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:
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.
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!
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!
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Together with our collaborators in Tromsø in Norway, we published a paper over the weekend in eBioMedicine describing the early colonization patterns of preterm infants, both in terms of the microbes that arrive early to the infants, but also in terms of the antibiotic resistance genes they carry.
In the paper (1), which is a continuation of an earlier study by part of the team (2), we analysed metagenomic data from six Norwegian neonatal intensive care units to better understand the bacterial microbiota of infants born preterm or on term and receiving different treatments. These groups included probiotic-supplemented and antibiotic-exposed extremely preterm infants (n = 29), antibiotic-exposed very preterm infants (n = 25), antibiotic-unexposed very preterm infants (n = 8), and antibiotic-unexposed full-term infants (n = 10). Stool samples were collected from the infants after 7, 28, 120, and 365 days of life and were analysed using shotgun metagenomics. We were particularly interested in the maturation of the preterm infant microbiome into a ‘normal’ healthy gut microbiome, and the colonization with bacteria carrying antibiotic resistance genes.
We found that microbiota maturation was largely determined by the length of hospitalisation for the infants and how much preterm they were. The use of probiotics rendered the gut microbiota and resistome of extremely preterm infants more alike to term infants on day 7 and partially restored the loss of species interconnectivity and stability associated with preterm delivery. Finally, colonisation with Escherichia coli was associated with the highest number of antibiotic-resistance genes in the infant microbiomes, followed by Klebsiella pneumoniae and Klebsiella aerogenes.
Being born very preterm, along with prolonged hospitalisation and frequent antibiotic use alters early life resistome and mobilome, leading to an increased gut carriage of antibiotic resistance genes and mobile genetic elements. On the other hand, the effect of probiotics was not unidirectional. Probiotics decreased resistome burden, but at the same time the bacterial strains in the probiotics appear to promote the activity of mobile genetic elements. Here, further study of the gut microbiota is necessary to be able to design strategies aiming to lower disease risk in vulnerable preterm infants.
As mentioned, this study was a collaboration with Veronika Pettersen‘s group in Tromsø, particularly Ahmed Bargheet, who have done a fabulous job on the bioinformatics and analysis of this study. I hope that we will continue this collaboration in the future (first step will be me visting Tromsø again in June!) This also continues a nice little “sidetrack” of the group’s research into the early life microbiome – previously represented by the work of Katariina Pärnänen (3) and Tove Wikström‘s vaginal microbiome study (4), which is a very interesting and relevant subject in terms of both medicine and microbial ecology. We are also setting up new collaborations in this area, so I hope that more will come out of this track in the next couple of years.
Finally, thank you Veronika for inviting me to participate in this great project!
- Bargheet A, Klingenberg C, Esaiassen E, Hjerde E, Cavanagh JP, Bengtsson-Palme J, Pettersen VK: Development of early life gut resistome and mobilome across gestational ages and microbiota-modifying treatments. eBio Medicine, 92, 104613 (2023). doi: 10.1016/j.ebiom.2023.104613
- Esaiassen E, Hjerde E, Cavanagh JP, Pedersen T, Andresen JH, Rettedal SI, Støen R, Nakstad B, Willassen NP, Klingenberg C: Effects of Probiotic Supplementation on the Gut Microbiota and Antibiotic Resistome Development in Preterm Infants. Frontiers in Pediatrics, 16, 6, 347 (2018). doi: 10.3389/fped.2018.00347
- Pärnänen K, Karkman A, Hultman J, Lyra C, Bengtsson-Palme J, Larsson DGJ, Rautava S, Isolauri E, Salminen S, Kumar H, Satokari R, Virta M: Maternal gut and breast milk microbiota affect infant gut antibiotic resistome and mobile genetic elements. Nature Communications, 9, 3891 (2018). doi: 10.1038/s41467-018-06393-w
- Wikström T, Abrahamsson S, Bengtsson-Palme J, Ek CJ, Kuusela P, Rekabdar E, Lindgren P, Wennerholm UB, Jacobsson B, Valentin L, Hagberg H: Microbial and human transcriptome in vaginal fluid at midgestation: association with spontaneous preterm delivery. Clinical and Translational Medicine, 12, 9, e1023 (2022). doi: 10.1002/ctm2.1023
What is the latent resistome? This is a term we coin in a new paper published yesterday in Microbiome. In the paper, we distinguish between the small number antibiotic resistance genes (ARGs) that are established, well-characterized, and available in existing resistance gene databases (what we refer to as “established ARGs”). These are typically ARGs encountered in clinical pathogens and are often already causing problems in human and animal infections. The remaining latently present ARGs, which we denote “latent ARGs”, are less or not at all studied, and are therefore much harder to detect (1). These latent ARGs are typically unknown and generally overlooked in most studies of resistance. They are also seldom accounted for in risk assessments of antibiotic resistance (2-4). This means that our view of the resistome and its diversity is incomplete, which hampers our ability to assess risk for promotion and spread of yet undiscovered resistance determinants.
