Category: Thoughts

Erik Kristiansson, who was co-supervisor for my PhD thesis, has an opening for a PhD student funded by the DDLS program. The project is combining bioinformatics and artificial intelligence with a focus on large-scale data analysis to better understand antibiotic resistance and the emergence of novel resistance genes. The research will be centered on DNA sequence analysis, inference in biological networks, and modelling of evolution. The primary applications will be related to antibiotic resistance and bacterial genomics.

I am particularly excited about this position because I will have the benefit of co-supervising the student. The student will also be part of the DDLS research school which is now being launched, which is also super-exciting for Swedish data driven life science.

The candidate is expected to have a degree in bioinformatics, mathematical statistics, mathematics, computer science, physics, molecular biology, or any equivalent topic. Previous experience in analysis of large-scale biological data is desirable. It is important to have good computing and programming skills (e.g. in Python and R), experience with the Linux/UNIX computer environment, and, to the extent possible, previous experience in working with machine learning and/or artificial intelligence.

I had such a good time with Erik as my co-supervisor, and he has put together a truly amazing supervision team with Joakim Larsson, Anna Johnning and myself. I could not imagine a better place to apply bioinformatics and ML/AI on antibiotic resistance! Deadline is June 7! Application link here: https://www.chalmers.se/om-chalmers/arbeta-hos-oss/lediga-tjanster/?rmpage=job&rmjob=12840&rmlang=SE

I have had the fortune to be involved in a study on the quality of reference material for mosquito barcoding for biodiversity studies. The study, which was led by Maurício Moraes Zenker at the Universidade Federal de São Carlos in Brazil, looked at the availability of public data for mosquitoes in online databases for two widely used DNA barcoding markers in Culicidae: the COI and ITS2 regions (1). Last week, this study was published in Scientific Reports.

The paper shows that around 30% of known species were covered for the COI gene in BOLD  and  GenBank, and 12% of species for ITS2 in GenBank. The Afrotropical, Australian and Oriental biogeographic regions had the lowest coverages, while the Nearctic, Palearctic and Oceanian regions had the highest. Countries with a higher diversity of mosquitoes tended to have lower coverage, which was surprisingly also the case for countries with higher numbers of medically important species. At the same time, countries with a higher number of endemic species tended to have a higher species coverage in the databases.

With this study, we would like to advocate for better curatorship of voucher specimens representing sequences in the databases. Also, an integrative taxonomic approach that combines various genetic markers with morphological analyses is important to allow a better use of DNA barcoding and metabarcoding in a diverse array of applications, including vector species detection and biodiversity monitoring.

Importantly, this work underscores how reliant DNA barcoding is on proper taxonomic foundations, including morphological characterisations. Molecular identification of species cannot happen in a vacuum! I would like to extend a big thanks for Maurício who invited me to take part in this study and who have done an excellent job putting it all together!

Reference

• Moraes Zenker M, Pineda Portella T, Costa Pessoa FA, Bengtsson-Palme J, Galetti PM: Low coverage of species constrains the use of DNA barcoding to assess mosquito biodiversity. Scientific Reports, 14, 7432 (2024). doi: 10.1038/s41598-024-58071-1 [Paper link]

I am very happy to welcome Agata Marchi back to the group as a PhD student! Agata was a master student in the group last year, doing a thesis focused on implementing a bioinformatic approach to identify differences between the genomes of host-associated and non host-associated strains of Pseudomonas aeruginosa. While one of her first tasks will be to complete this work and prepare it for publication, her doctoral studies will primarily be on the interactions between bacteria and between bacteria and host in the human microbiome and how these relate to complex diseases. She will focus on developing and applying machine learning methods to better understand this interplay.

I am – as the rest of the group – very happy to welcome Agata back to the lab!

My PhD supervisor Joakim Larsson has an opening for a PhD student at University of Gothenburg. The project is on the role of different wastewaters in the evolution of antibiotic resistance, and will be centered on bioinformatic analyses of large-scale data. The project will encompass analysis of bacterial growth curve data through machine learning to antibiotics with selective effects in different wastewaters. Comparative genomics and different AI-based approaches will be applied to large-scale public genome and metagenome data to better understand how resistance genes are mobilized and transferred to pathogens.

Joakim is a great scientist with a vibrant group, so if your interests is in line with the position, I strongly suggest you take a look at it! Deadline is October 30! Application link here: https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89bead79bb7258ad55c8d75228e5b7&job_id=30401

My colleague and friend Clemens Wittenbecher has an open doctoral student position at Chalmers in Data-Driven Precision Health Research. Clemens works with developing novel biomarker panels to quantify individual disease risk. The project itself will focus on innovative machine learning and artificial intelligence approaches to integrate multi-layer -omics data with bioimages of cardiovascular and metabolic tissues (computer tomography, ultrasound and magnetic resonance imaging).

Clemens is a fantastic person and a great supervisor so if your interests is in line with the position, I strongly suggest you take a look at it! Application link here:

https://www.chalmers.se/en/about-chalmers/work-with-us/vacancies/?rmpage=job&rmjob=11810&rmlang=UK

Time to do a rundown of conferences and meetings I will attend this fall. Double-check with your calendars and please reach out if you’re also going, so we can meet up!

