Category: Thoughts

I am very happy to announce that our paper on the metagenomes of periphyton communities (1) have been accepted in Frontiers in Microbiology (Aquatic Microbiology section). This project has been one of my longest running, as it started as my master thesis in 2010 and has gone through several metamorphoses before hitting its final form.

Briefly, our main findings are that:

1. Periphyton communities harbor an extraordinary diversity of organisms, including viruses, bacteria, algae, fungi, protozoans and metazoans
2. Bacteria are by far the most abundant
3. We find functional indicators of the biofilm form of life in periphyton involve genes coding for enzymes that catalyze the production and degradation of extracellular polymeric substances
4. Genes encoding enzymes that participate in anaerobic pathways are found in the biofilms suggesting that anaerobic or low-oxygen micro-zones within the biofilms exist

Most of this work has been carried out by my colleague Kemal Sanli, who have been doing a wonderful job pulling this together, with the help of Henrik Nilsson and Martin Eriksson. It also deserves to be noted that this work was the starting point for the Metaxa software (2,3), which recently reached version 2.1.1.

References

1. Sanli K, Bengtsson-Palme J, Nilsson RH, Kristiansson E, Alm Rosenblad M, Blanck H, Eriksson KM: Metagenomic sequencing of marine periphyton: Taxonomic and functional insights into biofilm communities. Frontiers in Microbiology, 6, 1192 (2015). doi: 10.3389/fmicb.2015.01192 [Paper link]
2. Bengtsson J, Eriksson KM, Hartmann M, Wang Z, Shenoy BD, Grelet G, Abarenkov K, Petri A, Alm Rosenblad M, Nilsson RH: Metaxa: A software tool for automated detection and discrimination among ribosomal small subunit (12S/16S/18S) sequences of archaea, bacteria, eukaryotes, mitochondria, and chloroplasts in metagenomes and environmental sequencing datasets. Antonie van Leeuwenhoek, 100, 3, 471-475 (2011). doi:10.1007/s10482-011-9598-6. [Paper link]
3. 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 [Paper link]

I am very happy to announce that Metaxa2 version 2.1 has been released today. This new version brings a lot of important improvements to the Metaxa2 software (1), in particular by the introduction of the Metaxa2 Diversity Tools. This is the list of new features (further elaboration follows below):

• The Metaxa2 Diversity Tools:
  • metaxa2_dc – a tool for collecting several .taxonomy.txt output files into one large abundance matrix, suitable for analysis in, e.g., R
  • metaxa2_rf – generates rarefaction curves based on the .taxonomy.txt output
  • metaxa2_si – species inference based on guessing species data from the other species present in the .taxonomy.txt output file
  • metaxa2_uc – a tool for determining if the community composition of a sample is significantly different from others through resampling analysis
• Added a new detection mode for detection of multiple rRNA in the same sequence, e.g. a genome
• Added the --reference option to improve the use of Metaxa2 as a tool to sort out host rRNA sequences from a dataset
• Added the --split_pairs option causing Metaxa2 to output paired-end sequences into two separate files, which is nice for further analysis of rRNA reads
• The default setting for the --align option has been changed to ‘none‘
• Automatic detection of which BLAST package that is installed
• Fixed a bug causing the last read of paired-end FASTA input to be ignored
• Fixed an occasionally occurring BLAST+ related warning message
• Fixed a bug that could cause the classifier to crash on highly divergent BLAST matches

The new version of Metaxa2 can be downloaded here, and for those interested I will spend the rest of this post outlining the new features.

