Category: Papers

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

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]

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!

Full reference:

• Ligouri R, Rommel SH, Bengtsson-Palme J, Helmreich B, Wurzbacher C: Microbial retention and resistances in stormwater quality improvement devices treating road runoff. bioRxiv, 426166 (2021). doi: 10.1101/2021.01.12.426166 [Link]

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.

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

Fun things on a Friday! First of all, we have updated our lab member page with some beautiful photos! Thanks Marcus for brining your camera today!

Second, our review of factors important for antibiotic resistance development in the environment has been recognised by FEMS Microbiology Reviews as one of the papers which have made the most impact across their portfolio. It has been added to a new (well, this is old news, so semi-new) collection of papers – ‘Articles with Impact’ – many of which are very well worth a read!

Have a nice weekend!

We have been awarded with the first best article award from FEMS Microbiology Reviews for our 2018 review Environmental factors influencing the development and spread of antibiotic resistance. I and my co-authors Joakim Larsson and Erik Kristiansson are honoured and – of course – very happy with this recognition of our work. I was interviewed in relation to the prize, an interview that can be read here. But, also, the paper is open access, so you can go and check it all out in its full glory right now!

I am happy to share the news that the paper describing out software tool Mumame is now out in its final form! (1) The paper got published today in the journal Metabarcoding and Metagenomics after being available as a preprint (2) since last autumn. This version has not changed a whole lot since the preprint, but it is more polished and better argued (thanks to a great review process). The software is virtually the same, but is not also available via Conda.

In the paper, we describe the Mumame software, which can be used to distinguish between wildtype and mutated sequences in shotgun metagenomic sequencing data and quantify their relative abundances. We further demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets (3-6), and find that the tool is useful but that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than is needed for most other applications of shotgun metagenomics. Since the preprint was published, Mumame has also found use in our recently published paper on selection for antibiotic resistance in a Croatian macrolide production wastewater treatment plant, unfortunately with inconclusive results (7). Mumame is freely available here.

I again want to stress the fantastic work that Shruthi Magesh did last year as a summer student at WID in the evaluation of this tool. As I have pointed out earlier, I did write the code for the software (with a lot of input from Viktor Jonsson), but Shruthi did the software testing and evaluations. Thanks and congratulations Shruthi, and good luck in pursuing your PhD program!

References

1. Magesh S, Jonsson V, Bengtsson-Palme J: Mumame: A software tool for quantifying gene-specific point-mutations in shotgun metagenomic data. Metabarcoding and Metagenomics, 3: 59–67 (2019). doi: 10.3897/mbmg.3.36236
2. Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572
3. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648
4. Lundström S, Östman M, Bengtsson-Palme J, Rutgersson C, Thoudal M, Sircar T, Blanck H, Eriksson KM, Tysklind M, Flach C-F, Larsson DGJ: Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms. Science of the Total Environment, 553, 587–595 (2016). doi: 10.1016/j.scitotenv.2016.02.103
5. Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ: The structure and diversity of human, animal and environmental resistomes. Microbiome, 4, 54 (2016). doi: 10.1186/s40168-016-0199-5
6. Kraupner N, Ebmeyer S, Bengtsson-Palme J, Fick J, Kristiansson E, Flach C-F, Larsson DGJ: Selective concentration for ciprofloxacin in Escherichia coli grown in complex aquatic bacterial biofilms. Environment International, 116, 255–268 (2018). doi: 10.1016/j.envint.2018.04.029
7. Bengtsson-Palme J, Milakovic M, Švecová H, Ganjto M, Jonsson V, Grabic R, Udiković Kolić N: Pharmaceutical wastewater treatment plant enriches resistance genes and alter the structure of microbial communities. Water Research, 162, 437-445 (2019). doi: 10.1016/j.watres.2019.06.073

I celebrate the fourth of July with the coincidental publishing of my most recent paper, in collaboration with the lab of Nikolina Udikovic-Kolic. The study used shotgun metagenomics to investigate the taxonomic structure and resistance gene composition of sludge communities in a treatment plant in Croatia receiving wastewater from production of the macrolide antibiotic azithromycin (1). We compared the levels of antibiotic resistance genes in sludge from this treatment plant and municipal sludge from a sewage treatment plant in Zagreb, and found that the total abundance of resistance genes was three times higher in sludge from the treatment plant receiving wastewater from pharmaceutical production. To our great surprise, this was not true for macrolide resistance genes, however. Instead, those genes had overall slightly lower abundances in the industrial sludge. At the same time, the genes that are associated with mobile genetic elements (such as integrons) had higher abundances in the industrial sludge.

This leads us to think that at high concentrations of antibiotics (such as in the industrial wastewater treatment plant), selection may favor taxonomic shifts towards intrinsically resistant species or strains harboring chromosomal resistance mutations rather than acquisition of mobile resistance genes. Unfortunately, the results regarding resistance mutation – obtained using our recent software tool Mumame (2) – were uninformative due to low number of reads mapping to the resistance regions of the 23S rRNA target gene for azithromycin.

Often, the problem of environmental pollution with pharmaceuticals is perceived as primarily being a concern in countries with poor pollution control, since price pressure has led to outsourcing of global antibiotics production to locations with lax environmental regulation (3). If this was the case, there would be much less incentive for improving legislation regarding emissions from pharmaceutical manufacturing at the EU level, as this would not move the needle in a significant way. However, the results of the paper (and other work by Nikolina’s group (4,5)) underscore the need for regulatory action also within Europe to avoid release of antibiotics into the environment.

References

1. Bengtsson-Palme J, Milakovic M, Švecová H, Ganjto M, Jonsson V, Grabic R, Udiković Kolić N: Pharmaceutical wastewater treatment plant enriches resistance genes and alter the structure of microbial communities. Water Research, accepted manuscript (2019). doi: 10.1016/j.watres.2019.06.073
2. Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572
3. Bengtsson-Palme J, Gunnarsson L, Larsson DGJ: Can branding and price of pharmaceuticals guide informed choices towards improved pollution control during manufacturing? Journal of Cleaner Production, 171, 137–146 (2018). doi: 10.1016/j.jclepro.2017.09.247
4. Bielen A, Šimatović A, Kosić-Vukšić J, Senta I, Ahel M, Babić S, Jurina T, González-Plaza JJ, Milaković M, Udiković-Kolić N: Negative environmental impacts of antibiotic-contaminated effluents from pharmaceutical industries. Water Research, 126, 79–87 (2017). doi: 10.1016/j.watres.2017.09.019
5. González-Plaza JJ, Šimatović A, Milaković M, Bielen A, Wichmann F, Udikovic-Kolic N: Functional repertoire of antibiotic resistance genes in antibiotic manufacturing effluents and receiving freshwater sediments. Frontiers in Microbiology, 8, 2675 (2017). doi: 10.3389/fmicb.2017.02675