EPPIcenter Seminar: Sylvia Portugal
Dr Silvia Portugal is a Group Leader at the Centre for Infectious Diseases, Parasitology Unit, Heidelberg University Hospital, Germany. She completed her PhD at the IMM Lisbon, supervised by Maria Mota, where showing in a mouse model that Plasmodium blood-stage infections suppress re-infection by newly inoculated sporozoites.She completed a post doc Pete Crompton’s lab at the NIH in Rockville, focusing on naturally acquired immunity to malaria in the seasonal transmission region of Mali, proposing a novel model of how children in endemic areas tend to remain asymptomatic and control parasite replication despite repeated P. falciparum infections.
Her current focusis understanding how the malaria parasite Plasmodium falciparum handles the absence of mosquitos during the dry season. In its lifecycle, the parasite needs to rotate between a human and mosquito. As long as there are mosquitos and humans, this cycle can constantly alternate. But, in most regions of the globe, transmission is seasonal: During dry season, it doesn’t rain for several months and when there is no water, there are no mosquitos. This could disrupt the cycle, but there are still new infections in the next wet season.
Our question is: How does the parasite survive the dry season and emerge again later? We know that Plasmodium “hides” in humans during the dry season, but no one presents clinical symptoms of malaria. So, we try to dissect the molecular mechanisms behind these silent infections and the strategies to overcome restrictions that are imposed by the surrounding environment. We want to know: How can people be infected the whole time, but only get sick when the mosquitos return in the wet season and how is the parasite able to restart transmission every year?
Join the EPPIcenter online in welcoming Dr Portugal
The EPPIcenter at UCSF aims to advance the understanding of infectious diseases to reduce global morbidity and mortality. We believe that the greatest success in the fight against infectious diseases will come through a highly interdisciplinary, systems epidemiology approach, connecting traditionally siloed theoretical work, technology development, generation and collection of empiric data, and analysis using statistical and mathematical modeling.