There is a growing recognition that interventions within the healthcare sector are not enough to curb antibiotic resistance development. Instead, a one-health perspective incorporating animal husbandry and external environments is needed. This calls for monitoring antibiotic resistance outside of the healthcare setting.
Unfortunately, antimicrobial resistance monitoring lacks comprehensive reference data for the vast majority of environments. Therefore, there is little knowledge on the range of background abundance and prevalence of antibiotic resistance genes (ARGs) occurring naturally. Furthermore, the few milieus where reference data exist are biased towards a small number of environments and there is no standardized methodology or any well-defined set of relevant ARGs that routinely are tested for monitoring purposes. This project will solve or alleviate these problems by integrating several approaches under one umbrella framework.
We will 1) establish baseline ranges for background ARG abundances and diversity in different environments, 2) standardize different methods for monitoring ARGs and provide a means for making them comparable, 3) identify sets of priority target ARGs for monitoring, 4) develop methods to detect emerging resistance threats and thereby provide an early-warning system for resistance, and 5) suggest a monitoring scheme that can be used in a modular fashion depending on the available resources. Establishing a coherent monitoring scheme is imperative for efficient monitoring, which in turn is essential for limiting future resistance development.
- Johan Bengtsson-Palme, University of Gothenburg, Sweden (Coordinator)
- Thomas U Berendonk, Technische Universität Dresden, Germany
- Etienne Ruppé, INSERM, France
- Sofia Forslund, Max-Delbrück-Centrum für Molekulare Medizin, Germany
- Luis Pedro Coelho, Fudan University, China
- Rabaab Zahra, Quaid-i-Azam University, Pakistan
Early and specific detection of microbial infections is crucial for the containment of diseases and for reducing the dependence on the use of antibiotics. There is however a lack of reliable, cheap and easy to use detection methods for day-to-day monitoring of infection and antimicrobial resistances in samples from patients, animals and the environment. This deficinecy is critical for the abuse of antibiotics and the diffusion of antimicrobial resistance.
The aim of this project is establishing a method based on yeast biosensors that can detect with high specificity pathogens from different sources to develop a new, fast and specific diagnostic tool for resistant pathogens. We will achieve this by joining together strong research groups on antimicrobial resistance, systems biology, and strain engineering at SINTEF, Chalmers University and National Medicines Institute. Particular focus will be given to the detection of ESBL or carbapenemase-producing strains belonging to the emerging ESKAPE group of resistant pathogens.
The biosensor is developed using the yeast Saccharomyces cerevisiae as host, which will be engineered to express specific receptors able to recognise unique molecules produced by the pathogens. The ligand-receptor binding initiates a cascade mechanism that activates the genes for the production of a red pigment visible to the naked eye. Using the biosensors, we aim to identify molecular markers specific for resistant pathogen strains, to enable fast, easy and inexpensive point-of-use profiling of resistant pathogens.
- Geir Klinkenberg, SINTEF, Norway (Coordinator)
- Verena Siewers, Chalmers University of Technology, Sweden
- Alicja Kuch, National Medicines Institute, Poland
Blood stream infection (BSI) is annually responsible of hundred thousand estimated deaths worlwide. The time frame for identification and antimicrobial susceptibility testing of the causative agent(s) of BSI directly impact the delay in the administration of appropriate antimicrobial therapy and, consequently, the clinical outcome of patients.
MALDI-TOF mass spectrometry obviously revolutionized routine microbial identification by drastically shortening the delay of the identification (ID). There is however no consensus on a universal and affordable tool for shortening the characterization of putative antibiotic resistance mechanisms. IDAREMS aims to introduce a disruptive tool for clinical diagnosis of blood stream infection (BSI) based on targeted proteomics carried out by tandem mass spectrometry (MS) to achieve concomitant pathogen identification and antibiotic resistance profiling directly from an aliquot of positive blood culture in less than one hour.
