» Articles » PMID: 16982645

Computational Analysis of Tissue-specific Combinatorial Gene Regulation: Predicting Interaction Between Transcription Factors in Human Tissues

Overview
Specialty Biochemistry
Date 2006 Sep 20
PMID 16982645
Citations 94
Authors
Affiliations
Soon will be listed here.
Abstract

Tissue-specific gene expression is generally regulated by more than a single transcription factor (TF). Multiple TFs work in concert to achieve tissue specificity. In order to explore these complex TF interaction networks, we performed a large-scale analysis of TF interactions for 30 human tissues. We first identified tissue-specific genes for 30 tissues based on gene expression databases. We then evaluated the relationships between TFs using the relative position and co-occurrence of their binding sites in the promoters of tissue-specific genes. The predicted TF-TF interactions were validated by both known protein-protein interactions and co-expression of their target genes. We found that our predictions are enriched in known protein-protein interactions (>80 times that of random expectation). In addition, we found that the target genes show the highest co-expression in the tissue of interest. Our findings demonstrate that non-tissue specific TFs play a large role in regulation of tissue-specific genes. Furthermore, they show that individual TFs can contribute to tissue specificity in different tissues by interacting with distinct TF partners. Lastly, we identified several tissue-specific TF clusters that may play important roles in tissue-specific gene regulation.

Citing Articles

Identification of EXPA4 as a key gene in cotton salt stress adaptation through transcriptomic and coexpression network analysis of root tip protoplasts.

Liu Q, Li P, Umer M, Abbas M, Zhao Y, Chen Y BMC Plant Biol. 2025; 25(1):65.

PMID: 39815183 PMC: 11736990. DOI: 10.1186/s12870-024-05958-w.


PPIA-coExp: Discovering Context-Specific Biomarkers Based on Protein-Protein Interactions, Co-Expression Networks, and Expression Data.

Yan D, Fan Z, Li Q, Chen Y Int J Mol Sci. 2024; 25(23).

PMID: 39684321 PMC: 11641600. DOI: 10.3390/ijms252312608.


Correlating gene expression levels with transcription factor binding sites facilitates identification of key transcription factors from transcriptome data.

Huang T, Niu S, Zhang F, Wang B, Wang J, Liu G Front Genet. 2024; 15:1511456.

PMID: 39678374 PMC: 11638204. DOI: 10.3389/fgene.2024.1511456.


Identification of DNA motif pairs on paired sequences based on composite heterogeneous graph.

Wu Q, Li Y, Wang Q, Zhao X, Sun D, Liu B Front Genet. 2024; 15:1424085.

PMID: 38952710 PMC: 11215013. DOI: 10.3389/fgene.2024.1424085.


Circadian-driven tissue specificity is constrained under caloric restricted feeding conditions.

Chen R, Zhang Z, Ma J, Liu B, Huang Z, Hu G Commun Biol. 2024; 7(1):752.

PMID: 38902439 PMC: 11190204. DOI: 10.1038/s42003-024-06421-0.


References
1.
Wingender E, Chen X, Hehl R, Karas H, Liebich I, Matys V . TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res. 1999; 28(1):316-9. PMC: 102445. DOI: 10.1093/nar/28.1.316. View

2.
Yu X, Lin J, Masuda T, Esumi N, Zack D, Qian J . Genome-wide prediction and characterization of interactions between transcription factors in Saccharomyces cerevisiae. Nucleic Acids Res. 2006; 34(3):917-27. PMC: 1361616. DOI: 10.1093/nar/gkj487. View

3.
Ashburner M, Ball C, Blake J, Botstein D, Butler H, Cherry J . Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000; 25(1):25-9. PMC: 3037419. DOI: 10.1038/75556. View

4.
GuhaThakurta D, Stormo G . Identifying target sites for cooperatively binding factors. Bioinformatics. 2001; 17(7):608-21. DOI: 10.1093/bioinformatics/17.7.608. View

5.
Krivan W, Wasserman W . A predictive model for regulatory sequences directing liver-specific transcription. Genome Res. 2001; 11(9):1559-66. PMC: 311083. DOI: 10.1101/gr.180601. View