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

Tomaru Y, Nakanishi M, Miura H, Kimura Y, Ohkawa H, Ohta Y, Hayashizaki Y, Suzuki M - Nucleic Acids Res. (2009)

Bottom Line: This approach focusing on several liver-enriched TRF families, each of which consists of structurally homologous members, revealed many significant regulatory relationships.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.

View Article: PubMed Central - PubMed

Affiliation: OMICS Sciences Center (OSC), RIKEN Yokohama Institute 1-7-22 Suehiro-Cho, Japan.

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.

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A highly probable static inter-TRF TRN in HepG2 cells. The edges presented were identified by Matrix RNAi perturbation. Highly significant edges confirmed by X-ChIP/qPCR are drawn in thick lines. Rhombus boxes represent TRFs that were tested in X-ChIP/qPCR. TRFs lacking a significant binding data are excluded from this figure. Autoregulation of all of these six TRFs was demonstrated but not drawn for clarity. Lines with arrowheads and T-shaped termini show positive and negative regulatory edges, respectively.
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Figure 8: A highly probable static inter-TRF TRN in HepG2 cells. The edges presented were identified by Matrix RNAi perturbation. Highly significant edges confirmed by X-ChIP/qPCR are drawn in thick lines. Rhombus boxes represent TRFs that were tested in X-ChIP/qPCR. TRFs lacking a significant binding data are excluded from this figure. Autoregulation of all of these six TRFs was demonstrated but not drawn for clarity. Lines with arrowheads and T-shaped termini show positive and negative regulatory edges, respectively.

Mentions: By using these identified significant edges, we depicted the inter-TRF TRN in HepG2 cells (Figure 8). Thick lines in Figure 8 are the set of finally validated 30 edges (25 downregulated and 5 upregulated) and comprise a static inter-TRF TRN in HepG2 cells. Figure 8 indicates that TCF1 and FOXA1 share a common set of regulated TRF genes (RORC, HNF4A, TCF2, ONECUT1, CEBPA, HNF4G and RXRA (suggested by only perturbation data). There is a negative feedback loop of FOXA1 → TCF1 → TCF2 ⊣ FOXA1. Various reciprocal regulations were also found between TCF1 and FOXA2, HNF4A, ONECUT1, RXRA as well as between FOXA1 and RXRA or RXRA and ONECUT1. Moreover, several regulatory circuitries were also found, for example, FOXA1 → TCF1 → RXRA → FOXA1, RXRA → TCF1 → ONECUT1 → RXRA, and TCF1 → RXRA → FOXA1 → HNF4A → TCF1. These molecular regulatory patterns characterize the static inter-TRF TRN in the human hepatoma cells. Figure 9 shows the outline of the strategy of Matrix RNAi-based inter-TRF TRN analysis used in the present investigation.Figure 8.


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

Tomaru Y, Nakanishi M, Miura H, Kimura Y, Ohkawa H, Ohta Y, Hayashizaki Y, Suzuki M - Nucleic Acids Res. (2009)

A highly probable static inter-TRF TRN in HepG2 cells. The edges presented were identified by Matrix RNAi perturbation. Highly significant edges confirmed by X-ChIP/qPCR are drawn in thick lines. Rhombus boxes represent TRFs that were tested in X-ChIP/qPCR. TRFs lacking a significant binding data are excluded from this figure. Autoregulation of all of these six TRFs was demonstrated but not drawn for clarity. Lines with arrowheads and T-shaped termini show positive and negative regulatory edges, respectively.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2651797&req=5

Figure 8: A highly probable static inter-TRF TRN in HepG2 cells. The edges presented were identified by Matrix RNAi perturbation. Highly significant edges confirmed by X-ChIP/qPCR are drawn in thick lines. Rhombus boxes represent TRFs that were tested in X-ChIP/qPCR. TRFs lacking a significant binding data are excluded from this figure. Autoregulation of all of these six TRFs was demonstrated but not drawn for clarity. Lines with arrowheads and T-shaped termini show positive and negative regulatory edges, respectively.
Mentions: By using these identified significant edges, we depicted the inter-TRF TRN in HepG2 cells (Figure 8). Thick lines in Figure 8 are the set of finally validated 30 edges (25 downregulated and 5 upregulated) and comprise a static inter-TRF TRN in HepG2 cells. Figure 8 indicates that TCF1 and FOXA1 share a common set of regulated TRF genes (RORC, HNF4A, TCF2, ONECUT1, CEBPA, HNF4G and RXRA (suggested by only perturbation data). There is a negative feedback loop of FOXA1 → TCF1 → TCF2 ⊣ FOXA1. Various reciprocal regulations were also found between TCF1 and FOXA2, HNF4A, ONECUT1, RXRA as well as between FOXA1 and RXRA or RXRA and ONECUT1. Moreover, several regulatory circuitries were also found, for example, FOXA1 → TCF1 → RXRA → FOXA1, RXRA → TCF1 → ONECUT1 → RXRA, and TCF1 → RXRA → FOXA1 → HNF4A → TCF1. These molecular regulatory patterns characterize the static inter-TRF TRN in the human hepatoma cells. Figure 9 shows the outline of the strategy of Matrix RNAi-based inter-TRF TRN analysis used in the present investigation.Figure 8.

Bottom Line: This approach focusing on several liver-enriched TRF families, each of which consists of structurally homologous members, revealed many significant regulatory relationships.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.

View Article: PubMed Central - PubMed

Affiliation: OMICS Sciences Center (OSC), RIKEN Yokohama Institute 1-7-22 Suehiro-Cho, Japan.

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.

Show MeSH
Related in: MedlinePlus