INTEGRATED ANALYSIS OF TRANSCRIPT-LEVEL REGULATION OF METABOLISM AND DISEASE-ASSOCIATION OF GENES UNDER HIGH REGULATORY LOAD

Mafalda_Galhardo

INTEGRATED ANALYSIS OF TRANSCRIPT-LEVEL REGULATION OF METABOLISM AND DISEASE-ASSOCIATION OF GENES UNDER HIGH REGULATORY LOAD

Mafalda Galhardo, EnvMetaGen Project, CIBIO-InBIO/UPorto | November 25, 2016 – 15h00 | CIBIO-InBIO’s Auditorium, Campus de Vairão

 

WELCOME SEMINAR IN BIODIVERSITY AND EVOLUTION

In this presentation, I will show an integrated analysis of human adipocyte differentiation, focusing on transcript-level regulation and its impact on the metabolic network. Diverse data types including gene expression microarrays, target gene identification for 3 down-regulated microRNAs, genome-wide binding profiles of 3 key adipogenic transcription factors and profiling of the active transcription mark H3K4me3, were used together with constraint-based metabolic modelling to obtain a global and concerted view of the regulatory and metabolic events occurring during adipogenesis. Such integrated analysis revealed the convergence of microRNAs and TFs on disease-associated genes from metabolic pathways predicted to change activity throughout differentiation. In order to test the hypothesis that genes under higher regulation are associated to a disease more often than expected, we analyzed a large set of public data from diverse tissues and cell types, from the genome-wide profiling of transcription factor binding or the location of active enhancers, revealing an enrichment for disease-associated genes among genes with the highest regulatory load. Further analysis taking into account diseases and their associated genes (based on DisGeNET) highlights the association of diseases relating to a particular cell type or function to the genes with the highest regulatory load on that particular cell type, such as the case of genes associated to Parkinson’s disease being enriched among the genes with more enhancers in samples from the substantia nigra (brain). Overall, integrated analyses can be very useful in understanding relationships and dependencies within biological processes or systems, as well as potentiate new testable hypotheses which are otherwise difficult to grasp using single methodologies.

Mafalda Galhardo has recently joined CIBIO-InBIO as a post-doc in the EnvMetaGen project, to work on data analysis and integration on topics such as epigenetics. She studied Biochemistry (BSc) in Lisbon (FCUL) and continued with Systems Biology (MSc) in Luxembourg (FSTC), where she also did her PhD. Her interests include genome regulation, epigenetics and metabolism.

Image credits: Mafalda Galhardo

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