Tag: Papers

Indian lake picked up by Indian media

It is nice to see that Indian media has picked up the story about antibiotic resistance genes in the heavily polluted Kazipally lake. In this case, it is the Deccan Chronicle who have been reporting on our findings and briefly interviewed Prof. Joakim Larsson about the study. The issue of pharmaceutical pollution of the environment in drug-producing countries is still rather under-reported and public perception of the problem might be rather low. Therefore, it makes me happy to see an Indian newspaper reporting on the issue. The scientific publication referred to can be found here.

Published paper: ITS chimera dataset

A couple of days ago, a paper I have co-authored describing an ITS sequence dataset for chimera control in fungi went online as an advance online publication in Microbes and Environments. There are several software tools available for chimera detection (e.g. Henrik Nilsson‘s fungal chimera checker (1) and UCHIME (2)), but these generally rely on the presence of a chimera-free reference dataset. Until now, there was no such dataset is for the fungal ITS region, and we in this paper (3) introduce a comprehensive, automatically updated reference dataset for fungal ITS sequences based on the UNITE database (4). This dataset supports chimera detection throughout the fungal kingdom and for full-length ITS sequences as well as partial (ITS1 or ITS2 only) datasets. We estimated the dataset performance on a large set of artificial chimeras to be above 99.5%, and also used the dataset to remove nearly 1,000 chimeric fungal ITS sequences from the UNITE database. The dataset can be downloaded from the UNITE repository. Thereby, it is also possible for users to curate the dataset in the future through the UNITE interactive editing tools.


  1. Nilsson RH, Abarenkov K, Veldre V, Nylinder S, Wit P de, Brosché S, Alfredsson JF, Ryberg M, Kristiansson E: An open source chimera checker for the fungal ITS region. Molecular Ecology Resources, 10, 1076–1081 (2010).
  2. Edgar RC, Haas BJ, Clemente JC, Quince C, Knight R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics, 27, 16, 2194-2200 (2011). doi:10.1093/bioinformatics/btr381
  3. Nilsson RH, Tedersoo L, Ryberg M, Kristiansson E, Hartmann M, Unterseher M, Porter TM, Bengtsson-Palme J, Walker D, de Sousa F, Gamper HA, Larsson E, Larsson K-H, Kõljalg U, Edgar R, Abarenkov K: A comprehensive, automatically updated fungal ITS sequence dataset for reference-based chimera control in environmental sequencing efforts. Microbes and Environments, Advance Online Publication (2015). doi: 10.1264/jsme2.ME14121
  4. 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: Metaxa2

After almost a year in different stages of review and revision, in which the paper (but not the software) saw a total transformation, I am happy to announce that the paper describing Metaxa2 has been accepted in Molecular Ecology Resources and is available in a rudimentary online early form. The figures in this version are not that pretty, but those who wants to read the paper asap, you have the possibility to do so.

This means that if you have been using Metaxa2 for a publication, there is now a new preferred way of citing this, namely:

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 (2015). doi: 10.1111/1755-0998.12399

The paper (1), apart from describing the new Metaxa version, also brings a very thorough evaluation of the software, compared to other tools for taxonomic classification implemented in QIIME (2). In short, we show that:

  • Metaxa2 can make trustworthy taxonomic classifications even with reads as short as 100 bp
  • Generally, the performance is reliable across the entire SSU rRNA gene, regardless of which V-region a read is derived from
  • Metaxa2 can reliably recapture species composition from short-read metagenomic data, comparable with results of amplicon sequencing
  • Metaxa2 outperforms other popular tools such as Mothur (3), the RDP Classifier (4), Rtax (5) and the QIIME implementation of Uclust (6) in terms of proportion of correctly classified reads from metagenomic data
  • The false positive rate of Metaxa2 is very close to zero; far superior to many of the above mentioned tools, many of which assume that reads must derive from the rRNA gene

Metaxa2 can be downloaded here. We have already used it for around two years internally, and it forms the base of the taxonomic classifications in e.g. our recently published paper on antibiotic resistance in a polluted Indian lake (7).