IDAREMS project is structured over three main work packages: WP1) the development by partner 1 (France) of a prototype assay for concomitant identification and rapid diagnostics of antimicrobial resistance in Gram-negative bacteria using a limited number of antibiotic-resistant bacterial isolates provided by partner 2 (France), partner 3 (Poland) and partner 4 (Thailand); WP2) the validation of the assay through blind testing of new clinical strains; WP3) technician training and deployment of the validated assay in the respective partner’s clinical platforms.
- Jérôme Lemoine, Jérôme Lemoine, France (Coordinator)
- Frederic Robin, Université Clermont Auvergne, France
- Marek Gniadkowski, National Medicines Institute, Poland
- Visanu Thamlikitkul, Mahidol University, Thailand
- Susan M. Poutanen, University of Toronto, Canada
Greater availability of fast and accurate diagnostics for infections would greatly reduce the over-prescription of antibiotics and slow the growth of antibiotic resistance which limits treatment options. It would also help prescribe the right drug at the right time, thus reducing suffering and increasing survival.
However, despite advances in technology, few useful diagnostics for bacterial infections have come to market and we are seeing a downward trend in innovation. This study proposes to identify the key barriers that remain once a new diagnostic has been developed, looking at recent technological advances that ultimately failed to be authorized, adopted, or able to change prescribing. It will also look at technologies that have helped in the fight against antimicrobial resistance. Are there any features or particularities that seem to have improved their chance of success?
Lessons from the failures and few successes will be used to assess chances for products in the pipeline, examining how regulation, reimbursement, technology transfer, and organizational characteristics might be improved to make them succeed within the clinical setting. The work will focus on each of these themes in developed world settings but also use them as a lens (in addition to a technical lens) to examine determinants of uptake in rural parts of South Africa, which are to some extent a proxy for LMIC.
- Olof Lindahl, Uppsala University, Sweden (Coordinator)
- Marc Mendelson, University of Cape Town, South Africa
- Eve Dubé, Université Laval, Canada
- Volkan Özenci, Karolinska Institutet, Sweden
- Florence Séjourné, BEAM Alliance, France
Lower respiratory tract infections (LRTI), such as pneumonia, are a leading cause of death especially in children below the age of 5 years. Low and middle-income countries (LMIC) suffer the highest burden of childhood pneumonia.
Most LRTIs are caused by viruses, but differentiating viral from bacterial causes is frequently impossible in LMIC due to lack of diagnostics. As a consequence, most cases are treated empirically with antibiotics leading to overuse and misuse of antibiotics, which is an important driver of the global epidemic of antimicrobial resistance.
Therefore, we propose to apply a newly developed diagnostic device, the modular breath sampler (MBS), which is based on the entrapment of aerosols from the lower respiratory tract to identify the etiological agent in children with LRTI. Because the MBS is a non-invasive, patient-friendly device and easy applicable for repeated measurements, it allows direct monitoring of the effect of antibiotic treatment. In addition, the identification of pathogens will not only be determined by PCR but also by loop-mediated isothermal amplification (LAMP) that amplifies DNA with high specificity, efficiency and rapidity in a single tube under isothermal conditions, and does not require a thermal cycler, which would make it easy to apply in LMICs.
- Marien de Jonge, Radboud University Medical Center, Netherlands (Coordinator)
- Markéta Martinkova, Charles University, Czech Republic
- Blandina Mmbaga, Kilimanjaro Christian Medical Centre, United Republic of Tanzania
- Corne van den Kieboom, Xheal Diagnostics, Netherlands
Antimicrobial resistance (AMR) is increasing worldwide, and surveillance activities play a key role in informing policies to contain AMR. Moreover, resistance to new antibiotics is emerging ever quicker after their introduction onto the market, rapidly reducing the effectiveness of even last-resort antibiotics.
As such, the sustainable introduction of a novel class antibiotic can only be achieved when accompanied by timely and informed surveillance and stewardship strategies. Affordable methodologies and tools to estimate the extent of national and local AMR are urgently needed to intelligently prioritise surveillance efforts, especially in low- and middle-income countries (LMICs).
