Microbiology, Metagenomics and Bioinformatics

Johan Bengtsson-Palme, University of Gothenburg | Wisconsin Institute for Discovery

Browsing Posts tagged Antibiotic resistance

The outcomes from a workshop arranged by JPIAMR, the Swedish Research Council (VR) and CARe were just published as a short review paper in Environment International. In the paper, which was mostly moved forward by Prof. Joakim Larsson at CARe, we describe four major areas of knowledge gaps in the realm of environmental antibiotic resistance (1). We then highlight several important sub-questions within these areas. The broad areas we define are:

  • What are the relative contributions of different sources of antibiotics and antibiotic resistant bacteria into the environment?
  • What is the role of the environment as affected by anthropogenic inputs (e.g. pollution and other activities) on the evolution (mobilization, selection, transfer, persistence etc.) of antibiotic resistance?
  • How significant is the exposure of humans to antibiotic resistant bacteria via different environmental routes, and what is the impact on human health?
  • What technological, social, economic and behavioral interventions are effective to mitigate the emergence and spread of antibiotic resistance via the environment?

Although much has been written on the topic before (e.g. 2-12), I think it is unique that we collect and explicitly point out areas in which we are lacking important knowledge to build accurate risk models and devise appropriate intervention strategies. The workshop was held in Gothenburg on the 27–28th of September 2017. The workshop leaders Joakim Larsson, Ana-Maria de Roda Husman and Ramanan Laxminarayan were each responsible for moderating a breakout group, and every breakout group was tasked to deal with knowledge gaps related to either evolution, transmission or interventions. The reports of the breakout groups were then discussed among all participants to clarify and structure the areas where more research is needed, which boiled down to the four overarching critical knowledge gaps described in the paper (1).

This is a short paper, and I think everyone with an interest in environmental antibiotic resistance should read it and reflect over its content (because, we may of course have overlooked some important aspect). You can find the paper here.

References

  1. Larsson DGJ, Andremont A, Bengtsson-Palme J, Brandt KK, de Roda Husman AM, Fagerstedt P, Fick J, Flach C-F, Gaze WH, Kuroda M, Kvint K, Laxminarayan R, Manaia CM, Nielsen KM, Ploy M-C, Segovia C, Simonet P, Smalla K, Snape J, Topp E, van Hengel A, Verner-Jeffreys DW, Virta MPJ, Wellington EM, Wernersson A-S: Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance. Environment International, 117, 132–138 (2018). doi: 10.1016/j.envint.2018.04.041
  2. Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews, 42, 1, 68–80 (2018). doi: 10.1093/femsre/fux053
  3. Martinez JL, Coque TM, Baquero F: What is a resistance gene? Ranking risk in resistomes. Nature Reviews Microbiology 2015, 13:116–123. doi:10.1038/nrmicro3399
  4. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
  5. Ashbolt NJ, Amézquita A, Backhaus T, Borriello P, Brandt KK, Collignon P, et al.: Human Health Risk Assessment (HHRA) for Environmental Development and Transfer of Antibiotic Resistance. Environmental Health Perspectives, 121, 993–1001 (2013)
  6. Pruden A, Larsson DGJ, Amézquita A, Collignon P, Brandt KK, Graham DW, et al.: Management options for reducing the release of antibiotics and antibiotic resistance genes to the environment. Environmental Health Perspectives, 121, 878–85 (2013).
  7. Gillings MR: Evolutionary consequences of antibiotic use for the resistome, mobilome and microbial pangenome. Frontiers in Microbiology, 4, 4 (2013).
  8. Baquero F, Alvarez-Ortega C, Martinez JL: Ecology and evolution of antibiotic resistance. Environmental Microbiology Reports, 1, 469–476 (2009).
  9. Baquero F, Tedim AP, Coque TM: Antibiotic resistance shaping multi-level population biology of bacteria. Frontiers in Microbiology, 4, 15 (2013).
  10. Berendonk TU, Manaia CM, Merlin C et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015).
  11. Hiltunen T, Virta M, Laine A-L: Antibiotic resistance in the wild: an eco-evolutionary perspective. Philosophical Transactions of the Royal Society B: Biological Sciences, 372 (2017) doi: 10.1098/rstb.2016.0039.
  12. Martinez JL: Bottlenecks in the transferability of antibiotic resistance from natural ecosystems to human bacterial pathogens. Frontiers in Microbiology, 2, 265 (2011).

