

Since 2022 the portal is part of the SciLifeLab Pandemic Laboratory Preparedness (PLP) Program. It is operated by the SciLifeLab Data Centre. Over time, the popularity of the Portal has increased within the research community, the general public, and those involved in healthcare, industry, and policy making. I very much look forward to work with Luisa Hugerth (Uppsala University), Laura Carroll (Umeå University), Benjamin Murrell (Karolinska Institute), Mahmoud Naguib (Uppsala University) and Johan Ankarklev (Stockholm University) on the future of the portal!

Friend, colleague and fellow DDLS Fellow Thomas van Boeckel has just established his research group here in Gothenburg. He is now looking for a new postdoc to identify and extract data to populate resistancebank.org, their database of AMR in animals. Ideally, they are looking for someone with training in microbiology. If you are interested in this position, I encourage you to take a look at this job posting!

I thought this could be interesting to some. SciLifeLab has opened 20 permanent staff positions for the new Data platform and Data Science Nodes (DSNs) organised within the DDLS program (that also funds my current position). These can be exciting opportunities to work with big data for someone who might not want to climb the academic group leader career ladder. The positions are spread out over Stockholm, Uppsala, Gothenburg and Linköping and can be found here.