Direct targets of the TRP63 transcription factor revealed by a combination of gene expression profiling and reverse engineering

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Figure 1.
Figure 1.

(A) Experimental and computational approach for the identification of TRP63 target genes. ΔNp63α was fused to a modified estrogen receptor domain (ERΔNp63α) and expressed in primary mouse keratinocytes by retroviral infection. Upon treatment with estrogen agonist tamoxifen, total RNA was collected at 10-min intervals for the first hour, and then at 20-min intervals until 4 h. Dynamic gene expression data were filtered by the TSNI algorithm to yield a ranked list of predicted direct TRP63 target genes. Validation was performed by analyzing global gene expression data upon Trp63 knockdown and by ChIP-chip analysis using TRP63 specific antibodies. (B) Expression profiles of a ΔNp63α responsive gene following its activation. Y-axis shows mRNA levels in the ERΔNp63α expressing keratinocytes treated with tamoxifen versus untreated ones expressed as log2 ratio (i.e., +1 corresponds to a twofold increase with respect to the 0 time point [untreated], −1 to a twofold decrease). Gene expression data were clustered using a Hierarchical Clustering approach with correlation metric and average linkage to generate the hierarchical tree. The number of clusters was set to 5. (C) Transcripts in Clusters 2 and 4 were classified using a functional annotation enrichment analysis (see Supplemental Table 3). The biological categories are as indicated in the pie charts, and the number of transcripts for each category is indicated in parenthesis.

This Article

  1. Genome Res. 18: 939-948

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