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R.R. Mohan, S.E. Wilson; Characterization of Gene Expression Profile in Human Corneal Fibroblast Cells in Response to TGF beta-1, beta-2 and beta-3 using Microarray Technology . Invest. Ophthalmol. Vis. Sci. 2003;44(13):842.
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© ARVO (1962-2015); The Authors (2016-present)
Purpose: To characterize and compare the changes occurring in the gene expression of the human corneal fibroblast (HSF) cells in response to TGF beta-1, beta-2 and beta-3 at two different time points using microarray technology. Methods: The Affymetrix GeneChip Human Gemone U133 set made of two microarray chips (U133A and U133B) was used. The 70-75% confluent first passage HSF cultures generated by seeding the same number of cells were exposed to either vehicle, TGF beta-1, beta-2 or beta-3 for 1hour and 8 hours. The total RNA was prepared using Qiagen method. Gene Microarray assays were performed following the vendor's instructions. The changes in the gene expression were analyzed using Affymetrix Microarray Suite version 5.0 (MAS 5.0), advanced statistical algorithms, after normalization of experimental data. Results: Out of the screened 39,000 genes and ESTs, 16,498, 16,988 and 16,622 genes at 1 hour and 17,398, 16,995 and 17,231 genes at 8 hours were present in response to TGF beta-1, beta-2 and beta-3, respectively. At 1 hour time point 440, 339 and 257 genes showed statistically significant increase and 132, 111 and 138 genes showed statistically significant decrease in the expression over control in response to TGF beta-1, beta-2 and beta-3, respectively. Whereas at 8 hours 3,780, 1,961 and 2,033 genes showed statistically significant increase and 4,118, 2,140 and 1,449 genes showed statistically significant decrease in the expression in response to TGF beta-1, beta-2 and beta-3, respectively. The statistically significant marginal increase or decrease was excluded from the analysis. The RAS-like estrogen-regulated growth-inhibitor, collagen type VIII alpha 2, neuropeptide Y receptor Y2, and actin gamma 1 genes are among the top 10 genes that showed most statistically significant increase in expression over control in response to TGF beta-1, beta-2 or beta-3 over time. However, the UDP-glucose ceramide glucosyltransferase, suppressor of cytokine signaling 5, ras homolog gene family member E and BMP-2 inducible kinase genes are among the top 10 genes that demonstrated most statistically significant decrease over control under similar conditions. A detailed analysis of the gene profile at two different time points in response to 3 TGF-beta isoforms is underway. Conclusions: The simultaneous screening of the several thousands genes by the Microarray analysis would help in identifying gene(s) primarily involved in TGF signaling and would enhance our understanding of the molecular mechanisms involved in the process.
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