Tag: Papers

Published paper: Temperature affects community interactions

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

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

FEMS Microbiology Reviews Award

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!

Published paper: benchmarking resistance gene identification

Since F1000Research uses a somewhat different publication scheme than most journals, I still haven’t understood if this paper is formally published after peer review, but I start to assume it is. There have been very little changes since the last version, so hence I will be lazy and basically repost what I wrote in April when the first version (the “preprint”) was posted online. The paper (1) is the result of a workshop arranged by the JRC in Italy in 2017. It describes various challenges arising from the process of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance genes in next generation sequencing data.

The paper discusses issues about the benchmarking datasets used, testing samples, evaluation criteria for the performance of different tools, and how the benchmarking dataset should be created and distributed. Specially, we address the following questions:

  • How should a benchmark strategy handle the current and expanding universe of NGS platforms?
  • What should be the quality profile (in terms of read length, error rate, etc.) of in silico reference materials?
  • Should different sets of reference materials be produced for each platform? In that case, how to ensure no bias is introduced in the process?
  • Should in silico reference material be composed of the output of real experiments, or simulated read sets? If a combination is used, what is the optimal ratio?
  • How is it possible to ensure that the simulated output has been simulated “correctly”?
  • For real experiment datasets, how to avoid the presence of sensitive information?
  • Regarding the quality metrics in the benchmark datasets (e.g. error rate, read quality), should these values be fixed for all datasets, or fall within specific ranges? How wide can/should these ranges be?
  • How should the benchmark manage the different mechanisms by which bacteria acquire resistance?
  • What is the set of resistance genes/mechanisms that need to be included in the benchmark? How should this set be agreed upon?
  • Should datasets representing different sample types (e.g. isolated clones, environmental samples) be included in the same benchmark?
  • Is a correct representation of different bacterial species (host genomes) important?
  • How can the “true” value of the samples, against which the pipelines will be evaluated, be guaranteed?
  • What is needed to demonstrate that the original sample has been correctly characterised, in case real experiments are used?
  • How should the target performance thresholds (e.g. specificity, sensitivity, accuracy) for the benchmark suite be set?
  • What is the impact of these performance thresholds on the required size of the sample set?
  • How can the benchmark stay relevant when new resistance mechanisms are regularly characterized?
  • How is the continued quality of the benchmark dataset ensured?
  • Who should generate the benchmark resource?
  • How can the benchmark resource be efficiently shared?

Of course, we have not answered all these questions, but I think we have come down to a decent description of the problems, which we see as an important foundation for solving these issues and implementing the benchmarking standard. Some of these issues were tackled in our review paper from last year on using metagenomics to study resistance genes in microbial communities (2). The paper also somewhat connects to the database curation paper we published in 2016 (3), although this time the strategies deal with the testing datasets rather than the actual databases. The paper is the first outcome of the workshop arranged by the JRC on “Next-generation sequencing technologies and antimicrobial resistance” held October 4-5 2017 in Ispra, Italy. You can find the paper here (it’s open access).

On another note, the new paper describing the UNITE database (4) has now got a formal issue assigned to it, as has the paper on tandem repeat barcoding in fungi published in Molecular Ecology Resources last year (5).

References and notes

  1. Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, Coque TM, Hammer P, Heß S, Kagkli DM, Krumbiegel C, Lanza VF, Madec J-Y, Naas T, O’Grady J, Paracchini V, Rossen JWA, Ruppé E, Vamathevan J, Venturi V, Van den Eede G: The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000Research, 7, 459 (2018). doi: 10.12688/f1000research.14509.1
  2. Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, 72, 2690–2703 (2017). doi: 10.1093/jac/dkx199
  3. Bengtsson-Palme J, Boulund F, Edström R, Feizi A, Johnning A, Jonsson VA, Karlsson FH, Pal C, Pereira MB, Rehammar A, Sánchez J, Sanli K, Thorell K: Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics, 16, 18, 2454–2460 (2016). doi: 10.1002/pmic.201600034
  4. Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, Kennedy P, Picard K, Glöckner FO, Tedersoo L, Saar I, Kõljalg U, Abarenkov K: The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research, 47, D1, D259–D264 (2019). doi: 10.1093/nar/gky1022
  5. Wurzbacher C, Larsson E, Bengtsson-Palme J, Van den Wyngaert S, Svantesson S, Kristiansson E, Kagami M, Nilsson RH: Introducing ribosomal tandem repeat barcoding for fungi. Molecular Ecology Resources, 19, 1, 118–127 (2019). doi: 10.1111/1755-0998.12944

