Tag: Metagenomics

TriMetAss has been updated to version 1.1. The new version addresses a number of minor issues and brings two new handy features. The update can be found here.

New features:

• Multiple input files can now be specified by adding several -1 and -2 options.
• TriMetAss now automatically stops if the candidate reads are the same for two iterations in a row.

Fixed issues:

• Support for recent versions of Trinity that no longer contain the Trinity.pl script.
• A minor bug causing TriMetAss to use more memory than necessary has been fixed.
• Fixed the --stop_total option so that TriMetAss actually uses this option (rather than --stop_length)
• Allowed complicated paths to be supplied for the output directory.

I would like to thank users Rickard Hammarén, Dr. Tatsuya Unno, Dr. Gisle Vestergaard and Dr. Joseph Nesme for providing me with the underlying information to provide these fixes. Thanks a lot!

Late last year, an opinion paper by José Martínez, Teresa Coque and Fernando Baquero was published in Nature Reviews Microbiology (1). In this paper, the authors present a system – resistance readiness conditions (RESCon) – for ranking the risks associated with the detection of antibiotic resistance genes. They also outline the obstacles associated with determining risks presented by antibiotic resistance genes in environmental microbial communities in terms of their potential to transfer to human pathogens. Generally, I am very positive about this paper, which I think is a must-read for anyone who works with antibiotic resistance genes in metagenomes, regardless of it they stem from the human gut or the external environment.

There is, however, one very important aspect that struck me and many other members of our research group as curious: the proposed system assign antibiotic resistance genes already present on mobile genetic elements in human pathogens to the highest risk category (RESCon 1), while resistance genes encoding novel resistance mechanisms not yet been found on mobile elements in a pathogen are considered to be part of lower risk categories. We believe that this system will overestimate the risks associated with well-known resistance factors that are already circulating among human pathogens and under-appreciate the potentially disastrous consequences that the transfer of previously unknown resistance determinants from the environmental resistome could have (exemplified by the rapid clinical spread of the NDM-1 metallo-beta-lactamase gene (2,3)).

With this in mind me and Joakim Larsson wrote a response letter to Nature Reviews Microbiology that went online last monday (4), together with the authors’ reply to us (5). (I strongly suggest that you read the entire original paper (1) before you read the reply (5) to our response letter (4), since Martinez et al. changes the scope slightly from the original paper in their response letter, and these clarifications may (or may not) have been in response to our arguments.)

In our response, we also stress that the abundances of resistance genes, and not only their presence, should be accounted for when estimating risks (although that last point might have been slightly obscured due to the very low word limit). In other words, we think that identifying environmental hotspots for antibiotic resistance genes, where novel resistance genes could be selected for (6,7,8), is of great importance for mitigating public health risks related to environmental antibiotic resistance. Please read our full thoughts on the matter in Nature Reviews Microbiology.

Similar issues will be touched upon in my talk at the EDAR2015 conference later in May. Hope to see you there!

References

1. Martinez JL, Coque TM, Baquero F: What is a resistance gene? Ranking risk in resistomes. Nat Rev Microbiol 2015, 13:116–123.
2. Kumarasamy KK, et al.: Emergence of a new antibiotic resistance mechanism in India, Pakistan, and the UK: a molecular, biological, and epidemiological study. Lancet Infect Dis 2010, 10:597–602.
3. Walsh TR, Weeks J, Livermore DM, Toleman MA: Dissemination of NDM‐1 positive bacteria in the New Delhi environment and its implications for human health: an environmental point prevalence study. Lancet Infect Dis 2011, 11:355–362.
4. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nat Rev Microbiol 2015, Advance online publication. doi:10.1038/nrmicro3399‐c1
5. Martinez JL, Coque TM, Baquero F: Prioritizing risks of antibiotic resistance genes in all metagenomes. Nat Rev Microbiol 2015, Advance online publication. doi:10.1038/nrmicro3399‐c2
6. Kristiansson E, et al.: Pyrosequencing of antibiotic‐contaminated river sediments reveals high levels of resistance and gene transfer elements. PLoS ONE 2011, 6:e17038.
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. Front Microbiol 2014, 5:648.
8. Marathe NP, et al.: A treatment plant receiving waste water from multiple bulk drug manufacturers is a reservoir for highly multi‐drug resistant integron‐bearing bacteria. PLoS ONE 2013, 8:e77310.

