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Nobre, C.

Publications and source records attributed to Nobre, C..

2 recordsLinked to original sources

NOX4 prevents the recruitment of PAX8 and NKX2.1 to chromatin in BRAF-mutated thyroid cancer cells.

Radioiodine (RAI) therapy, used for treating thyroid cancers, hinges on the expression of the Sodium Iodide Symporter (NIS). The majority of differentiated thyroid cancers (DTCs) are papillary, with a BRAFV600E mutation. This mutation correlates with an absence of RAI uptake, due to low NIS expression and a low differentiation score. NADPH oxidase 4 (NOX4)-derived ROS contribute to NIS repression in BRAFV600E-mutated thyroid cancer cells. Depleting NOX4 enhances the reactivation of NIS. This reversibility implies an epigenetic mechanisms contribution. Our findings indicate that NOX4 generates oxidative DNA damage in BRAFV600E-mutated thyroid cancer cells. DNA repair proteins such as OGG1 and MSH2/MSH6 proteins, in cooperation with DNMT1, turn these damages into transcription-blocking damages. This prevents the binding of PAX8 and NKX2.1 - two key transcription factors involved in thyroid differentiation - to the chromatin. Co-inhibition of the MAPK pathway, which regulates MSH2/MSH6 and DNMT1 expressions, and the TGF-{beta}1 pathway, which regulates NOX4 expression, fortifies the recruitment of the two transcription factors to the chromatin. Collectively, our findings present a molecular basis for NOX4s role in thyroid dedifferentiation.

cancer biology↗

Lineage: Visualizing Multivariate Clinical Data in Genealogy Graphs

The majority of diseases that are a significant challenge for public and individual heath are caused by a combination of hereditary and environmental factors. In this paper we introduce Lineage, a novel visual analysis tool designed to support domain experts who study such multifactorial diseases in the context of genealogies. Incorporating familial relationships between cases with other data can provide insights into shared genomic variants and shared environmental exposures that may be implicated in such diseases. We introduce a data and task abstraction, and argue that the problem of analyzing such diseases based on genealogical, clinical, and genetic data can be mapped to a multivariate graph visualization problem. The main contribution of our design study is a novel visual representation for tree-like, multivariate graphs, which we apply to genealogies and clinical data about the individuals in these families. We introduce data-driven aggregation methods to scale to multiple families. By designing the genealogy graph layout to align with a tabular view, we are able to incorporate extensive, multivariate attributes in the analysis of the genealogy without cluttering the graph. We validate our designs by conducting case studies with our domain collaborators.

bioinformatics↗