My colleagues in Gothenburg have published a new paper in Environment International, in which I was involved in the bioinformatics analyses. In the paper, for which Nadine Kraupner did the lion’s share of the work, we establish minimal selective concentrations (MSCs) for resistance to the antibiotic ciprofloxacin in Escherichia coli grown in complex microbial communities (1). We also determine the community responses at the taxonomic and resistance gene levels. Nadine has made use of Sara Lundström’s aquarium system (2) to grow biofilms in the exposure of sublethal levels of antibiotics. Using the system, we find that 1 μg/L ciprofloxacin selects for the resistance gene qnrD, while 10 μg/L ciprofloxacin is required to detect changes of phenotypic resistance. In short, the different endpoints studied (and their corresponding MSCs) were:

  • CFU counts from test tubes, grown on R2A plates with 2 mg/L ciprofloxain – MSC = 5 μg/L
  • CFU counts from aquaria, grown on R2A plates with 0.25 or 2 mg/L ciprofloxain – MSC = 10 μg/L
  • Chromosomal resistance mutations – MSC ~ 10 μg/L
  • Increased resistance gene abundances, metagenomics – MSC range: 1 μg/L
  • Changes to taxonomic diversity1 µg/L
  • Changes to taxonomic community composition – MSC ~ 1-10 μg/L

We have previously reported a predicted no-effect concentration for resistance of 0.064 µg/L for ciprofloxacin (3), which corresponds fairly well with the MSCs determined experimentally here, being around a factor of ten off. However, we cannot exclude that in other experimental systems, the selective effects of ciprofloxacin could be even lower and thus the predicted PNEC may still be relevant. The selective concentrations we report for ciprofloxacin are close to those that have been reported in sewage treatment plants (3-5), suggesting the possibility for weak selection of resistance. Several recent reports have underscored the need to populate the this far conceptual models for resistance development in the environment with actual numbers (6-10). Determining selective concentrations for different antibiotics in actual community settings is an important step on the road towards building accurate quantitative models for resistance emergence and propagation.

References

  1. Kraupner N, Ebmeyer S, Bengtsson-Palme J, Fick J, Kristiansson E, Flach C-F, Larsson DGJ: Selective concentration for ciprofloxacin in Escherichia coli grown in complex aquatic bacterial biofilms. Environment International, 116, 255–268 (2018). doi: 10.1016/j.envint.2018.04.029 [Paper link]
  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. 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
  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. Bengtsson-Palme J, Hammarén R, Pal C, Östman M, Björlenius B, Flach C-F, Kristiansson E, Fick J, Tysklind M, Larsson DGJ: Elucidating selection processes for antibiotic resistance in sewage treatment plants using metagenomics. Science of the Total Environment, 572, 697–712 (2016). doi: 10.1016/j.scitotenv.2016.06.228
  6. 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
  7. Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews, 42, 1, 68–80 (2018). doi: 10.1093/femsre/fux053
  8. Joint Programming Initiative on Antimicrobial Resistance: JPIAMR Workshop on Environmental Dimensions of AMR: Summary and recommendations. JPIAMR (2017). [Link]
  9. Angers A, Petrillo P, Patak, A, Querci M, Van den Eede G: The Role and Implementation of Next-Generation Sequencing Technologies in the Coordinated Action Plan against Antimicrobial Resistance. JRC Conference and Workshop Report, EUR 28619 (2017). doi: 10.2760/745099
  10. Larsson DGJ, Andremont A, Bengtsson-Palme J, Brandt KK, de Roda Husman AM, Fagerstedt P, Fick J, Flach C-F, Gaze WH, Kuroda M, Kvint K, Laxminarayan R, Manaia CM, Nielsen KM, Ploy M-C, Segovia C, Simonet P, Smalla K, Snape J, Topp E, van Hengel A, Verner-Jeffreys DW, Virta MPJ, Wellington EM, Wernersson A-S: Critical knowledge gaps and research needs related to the environmental dimensions of antibiotic resistance. Environment International, in press (2018). doi: 10.1016/j.envint.2018.04.041

