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

Abstract

Genome-wide identification of bona-fide targets of transcription factors in mammalian cells is still a challenge. We present a novel integrated computational and experimental approach to identify direct targets of a transcription factor. This consists of measuring time-course (dynamic) gene expression profiles upon perturbation of the transcription factor under study, and in applying a novel "reverse-engineering" algorithm (TSNI) to rank genes according to their probability of being direct targets. Using primary keratinocytes as a model system, we identified novel transcriptional target genes of TRP63, a crucial regulator of skin development. TSNI-predicted TRP63 target genes were validated by Trp63 knockdown and by ChIP-chip to identify TRP63-bound regions in vivo. Our study revealed that short sampling times, in the order of minutes, are needed to capture the dynamics of gene expression in mammalian cells. We show that TRP63 transiently regulates a subset of its direct targets, thus highlighting the importance of considering temporal dynamics when identifying transcriptional targets. Using this approach, we uncovered a previously unsuspected transient regulation of the AP-1 complex by TRP63 through direct regulation of a subset of AP-1 components. The integrated experimental and computational approach described here is readily applicable to other transcription factors in mammalian systems and is complementary to genome-wide identification of transcription-factor binding sites.

Transcription factor p63 bookmarks and regulates dynamic enhancers during epidermal differentiation

“…Knockdown or knockout of p63 in epithelial cells to identify de-regulated genes [14, 17] has been the major approach in previous p63 target gene studies. The main drawback of this approach is that loss of p63 results in epithelial differentiation defects, and genes that are expressed at a later stage of differentiation and regulated by p63 may not be identified.…”

“…A number of studies have aimed at the identification of p63 target genes to understand the role of p63 in epidermal differentiation. Some studies took the RNAi approach to knock down p63 in proliferating keratinocytes and identified several hundreds of potentially p63-regulated genes [14, 17]. However, as p63 -depleted keratinocytes have differentiation defects [14, 17], potential direct p63 target genes at later stages of epidermal differentiation might not be identified using this approach.…”

“…Some studies took the RNAi approach to knock down p63 in proliferating keratinocytes and identified several hundreds of potentially p63-regulated genes [14, 17]. However, as p63 -depleted keratinocytes have differentiation defects [14, 17], potential direct p63 target genes at later stages of epidermal differentiation might not be identified using this approach. Furthermore, ChIP-seq analysis in human epidermal keratinocytes identified tens of thousands p63 binding sites in the human genome [18, 19].…”

“…Furthermore, ChIP-seq analysis in human epidermal keratinocytes identified tens of thousands p63 binding sites in the human genome [18, 19]. The large difference between the number of p63 binding sites [18, 19] and p63-regulated genes found using RNAi approaches [14, 17] raises the question whether all identified p63 binding sites are functional in gene regulation in epidermal keratinocytes. Here, we present the first comprehensive catalog of p63-regulated genes and enhancers during epidermal differentiation through epigenomics and transcriptomics profiling.…”

Utilizing cis-elements to refine gene regulatory networks

“…ARACNe [3] focuses on the targets prediction using mutual information measures. Ordinary Differential Equations (ODEs) and controlled perturbation are used in TSNI [4]. Bayesian network and Dynamic Bayesian network inference are employed in Banjo [5].…”