Metaxa2 Diversity Tools
One often requested feature of Metaxa2 is the ability to further make simple analysis from the data after classification. The Metaxa2 Diversity Tools included in Metaxa2 2.1 is a seed for such an effort (although not close to a full-fledge community analysis package compared to QIIME (2) or Mothur (3)). The set currently consist of four tools

The Metaxa2 Data Collector (metaxa2_dc) is the simplest of them (but probably the most requested), designed to merge the output of several *.level_X.txt files from the Metaxa2 Taxonomic Traversal Tool into one large abundance matrix, suitable for further analysis in, for example, R. The Metaxa2 Species Inference tool (metaxa2_si) can be used to further infer taxon information on, for example, the species level at a lower reliability than what would be permitted by the Metaxa2 classifier, using a complementary algorithm. The idea is that is if only a single species is present in, e.g., a family and a read is assigned to this family, but not classified to the species level, that sequence will be inferred to the same species as the other reads, given that it has more than 97% sequence identity to its best reference match. This can be useful if the user really needs species or genus classifications but many organisms in the studied species group have similar rRNA sequences, making it hard for the Metaxa2 classifier to classify sequences to the species level.

The Metaxa2 Rarefaction analysis tool (metaxa2_rf) performs a rarefaction analysis based on the output from the Metaxa2 classifier, taking into account also the unclassified portion of rRNAs. The Metaxa2 Uniqueness of Community analyzer (metaxa2_uc), finally, allows analysis of whether the community composition of two or more samples or groups is significantly different. Using resampling of the community data, the null hypothesis that the taxonomic content of two communities is drawn from the same set of taxa (given certain abundances) is tested. All these tools are further described in the manual.

The genome mode
Metaxa2 has long been said not to be useful for predicting rRNA in longer sequences, such as full genomes or chromosomes, since it has traditionally only looked for a single rRNA hit. With Metaxa2 2.1, it is now possible to use Metaxa2 on longer sequences to detect multiple rRNA occurrences. To do this, you need to change the operating mode using the new --mode option to either ‘auto‘ or ‘genome‘. The auto mode will treat sequences longer than 2500 bp as “genome” sequences and look for multiple matches in these.

The reference mode
Another feature request that has been addressed in the new Metaxa2 version is the ability to filter out certain sequences from the data set. For example, you may want to exclude all rRNA sequences that are derived from to host organism, but keep the analysis of all other rRNA reads. This is now possible by supplying a file of reference rRNA sequences to exclude in FASTA format to the --reference option.

Experimental Usearch support
Finally, we have toyed around with support for Usearch (4) instead of BLAST (5) as the search algorithm for the classification step. However, this is far from fine-tuned and it is included as an experimental feature that you may use on your own risk! We recommend that you not use it for classification of data for publication yet. However, we are interested in how this works for you, so if you like you may test to run the Usearch algorithm in parallel with your BLAST-based analysis and compare the results and send me your input on how it works. You can read more about using Usearch at the end of the Metaxa2 manual.

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. Edgar RC: Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 26, 2460–2461 (2010).
5. Altschul SF, Madden TL, Schäffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res, 25, 3389–3402 (1997).

UPDATE: This post has been updated to reflect a valid comment that I messed up in the original post. Specific use of antibiotics as feed additives has been forbidden for years in the EU. The petition was about cutting the prophylactic use of antibiotics in animals, but that was very unclear from the original post. I thank my readers for pointing this unclarity out.

I’m going to do something unusual and ask you to sign a petition targeted at European Union ministers to support new EU laws to drastically cut the prophylactic use of antibiotics in agriculture as growth promoters. The problem is that if ministers don’t feel the public pressure to act, the laws may be delayed or not be implemented. Examples from Denmark, Sweden, Norway and the Netherlands show that it is possible to produce meat with little or no antibiotics, but since bacteria can travel across borders (1), we need to bring the rest of the world onboard, and the EU is good first step. Therefore I ask you to sign the Avaaz petition here.

1. Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DGJ, Johansson A: The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrobial Agents and Chemotherapy, 59, 10, 6551-6560 (2015). doi: 10.1128/AAC.00933-15 [Paper link]

The paper we published in August on travelers carrying resistance genes with them in their gut microbiota has now been typeset and got proper volume and issue numbers assigned to it in Antimicrobial Agents and Chemotherapy. Take a look at it, I personally think it’s quite good-looking.

Also, if you understand Swedish, here is an interview with me broadcasted on Swedish Radio last month about this study and the consequences of it.

