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Nuclear colocalization of transcription factor target genes strengthens coregulation in yeast.

ABSTRACT: Eukaryotic chromosomes are not randomly distributed in the interphase nucleus, but instead occupy distinct territories. Nonetheless, the genome-wide relationships of gene regulation to gene nuclear location remain poorly understood in yeast. In the three-dimensional view of gene regulation, we found that a considerable number of transcription factors (TFs) regulate genes that are colocalized in the nucleus. Colocalized TF target genes are more strongly coregulated compared with the other TF target genes. Target genes of chromatin regulators are also colocalized. These results demonstrate that colocalization of coregulated genes is a common process, and three-dimensional gene positioning is an important part of gene regulation. Our findings will have implications in understanding nuclear architecture and function.

SUBMITTER: Dai Z

PROVIDER: S-EPMC3245921 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature

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Nuclear colocalization of transcription factor target genes strengthens coregulation in yeast.

Dai Zhiming Z Dai Xianhua X

Nucleic acids research 20110831 1

Eukaryotic chromosomes are not randomly distributed in the interphase nucleus, but instead occupy distinct territories. Nonetheless, the genome-wide relationships of gene regulation to gene nuclear location remain poorly understood in yeast. In the three-dimensional view of gene regulation, we found that a considerable number of transcription factors (TFs) regulate genes that are colocalized in the nucleus. Colocalized TF target genes are more strongly coregulated compared with the other TF targ ...[more]

PMID: 21880591

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Project description:High throughput technologies, including array-based chromatin immunoprecipitation, have rapidly increased our knowledge of transcriptional maps-the identity and location of regulatory binding sites within genomes. Still, the full identification of sites, even in lower eukaryotes, remains largely incomplete. In this paper we develop a supervised learning approach to site identification using support vector machines (SVMs) to combine 26 different data types. A comparison with the standard approach to site identification using position specific scoring matrices (PSSMs) for a set of 104 Saccharomyces cerevisiae regulators indicates that our SVM-based target classification is more sensitive (73 vs. 20%) when specificity and positive predictive value are the same. We have applied our SVM classifier for each transcriptional regulator to all promoters in the yeast genome to obtain thousands of new targets, which are currently being analyzed and refined to limit the risk of classifier over-fitting. For the purpose of illustration we discuss several results, including biochemical pathway predictions for Gcn4 and Rap1. For both transcription factors SVM predictions match well with the known biology of control mechanisms, and possible new roles for these factors are suggested, such as a function for Rap1 in regulating fermentative growth. We also examine the promoter melting temperature curves for the targets of YJR060W, and show that targets of this TF have potentially unique physical properties which distinguish them from other genes. The SVM output automatically provides the means to rank dataset features to identify important biological elements. We use this property to rank classifying k-mers, thereby reconstructing known binding sites for several TFs, and to rank expression experiments, determining the conditions under which Fhl1, the factor responsible for expression of ribosomal protein genes, is active. We can see that targets of Fhl1 are differentially expressed in the chosen conditions as compared to the expression of average and negative set genes. SVM-based classifiers provide a robust framework for analysis of regulatory networks. Processing of classifier outputs can provide high quality predictions and biological insight into functions of particular transcription factors. Future work on this method will focus on increasing the accuracy and quality of predictions using feature reduction and clustering strategies. Since predictions have been made on only 104 TFs in yeast, new classifiers will be built for the remaining 100 factors which have available binding data.

Dataset Information

Nuclear colocalization of transcription factor target genes strengthens coregulation in yeast.

Publications

Nuclear colocalization of transcription factor target genes strengthens coregulation in yeast.

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