Luce Landraud

Luce Landraud is an Associate Professor (MCU-PH) and permanent researcher at University of Paris, Faculty of Medicine, affiliated with IAME INSERM U1137. She serves as a core investigator in the EVREST team, which addresses antibiotic resistance and virulence evolution in bacterial pathogens through ecological and evolutionary frameworks.

Her academic credentials include:

• PhD in Bacteriology, Medical School, Nice Sophia Antipolis (2004-2015)
• PhD in Microbiology, Medical School, Paris Sorbonne Cité (2015-present)

Dr. Landraud's research centers on antibiotic resistance dynamics in Enterobacterales, with emphasis on plasmid fitness costs, virulence-resistance coevolution, and E. coli pathogenomics. She integrates genomic surveillance, experimental evolution, and clinical data to investigate how resistance mechanisms emerge and persist in bacterial populations across healthcare and environmental settings.

Analysis of her 2020-2023 publications reveals consistent focus on genomic epidemiology of resistance plasmids, iron-mediated virulence pathways, and therapeutic alternatives like phage therapy. Key contributions appear in high-impact venues including Nature Communications and Antimicrobial Agents and Chemotherapy, demonstrating methodological innovation in resistance tracking and evolutionary modeling.

No specific scientific awards were documented in available sources.

Dr. Landraud actively supervises research within the EVREST framework and participates in multicenter clinical trials. Her work receives support from INSERM and national research agencies, enabling large-scale genomic studies and translational projects addressing urgent antibiotic resistance challenges.

As an EVREST team member, she contributes to three research axes: (1) virulence/resistance evolution, (2) antibiotic response in microbial communities, and (3) therapeutic repurposing. The team employs advanced genomic, microbiological, and computational approaches to investigate resistance mechanisms in clinical isolates and experimental models.