This weekend, F1000Research put online the non-peer-reviewed version of the paper resulting from a workshop arranged by the JRC in Italy last year (1). (I will refer to this as a preprint, but at F1000Research the line is quite blurry between preprint and published paper.) The paper describes various challenges arising from the process of designing a benchmark strategy for bioinformatics pipelines (2) in the identification of antimicrobial resistance genes in next generation sequencing data.

The paper discusses issues about the benchmarking datasets used, testing samples, evaluation criteria for the performance of different tools, and how the benchmarking dataset should be created and distributed. Specially, we address the following questions:

  • How should a benchmark strategy handle the current and expanding universe of NGS platforms?
  • What should be the quality profile (in terms of read length, error rate, etc.) of in silico reference materials?
  • Should different sets of reference materials be produced for each platform? In that case, how to ensure no bias is introduced in the process?
  • Should in silico reference material be composed of the output of real experiments, or simulated read sets? If a combination is used, what is the optimal ratio?
  • How is it possible to ensure that the simulated output has been simulated “correctly”?
  • For real experiment datasets, how to avoid the presence of sensitive information?
  • Regarding the quality metrics in the benchmark datasets (e.g. error rate, read quality), should these values be fixed for all datasets, or fall within specific ranges? How wide can/should these ranges be?
  • How should the benchmark manage the different mechanisms by which bacteria acquire resistance?
  • What is the set of resistance genes/mechanisms that need to be included in the benchmark? How should this set be agreed upon?
  • Should datasets representing different sample types (e.g. isolated clones, environmental samples) be included in the same benchmark?
  • Is a correct representation of different bacterial species (host genomes) important?
  • How can the “true” value of the samples, against which the pipelines will be evaluated, be guaranteed?
  • What is needed to demonstrate that the original sample has been correctly characterised, in case real experiments are used?
  • How should the target performance thresholds (e.g. specificity, sensitivity, accuracy) for the benchmark suite be set?
  • What is the impact of these performance thresholds on the required size of the sample set?
  • How can the benchmark stay relevant when new resistance mechanisms are regularly characterized?
  • How is the continued quality of the benchmark dataset ensured?
  • Who should generate the benchmark resource?
  • How can the benchmark resource be efficiently shared?

Of course, we have not answered all these questions, but I think we have come down to a decent description of the problems, which we see as an important foundation for solving these issues and implementing the benchmarking standard. Some of these issues were tackled in our review paper from last year on using metagenomics to study resistance genes in microbial communities (3). The paper also somewhat connects to the database curation paper we published in 2016 (4), although this time the strategies deal with the testing datasets rather than the actual databases. The paper is the first outcome of the workshop arranged by the JRC on “Next-generation sequencing technologies and antimicrobial resistance” held October 4-5 last year in Ispra, Italy. You can find the paper here (it’s open access).

References and notes

  1. Angers-Loustau A, Petrillo M, Bengtsson-Palme J, Berendonk T, Blais B, Chan KG, Coque TM, Hammer P, Heß S, Kagkli DM, Krumbiegel C, Lanza VF, Madec J-Y, Naas T, O’Grady J, Paracchini V, Rossen JWA, Ruppé E, Vamathevan J, Venturi V, Van den Eede G: The challenges of designing a benchmark strategy for bioinformatics pipelines in the identification of antimicrobial resistance determinants using next generation sequencing technologies. F1000Research, 7, 459 (2018). doi: 10.12688/f1000research.14509.1
  2. You may remember that I hate the term “pipeline” for bioinformatics protocols. I would have preferred if it was called workflows or similar, but the term “pipeline” has taken hold and I guess this is a battle where I have essentially lost. The bioinformatics workflows will be known as pipelines, for better and worse.
  3. Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, 72, 2690–2703 (2017). doi: 10.1093/jac/dkx199
  4. Bengtsson-Palme J, Boulund F, Edström R, Feizi A, Johnning A, Jonsson VA, Karlsson FH, Pal C, Pereira MB, Rehammar A, Sánchez J, Sanli K, Thorell K: Strategies to improve usability and preserve accuracy in biological sequence databases. Proteomics, 16, 18, 2454–2460 (2016). doi: 10.1002/pmic.201600034

