Adaptively inferring human transcriptional subnetworks

Abstract

Although the human genome has been sequenced, progress in understanding gene regulation in humans has been particularly slow. Many computational approaches developed for lower eukaryotes to identify cis-regulatory elements and their associated target genes often do not generalize to mammals, largely due to the degenerate and interactive nature of such elements. Motivated by the switch-like behavior of transcriptional responses, we present a systematic approach that allows adaptive determination of active transcriptional subnetworks (cis-motif combinations, the direct target genes and physiological processes regulated by the corresponding transcription factors) from microarray data in mammals, with accuracy similar to that achieved in lower eukaryotes. Our analysis uncovered several new subnetworks active in human liver and in cell-cycle regulation, with similar functional characteristics as the known ones. We present biochemical evidence for our predictions, and show that the recently discovered G2/M-specific E2F pathway is wider than previously thought; in particular, E2F directly activates certain mitotic genes involved in hepatocellular carcinomas. Additionally, we demonstrate that this method can predict subnetworks in a condition-specific manner, as well as regulatory crosstalk across multiple tissues. Our approach allows systematic understanding of how phenotypic complexity is regulated at the transcription level in mammals and offers marked advantage in systems where little or no prior knowledge of transcriptional regulation is available.

Figure 1

Modeling mammalian transcription with linear splines. (A) Sigmoidal transcriptional response (Carey, 1998). The response is flat below a binding affinity threshold, namely, the gene activation threshold, and varies exponentially above it. It saturates at high binding energies. (B) Example of a linear spline. A linear spline is a piecewise linear function: it is zero below (above) a threshold, termed knot (ξ), and changes linearly above (below) it. E_g refers to the observed mRNA level of gene g, whereas E_gC is its mRNA level in the reference sample. Knots are related to gene activation thresholds. All genes with PWM scores S_g>ξ are predicted targets of the motif contributing to this spline, shaded in blue. (C) A schematic view of the key steps in identification of significant motifs and motif pairs using linear splines.

Figure 2

Schematic representation of our analysis. A snapshot of the tissue-specific transcriptional subnetworks discovered from microarray data on adult human liver under a normal condition.

Figure 3

Biochemical validation of target genes. Results of RT–PCR analysis in NIH3T3 fibroblasts expressing an inducible ERE2F1 or a transactivation-deficient mutant, MERE2F1. At time zero, medium containing 500 nM of 4-hydroxytamoxifen (OHT) was added and transcript levels of (A) DLG7 and (B) CDC16 were determined using gene-specific primers at the indicated times by RT–PCR. The respective transcript levels with 10 μg/ml of cyclohexamide (CTX) added are shown in (C) DLG7 and (D) CDC16. GAPDH was used as a standardization control. Plotted data reflect results after normalization with GAPDH.