About
We are interested in how bacteria respond and evolve in the presence of the host. Integrating molecular microbiology, systems biology approaches, organoid models and clinical data, we aim to connect fundamental microbiology concepts with their medical implications. Our long-term goal is to use this knowledge to improve infection treatments and antibiotic usage in the age of antimicrobial resistance (AMR).
Research
AMR is one of the most pressing challenges in modern medicine. Antibiotic treatments in the clinics are guided by standardized, host-free testing pipelines. Yet, bacteria experience antibiotics in the context of the host, where immune defenses, tissue-specific conditions, and microbial interactions profoundly influence bacterial physiology and evolution. These factors can alter antibiotic efficacy and impose selective pressures that differ fundamentally from those observed in vitro. Therefore, characterizing these interactions is key to understand why antibiotics succeed or fail in clinical settings.
Our research is guided by the following questions:
How do host-derived factors modulate antibiotic activity and shape the evolutionary paths leading to resistance?
How do bacterial pathogenicity traits co-evolve with AMR and spread across ecological and host-associated niches?
Is AMR conditional on environmental or host context, and can such context-dependent resistance be predicted?
To address these questions, we combine molecular microbiology with experimental and computational systems approaches, including high-throughput phenotyping, (meta)genomics, and microphysiological models. This interdisciplinary framework allows us to map bacterial adaptation across scales, from molecular mechanisms to population-level evolutionary dynamics.
Ongoing Projects
Impact of immune mediators on antibiotic resistance
Bacteria are constantly exposed to the host immunity in both their commensal and pathogenic lifestyles. Immune mediators affect bacterial functions and can interact with antibiotics, reshaping resistance evolution trajectories. To investigate this, we combine large-scale phenotyping, experimental evolution, and organoid-based infection models, with the overarching goal to reveal context-dependent resistance and its mechanisms.
Co-evolution of antimicrobial resistance and virulence
Resistance and virulence traits can co-occur in clinically relevant bacteria, but their joint evolutionary constraints are unclear. We investigate how adaptation to host-associated pressures influences the emergence and spread of these traits. By integrating comparative genomics of large bacterial genome collections and experimental validation, we assess how resistance, virulence, and mobile genetic elements co-evolve in natural populations. Integrating clinical data, we map these associations across health and disease including undersampled niches, with the broader goal to assess commensal-pathogen transitions in a One Health perspective.
Prediction of antimicrobial resistance and interactions
In clinics, antibiotic susceptibility testing is often constrained by time and costs. We integrate large-scale phenotypic data on single and combined antimicrobials with clinical data to develop predictive frameworks of antimicrobial susceptibility. Our goal is to identify new markers of antibiotic susceptibility that can be used to accelerate antibiotic choice in the clinics. The interpretability of our models also provides mechanistic insights for new antimicrobial targets and rational design of drug combinations.
Funding