Recently, Le Page et al. published a paper in Environmental International (1), partially building on the predicted no-effect concentrations for resistance selection for 111 antibiotics that me and Joakim Larsson published around two years ago (2). In their paper, the authors stress that discharge limits for antibiotics need to consider their potency to affect both environmental and human health, which we believe is a very reasonable standpoint, and to which we agree. However, we do not agree on the authors’ claim that cyanobacteria would often be more sensitive to antibiotics than the most sensitive human-associated bacteria (1). Importantly, we also think that it is a bit unclear from the paper which protection goals are considered. Are the authors mainly concerned with protecting microbial diversity in ecosystems, protecting ecosystem functions and services, or protecting from risks for resistance selection? This is important because it influence why one would want to mitigate, and therefore who would perform which actions. To elaborate a little on our standpoints, we wrote a short correspondence piece to Environment International, which is now published (3). (It has been online for a few days, but without a few last-minute changes we did to the proof, and hence I’m only posting about it now when the final version is online.) There is indeed an urgent need for discharge limits for antibiotics, particularly for industrial sources (4) and such limits would have tremendous value in regulation efforts, and in development of environmental criteria within public procurement and generic exchange programs (5). Importantly, while we are all for taking ecotoxicological data into account when doing risk assessment, we think that there should be solid scientific ground for mitigations and that regulations need to consider the benefits versus the costs, which is what we want to convey in our response to Le Page et al.

References

  1. Le Page G, Gunnarsson L, Snape J, Tyler CR: Integrating human and environmental health in antibiotic risk assessment: a critical analysis of protection goals, species sensitivity and antimicrobial resistance. Environment International, in press (2017). doi: 10.1016/j.envint.2017.09.013
  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
  3. Bengtsson-Palme J, Larsson DGJ: Protection goals must guide risk assessment for antibiotics. Environment International, in press (2017). doi: 10.1016/j.envint.2017.10.019
  4. Bengtsson-Palme J, Larsson DGJ: Time to limit antibiotic pollution. The Medicine Maker, 0416, 302, 17–18 (2016). [Paper link]
  5. Bengtsson-Palme J, Gunnarsson L, Larsson DGJ: Can branding and price of pharmaceuticals guide informed choices towards improved pollution control during manufacturing? Journal of Cleaner Production, 171, 137–146 (2018). doi: 10.1016/j.jclepro.2017.09.247

Yesterday, Swedish television channel TV4 highlighted a recent publication by myself, Lina Gunnarsson and Joakim Larsson, in which we show that the price of pharmaceuticals is linked to the environmental standards of production countries. Surprisingly, however, this link seems to be mostly driven by whether the product is generic or original (branded), which in turns affect the prices.

In the study (1), published in Journal of Cleaner Production, we have used an exclusive set of Swedish sales data for pharmaceuticals combined with data on the origin of the active ingredients, obtained under an agreement to not identify individual manufacturers or products. We used this data to determine if price pressure and generic substitution could be linked to the general environmental performance and the corruption levels of the production countries, as measured by the Environmental Performance Index (2) and the Corruption Perception Index (3). In line with what we believed, India was the largest producer of generics, while Europe and the USA dominated the market for branded products (1). Importantly, we found that the price and environmental performance index of the production countries were linked, but that this relationship was largely explained by whether the product was original or generic.