Published paper: Diarrhea-causing bacteria in the Choqueyapu River in Bolivia

My first original paper of the year was just published in PLoS ONE. This is a collaboration with Åsa Sjöling’s group at the Karolinska Institute and the Universidad Mayor de San Andrés in Bolivia, and the project has been largely run by Jessica Guzman-Otazo.

Poor drinking water quality is a major cause of diarrhea, especially in the absence of well-working sewage treatment systems. In the study, we investigate the numbers of bacteria causing diarrhea (or actually, marker genes for those bacteria) in water, soil and vegetable samples from the Choqueyapu River area in La Paz – Bolivia’s third largest city (1). The river receives sewage and wastewater from industries and hospitals while flowing through La Paz. We found that levels of ETEC – a bacterium that causes severe diarrhea – were much higher in the city than upstream of it, including at a site where the river water is used for irrigation of crops.

In addition, several multi-resistant bacteria could be isolated from the samples, of which many were emerging, globally spreading, multi-resistant variants. The results of the study indicate that there is a real risk for spreading of diarrheal diseases by using the contaminated water for drinking and irrigation (2,3). Furthermore, the identification of multi-resistant bacteria that can cause human diseases show that water contamination is an important route through which antibiotic resistance can be transferred from the environment back to humans (4).

The study was published in PLoS ONE and can be found here.

References

  1. Guzman-Otazo J, Gonzales-Siles L, Poma V, Bengtsson-Palme J, Thorell K, Flach C-F, Iñiguez V, Sjöling Å: Diarrheal bacterial pathogens and multi-resistant enterobacteria in the Choqueyapu River in La Paz, Bolivia. PLoS ONE, 14, 1, e0210735 (2019). doi: 10.1371/journal.pone.0210735
  2. Graham DW, Collignon P, Davies J, Larsson DGJ, Snape J: Underappreciated Role of Regionally Poor Water Quality on Globally Increasing Antibiotic Resistance. Environ Sci Technol 141001154428000 (2014). doi: 10.1021/es504206x
  3. Bengtsson-Palme J: Antibiotic resistance in the food supply chain: Where can sequencing and metagenomics aid risk assessment? Current Opinion in Food Science, 14, 66–71 (2017). doi: 10.1016/j.cofs.2017.01.010
  4. 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

Published paper: The UNITE database

In the 2019 database issue, Nucleic Acids Research will include a new paper on the UNITE database for molecular identification of fungi (1). I have been involved in the development of UNITE in different ways since 2012, most prominently via the ITSx (2) and Atosh software which are ticking under the hood of the database.

In this update paper, we introduce a redesigned handling of unclassifiable species hypotheses, integration with the taxonomic backbone of the Global Biodiversity Information Facility, and support for an unlimited number of parallel taxonomic classification systems. The database now contains around one million fungal ITS sequences that can be used for reference, which are clustered into roughly 459,000 species hypotheses (3). Each species hypothesis is assigned a digital object identifier (DOI), which enables unambiguous reference across studies. The paper is available as open access and the UNITE database is available open source from here.