In our new study, we try to alleviate this issue by analyzing more than 10,000 metagenomic samples. We show that the latent ARGs are more abundant and diverse than established ARGs in all studied environments, including the human- and animal-associated microbiomes. The total pan-resistomes, i.e., all ARGs present in an environment (including the latent ARGs), are heavily dominated by these latent ARGs. In contrast, the core resistome (the ARGs that are commonly encountered) comprise both latent and established ARGs.
In the study, we identified several latent ARGs that were shared between environments or that are already present in human pathogens. These are often located on mobile genetic elements that can be transferred between bacteria. Finally, we also show that wastewater microbiomes have surprisingly large pan- and core-resistomes, which makes this environment a potent high-risk environment for mobilization and promotion of latent ARGs, which may make it into pathogens in the future.
It is also interesting to note that this new study echoes the results of my own study from 2018, showing that soil and water environments contain a high diversity of latent ARGs (or ARGs not found in pathogens as I put it in the 2018 study), despite being almost devoid of established ARGs (5).
This project has been a collaboration with Erik Kristiansson’s research group, and particularly with Juan Inda-Diáz. It has been great fun to work with them and I hope that we will keep this collaboration going into the future! The study can be read in its entirety here.
- 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 [Paper link]
- 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
- Bengtsson-Palme J, Larsson DGJ: Antibiotic resistance genes in the environment: prioritizing risks. Nature Reviews Microbiology, 13, 369 (2015). doi: 10.1038/nrmicro3399-c1
- Bengtsson-Palme J: Assessment and management of risks associated with antibiotic resistance in the environment. In: Roig B, Weiss K, Thoreau V (Eds.) Management of Emerging Public Health Issues and Risks: Multidisciplinary Approaches to the Changing Environment, 243–263. Elsevier, UK (2019). doi: 10.1016/B978-0-12-813290-6.00010-X
- Bengtsson-Palme J: The diversity of uncharacterized antibiotic resistance genes can be predicted from known gene variants – but not always. Microbiome, 6, 125 (2018). doi: 10.1186/s40168-018-0508-2
As I wrote a few days ago, I have now started my new position at Chalmers SysBio. This position is funded by the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS), which also funds PhD and postdoc positions. We are now announcing two doctoral student projects and one postdoc project within the DDLS program in my lab.
Common to all projects is that they will the use of large-scale data-driven approaches (including machine learning and (meta)genomic sequence analysis), high-throughput molecular methods and established theories developed for macro-organism ecology to understand biological phenomena. We are for all three positions looking for people with a background in bioinformatics, computational biology or programming. In all three cases, there will be at least some degree of analysis and interpretation of large-scale data from ongoing and future experiments and studies performed by the group and our collaborators. The positions are all part of the SciLifeLab national research school on data-driven life science, which the students and postdoc will be expected to actively participate in.