To some extent, this relationship would allow buyers to select products that likely originate from countries that, in general terms, have better pollution control, which was also highlighted in the news clip that TV4 produced. However, what was lacking from that clip was the fact that this approach lacks resolution, because it does not say anything about the environmental footprint of individual products. We therefore conclude that to better allow consumers, hospitals and pharmacies to influence the environmental impact of their product choices, there is need for regulation and, importantly, transparency in the production chain, as has also been pointed out earlier (4,5). To this end, emissions from manufacturing need to be measured, allowing for control and follow-up on industry commitments towards sustainable manufacturing of pharmaceuticals (6). Since the discharges from pharmaceutical manufacturing not only leads to consequences to the local environment (7,8), but also in the case of antibiotics has potentially global consequences in terms of increasing risks for resistance development (9), limiting discharges is an urgent need to avoid a looming antibiotic resistance crisis (10).

The paper was also highlighted by the Centre for Antibiotic Resistance Research, and can be read here or here.

References

  1. Bengtsson-Palme J, Gunnarsson L, Larsson DGJ: Can branding and price of pharmaceuticals guide informed choices towards improved pollution control during manufacturing? Journal of Cleaner Production, 171, 137–146 (2018). doi: 10.1016/j.jclepro.2017.09.247
  2. Hsu A, Alexandre N, Cohen S, Jao P, Khusainova E: 2016 Environmental Performance Index. Yale University, New Haven, CT, USA (2016). http://epi.yale.edu/reports/2016-report
  3. Transparency International: Corruption Perceptions Index 2014. Transparency International, Berlin, Germany (2014). http://www.transparency.org/cpi2014/in_detail
  4. Larsson DGJ, Fick J: Transparency throughout the production chain–a way to reduce pollution from the manufacturing of pharmaceuticals? Regulatory Toxicology and Pharmacology, 53, 161–163 (2009). doi:10.1016/j.yrtph.2009.01.008
  5. Ågerstrand M, Berg C, Björlenius B, Breitholtz M, Brunström B, Fick J, Gunnarsson L, Larsson DGJ, Sumpter JP, Tysklind M, Rudén C: Improving environmental risk assessment of human pharmaceuticals. Environmental Science & Technology, 49, 5336–5345 (2015). doi:10.1021/acs.est.5b00302
  6. Industry Roadmap for Progress on Combating Antimicrobial Resistance: Industry Roadmap for Progress on Combating Antimicrobial Resistance – September 2016. (2016). http://www.ifpma.org/wp-content/uploads/2016/09/Roadmap-for-Progress-on-AMR-FINAL.pdf
  7. 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
  8. aus der Beek T, Weber FA, Bergmann A, Hickmann S, Ebert I, Hein A, Küster A: Pharmaceuticals in the environment–Global occurrences and perspectives. Environmental Toxicology and Chemistry, 35, 823–835 (2016). doi:10.1002/etc.3339
  9. 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
  10. Bengtsson-Palme J, Larsson DGJ: Time to limit antibiotic pollution. The Medicine Maker, 0416, 302, 17–18 (2016). [Paper link]

Myself, Joakim Larsson and Erik Kristiansson have written a review on the environmental factors that influence development and spread of antibiotic resistance, which was published today in FEMS Microbiology Reviews. The review (1) builds on thoughts developed in the latter parts of my PhD thesis (2), and seeks to provide a synthesis knowledge gained from different subfields towards the current understanding of evolutionary and ecological processes leading to clinical appearance of resistance genes, as well as the important environmental dispersal barriers preventing spread of resistant pathogens.

We postulate that emergence of novel resistance factors and mobilization of resistance genes are likely to occur continuously in the environment. However, the great majority of such genetic events are unlikely to lead to establishment of novel resistance factors in bacterial populations, unless there is a selection pressure for maintaining them or their fitness costs are negligible. To enable measures to prevent resistance development in the environment, it is therefore critical to investigate under what conditions and to what extent environmental selection for resistance takes place. Selection for resistance is likely less important for the dissemination of resistant bacteria, but will ultimately depend on how well the species or strain in question thrives in the external environment. Metacommunity theory (3,4) suggests that dispersal ability is central to this process, and therefore opportunistic pathogens with their main habitat in the environment may play an important role in the exchange of resistance factors between humans and the environment. Understanding the dispersal barriers hindering this exchange is not only key to evaluate risks, but also to prevent resistant pathogens, as well as novel resistance genes, from reaching humans.

