Identification of interacting transcription factors regulating tissue gene expression in human. 2010

Zihua Hu, and Steven M Gallo
Center for Computational Research, New York State Center of Excellence in Bioinformatics & Life Sciences, Department of Biostatistics, Department of Medicine, State University of New York (SUNY), Buffalo, NY 14260, USA. zihuahu@ccr.buffalo.edu

BACKGROUND Tissue gene expression is generally regulated by multiple transcription factors (TFs). A major first step toward understanding how tissues achieve their specificity is to identify, at the genome scale, interacting TFs regulating gene expression in different tissues. Despite previous discoveries, the mechanisms that control tissue gene expression are not fully understood. RESULTS We have integrated a function conservation approach, which is based on evolutionary conservation of biological function, and genes with highest expression level in human tissues to predict TF pairs controlling tissue gene expression. To this end, we have identified 2549 TF pairs associated with a certain tissue. To find interacting TFs controlling tissue gene expression in a broad spatial and temporal manner, we looked for TF pairs common to the same type of tissues and identified 379 such TF pairs, based on which TF-TF interaction networks were further built. We also found that tissue-specific TFs may play an important role in recruiting non-tissue-specific TFs to the TF-TF interaction network, offering the potential for coordinating and controlling tissue gene expression across a variety of conditions. CONCLUSIONS The findings from this study indicate that tissue gene expression is regulated by large sets of interacting TFs either on the same promoter of a gene or through TF-TF interaction networks.

UI MeSH Term Description Entries
D008099 Liver A large lobed glandular organ in the abdomen of vertebrates that is responsible for detoxification, metabolism, synthesis and storage of various substances. Livers
D009132 Muscles Contractile tissue that produces movement in animals. Muscle Tissue,Muscle,Muscle Tissues,Tissue, Muscle,Tissues, Muscle
D011401 Promoter Regions, Genetic DNA sequences which are recognized (directly or indirectly) and bound by a DNA-dependent RNA polymerase during the initiation of transcription. Highly conserved sequences within the promoter include the Pribnow box in bacteria and the TATA BOX in eukaryotes. rRNA Promoter,Early Promoters, Genetic,Late Promoters, Genetic,Middle Promoters, Genetic,Promoter Regions,Promoter, Genetic,Promotor Regions,Promotor, Genetic,Pseudopromoter, Genetic,Early Promoter, Genetic,Genetic Late Promoter,Genetic Middle Promoters,Genetic Promoter,Genetic Promoter Region,Genetic Promoter Regions,Genetic Promoters,Genetic Promotor,Genetic Promotors,Genetic Pseudopromoter,Genetic Pseudopromoters,Late Promoter, Genetic,Middle Promoter, Genetic,Promoter Region,Promoter Region, Genetic,Promoter, Genetic Early,Promoter, rRNA,Promoters, Genetic,Promoters, Genetic Middle,Promoters, rRNA,Promotor Region,Promotors, Genetic,Pseudopromoters, Genetic,Region, Genetic Promoter,Region, Promoter,Region, Promotor,Regions, Genetic Promoter,Regions, Promoter,Regions, Promotor,rRNA Promoters
D005786 Gene Expression Regulation Any of the processes by which nuclear, cytoplasmic, or intercellular factors influence the differential control (induction or repression) of gene action at the level of transcription or translation. Gene Action Regulation,Regulation of Gene Expression,Expression Regulation, Gene,Regulation, Gene Action,Regulation, Gene Expression
D006801 Humans Members of the species Homo sapiens. Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D014157 Transcription Factors Endogenous substances, usually proteins, which are effective in the initiation, stimulation, or termination of the genetic transcription process. Transcription Factor,Factor, Transcription,Factors, Transcription
D015233 Models, Statistical Statistical formulations or analyses which, when applied to data and found to fit the data, are then used to verify the assumptions and parameters used in the analysis. Examples of statistical models are the linear model, binomial model, polynomial model, two-parameter model, etc. Probabilistic Models,Statistical Models,Two-Parameter Models,Model, Statistical,Models, Binomial,Models, Polynomial,Statistical Model,Binomial Model,Binomial Models,Model, Binomial,Model, Polynomial,Model, Probabilistic,Model, Two-Parameter,Models, Probabilistic,Models, Two-Parameter,Polynomial Model,Polynomial Models,Probabilistic Model,Two Parameter Models,Two-Parameter Model
D016000 Cluster Analysis A set of statistical methods used to group variables or observations into strongly inter-related subgroups. In epidemiology, it may be used to analyze a closely grouped series of events or cases of disease or other health-related phenomenon with well-defined distribution patterns in relation to time or place or both. Clustering,Analyses, Cluster,Analysis, Cluster,Cluster Analyses,Clusterings
D053263 Gene Regulatory Networks Interacting DNA-encoded regulatory subsystems in the GENOME that coordinate input from activator and repressor TRANSCRIPTION FACTORS during development, cell differentiation, or in response to environmental cues. The networks function to ultimately specify expression of particular sets of GENES for specific conditions, times, or locations. Gene Circuits,Gene Modules,Gene Networks,Transcriptional Networks,Gene Module,Circuit, Gene,Circuits, Gene,Gene Circuit,Gene Network,Gene Regulatory Network,Module, Gene,Modules, Gene,Network, Gene,Network, Gene Regulatory,Network, Transcriptional,Networks, Gene,Networks, Gene Regulatory,Networks, Transcriptional,Regulatory Network, Gene,Regulatory Networks, Gene,Transcriptional Network
D019295 Computational Biology A field of biology concerned with the development of techniques for the collection and manipulation of biological data, and the use of such data to make biological discoveries or predictions. This field encompasses all computational methods and theories for solving biological problems including manipulation of models and datasets. Bioinformatics,Molecular Biology, Computational,Bio-Informatics,Biology, Computational,Computational Molecular Biology,Bio Informatics,Bio-Informatic,Bioinformatic,Biologies, Computational Molecular,Biology, Computational Molecular,Computational Molecular Biologies,Molecular Biologies, Computational

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