The new citation for the paper is:

• Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DGJ, Johansson A: The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrobial Agents and Chemotherapy, 59, 10, 6551-6560 (2015). doi: 10.1128/AAC.00933-15 [Paper link]

I have had the pleasure to be chosen as a speaker for next week’s (ten days from now) Swedish Bioinformatics Workshop. My talk is entitled “Turn up the signal – wipe out the noise: Gaining insights into bacterial community functions using metagenomic data“, and will largely deal with the same questions as my talk on EDAR3 in May this year. As then, 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 community function, and use examples 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 Thursday, September 24, 2015 at 16:30. The full program for the conference can be found here. And also, if you want a sneak peak of the talk, you can drop by on Friday 13.00 at Chemistry and Molecular Biology, where I will give a seminar on the same topic in the Monthly Bioinformatic Practical Meetings series.

There have been quite a lot of buzz this week around the travel paper we published earlier this month. Twitter aside, the findings of the paper has also been covered by a range of news outlets, both in Sweden and internationally. Today, I was on Swedish radio talking resistance problems for about ten minutes (listen here; in Swedish). Here’s a few takes on the story I gathered around the web:

Science Daily
Business Standard
Z News
Englemed Health News
Läkemedelsvärlden (in Swedish)
Sveriges Radio (in Swedish)
Göteborgs-Posten (in Swedish)

Earlier today, my most recent paper (1) became available online, describing resistance gene patterns in the gut microbiota of Swedes before and after travel to the Indian peninsula and central Africa. In this work, we have used metagenomic sequencing of the intestinal microbiome of Swedish students returning from exchange programs to show that the abundance of antibiotic resistance genes in several classes are increased after travel. This work reiterates the findings of several papers describing uptake of resistant bacteria (2-8) or resistance genes (9-11) after travel to destinations with worse resistance situation.

Our paper is important because it:

1. Addresses the abundance of a vast range of resistance genes (more than 300).
2. Finds evidence for that the overall relative abundance of antibiotic resistance genes increased after travel, without any intake of antibiotics.
3. Shows that the sensitivity of metagenomics was, despite very deep sequencing efforts, not sufficient to detect acquisition of the low-abundant (CTX-M) resistance genes responsible for observed ESBL phenotypes.
4. Reveals a “core resistome” of resistance genes that are more or less omnipresent, and remain relatively stable regardless of travel, while changes seem to occur in the more variable part of the resistome.
5. Hints at increased abundance of Proteobacteria after travel, although this increase could not specifically be linked to resistance gene increases.
6. Uses de novo metagenomic assembly to physically link resistance genes in the same sample, giving hints of co-resistance patterns in the gut microbiome.

The paper was a collaboration with Martin Angelin, Helena Palmgren and Anders Johansson at Umeå University, and was made possible by bioinformatics support from SciLifeLab in Stockholm. I highly recommend reading it as a complement to e.g. the Forslund et al. paper (12) describing country-specific antibiotic resistance patterns in the gut microbiota.

Taken together, this study offers a broadened perspective on how the antibiotic resistance potential of the human gut microbiome changes after travel, providing an independent complement to previous studies targeting a limited number of bacterial species or antibiotic resistance genes. Understanding how resistance genes travels the globe is hugely important, since resistance in principle only need to appear in a pathogen once; improper hygiene and travel may then spread novel resistance genes across continents rapidly (13,14).

