Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells

Abstract

Dissection of regulatory networks that control gene transcription is one of the greatest challenges of functional genomics. Using human genomic sequences, models for binding sites of known transcription factors, and gene expression data, we demonstrate that the reverse engineering approach, which infers regulatory mechanisms from gene expression patterns, can reveal transcriptional networks in human cells. To date, such methodologies were successfully demonstrated only in prokaryotes and low eukaryotes. We developed computational methods for identifying putative binding sites of transcription factors and for evaluating the statistical significance of their prevalence in a given set of promoters. Focusing on transcriptional mechanisms that control cell cycle progression, our computational analyses revealed eight transcription factors whose binding sites are significantly overrepresented in promoters of genes whose expression is cell-cycle-dependent. The enrichment of some of these factors is specific to certain phases of the cell cycle. In addition, several pairs of these transcription factors show a significant co-occurrence rate in cell-cycle-regulated promoters. Each such pair indicates functional cooperation between its members in regulating the transcriptional program associated with cell cycle progression. The methods presented here are general and can be applied to the analysis of transcriptional networks controlling any biological process.

Figure 1.

Representation of TF PWMs in the cell cycle phase clusters. The eight circles correspond to the PWMs that were highly enriched in promoters of cell-cycle-regulated genes (Table 3). Each circle is divided into five zones, corresponding to the phase clusters. The number adjacent to the zone represents the ratio of its prevalence in promoters contained in each of the cell cycle phase clusters to its prevalence in the set of 13K background promoters. Note that several TFs show a tendency toward specific cell cycle phases, for example, overrepresentation of the E2F PWM in promoters of the G₁/S and S clusters and its underrepresentation in promoters of the M/G₁ cluster.

Figure 2.

Distribution of locations of TF putative binding sites found in 568 cell-cycle-regulated promoters. Promoters were divided into six intervals, 200 bp each. For each of the PWMs listed in Table 3, the number of times its computationally identified binding sites appeared in each interval was counted (after accounting for the actual number of base pairs scanned in each interval; this number changes as the masked sequences are not uniformly distributed among the six intervals). Locations of NRF-1, CREB, NF-Y, Sp1, ATF, and E2F binding sites tend to concentrate in the vicinity of the TSSs (χ² test, p < 0.01).

Figure 3.

Pairs of PWMs that co-occur significantly in promoters of genes regulated in a cell cycle manner. We examined whether the nine PWMs reported in Tables 1 can be organized into regulatory modules. For each possible pair formed by these PWMs, we tested whether the prevalence of cell-cycle-regulated promoters that contain hits for both PWMs is significantly higher than would be expected if the PWMs occurred independently. Eight significant pairs were identified, each connected by an edge. The corresponding p-value is indicated next to the edge. The edge connecting the E2F–NRF1 pair is dashed to indicate that its significance is borderline.