My name is Johan Bengtsson-Palme. I am an assistant professor at the Sahlgrenska Academy at University of Gothenburg, Sweden. My research group works with microbiology and microbial ecology, primarily focusing on investigating antibiotic resistance, pathogenesis and interactions in bacterial communities through large-scale experimental work, metagenomics and bioinformatics. I also have an interest in molecular taxonomy and improving the quality of reference databases. You can read more about our research interests here. We work closely with the groups of Joakim Larsson, Jo Handelsman, Erik Kristiansson and Henrik Nilsson. To contact me, feel free to send an e-mail to my email@example.com
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!
- 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
- 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
- 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
- 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.
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.
- 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]
- 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]
The Microbiology Lab Pod is back with season two. This first episode was recorded on February 4 and has the theme of global change and effects on microbes. The crew (Johan Bengtsson-Palme, Emil Burman, Anna Abramova, Marcus Wenne, Sebastian Wettersten and Mahbuba Lubna Akter) is joined by two guests – Shumaila Malik and Emilio Rudbeck – and talks about the lab’s most recent publication, the one-year covid anniversary, the effects of global warming and other global change factors on soil microbial communities, and thawing permafrost.
The specific papers discussed in the pod (with approximate timings) are as follows:
- 5:45 – 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. https://doi.org/10.1093/bioinformatics/btaa1086
- 8:00 – Bengtsson, J., et al., 2011. Metaxa: a software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets. Antonie van Leeuwenhoek 100, 471–475. https://doi.org/10.1007/s10482-011-9598-6
- 29:30 – Donhauser, J., Niklaus, P.A., Rousk, J., Larose, C., Frey, B., 2020. Temperatures beyond the community optimum promote the dominance of heat-adapted, fast growing and stress resistant bacteria in alpine soils. Soil Biology and Biochemistry 148, 107873. https://doi.org/10.1016/j.soilbio.2020.107873
- 54:30 – Zhou, Z., Wang, C., Luo, Y., 2020. Meta-analysis of the impacts of global change factors on soil microbial diversity and functionality. Nat Commun 11, 3072. https://doi.org/10.1038/s41467-020-16881-7
- 60:45 – Bahram, M., et al., 2018. Structure and function of the global topsoil microbiome. Nature 320, 1039. https://doi.org/10.1038/s41586-018-0386-6
- 68:15 – Lozano, G.L., et al., 2019. Introducing THOR, a Model Microbiome for Genetic Dissection of Community Behavior. mBio 10. https://doi.org/10.1128/mBio.02846-18
- 70:15 – Bengtsson-Palme, J., 2020. Microbial model communities: To understand complexity, harness the power of simplicity. Computational and Structural Biotechnology Journal 18, 3987–4001. https://doi.org/10.1016/j.csbj.2020.11.043
- 72:00 – Sajjad, W., et al., 2020. Resurrection of inactive microbes and resistome present in the natural frozen world: Reality or myth? Science of The Total Environment 735, 139275. https://doi.org/10.1016/j.scitotenv.2020.139275
- 74:00 – Yashina, S., et al., 2012. Regeneration of whole fertile plants from 30,000-y-old fruit tissue buried in Siberian permafrost. Proceedings of the National Academy of Sciences 109, 4008–4013. https://doi.org/10.1073/pnas.1118386109
- 74:30 – Pikuta, E.V., et al., 2005. Carnobacterium pleistocenium sp. nov., a novel psychrotolerant, facultative anaerobe isolated from permafrost of the Fox Tunnel in Alaska. International Journal of Systematic and Evolutionary Microbiology 55, 473–478. https://doi.org/10.1099/ijs.0.63384-0
- 75:00 – Bidle, K.D., Lee, S., Marchant, D.R., Falkowski, P.G., 2007. Fossil genes and microbes in the oldest ice on Earth. Proceedings of the National Academy of Sciences 104, 13455–13460. https://doi.org/10.1073/pnas.0702196104
- 75:15 – Timofeev, V., et al., 2019. Insights from Bacillus anthracis strains isolated from permafrost in the tundra zone of Russia. PLoS ONE 14, e0209140. https://doi.org/10.1371/journal.pone.0209140
- 83:15 – Bengtsson-Palme, J., Boulund, F., Fick, J., Kristiansson, E., Larsson, D.G.J., 2014. Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in microbiology 5, 648. https://doi.org/10.3389/fmicb.2014.00648
- 84:00 – 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
The podcast was recorded on February 4, 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.
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.
A new version of ITSx is released today. This minor update contains two minor bug fixes and two small new features.
The first bug was that ITSx returned empty sequences in the FASTA file for no detections for large input files. This has now been fixed.
The second bug fix is a bit more fuzzy and involved some fine-tuning of how large input files are handled in ITSx to stabilise E-value and score cut-offs.
The two new features are:
- The possbility to put the temporary directory in a custom location using the
- ITSx now warns when the input file contains sequences with identical identifiers, which usually leads to sequences being dropped from the input file.
The new update brings ITSx to version 1.1.3. Thanks for the users who have spotted bugs and suggested new features! Happy barcoding everyone!
I am happy to report that today a preprint on a recent collaboration with Christian Wurzbacher‘s group came out on bioRxiv. In the preprint study, we explore microbial communities in stormwater runoff from roads in terms of microbial composition and the potential for these settings to disseminate and select for antibiotic resistance, as well as metal resistance. My part of this study is quite small; I mostly provided the analysis of resistance genes on integrons, but it was a fun study and I look forward to work more with Christian and his excellent team!
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.
- 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
- Chao,M.C. et al. (2016) The design and analysis of transposon insertion sequencing experiments. Nature reviews Microbiology, 14, 119–128.
- 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.
- Goodman,A.L. et al. (2011) Identifying microbial fitness determinants by insertion sequencing using genome-wide transposon mutant libraries. Nature Protocols, 6, 1969–1980.
- McCoy,K.M. et al. (2017) MAGenTA: a Galaxy implemented tool for complete Tn- Seq analysis and data visualization. Bioinformatics, 33, 2781– 2783.
- Zhao,L. et al. (2017) TnseqDiff: identification of conditionally essential genes in transposon sequencing studies. BMC Bioinformatics, 18.
- Zomer,A. et al. (2012) ESSENTIALS: Software for Rapid Analysis of High Throughput Transposon Insertion Sequencing Data. PLoS ONE, 7, e43012.