  1. 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 (2015). doi: 10.1111/1755-0998.12399 [Paper link]
  2. Caporaso JG, Kuczynski J, Stombaugh J et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336 (2010).
  3. Schloss PD, Westcott SL, Ryabin T et al.: Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environmental Microbiology, 75, 7537–7541 (2009).
  4. Wang Q, Garrity GM, Tiedje JM, Cole JR: Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Applied and Environmental Microbiology, 73, 5261–5267 (2007).
  5. Soergel DAW, Dey N, Knight R, Brenner SE: Selection of primers for optimal taxonomic classification of environmental 16S rRNA gene sequences. The ISME Journal, 6, 1440–1444 (2012).
  6. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461 (2010).
  7. 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).

A novel antibiotic? Pretty cool, but…

In a recent paper in Nature, a completely new antibiotic – teixobactin – is described (1). The really cool thing about this antibiotic is that it was discovered in a screen of uncultured bacteria, grown using new technology that enable controlled growth of single colonies in situ. I really like this idea, and I think the prospect of a novel antibiotic using a previously unexploited mechanism is super-promising, particularly in the light of alarming resistance development in clinically important pathogens (2,3). What really annoys me about the paper is the claim (already in the abstract) that since “we did not obtain any mutants of Staphylococcus aureus or Mycobacterium tuberculosis resistant to teixobactin (…) the properties of this compound suggest a path towards developing antibiotics that are likely to avoid development of resistance.” To me, this sounds pretty much like a bogus statement; in essence telling me that we apparently have not learned anything from the 70 years of antibiotics usage and resistance development. After working with antibiotic resistance a couple of years, particularly from the environmental perspective, I have a very disturbing feeling that there is already resistance mechanisms against teixobactin waiting out in the wild (4,5). Pretending that lack of mutation-associated resistance development means that there could not be resistance development did not help vancomycin (6,7), and we now see VRE (Vancomycin Resistant Enterococcus) showing up as a major problem in clinics. The “avoid development of resistance” claim is downright irresponsible, and the cynic in me cannot help to think that NovoBiotic Pharmaceuticals (the affiliation of almost half of the authors) has a monetary finger in this jar. In the end, time will tell how “resistance-resilient” teixobactin is and how well we can handle the gift of a novel antibiotic.

  1. Ling LL, Schneider T, Peoples AJ, Spoering AL, Engels I, Conlon BP, Mueller A, Schäberle TF, Hughes DE, Epstein S, Jones M, Lazarides L, Steadman VA, Cohen DR, Felix CR, Fetterman KA, Millett WP, Nitti AG, Zullo AM, Chen C, Lewis K: A new antibiotic kills pathogens without detectable resistance. Nature (2015). doi:10.1038/nature14098
  2. Finley RL, Collignon P, Larsson DGJ, McEwen SA, Li X-Z, Gaze WH, Reid-Smith R, Timinouni M, Graham DW, Topp E: The scourge of antibiotic resistance: the important role of the environment. Clin Infect Dis, 57: 704–710 (2013).
  3. French GL: The continuing crisis in antibiotic resistance. Int J Antimicrob Agents, 36 Suppl 3:S3–7 (2010).
  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).
  5. Larsson DGJ: Antibiotics in the environment. Ups J Med Sci, 119: 108–112 (2014).
  6. Wright GD: Mechanisms of resistance to antibiotics. Curr Opin Chem Biol, 7:563–569 (2003).
  7. Werner G, Strommenger B, Witte W: Acquired vancomycin resistance in clinically relevant pathogens. Future Microbiol, 3: 547–562 (2008).