Towards the end of the paper, we suggest certain environments that seem to be more important from a risk management perspective. We also discuss additional problems linked to the development of antibiotic resistance, such as increased evolvability of bacterial genomes (5) and which other types of genes that may be mobilized in the future, should the development continue (1,6). In this review, we also further develop thoughts on the relative risks of re-recruiting and spreading well-known resistance factors already circulating in pathogens, versus recruitment of completely novel resistance genes from environmental bacteria (7). While the latter case is likely to be very rare, and thus almost impossible to quantify the risks for, the consequences of such (potentially one-time) events can be dire.

I personally think that this is one of the best though-through pieces I have ever written, and since it is open access and (in my biased opinion) written in a fairly accessible way, I recommend everyone to read it. It builds on the ecological theories for resistance ecology developed by, among others, Fernando Baquero and José Martinez (8-13). Over the last year, it has been stressed several times at meetings (e.g. at the EDAR conferences in August) that there is a need to develop an ecological framework for antibiotic resistance genes. I think this paper could be one of the foundational pillars on such an endeavor and look forward to see how it will fit into the growing literature on the subject!

References

  1. Bengtsson-Palme J, Kristiansson E, Larsson DGJ: Environmental factors influencing the development and spread of antibiotic resistance. FEMS Microbiology Reviews, accepted manuscript (2017). doi: 10.1093/femsre/fux053
  2. Bengtsson-Palme J: Antibiotic resistance in the environment: a contribution from metagenomic studies. Doctoral thesis (medicine), Department of Infectious Diseases, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg, 2016. [Link]
  3. Bengtsson J: Applied (meta)community ecology: diversity and ecosystem services at the intersection of local and regional processes. In: Verhoef HA, Morin PJ (eds.). Community Ecology: Processes, Models, and Applications. Oxford: Oxford University Press, 115–130 (2009).
  4. Leibold M, Norberg J: Biodiversity in metacommunities: Plankton as complex adaptive systems? Limnology and Oceanography, 1278–1289 (2004).
  5. Gillings MR, Stokes HW: Are humans increasing bacterial evolvability? Trends in Ecology and Evolution, 27, 346–352 (2012).
  6. Gillings MR: Evolutionary consequences of antibiotic use for the resistome, mobilome and microbial pangenome. Frontiers in Microbiology, 4, 4 (2013).
  7. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
  8. Baquero F, Alvarez-Ortega C, Martinez JL: Ecology and evolution of antibiotic resistance. Environmental Microbiology Reports, 1, 469–476 (2009).
  9. Baquero F, Tedim AP, Coque TM: Antibiotic resistance shaping multi-level population biology of bacteria. Frontiers in Microbiology, 4, 15 (2013).
  10. Berendonk TU, Manaia CM, Merlin C et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015).
  11. Hiltunen T, Virta M, Laine A-L: Antibiotic resistance in the wild: an eco-evolutionary perspective. Philosophical Transactions of the Royal Society B: Biological Sciences, 372 (2017) doi: 10.1098/rstb.2016.0039.
  12. Martinez JL: Bottlenecks in the transferability of antibiotic resistance from natural ecosystems to human bacterial pathogens. Frontiers in Microbiology, 2, 265 (2011).
  13. Salyers AA, Amábile-Cuevas CF: Why are antibiotic resistance genes so resistant to elimination? Antimicrobial Agents and Chemotherapy, 41, 2321–2325 (1997).

Today, Microbiome put online a paper lead-authored by my colleague Fanny Berglund – one of Erik Kristiansson’s brilliant PhD students – in which we identify 76 novel metallo-ß-lactamases (1). This feat was made possible because of a new computational method designed by Fanny, which uses a hidden Markov model based on known B1 metallo-ß-lactamases. We analyzed over 10,000 bacterial genomes and plasmids and over 5 terabases of metagenomic data and could thereby predict 76 novel genes. These genes clustered into 59 new families of metallo-β-lactamases (given a 70% identity threshold). We also verified the functionality of 21 of these genes experimentally, and found that 18 were able to hydrolyze imipenem when inserted into Escherichia coli. Two of the novel genes contained atypical zinc-binding motifs in their active sites. Finally, we show that the B1 metallo-β-lactamases can be divided into five major groups based on their phylogenetic origin. It seems that nearly all of the previously characterized mobile B1 β-lactamases we identify in this study were likely to have originated from chromosomal genes present in species within the Proteobacteria, particularly Shewanella spp.

