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Inhibitory effect of TIS7 on Sp1-C/EBPalpha transcription factor module activity.

N Wick¹, A Schleiffer, L A Huber

¹Clinical Institute for Pathology, University of Vienna, Austria Waehringer Guertel 18-20, A-1190 Vienna, Austria.

Journal of Molecular Biology

|April 21, 2004

Summary

Transcriptional co-repressor TIS7 inhibits gene expression by interfering with the C/EBPalpha-Sp1 transcription factor module. This study details TIS7

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Area of Science:

Molecular Biology
Gene Regulation
Transcription Factors

Background:

C/EBPalpha and Sp1 transcription factors form a module that drives gene expression during cell differentiation.
TIS7 (TPA inducible sequence 7) acts as a co-repressor, down-regulating specific gene transcription in epithelial cells.
Previous work identified TIS7 as a co-repressor in epithelial cells experiencing loss of polarity.

Purpose of the Study:

To investigate the mechanism by which TIS7 inhibits gene transcription.
To determine if TIS7 interacts with the C/EBPalpha-Sp1 transcription factor module.
To validate the utility of bioinformatic analysis in predicting transcription factor module interactions.

Main Methods:

Bioinformatic analysis to identify common binding sites for the C/EBPalpha-Sp1 module in TIS7-regulated genes.
Reporter assays to confirm the inhibitory effect of TIS7 on C/EBPalpha-Sp1-mediated transcription.
Experiments to elucidate the specific interference of TIS7 with Sp1 transcriptional activity and DNA binding.

Main Results:

A common binding site for the C/EBPalpha-Sp1 module was identified in the regulatory regions of TIS7-regulated genes.
TIS7 was confirmed to inhibit C/EBPalpha-Sp1-mediated transcription.
TIS7 was shown to interfere with Sp1 transcriptional activity and prevent Sp1 binding to its consensus DNA site.

Conclusions:

TIS7 functions as a transcriptional co-repressor by specifically interfering with Sp1 activity within the C/EBPalpha-Sp1 module.
This interference prevents the formation of a functional Sp1-DNA complex, thereby down-regulating target gene expression.
The study highlights the effectiveness of bioinformatic approaches for predicting and analyzing transcription factor module functions.