ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells

Abstract

Next-generation sequencing has greatly increased the scope and the resolution of transcriptional regulation study. RNA sequencing (RNA-Seq) and ChIP-Seq experiments are now generating comprehensive data on transcript abundance and on regulator-DNA interactions. We propose an approach for an integrated analysis of these data based on feature extraction of ChIP-Seq signals, principal component analysis, and regression-based component selection. Compared with traditional methods, our approach not only offers higher power in predicting gene expression from ChIP-Seq data but also provides a way to capture cooperation among regulators. In mouse embryonic stem cells (ESCs), we find that a remarkably high proportion of variation in gene expression (65%) can be explained by the binding signals of 12 transcription factors (TFs). Two groups of TFs are identified. Whereas the first group (E2f1, Myc, Mycn, and Zfx) act as activators in general, the second group (Oct4, Nanog, Sox2, Smad1, Stat3, Tcfcp2l1, and Esrrb) may serve as either activator or repressor depending on the target. The two groups of TFs cooperate tightly to activate genes that are differentially up-regulated in ESCs. In the absence of binding by the first group, the binding of the second group is associated with genes that are repressed in ESCs and derepressed upon early differentiation.

Fig. 1.

Illustration of the binding peaks of E2f1 around three genes. The vertical axis represents the amplitude of the ChIP-Seq signals.

Fig. 2.

Model assessments. (A) Predicted versus observed ESC gene expression values for the RNA-Seq dataset on the binary TFAS. (B) RNA-Seq dataset on the continuous TFAS. (C) Microarray dataset on the binary TFAS. (D) Microarray dataset on the continuous TFAS. r is the Pearson correlation coefficient. (E) The R² statistics of individual TFPCs for the prediction of RNA-Seq gene expression. (F) The overall R² statistics for the predictions of gene expression under various conditions from the ESC ChIP-Seq data.

Fig. 3.

TFPCs capture the roles of TFs. (A) Regression coefficients of the model using individual TFASs as predictors. (B) Regression coefficients using TFPCs as predictors. (C) Loadings of TFs in the top three selected TFPCs, weighted by the fractions of variance in TFASs explained by the TFPCs. The error bars give the 95% bootstrap confidence intervals.