Polluted lake paper in final form

Our paper describing the bacterial community of a polluted lake in India has now been typeset and appears in its final form in Frontiers in Microbiology. If I may say so, I think that the paper turned out to be very goodlooking and it is indeed nice to finally see it in print. The paper describes an unprecedented diversity and abundance of antibiotic resistance genes and genes enabling transfer of DNA between bacteria. We also describe a range of potential novel plasmids from the lake. Finally, the paper briefly describes a new approach to targeted assembly of metagenomic data — TriMetAss — which can be downloaded here.

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

TriMetAss – A Trinity-based targeted metagenomics assembler

With the publication of my latest paper last week (1), I also would like to highlight some of the software underpinning the findings a bit. To get around the problem that extremely common resistance genes could be present in multiple contexts and variants, causing assembler such as Velvet (2) to perform sub-optimally, we have written a software tool that utilizes Vmatch (3) and Trinity (4) to iteratively construct contigs from reads associated with resistance genes. This could of course be used in many other situations as well, when you want to specifically assemble a certain portion of a metagenome, but suspect that that portion might be found in multiple contexts.

TriMetAss is a Perl program, employing Vmatch and Trinity to construct multi-context contigs. TriMetAss uses extracted reads associated with, e.g., resistance genes as seeds for a Vmatch search against the complete set of read pairs, extracting reads matching with at least 49 bp (by default) to any of the seed reads. These reads are then assembled using Trinity. The resulting contigs are then used as seeds for another search using Vmatch to the complete set of reads, as above. All matches (including the previously matching read pairs) are again then used for a Trinity assembly. This iterative process is repeated until a stop criteria is met, e.g. when the total number of assembled nucleotides starts to drop rather than increase. The software can be downloaded here.


  1. 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
  2. Zerbino DR, Birney E: Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 18, 821–829 (2008). doi:10.1101/gr.074492.107
  3. Kurtz S: The Vmatch large scale sequence analysis software (2010). http://vmatch.de/
  4. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, Amit I, Adiconis X, Fan L, Raychowdhury R, Zeng Q, et al.: Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nat Biotechnol 29, 644–652 (2011). doi:10.1038/nbt.1883

Published paper: Antibiotic resistance genes in a polluted lake

The first work in which I have employed metagenomics to investigate antibiotic resistance has been accepted in Frontiers in Microbiology, and is (at the time of writing) available as a provisional PDF. In the paper (1), which is co-authored by Fredrik Boulund, Jerker Fick, Erik Kristiansson and Joakim Larsson, we have used shotgun metagenomic sequencing of an Indian lake polluted by dumping of waste from pharmaceutical production. We used this data to describe the diversity of antibiotic resistance genes and the genetic context of those, to try to predict their genetic transferability. We found resistance genes against essentially every major class of antibiotics, as well as large abundances of genes responsible for mobilization of genetic material. Resistance genes were estimated to be 7000 times more abundant in the polluted lake than in a Swedish lake included for comparison, where only eight resistance genes were found. The abundances of resistance genes have previously only been matched by river sediment subject to pollution from pharmaceutical production (2). In addition, we describe twenty-six known and twenty-one putative novel plasmids from the Indian lake metagenome, indicating that there is a large potential for horizontal gene transfer through conjugation. Based on the wide range and high abundance of known resistance factors detected, we believe that it is plausible that novel resistance genes are also present in the lake. We conclude that environments polluted with waste from antibiotic manufacturing could be important reservoirs for mobile antibiotic resistance genes. This work further highlights previous findings that pharmaceutical production settings could provide sufficient selection pressure from antibiotics (3) to drive the development of multi-resistant bacteria (4,5), resistance which may ultimately end up in pathogenic species (6,7). The paper can be read in its entirety here.