References

  1. Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, Kennedy P, Picard K, Glöckner FO, Tedersoo L, Saar I, Kõljalg U, Abarenkov K: The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research, Advance article, gky1022 (2018). doi: 10.1093/nar/gky1022
  2. Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, De Wit P, Sánchez-García M, Ebersberger I, de Souza F, Amend AS, Jumpponen A, Unterseher M, Kristiansson E, Abarenkov K, Bertrand YJK, Sanli K, Eriksson KM, Vik U, Veldre V, Nilsson RH: Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for use in environmental sequencing. Methods in Ecology and Evolution, 4, 10, 914–919 (2013). doi: 10.1111/2041-210X.12073
  3. Kõljalg U, Nilsson RH, Abarenkov K, Tedersoo L, Taylor AFS, Bahram M, Bates ST, Bruns TT, Bengtsson-Palme J, Callaghan TM, Douglas B, Drenkhan T, Eberhardt U, Dueñas M, Grebenc T, Griffith GW, Hartmann M, Kirk PM, Kohout P, Larsson E, Lindahl BD, Lücking R, Martín MP, Matheny PB, Nguyen NH, Niskanen T, Oja J, Peay KG, Peintner U, Peterson M, Põldmaa K, Saag L, Saar I, Schüßler A, Senés C, Smith ME, Suija A, Taylor DE, Telleria MT, Weiß M, Larsson KH: Towards a unified paradigm for sequence-based identification of Fungi. Molecular Ecology, 22, 21, 5271–5277 (2013). doi: 10.1111/mec.12481

Published paper: Breast milk and the infant gut resistome

This week, a paper by my former roommate Katariina Pärnänen was published by Nature Communications. In the paper (1), we use shotgun metagenomics to show that infants carry more resistant bacteria in their gut than adults do, irrespective of whether they themselves have been treated with antibiotics or not. We also found that the antibiotic resistance gene and mobile genetic element profiles of infant feces are more similar to those of their own mothers than to those of unrelated mothers. This is suggestive of a pathway of transmission of resistance genes from the mothers, and importantly we find that the mobile genetic elements in breastmilk are shared with those of the infant feces, despite vast differences in their microbiota composition. Finally, we find that termination of breastfeeding and intrapartum antibiotic prophylaxis of mothers are associated with higher abundances of specific ARGs in the infant gut. Our results suggest that infants inherit the legacy of past antibiotic consumption of their mothers via transmission of genes, but that the taxonomic composition of the microbiota still strongly dictates the overall load of resistance genes.

I am not going to dwell in to details of the study here, but I instead encourage you to read the paper (hey, it’s open access!) or the excellent popular summary that Katariina has already written. Finally, I want to emphasize the great work Katariina has put into this (I would know, since I shared room with her) and congratulate her on her own little infant!

Reference

  1. Pärnänen K, Karkman A, Hultman J, Lyra C, Bengtsson-Palme J, Larsson DGJ, Rautava S, Isolauri E, Salminen S, Kumar H, Satokari R, Virta M: Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements. Nature Communications, 9, 3891 (2018). doi: 10.1038/s41467-018-06393-w [Paper link]

Published paper: Ribosomal tandem repeat barcoding for fungi

On Friday, Molecular Ecology Resources put online Christian Wurzbacher‘s latest paper, of which I am also a coauthor. The paper presents three sets of general primers that allow for amplification of the complete ribosomal operon from the ribosomal tandem repeats, covering all the ribosomal markers (ETS, SSU, ITS1, 5.8S, ITS2, LSU, and IGS) (1). This paper is important because it introduces a technique to utilize third generation sequencing (PacBio and Nanopore) to generate high‐quality reference data (equivalent or better than Sanger sequencing) in a high‐throughput manner. The paper shows that the quality of the Nanopore generated sequences was 99.85%, which is comparable with the 99.78% accuracy described for Sanger sequencing.

My main contribution to this paper is the consensus sequence generation script – Consension – which is available from my software page. Importantly, there are huge gaps in the reference databases we use for taxonomic classification and this method will facilitate the integration of reference data from all of the ribosomal markers. We hope that this work will stimulate large-scale generation of ribosomal reference data covering several marker genes, linking previously spread-out information together.

