In the very first episode of the Bengtsson-Palme lab podcast, a crew consisting of Johan Bengtsson-Palme, Emil Burman, Haveela Kunche and Anna Abramova discusses the origin of the novel coronavirus, interactions between influenza and the respiratory tract microbiome, resistant bacteria in glaciers, pathway analysis methods, a new genus of bacteria discovered in Gothenburg, as well as life in research during a global pandemic.
The specific papers discussed in the pod (with approximate timings) are as follows:
- 10:15 – Andersen, K.G., Rambaut, A., Lipkin, W.I., Holmes, E.C., Garry, R.F., 2020. The proximal origin of SARS-CoV-2. Nature Medicine 26, 450–452. https://doi.org/10.1038/s41591-020-0820-9
- 17:30 – Zhou, P., et al., 2020. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 579, 270–273. https://doi.org/10.1038/s41586-020-2012-7
- 19:30 – https://www.fli.de/en/press/press-releases/press-singleview/novel-coronavirus-sars-cov-2-fruit-bats-and-ferrets-are-susceptible-pigs-and-chickens-are-not/
- 20:45 – Kadioglu, O., Saeed, M., Greten, H.J., Efferth, T, 2020. Identification of novel compound against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning. Bulletin of the World Health Organization. https://doi.org/10.2471/BLT.20.255943
- 21:45 – Cheng, V.C.C., Lau, S.K.P., Woo, P.C.Y., Yuen, K.Y., 2007. Severe Acute Respiratory Syndrome Coronavirus as an Agent of Emerging and Reemerging Infection. Clinical Microbiology Reviews 20, 660–694. https://doi.org/10.1128/CMR.00023-07
- 22:15 – Fan, Y., Zhao, K., Shi, Z.-L., Zhou, P., 2019. Bat Coronaviruses in China. Viruses 11, 210. https://doi.org/10.3390/v11030210
- 29:15 – Zhang, L., et al., 2020. Characterization of antibiotic resistance and host-microbiome interactions in the human upper respiratory tract during influenza infection. Microbiome 8. https://doi.org/10.1186/s40168-020-00803-2
- 39:15 – Makowska, N., et al., 2020. Occurrence of integrons and antibiotic resistance genes in cryoconite and ice of Svalbard, Greenland, and the Caucasus glaciers. Science of The Total Environment 716, 137022. https://doi.org/10.1016/j.scitotenv.2020.137022
- 49:45 – 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
- 58:45 – Gillings, M.R., 2014. Integrons: past, present, and future. Microbiology and molecular biology reviews : MMBR 78, 257–277. https://doi.org/10.1128/MMBR.00056-13
- 60:45 – Moradi, E., Marttinen, M., Häkkinen, T., Hiltunen, M., Nykter, M., 2019. Supervised pathway analysis of blood gene expression profiles in Alzheimer’s disease. Neurobiology of Aging 84, 98–108. https://doi.org/10.1016/j.neurobiolaging.2019.07.004
- 62:15 – Johnson, W.E., Li, C., Rabinovic, A., 2007. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127. https://doi.org/10.1093/biostatistics/kxj037
- 72:15 – Marathe, N.P., et al., 2019. Scandinavium goeteborgense gen. nov., sp. nov., a New Member of the Family Enterobacteriaceae Isolated From a Wound Infection, Carries a Novel Quinolone Resistance Gene Variant. Frontiers in Microbiology 10. https://doi.org/10.3389/fmicb.2019.02511
- 76:00 – Boulund, F., et al., 2017. Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics 18, 438. https://doi.org/10.1186/s12864-017-4064-0
The podcast was recorded on April 9, 2020. 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.
We are hiring a postdoc to work with environmental monitoring of antimicrobial resistance. The project is part of the EMBARK program and will consider different aspects of establishing a baseline for background antibiotic resistance in the environment, standardization of monitoring protocols and development of methods to detect emerging resistance threats. The project will involve work with environmental sampling, DNA extractions, bacterial culturing and generation of large-scale DNA sequence data. In terms of bioinformatic analyses, the project will encompass analysis of next-generation sequence data, genome-resolved metagenomics, short-read assembly and network analysis.
We look for a skilled bioinformatician, preferably with experience of experimental laboratory work. 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 January 3.
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!
- 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
- Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572
- 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
- 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
- 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
- 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
- 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
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
- 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
- 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
- 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
- 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
- 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
I just uploaded a mini update to ITSx, fixing a bug that caused the
--truncate option not to be accepted by the software in ITSx 1.1. This bug fix brings the software to version 1.1.1. No other changes have been introduced in this version. Download the update here. Happy barcoding!
A few days ago, my attention was turned to a duplicate in the COI database bundled with Metaxa2 2.2. While this duplicate sequence should not cause any troubles for Metaxa2 itself, it has created issues for people using the database itself together with, e.g., QIIME. Therefore, I have today issued a very very minor update to the Metaxa2 2.2 package as well as the entry in the Metaxa2 Database Repository, removing the duplicate sequence. I deemed that this was not significant enough to issue a new version, particularly as no code was changed and it did not cause issues for the software itself, so the version will stay at 2.2 for the time being. Happy barcoding!
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.
- Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572 [Link]
- 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]
- 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]
- 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]
- 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.
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.
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!
- 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
- 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
- Edgar RC: Accuracy of taxonomy prediction for 16S rRNA and fungal ITS sequences. PeerJ, 6, e4652 (2018). doi: 10.7717/peerj.4652
- 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
- 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