Purchase this article with an account.
P. Belleau, E. Deilhes, N. Boivin, R. Arseneault, E. Calvo, V. Raymond; Relational Network of Differentially Expressed Candidate Genes for Glaucoma in Human Retina and Ciliary Body. Invest. Ophthalmol. Vis. Sci. 2007;48(13):5614.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
We previously exploited genomic convergence, a multistep approach that combines gene expression with genomic linkage, to identify and prioritize candidate genes for primary open angle glaucoma (POAG) within 10 GLC1 loci for which no disease causing-gene has yet been characterized. Out of 795 differentially expressed genes detected in this study, 74 mapped within 1 of the loci. In order to integrate the known information about the mapping, roles and functions of these 74 candidate genes, a data mining strategy was developed to subgroup them in relation with the other 721 differentially expressed genes.
Candidate gene to inherited diseases (G2D) was used to identify more than 1000 Gene Ontology (GO) terms associated with glaucoma. Eight keywords were used with Entrez Programming Utilities to search our differentially expressed genes on pubmed. We drew our network using the GUESS program that associated the differentially expressed genes with the GO, POAG loci and keywords.
In our microarray experiments, 530 and 265 transcripts were differentially expressed, respectively, in the retina and ciliary body of one glaucomatous patient as compared to two controls. From these results, we generated 795 graphs; each one of them gave us an overview of the global implication of each group of genes in glaucoma. We then identified the highest scored genes to establish the best candidates for glaucoma. For instance, the BCL2L1 gene was found to interconnect with 6 keywords and 20 other differentially expressed genes, 4 of them mapping to one of the GLC1 locus. This network was highly related to apoptosis GO term.
Microarray is one of the best tools available to study the transcriptomes of ocular tissues but analysis of this data is often time consuming. We created a method to facilitate the study of microarray results. By focusing on pathways and genes cited together, we overviewed different mechanisms that could be involved in glaucoma. This strategy allowed us to visualize relevant information to identify the best candidate genes for glaucoma.
This PDF is available to Subscribers Only