Functional inference from non-random distributions of conserved predicted transcription factor binding sites

MOTIVATION Our understanding of how genes are regulated in a concerted fashion is still limited. Especially, complex phenomena like cell cycle regulation in multicellular organisms are poorly understood. Therefore, we investigated conserved predicted transcription factor binding sites (TFBSs) in man-mouse upstream regions of genes that can be associated to a particular cell cycle phase in HeLa cells. TFBSs were predicted from selected binding site motifs (represented by position weight matrices, PWMs) based on a statistical approach. A regulatory role for a transcription factor is more probable if its predicted TFBSs are enriched in upstream regions of genes, that are associated with a subset of cell cycle phases. We tested for this association by computing exact P-values for the observed phase distributions under the null distribution defined by the relative amount of conserved upstream sequence of genes per cell cycle phase. We considered non-exonic and 5'-untranslated region (5'-UTR) binding sites separately and corrected for multiple testing by taking the false discovery rate into account. RESULTS We identified 22 non-exonic and 11 5'-UTR significant PWM phase distributions although expecting one false discovery. Many of the corresponding transcription factors (e.g. members of the thyroid hormone/retinoid receptor subfamily) have already been associated with cell cycle regulation, proliferation and development. It appears that our method is a suitable tool for detecting putative cell cycle regulators in the realm of known human transcription factors. AVAILABILITY Further details and supplementary data can be obtained from http://corg.molgen.mpg.de/cellcycle

[1]  Martin Vingron,et al.  Exploring potential target genes of signaling pathways by predicting conserved transcription factor binding sites , 2003, ECCB.

[2]  V K Chatterjee,et al.  Inhibition of cellular proliferation through IkappaB kinase-independent and peroxisome proliferator-activated receptor gamma-dependent repression of cyclin D1. , 2001, Molecular and cellular biology.

[3]  P. Qiu.  Recent advances in computational promoter analysis in understanding the transcriptional regulatory network. , 2003, Biochemical and biophysical research communications.

[4]  Sven Rahmann,et al.  Dynamic Programming Algorithms for Two Statistical Problems in Computational Biology , 2003, WABI.

[5]  M Zweyer,et al.  The phosphoinositide 3-kinase/Akt pathway regulates cell cycle progression of HL60 human leukemia cells through cytoplasmic relocalization of the cyclin-dependent kinase inhibitor p27Kip1 and control of cyclin D1 expression , 2003, Leukemia.

[6]  Alexander E. Kel,et al.  TRANSFAC®: transcriptional regulation, from patterns to profiles , 2003, Nucleic Acids Res..

[7]  M. Tsai,et al.  Androgen regulation of the cyclin-dependent kinase inhibitor p21 gene through an androgen response element in the proximal promoter. , 1999, Molecular endocrinology.

[8]  John Gaspar,et al.  Opposing Functions of the Ets Factors NERF and ELF-1 During Chicken Blood Vessel Development , 2002, Arteriosclerosis, thrombosis, and vascular biology.

[9]  Luquan Wang,et al.  Comparative promoter analysis and its application in analysis of PTH-regulated gene expression. , 2003, Journal of molecular biology.

[10]  Martin Vingron,et al.  Annotating regulatory DNA based on man-mouse genomic comparison , 2002, ECCB.

[11]  Gill Bejerano.  Efficient exact value computation and applications to biosequence analysis , 2003, RECOMB '03.

[12]  R. Hardison.  Conserved noncoding sequences are reliable guides to regulatory elements. , 2000, Trends in genetics : TIG.

[13]  H. Samuels,et al.  Regulation of the mdm2 Oncogene by Thyroid Hormone Receptor , 1999, Molecular and Cellular Biology.

[14]  Tim Jordan,et al.  Fox's in development and disease. , 2003, Trends in genetics : TIG.

[15]  C. Ball,et al.  Identification of genes periodically expressed in the human cell cycle and their expression in tumors. , 2002, Molecular biology of the cell.

[16]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Douglas R Lowy,et al.  p16INK4a gene promoter variation and differential binding of a repressor, the ras-responsive zinc-finger transcription factor, RREB , 2003, Oncogene.

[18]  C. Lawrence,et al.  Human-mouse genome comparisons to locate regulatory sites , 2000, Nature Genetics.

[19]  Michael Brownlee,et al.  Inhibition of Cellular Proliferation through IκB Kinase-Independent and Peroxisome Proliferator-Activated Receptor γ-Dependent Repression of Cyclin D1 , 2001, Molecular and Cellular Biology.

[20]  R. Sharan,et al.  Genome-wide in silico identification of transcriptional regulators controlling the cell cycle in human cells. , 2003, Genome research.

[21]  M. Waterman,et al.  A new algorithm for best subsequence alignments with application to tRNA-rRNA comparisons. , 1987, Journal of molecular biology.

[22]  Martin Vingron,et al.  SYSTERS, GeneNest, SpliceNest: exploring sequence space from genome to protein , 2002, Nucleic Acids Res..

[23]  J. Nevins,et al.  Expression of transcription factor E2F1 induces quiescent cells to enter S phase , 1993, Nature.

[24]  Martin Vingron,et al.  CORG: a database for COmparative Regulatory Genomics , 2003, Nucleic Acids Res..

[25]  T. Volkert,et al.  E2F integrates cell cycle progression with DNA repair, replication, and G(2)/M checkpoints. , 2002, Genes & development.

[26]  Martin Vingron,et al.  On the Power of Profiles for Transcription Factor Binding Site Detection , 2003, Statistical applications in genetics and molecular biology.