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Department of Plant and Microbial Biology IPMB - Department of Plant and Microbial Biology


Population genetics of wheat powdery mildew in Europe

It has long been recognized that understanding the population genetics of agricultural plant pathogens could contribute to improve disease control, and in the past decades much effort was placed to study the genetic structure of pathogen populations, their migration routes, host shifts and reproductive strategies. However, because of reduced resources and technical challenges such as the large genome size of many plant pathogens, population genetics and molecular epidemiology of agricultural pathogens have not been characterized by the large scale real-time genomic approach that was so fruitful in the study of human infectious diseases.

In this project we use large scale genomics to study wheat powdery mildew (Blumeria graminis f. sp. tritici), a fungal pathogen of wheat. We have sampled about 300 isolates from different European countries during two seasons (2022 and 2023). We aim to investigate the population genetics of wheat powdery mildew, shedding light on the genetic variation, population structure, migration patterns, and adaptive evolution of this pathogen in Europe (e.g., adaptation to local climatic conditions, to different hosts, or the evolution of fungicides resistance). This knowledge will be useful to better understand the disease dynamics, and it will inform the development of sustainable control strategies.

Molecular epidemiology of Mycobacterium tuberculosi

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), remains a major global health concern, affecting millions of people each year. Understanding the epidemiological and evolutionary dynamics of M. tuberculosis can contribute to designing effective strategies for disease control. Molecular epidemiology, combined with model-based approaches, has emerged as a powerful tool to study this pathogen. This research project aims to investigate the molecular epidemiology of M. tuberculosis using mathematical models to disentangle different aspects of TB epidemics, such as transmission, latency, and the length of the infectious period.