Combining clinical, microbiological, epidemiological, and computational modelling expertise in one consortium, the project aims to satisfy that need through advanced data science and machine learning techniques at the global and (sub-)national scale, and multi-scale holistic dynamic network models at the local scale. Data and models resulting from the project will be disseminated to the benefit of various stakeholders, via their active participation in the project’s sounding board workshop, their involvement in our advisory board, the release of data and models in an international repository, and the initiation of integrating our modelling system in a web-based interactive platform.
Ultimately, our approaches aim to facilitate the sustainable potential future introduction of novel class and last-resort antimicrobial drugs. We illustrate this capacity in the specific case of Neisseria gonorrhoeae.
- Filippo Castiglione, National Research Coucil of Italy, Italy (Coordinator)
- Constance Schultsz, Amsterdam UMC, Faculty of Medicine, University of Amsterdam/Stichting Amsterdam Institute for Global Health & Development, Netherlands
- Peteris Daugulis, Daugavpils University, Institute of Life Sciences and Technology, Latvia
- Raquel Abad Torreblanca, Instituto de Salud Carlos III, Spain
Pseudomonas aeruginosa causes severe infections in hospitalized patients. The worldwide emergence of carbapenem-resistant P. aeruginosa (CR-PA) makes infections by these pathogens almost untreatable. The World Health Organization now ranks CR-PA highest in the list of ‘urgent threats’.
Information for action to prevent further emergence has to come from insight into sources and transmission routes through smart surveillance. At present, a smart surveillance strategy is not available for CR-PA.
The aim of this project is to develop a globally-applicable smart surveillance strategy to guide action against the spread of CR-PA. Since P. aeruginosa prefers moist niches, we will focus on the human-water interface. First, highly-sensitive methods to detect CR-PA in specific environmental and human niches will be developed. Subsequently, CR-PA will be collected in three study sites with increasing prevalences of CR-PA, increasingly warmer climates, and different water situations: Rotterdam (The Netherlands), Rome (Italy), Jakarta (Indonesia). CR-PA will be searched for in a variety of niches in the environment outside and inside the hospital, and in healthy humans and hospitalized patients. Whole genome sequencing will be performed to compare the CR-PA from different sources and identify transmission routes.
Our project will provide insight into the relative contribution of the different potential reservoirs of CR-PA to its spread in different settings which will be used for the development of a globally-applicable surveillance strategy for CR-PA to guide preventive actions.
- Juliëtte Severin, Erasmus MC University Medical Center, Netherlands (Coordinator)
- Heike Schmitt, National Institute for Public Health and the Environment (RIVM), Netherlands
- Anis Karuniawati, Universitas Indonesia, Indonesia
- Bas van der Zaan, Deltares, Netherlands
- Roger Levesque, University of Laval, Canada
- Nicola Petrosillo, National Institute for Infectious Diseases “Lazzaro Spallanzani”, IRCCS, Italy
OASIS aims to develop an antimicrobial resistance (AMR) surveillance strategy in a One Health context, and applicable in high-, middle-, and low-income countries. The proposed strategy challenges the strong reliance on laboratory-based AMR surveillance for meeting objectives of the Global Action Plan on AMR.
Laboratory-based AMR surveillance is hampered by selection bias and unrepresentativeness for local settings, precluding guidance on empirical treatment decisions in the human or veterinary domains. Population-based AMR surveillance is preferred but is time-, labour- and cost intensive due to large sample sizes required. OASIS moves from estimating AMR prevalence to classifying populations/settings as having a high/low AMR prevalence, by applying a Lot Quality Assurance Sampling approach, which requires much smaller sample sizes and is uniquely positioned for population-based AMR surveillance.