September 21-24: Nordic Society of Clinical Microbiology and Infectious Diseases (NSCMID), in Örebro, Sweden. I will give a talk about the EMBARK work in the Saturday session on Metagenomics in infection, inflammatory disease and the environment

October 5-6: Conference on ‘Optimal practices to protect human health care from antimicrobial resistance selected in the veterinary domain’ organised by The Netherlands Food and Consumer Product Safety Authority (NVWA) in Amsterdam, the Netherlands. I will chair a session on October 6 on Next generation sequencing for bioinformatic based surveillance.

October 18-22: 32º Congresso Brasileiro de Microbiologia, in Foz do Iguaçu, Brazil. I will give a talk in the Saturday session (the 21st) on the use of model systems all the way to global surveillance systems to prevent future pandemics.

November 15-16: DDLS Annual Meeting, in Stockholm Sweden. I am in the organising committee for this event with the theme “The emerging role of AI in data-driven life science”.

November 17: DDLS Cell and Molecular Biology Minisymposium.

November 29: GOTBIN Annual Workshop, in Gothenburg Sweden.

This will be a fun (but intense!) fall!

I just want to point potential doctoral students’ attention to this fantastic opportunity to work with my EMBARK colleague Luis Pedro Coelho as he sets up his new lab in Brisbane in Australia at the relatively new Centre for Microbiome Research. Luis is looking for two PhD students, one who will focus on identifying and characterising the small proteins of the global microbiome and one more related to developing novel bioinformatic methods for studying microbial communities.

I can highly recommend this opportunity given that you are willing to move to Australia, as Luis is one of the most brilliant scientists I have worked with, is incredibly easy-going and fosters a lab culture I strong support. More information and application here.

On the 5th and 6th of October this year, I will be taking part in a relatively small, but very interesting conference on veterinary and environmental AMR, held in Amsterdam. The theme of the event will be “Optimal practices to protect human health care from antimicrobial resistance selected in the veterinary domain”.

The aim of the conference is to discuss innovative additional measures to prevent development of resistance in animals. In addition, the participants will explore possibilities to prevent transfer to human health care after selection has taken place. The conference program will consist of both lectures and break-out sessions and is intended for researchers as well as for policy makers involved in the battle against antimicrobial resistance. The results of the break-out sessions are to be published in an open access journal, and I will be charing one of these break-out sessions. Please see the conference web site for the entire program: https://www.nvwa.nl/amrconference

There is an upper cap of 120 participants for the event, so if you’re interested make sure to register soon! The conference will be held on October 5 and 6 2023 in the Park Inn by Radisson in Amsterdam, The Netherlands. The hotel is easy to reach by a 12 minutes train ride from Schiphol airport and a 5 minutes train ride from the city center. 

I look forward to seeing you in Amsterdam!

I am happy to share with you that since a couple of months back there is an up-to-date version of ITSx available through Galaxy! The tool can be found here: https://usegalaxy.eu/root?tool_id=itsx

The person behind this is really Björn Grüning at the University of Freiburg. I am immensely thankful for the work he has put into this. Our intention to make sure that both the Galaxy version and the bioconda version are maintained in parallel to the one on this website, and continuously up to date!

Happy barcoding!

In just a few days, Environment International has published two papers coming out from the EMBARK consortium which are somewhat connected to each other.

The first (or technically the second, but the other order makes more sense when explaining this…) is the first paper involving most of the people who have been working in the EMBARK consortium for an extended period of time. It’s an overview paper titled “Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement Itit, and what are the data needs?” (1) and I think the title describes the topic pretty well. Basically, we go through why it would be interesting to monitor for antibiotic resistance in the environments, how that could be implemented and what we would need to know to get there.

The very condensed story is that if one is considering implementing monitoring for environmental resistance, these are a few things that should be considered:

• The purpose of monitoring: What is the motivation? What should be achieved? What type of risk should be assessed? What type of action would monitoring enable?
• Choice of methods: Which methods are economically feasible? Which methods would deliver results within a useful timeframe for taking appropriate actions?
• Targeted environments: In what type of environment would monitoring for a given purpose be worthwhile?
• Intended users: Who would be able to use, implement and act upon this strategy?
• Integration potential: How does this monitoring integrate with other monitoring efforts? How can the resulting data be communicated?

We then dive into the knowledge gaps we are currently facing, and particularly highlight the following areas:

• Establish how different existing methods for monitoring resistance compare to each other
• Extend pathogen-centric databases for resistance genes with latent resistance genes (2)
• Determine the locations and type of environments relevant for resistance monitoring
  To reduce costs, utilizing already existing environmental monitoring should be prioritized, as should locations integrated into operating or planned surveillance programs. More efforts should also be made to identify additional pathways for resistance transmission through the environment.
• Study the environment as a source and transmission route for antibiotic resistance
  Stratify risks associated with resistance genes found in the environment. Define typical levels of antibiotic resistance in different environments (3), and define how these levels change over time.
• Identify settings where the relationship between fecal bacteria and antibiotic resistance is absent
  Usually, these levels follow each other, but the environments where they don’t are important as they deviate from the expected baseline of resistance. This knowledge can aid in identifying situations in which it would be helpful to investigate a microbial community for resistance to specific antibiotics.
• Identify the origins for more antibiotic resistance genes (4)
  This knowledge will be instrumental in preventing the emergence of new forms of resistance in pathogens in the future.

