Purchase this article with an account.
Ilona Liesenborghs, Lars M.T. Eijssen, Martina Kutmon, Chris T. Evelo, Theo G.M.F. Gorgels, Wouter H.G. Hubens, Henny J Beckers, Carroll A.B. Webers, Jan S.A.G. Schouten; The signature of the healthy trabecular meshwork. Invest. Ophthalmol. Vis. Sci. 2019;60(9):3432.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Trabecular meshwork (TM) tissue plays an important role in glaucoma, however, its molecular pathogenesis is not yet completely understood. Identifying the signature genes and pathways in healthy TM tissue can provide a reference for the study of molecular disease processes and supports the selection of candidate target genes for new treatment options. Therefore, we performed in silico bioinformatics analyses based on all available gene expression datasets of healthy human TM tissue.
A systematic search for gene expression datasets was performed in Gene Expression Omnibus and ArrayExpress. We identified 17 datasets, containing data of 61 healthy individuals. Quality control and pre-processing of the datasets were performed with ArrayAnalysis.org. After removal of deviating samples, the datasets were jointly normalized and integrated into one database. Then, the average gene expression of each tested gene was calculated. If a gene was tested multiple times within one dataset, the reporter with the highest expression value was used. The top 10% genes with the highest average expression values were identified as the most active genes in the healthy TM. A broader cut-off of the top 25% genes was used as input for pathway analysis. This was performed with PathVisio using the pathways from two commonly used pathway databases: KEGG and WikiPathways. Pathways with a Z-score > 1.96 were selected as these are likely the most relevant TM active pathways. These pathways were clustered into functional categories. In addition, ubiquitous pathways and genes were identified using tissue-wide expression data in WikiPathways TissueAnalyzer. Thereafter, they were excluded from the results, enabling us to identify signature genes and pathways of the TM.
We identified 837 ubiquitous genes and 18 ubiquitous pathways which were excluded from our results. The remaining 1782 genes and 173 pathways were identified as the signature genes and pathways of the healthy TM tissue. Multiple of the identified genes are already known to be highly expressed in the TM, for example MYOC, VCL, MYL9, SEC23A, TAGLN, and SERPINA3. The pathways could be clustered in multiple functional clusters as for example VEGF, adhesion, and TGF-β, which already have been associated with processes in the TM.
We identified multiple non-ubiquitous genes and pathways that form the signature of the healthy TM.
This abstract was presented at the 2019 ARVO Annual Meeting, held in Vancouver, Canada, April 28 - May 2, 2019.
This PDF is available to Subscribers Only