OASIS optimises the LQAS approach as a rapid, domain-, and setting-appropriate AMR surveillance strategy, within a One Health context that appreciates the close interplay of drivers of AMR emergence and transmission in human and livestock populations. Surveillance strategies that use a similar methodology to assess AMR prevalence in multiple domains are highly desired, strengthen the knowledge and evidence base on AMR, and optimise the use of antimicrobials in both human and animal health. Oasis’ implementation research component engages domain-specific stakeholders throughout the project to optimise knowledge utilisation, and facilitate the translation of results into policy.
- Frank van Leth, Amsterdam Institute for Global Health and Development, Amsterdam UMC, Netherlands (Coordinator)
- Hubert Ph. Endtz, Foundation Merieux, France
- Christian Menge, Friedrich-Loeffler-Institut , Germany
- Christa Ewers, Institute of Hygiene and Infectious Diseases of Animals, Justus Liebig University, Germany
- Mounerou Salou, University of Lomé, Togo
- Abdoul-Salam Ouedraogo, Higher National Institute of Health Sciences, Nazi Boni University, Burkina Faso
Antibiotic resistant organisms (AROs) have become increasingly difficult to treat, with rising morbidity and mortality worldwide. Healthcare institutions are often the epicenter for outbreaks of these antibiotic resistant organisms, and are also windows into their circulation within the broader community.
Transmission of antibiotic resistant organisms within hospitals is under appreciated. Moreover, identification of linked strains that may be causing occult outbreaks is often not systematically performed. Genomic approaches can provide a better understanding of within-hospital transmission of AROs, which can be used to guide infection control practices. Some institutions have augmented their ARO surveillance with whole genome sequencing, but this is both expensive and time consuming, making it unsuitable for routine use.
However, new approaches that use k-mer based algorithms along with genomic reference databases can provide rapid evaluation of pathogen lineage and potential for linked transmission. These same approaches can also be used to provide important rapid diagnostic information about the pathogen and likelihood of resistance to a given antibiotic. While there is much potential in these approaches, they need to be formally evaluated across care settings and geography before they can be trialled in the clinical setting. Here we propose a multi-continental prospective evaluation of the performance of a k-mer based approach for institutional surveillance of common multidrug resistant Gram-negative pathogens as well rapid prediction of antibiotic resistance patterns.
- Derek MacFadden, Ottawa Hospital Research Institute, Canada (Coordinator)
- Allison McGeer, Mt. Sinai Hospital, University of Toronto, Canada
- Hajo Grundmann, University of Freiberg, Germany
- Martin Antonio, Medical Research Council Unit, The Gambia, Gambia
- William Hanage, Harvard Chan School of Public Health, USA
The discovery of antimicrobial agents was one of the great triumphs of the 20th century. The sobering news is that antibiotic resistance was part of the process as well.
If nothing is done by 2050, AMR will cost $100 trillion with 10M people/year expected to die (https://amr-review.org). Factors driving AMR extend beyond human healthcare with implications in veterinary medicine, agriculture and the environment (the One Health approach). New and improved approaches for tackling AMR include better surveillance; rational drug use, different business model for generating antibiotics, innovation at all levels and most importantly a global approach.
This transnational team grant proposal is tasked to apply new machine learning approaches for modelling AMR for faster diagnosis, better surveillance and prediction of resistance emergence. Specifically, we will develop machine learning implementation that can orient the selection of treatments by assessing the level of resistance, provide rational for the generation of novel antibiotics, and assist in the surveillance of human and livestock AMR around the globe. To achieve this, we have assembled a transnational team (Canada, China, Finland, France) with complementary skills with demonstrated expertise in machine learning applied to both genomics and metabolomics and AMR domain experts.
Our transnational team has all the elements to be highly impactful and to continue collaborating well past the JPIAMR funding period. The complementarity of our expertise will help us tackle the challenges ahead and ensure our continued success.
- Jacques Corbeil, Université Laval, Canada (Coordinator)
- Jie Feng, Institute of Microbiology, Chinese Academy of Sciences, China
- Juho Rousu, Aalto University, Finland
- Véronique Dubois, Université de Bordeaux, CNRS, France