This study more than doubles the number of known B1 metallo-β-lactamases. As with the study by Boulund et al. (2) which we published last month on computational discovery of novel fluoroquinolone resistance genes (which used a very similar approach but on a completely different type of genes), this study also supports the hypothesis that environmental bacterial communities act as sources of uncharacterized antibiotic resistance genes (3-7). Fanny have done a fantastic job on this paper, and I highly recommend reading it in its entirety (it’s open access so you have virtually no excuse not to). It can be found here.

References

  1. Berglund F, Marathe NP, Österlund T, Bengtsson-Palme J, Kotsakis S, Flach C-F, Larsson DGJ, Kristiansson E: Identification of 76 novel B1 metallo-β-lactamases through large-scale screening of genomic and metagenomic data. Microbiome, 5, 134 (2017). doi: 10.1186/s40168-017-0353-8
  2. Boulund F, Berglund F, Flach C-F, Bengtsson-Palme J, Marathe NP, Larsson DGJ, Kristiansson E: Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics, 18, 682 (2017). doi: 10.1186/s12864-017-4064-0
  3. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
  4. Allen HK, Donato J, Wang HH et al.: Call of the wild: antibiotic resistance genes in natural environments. Nature Reviews Microbiology, 8, 251–259 (2010).
  5. Berendonk TU, Manaia CM, Merlin C et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015).
  6. Martinez JL: Bottlenecks in the transferability of antibiotic resistance from natural ecosystems to human bacterial pathogens. Frontiers in Microbiology, 2, 265 (2011).
  7. Finley RL, Collignon P, Larsson DGJ et al.: The scourge of antibiotic resistance: the important role of the environment. Clinical Infectious Diseases, 57, 704–710 (2013).

BMC Genomics today published a paper first-authored by my long-time colleague Fredrik Boulund, which describes a computational screen of genomes and metagenomes for novel qnr fluoroquinolone resistance genes (1). The study makes use of Fredrik’s well-designed and updated qnr-prediction pipeline, but in contrast to his previous publication based on the pipeline from 2012 (2), we here study a 20-fold larger dataset of almost 13 terabases of sequence data. Based on this data, the pipeline predicted 611 putative qnr genes, including all previously described plasmid-mediated qnr gene families. 20 of the predicted genes were previously undescribed, and of these nine were selected for experimental validation. Six of those tested genes improved the survivability under ciprofloxacin exposure when expressed in Escherichia coli. The study shows that qnr genes are almost ubiquitous in environmental microbial communities. This study also lends further credibility to the hypothesis that environmental bacterial communities can act as sources of previously uncharacterized antibiotic resistance genes (3-7). The study can be read in its entirety here.

References

  1. Boulund F, Berglund F, Flach C-F, Bengtsson-Palme J, Marathe NP, Larsson DGJ, Kristiansson E: Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets. BMC Genomics, 18, 682 (2017). doi: 10.1186/s12864-017-4064-0
  2. Boulund F, Johnning A, Pereira MB, Larsson DGJ, Kristiansson E: A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences. BMC Genomics, 13, 695 (2012). doi: 10.1186/1471-2164-13-695
  3. Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
  4. Allen HK, Donato J, Wang HH et al.: Call of the wild: antibiotic resistance genes in natural environments. Nature Reviews Microbiology, 8, 251–259 (2010).
  5. Berendonk TU, Manaia CM, Merlin C et al.: Tackling antibiotic resistance: the environmental framework. Nature Reviews Microbiology, 13, 310–317 (2015).
  6. Martinez JL: Bottlenecks in the transferability of antibiotic resistance from natural ecosystems to human bacterial pathogens. Frontiers in Microbiology, 2, 265 (2011).
  7. Finley RL, Collignon P, Larsson DGJ et al.: The scourge of antibiotic resistance: the important role of the environment. Clinical Infectious Diseases, 57, 704–710 (2013).

