Towards an integrated metabolic and gene regulatory network model of human endothelial function
BMC Seminar Thursday 25 February at 12:00
Speaker: Adrián López García de Lomana, Research Specialist in Óttar Rolfsson lab, Center for Systems Biology, University of Iceland
Title: Towards an integrated metabolic and gene regulatory network model of human endothelial function
Abstract: The endothelium is a cell monolayer that covers the blood and lymphatic vessels. It regulates vital processes including nutrient distribution, inflammation and metastasis. Gene regulatory network models are useful tools to reveal which transcriptome changes at large are associated with phenotypic changes and also to identify the particular mechanistic mechanisms at play. The molecular regulators of endothelial function are not totally understood.
I will present ongoing efforts towards the inference of a gene regulatory network model for endothelium function from all publicly available bulk and single-cell expression data sets, including EndoDB that contains up to 4,741 bulk and 5,847 single cell endothelial transcriptomes. Our model will include mechanistic details from TF-target and miRNA-target interactions from curated databases like DoRothEA and miRTarBase respectively. Finally, I plan to predict metabolic fluxes for each endothelial functional state by integrating particular transcriptome levels to the human genome-scale metabolic network reconstruction RECON3D. Model performance will be assessed using a cross-validation approach over excluded data sets. Ultimately, novel genetic and metabolic perturbation experiments can be carried out in vitro, paving the path towards clinical interventions.
Such methodology has been recently applied to a diverse set of other systems and I will briefly summarize its outcomes as a proof-of-concept of this approach: (1) a more fine-grained molecular classification of a cohort of multiple myeloma patients, (2) a map of transcriptional activity regulators upon treatment before the onset of recurrence in glioblastoma and (3) the prediction of metabolic vulnerabilities in Mycobacterium tuberculosis.