Abstract
Background: The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. TF knockdown experiments have revealed that TF binding profiles and gene expression levels are correlated. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets. Methods: Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. Corresponding promoter sequences were reduced to DNase I-accessible intervals; TFBSs were then identified within these intervals using information theory-based position weight matrices for each TF (iPWMs) and clustered. Features from information-dense TFBS clusters predicted these genes with machine learning classifiers, which were evaluated for accuracy, specificity and sensitivity. Mutations in TFBSs were analyzed to in silico examine their impact on cluster densities and the regulatory states of target genes. Results: We initially chose the glucocorticoid receptor gene ( NR3C1), whose regulation has been extensively studied, to test this approach. SLC25A32 and TANK were found to exhibit the most similar expression patterns to NR3C1. A Decision Tree classifier exhibited the largest area under the Receiver Operating Characteristic (ROC) curve in detecting such genes. Target gene prediction was confirmed using siRNA knockdown of TFs, which was found to be more accurate than those predicted after CRISPR/CAS9 inactivation. In-silico mutation analyses of TFBSs also revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes.
Highlights
The distinctive organization and combination of transcription factor binding sites (TFBSs) and regulatory modules in promoters dictates specific expression patterns within a set of genes1.Clustering of multiple adjacent binding sites for the same TF and for different TFs defines cis-regulatory modules (CRMs) in human gene promoters
Mutation analyses on promoters of TF targets To better understand the significance of individual binding sites for information-dense clusters and the regulatory state of direct targets, we evaluated the effects of sequence changes that altered the R values of these sites on cluster formation and i whether a gene was predicted to be a TF target
Similarity between Genotype-Tissue Expression (GTEx) tissue-wide expression profiles of genes To confirm that the Bray-Curtis Similarity can effectively measure how akin the tissue-wide expression profiles of two genes are to one another, Equation 2 was applied to compute the similarity values between the tissue-wide expression profiles of the glucocorticoid receptor (GR or NR3C1) gene and all other 18,812 PC genes
Summary
The distinctive organization and combination of transcription factor binding sites (TFBSs) and regulatory modules in promoters dictates specific expression patterns within a set of genes1.Clustering of multiple adjacent binding sites for the same TF (homotypic clusters) and for different TFs (heterotypic clusters) defines cis-regulatory modules (CRMs) in human gene promoters. The distinctive organization and combination of transcription factor binding sites (TFBSs) and regulatory modules in promoters dictates specific expression patterns within a set of genes. The distribution and composition of cis-regulatory modules composed of transcription factor (TF) binding site (TFBS) clusters in promoters substantially determine gene expression patterns and TF targets. We use TFBS features within accessible promoter intervals to predict genes with similar tissue-wide expression patterns and TF targets. Methods: Genes with correlated expression patterns across 53 tissues and TF targets were respectively identified from Bray-Curtis Similarity and TF knockdown experiments. In-silico mutation analyses of TFBSs revealed that one or more information-dense TFBS clusters in promoters are required for accurate target gene prediction. Conclusions: Machine learning based on TFBS information density, organization, and chromatin accessibility accurately identifies gene targets with comparable tissue-wide expression patterns. Multiple information-dense TFBS clusters in promoters appear to protect promoters from effects of deleterious binding site mutations in a single TFBS that would otherwise alter regulation of these genes
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