References

1. Bengtsson-Palme J, Angelin M, Huss M, Kjellqvist S, Kristiansson E, Palmgren H, Larsson DGJ, Johansson A: The human gut microbiome as a transporter of antibiotic resistance genes between continents. Antimicrob Agents Chemother Accepted manuscript posted online (2015). doi: 10.1128/AAC.00933-15 [Paper link]
2. Gaarslev K, Stenderup J: Changes during travel in the composition and antibiotic resistance pattern of the intestinal Enterobacteriaceae flora: results from a study of mecillinam prophylaxis against travellers’ diarrhoea. Curr Med Res Opin 9:384–387 (1985).
3. Paltansing S, Vlot JA, Kraakman MEM, Mesman R, Bruijning ML, Bernards AT, Visser LG, Veldkamp KE: Extended-spectrum β-lactamase-producing enterobacteriaceae among travelers from the Netherlands. Emerging Infect. Dis. 19:1206–1213 (2013).
4. Ruppé E, Armand-Lefèvre L, Estellat C, El-Mniai A, Boussadia Y, Consigny PH, Girard PM, Vittecoq D, Bouchaud O, Pialoux G, Esposito-Farèse M, Coignard B, Lucet JC, Andremont A, Matheron S: Acquisition of carbapenemase-producing Enterobacteriaceae by healthy travellers to India, France, February 2012 to March 2013. Euro Surveill. 19 (2014).
5. Kennedy K, Collignon P: Colonisation with Escherichia coli resistant to “critically important” antibiotics: a high risk for international travellers. Eur J Clin Microbiol Infect Dis 29:1501–1506 (2010).
6. Tham J, Odenholt I, Walder M, Brolund A, Ahl J, Melander E: Extended-spectrum beta-lactamase-producing Escherichia coli in patients with travellers’ diarrhoea. Scand. J. Infect. Dis. 42:275–280 (2010).
7. Östholm-Balkhed Å, Tärnberg M, Nilsson M, Nilsson LE, Hanberger H, Hällgren A, Travel Study Group of Southeast Sweden: Travel-associated faecal colonization with ESBL-producing Enterobacteriaceae: incidence and risk factors. J Antimicrob Chemother 68:2144–2153 (2013).
8. Kantele A, Lääveri T, Mero S, Vilkman K, Pakkanen SH, Ollgren J, Antikainen J, Kirveskari J: Antimicrobials increase travelers’ risk of colonization by extended-spectrum betalactamase-producing enterobacteriaceae. Clin Infect Dis 60:837–846 (2015).
9. von Wintersdorff CJH, Penders J, Stobberingh EE, Oude Lashof AML, Hoebe CJPA, Savelkoul PHM, Wolffs PFG: High rates of antimicrobial drug resistance gene acquisition after international travel, The Netherlands. Emerging Infect. Dis. 20:649–657 (2014).
10. Tängdén T, Cars O, Melhus A, Löwdin E: Foreign travel is a major risk factor for colonization with Escherichia coli producing CTX-M-type extended-spectrum beta-lactamases: a prospective study with Swedish volunteers. Antimicrob Agents Chemother 54:3564–3568 (2010).
11. Dhanji H, Patel R, Wall R, Doumith M, Patel B, Hope R, Livermore DM, Woodford N: Variation in the genetic environments of bla(CTX-M-15) in Escherichia coli from the faeces of travellers returning to the United Kingdom. J Antimicrob Chemother 66:1005–1012 (2011).
12. Forslund K, Sunagawa S, Kultima JR, Mende DR, Arumugam M, Typas A, Bork P: Country-specific antibiotic use practices impact the human gut resistome. Genome Res 23:1163–1169 (2013).
13. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nat Rev Microbiol 13:396 (2015).
14. Larsson DGJ: Antibiotics in the environment. Ups J Med Sci 119:108–112 (2014).

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!

This week is the first of my long summer break, and I will be on vacation until mid-August. This means that I will only read mail sporadically, if at all. For very urgent issues, please give me a call or send me an sms, and I will attend to your message as soon as possible (this of course only applies to those of you who have my number in the first place).

For support questions, there are a few options:

• For questions regarding Metaxa or Metaxa2, please add “METAXA” to the beginning of the subject line of the e-mail.
• For questions regarding ITSx, please add “ITSX” to the beginning of the subject line.
• For other support questions, please add “SUPPORT” to the beginning of the subject line.

This way I can easily assess which mails that are urgent to reply to. Don’t add “IMPORANT” or “URGENT” since that will just invoke the spam filter.

I wish you all a very very great summer!

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.