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A systematic model to predict transcriptional regulatory mechanisms based on overrepresentation of transcription factor binding profiles.

ABSTRACT: An important aspect of understanding a biological pathway is to delineate the transcriptional regulatory mechanisms of the genes involved. Two important tasks are often encountered when studying transcription regulation, i.e., (1) the identification of common transcriptional regulators of a set of coexpressed genes; (2) the identification of genes that are regulated by one or several transcription factors. In this study, a systematic and statistical approach was taken to accomplish these tasks by establishing an integrated model considering all of the promoters and characterized transcription factors (TFs) in the genome. A promoter analysis pipeline (PAP) was developed to implement this approach. PAP was tested using coregulated gene clusters collected from the literature. In most test cases, PAP identified the transcription regulators of the input genes accurately. When compared with chromatin immunoprecipitation experiment data, PAP's predictions are consistent with the experimental observations. When PAP was used to analyze one published expression-profiling data set and two novel coregulated gene sets, PAP was able to generate biologically meaningful hypotheses. Therefore, by taking a systematic approach of considering all promoters and characterized TFs in our model, we were able to make more reliable predictions about the regulation of gene expression in mammalian organisms.

SUBMITTER: Chang LW

PROVIDER: S-EPMC1415218 | biostudies-literature | 2006 Mar

REPOSITORIES: biostudies-literature

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A systematic model to predict transcriptional regulatory mechanisms based on overrepresentation of transcription factor binding profiles.

Chang Li-Wei LW Nagarajan Rakesh R Magee Jeffrey A JA Milbrandt Jeffrey J Stormo Gary D GD

Genome research 20060131 3

An important aspect of understanding a biological pathway is to delineate the transcriptional regulatory mechanisms of the genes involved. Two important tasks are often encountered when studying transcription regulation, i.e., (1) the identification of common transcriptional regulators of a set of coexpressed genes; (2) the identification of genes that are regulated by one or several transcription factors. In this study, a systematic and statistical approach was taken to accomplish these tasks b ...[more]

PMID: 16449500

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Project description:BACKGROUND: The human genome contains a large amount of cis-regulatory DNA elements responsible for directing both spatial and temporal gene-expression patterns. Previous studies have shown that based on their mRNA expression breast tumors could be divided into five subgroups (Luminal A, Luminal B, Basal, ErbB2(+) and Normal-like), each with a distinct molecular portrait. Whole genome gene expression analysis of independent sets of breast tumors reveals repeatedly the robustness of this classification. Furthermore, breast tumors carrying a TP53 mutation show a distinct gene expression profile, which is in strong association to the distinct molecular portraits. The mRNA expression of 552 genes, which varied considerably among the different tumors, but little between two samples of the same tumor, has been shown to be sufficient to separate these tumor subgroups. RESULTS: We analyzed in silico the transcriptional regulation of genes defining the subgroups at 3 different levels: 1. We studied the pathways in which the genes distinguishing the subgroups of breast cancer may be jointly involved including upstream regulators (1st and 2nd level of regulation) as well as downstream targets of these genes. 2. Then we analyzed the promoter areas of these genes (-500 bp tp +100 bp relative to the transcription start site) for canonical transcription binding sites using Genomatix. 3. We looked for the actual expression levels of the identified TF and how they correlate with the overrepresentation of their TF binding sites in the separate groups. We report that promoter composition of the genes that most strongly predict the patient subgroups is distinct. The class-predictive genes showed a clearly different degree of overrepresentation of transcription factor families in their promoter sequences. CONCLUSION: The study suggests that transcription factors responsible for the observed expression pattern in breast cancers may lead us to important biological pathways.

Dataset Information

A systematic model to predict transcriptional regulatory mechanisms based on overrepresentation of transcription factor binding profiles.

Publications

A systematic model to predict transcriptional regulatory mechanisms based on overrepresentation of transcription factor binding profiles.

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