Tag: Antibiotic resistance

We have been awarded with the first best article award from FEMS Microbiology Reviews for our 2018 review Environmental factors influencing the development and spread of antibiotic resistance. I and my co-authors Joakim Larsson and Erik Kristiansson are honoured and – of course – very happy with this recognition of our work. I was interviewed in relation to the prize, an interview that can be read here. But, also, the paper is open access, so you can go and check it all out in its full glory right now!

We are hiring a postdoc to work with environmental monitoring of antimicrobial resistance. The project is part of the EMBARK program and will consider different aspects of establishing a baseline for background antibiotic resistance in the environment, standardization of monitoring protocols and development of methods to detect emerging resistance threats. The project will involve work with environmental sampling, DNA extractions, bacterial culturing and generation of large-scale DNA sequence data. In terms of bioinformatic analyses, the project will encompass analysis of next-generation sequence data, genome-resolved metagenomics, short-read assembly and network analysis.

We look for a skilled bioinformatician, preferably with experience of experimental laboratory work. If you feel that you are the right person for this position, you can apply here. More information is also available here. We look forward to your application! The deadline for applications is January 3.

I am very happy to announce today (on the European Antibiotic Awareness Day), that the EMBARK project that I am coordinator for got funded by JPIAMR with almost 1.4 million Euros over three years!

The primary goal of EMBARK is to establish a baseline for how common resistance is in the environment and what resistance types that can be expected where. That background data will then underpin efforts to standardize different methods for resistance surveillance and identify high-priority targets that should be used for efficient monitoring. In addition, EMBARK will develop and evaluate methods to detect new resistance factors and thereby provide an early-warning system for emerging resistance threats.

EMBARK is an international collaboration funded by JPIAMR. The consortium consists of myself, Thomas Berendonk (TU-Dresden, Germany), Luis Pedro Coelho (Fudan University, China), Sofia Forslund (ECRC Max-Delbrück-Centrum für Molekulare Medizin, Germany), Etienne Ruppé (INSERM, France) and Rabaab Zahra (Quaid-i-Azam University, Pakistan).

EMBARK has a website where the protocols and data generated during the project will be released. Follow our progress towards better monitoring of antimicrobial resistance in the environment here and on the EMBARK Twitter account: @EMBARK_JPIAMR!

I have been slow at picking this ball up, but the book chapter that I coauthored with Stefanie Hess is now available online (and has been for almost a month). It is part of the book Antibiotic Drug Resistance, edited by José-Luis Capelo-Martínez and Gilberto Igrejas and was available in print on September 9th.

Our chapter deals with sources of resistant bacteria to the environment, and in particular the roles of sewage, wastewater and agriculture in resistance dissemination. Furthermore, the chapter discusses de novo selection of resistance and defines relevant risk scenarios. Finally, we outline the different management options available and discuss their feasibility.

The chapter boils down to that the available strategies for limiting antibiotic resistance dissemination and selection in the environment are overall quite clear. Larger problems that remain to be solved are how to prioritize between different strategies, which technologies that would provide the largest benefits and to achieve the political willingness to pursue these strategies. We note that several of the most efficient resistance prevention options involve high costs, investments in technology and infrastructure in other countries or proposals that are likely to be rather unpopular with the general public. For example, investing in sewage treatment and water infrastructure in low-income countries would likely be among the most effective means to reduce releases of resistant bacteria into the environment and reduced meat consumption would contribute to lower the use of antibiotics in animal husbandry, but neither is a very popular proposal for tax payers in high-income countries.

I have not yet read the entire book myself, but the table of content shows a very wide-reaching and comprehensive picture of the antibiotic resistance field, with a range of prominent authors. The editors have made a good job collecting this many interesting book chapters in the same volume!

Reference

Bengtsson-Palme J, Heß S: Strategies to reduce or eliminate resistant pathogens in the environment. In: Capelo Martinez JL, Igrejas G (Eds.) Antibiotic Drug Resistance, 637–673. Wiley, NJ, USA (2020). doi: 10.1002/9781119282549.ch24[Link]

I am happy to share the news that the paper describing out software tool Mumame is now out in its final form! (1) The paper got published today in the journal Metabarcoding and Metagenomics after being available as a preprint (2) since last autumn. This version has not changed a whole lot since the preprint, but it is more polished and better argued (thanks to a great review process). The software is virtually the same, but is not also available via Conda.

