Lung microbiome and mucosal immunity
Recent studies have successfully implicated several factors in the susceptibility to fungal infection, but have provided little insight into the nature of the underlying biological mechanisms. We propose a multiomics approach to link deep clinical phenotyping to molecular and genetic signatures of the host-fungus-microbiota interaction towards personalized prognosis, diagnosis, and treatment of fungal disease. We aim to dissect the contribution of host genetics and immune phenotypes to the risk and outcome of fungal disease, to generate a time-resolved annotation of microbiota dynamics during infection, discover new metabolites and pathways involved in the development of infection, and decipher the host-fungus-microbiota crosstalk to evaluate its diagnostic and therapeutic potential. By resorting to the longitudinal study of large populations of immunocompromised patients and using cutting-edge research tools that combine genome sequencing, metagenomic profiling, and global metabolomics, as well as advanced lung-on-a-chip systems, we aim to establish a systematic understanding of the host-fungus-microbiota crosstalk and how it defines interindividual susceptibility to fungal disease with unprecedent resolution.
“la Caixa” Foundation
Samuel M Gonçalves
Main Project Outcomes
S. Queirós, “Right ventricular segmentation in multi-view cardiac MRI using a unified U-net model”, in E. Puyol Antón et al. (eds) Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science, vol 13131, pp. 287-295, Springer, Cham, 2022.
“Best Paper Award in the M&Ms-2 Challenge”, by M&Ms2 Challenge organizers and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society.