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An integrative ChIP-chip and gene expression profiling to model SMAD regulatory modules.

Qin H, Chan MW, Liyanarachchi S, Balch C, Potter D, Souriraj IJ, Cheng AS, Agosto-Perez FJ, Nikonova EV, Yan PS, Lin HJ, Nephew KP, Saltz JH, Showe LC, Huang TH, Davuluri RV - BMC Syst Biol (2009)

Bottom Line: The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context.The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1).Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.

View Article: PubMed Central - HTML - PubMed

Affiliation: Human Cancer Genetics Program, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA. huaxia.qin@gmail.com

ABSTRACT

Background: The TGF-beta/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-beta/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context.

Results: To address this question and develop a computational model for SMAD modules, we simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-beta/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-beta activation. We developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1).

Conclusion: Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.

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Related in: MedlinePlus

Experimental validation of microarray results by RT-PCR analysis of 10 randomly selected TGF-β/SMAD target genes. The mRNAs of 10 genes from IOSE cells, treated with TGF-β 1, were amplified by RT-PCR and measured by a real-time PCR machine. The fold-change in mRNA expression for each gene was calculated by setting the expression (RT-PCR or microarray) values at 0 hr to 1. The plots are in log2 scale.
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Figure 3: Experimental validation of microarray results by RT-PCR analysis of 10 randomly selected TGF-β/SMAD target genes. The mRNAs of 10 genes from IOSE cells, treated with TGF-β 1, were amplified by RT-PCR and measured by a real-time PCR machine. The fold-change in mRNA expression for each gene was calculated by setting the expression (RT-PCR or microarray) values at 0 hr to 1. The plots are in log2 scale.

Mentions: SMAD4 binding of 10 randomly selected loci of the 150 targets that were shown to both bind SMAD4 and change gene expression in response to TGF-β, were confirmed in individual ChIP assays. On average, greater than 1.5 fold-enrichment was observed in IOSE cells after 3 hrs treatment with TGF-β1 (Figure 2). RT-PCR analysis was used to confirm altered expression of five group 2, two group 3, and three group 4 genes at 0, 3, 6 and 12 hrs after TGF-β stimulation (Figure 3). Specifically, we observed that the increase in expression of ADAM19, FBXO32, RunX1T1, and DDAH (group 2, sustained up-regulated) was maintained at the time-course between 3 and 12 hrs after treatment, while ZNF638 (group 2, transient up-regulated) showed increased expression at the 3 hr time point and gradual decrease to base-line level at the 12 hr time point. Decreased expression of FRAT and CXXC6 (group 3) was observed at 3 hrs after treatment. Expression levels of these two genes tended to increase afterwards, but remained below baseline levels by 12 hrs of treatment. On the other hand, expression of NTN4, ADPN, and RGS17 (group 4) continued to decrease at 6 hrs or 12 hrs after treatment. To summarize, the overall trends of temporal changes of expression and binding by SMAD4 observed in our microarray platforms were recapitulated by the RT-PCR and ChIP-PCR experiments, respectively.


An integrative ChIP-chip and gene expression profiling to model SMAD regulatory modules.

Qin H, Chan MW, Liyanarachchi S, Balch C, Potter D, Souriraj IJ, Cheng AS, Agosto-Perez FJ, Nikonova EV, Yan PS, Lin HJ, Nephew KP, Saltz JH, Showe LC, Huang TH, Davuluri RV - BMC Syst Biol (2009)

Experimental validation of microarray results by RT-PCR analysis of 10 randomly selected TGF-β/SMAD target genes. The mRNAs of 10 genes from IOSE cells, treated with TGF-β 1, were amplified by RT-PCR and measured by a real-time PCR machine. The fold-change in mRNA expression for each gene was calculated by setting the expression (RT-PCR or microarray) values at 0 hr to 1. The plots are in log2 scale.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2724489&req=5

Figure 3: Experimental validation of microarray results by RT-PCR analysis of 10 randomly selected TGF-β/SMAD target genes. The mRNAs of 10 genes from IOSE cells, treated with TGF-β 1, were amplified by RT-PCR and measured by a real-time PCR machine. The fold-change in mRNA expression for each gene was calculated by setting the expression (RT-PCR or microarray) values at 0 hr to 1. The plots are in log2 scale.
Mentions: SMAD4 binding of 10 randomly selected loci of the 150 targets that were shown to both bind SMAD4 and change gene expression in response to TGF-β, were confirmed in individual ChIP assays. On average, greater than 1.5 fold-enrichment was observed in IOSE cells after 3 hrs treatment with TGF-β1 (Figure 2). RT-PCR analysis was used to confirm altered expression of five group 2, two group 3, and three group 4 genes at 0, 3, 6 and 12 hrs after TGF-β stimulation (Figure 3). Specifically, we observed that the increase in expression of ADAM19, FBXO32, RunX1T1, and DDAH (group 2, sustained up-regulated) was maintained at the time-course between 3 and 12 hrs after treatment, while ZNF638 (group 2, transient up-regulated) showed increased expression at the 3 hr time point and gradual decrease to base-line level at the 12 hr time point. Decreased expression of FRAT and CXXC6 (group 3) was observed at 3 hrs after treatment. Expression levels of these two genes tended to increase afterwards, but remained below baseline levels by 12 hrs of treatment. On the other hand, expression of NTN4, ADPN, and RGS17 (group 4) continued to decrease at 6 hrs or 12 hrs after treatment. To summarize, the overall trends of temporal changes of expression and binding by SMAD4 observed in our microarray platforms were recapitulated by the RT-PCR and ChIP-PCR experiments, respectively.

Bottom Line: The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context.The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1).Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.

View Article: PubMed Central - HTML - PubMed

Affiliation: Human Cancer Genetics Program, Department of Molecular Virology, Immunology, and Medical Genetics, The Ohio State University, Columbus, OH 43210, USA. huaxia.qin@gmail.com

ABSTRACT

Background: The TGF-beta/SMAD pathway is part of a broader signaling network in which crosstalk between pathways occurs. While the molecular mechanisms of TGF-beta/SMAD signaling pathway have been studied in detail, the global networks downstream of SMAD remain largely unknown. The regulatory effect of SMAD complex likely depends on transcriptional modules, in which the SMAD binding elements and partner transcription factor binding sites (SMAD modules) are present in specific context.

Results: To address this question and develop a computational model for SMAD modules, we simultaneously performed chromatin immunoprecipitation followed by microarray analysis (ChIP-chip) and mRNA expression profiling to identify TGF-beta/SMAD regulated and synchronously coexpressed gene sets in ovarian surface epithelium. Intersecting the ChIP-chip and gene expression data yielded 150 direct targets, of which 141 were grouped into 3 co-expressed gene sets (sustained up-regulated, transient up-regulated and down-regulated), based on their temporal changes in expression after TGF-beta activation. We developed a data-mining method driven by the Random Forest algorithm to model SMAD transcriptional modules in the target sequences. The predicted SMAD modules contain SMAD binding element and up to 2 of 7 other transcription factor binding sites (E2F, P53, LEF1, ELK1, COUPTF, PAX4 and DR1).

Conclusion: Together, the computational results further the understanding of the interactions between SMAD and other transcription factors at specific target promoters, and provide the basis for more targeted experimental verification of the co-regulatory modules.

Show MeSH
Related in: MedlinePlus