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Emilie Guérin

Publications and source records attributed to Emilie Guérin.

2 recordsLinked to original sources

Hepatocyte iron loading capacity is associated with differentiation and repression of motility in the HepaRG cell line.

High liver iron content is a risk factor for developing hepatocellular carcinoma (HCC). However, HCC cells are always iron-poor. Therefore, an association between hepatocyte iron storage capacity and differentiation is suggested. To characterize biological processes involved in iron loading capacity, we used a cDNA microarray to study the differentiation of the human HepaRG cell line, from undifferentiated proliferative cells to hepatocyte differentiated cells. We were able to identify genes modulated along HepaRG differentiation, leading us to propose new genes not previously associated with HCC. Moreover, using Gene Ontology annotations, we demonstrated that HepaRG hepatocyte iron loading capacity occurred both with the repression of genes involved in cell motility, signal transduction, and biosynthesis and with the appearance of genes linked to lipid metabolism and immune response. These results provide new insights in the understanding of the relationship between iron and hepatocyte differentiation during iron-related hepatic diseases.

Carcinoma, Hepatocellular↗

BioMeKe: an ontology-based biomedical knowledge extraction system devoted to transcriptome analysis.

Semantic interoperability between knowledge bases in medicine, and knowledge base in genomics and molecular biology will lead to advances in fundamental research as well as to improved patient care. DNA chips strategy is used for transcriptome analysis in order to identify deregulated genes in physio-pathological conditions. The objective of the BioMedical Knowledge Extraction project (BioMeKe) is to develop a knowledge warehouse in the context of transcriptome analysis during liver diseases. Knowledge sources include ontologies, related terminologies and annotations linked towards public databases (e.g., SWISSPROT). BioMeKe has been developed to have access to information using systematic investigation upon a concept, gene, gene products, pathology, or any target keyword, and is based on the combination of several relevant resources: UMLS, GeneOntology, MeSH supplementary terms, GOA, and HUGO. Current efforts are focusing on exploiting this ontology-based Knowledge Extractor, to enrich the expression data on genes delivered by a liver specific DNA microarray for better assistance of analysis.

Databases, Genetic↗