Research team
Expertise
My research focuses on microbial genomics and antimicrobial resistance, with particular expertise in Salmonella. I study how bacterial pathogens evolve, adapt to the human host and develop resistance to antibiotics, combining genome sequencing, gene expression analysis and laboratory experiments. As a complementary research line, I develop and evaluate applications of metagenomic sequencing for studying pathogens and monitoring bacteria, viruses and antimicrobial resistance in wastewater. My expertise also includes nanopore sequencing, benchmarking and laboratory techniques such as bacterial culture, antimicrobial susceptibility testing, nucleic acid extraction, library preparation and sequencing. I also contribute to bioinformatics training and strengthening research capacity in low- and middle-income countries. Beyond research, I value connecting people and contributing to the scientific community, particularly by supporting students, PhD candidates and researchers in these countries.
Unveiling the Hidden Human Virome: A Genomic and Spatiotemporal Approach to Viral Surveillance in Wastewater.
Abstract
Wastewater-based surveillance (WBS) analyzes human-excreted substances in wastewater to track infectious diseases at the population level. In Belgium, current efforts focus on detecting SARS-CoV-2, respiratory syncytial virus (RSV), and influenza. However, many other potential viral threats remain unmonitored, highlighting the need for a more comprehensive, multi-virus surveillance system. Although existing methods are effective, enhancing virus concentration and detection techniques can further improve accuracy. Expanding genomic surveillance will allow for tracking of viral evolution, while refining predictive modelling can enhance outbreak forecasting. Using our unique wastewater databank from various wastewater stations, and the close collaboration with Sciensano, this project aims to strengthen Belgium's wastewater-based surveillance system by improving virus concentration and extraction methods, expanding genomic surveillance, and strengthening predictive modelling. Ultimately, this project will enhance early detection, outbreak prediction, and public health preparedness, enabling more effective real-time monitoring and response to emerging and re-emerging viral threats.Researcher(s)
- Promoter: Delputte Peter
- Co-promoter: Cuypers Wim
- Fellow: De Smedt Marie
Research team(s)
Funding
- BOF
Project type(s)
- Research Project
Pioneering portable pathogen monitoring: LeapSEQ's lean and adaptive sequencing solutions.
Abstract
The main goal of this VLAIO Innovation Mandate is to create LeapSEQ, an innovative software platform designed for the effective and real-time surveillance of viruses and bacterial antimicrobial resistance (AMR) in wastewater through the use of portable nanopore technology.Researcher(s)
- Promoter: Laukens Kris
- Co-promoter: Delputte Peter
- Fellow: Cuypers Wim
Research team(s)
Funding
- VLAIO
Project type(s)
- Research Project
Systems biological analysis of niche adaptation in resistant and virulent Salmonella pathogens.
Abstract
The foodborne pathogen Salmonella poses a significant threat to human health worldwide. This is further complicated by the emergent spread of antibiotic resistant strains. Salmonella serotypes and subtypes can have different niches, from a broad range to a very specific niche, e.g. humans. Such bacteria can become very efficient in infecting humans and will contribute even more to the spread of antibiotic resistance. To combat the emergent spread of multiresistant bacteria, molecular monitoring of bacterial strains that show increased adaptation towards the human host, combined with high resistance and virulence, it is vital. While researchers can relatively accurately predict alarming resistant and virulent phenotypes based on whole genome sequencing data, niche adaptation prediction techniques are lagging behind. I will solve these problems by (i) analysing niche adaptation from a broad perspective and (ii) implementing cutting edge computational technologies to predict niche adaptation in Salmonella. This methodology will be built and tested on a model Salmonella serotype, Salmonella Concord. Salmonella Concord is intrinsically a highly virulent and resistant serotype, and shows geographical restriction (the Horn of Africa). It has been reported in Belgium through adopted children, mainly from Ethiopia. Insights from my research will empower health care innovations, and the predictive model will significantly improve risk assessment of pathogenic bacteria.Researcher(s)
- Promoter: Laukens Kris
- Co-promoter: Meysman Pieter
- Fellow: Cuypers Wim
Research team(s)
Funding
- FWO
Project type(s)
- Research Project