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Comprehensive bioinformatics analysis of trabecular meshwork gene expression data to unravel the molecular pathogenesis of primary open-angle glaucoma

Publication year 2020
Published in Acta Ophthalmologica
Authors Ilona Liesenborghs, Lars M T Eijssen, Martina Kutmon, T.G.M.F. Gorgels, Chris T. Evelo, Henny J M Beckers, Carroll A B Webers, Johannes S A G Schouten

PURPOSE: Performing bioinformatics analyses using trabecular meshwork (TM) gene expression data in order to further elucidate the molecular pathogenesis of primary open-angle glaucoma (POAG), and to identify candidate target genes.

METHODS: A systematic search in Gene Expression Omnibus and ArrayExpress was conducted, and quality control and preprocessing of the data was performed with Molecular pathway overrepresentation analysis was performed with PathVisio using pathway content from three pathway databases: WikiPathways, KEGG and Reactome. In addition, Gene Ontology (GO) analysis was performed on the gene expression data. The significantly changed pathways were clustered into functional categories which were combined into a network of connected genes.

RESULTS: Ninety-two significantly changed pathways were clustered into five functional categories: extracellular matrix (ECM), inflammation, complement activation, senescence and Rho GTPase signalling. ECM included pathways involved in collagen, actin and cell-matrix interactions. Inflammation included pathways entailing NF-κB and arachidonic acid. The network analysis showed that several genes overlap between the inflammation cluster on the one hand, and the ECM, complement activation and senescence clusters on the other hand. GO analysis, identified additional clusters, related to development and corticosteroids.

CONCLUSION: This study provides an overview of the processes involved in the molecular pathogenesis of POAG in the TM. The results show good face validity and confirm findings from histological, biochemical, genome-wide association and transcriptomics studies. The identification of known points of action for drugs, such as Rho GTPase, arachidonic acid, NF-κB, prostaglandins and corticosteroid clusters, supports the value of this approach to identify potential drug targets.

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