Tag: Antibiotic resistance

I will be giving talks on data driven life science – specifically on antibiotic resistance and pathogenicity – on two different events organised within the Data Driven Life Science program (DDLS) in the next month. First up is on the DDLS Annual Conference, coming up already next week (15-16 November). Here, I will give a talk on the evolution of pathogenicity, outlining some of our ongoing work towards finding novel virulence factors. There will also be talk from the other DDLS fellows, as well as Samuli Ripatti and Cecilia Clementi.

On-site registration closes on November 9 so make sure to grab one of the last spots at this exciting event! Register here – online attendance is also possible for those who don’t want to travel to Stockholm.

Then in December, I will be talking at the Data-driven Epidemiology and biology of infections Research Area Symposium in Gothenburg on how to predict the disease threats of the future. This symposium takes place in Gothenburg on December 7 to 8, but again online participation is also possible. Aside from me, Nicholas Croucher will talk about genomic surveillance data and bacterial epidemiology, Bill Hanage will talk about decisions in an imperfect world and Tove Fall will talk about dynamic disease surveillance. There will also be talks about the new DDLS fellows in epidemiology and infection biology, which is what I am perhaps most excited about: Thomas van Boeckel, Luisa Hugerth and Laura Carroll! It seems like registration has not yet opened for this event, but keep monitoring this site.

I look forward to see you at these events!

Finally the lab podcast is back! In this episode Microbiology Lab Pod, the team (Johan Bengtsson-Palme, Emil Burman, Anna Abramova, Marcus Wenne, Mirjam Dannborg and Agata Marchi) discusses the environmental antibiotic resistance in anticipation of the EDAR conference coming up later this week!

The specific papers discussed in the pod are as follows:

• Marcoleta, Andrés E., Patricio Arros, Macarena A. Varas, José Costa, Johanna Rojas-Salgado, Camilo Berríos-Pastén, Sofía Tapia-Fuentes, et al. “The Highly Diverse Antarctic Peninsula Soil Microbiota as a Source of Novel Resistance Genes.” Science of The Total Environment 810 (March 2022): 152003. https://doi.org/10.1016/j.scitotenv.2021.152003
• Yi, Xinzhu, Jie-Liang Liang, Jian-Qiang Su, Pu Jia, Jing-li Lu, Jin Zheng, Zhang Wang, et al. “Globally Distributed Mining-Impacted Environments Are Underexplored Hotspots of Multidrug Resistance Genes.” The ISME Journal 16, no. 9 (September 2022): 2099–2113. https://doi.org/10.1038/s41396-022-01258-z
• Johnning, Anna, Erik Kristiansson, Jerker Fick, Birgitta Weijdegård, and DG Joakim Larsson. “Resistance Mutations in GyrA and ParC Are Common in Escherichia Communities of Both Fluoroquinolone-Polluted and Uncontaminated Aquatic Environments.” Frontiers in Microbiology 6 (2015): 1355. https://doi.org/10.3389/fmicb.2015.01355
• Flach, Carl-Fredrik, Chandan Pal, Carl Johan Svensson, Erik Kristiansson, Marcus Östman, Johan Bengtsson-Palme, Mats Tysklind, and D. G. Joakim Larsson. “Does Antifouling Paint Select for Antibiotic Resistance?” The Science of the Total Environment 590–591 (July 15, 2017): 461–68. https://doi.org/10.1016/j.scitotenv.2017.01.213

The podcast was recorded on September 12, 2022. 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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Our open doctoral student and postdoc positions closed over the weekend, and in total we had 110 applications, although some persons applied to more than one of the positions, bringing the total number of applicants down a bit. Still, this will be a lot of work for me. I will prioritize the postdoc position, as this had the fewest applications. So if you applied to one of the two PhD student positions, please give it some time.

A quick skimming of the applications shows that we have had extraordinary high quality of applications overall, although some of the applicants will be a bit too wet-lab oriented for these specific positions.

Thanks a lot for your interest in the lab’s work! I appreciate all of your efforts!

As I wrote a few days ago, I have now started my new position at Chalmers SysBio. This position is funded by the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), which also funds PhD and postdoc positions. We are now announcing two doctoral student projects and one postdoc project within the DDLS program in my lab.

Common to all projects is that they will the use of large-scale data-driven approaches (including machine learning and (meta)genomic sequence analysis), high-throughput molecular methods and established theories developed for macro-organism ecology to understand biological phenomena. We are for all three positions looking for people with a background in bioinformatics, computational biology or programming. In all three cases, there will be at least some degree of analysis and interpretation of large-scale data from ongoing and future experiments and studies performed by the group and our collaborators. The positions are all part of the SciLifeLab national research school on data-driven life science, which the students and postdoc will be expected to actively participate in.

The postdoc and one of the doctoral students are expected to be involved in a project aiming to uncover interactions between the bacteria in microbiomes that are important for community stability and resilience to being colonized by pathogens. This project also seeks to unearth which environmental and genetic factors that are important determinants of bacterial invasiveness and community stability. The project tasks may include things like predicting genes involved in pathogenicity and other interactions from sequencing data, and performing large-scale screening for such genes in microbiomes.

The second doctoral student is expected to work in a project dealing with understanding and limiting the spread of antibiotic resistance through the environment, identifying genes involved in antibiotic resistance, defining the conditions that select for antibiotic resistance in different settings, and developing approaches for monitoring for antibiotic resistance in the environment. Specifically, the tasks involved in this project may be things like identifying risk environments for AMR, define potential novel antibiotic resistance genes, and building a platform for AMR monitoring data.

For all these three positions, there is some room for adapting the specific tasks of the projects to the background and requests of the recruited persons!

We are very excited to see your applications and to jointly build the next generation of data driven life scientist! Read more about the positions here.

Together with Joakim Larsson‘s lab, we now have an open two-year postdoc position in bioinformatics on antibiotic resistance and biocide resistance. The development of antibiotic resistance has been driven by use of antibiotics, but antibacterial biocides also have the potential to select for antibiotic resistance. However, knowledge of which genes that contribute to biocide resistance and could be associated with antibiotic resistance is sparse. To some extent, such genes are documented in the BacMet database which we have developed, but this collection of resistance genes is only scratching the surface of all biocide resistance that exists among bacteria in the environment.

We are now looking for a postdoctoral fellow to continue the important work on bioinformatic analysis of biocide and antibiotic resistance to answer the question whether increasing biocide resistance would be a threat to human health. The postdoc will be working with the development of the BacMet database to make it more targeted towards biocidal substances and products in addition to resistance genes. The tasks include bioinformatic sequence analysis, literature studies and database and web programming. The work will also include investigations of the prevalence of the identified resistance genes in genomes and metagenomes.

The recruited person will work closely with both my group and the group of Prof. Joakim Larsson, and will participate in the JPIAMR-funded BIOCIDE project. You can apply to the postdoc position at the University of Gothenburg application portal: https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89bead79bb7258ad55c8d75228e5b7&job_id=25122

The deadline is May 4, 2022. Come work with us on this exciting topic in the intersect between two great research environments (if I may say it myself!) We look forward to your application!

As part of a series highlighting the research at the Institute of Biomedicine, I was a few weeks a go interviewed about the research in the lab and my history. This interview has now been published on the department website, both in Swedish and English. I think it is a pretty nice read and a good introduction to our work and why we do what we do. Could make for a good weekend read!

The newly formed BIOCIDE program, seeking to determine how antibacterial biocides contribute to the development of biocide resistance and spread of antibiotic resistant bacteria, is organizing a workshop on risk assessment of biocide and antibiotic resistance on the 9th of March this year. I will be giving a talk on the BacMet database and how that will be integrated in risk assessment and the research program. If you have an interest in the risk associated with biocides, or antibiotic resistance development, I strongly suggest that you take part in this exciting workshop, particularly if you are working for a regulatory authority.

The program targets biocide resistance and cross-resistance to antibiotics, guidance development for assessment of biocide resistance, the BacMet biocide resistance database, and the assessment of environmental exposure to biocides and risks for co-selection. The workshop will include speakers such as Joakim Larsson, Nabila Haddache, Marlene Ågerstrand and Frank Schreiber.

