Category: Thoughts

So, on Thursday (May 26th) I will defend my thesis, titled “Antibiotic resistance in the environment: a contribution from metagenomic studies”. I will not dwell into this by writing a novel text, but will instead shamelessly reproduce the press release, which should give a reasonable overview of what I have been doing:

More and more people are infected with antibiotic resistant bacteria. But how do bacteria become resistant? A doctoral thesis from the Centre for Antibiotic Resistance Research at University of Gothenburg has investigated the role of the environment in the development of antibiotic resistance.

“An important question we asked was how low concentrations of antibiotics that can favour the growth of resistant bacteria in the environment”, says Johan Bengtsson-Palme, author of the thesis.

“Based on our analyses, we propose emission limits for 111 antibiotics that should not be exceeded in order to avoid that environmental bacteria become more resistant.”

A starting point to regulate antibiotic pollution
A recent report, commissioned by the British Prime Minister David Cameron, proposes that the emission limits suggested in Johan’s thesis should be used as a starting point to regulate antibiotic pollution from, for example, pharmaceutical production – globally.

“Many people are surprised that such regulations are not already in place, but today it is actually not a crime to discharge wastewater contaminated with large amounts of antibiotics, not even in Europe”, says Johan Bengtsson-Palme.

Resistance genes
In one of the studies in the thesis, the researchers show that resistance genes against a vast range of antibiotics are enriched in an Indian lake polluted by dumping of wastewater from pharmaceutical production.

“It’s scary. Not only do the bacteria carry a multitude of resistance genes. They are also unusually well adapted to share those genes with other bacteria. If a disease-causing bacterium ends up in the lake, it may quickly pick up the genes it needs to become resistant. Since the lake is located close to residential areas, such spread of resistant bacteria to humans is not hard to imagine”, says Johan Bengtsson-Palme.

Spreading by travelers
The thesis also shows that resistant bacteria spread in the intestines of travelers who have visited India or Central Africa, even if the travelers themselves have not become sick.

“That resistant bacteria spread so quickly across the planet highlights that we must adopt a global perspective on the resistance problem”, says Johan Bengtsson-Palme. “Furthermore, it is not enough to reduce the use of antibiotics in healthcare. We must also reduce the use of antibiotics for animals, and try to limit the releases of antibiotics into the environment to try to get control over the growing antibiotic resistance problem before it is too late”.

The thesis Antibiotic resistance in the environment: a contribution from metagenomic studies will be defended on a dissertation on May 26th.

Today marks the five year anniversary for the Metaxa software’s initial release. Much has happened to the software since; Metaxa started off as an rRNA extraction utility for metagenomic data (1), including coarse classification to organism/organelle type. Since it has gained full-scale taxonomic classification ability better or on par with other software packages (2), much greater speed, support for the LSU gene, gained a range of related software tools (3), and spurred development of other tools such as ITSx (4). I have also been involved in no less than four peer-reviewed publications directly related to the software (1-3,5).

But it does not end here; these five years were just the beginning. We are – in different constellations – working on further enhancements to Metaxa2, including support for more genes, an updated classification database, and better customization options. I am very much still devoted to keep Metaxa2 alive and relevant as a tool for taxonomic analysis of metagenomes, applicable whenever accuracy is a key parameter. Thanks for being part of the community for these five years!

References

1. 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]
2. Bengtsson-Palme J, Hartmann M, Eriksson KM, Pal C, Thorell K, Larsson DGJ, Nilsson RH: Metaxa2: Improved identification and taxonomic classification of small and large subunit rRNA in metagenomic data. Molecular Ecology Resources, 15, 6, 1403–1414 (2015). doi: 10.1111/1755-0998.12399 [Paper link]
3. Bengtsson-Palme J, Thorell K, Wurzbacher C, Sjöling Å, Nilsson RH: Metaxa2 Diversity Tools: Easing microbial community analysis with Metaxa2. Ecological Informatics, 33, 45–50 (2016). doi: 10.1016/j.ecoinf.2016.04.004 [Paper link]
4. Bengtsson-Palme J, Ryberg M, Hartmann M, Branco S, Wang Z, Godhe A, De Wit P, Sánchez-García M, Ebersberger I, de Souza F, Amend AS, Jumpponen A, Unterseher M, Kristiansson E, Abarenkov K, Bertrand YJK, Sanli K, Eriksson KM, Vik U, Veldre V, Nilsson RH: Improved software detection and extraction of ITS1 and ITS2 from ribosomal ITS sequences of fungi and other eukaryotes for use in environmental sequencing. Methods in Ecology and Evolution, 4, 10, 914–919 (2013). doi: 10.1111/2041-210X.12073 [Paper link]
5. Bengtsson-Palme J, Hartmann M, Eriksson KM, Nilsson RH: Metaxa, overview. In:Nelson K. (Ed.) Encyclopedia of Metagenomics: SpringerReference (www.springerreference.com). Springer-Verlag Berlin Heidelberg (2013). doi: 10.1007/978-1-4614-6418-1_239-6 [Link]

