Interacting models of cooperative gene regulation

Abstract

Cooperativity between transcription factors is critical to gene regulation. Current computational methods do not take adequate account of this salient aspect. To address this issue, we present a computational method based on multivariate adaptive regression splines to correlate the occurrences of transcription factor binding motifs in the promoter DNA and their interactions to the logarithm of the ratio of gene expression levels. This allows us to discover both the individual motifs and synergistic pairs of motifs that are most likely to be functional, and enumerate their relative contributions at any arbitrary time point for which mRNA expression data are available. We present results of simulations and focus specifically on the yeast cell-cycle data. Inclusion of synergistic interactions can increase the prediction accuracy over linear regression to as much as 1.5-to 3.5-fold. Significant motifs and combinations of motifs are appropriately predicted at each stage of the cell cycle. We believe our multivariate adaptive regression splines-based approach will become more significant when applied to higher eukaryotes, especially mammals, where cooperative control of gene regulation is absolutely essential.cooperativity ͉ correlation ͉ expression data ͉ transcription regulation R egulation of gene transcription in eukaryotes is complex and is inherently combinatorial in nature (1, 2). Transcriptional synergy is a key element of such combinatorial control in gene regulation networks. It requires cooperative binding of multiple transcription factors (TFs) and is intrinsically nonlinear in nature (2). Taking adequate account of such synergy in computational models is extremely important to have an accurate view of the underlying biology.Conventional computational methods (3) have focused on identifying motifs upstream of the clusters of coexpressed genes. However, many genes fail to cluster and, therefore, regulatory elements of a large number of genes are unknown. Recent work (4, 5) has attempted to overcome this problem by correlating the frequency of DNA motifs with the logarithm of expression levels by using multivariate linear regression. Despite the success in identifying many known important motifs, this method does not account for the synergistic effects and nonlinearities present during transcription regulation. When applied to the yeast cell-cycle data, we found that these methods can explain only 10% of the variations in the data on an average (noise level accounts for Ϸ50%; ref. 4).More recently, models that account for cooperativity between TFs during transcription regulation have been developed (6-10). However, all of these models are limited by one or more of the following factors. Some of these methods (6-8), like expression coherence (EC) score approach (6, 7), require data from multiple time points, which are not always available. Methods based on regression trees (8), on the other hand, cannot take proper account of additive effects. In other cases (9, 10), we found either the known pairs of motifs ...

DNA motifs in human and mouse proximal promoters predict tissue-specific expression

“…Motifs are combined into pairs and triples to form putative CRMs based on their combined ability to differentiate positive from negative promoters. Predictors are constructed from CRMs using multivariate adaptive regression splines (MARS) (9,16,50). MARS can predict the linear and nonlinear relation between CRMs, capturing a multitude of interactive behaviors.…”

“…Predictors are constructed with the MARS algorithm (9,16,50) by using motif and module features. We used MARS to construct predictors that include seven terms using stepwise forward addition of linear splines and their products.…”

“…Identification and categorization of the entire repertoire of TFBSs are among the greatest challenges in systems biology (5)(6)(7). Although CRM identification in lower eukaryotes, such as various yeast species, has been progressing rapidly (8,9), similar efforts in vertebrates have proven to be especially difficult due to the genomic and regulatory complexity of the higher organisms (10). CRMs in vertebrate regulatory regions not only control transcription during the life of individual cells but also must orchestrate cellular communication during tissue differentiation, morphogenesis, and body-part formation (11), as well as maintain specific patterns of transcription in terminally differentiated tissues (12).…”

“…Although CRM identification in lower eukaryotes, such as various yeast species, has been progressing rapidly (8, 9), similar efforts in vertebrates have proven to be especially difficult due to the genomic and regulatory complexity of the higher organisms (10). CRMs in vertebrate regulatory regions not only control transcription during the life of individual cells but also must orchestrate cellular communication during tissue differentiation, morphogenesis, and body-part formation (11), as well as maintain specific patterns of transcription in terminally differentiated tissues (12).Recent work to predict expression using cis-regulatory elements includes studies on yeast (6,9,13,14), identification of target genes and binding sites for specific transcription factors (15, 16), and an evaluation of the effect of TFBS quality (17) on target regulation. Bussemaker et al (13) and Conlon et al (14) used linear regression to fit the count of predicted cis-regulatory elements (13) or the sum of the likelihood ratios of all potential cis-regulatory elements (14) to expression intensity.…”

“…Recent work to predict expression using cis-regulatory elements includes studies on yeast (6,9,13,14), identification of target genes and binding sites for specific transcription factors (15, 16), and an evaluation of the effect of TFBS quality (17) on target regulation. Bussemaker et al (13) and Conlon et al (14) used linear regression to fit the count of predicted cis-regulatory elements (13) or the sum of the likelihood ratios of all potential cis-regulatory elements (14) to expression intensity.…”