In the paper, we describe the Mumame software, which can be used to distinguish between wildtype and mutated sequences in shotgun metagenomic sequencing data and quantify their relative abundances. We further demonstrate the utility of the tool by quantifying antibiotic resistance mutations in several publicly available metagenomic data sets (3-6), and find that the tool is useful but that sequencing depth is a key factor to detect rare mutations. Therefore, much larger numbers of sequences may be required for reliable detection of mutations than is needed for most other applications of shotgun metagenomics. Since the preprint was published, Mumame has also found use in our recently published paper on selection for antibiotic resistance in a Croatian macrolide production wastewater treatment plant, unfortunately with inconclusive results (7). Mumame is freely available here.

I again want to stress the fantastic work that Shruthi Magesh did last year as a summer student at WID in the evaluation of this tool. As I have pointed out earlier, I did write the code for the software (with a lot of input from Viktor Jonsson), but Shruthi did the software testing and evaluations. Thanks and congratulations Shruthi, and good luck in pursuing your PhD program!

References

1. Magesh S, Jonsson V, Bengtsson-Palme J: Mumame: A software tool for quantifying gene-specific point-mutations in shotgun metagenomic data. Metabarcoding and Metagenomics, 3: 59–67 (2019). doi: 10.3897/mbmg.3.36236
2. Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572
3. 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
4. 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
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
6. 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
7. Bengtsson-Palme J, Milakovic M, Švecová H, Ganjto M, Jonsson V, Grabic R, Udiković Kolić N: Pharmaceutical wastewater treatment plant enriches resistance genes and alter the structure of microbial communities. Water Research, 162, 437-445 (2019). doi: 10.1016/j.watres.2019.06.073

This week marked the departure of our summer internship student Alice Zublena, who is now heading back to France to finish her masters program. Alice has been working on establishing effect concentrations for beta-lactam antibiotics for different bacteria, and has generated a very exciting and useful data set for our work in the coming years. I am tremendously happy that I have got to work with Alice this summer and very thankful for having the opportunity to supervise such a talented student. Thanks for your great work this summer Alice and good luck with everything you pursue in the future! 

I celebrate the fourth of July with the coincidental publishing of my most recent paper, in collaboration with the lab of Nikolina Udikovic-Kolic. The study used shotgun metagenomics to investigate the taxonomic structure and resistance gene composition of sludge communities in a treatment plant in Croatia receiving wastewater from production of the macrolide antibiotic azithromycin (1). We compared the levels of antibiotic resistance genes in sludge from this treatment plant and municipal sludge from a sewage treatment plant in Zagreb, and found that the total abundance of resistance genes was three times higher in sludge from the treatment plant receiving wastewater from pharmaceutical production. To our great surprise, this was not true for macrolide resistance genes, however. Instead, those genes had overall slightly lower abundances in the industrial sludge. At the same time, the genes that are associated with mobile genetic elements (such as integrons) had higher abundances in the industrial sludge.

This leads us to think that at high concentrations of antibiotics (such as in the industrial wastewater treatment plant), selection may favor taxonomic shifts towards intrinsically resistant species or strains harboring chromosomal resistance mutations rather than acquisition of mobile resistance genes. Unfortunately, the results regarding resistance mutation – obtained using our recent software tool Mumame (2) – were uninformative due to low number of reads mapping to the resistance regions of the 23S rRNA target gene for azithromycin.

Often, the problem of environmental pollution with pharmaceuticals is perceived as primarily being a concern in countries with poor pollution control, since price pressure has led to outsourcing of global antibiotics production to locations with lax environmental regulation (3). If this was the case, there would be much less incentive for improving legislation regarding emissions from pharmaceutical manufacturing at the EU level, as this would not move the needle in a significant way. However, the results of the paper (and other work by Nikolina’s group (4,5)) underscore the need for regulatory action also within Europe to avoid release of antibiotics into the environment.