In the most recent issue of the Medicine Maker (#0416), there is a short opinion piece by me and Joakim Larsson, in which we argue for that pharmaceutical companies should live up to their ethical responsibilities, and may actually benefit from doing so (1). We were invited to write for the Medicine Maker based on our recent papers on proposed limits for antibiotic discharges into the environment (2) and minimal selective concentrations (3).

We argue that now as PNECs for resistance selection are available, they should be applied in regulatory contexts. The recent O’Neill report on antimicrobial resistance (commissioned by the British Government) specifically highlighted the urgent need for enforceable regulations on antibiotic discharges (4). The concentrations we reported in our Environment International paper (2) can be used by local, national and international authorities to define emission limits for antibiotic-producing factories, but also for pharmaceutical companies to assess and manage risks for resistance selection associated with their own discharges.

The Medicine Maker can be read for free, but requires registration to access its full content. The full opinion piece can be found here.

References

1. Bengtsson-Palme J, Larsson DGJ: Time to limit antibiotic pollution. The Medicine Maker, 0416, 302, 17–18 (2016). [Paper link]
2. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140–149 (2016). doi: 10.1016/j.envint.2015.10.015 [Paper link]
3. Lundström S, Östman M, Bengtsson-Palme J, Rutgersson C, Thoudal M, Sircar T, Blanck H, Eriksson KM, Tysklind M, Flach C-F, Larsson DGJ: Minimal selective concentrations of tetracycline in complex aquatic bacterial biofilms. Science of the Total Environment, 553, 587–595 (2016). doi: 10.1016/j.scitotenv.2016.02.103 [Paper link]
4. Review on Antimicrobial Resistance: Antimicrobials in agriculture and the environment: Reducing unnecessary use and waste (J O’Neill, Ed,) (2015). [Link].

Yesterday, Ecological Informatics put our paper describing Metaxa2 Diversity Tools online (1). Metaxa2 Diversity Tools was introduced with Metaxa2 version 2.1 and consists of

• 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 resampling rarefaction curves (2) 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

At the same time as I did this update to the web site, I also took the opportunity to update the Metaxa2 FAQ to better reflect recent updates to the Metaxa2 software.

Metaxa2 Diversity Tools
One often requested feature of Metaxa2 (3) has been the ability to make simple analyses 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-fledged community analysis package comparable to QIIME (4) or Mothur (5)). It 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 resampling rarefaction analysis (2) 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 and the recent paper (1).

The latest version of Metaxa2, including the Metaxa2 Diversity Tools, can be downloaded here.

References

1. Bengtsson-Palme J, Thorell K, Wurzbacher C, Sjöling Å, Nilsson RH: Metaxa2 Diversity Tools: Easing microbial community analysis with Metaxa2. Ecological Informatics, 33, 45–50 (2016). doi: 10.1016/j.ecoinf.2016.04.004 [Paper link]
2. Gotelli NJ, Colwell RK: Quantifying biodiversity: procedures and pitfalls in the measurement and comparison of species richness. Ecology Letters, 4, 379–391 (2000). doi:10.1046/j.1461-0248.2001.00230.x
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 (2015). doi: 10.1111/1755-0998.12399 [Paper link]
4. Caporaso JG, Kuczynski J, Stombaugh J et al.: QIIME allows analysis of high-throughput community sequencing data. Nature Methods, 7, 335–336 (2010).
5. 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).