An important outcome of this paper is that we realise that we are still not at a level of understanding where routine monitoring for resistance in the environment can be easily justified or implemented. Still, there is a need for monitoring data in natural environments to even get started, and therefore we support the implementation of national, regional and global of initiatives without having all the scientific answers. The lack of comprehensive understanding should not be an obstacle to starting environmental monitoring for AMR, nor for action against environmental development and spread of AMR.

The second paper is very much related to the first, in that it actually tries to address one of these knowledge gaps: the need for normal background levels of antibiotic resistance in different environments. In this paper, Anna Abramova did an herculean effort collecting (we hope) all qPCR data on antibiotic resistance gene abundances in the environment for the past two decades. All in all, she collected data for more than 1500 samples across 150 studies and integrated these into an analys of what we could consider normal levels of resistance in different environments.

For an ‘average’ resistance gene, we found that the normal relative abundance range was form 10^-5 to 10^-3 copies per bacterial 16S rRNA, or that around one in 1,000 bacteria would carry a given resistance gene. This level varied quite a bit between different resistance genes, however, but not so much between environmental types (except for in human and animal feces, where some resistance genes were clearly more abundant, most prominently tetracycline resistance genes). What was more striking was that there was a clear difference between environments impacted or likely impacted by human activities, as opposed to more pristine environments with little to none human impact. Some resistance genes, such as tetA, tetG, blaTEM and blaCTX-M, showed very marked differences between these impacted and non-impacted environments, making them great markers of human-activity-associated resistance.

Our final recommendations with regards to monitoring include:

• Include the intI1, sul1, blaTEM, blaCTX-M and qnrS genes in environmental monitoring, along with a selection of tetracycline resistance genes, including either tetA or tetG.
• Other potential target genes could be sul3, vanA, tetH, aadA2, floR, ereA and mexF, which are abundant in some environments, but are not often included in qPCR studies of environmental AMR
• If a gene deviates from the expected 10^-5 to 10^-3 interval, this warrants further investigation of the causes.
• Maximum acceptable levels need to be determined not only taking relative abundances of genes into account, but also risks to human health as well as the numbers of bacteria in a given volume of sample into account (5,6) and transmission routes to humans (7)
• The different standards of reporting DNA abundances constituted a complicating factor for this study. Both abundances of resistance genes relative to the 16S rRNA gene and to the sample volume or weight should be reported.
• The absence of clear trends of increases or decreases in resistance gene abundances over time indicates a need for more systematic time series data in a variety of environments.

Our results also highlighted the scarcity of resistance gene data from parts of the world, particularly from Africa and South America, and underscores the need for a concerted effort to quantify typical background levels of resistance in the environment more broadly to enable efficient environmental surveillance schemes akin to those that exist in clinical and veterinary settings.

I encourage anyone with an interesting these topics to at least skim the full papers [Monitoring overview paper here, Normal qPCR resistance abundances here]. These will be great resources and I am very proud of them both. I would really like to thank the entire EMBARK team and our collaborators in CORNELIA, WastPAN and in other organisations. I would also like to thank Anna for her hard work on collecting and analysing the qPCR data for around two years. It has been a long ride, and I think we are both happy, proud and a bit relieved to finally see this paper published!

References

1. Bengtsson-Palme J, Abramova A, Berendonk TU, Coelho LP, Forslund SK, Gschwind R, Heikinheimo A, Jarquin-Diaz VH, Khan AA, Klümper U, Löber U, Nekoro M, Osińska AD, Ugarcina Perovic S, Pitkänen T, Rødland EK, Ruppé E, Wasteson Y, Wester AL, Zahra R: Towards monitoring of antimicrobial resistance in the environment: For what reasons, how to implement it, and what are the data needs? Environment International, 108089 (2023). doi: 10.1016/j.envint.2023.108089
2. Inda-Díaz JS, Lund D, Parras-Moltó M, Johnning A, Bengtsson-Palme J, Kristiansson E: Latent antibiotic resistance genes are abundant, diverse, and mobile in human, animal, and environmental microbiomes. Microbiome, 11, 44 (2023). doi: 10.1186/s40168-023-01479-0
3. Abramova A, Berendonk TU, Bengtsson-Palme J: A global baseline for qPCR-determined antimicrobial resistance gene prevalence across environments. Environment International, 178, 108084 (2023). doi: 10.1016/j.envint.2023.108084
4. Ebmeyer S, Kristiansson E, Larsson DGJ: A framework for identifying the recent origins of mobile antibiotic resistance genes. Communications Biology, 4 (2021). doi:10.1038/s42003-020-01545-5
5. 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
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, 878–885 (2013). doi:10.1289/ehp.1206446
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