Dataset Information

Identification of an inter-transcription factor regulatory network in human hepatoma cells by Matrix RNAi.

ABSTRACT: Transcriptional regulation by transcriptional regulatory factors (TRFs) of their target TRF genes is central to the control of gene expression. To study a static multi-tiered inter-TRF regulatory network in the human hepatoma cells, we have applied a Matrix RNAi approach in which siRNA knockdown and quantitative RT-PCR are used in combination on the same set of TRFs to determine their interdependencies. This approach focusing on several liver-enriched TRF families, each of which consists of structurally homologous members, revealed many significant regulatory relationships. These include the cross-talks between hepatocyte nuclear factors (HNFs) and the other TRF groups such as CCAAT/enhancer-binding proteins (CEBPs), retinoic acid receptors (RARs), retinoid receptors (RXRs) and RAR-related orphan receptors (RORs), which play key regulatory functions in human hepatocytes and liver. In addition, various multi-component regulatory motifs, which make up the complex inter-TRF regulatory network, were identified. A large part of the regulatory edges identified by the Matrix RNAi approach could be confirmed by chromatin immunoprecipitation. The resultant significant edges enabled us to depict the inter-TRF TRN forming an apparent regulatory hierarchy of (FOXA1, RXRA) --> TCF1 --> (HNF4A, ONECUT1) --> (RORC, CEBPA) as the main streamline.

SUBMITTER: Tomaru Y

PROVIDER: S-EPMC2651797 | biostudies-other | 2009 Mar

REPOSITORIES: biostudies-other

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Identification of an inter-transcription factor regulatory network in human hepatoma cells by Matrix RNAi.

Tomaru Yasuhiro Y Nakanishi Misato M Miura Hisashi H Kimura Yasumasa Y Ohkawa Hiroki H Ohta Yusuke Y Hayashizaki Yoshihide Y Suzuki Masanori M

Nucleic acids research 20090107 4

Transcriptional regulation by transcriptional regulatory factors (TRFs) of their target TRF genes is central to the control of gene expression. To study a static multi-tiered inter-TRF regulatory network in the human hepatoma cells, we have applied a Matrix RNAi approach in which siRNA knockdown and quantitative RT-PCR are used in combination on the same set of TRFs to determine their interdependencies. This approach focusing on several liver-enriched TRF families, each of which consists of stru ...[more]

PMID: 19129217

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Project description:BACKGROUND: MicroRNA (miRNA) is a class of small RNAs of ~22nt which play essential roles in many crucial biological processes and numerous human diseases at post-transcriptional level of gene expression. It has been revealed that miRNA genes tend to be clustered, and the miRNAs organized into one cluster are usually transcribed coordinately. This implies a coordinated regulation mode exerted by clustered miRNAs. However, how the clustered miRNAs coordinate their regulations on large scale gene expression is still unclear. RESULTS: We constructed the miRNA-transcription factor regulatory network that contains the interactions between transcription factors (TFs), miRNAs and non-TF protein-coding genes, and made a genome-wide study on the regulatory coordination of clustered miRNAs. We found that there are two types of miRNA clusters, i.e. homo-clusters that contain miRNAs of the same family and hetero-clusters that contain miRNAs of various families. In general, the homo-clustered as well as the hetero-clustered miRNAs both exhibit coordinated regulation since the miRNAs belonging to one cluster tend to be involved in the same network module, which performs a relatively isolated biological function. However, the homo-clustered miRNAs show a direct regulatory coordination that is realized by one-step regulation (i.e. the direct regulation of the coordinated targets), whereas the hetero-clustered miRNAs show an indirect regulatory coordination that is realized by a regulation comprising at least three steps (e.g. the regulation on the coordinated targets by a miRNA through a sequential action of two TFs). The direct and indirect regulation target different categories of genes, the former predominantly regulating genes involved in emergent responses, the latter targeting genes that imply long-term effects. CONCLUSION: The genomic clustering of miRNAs is closely related to the coordinated regulation in the gene regulatory network. The pattern of regulatory coordination is dependent on the composition of the miRNA cluster. The homo-clustered miRNAs mainly coordinate their regulation rapidly, while the hetero-clustered miRNAs exert control with a delay. The diverse pattern of regulatory coordination suggests distinct roles of the homo-clustered and the hetero-clustered miRNAs in biological processes.

Project description:PurposeNasopharyngeal carcinoma (NPC) is one of the most common malignant tumors of the head and neck. This study aimed to investigate the crucial genes and regulatory networks involved in the carcinogenesis of NPC using a bioinformatics approach.MethodsFive mRNA and two miRNA expression datasets were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and miRNAs (DEMs) between NPC and normal samples were analyzed using R software. The WebGestalt tool was used for functional enrichment analysis, and protein-protein interaction (PPI) network analysis of DEGs was performed using STRING database. Transcription factors (TFs) were predicted using TRRUST and Transcriptional Regulatory Element Database (TRED). Kinases were identified using X2Kgui. The miRNAs of DEGs were predicted using miRWalk database. A kinase-TF-mRNA-miRNA integrated network was constructed, and hub nodes were selected. The hub genes were validated using NPC datasets from the GEO and Oncomine databases. Finally, candidate small-molecule agents were predicted using CMap.ResultsA total of 122 DEGs and 44 DEMs were identified. DEGs were associated with the immune response, leukocyte activation, endoplasmic reticulum stress in GO analysis, and the NF-κB signaling pathway in KEGG analysis. Four significant modules were identified using PPI network analysis. Subsequently, 26 TFs, 73 kinases, and 2499 miRNAs were predicted. The predicted miRNAs were cross-referenced with DEMs, and seven overlapping miRNAs were selected. In the kinase-TF-mRNA-miRNA integrated network, eight genes (PTGS2, FN1, MMP1, PLAU, MMP3, CD19, BMP2, and PIGR) were identified as hub genes. Hub genes were validated with consistent results, indicating the reliability of our findings. Finally, six candidate small-molecule agents (phenoxybenzamine, luteolin, thioguanosine, reserpine, blebbistatin, and camptothecin) were predicted.ConclusionWe identified DEGs and an NPC regulatory network involving kinases, TFs, mRNAs, and miRNAs, which might provide promising insight into the pathogenesis, treatment, and prognosis of NPC.

Dataset Information

Identification of an inter-transcription factor regulatory network in human hepatoma cells by Matrix RNAi.

Publications

Identification of an inter-transcription factor regulatory network in human hepatoma cells by Matrix RNAi.

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