Dataset Information

A systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response.

ABSTRACT: Duplication of genes encoding transcription factors plays an essential role in driving phenotypic variation. Because regulation can occur at multiple levels, it is often difficult to discern how each duplicated factor achieves its regulatory specificity. In these cases, a "systems approach" may distinguish the role of each factor by integrating complementary large-scale measurements of the regulatory network. To explore such an approach, we integrate growth phenotypes, promoter binding profiles, and gene expression patterns to model the DNA damage response network controlled by the Yeast-specific AP-1 (YAP) family of transcription factors. This analysis reveals that YAP regulatory specificity is achieved by at least three mechanisms: (i) divergence of DNA-binding sequences into two subfamilies; (ii) condition-specific combinatorial regulation by multiple Yap factors; and (iii) interactions of Yap 1, 4, and 6 with chromatin remodeling proteins. Additional microarray experiments establish that Yap 4 and 6 regulate gene expression through interactions with the histone deacetylase, Hda1. The data further highlight differences among Yap paralogs in terms of their regulatory mode of action (activation vs. repression). This study suggests how other large TF families might be disentangled in the future.

SUBMITTER: Tan K

PROVIDER: S-EPMC2268563 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

A systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response.

Tan Kai K Feizi Hoda H Luo Colin C Fan Stephanie H SH Ravasi Timothy T Ideker Trey G TG

Proceedings of the National Academy of Sciences of the United States of America 20080219 8

Duplication of genes encoding transcription factors plays an essential role in driving phenotypic variation. Because regulation can occur at multiple levels, it is often difficult to discern how each duplicated factor achieves its regulatory specificity. In these cases, a "systems approach" may distinguish the role of each factor by integrating complementary large-scale measurements of the regulatory network. To explore such an approach, we integrate growth phenotypes, promoter binding profiles, ...[more]

PMID: 18287073

Similar Datasets

Project description:BackgroundBiological entities do not perform in isolation, and often, it is the nature and degree of interactions among numerous biological entities which ultimately determines any final outcome. Hence, experimental data on any single biological entity can be of limited value when considered only in isolation. To address this, we propose that augmenting individual entity data with the literature will not only better define the entity's own significance but also uncover relationships with novel biological entities.To test this notion, we developed a comprehensive text mining and computational methodology that focused on discovering new targets of one class of molecular entities, transcription factors (TF), within one particular disease, colorectal cancer (CRC).MethodsWe used 39 molecular entities known to be associated with CRC along with six colorectal cancer terms as the bait list, or list of search terms, for mining the biomedical literature to identify CRC-specific genes and proteins. Using the literature-mined data, we constructed a global TF interaction network for CRC. We then developed a multi-level, multi-parametric methodology to identify TFs to CRC.ResultsThe small bait list, when augmented with literature-mined data, identified a large number of biological entities associated with CRC. The relative importance of these TF and their associated modules was identified using functional and topological features. Additional validation of these highly-ranked TF using the literature strengthened our findings. Some of the novel TF that we identified were: SLUG, RUNX1, IRF1, HIF1A, ATF-2, ABL1, ELK-1 and GATA-1. Some of these TFs are associated with functional modules in known pathways of CRC, including the Beta-catenin/development, immune response, transcription, and DNA damage pathways.ConclusionsOur methodology of using text mining data and a multi-level, multi-parameter scoring technique was able to identify both known and novel TF that have roles in CRC. Starting with just one TF (SMAD3) in the bait list, the literature mining process identified an additional 116 CRC-associated TFs. Our network-based analysis showed that these TFs all belonged to any of 13 major functional groups that are known to play important roles in CRC. Among these identified TFs, we obtained a novel six-node module consisting of ATF2-P53-JNK1-ELK1-EPHB2-HIF1A, from which the novel JNK1-ELK1 association could potentially be a significant marker for CRC.

Dataset Information

A systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response.

Publications

A systems approach to delineate functions of paralogous transcription factors: role of the Yap family in the DNA damage response.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets