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.

There was a minor site-wide outage today (resulting on 404 errors all across the site) due to an automatic WordPress update that went wrong. It now looks like this is fixed and the web site is back online. However, if you notice any strange behavior, please drop me a line!

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]

Thanks a lot to all of those who applied to the PhD position opening that closed a week ago. In total we received 59 applications, of which the vast majority were of high quality – I am sure that at least half the candidates would have made a great job in the position. However, we have to make a selection among these 59 candidates, so after reading and evaluating all 59 applications, we have now nailed down ten top candidates that we will initially move forward with. Those ten candidates should have received an e-mail today about how the process will move forward.

If you have not received an e-mail from us, the most likely explanation is that you were not among these top ten candidates (but remember to also check your spam!) In that case, you will get a follow-up once the position is filled.

Again, thanks a lot for your interest. I have been overwhelmed by the high quality and relevance of the applications.

We are hiring a PhD student to work with interactions between the bacteria in human and environmental microbiomes that are important for community stability and resilience to being colonized by unwanted bacteria (including pathogens). The project seeks to unearth which environmental and genetic factors that are important determinants of bacterial invasiveness and community stability. You can read more at our Open Positions page.

We are looking for a candidate with experience with both bioinformatics and experimental microbiology. Previous experience with microbial communities is a plus, but not a must, as is work with human cell lines.

The project is fully funded by a grant from the Swedish Research Council and the position is planned for 4.5 years, with 4 years of research and course work and half a year of teaching.

If you feel that you are the right person for this position, you can apply here. We look forward to your application! The deadline for applications is October 21.

Here’s a nice popular summary of the paper that I published with Emil Burman last month on how temperature affects the microbial model community THOR. I think Miles Martin at The Academic Times did a great distilling my ramblings into a coherent story. Good job Miles!

I did not know about The Academic Times before this but will keep an eye on this relatively new publication aiming to popularize and distill scientific content for other scientists.

In other popularization-of-science-news, I got interviewed last week by New Scientist about a very exciting paper that came out this week on travelers picking up antibiotic resistance genes in Africa and Asia. The study was quite similar to what we did back in 2015, but used a much larger data set and uncovered that there are many, many more resistance genes that are enriched after travel than what we found using our more limited dataset. Very cool study, and you can read the New Scientist summary here.

I am very happy to announce that Emil Burman‘s (doctoral student in the lab) first first-author paper was published today in Frontiers in Microbiology. In this paper (1), we explored how temperature affected the interactions in the model microbial community THOR (2). Somewhat surprisingly, we found that even a small difference in temperature changed the community intrinsic properties (3) of this model community a lot. We furthermore find that changes in growth rates of the members of the community partially explains the changed interaction patterns, but only to some extent. Finally, we also found that biofilm production overall was much higher at lower temperatures (9-15°C) than at room temperature, and that at around 25°C and above the community formed virtually no biofilm.

The temperature range we tested is not unlikely to be encountered when incubating the community in a thermally unregulated environment. Thus, our results show that a high degree of temperature control is crucial between experiments, particularly when reproducing results across different laboratories, equipment, and personnel. This highlights the need for standards and transparency in research on microbial model communities (4).

Another important, related, aspect is that disruptive factors that discriminate against single members of the community are not unique to THOR. Instead, this is likely to be the case for other microbial model (as well as natural communities). Since only a few of these model communities have been elucidated for community behaviors outside of specific culturing conditions they were first contrived under, this may severely limit our view of interactions between microbes to specific laboratory settings. This casts some doubt on the validity of extrapolation from results obtained from microbial model communities. It seems to be important moving forward to establish that community-intrinsic behaviors in model communities are stable in the face of variable environmental conditions, such as temperature, pH, nutrient availability, and initial inoculum size.

A short backstory to this paper: this begun when Emil could not consistently replicate the results I had obtained during my postdoc (working on THOR) in Prof. Jo Handelsman’s lab at the University of Wisconsin-Madison. After a long time of troubleshooting, we realized that our lab did not hold a stable room temperature. We bought a cold incubator, and – boom – after that the expected community behavior came back. This made us realize the importance of temperature for the community-intrinsic properties of THOR, which then led to this more systematic investigation.

Great work Emil! It is nice to finally see this in its published form. Read the entire paper (open access) here!

References

1. Burman E, Bengtsson-Palme J: Microbial community interactions are sensitive to small differences in temperature. Frontiers in Microbiology, 12, 672910 (2021). doi: 10.3389/fmicb.2021.672910
2. Lozano GL, Bravo JI, Garavito Diago MF, Park HB, Hurley A, Peterson SB, Stabb EV, Crawford JM, Broderick NA, Handelsman J: Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior. mBio, 10, 2, e02846-18 (2019). doi: 10.1128/mBio.02846-18
3. Madsen JS, Sørensen SJ, Burmølle M: Bacterial social interactions and the emergence of community-intrinsic properties. Current Opinion in Microbiology 42, 104–109 (2018). doi: 10.1016/j.mib.2017.11.018
4. Bengtsson-Palme J: Microbial model communities: To understand complexity, harness the power of simplicity. Computational and Structural Biotechnology Journal, 18, 3987-4001 (2020). doi: 10.1016/j.csbj.2020.11.043

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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Last week, a preprint describing a study which I have played a small part in was posted on bioRxiv. This paper (1) uses the Metaxa2 database (2) to tease out how much of an effect mitochondrial rRNA sequences have on studies of bacterial diversity in corals. And it turns out that it matters… a lot. Importantly, by supplementing the taxonomic databases with diverse mitochondrial rRNA sequences from the Metaxa2 database, ~97% of unique unclassified sequences could be resolved as mitochondrial, without increasing the level of misannotation in mock communities. Thus the study not only points to a problem, but also to its solution! You can read it all here.

References

1. Sonnet D, Brown T, Bengtsson-Palme J, Padilla-Gamiño J, Zaneveld JR: The Organelle in the Room: Under-annotated Mitochondrial Reads Bias Coral Microbiome Analysis. bioRxiv, 431501 (2021). doi: 10.1101/2021.02.23.431501 [Link]
2. Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15, 6, 1403–1414 (2015). doi: 10.1111/1755-0998.12399 [Paper link]