After a long wait (1) Sara Lundström’s paper establishing minimal selective concentrations (MSCs) for the antibiotic tetracycline in complex microbial communities (2), of which I am a co-author, has gone online. Personally, I think this paper is among the finest work I have been involved in; a lot of good science have gone into this publication. Risk assessment and management of antibiotics pollution is in great need of scientific data to underpin mitigation efforts (3). This paper describes a method to determine the minimal selective concentrations of antibiotics, and investigates different endpoints for measuring those MSCs. The method involves a testing system highly relevant for aquatic communities, in which bacteria are allowed to form biofilms in aquaria under controlled antibiotic exposure. Using the system, we find that 1 μg/L tetracycline selects for the resistance genes tetA and tetG, while 10 μg/L tetracycline is required to detect changes of phenotypic resistance. In short, the different endpoints studied (and their corresponding MSCs) were:

• CFU counts on R2A plates with 20 μg/mL tetracycline – MSC = 10 μg/L
• MIC range – MSC ~ 10-100 μg/L
• PICT, leucine uptake after short-term TC challenge – MSC ~ 100 μg/L
• Increased resistance gene abundances, metagenomics – MSC range: 0.1-10 μg/L
• Increased resistance gene abundances, qPCR (tetA and tetG) – MSC ≤ 1 μg/L
• Changes to taxonomic diversity – no significant changes detected
• Changes to taxonomic community composition – MSC ~ 1-10 μg/L

This study confirms that the estimated PNECs we reported recently (4) correspond well to experimentally determined MSCs, at least for tetracycline. Importantly, the selective concentrations we report for tetracycline overlap with those that have been reported in sewage treatment plants (5). We also see that tetracycline not only selects for tetracycline resistance genes, but also resistance genes against other classes of antibiotics, including sulfonamides, beta-lactams and aminoglycosides. Finally, the approach we describe can be used for improved in risk assessment for (also other) antibiotics, and to refine the emission limits we suggested in a recent paper based on theoretical calculations (4).

References and notes

1. Okay, seriously: how can a journal’s production team return the proofs for a paper within 24 hours of acceptance, and then wait literally five weeks before putting the final proofs online? Nothing against STOTEN, but I honestly wonder what was going on beyond the scenes here.
2. Lundström SV, 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]
3. Ågerstrand M, Berg C, Björlenius B, Breitholtz M, Brunstrom B, Fick J, Gunnarsson L, Larsson DGJ, Sumpter JP, Tysklind M, Rudén C: Improving environmental risk assessment of human pharmaceuticals. Environmental Science and Technology (2015). doi:10.1021/acs.est.5b00302
4. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140-149 (2016). doi: 10.1016/j.envint.2015.10.015
5. Michael I, Rizzo L, McArdell CS, Manaia CM, Merlin C, Schwartz T, Dagot C, Fatta-Kassinos D: Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review. Water Research, 47, 957–995 (2013). doi:10.1016/j.watres.2012.11.027

The Royal Swedish Academy of Sciences (KVA) is, together with Joakim Larsson, arranging a conference on the mechanisms and evidence for the involvement of metals and biocides in selection of antibiotic resistant bacteria. Several experts from around Europe will attend and give talks, including for example Dan Andersson, Kristian Brandt, Teresa Coque, Will Gaze, Åsa Melhus and Chris Rensing. The symposium is open to everyone and is free of charge (although registration is binding).

The conference take place between 15th and 16th of March, at The Royal Swedish Academy of Sciences’ facilities at Lilla Frescativägen in Stockholm. And you should join us; register here!

Even for Sweden, this was a pleasant surprise to come to work to this morning.

My workplace, in the winter.

The view from my office.

I have made my yearly updates to the web site (changing pictures and adding the yearly summary), and I just want to take the opportunity to wish all my visitors a happy 2016! My little family has been sick (at least one of us) during most of the holidays, so we have had a very calm Christmas and a very calm New Year’s. Hope you have had more fun!

Yesterday was an intensive day for typesetters apparently, since they put two of my papers online on the same day. This second paper was published in Environment International, and focuses on predicting minimal selective concentrations for all antibiotics present in the EUCAST database (1).

