My lab investigates the complex dynamics and interactions of microbial communities in diverse food products, including vacuum-packed beef, plant-based meat alternatives, and cheese. Current projects explore how microbial populations evolve during ripening or storage and how their interactions influence food quality, spoilage and preservation. For example, we recently uncovered a triangular interaction network involving lactic acid bacteria, Enterobacterales, and yeasts that plays a key role in meat spoilage (Figure 1). To study these communities, we combine classical culture-based methods and co-cultivation experiments with whole-genome sequencing, metagenomic and functional analysis. Advanced bioinformatics tools allow us to identify microbial taxa, understand metabolic functions, and unravel interaction mechanisms. Our goal is to lay the groundwork for microbiome-informed strategies that enhance food preservation, reduce spoilage, and improve quality control across the food chain.

Further reading about microbial interactions and dynamics in foods:

Microbial community structure of plant-based meat alternatives

Microbial succession and interaction in vacuum-packed beef: a longitudinal study of bacterial and fungal dynamics

Microbial contamination in food production environments poses a significant risk to food safety and public health. Pathogens such as Salmonella spp., Listeria monocytogenes, and E. coli can persist in niches like drains, equipment crevices, and biofilms. These microbes can spread through direct contact with contaminated surfaces, aerosols, personnel, and improperly cleaned tools. Cross-contamination is a critical concern, especially when raw and ready-to-eat products are handled in close proximity. Environmental monitoring helps identify microbial hotspots and assess hygiene effectiveness in processing facilities. Genomic tools allow us to trace transmission pathways and identify contamination sources with high resolution (Figure 2). Our research aims to uncover how microbes move and survive in food production settings to inform smarter, safer hygiene and control strategies.

Further reading about microbial transmission patterns in food production environments:

The sources and transmission routes of microbial populations throughout a meat processing facility

The project aims to predict and test genomic adaptations that enable the persistence of Salmonella populations in food and food processing environments using a reverse ecology approach. It focuses on identifying fine-scale population differences within serotypes that contribute to persistence, leveraging whole-genome sequencing data and metadata. Differential adaptations will be predicted using signatures of selection in persistent populations compared to their closest sister populations. These predictions will then be tested in vitro through growth kinetic experiments and phenotypic assays under various environmental conditions, such as pH, temperature, and salinity. Ultimately, the findings aim to enable targeted strategies for preventing and controlling recurring Salmonella contamination in the food industry (Figure 3).

Further reading about the reverse ecology approach:

Differential carbon utilization enables co-existence of recently speciated Campylobacteraceae in the cow rumen epithelial microbiome

Distinct lactate utilization strategies drive niche differentiation between two co-existing Megasphaera species in the rumen microbiome