More information can be found here: https://www.gu.se/en/biocide/risk-assessment-of-biocide-and-antibiotic-resistance

If you are skilled in bioinformatics and want to work with one of my favorite persons, you should check out this postdoc ad closing January 9. This two-year position in Erik Kristiansson‘s lab at Chalmers University of Technology is a great opportunity to work with fantastic people on highly interesting questions. It has applications in infectious diseases and antibiotic resistance, and will be focused on genomic analysis of antibiotic resistance and virulence and their evolutionary history. The work includes both the development of new data-driven methodologies and the application of existing methodology to new datasets. The position will involve collaborations with researchers from clinical microbiology and the environmental sciences within the Centre for Antibiotic Resistance Research.

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]

Last week, I published 13 (!!) papers in the EFSA Journal on how to assess concentrations of antibiotics that could select for antibiotic resistance in animal feed (1-13). Or, well, you could also look at it as that the EFSA Biohaz panel that I have been a part of for more than two years published our final 13-part report.

Regardless of how you view it, this set of papers have two main takeaways:

1. We present a method to establish Predicted Minimal Selective Concentrations (PMSCs) for antibiotics. This method uses a combination of Dan Andersson’s approach to MSCs (14) and the method I published with Joakim Larsson around five years ago to establish predicted no-effect concentrations (PNECs) for antibiotics based on MIC data (15). The combination is a powerful (but very cautious) tool to estimate minimal selective concentrations for antibiotics (1), and we have subsequently applied this method to animal feed contamination with antibiotics, but…
2. There is way too little data to establish PMSCs for most antibiotics with any certainty. Really, the lack of data is so bad that for many of the antibiotic classes we could not make a reasonable assessment. Thus the main conclusion might be that we need a lot more data on how low concentrations of antibiotics that select for antibiotic resistance, both in laboratory systems and in more realistic settings.

References

1. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 1: Methodology, general data gaps and uncertainties. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6852 [Paper link]
2. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 2: Aminoglycosides/aminocyclitols: apramycin, paromomycin, neomycin and spectinomycin. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6853 [Paper link]
3. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 3: Amprolium. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6854 [Paper link]
4. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 4: ß-Lactams: amoxicillin and penicillin. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6855 [Paper link]
5. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 5: Lincosamides: lincomycin. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6856 [Paper link]
6. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 6: Macrolides: tilmicosin, tylosin and tylvalosin. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6858 [Paper link]
7. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 7: Amphenicols: florfenicol and thiamphenicol. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6859 [Paper link]
8. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 8: Pleuromutilins: tiamulin and valnemulin. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6860 [Paper link]
9. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 9: Polymyxins: colistin. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6861 [Paper link]
10. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 10: Quinolones: flumequine and oxolinic acid. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6862 [Paper link]
11. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 11: Sulfonamides. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6863 [Paper link]
12. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 12: Tetracycline, chlortetracycline, oxytetracycline, and doxycycline. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6864[Paper link]
13. EFSA Panel on Biological Hazards (BIOHAZ)*, Allende A, Koutsoumanis K, Alvarez-Ordóñez A, Bolton D, Bover-Cid S, Chemaly M, Davies R, De Cesare A, Herman L, Hilbert F, Lindqvist R, Nauta M, Ru G, Simmons M, Skandamis P, Suffredini E, Andersson DI, Bampidis V, Bengtsson-Palme J, Bouchard D, Ferran A, Kouba M, López Puente S, López-Alonso M, Saxmose Nielsen S, Pechová A, Petkova M, Girault S, Broglia A, Guerra B, Lorenzo Innocenti M, Liébana E, López-Gálvez G, Manini P, Stella P, Peixe L: Maximum levels of cross-contamination for 24 antimicrobial active substances in non-target feed. Part 13: Trimethoprim. EFSA Journal, 19, 10 (2021). doi: 10.2903/j.efsa.2021.6865 [Paper link]
14. Gullberg E, Cao S, Berg OG, Ilbäck C, Sandegren L, Hughes D, et al.: Selection of resistant bacteria at very low antibiotic concentrations. PLoS Pathogens 7, e1002158 (2011). doi: 10.1371/journal.ppat.1002158
15. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140-149 (2016). doi: 10.1016/j.envint.2015.10.015 [Paper link]