Today (well, up until yesterday at least), we have virtually no knowledge of which environmental concentrations that can exert a selection pressure for antibiotic resistant bacteria. However, experimentally determining minimal selective concentrations (MSCs) in complex ecosystems would involve immense efforts if done for all antibiotics. Therefore, efforts to theoretically determine MSCs for different antibiotics have been suggested (2,3). In this paper we therefore estimate upper boundaries for selective concentrations for all antibiotics in the EUCAST database, based on the assumption that selective concentrations a priori must be lower than those completely inhibiting growth. Data on Minimal Inhibitory Concentrations (MICs) were obtained for 122 antibiotics and antibiotics combinations, the lowest observed MICs were identified for each of those across all tested species, and to compensate for limited species coverage, we adjusted the lowest MICs for the number of tested species. We finally assessed Predicted No Effect Concentrations (PNECs) for resistance selection using an assessment factor of 10 to account for the differences between MICs and MSCs. Since we found that the link between taxonomic similarity between species and lowest MIC was weak, we have not compensated for the taxonomic diversity that each antibiotic was tested against – only for limited number of species tested. In most cases, our PNECs for selection of resistance were below available PNECs for ecotoxicological effects retrieved from FASS. Also, concentrations predicted to be selective have, for some antibiotics, been detected in regular sewage treatment plants (4), and are greatly exceeded in environments polluted by pharmaceutical pollution (5-7), often with drastic consequences in terms of resistance gene enrichments (8-10). This is a central issue since in principle a transfer event of a novel resistance determinant from an environmental bacteria to an (opportunistic) human pathogen only need to occur once to become a clinical problem (11). Once established, the gene could then spread through human activities, such as trade and travel (7,13). Importantly, this paper:

• Provides upper boundaries for selective concentrations (MSCs) for 111 antibiotics
• Predicts no effect concentrations (PNECs) for resistance selection
• Can guide implementation of compound-specific emission limits based on the provided concentrations

The paper is available under open access here. We hope, and believe, that the data will be of great use in environmental risk assessments, in efforts by industries, regulatory agencies or purchasers of medicines to define acceptable environmental emissions of antibiotics, in the implementation of environmental monitoring programs, for directing mitigations, and for prioritizing future studies on environmental antibiotic resistance.

References:

1. Bengtsson-Palme J, Larsson DGJ: Concentrations of antibiotics predicted to select for resistant bacteria: Proposed limits for environmental regulation. Environment International, 86, 140-149 (2016). doi: 10.1016/j.envint.2015.10.015 [Paper link]
2. Ågerstrand M, Berg C, Björlenius B, Breitholtz M, Brunstrom B, Fick J, Gunnarsson L, Larsson DGJ, Sumpter JP, Tysklind M, Rudén C: Improving environmental risk assessment of human pharmaceuticals. Environmental Science and Technology (2015). doi:10.1021/acs.est.5b00302
3. Tello A, Austin B, Telfer TC: Selective pressure of antibiotic pollution on bacteria of importance to public health. Environmental Health Perspectives, 120, 1100–1106 (2012). doi:10.1289/ehp.1104650
4. Michael I, Rizzo L, McArdell CS, Manaia CM, Merlin C, Schwartz T, Dagot C, Fatta-Kassinos D: Urban wastewater treatment plants as hotspots for the release of antibiotics in the environment: a review. Water Research, 47, 957–995 (2013). doi:10.1016/j.watres.2012.11.027
5. Larsson DGJ, de Pedro C, Paxeus N: Effluent from drug manufactures contains extremely high levels of pharmaceuticals. Journal of Hazardous Materials, 148, 751–755 (2007). doi:10.1016/j.jhazmat.2007.07.008
6. Fick J, Söderström H, Lindberg RH, Phan C, Tysklind M, Larsson DGJ: Contamination of surface, ground, and drinking water from pharmaceutical production. Environmental Toxicology and Chemistry, 28, 2522–2527 (2009). doi:10.1897/09-073.1
7. Larsson DGJ: Pollution from drug manufacturing: review and perspectives. Philosophical Transactions of the Royal Society London, Series B Biological Sciences, 369 (2014). doi:10.1098/rstb.2013.0571
8. 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 [Paper link]
9. 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.
10. 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
11. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1 [Paper link]
12. 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 got a very nice little e-mail yesterday evening, which made me realize that when I posted the Metaxa 2.1 update, I forgot to thank and credit the wonderful Metaxa/Metaxa2 community who have contributed with input on which Metaxa2 features that they would like to see implemented. Particularly, I would like to thank Thomas Haverkamp who suggested the reference option, Åsa Sjöling who brainstormed what led to the metaxa2_uc tool with me, and everyone who have suggested various downstream analysis tricks that have got baked into the Metaxa2 Diversity Tools.

Within the Metaxa team I would like to specifically thank Kaisa Thorell (particularly for the --split_pairs option) and Martin Hartmann (who said that the software should obviously be able to detect which BLAST version that was installed), who keep pushing for features and ideas to make the software better. Thanks a lot to all of you, and have a nice weekend!