Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells.
Dissection of regulatory networks that control gene transcription is one of the greatest challenges of functional genomics. Using human genomic sequences, models for binding sites of known transcription factors, and gene expression data, we demonstrate that the reverse engineering approach, which infers regulatory mechanisms from gene expression patterns, can reveal transcriptional networks in human cells. To date, such methodologies were successfully demonstrated only in prokaryotes and low eukaryotes. We developed computational methods for identifying putative binding sites of transcription factors and for evaluating the statistical significance of their prevalence in a given set of promoters. Focusing on transcriptional mechanisms that control cell cycle progression, our computational analyses revealed eight transcription factors whose binding sites are significantly overrepresented in promoters of genes whose expression is cell-cycle-dependent. The enrichment of some of these factors is specific to certain phases of the cell cycle. In addition, several pairs of these transcription factors show a significant co-occurrence rate in cell-cycle-regulated promoters. Each such pair indicates functional cooperation between its members in regulating the transcriptional program associated with cell cycle progression. The methods presented here are general and can be applied to the analysis of transcriptional networks controlling any biological process.
The David and Inez Myers Laboratory for Genetic Research, Department of Human Genetics, Sackler School of Medicine, and School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
Ran
R
Elkon
Chaim
C
Linhart
Roded
R
Sharan
Ron
R
Shamir
Yosef
Y
Shiloh
D001665:Binding Sites / Q000235:genetics; D002453:Cell Cycle / Q000235:genetics*; D018797:Cell Cycle Proteins / Q000096:biosynthesis; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D004268:DNA-Binding Proteins / Q000235:genetics; D030541:Databases, Genetic / Q000706:statistics & numerical data; D050684:E2F Transcription Factors; D020869:Gene Expression Profiling / Q000379:methods / Q000706:statistics & numerical data; D005786:Gene Expression Regulation / Q000235:genetics*; D018816:Genes, cdc; D015894:Genome, Human; D006801:Humans; D011401:Promoter Regions, Genetic / Q000235:genetics; D014157:Transcription Factors / Q000235:genetics; D014158:Transcription, Genetic / Q000235:genetics*
D018797:Cell Cycle Proteins; D004268:DNA-Binding Proteins; D050684:E2F Transcription Factors; D014157:Transcription Factors