References

1. Bengtsson-Palme J, Milakovic M, Švecová H, Ganjto M, Jonsson V, Grabic R, Udiković Kolić N: Pharmaceutical wastewater treatment plant enriches resistance genes and alter the structure of microbial communities. Water Research, accepted manuscript (2019). doi: 10.1016/j.watres.2019.06.073
2. Magesh S, Jonsson V, Bengtsson-Palme J: Quantifying point-mutations in metagenomic data. bioRxiv, 438572 (2018). doi: 10.1101/438572
3. 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
4. Bielen A, Šimatović A, Kosić-Vukšić J, Senta I, Ahel M, Babić S, Jurina T, González-Plaza JJ, Milaković M, Udiković-Kolić N: Negative environmental impacts of antibiotic-contaminated effluents from pharmaceutical industries. Water Research, 126, 79–87 (2017). doi: 10.1016/j.watres.2017.09.019
5. González-Plaza JJ, Šimatović A, Milaković M, Bielen A, Wichmann F, Udikovic-Kolic N: Functional repertoire of antibiotic resistance genes in antibiotic manufacturing effluents and receiving freshwater sediments. Frontiers in Microbiology, 8, 2675 (2017). doi: 10.3389/fmicb.2017.02675

AMR Control just released (some of) the articles of their 2019-20 issue, and among the papers hot of the press is one that I have co-authored with Etienne Ruppé, Yannick Charretier and Jacques Schrenzel on how next-generation sequencing can be used to address antibiotic resistance problems (1).

The paper contains a brief overview of next-generation sequencing platforms and tools, the resources that can be used to detect and quantify resistance from sequencing data, and descriptions of applications in clinical genomics, clinical/human metagenomics as well as in environmental settings (the latter being the part where I contributed the most). Compared to much of the writing on antibiotic resistance and sequencing applications, I think this paper is pretty easily accessible to a general audience.

I first met Etienne on the JRC workshops for how next-generation sequencing could be implemented in the EU’s Coordinated Action Plan against Antimicrobial Resistance (2,3), and it seems quite fitting that we now ended up writing a paper on such implementations together.

1. Ruppé E, Bengtsson-Palme J, Charretier Y, Schrenzel J: How next-generation sequencing can address the antimicrobial resistance challenge. AMR Control, 2019-20, 60-65 (2019). [Paper link]
2. 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 [Link]
3. 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.14509.2 [Paper link]

I am happy to announce that Cancer- och Allergifonden [the Cancer and Allergy Foundation] have awarded me with the first Lennart Sparell prize. The prize was instated in memory of the foundations founder – Lennart Sparell, who passed away last year – and is awarded to researchers (or other persons) who have thought outside-of-the-box or challenged the current paradigms. A particular emphasis is given to research on environmental pollutants that affect human health through food or environmental exposure.

Naturally, I am honored to be the recipient of this prize. The award was motivated by the research I have done on the role of ecological and evolutionary processes in the external environment in driving antibiotic resistance development, and how that can have consequences for human health. Particularly, I am happy that the research that I, Joakim Larsson, Erik Kristiansson and a few others on the role of environmental processes in the development of antibiotic resistance and the recruitment of novel resistance genes are given attention. This view, which perhaps do not challenge the paradigm but at the very least points to an alternative risk scenario, has often been neglected when environmental antibiotic resistance has been discussed.

The prize will be awarded on a ceremony on June 10 in Stockholm, but I would already now take the opportunity to thank everyone who has been involved in the research being recognized, particularly Joakim Larsson and Erik Kristiansson – this award is to a very very large extent to your merit.

Since F1000Research uses a somewhat different publication scheme than most journals, I still haven’t understood if this paper is formally published after peer review, but I start to assume it is. There have been very little changes since the last version, so hence I will be lazy and basically repost what I wrote in April when the first version (the “preprint”) was posted online. The paper (1) is the result of a workshop arranged by the JRC in Italy in 2017. It describes various challenges arising from the process of designing a benchmark strategy for bioinformatics pipelines 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 (2). The paper also somewhat connects to the database curation paper we published in 2016 (3), 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 2017 in Ispra, Italy. You can find the paper here (it’s open access).

On another note, the new paper describing the UNITE database (4) has now got a formal issue assigned to it, as has the paper on tandem repeat barcoding in fungi published in Molecular Ecology Resources last year (5).

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. 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
3. 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
4. Nilsson RH, Larsson K-H, Taylor AFS, Bengtsson-Palme J, Jeppesen TS, Schigel D, Kennedy P, Picard K, Glöckner FO, Tedersoo L, Saar I, Kõljalg U, Abarenkov K: The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Research, 47, D1, D259–D264 (2019). doi: 10.1093/nar/gky1022
5. Wurzbacher C, Larsson E, Bengtsson-Palme J, Van den Wyngaert S, Svantesson S, Kristiansson E, Kagami M, Nilsson RH: Introducing ribosomal tandem repeat barcoding for fungi. Molecular Ecology Resources, 19, 1, 118–127 (2019). doi: 10.1111/1755-0998.12944