Today, I started my new position at the University of Gothenburg as a non-tenured assistant professor (forskarassistent)*. In essence, this means that I have a position funded by my own grant until the end of 2020, although I will be on a leave-of-absence while doing my PostDoc with Jo Handelsman in Wisconsin. Speaking of which, I will be leaving to the US on Thursday next week for a month of setting things up at her lab (and also going to the EDAR4 conference in Lansing). I will return to Sweden in mid-September and leave for the US for real early next year.

In terms of actual work, this change of position will not mean very much at the moment. I will continue to do the same things for some time, and I will remain closely associated with Joakim Larsson’s lab at the Dept. of Infectious Diseases. And luckily, I will retain my lovely roommates for at least the time being. In the long run, however, this means that I will shift my research focus slightly, away from antibiotic resistance risk management towards interactions in microbial communities (still related to antibiotics though). Exciting times ahead!

Note
* For some reason, the university administration refuses to call this position assistant professor in English at this time, instead referring to the position as “Postdoctoral research fellow”. I guess that it might be bloody annoying explaining that this is not the same as “postdoctoral researcher” and virtually everywhere else would be called “(non-tenured) assistant professor”, but then on the other hand, who cares about titles anyway?

Today, a review paper which I wrote together with Joakim Larsson and Erik Kristiansson was published in Journal of Antimicrobial Chemotherapy (1). We have for a long time used metagenomic DNA sequencing to study antibiotic resistance in different environments (2-6), including in the human microbiota (7). Generally, our ultimate purpose has been to assess the risks to human health associated with resistance genes in the environment. However, a multitude of methods exist for metagenomic data analysis, and over the years we have learned that not all methods are suitable for the investigation of resistance genes for this purpose. In our review paper, we describe and discuss current methods for sequence handling, mapping to databases of resistance genes, statistical analysis and metagenomic assembly. We also provide an overview of important considerations related to the analysis of resistance genes, and end by recommending some of the currently used tools, databases and methods that are best equipped to inform research and clinical practice related to antibiotic resistance (see the figure from the paper below). We hope that the paper will be useful to researchers and clinicians interested in using metagenomic sequencing to better understand the resistance genes present in environmental and human-associated microbial communities.

References

  1. Bengtsson-Palme J, Larsson DGJ, Kristiansson E: Using metagenomics to investigate human and environmental resistomes. Journal of Antimicrobial Chemotherapy, advance access (2017). doi: 10.1093/jac/dkx199 [Paper link]
  2. Bengtsson-Palme J, Boulund F, Fick J, Kristiansson E, Larsson DGJ: Shotgun metagenomics reveals a wide array of antibiotic resistance genes and mobile elements in a polluted lake in India. Frontiers in Microbiology, 5, 648 (2014). doi: 10.3389/fmicb.2014.00648 [Paper link]
  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. Bengtsson-Palme J, Hammarén R, Pal C, Östman M, Björlenius B, Flach C-F, Kristiansson E, Fick J, Tysklind M, Larsson DGJ: Elucidating selection processes for antibiotic resistance in sewage treatment plants using metagenomics. Science of the Total Environment, 572, 697–712 (2016). doi: 10.1016/j.scitotenv.2016.06.228 [Paper link]
  5. Pal C, Bengtsson-Palme J, Kristiansson E, Larsson DGJ: The structure and diversity of human, animal and environmental resistomes. Microbiome, 4, 54 (2016). doi: 10.1186/s40168-016-0199-5 [Paper link]
  6. Flach C-F, Pal C, Svensson CJ, Kristiansson E, Östman M, Bengtsson-Palme J, Tysklind M, Larsson DGJ: Does antifouling paint select for antibiotic resistance? Science of the Total Environment, 590–591, 461–468 (2017). doi: 10.1016/j.scitotenv.2017.01.213 [Paper link]
  7. 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]