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Dubessy, C.

Publications and source records attributed to Dubessy, C..

2 recordsLinked to original sources

Dysfunction of exocytosis causes catecholamine hypersecretion in patient with pheochromocytoma

Pheochromocytoma (Pheo) is a neuroendocrine tumor that develops from chromaffin cells of the adrenal medulla, and is responsible of an excess of catecholamines secretion leading to severe clinical symptoms such as hypertension, elevated stroke risk and various cardiovascular complications. Surprisingly, hypersecretory activity of Pheo has never been explored at the cellular and molecular levels from individual tumor cells. In the present study, we have combined catecholamine secretion measurement by carbon fiber amperometry on human tumor cells directly cultured from freshly resected Pheo, with the analysis by mass spectrometry of the exocytotic proteins differentially expressed between the tumor and the matched adjacent non-tumor tissue. Catecholamine secretion recordings from individual Pheo cells obtained from most patients reveal a higher number of exocytic events per cell associated with faster kinetic parameters. Accordingly, we unravel significant tumor-associated modifications in the expression of key proteins involved in different steps of the calcium-regulated exocytic pathway. Altogether, our findings indicate that dysfunction of the calcium-regulated exocytosis at the level of individual Pheo cell is a cause of the tumor-associated hypersecretion of catecholamines.

cancer biology↗

Improving bioinformatics prediction of microRNA targets by ranks aggregation

microRNAs are non-coding RNAs which down-regulate a large number of target mRNAs and modulate cell activity. Despite continued progress, bioinformatics prediction of microRNA targets remains a challenge since available softwares still suffer from a lack of accuracy and sensitivity. Moreover, these tools show fairly inconsistent results from one another. Thus, in an attempt to circumvent these difficulties, we aggregated all human results of three important prediction algorithms (miRanda, PITA and SVmicrO) showing additional characteristics in order to rerank them into a single list. This database is freely available through a webtool called miRabel (http://bioinfo.univ-rouen.fr/mirabel/) which can take either a list of miRNAs, genes or signaling pathways as search inputs. Receiver Operating Characteristic curves and Precision-Recall curves analysis carried out using experimentally validated data and very large datasets show that miRabel significantly improves the prediction of miRNA targets compared to the three algorithms used separatly. Moreover, using the same analytical methods, miRabel shows significantly better predictions than other popular algorithms such as MBSTAR and miRWalk. Interestingly, a F-score analysis revealed that miRabel also significantly improves the relevance of the top results. The aggregation of results from different databases is therefore a powerful and generalizable approach to many other species to improve miRNA target predictions. Thus, miRabel is an efficient tool to accurately identify miRNA targets and integrate them into a biological context.

bioinformatics↗