June 2013
Volume 54, Issue 15
Free
ARVO Annual Meeting Abstract  |   June 2013
Predicting Gene Networks and Transcription Factors in TNFα Treatment of Trabecular Meshwork Cells
Author Affiliations & Notes
  • Dongseok Choi
    Biostatistics, Public Hlth & Preventive Med, Oregon Health & Science Univ, Portland, OR
  • Lauren Hayashi
    Casey Eye Institute, Oregon Health & Science Univ, Portland, OR
  • Kathryn Carr
    Biostatistics, Public Hlth & Preventive Med, Oregon Health & Science Univ, Portland, OR
  • Mary Kelley
    Casey Eye Institute, Oregon Health & Science Univ, Portland, OR
  • Ted Acott
    Casey Eye Institute, Oregon Health & Science Univ, Portland, OR
Investigative Ophthalmology & Visual Science June 2013, Vol.54, 6225. doi:
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    • Get Citation

      Dongseok Choi, Lauren Hayashi, Kathryn Carr, Mary Kelley, Ted Acott; Predicting Gene Networks and Transcription Factors in TNFα Treatment of Trabecular Meshwork Cells. Invest. Ophthalmol. Vis. Sci. 2013;54(15):6225.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose: Tumor necrosis factor alpha (TNFα) is a crucial mediator of the therapeutic effects of laser trabeculoplasty on glaucoma. TNFα treatment increases matrix metalloproteinases (MMPs), enzymes that initiate turnover in the extracellular matrix (ECM), of trabecular meshwork (TM) cells. To further study ECM remodeling and its effects on aqueous humor outflow resistance, we conducted analyses to identify gene networks and their putative transcription factor binding sites by gene expression in response to TNFα treatment in TM cells.

Methods: Primary porcine TM cells were treated with recombinant human TNFα (10 ng/ml). At 12, 24, and 48 hours after treatment, total RNA was collected for gene expression profiling. The Significance Analysis of Microarrays was performed to identify differentially expressed genes with a q-value < 5%. TightClust cluster analysis produced small groups of significant genes by similar temporal patterns. Tight clusters up-regulated at 24 and 48 hr were analyzed for gene networks by Metacore. DNA promoter sequences for those genes were extracted using Ensembl Genome Browser. Masked sequences were collected in order to remove regions of interspersed repeats or areas of low complexity for 5K bp upstream with 2K bp downstream. The sequences were analyzed by CisModule to identify putative cis-regulatory motifs. The consensus sequences of binding sites predicted by CisModule were identified by TRANSFAC Professional database against known cis-regulatory motifs.

Results: The Metacore analysis produced several potential gene networks. At 24 hr, cluster 45 mapped to a network around CD44 and JNK1. At 48hr, cluster 14 mapped to a network surrounding BMP7 and cluster 16 at around SP1, STAT1 and SMAD. The CisModule and TRANSFAC analyses predicted potential transcription factors for each cluster. LRF, TFII-I, and unknown were predicted for cluster 45; MSX-1, CDXA, and RFX for cluster 14; and MAX, KID3, and WT1 for cluster 16.

Conclusions: We identified potential key networks of genes and their putative transcription factors in response to TNFα treatment of TM cells by combining biostatistics and bioinformatics tools. Transcription factors play a key role in regulating gene expression patterns. Further validation studies will be required to confirm the functionality of these networks and transcription factors.

Keywords: 533 gene/expression • 739 transcription factors • 735 trabecular meshwork  
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