Integration of genome and chromatin structure with gene expression profiles to predict c-MYC recognition site binding and function.
The MYC genes encode nuclear sequence specific-binding DNA-binding proteins that are pleiotropic regulators of cellular function, and the c-MYC proto-oncogene is deregulated and/or mutated in most human cancers. Experimental studies of MYC binding to the genome are not fully consistent. While many c-MYC recognition sites can be identified in c-MYC responsive genes, other motif matches-even experimentally confirmed sites-are associated with genes showing no c-MYC response. We have developed a computational model that integrates multiple sources of evidence to predict which genes will bind and be regulated by MYC in vivo. First, a Bayesian network classifier is used to predict those c-MYC recognition sites that are most likely to exhibit high-occupancy binding in chromatin immunoprecipitation studies. This classifier incorporates genomic sequence, experimentally determined genomic chromatin acetylation islands, and predicted methylation status from a computational model estimating the likelihood of genomic DNA methylation. We find that the predictions from this classifier are also applicable to other transcription factors, such as cAMP-response element-binding protein, whose binding sites are sensitive to DNA methylation. Second, the MYC binding probability is combined with the gene expression profile data from nine independent microarray datasets in multiple tissues. Finally, we may consider gene function annotations in Gene Ontology to predict the c-MYC targets. We assess the performance of our prediction results by comparing them with the c-myc targets identified in the biomedical literature. In total, we predict 460 likely c-MYC target genes in the human genome, of which 67 have been reported to be both bound and regulated by MYC, 68 are bound by MYC, and another 80 are MYC-regulated. The approach thus successfully identifies many known c-MYC targets and suggests many novel sites. Our findings suggest that to identify c-MYC genomic targets, integration of different data sources helps to improve the accuracy.
Chen
Yili
Y
Bioinformatics Program, University of Michigan Medical School, Ann Arbor, Michigan, United States of America.
Blackwell
Thomas W
TW
Chen
Ji
J
Gao
Jing
J
Lee
Angel W
AW
States
David J
DJ
D001665:Binding Sites; D002843:Chromatin / Q000737:chemistry* / Q000235:genetics*; D002874:Chromosome Mapping; D003198:Computer Simulation; D004268:DNA-Binding Proteins / Q000737:chemistry* / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D008956:Models, Chemical; D011485:Protein Binding; D025941:Protein Interaction Mapping; D000090063:Proto-Oncogene Mas; D020539:Sequence Analysis, Protein / Q000379:methods*; D013329:Structure-Activity Relationship; D018511:Systems Integration; D014157:Transcription Factors / Q000737:chemistry* / Q000235:genetics*
D002843:Chromatin; D004268:DNA-Binding Proteins; C000715470:MAS1 protein, human; C116115:MYCBP protein, human; D000090063:Proto-Oncogene Mas; D014157:Transcription Factors