Tag: Microbial ecology

We are hiring a PhD student to work with effects of antibiotics on microbial communities! The project will use large-scale techniques to investigate how sub-inhibitory concentrations of antibiotics affect microbial communities. Specifically, the project will examine how the ability for bacteria to colonize and invade established microbial communities is impacted by antibiotics. The project will also explore how antibiotics influence the interactions between different species in bacterial communities and if this may change their ability to withstand invasions. The goal is to identify the genes and mechanisms that contribute to change and stability in microbial communities.

A cool thing about this position is that it is fairly adaptable to the eventual candidate, and the project can be somewhat tailored to suit the profile of the PhD student. This means that we’re looking for someone who is either a bioinformatician or an experimentalist (or both). Previous experience with microbial communities is a plus, but not a must.

If you feel that you are the right person for this position, you can apply here. More information is also available here. We look forward to your application! The deadline for applications is December 9.

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

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

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

Let me get straight to something somewhat besides the point here: summer students can achieve amazing things! One such student I had the pleasure to work with this summer is Shruthi Magesh, and a preprint based on work she did with me at the Wisconsin Institute for Discovery this summer just got published on bioRxiv (1). The preprint describes a software tool called Mumame, which uses database information on mutations in DNA or protein sequences to search metagenomic datasets and quantifies the relative proportion of resistance mutations over wild type sequences.

In the preprint (1), we first of all show that Mumame works on amplicon data where we already knew the true outcome (2). Second, we show that we can detect differences in mutation frequencies in controlled experiments (2,3). Lastly, we use the tool to gain some further information about resistance patterns in sediments from polluted environments in India (4,5). Together these analyses show that one of the most central aspects for Mumame to be able to find mutations is having a very high number of sequenced reads in all libraries (preferably more than 50 million per library), because these mutations are generally rare – even in polluted environments and microcosms exposed to antibiotics. We expect Mumame to be a useful addition to metagenomic studies of e.g. antibiotic resistance, and to increase the detail by which metagenomes can be screened for phenotypically important differences.

While I did write the code for the software (with a lot of input from Viktor Jonsson, who also is a coauthor on the preprint, on the statistical analysis), Shruthi did the software testing and evaluations, and the paper would not have been possible hadn’t she wanted a bioinformatic summer project related to metagenomics, aside from her laboratory work. The resulting preprint is available from bioRxiv and the Mumame software is freely available from this site.

References

1. Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572 [Link]
2. 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 [Paper link]
3. 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 [Paper link]
4. 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 [Paper link]
5. Kristiansson E, Fick J, Janzon A, Grabic R, Rutgersson C, Weijdegård B, Söderström H, Larsson DGJ: Pyrosequencing of antibiotic-contaminated river sediments reveals high levels of resistance and gene transfer elements. PLoS ONE, Volume 6, e17038 (2011). doi:10.1371/journal.pone.0017038.

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]

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]

Last week, I uploaded a new database to the Metaxa2 Database Repository, called DAIRYdb. DAIRYdb (1) is a manually curated reference database for 16S rRNA amplicon sequences from dairy products. Significant efforts have been put into improving annotation algorithms, such as Metaxa2 (2), while less attention has been put into curation of reliable and consistent databases (3). Previous studies have shown that databases restricted to the studied environment improve unambiguous taxonomy annotation to the species level, thanks to consistent taxonomy, lack of blanks and reduced competition between different reference taxonomies (4-5). The usage of DAIRYdb in combination with different classification tools allows taxonomy annotation accuracy of over 90% at species level for microbiome samples from dairy products, where species identification is mandatory due to the affiliation to few closely related genera of most dominant lactic acid bacteria.

The database can be added to your Metaxa2 (version 2.2 or later) installation by using the following command:

metaxa2_install_database -g SSU_DAIRYdb_v1.1.2

Further adaptations of the DAIRYdb can be found on GitHub and the preprint has been deposited in BioRxiv (1). DAIRYdb was developed by Marco Meola, Etienne Rifa and their collaborators, who also provided most of the text for this post. Thanks Marco for this excellent addition to the database collection!

References

1. Meola M, Rifa E, Shani N, Delbes C, Berthoud H, Chassard C: DAIRYdb: A manually curated gold standard reference database for improved taxonomy annotation of 16S rRNA gene sequences from dairy products. bioRxiv, 386151 (2018). doi: 10.1101/386151
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
3. Edgar RC: Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ, 6, e4652 (2018). doi: 10.7717/peerj.4652
4. Ritari J, Salojärvi J, Last L, de Vos WM: Improved taxonomic assignment of human intestinal 16S rRNA sequences by a dedicated reference database. BMC Genomics, 16, 1, 1056 (2015). doi: 10.1186/s12864-015-2265-y
5. Newton ILG, Roeselers G: The effect of training set on the classification of honey bee gut microbiota using the naïve bayesian classifier. BMC Microbiology, 12, 1, 221 (2012). doi: 10.1186/1471-2180-12-221

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).

I have just published a popular-science-for-scientists type of post at the Nature Microbiology Community about my recent paper published in Microbiome. I personally think that it might be worth a read, so feel free to head over here and read it!