Reference

  1. Wurzbacher C, Larsson E, Bengtsson-Palme J, Van den Wyngaert S, Svantesson S, Kristiansson E, Kagami M, Nilsson RH: Introducing ribosomal tandem repeat barcoding for fungi. Molecular Ecology Resources, Accepted article (2018). doi: 10.1111/1755-0998.12944 [Paper link]

Published paper: The global topsoil microbiome

I’m really late at this ball for a number of reasons, but last week Nature published our paper on the structure and function of the global topsoil microbiome (1). This paper has a long story, but in short I got contacted by Mohammad Bahram (the first author) about two years ago about a project using metagenomic sequencing to look at a lot of soil samples for patterns of antibiotic resistance gene abundances and diversity. The project had made the interesting discovery that resistance gene abundances were linked to the ratio of fungi and bacteria (so that more fungi was linked to more resistance genes). During the following year, we together worked on deciphering these discoveries, which are now published in Nature. The paper also deals with the taxonomic patterns linked to geography (1), but as evident from the above, my main contribution here has been on the antibiotic resistance side.

In short, we find that:

  • Bacterial diversity is highest in temperate habitats, and lower both closer to the equator and the poles
  • For bacteria, the diversity of biological functions follows the same pattern, but for fungi, the functional diversity is higher closer to the poles and the equator
  • Higher abundance of fungi is linked to higher abundance and diversity of antibiotic resistance genes. Specifically, this is related to known antibiotic producing fungal lineages, such as Penicillium and Oidiodendron. There also seems to be a link between the Actinobacteria, encompassing the antibiotic-producing bacterial genus of Streptomyces and higher resistance gene diversity.
  • Similar relationships between the fungus-like Oomycetes and resistance genes was also found in ocean samples from the Tara Oceans project (2)

The results of this study indicate that both environmental filtering and niche differentiation determine soil microbial composition, and that the role of dispersal limitation is minor at this scale. Soil pH and precipitation seems to be the strongest drivers of community composition. Furthermore, we interpret our data to reveal that inter-kingdom antagonism is important in structuring microbial communities. This speaks against the notion put forward that antibiotic resistance genes might not have a resistance function in natural settings (3). That said, the most likely explanation here is probably a bit of both warfare and repurposing of genes. Soil seems to be the largest untapped source of resistance genes for human pathogens (4), and the finding that natural antagonism may be driving resistance gene diversification and enrichment may be important for future management of environmental antibiotic resistance (5,6).

It was really great to work with Mohammad and his team, and I sure hope that we will collaborate again in the future. The entire paper can be found in the issue of Nature coming out this week, and is already online at Nature’s website.

References

  1. Bahram M°, Hildebrand F°, Forslund SK, Anderson JL, Soudzilovskaia NA, Bodegom PM, Bengtsson-Palme J, Anslan S, Coelho LP, Harend H, Huerta-Cepas J, Medema MH, Maltz MR, Mundra S, Olsson PA, Pent M, Põlme S, Sunagawa S, Ryberg M, Tedersoo L, Bork P: Structure and function of the global topsoil microbiome. Nature, 560, 233–237 (2018). doi: 10.1038/s41586-018-0386-6
  2. Sunagawa S et al. Structure and function of the global ocean microbiome. Science 348, 6237, 1261359 (2015). doi: 10.1126/science.1261359
  3. Aminov RI: The role of antibiotics and antibiotic resistance in nature. Environmental Microbiology, 11, 12, 2970-2988 (2009). doi: 10.1111/j.1462-2920.2009.01972.x
  4. Bengtsson-Palme J: The diversity of uncharacterized antibiotic resistance genes can be predicted from known gene variants – but not always. Microbiome, 6, 125 (2018). doi: 10.1186/s40168-018-0508-2
  5. 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
  6. Larsson DGJ, Andremont A, Bengtsson-Palme J, Brandt KK, de Roda Husman AM, Fagerstedt P, Fick J, Flach C-F, Gaze WH, Kuroda M, Kvint K, Laxminarayan R, Manaia CM, Nielsen KM, Ploy M-C, Segovia C, Simonet P, Smalla K, Snape J, Topp E, van Hengel A, Verner-Jeffreys DW, Virta MPJ, Wellington EM, Wernersson A-S: Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance. Environment International, 117, 132–138 (2018).