Category: EMBARK

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’ve been terribly bad at keeping updates on what we have published in the last year or so – there have just been a bit too much other things to do. So I thought it was time to take a look at what we have published in the last year before some really cool stuff hits the press this summer and fall (hopefully more on that soon!!)

Let’s start with some EMBARK/SEARCHER output. Several lab members (Anna, Marcus and I) have been involved in a paper using functional metagenomics to find novel cefiderocol resistance genes (1). We found four resistance genes, including three ꞵ-lactamases (VEB-3, an OXA-372 homolog, and a YbxI homolog) and a partial penicillin-binding protein homolog, none of which had been previously reported as a cefiderocol resistance gene. The blaVEB-3 gene was associated with a mobile genetic element. We could find three of them using shotgun metagenomics, showing that the blaVEB-3 gene was widespread across France, Sweden, Germany and Pakistan, hinting at efficient dissemination of this gene.

I have also been involved in a collaboration paper with Thomas Berendonk and Uli Klümper‘s labs, where we investigate if fish can be sentinels of environmental antibiotic resistance, and it turns out that they are… not great for that (2).

On the topic of antibiotic resistance gene (ARG) dissemination, Máté Vass (now at SLU) lead a study published in Communications Biology investigating how water stratification affects horizontal gene transfer, with a focus on ARGs (3). The main finding of this paper is that water stratification is a constraint on horizontal gene transfer, which may have implications on how we think about ARG spread through water environments.

While we are at the topic of large-scale quantifications of ARGs in big data sets, I was super-happy to be part of a collaboration with Katariina Pärnänen on how gender (and other factors) impact ARGs in the human microbiome (4). I kept telling Katariina that this would probably yield nothing – the microbiome data was too noisy, and the signal will get lost. Yet, she persisted, and indeed it turned out we are at the point where there is enough human microbiome data to get a signal even if there is a lot of noise. So hats off to Katariina, this was your “what did I say” moment with me!

Then we have a set of mechanistic AMR studies on ARG evolution. First, Lisa Teichmann published parts of her PhD thesis, first on the gradual evolution of fluoroquinolone resistance in E. coli (5) and how this is related to the SOS response in bacteria. She then followed up with a somewhat similar paper on amoxicillin evolution in E. coli (6). The general picture of these two papers on how E. coli adapts genetically to antibiotic stress is that resistance evolution is highly antibiotic-specific and that canonical stress-response or mutagenic pathways do not uniformly explain adaptive trajectories.

Somewhat connected, Nathália Abichabki recently published a paper where we propose screening cut-off values and tolerance disk tests (TDtests) for detection of tolerance/persistence to ceftazidime-avibactam in Klebsiella pneumoniae (7). This is also related to a bunch of papers on tolerance and low-level resistance to antibiotics that will be coming out of Nathália’s thesis, so there is more coming on this front soon!

Finally – on the AMR front – Anna Abramova led an effort together with Veronika Pettersen to investigate possibility for integration of AMR surveillance systems in the Nordic countries that recently got published in Public Health (8). Anna and Veronika did a huge amount of work on this paper, but this was largely the outcome of several meetings on the NoMoReAMR consortium, where we pinpointed missed opportunities for surveillance in the otherwise relatively homogenous Nordic countries. I hope to get to work more with this consortium in the future, as I think that we have had very fruitful discussions on both AMR research and monitoring and when and where it is useful.

And so two papers not related to AMR: We had a very nice collaboration with Daniel Bojar‘s group coming out late last year in Nature Communications, looking – from many different angles – at seal milk oligosaccharides and their potential uses. While the cool finding in this paper is that seal milk seems even more complex than human breast milk in terms of milk oligosaccharides (9), we did not contribute too much in that part. Instead, Mirjam Dannborg was studying the effects of these oligosaccharides on pathogen biofilms, work that will also be part of her PhD thesis when she defends this fall!

Finally, in a collaboration with colleagues in Brazil, we published a review article on the outlook for combining 3D organoid cultures and high-throughput analysis techniques to better understand host-pathogen interactions (10). This was the result of a cross-visit collaboration between Brazil and Sweden, where me and Mirjam visited the lab of Elaine de Martinis, and Elaine, Leonardo Andrade and Nathália Abichabki visited our lab back in 2023. It’s nice to see our discussions take paper form and I hope to be working more with this wonderful team in Brazil!

