Open Access Research

Regulatory interdependence of myeloid transcription factors revealed by Matrix RNAi analysis

Yasuhiro Tomaru12, Christophe Simon1, Alistair RR Forrest13, Hisashi Miura12, Atsutaka Kubosaki1, Yoshihide Hayashizaki12 and Masanori Suzuki12*

Author Affiliations

1 RIKEN Omics Science Center, RIKEN Yokohama Institute 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan

2 International Graduate School of Arts and Sciences, Yokohama City University, 1-7-29 Suehiro-Cho, Tsurumi-Ku, Yokohama 230-0045, Japan

3 The Eskitis Institute for Cell and Molecular Therapies, Griffith University, Brisbane Innovation Park, Don Young Road, Nathan, QLD 4111, Australia

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Genome Biology 2009, 10:R121  doi:10.1186/gb-2009-10-11-r121

Published: 2 November 2009

Additional files

Additional data file 1:

This table shows siRNA and primer sequences for the knockdown analysis, expression data (ΔCT value) and knockdown efficiencies of siRNAs targeting a TF gene in the Matrix RNAi analysis. Expression patterns analyzed in FANTOM4 were classified into pro-differentiated, anti-differentiated, static, transient and dynamic according to the expression profiles of the TF genes examined in the present Matrix RNAi research: 'static', 'transient' and 'dynamic' represent the profiles in which the expression level of a given TF gene is constant or significantly unchanged, having one peak or valley, and having multiple peaks and/or valleys, respectively, during PMA-induced THP-1 differentiation.

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Additional data file 2:

Western blotting with specific antibodies against each of the TFs and the protein extracts prepared from the THP-1 cells transfected with 20 nM negative control siRNA or each of the TF-specific siRNAs was carried out to evaluate TF knockdown efficiency at the protein level. Control: protein extracts from THP-1 cells transfected with negative control siRNA. siRNA: protein extracts from THP-1 cells transfected with each TF-specific siRNA. The levels of actin and TATA binding protein (TBP) were also examined as internal references (controls 1 and 2, respectively).

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Additional data file 3:

Figure S1: distribution of perturbation magnitudes between significant and non-significant edges. The 927 edges and 83 auto-perturbation edges that corresponded to 78 TF and 5 non-TF genes that were knocked down and had a low SD (mean > 2 SD) and a low P-value (P < 0.05) in Student's t-test were selected as significant edge candidates. The remaining 6,958 edges were grouped together as non-significant edges. The edges in each group were divided according to their perturbation magnitudes, which were calculated on the basis of the data from the qRT-PCR assay (see qRT-PCR in Expression analysis, Materials and methods for details). Perturbation magnitude was represented by absolute ΔΔCT, in every 0.2 absolute ΔΔCT and the percentages of the number of edges in each fraction to the total number of the edges were plotted. Red bars represent the percentage of the number of significant perturbation edges, black bars non-significant ones and yellow bars TF genes knocked down. Figure S2: magnitude of perturbation for significant and non-significant groups. Mean and SD values of ΔΔCTs of high (< 2 SD and P > 0.05) and low (> 2 SD and P < 0.05) SD and P-value groups were calculated. ΔΔCT values for knockdown of the TF genes (siRNA) are much larger than perturbation magnitudes, indicating that the influence of knockdown of TF genes on their downstream TF genes tends to be attenuated.

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Additional data file 4:

All qRT-PCR data used for the Matrix RNAi analysis. ddCt indicates the average ddCt from four biological replicates. SD indicates the standard deviation of ddCt from four biological replicates. Ttest indicates the P-value of the dCt between the knockdown and control siRNA transfected samples.

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Additional data file 5:

The 927 edges and 83 autoregulatory edges that showed a low SD (mean > 2 SD) and a low P-value (P < 0.05) in Student's-t-test were selected as significant edge candidates. The remaining 6,958 edges were removed from the qRT-PCR data.

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Additional data file 6:

THP-1 cells (1 × 106 cells) were transfected with an individual siRNA species against each of the TF genes. The total RNA was extracted 48 h after the transfection and used for qRT-PCR. The changes in expression levels (perturbations) were evaluated by CT calculated according to the method described by Livak et al. [37]. Quadruplicated experiments were carried out to obtain the average CT, SD and P-value. Only the edges that gave a low SD (mean of ΔΔCT > 2 SD) and P-value (< 0.05) were selected as significant regulatory TF-TF gene edges for preparing this table. 'Input gene' and 'target gene' indicates genes knocked down by a specific siRNA and genes perturbed significantly after siRNA transfection, respectively. 'Activate' and 'suppress' indicate that knockdown of one TF led to a significant decrease in the expression of another and to a significant increase in the expression of another, respectively. The actual data for RNAi perturbation are in Additional data file 4 (for all of the edges tested) and Additional data file 5 (for only significant edges).

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Additional data file 7:

For depiction of the putative networks, only significant edges (> 2 SD and P < 0.05) were extracted based on the Matrix RNAi data in Additional data files 4 and 5. The network was drawn by Cytoscape [33]. In these networks, TFs and TF genes regulated by them are not distinguished from each other, but the nodes emitting and accepting an arrow represent the putative regulators and regulated genes, respectively. Figure S1: perturbation network of significant regulatory edges based on Matrix RNAi data. Figure S2: pro-differentiative edge network. Figure S3: anti-differentiative edge network.

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Additional data file 8:

Figure S1: comparison of the extent of THP-1 cell adhesion between individual knockdown of either MYB or MLLT3 and their double knockdown. Blue bars indicate floating cells and red bars indicate attached cells counted 96 h after siRNA transfection. M & M indicates double knockdown. NC indicates cells transfected with siRNA negative control. Figure S2: Venn diagram of genes affected by knockdown of MYB and MLLT3 and their double knockdown.

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Additional data file 9:

Only the data showing a positive TF binding (ΔCT > 1.0 corresponding to two-fold enrichment of the TF-specific DNA fragments and P < 0.05 in Student's t-test) in three separate X-ChIP/qPCR experiments are indicated.

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Additional data file 10:

Figure S1: comparison between P-value threshold (P < 0.05), q-value threshold (q-value < 0.05) and 2-SD threshold (a signal average < 2 × SD). Figure S2: comparison between 2-SD/P-value threshold and 2-SD/P-value threshold and ChIP/qPCR confirmation. Figure S3: comparison between 2-SD/P-value/q-value threshold and 2-SD/P-value/q-value threshold and ChIP/qPCR confirmation. FDR (q-value) was calculated by using the QVALUE program and R software as described in Materials and methods. The number in parentheses indicates the number of edges excluding auto-perturbation edges. Figure S4: accumulative number of TF-binding positive and negative edges with q-value. Regulatory edges tested for TF-binding were separated into two groups for significance in ChIP assay (ChIP-negative and -positive) and the numbers determined together with the q-values for their perturbations.

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Additional data file 11:

Antibodies used in western blotting and X-ChIP analysis.

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Additional data file 12:

Primers used in X-ChIP/qPCR analysis. Primer pairs were designed around 500 nt upstream from the TSSs of the respective genes, except for some genes that were difficult to design their primers in the very proximal region.

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