Tag: Novel resistance genes

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]

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

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

Last week, a paper resulting from a collaboration with Stefanie Heß and Viktor Jonsson was published in Environmental Science & Technology. In the paper, we build a quantitative model for the emergence of antibiotic resistance genes in human pathogens and populate it using the few numbers that are available on different processes (bacterial uptake, horizontal gene transfer rates, rate of mobilization of chromosomal genes, etc.) in the literature (1).

In short, we find that in order for the environment to play an important role in the appearance of novel resistance genes in pathogens, there needs to be a substantial flow of bacteria from the environment to the human microbiome. We also find that most likely the majority of resistance genes in human pathogens have very small fitness costs associated with them, if any cost at all.

The model makes three important predictions:

1. The majority of ARGs present in pathogens today should have very limited effects on fitness. The model caps the average fitness impact for ARGs currently present in human pathogens between −0.2 and +0.1% per generation. By determining the fitness effects of carrying individual ARGs in their current hosts, this prediction could be experimentally tested.
2. The most likely location of ARGs 70 years ago would have been in human-associated bacteria. By tracking ARGs currently present in human pathogens across bacterial genomes, it may be possible to trace the evolutionary history of these genes and thereby identify their likely hosts at the beginning of the antibiotic era, similar to what was done by Stefan Ebmeyer and his colleagues (2). What they found sort-of corroborate the findings of our model and lend support to the idea that most ARGs may not originate in the environment. However, this analysis is complicated by the biased sampling of fully sequenced bacterial genomes, most of which originate from human specimens. That said, the rapid increase in sequencing capacity may make full-scale analysis of ARG origins using genomic data possible in the near future, which would enable testing of this prediction of the model.
3. If the origins of ARGs currently circulating in pathogens can be established, the range of reasonable dispersal ability levels from the environment to pathogens narrows dramatically. Similarly, if the rates of mobilization and horizontal transfer of resistance genes could be better determined by experiments, the model would predict the likely origins more precisely. Just establishing a ball-park range for the mobilization rate would dramatically restrict the possible outcomes of the model. Thus, a more precise determination of any of these parameters would enable several more specific predictions by the model.

This paper has a quite interesting backstory, beginning with me having leftover time on a bus ride in Madison (WI), thinking about whether you could quantize the conceptual framework for resistance gene emergence we described in our 2018 review paper in FEMS Reviews Microbiology (3). This spurred the first attempt at such a model, which then led to Stefanie Heß and me applying for support from the Centre for Antibiotic Resistance Research at the University of Gothenburg (CARe) to develop this idea further. We got this support and Stefanie spent a few days with me in Gothenburg developing this idea into a model we could implement in R.

However, at that point we realized we needed more modeling expertise and brought in Viktor Jonsson to make sure the model was robust. From there, it took us about 1.5 years to refine and rerun the model about a million times… By the early spring this year, we had a reasonable model that we could write a manuscript around, and this is what now is published. It’s been an interesting and very nice ride together with Stefanie and Viktor!

References

1. Bengtsson-Palme J, Jonsson V, Heß S: What is the role of the environment in the emergence of novel antibiotic resistance genes? A modelling approach. Environmental Science & Technology, Article ASAP (2021). doi: 10.1021/acs.est.1c02977 [Paper link]
2. Ebmeyer S, Kristiansson E, Larsson DGJ: A framework for identifying the recent origins of mobile antibiotic resistance genes. Communications Biology 4 (2021). doi: 10.1038/s42003-020-01545-5
3. Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews, 42, 1, 68–80 (2018). doi: 10.1093/femsre/fux053 [Paper link]

In this episode Microbiology Lab Pod, the team (Johan Bengtsson-Palme, Emil Burman, Anna Abramova, Marcus Wenne, Sebastian Wettersten and Mahbuba Lubna Akter, Shumaila Malik, Emilio Rudbeck and Camille Wuyts) discusses the evolution of antibiotic resistance from different perspectives. We also interview Rémi Gschwind about his work on novel antibiotic resistance genes in the EMBARK program.

The specific papers discussed in the pod (with approximate timings) are as follows:

• 7:45 – EMBARK website: http://antimicrobialresistance.eu
• 26:15 – Seemann, T., 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069. https://doi.org/10.1093/bioinformatics/btu153
• 29:00 – Bengtsson-Palme, J., Larsson, D.G.J., 2015. Antibiotic resistance genes in the environment: prioritizing risks. Nature reviews Microbiology 13, 396. https://doi.org/10.1038/nrmicro3399-c1
• 29:30 – Ebmeyer, S., Kristiansson, E., Larsson, D.G.J., 2021. A framework for identifying the recent origins of mobile antibiotic resistance genes. Communications Biology 4. https://doi.org/10.1038/s42003-020-01545-5
• 54:15 – Gillings, M.R., Stokes, H.W., 2012. Are humans increasing bacterial evolvability? Trends in Ecology & Evolution 27, 346–352. https://doi.org/10.1016/j.tree.2012.02.006
• 55:15 – Woods, L.C., et al., 2020. Horizontal gene transfer potentiates adaptation by reducing selective constraints on the spread of genetic variation. Proc Natl Acad Sci USA 117, 26868–26875. https://doi.org/10.1073/pnas.2005331117
• 76:15 – Card, K.J., Thomas, M.D., Graves, J.L., Barrick, J.E., Lenski, R.E., 2021. Genomic evolution of antibiotic resistance is contingent on genetic background following a long-term experiment with Escherichia coli. Proc Natl Acad Sci USA 118, e2016886118. https://doi.org/10.1073/pnas.2016886118

The podcast was recorded on March 18, 2021. If you want to reach out to us with comments, suggestions, or other feedback, please send an e-mail to podcast at microbiology dot se or contact @bengtssonpalme via Twitter. The music that can be heard on the pod is composed by Johan Bengtsson-Palme and is taken from the album Cafe Phonocratique.

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This is the fifth episode of the Microbiology Lab Pod and has been lying around on my computer almost finished for way too long. It was recorded on September 23, and the bigger-than-ever-before crew (Johan Bengtsson-Palme, Emil Burman, Haveela Kunche, Anna Abramova, Marcus Wenne, Sebastian Wettersten and Mahbuba Lubna Akter) is joined by Fanny Berglund to discuss computational discovery of novel resistance genes. We also discuss antibiotic resistance mechanisms, particularly in Pseudomonas aeruginosa.

The specific papers discussed in the pod (with approximate timings) are as follows:

• 5:30 – Berglund, F., Johnning, A., Larsson, D.G.J., Kristiansson, E., 2020. An updated phylogeny of the metallo-b-lactamases. Journal of Antimicrobial Chemotherapy 7. https://doi.org/10.1093/jac/dkaa392
• 5:45 – Berglund, F., Österlund, T., Boulund, F., Marathe, N.P., Larsson, D.G.J., Kristiansson, E., 2019. Identification and reconstruction of novel antibiotic resistance genes from metagenomes. Microbiome 7, 52. https://doi.org/10.1186/s40168-019-0670-1
• 6:00 – Berglund, F., Marathe, N.P., Österlund, T., Bengtsson-Palme, J., Kotsakis, S., Flach, C.-F., Larsson, D.G.J., Kristiansson, E., 2017. Identification of 76 novel B1 metallo-β-lactamases through large-scale screening of genomic and metagenomic data. Microbiome 5, i29. https://doi.org/10.1186/s40168-017-0353-8
• 6:15 – Boulund, F., Berglund, F., Flach, C.-F., Bengtsson-Palme, J., Marathe, N.P., Larsson, D.G.J., Kristiansson, E., 2017. Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics 18, 438. https://doi.org/10.1186/s12864-017-4064-0
• 37:15 – Crippen, C.S., Jr., M.J.R., Sanchez, S., Szymanski, C.M., 2020. Multidrug Resistant Acinetobacter Isolates Release Resistance Determinants Through Contact-Dependent Killing and Bacteriophage Lysis. Frontiers in Microbiology 11. https://doi.org/10.3389/fmicb.2020.01918
• 52:15 – Leonard, A.F.C., Zhang, L., Balfour, A.J., Garside, R., Hawkey, P.M., Murray, A.K., Ukoumunne, O.C., Gaze, W.H., 2018. Exposure to and colonisation by antibiotic-resistant E. coli in UK coastal water users: Environmental surveillance, exposure assessment, and epidemiological study (Beach Bum Survey). Environment International 114, 326–333. https://doi.org/10.1016/j.envint.2017.11.003
• 53:30 – Bengtsson-Palme, J., Kristiansson, E., Larsson, D.G.J., 2018. Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews 42, 25. https://doi.org/10.1093/femsre/fux053
• 54:30 – Leonard, A.F.C., Zhang, L., Balfour, A.J., Garside, R., Gaze, W.H., 2015. Human recreational exposure to antibiotic resistant bacteria in coastal bathing waters. Environment International 82, 92–100. https://doi.org/10.1016/j.envint.2015.02.013
• 55:30 – Ahmed, M.N., Abdelsamad, A., Wassermann, T., et al., 2020. The evolutionary trajectories of P. aeruginosa in biofilm and planktonic growth modes exposed to ciprofloxacin: beyond selection of antibiotic resistance. npj Biofilms and Microbiomes 6. https://doi.org/10.1038/s41522-020-00138-8
• 69:30 – Rezzoagli, C., Archetti, M., Mignot, I., Baumgartner, M., Kümmerli, R., 2020. Combining antibiotics with antivirulence compounds can have synergistic effects and reverse selection for antibiotic resistance in Pseudomonas aeruginosa. PLOS Biology 18, e3000805. https://doi.org/10.1371/journal.pbio.3000805
• 79:45 – Allen, R.C., Popat, R., Diggle, S.P., Brown, S.P., 2014. Targeting virulence: can we make evolution-proof drugs? Nature reviews Microbiology 12, 300–308. https://doi.org/10.1038/nrmicro3232
• 80:45 – Köhler, T., Perron, G.G., Buckling, A., van Delden, C., 2010. Quorum Sensing Inhibition Selects for Virulence and Cooperation in Pseudomonas aeruginosa. PLoS Pathogens 6, e1000883. https://doi.org/10.1371/journal.ppat.1000883

The podcast was recorded on September 23, 2020. If you want to reach out to us with comments, suggestions or other feedback, please send an e-mail to podcast at microbiology dot se or contact @bengtssonpalme via Twitter. The music that can be heard on the pod is composed by Johan Bengtsson-Palme and is taken from the album Cafe Phonocratique.

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In the second episode of Microbiology Lab Pod, a crew consisting of Johan Bengtsson-Palme, Emil Burman, Haveela Kunche and Anna Abramova discusses how to identify novel resistance genes with our special guest Marlies Böhm. We also talk about bacterial virulence: how do bacteria become virulent, how do virulence relate to competition, how do bacteria evade the immune system and can we attenuate virulence using fatty acids?

The specific papers discussed in the pod (with approximate timings) are as follows:

• 7:15 – Böhm, M.-E., Razavi, M., Flach, C.-F., Larsson, D.G.J., 2020a. A Novel, Integron-Regulated, Class C β-Lactamase. Antibiotics 9, 123. https://doi.org/10.3390/antibiotics9030123
• 7:15 – Böhm, M.-E., Razavi, M., Marathe, N.P., Flach, C.-F., Larsson, D.G.J., 2020b. Discovery of a novel integron-borne aminoglycoside resistance gene present in clinical pathogens by screening environmental bacterial communities. Microbiome 8. https://doi.org/10.1186/s40168-020-00814-z
• 9:15 – Makowska, N., et al., 2020. Occurrence of integrons and antibiotic resistance genes in cryoconite and ice of Svalbard, Greenland, and the Caucasus glaciers. Science of The Total Environment 716, 137022. https://doi.org/10.1016/j.scitotenv.2020.137022
• 20:45 – Marathe, N.P., et al., 2019. Scandinavium goeteborgense gen. nov., sp. nov., a New Member of the Family Enterobacteriaceae Isolated From a Wound Infection, Carries a Novel Quinolone Resistance Gene Variant. Frontiers in Microbiology 10. https://doi.org/10.3389/fmicb.2019.02511
• 33:45 – Kaito, C., Yoshikai, H., Wakamatsu, A., Miyashita, A., Matsumoto, Y., Fujiyuki, T., Kato, M., Ogura, Y., Hayashi, T., Isogai, T., Sekimizu, K., 2020. Non-pathogenic Escherichia coli acquires virulence by mutating a growth-essential LPS transporter. PLOS Pathogens 16, e1008469. https://doi.org/10.1371/journal.ppat.1008469
• 43:45 – Lories, B., Roberfroid, S., Dieltjens, L., De Coster, D., Foster, K.R., Steenackers, H.P., 2020. Biofilm Bacteria Use Stress Responses to Detect and Respond to Competitors. Current Biology 30, 1231-1244.e4. https://doi.org/10.1016/j.cub.2020.01.065
• 45:45 – Lozano, G.L., Bravo, J.I., Garavito Diago, M.F., Park, H.B., Hurley, A., Peterson, S.B., Stabb, E.V., Crawford, J.M., Broderick, N.A., Handelsman, J., 2019. Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior. mBio 10. https://doi.org/10.1128/mBio.02846-18
• 55:45 – Kumar, P., Lee, J.-H., Beyenal, H., Lee, J., 2020. Fatty Acids as Antibiofilm and Antivirulence Agents. Trends in Microbiology. https://doi.org/10.1016/j.tim.2020.03.014
• 60:15 – Gullberg, E., Cao, S., Berg, O.G., Ilbäck, C., Sandegren, L., Hughes, D., Andersson, D.I., 2011. Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathogens 7, e1002158. https://doi.org/10.1371/journal.ppat.1002158
• 61:15 – Larsson, D.G.J., 2018. Risks of using the natural defence of commensal bacteria as antibiotics call for research and regulation. International Journal of Antimicrobial Agents 51, 277–278. https://doi.org/10.1016/j.ijantimicag.2017.12.018
• 65:15 – Lone, A.G., Bankhead, T., 2020. The Borrelia burgdorferi VlsE Lipoprotein Prevents Antibody Binding to an Arthritis-Related Surface Antigen. Cell Reports 30, 3663-3670.e5. https://doi.org/10.1016/j.celrep.2020.02.081

The podcast was recorded on May 7, 2020. If you want to reach out to us with comments, suggestions or other feedback, please send an e-mail to podcast at microbiology dot se or contact @bengtssonpalme via Twitter. The music that can be heard on the pod is composed by Johan Bengtsson-Palme and is taken from the album Cafe Phonocratique.

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Here’s some updates on my Spring schedule.

On March 19, I will be presenting the EMBARK program and what we aim to achieve at a conference organised by the Swedish Medical Products Agency called NordicMappingAMR. The event will feature an overview of existing monitoring of antibiotics and antibiotic resistant bacteria in the environment. The conference aims to present the results from this survey, to listen to experts in the field and to discuss possible progress. It takes place in Uppsala. For any further questions, contact Kia Salin at NordicMappingAMR@lakemedelsverket.se

Then on May 18 to 20 I will participate in the 7th Microbiome & Probiotics R&D and Business Collaboration Forum in Rotterdam. This industry/academia cross-over event focuses on cutting-edge microbiome and probiotics research, and challenges and opportunities in moving research towards commercialisation. I will talk on the work we do on deciphering genetic mechanisms behind microbial interactions in microbiomes on May 20.

And finally, I also want to bring the attention to that my collaborator Erik Kristiansson has an open PhD position in his lab. The position is funded by the Environmental Dimensions of Antibiotic Resistance (EDAR) research project, aiming to describe the environmental role in the development and promotion of antibiotic resistance. The focus of the PhD position will be on analysis of large-scale data, with special emphasis on the identification of new forms of resistance genes. The project also includes phylogenetic analysis and development of methods for assessment of gene evolution. More info can be found here.