Papers mentioned:

1. Gschwind R, Bonnet M, Abramova A, Jarquín-Díaz VH, Wenne M, Löber U, Godron N, Kampouris ID, Tskhay F, Nahid F, Debroucker C, Bui-Hai M, El Aiba I, Klümper U, Berendonk TU, Forslund-Startceva SK, Zahra R, Bengtsson-Palme J, Ruppé E: Cefiderocol resistance genes identified in environmental samples using functional metagenomics. ISME Journal, 20, 1, wrag010 (2026). doi: 10.1093/ismejo/wrag010 [Paper link]
2. Tskhay F, Köbsch C, Elena AX, Bengtsson-Palme J, Berendonk TU, Klümper U: Fish are poor sentinels for surveillance of riverine antimicrobial resistance. One Health, 20, 101026 (2025). doi: 10.1016/j.onehlt.2025.101026 [Paper link]
3. Vass M, Abramova A, Bengtsson-Palme J: Antimicrobial resistance dissemination via horizontal gene transfer is constrained in stratified waters. Communications Biology, 9, 435 (2026). doi: 10.1038/s42003-026-09857-8 [Paper link]
4. Salehi M, Laitinen V, Bhanushali S, Bengtsson-Palme J, Collignon P, Beggs JJ, Pärnänen K, Lahti L: Gender differences in global antimicrobial resistance. npj Biofilms and Microbiomes, 11, 79 (2025). doi: 10.1038/s41522-025-00715-9 [Paper link]
5. Teichmann L, Luitwieler SH, Bengtsson-Palme J, ter Kuile BH: Fluoroquinolone-specific resistance trajectories in E. coli and their dependence on the SOS-response. BMC Microbiology, 27, 37 (2025). doi: 10.1186/s12866-025-03771-5 [Paper link]
6. Teichmann L, Wenne M, Luitweiler S, Dugar G, Bengtsson-Palme J, ter Kuile B: Genetic Adaptation to Amoxicillin in Escherichia coli: The Limited Role of dinB and katE. PLoS ONE, 20, 2, e0312223 (2025). doi: 10.1371/journal.pone.0312223 [Paper link]
7. Abichabki N, Bellissimo-Rodrigues F, Gaspar GG, Pocente RHC, Lima DAFS, Bollela VR, Braga GUL, De Martinis ECP, Ferreira JC, Darini ALC, Bengtsson-Palme J, Andrade LN: Proposal for screening cut-off values and use of Tolerance Disk Test (TDtest) for detection of tolerance/persistence to ceftazidime-avibactam in Klebsiella pneumoniae. Diagnostic Microbiology and Infectious Disease, 116, 3, 117515 (2026). doi: 10.1016/j.diagmicrobio.2026.117515 [Paper link]
8. Abramova A, Baral A, Osińska AD, Metsä-Simola N, Räisänen K, Ribeiro Duarte AS, Helgason KO, Halldórsdóttir AM, Pärnänen K, Skov Simonsen G, Sariola S, Lahti L, Bengtsson-Palme J, Wasteson Y, Munk P, Pettersen VK: Roadmap for integrated One Health AMR surveillance in Nordic countries. Public Health, 255, 106285 (2026). doi: 10.1016/j.puhe.2026.106285 [Paper link]
9. Jin C, Lundstrøm J, Cori CR, Guu S-Y, Bennett AR, Dannborg M, Bengtsson-Palme J, Hevey R, Khoo K-H, Bojar D: Seal milk oligosaccharides rival human milk complexity and exhibit functional dynamics during lactation. Nature Communications, 16, 10067 (2025). doi: 10.1038/s41467-025-66075-2 [Paper link]
10. de Martinis ECP, Alves VF, Pereira MG, Andrade LN, Abichabki N, Abramova A, Dannborg M, Bengtsson-Palme J: Applying 3D cultures and high-throughput technologies to study host-pathogen interactions. Frontiers in Immunology, 16 (2025). doi: 10.3389/fimmu.2025.1488699[Paper link]

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!

