Category: Thoughts

Postdoc with Erik Kristiansson

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.

Short site outage

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!

Published paper: Modeling antibiotic resistance gene emergence

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]

13 papers published on antibiotics in feed

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]

Update on the PhD candidate evaluation

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.

Open PhD position

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 hereWe look forward to your application! The deadline for applications is October 21.

Some media coverage

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.

Metaxa2 Genome mode fixes

Yes, Saturdays are somewhat weird days for software updates, but if you’re doing weekend work anyway, why wait to push bug fixes to the community? A very minor bug-fix update to Metaxa2 was released today, bringing the software to version 2.2.3.

Two things have changed in this version, both related to the genome mode. 1) We fixed a file reading bug in the ‘genome’ mode of the software. This bug caused the last sequence in an input FASTA file not to be read unless there was a newline after it. Since the ‘genome’ mode is rarely used by most users, we suspect not a lot of users have been affected by this bug.
2) While we were at it, we changed the behavior of the ‘genome’ mode to mirror that of the ‘auto’ mode, as the strict genome mode dropped sequences shorter than 2500 bp. We considered this behavior counter-intuitive to what most users would want, and has now changed the ‘genome’ mode to behave the same as the ‘auto’ mode and not drop short sequences.

No other changes have been made in this version. The update can be found at the Metaxa2 software page.

Published paper: CAFE

We start the new year with a bang, or at least a new paper published. Bioinformatics put our paper (1) describing the software package CAFE online today (although it was accepted late last year). The CAFE package is a combination of Perl and R tools that can analyze data from paired transposon mutant sequencing experiments (2-4), generate fitness coefficients for each gene and condition, and perform appropriate statistical testing on these fitness coefficients. The paper is short, but shows that CAFE performs as good as the best competing tools (5-7) while being superior at controlling for false positives (you’ll have to dig into the supplement to find the data for that though).

Importantly, this is a collaborative effort by basically the entire research group from last spring: me, Haveela, Emil, Anna and our visiting student Adriana. A big thanks to all of you for working on this short but important paper! You can read the full paper here.

References

  1. Abramova A, Osińska A, Kunche H, Burman E, Bengtsson-Palme J (2021) CAFE: A software suite for analysis of paired-sample transposon insertion sequencing data. Bioinformatics, advance article doi: 10.1093/bioinformatics/btaa1086
  2. Chao,M.C. et al. (2016) The design and analysis of transposon insertion sequencing experiments. Nature reviews Microbiology, 14, 119–128.
  3. van Opijnen,T. and Camilli,A. (2013) Transposon insertion sequencing: a new tool for systems-level analysis of microorganisms. Nature reviews Microbiology, 11, 435–442.
  4. Goodman,A.L. et al. (2011) Identifying microbial fitness determinants by insertion sequencing using genome-wide transposon mutant libraries. Nature Protocols, 6, 1969–1980.
  5. McCoy,K.M. et al. (2017) MAGenTA: a Galaxy implemented tool for complete Tn- Seq analysis and data visualization. Bioinformatics, 33, 2781– 2783.
  6. Zhao,L. et al. (2017) TnseqDiff: identification of conditionally essential genes in transposon sequencing studies. BMC Bioinformatics, 18.
  7. Zomer,A. et al. (2012) ESSENTIALS: Software for Rapid Analysis of High Throughput Transposon Insertion Sequencing Data. PLoS ONE, 7, e43012.

Published paper: Microbial model communities

This week, in a stroke of luck coinciding with my conference presentation on the same topic, my review paper on microbial model communities came out in Computational and Structural Biotechnology Journal. The paper (1) provides an overview of the existing microbial model communities that have been developed for different purposes and makes some recommendations on when to use what kind of community. I also make a deep-dive into community intrinsic-properties and how to capture and understand how microbes growing together interact in a way that is not predictable from how they grow in isolation.

The main take-home messages of the paper are that 1) there already exists a quite diverse range of microbial model communities – we probably don’t need a wealth of additional model systems, 2) there need to be better standardization and description of the exact protocols used – this is more important in multi-species communities than when species are grown in isolation, and 3) the researchers working with microbial model communities need to settle on a ‘gold standard’ set of model communities, as well as common definitions, terms and frameworks, or the complexity of the universe of model systems itself may throw a wrench into the research made using these model systems.

The paper was inspired by the work I did in Jo Handelsman‘s lab on the THOR model community (2), which I then have brought with me to the University of Gothenburg. In the lab, we are also setting up other model systems for microbial interactions, and in this process I thought it would be useful to make an overview of what is already out there. And that overview then became this review paper.

The paper is fully open-access, so there is really not much need to go into the details here. Go and read the entire thing instead (or just get baffled by Table 1, listing the communities that are already out there!)

References

  1. Bengtsson-Palme J: Microbial model communities: To understand complexity, harness the power of simplicity. Computational and Structural Biotechnology Journal, in press (2020). doi: 10.1016/j.csbj.2020.11.043
  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