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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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Correlation between TRF binding- and perturbation-positive edges. A total of 40 regulatory edges that showed a perturbation with a low 2 SD value and a low P-value (<0.05) targeted by any of the six TRFs whose chromatin bindings were examined and were selected to determine the correlation with TRF–TRF gene interaction. Binding-positive 73 TRF edges that showed more than 1.0 of the enrichment index ΔCT with a low 2 SD value and P < 0.05 in Student's t-test were also selected. Autoregulatory edges are not included in this figure because Matrix RNAi cannot identify them through perturbation. Numbers in parentheses indicate the edges that have been reported in literature (Supplementary Table 8).
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Figure 7: Correlation between TRF binding- and perturbation-positive edges. A total of 40 regulatory edges that showed a perturbation with a low 2 SD value and a low P-value (<0.05) targeted by any of the six TRFs whose chromatin bindings were examined and were selected to determine the correlation with TRF–TRF gene interaction. Binding-positive 73 TRF edges that showed more than 1.0 of the enrichment index ΔCT with a low 2 SD value and P < 0.05 in Student's t-test were also selected. Autoregulatory edges are not included in this figure because Matrix RNAi cannot identify them through perturbation. Numbers in parentheses indicate the edges that have been reported in literature (Supplementary Table 8).

Mentions: The edges whose TRF gene bindings were demonstrated included 30 perturbation-positive edges, making up more than 41% of the 73 edges deduced by the perturbation analysis with six TRFs that were tested by both Matrix RNAi and X-ChIP/qPCR (Figure 7). On the other hand, binding-positive edges constitute 75% (30 in 40 edges) of the perturbation-positive edges. The high concordance between the perturbation and chromatin binding suggests that the Matrix RNAi approach is highly effective in identifying real edges between TRFs.Figure 7.


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)

Correlation between TRF binding- and perturbation-positive edges. A total of 40 regulatory edges that showed a perturbation with a low 2 SD value and a low P-value (<0.05) targeted by any of the six TRFs whose chromatin bindings were examined and were selected to determine the correlation with TRF–TRF gene interaction. Binding-positive 73 TRF edges that showed more than 1.0 of the enrichment index ΔCT with a low 2 SD value and P < 0.05 in Student's t-test were also selected. Autoregulatory edges are not included in this figure because Matrix RNAi cannot identify them through perturbation. Numbers in parentheses indicate the edges that have been reported in literature (Supplementary Table 8).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

Figure 7: Correlation between TRF binding- and perturbation-positive edges. A total of 40 regulatory edges that showed a perturbation with a low 2 SD value and a low P-value (<0.05) targeted by any of the six TRFs whose chromatin bindings were examined and were selected to determine the correlation with TRF–TRF gene interaction. Binding-positive 73 TRF edges that showed more than 1.0 of the enrichment index ΔCT with a low 2 SD value and P < 0.05 in Student's t-test were also selected. Autoregulatory edges are not included in this figure because Matrix RNAi cannot identify them through perturbation. Numbers in parentheses indicate the edges that have been reported in literature (Supplementary Table 8).
Mentions: The edges whose TRF gene bindings were demonstrated included 30 perturbation-positive edges, making up more than 41% of the 73 edges deduced by the perturbation analysis with six TRFs that were tested by both Matrix RNAi and X-ChIP/qPCR (Figure 7). On the other hand, binding-positive edges constitute 75% (30 in 40 edges) of the perturbation-positive edges. The high concordance between the perturbation and chromatin binding suggests that the Matrix RNAi approach is highly effective in identifying real edges between TRFs.Figure 7.

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