  1. 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, Volume 5, Issue 648 (2014). doi: 10.3389/fmicb.2014.00648
  2. 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.
  3. Larsson DGJ, de Pedro C, Paxeus N: Effluent from drug manufactures contains extremely high levels of pharmaceuticals. J Hazard Mater, Volume 148, 751–755 (2007). doi:10.1016/j.jhazmat.2007.07.008
  4. Marathe NP, Regina VR, Walujkar SA, Charan SS, Moore ERB, Larsson DGJ, Shouche YS: A Treatment Plant Receiving Waste Water from Multiple Bulk Drug Manufacturers Is a Reservoir for Highly Multi-Drug Resistant Integron-Bearing Bacteria. PLoS ONE, Volume 8, e77310 (2013). doi:10.1371/journal.pone.0077310
  5. Johnning A, Moore ERB, Svensson-Stadler L, Shouche YS, Larsson DGJ, Kristiansson E: Acquired genetic mechanisms of a multiresistant bacterium isolated from a treatment plant receiving wastewater from antibiotic production. Appl Environ Microbiol, Volume 79, 7256–7263 (2013). doi:10.1128/AEM.02141-13
  6. Pruden A, Larsson DGJ, Amézquita A, Collignon P, Brandt KK, Graham DW, Lazorchak JM, Suzuki S, Silley P, Snape JR., et al.: Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environ Health Perspect, Volume 121, 878–885 (2013). doi:10.1289/ehp.1206446
  7. Finley RL, Collignon P, Larsson DGJ, McEwen SA, Li X-Z, Gaze WH, Reid-Smith R, Timinouni M, Graham DW, Topp E: The scourge of antibiotic resistance: the important role of the environment. Clin Infect Dis, Volume 57, 704–710 (2013). doi:10.1093/cid/cit355

Published paper: Is ITS1 a better barcode than ITS2?

Another paper I have made a contribution to have just recently been published in Molecular Ecology Resources. The paper (1), which is lead-authored by Xin-Cun Wang and Chang Liu at the Institute of Medicinal Plant Development in Beijing, investigates the usability of the ITS1 and ITS2 as separate barcodes across the Eukaryotes. The study is a large scale meta-analysis comparing available high-quality sequence data in as many taxonomic groups at possible from three different aspects: PCR amplification, DNA sequencing efficiency and species discrimination ability. Specifically, we have looked for the presence of DNA barcoding gaps, species discrimination efficiency, sequence length distribution, GC content distribution and primer universality, using bioinformatic approaches. We found that the ITS1 had significantly higher efficiencies than the ITS2 in 17 of 47 families and 20 of 49 investigated genera, which was markedly better than the performance of ITS2. We conclude that, in general, ITS1 represents a better DNA barcode than ITS2 for a majority of eukaryotic taxonomic groups. This of course doesn’t mean that using the ITS2 or the ITS region in its entirety should be dismissed, but our results can serve as a ground for making informed decisions about which region to choose for your amplicon sequencing project. The results complement what have previously been observed for e.g. fungi, where the difference between ITS1 and ITS2 were much less pronounced (2).


  1. Wang X-C, Liu C, Huang L, Bengtsson-Palme J, Chen H, Zhang J-H, Cai D, Li J-Q: ITS1: A DNA barcode better than ITS2 in eukaryotes? Molecular Ecology Resources. Early view. doi: 10.1111/1755-0998.12325 [Paper link]
  2. Blaalid R, Kumar S, Nilsson RH, Abarenkov K, Kirk PM, Kauserud H: ITS1 versus ITS2 as DNA metabarcodes for fungi. Molecular Ecology Resources. Volume 13, Issue2, Page 218-224. doi: 10.1111/1755-0998.12065 [Paper link]

Published paper: Detoxification genes in marine bacteria

I just got word from BMC Genomics that my most recent paper has just been published (in provisional form; we still have not seen the edited proofs). In this paper (1), which I have co-authored with Anders Blomberg, Magnus Alm Rosenblad and Mikael Molin, we utilize metagenomic data from the GOS-expedition (2) together with fully sequenced bacterial genomes to show that:

  1. Detoxification genes in general are underrepresented in marine planktonic bacteria
  2. Surprisingly, the detoxification that show a differential distribution are more abundant in open ocean water than closer to the coast
  3. Peroxidases and peroxiredoxins seem to be the main line of defense against oxidative stress for bacteria in the marine milieu, rather than e.g. catalases
  4. The abundance of detoxification genes does not seem to increase with estimated pollution.