Ákos Kovács had the brilliant idea of putting up a temporary resource for things you bring up in a talk that you can point people to. I did not do this before my talk at ISME today, but I thought the idea was so good, so here’s a summary and collection of my ISME short-talk on the EMBARK outcomes today:

• More information on EMBARK and its successor SEARCHER can be found on the project website, here: http://antimicrobialresistance.eu Importantly, this is a team effort over four years and I only touched on a few selected things
• Within the project we have looked at typical background levels of antibiotic resistance in the environment. We have already published some of these results (for qPCR abundances) in Abramova et al. 2023
• The average resistance gene in the average environment is present in ~1 in 1000 bacteria, but the variation between different genes is huge
• Depending on monitoring goal, different target genes are relevant to use. See this table adapted from Abramova et al. 2023:

• We have also tried to make different monitoring methods for environmental AMR comparable. Those mentioned in the talk were selective culturing for resistant bacteria, qPCR and shotgun metagenomics
• This data is not yet published, but overall we see relatively good correlation between qPCR and metagenomics. This is not true for all genes, though, and unfortunately neither qPCR nor metagenomics is always better than the other
• Culturing data is not very good at predicting specific antibiotic resistance gene abundances as the class level
• Finally, we have developed methods for discovering new types of ARGs, as seen in the ResFinderFG database: Gschwind et al. 2023
• We have also used these new methods to look at differences between established ARGs and latent ARGs in a variety of environments: Inda-Díaz et al. 2023
• Our ultimate goal in EMBARK would be to develop a modular framework for environmental monitoring of antibiotic resistance. You can read more about our thinking and goals in the review paper we published last year: Bengtsson-Palme et al. 2023

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.

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!

This paper came out just about the same time as the PhD position with Erik Kristiansson where I will be co-supervisor was announced, and I did not want to steal that thunder with another news item, but it is now time to highlight the fantastic work of Víctor Hugo Jarquín-Díaz on antibiotic resistance genes in the gut microbiomes of mice across a gradient of pure and hybrid genotypes in the European house mouse hybrid zone. This came out in mid-April in ISME Communications and presents the interesting hypothesis that hybridisation not only shapes bacterial communities, but also antibiotic resistance gene occurrences (1).

This study is based on 16S rRNA amplicon sequencing of gut bacteria in natural populations of house mice. From this we have predicted the antibiotic resistance gene composition in the microbiomes, and found a significant increase in the predicted antibiotic resistance gene richness in hybrid mice. In other words, more and different antibiotic resistance genes were found in the hybrid mice than in the non-hybridised individuals. We believe that this could be due to a disruption of the microbiome composition in hybrid mice. The aberrant microbiomes in hybrids represent less complex communities, potentially promoting selection for resistance.

It deserves to be mentioned that this is more of a pilot study, which we hope to follow up with a more proper study targeting the resistance genes in the mice microbiomes. That said, our work suggests that host genetic variation impacts the gut microbiome and antibiotic resistance gene, at least in mice. This raises further questions on how the mammalian host genetics impact antibiotic resistance carriage in bacteria via microbiome dynamics or interaction with the environment.

I am very happy to have been part of this EMBARK collaboration with the Sofia Forslund-Startceva and Emanuel Heitlinger labs! And I am especially thankful to Víctor who pulled off this very thought-inducing study!

Reference

1. Jarquín-Díaz VH, Ferreira SCM, Balard A, Ďureje Ľ, Macholán M, Piálek J, Bengtsson-Palme J, Kramer-Schadt S, Forslund-Startceva SK, Heitlinger E: Aberrant microbiomes are associated with increased antibiotic resistance gene load in hybrid mice. ISME Communications, ycae053 (2024). doi: 10.1093/ismeco/ycae053 [Paper link]

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

Time to do a rundown of conferences and meetings I will attend this fall. Double-check with your calendars and please reach out if you’re also going, so we can meet up!

September 21-24: Nordic Society of Clinical Microbiology and Infectious Diseases (NSCMID), in Örebro, Sweden. I will give a talk about the EMBARK work in the Saturday session on Metagenomics in infection, inflammatory disease and the environment

October 5-6: Conference on ‘Optimal practices to protect human health care from antimicrobial resistance selected in the veterinary domain’ organised by The Netherlands Food and Consumer Product Safety Authority (NVWA) in Amsterdam, the Netherlands. I will chair a session on October 6 on Next generation sequencing for bioinformatic based surveillance.

October 18-22: 32º Congresso Brasileiro de Microbiologia, in Foz do Iguaçu, Brazil. I will give a talk in the Saturday session (the 21st) on the use of model systems all the way to global surveillance systems to prevent future pandemics.

November 15-16: DDLS Annual Meeting, in Stockholm Sweden. I am in the organising committee for this event with the theme “The emerging role of AI in data-driven life science”.

November 17: DDLS Cell and Molecular Biology Minisymposium.

November 29: GOTBIN Annual Workshop, in Gothenburg Sweden.

This will be a fun (but intense!) fall!