The postdoc and one of the doctoral students are expected to be involved in a project aiming to uncover interactions between the bacteria in microbiomes that are important for community stability and resilience to being colonized by pathogens. This project also seeks to unearth which environmental and genetic factors that are important determinants of bacterial invasiveness and community stability. The project tasks may include things like predicting genes involved in pathogenicity and other interactions from sequencing data, and performing large-scale screening for such genes in microbiomes.
The second doctoral student is expected to work in a project dealing with understanding and limiting the spread of antibiotic resistance through the environment, identifying genes involved in antibiotic resistance, defining the conditions that select for antibiotic resistance in different settings, and developing approaches for monitoring for antibiotic resistance in the environment. Specifically, the tasks involved in this project may be things like identifying risk environments for AMR, define potential novel antibiotic resistance genes, and building a platform for AMR monitoring data.
For all these three positions, there is some room for adapting the specific tasks of the projects to the background and requests of the recruited persons!
We are very excited to see your applications and to jointly build the next generation of data driven life scientist! Read more about the positions here.
Together with Joakim Larsson‘s lab, we now have an open two-year postdoc position in bioinformatics on antibiotic resistance and biocide resistance. The development of antibiotic resistance has been driven by use of antibiotics, but antibacterial biocides also have the potential to select for antibiotic resistance. However, knowledge of which genes that contribute to biocide resistance and could be associated with antibiotic resistance is sparse. To some extent, such genes are documented in the BacMet database which we have developed, but this collection of resistance genes is only scratching the surface of all biocide resistance that exists among bacteria in the environment.
We are now looking for a postdoctoral fellow to continue the important work on bioinformatic analysis of biocide and antibiotic resistance to answer the question whether increasing biocide resistance would be a threat to human health. The postdoc will be working with the development of the BacMet database to make it more targeted towards biocidal substances and products in addition to resistance genes. The tasks include bioinformatic sequence analysis, literature studies and database and web programming. The work will also include investigations of the prevalence of the identified resistance genes in genomes and metagenomes.
The recruited person will work closely with both my group and the group of Prof. Joakim Larsson, and will participate in the JPIAMR-funded BIOCIDE project. You can apply to the postdoc position at the University of Gothenburg application portal: https://web103.reachmee.com/ext/I005/1035/job?site=7&lang=UK&validator=9b89bead79bb7258ad55c8d75228e5b7&job_id=25122
The deadline is May 4, 2022. Come work with us on this exciting topic in the intersect between two great research environments (if I may say it myself!) We look forward to your application!
I have very big and exciting news to share with you. After more than 10 years at the Sahlgrenska Academy, me and my lab will be moving from the University of Gothenburg to Chalmers University of Technology (which is physically a move of less than a kilometer, so still within Gothenburg). I have been offered a position at the Division of Systems Biology, funded by the SciLifeLab and Wallenberg National Program for Data-Driven Life Science (DDLS). The total funding to my lab will be 17 million SEK, with some co-funding from Chalmers added in on top of that.
I am of course very excited about this opportunity, which will bring some infrastructure that we need in-house that we don’t have easy access to today. At the same time, I am sad to leave my academic ‘home’, and the fantastic people we have been working with there for the years. I am also endlessly thankful for the support and trust that the Sahlgrenska Academy, the Institute of Biomedicine and the Department of Infectious Diseases have put into me and my research over the past years.
The transition to Chalmers will start already in May, but will be gradual and continue for a long time. We have close ties to the Sahlgrenska Academy and we will keep closely collaborating with researchers there. I will also retain an affiliation to the University of Gothenburg, at least for the near future.
All in all, this year will bring very interesting development, and this additional funding from the DDLS program will allow us to venture into new areas of bioinformatics and try out ideas that have previously been out of reach. I look forward to work with our new colleagues at Chalmers and within the DDLS program in the coming years!