From this we conclude that other selective pressures than pollution likely play the largest role in shaping marine planktonic bacterial communities, such as for example nutrient limitations. This suggests substantial streamlining of gene copy number and genome sizes, in line with observations made in previous studies (3). Along the same lines, our findings indicate that the majority of marine bacteria would have a low capacity to adapt to increased pollution, which is relevant as large amounts of human pollutants and waste end up in the oceans every year. The study exemplifies the use of metagenomics data in ecotoxicology, and how we can examine anthropogenic consequences on life in the sea using approaches derived from genomics. You can read the paper in its entirety here.


  1. Bengtsson-Palme J, Alm Rosenblad M, Molin M, Blomberg A: Metagenomics reveals that detoxification systems are underrepresented in marine bacterial communities. BMC Genomics. Volume 15, Issue 749 (2014). doi: 10.1186/1471-2164-15-749 [Paper link]

  2. Yooseph S, Sutton G, Rusch DB, Halpern AL, Williamson SJ, Remington K, Eisen JA, Heidelberg KB, Manning G, Li W, Jaroszewski L, Cieplak P, Miller CS, Li H, Mashiyama ST, Joachimiak MP, Van Belle C, Chandonia J-M, Soergel DA, Zhai Y, Natarajan K, Lee S, Raphael BJ, Bafna V, Friedman R, Brenner SE, Godzik A, Eisenberg D, Dixon JE, Taylor SS, et al: The Sorcerer II Global Ocean Sampling expedition: expanding the universe of protein families. PLoS Biology. 5:e16 (2007).
  3. Yooseph S, Nealson KH, Rusch DB, McCrow JP, Dupont CL, Kim M, Johnson J, Montgomery R, Ferriera S, Beeson KY, Williamson SJ, Tovchigrechko A, Allen AE, Zeigler LA, Sutton G, Eisenstadt E, Rogers Y-H, Friedman R, Frazier M, Venter JC: Genomic and functional adaptation in surface ocean planktonic prokaryotes. Nature. 468:60–66 (2010).

Scientific Data – a way of getting credit for data

In an interesting development, Nature Publishing Group has launched a new initiative: Scientific Data – a online-only open access journal that publishes data sets without the demand of testing scientific hypotheses in connection to the data. That is, the data itself is seen as the valuable product, not any findings that might result from it. There is an immediate upside of this; large scientific data sets might be accessible to the research community in a way that enables proper credit for the sample collection effort. Since there is no demand for a full analysis of the data, the data itself might quicker be of use to others, without worrying that someone else might steal the bang of the data per se. I also see a possible downside, though. It would be easy to hold on to the data until you have analyzed it yourself, and then release it separately just about when you submit the paper on the analysis, generating extra papers and citation counts. I don’t know if this is necessarily bad, but it seems it could contribute to “publishing unit dilution”. Nevertheless, I believe that this is overall a good initiative, although how well it actually works will be up to us – the scientific community. Some info copied from the journal website:

Scientific Data’s main article-type is the Data Descriptor: peer-reviewed, scientific publications that provide an in-depth look at research datasets. Data Descriptors are a combination of traditional scientific publication content and structured information curated in-house, and are designed to maximize reuse and enable searching, linking and data mining. (…) Scientific Data aims to address the increasing need to make research data more available, citable, discoverable, interpretable, reusable and reproducible. We understand that wider data-sharing requires credit mechanisms that reward scientists for releasing their data, and peer evaluation mechanisms that account for data quality and ensure alignment with community standards.