I will be giving a talk at the Third International symposium on the environmental dimension of antibiotic resistance (EDAR2015) next month (five weeks from now. The talk is entitled “Turn up the signal – wipe out the noise: Gaining insights into antibiotic resistance of bacterial communities using metagenomic data“, and will deal with handling of metagenomic data in antibiotic resistance gene research. The talk will highlight the some particular pitfalls related to interpretation of data, and exemplify how flawed analysis practices can result in misleading conclusions regarding antibiotic resistance risks. I will particularly address how taxonomic composition influences the frequencies of resistance genes, the importance of knowledge of the functions of the genes in the databases used, and how normalization strategies influence the results. Furthermore, we will show how the context of resistance genes can allow inference of their potential to spread to human pathogens from environmental or commensal bacteria. All these aspects will be exemplified by data from our studies of environments subjected to pharmaceutical pollution in India, the effect of travel on the human resistome, and modern municipal wastewater treatment processes.

The talk will take place on Monday, May 18, 2015 at 13:20. The full scientific program for the conference can be found here. Registration for the conference is still possible, although not for the early-bird price. I look forward to see a lot of the people who will attend the conference, and hopefully also you!

Metaxa2 has been updated to version 2.0.2 and can be downloaded from the Metaxa2 web site. The 2.0.2 update fixes two minor bugs; one causing the “.graph” file to display incorrect or no names for the regions of the LSU regions, and one causing misreporting of the number of sequences in single-end FASTQ files (paired-end files were reported correctly). The update also brings a slightly improved classifier. Thanks to Marco Severgnini for reporting the FASTQ file issue! The update is available here.

A couple of days ago a paper was published in Environmental Sciences Europe summarizing the EU report on effect-based tools for use in toxicology in the aquatic environment I have been involved in (1). This report was officially published last spring (2), and can be found here, with the annex available on the European Commission document website. My contribution to the paper was, as with the report, in the genomics and metagenomics section. The paper briefly presents modern bioassays, biomarkers and ecological methods that can be used for aquatic monitoring of the environment.

References:

1. Wernersson A-S, Carere M, Maggi C, Tusil P, Soldan P, James A, Sanchez W, Dulio V, Broeg K, Reifferscheid G, Buchinger S, Maas H, Van Der Grinten E, O’Toole S, Ausili A, Manfra L, Marziali L, Polesello S, Lacchetti I, Mancini L, Lilja K, Linderoth M, Lundeberg T, Fjällborg B, Porsbring T, Larsson DGJ, Bengtsson-Palme J, Förlin L, Kienle C, Kunz P, Vermeirssen E, Werner I, Robinson CD, Lyons B, Katsiadaki I, Whalley C, den Haan K, Messiaen M, Clayton H, Lettieri T, Negrão Carvalho R, Gawlik BM, Hollert H, Di Paolo C, Brack W. Kammann U, Kase R: The European technical report on aquatic effect-based monitoring tools under the water framework directive. Environmental Sciences Europe, 27, 7 (2015). doi: 10.1186/s12302-015-0039-4 [Paper link]
2. Wernersson A-S, Carere M, Maggi C, Tusil P, Soldan P, James A, Sanchez W, Broeg K, Kammann U, Reifferscheid G, Buchinger S, Maas H, Van Der Grinten E, Ausili A, Manfra L, Marziali L, Polesello S, Lacchetti I, Mancini L, Lilja K, Linderoth M, Lundeberg T, Fjällborg B, Porsbring T, Larsson DGJ, Bengtsson-Palme J, Förlin L, Kase R, Kienle C, Kunz P, Vermeirssen E, Werner I, Robinson CD, Lyons B, Katsiadaki I, Whalley C, den Haan K, Messiaen M, Clayton H, Lettieri T, Negrão Carvalho R, Gawlik BM, Dulio V, Hollert H, Di Paolo C, Brack W (2014). Technical Report on Aquatic Effect-Based Monitoring Tools. European Commission. Technical Report 2014-077, Office for Official Publications of European Communities, ISBN: 978-92-79-35787-9. doi:10.2779/7260

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).

References

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).

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.

Reference:
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

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.

References:

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

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.

References:

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

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.

References:

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).