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Dissecting microregulation of a master regulatory network.

Sinha AU, Kaimal V, Chen J, Jegga AG - BMC Genomics (2008)

Bottom Line: Here, we use bioinformatics-based integrative approach to identify and prioritize putative p53-regulated miRNAs, and unravel the miRNA-based microregulation of the p53 master regulatory network.Specifically, we identify putative microRNA regulators of a) transcription factors that are upstream or downstream to p53 and b) p53 interactants.Our predicted p53-miRNA-gene networks strongly suggest that coordinated transcriptional and p53-miR mediated networks could be integral to tumorigenesis and the underlying processes and pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pediatrics, University of Cincinnati College of Medicine, Ohio, USA. sinhaam@ececs.uc.edu

ABSTRACT

Background: The master regulator p53 tumor-suppressor protein through coordination of several downstream target genes and upstream transcription factors controls many pathways important for tumor suppression. While it has been reported that some of the p53's functions are microRNA-mediated, it is not known as to how many other microRNAs might contribute to the p53-mediated tumorigenesis.

Results: Here, we use bioinformatics-based integrative approach to identify and prioritize putative p53-regulated miRNAs, and unravel the miRNA-based microregulation of the p53 master regulatory network. Specifically, we identify putative microRNA regulators of a) transcription factors that are upstream or downstream to p53 and b) p53 interactants. The putative p53-miRs and their targets are prioritized using current knowledge of cancer biology and literature-reported cancer-miRNAs.

Conclusion: Our predicted p53-miRNA-gene networks strongly suggest that coordinated transcriptional and p53-miR mediated networks could be integral to tumorigenesis and the underlying processes and pathways.

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Related in: MedlinePlus

p53 interactome along with the putative miR regulators that are shown to be induced following the activation of p53 [11]. miRNAs are represented by yellow ellipses, target genes are represented by green boxes. p53 is represented in the center as blue circle. Induction of the miRNAs by p53 is represented by directed red lines. Negative regulation of the target genes by miRNAs are represented by dark grey lines. The protein interactions are represented by undirected light blue lines. Networks (using force-directed layouts) were generated using aiSee [76] network visualization software.
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Figure 5: p53 interactome along with the putative miR regulators that are shown to be induced following the activation of p53 [11]. miRNAs are represented by yellow ellipses, target genes are represented by green boxes. p53 is represented in the center as blue circle. Induction of the miRNAs by p53 is represented by directed red lines. Negative regulation of the target genes by miRNAs are represented by dark grey lines. The protein interactions are represented by undirected light blue lines. Networks (using force-directed layouts) were generated using aiSee [76] network visualization software.

Mentions: Since the functional state of a protein-protein interaction network depends on gene expression, a fundamental question is what relationships exist between protein interaction network and gene regulation [70]. Liang and Li in their recent study of evidence for global correlation between microRNA repression and protein-protein interactions reported that interacting proteins tend to share more microRNA target-site types than random pairs [70]. In the current study, using the known p53-interactants, we calculated the probability of p53-miRs regulating the p53 interactants. We mined the BioGRID database [71], and currently there are 141 known interactants of p53. Surprisingly, 114 of these 141 p53-interactants were predicted to be regulated by p53-miRs and were shown to be statistically significant (p = 1.41e-21) (see Additional File 5 for details). Using the known data of miRNAs induced or repressed following p53 activation [11], we further filtered the p53-miRs that could be associated with the p53 interactome. Figures 5 and 6 show the p53 interactome along with the putative p53-miR regulators that are shown to be induced or repressed [11] respectively following the activation of p53.


Dissecting microregulation of a master regulatory network.

Sinha AU, Kaimal V, Chen J, Jegga AG - BMC Genomics (2008)

p53 interactome along with the putative miR regulators that are shown to be induced following the activation of p53 [11]. miRNAs are represented by yellow ellipses, target genes are represented by green boxes. p53 is represented in the center as blue circle. Induction of the miRNAs by p53 is represented by directed red lines. Negative regulation of the target genes by miRNAs are represented by dark grey lines. The protein interactions are represented by undirected light blue lines. Networks (using force-directed layouts) were generated using aiSee [76] network visualization software.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2289817&req=5

Figure 5: p53 interactome along with the putative miR regulators that are shown to be induced following the activation of p53 [11]. miRNAs are represented by yellow ellipses, target genes are represented by green boxes. p53 is represented in the center as blue circle. Induction of the miRNAs by p53 is represented by directed red lines. Negative regulation of the target genes by miRNAs are represented by dark grey lines. The protein interactions are represented by undirected light blue lines. Networks (using force-directed layouts) were generated using aiSee [76] network visualization software.
Mentions: Since the functional state of a protein-protein interaction network depends on gene expression, a fundamental question is what relationships exist between protein interaction network and gene regulation [70]. Liang and Li in their recent study of evidence for global correlation between microRNA repression and protein-protein interactions reported that interacting proteins tend to share more microRNA target-site types than random pairs [70]. In the current study, using the known p53-interactants, we calculated the probability of p53-miRs regulating the p53 interactants. We mined the BioGRID database [71], and currently there are 141 known interactants of p53. Surprisingly, 114 of these 141 p53-interactants were predicted to be regulated by p53-miRs and were shown to be statistically significant (p = 1.41e-21) (see Additional File 5 for details). Using the known data of miRNAs induced or repressed following p53 activation [11], we further filtered the p53-miRs that could be associated with the p53 interactome. Figures 5 and 6 show the p53 interactome along with the putative p53-miR regulators that are shown to be induced or repressed [11] respectively following the activation of p53.

Bottom Line: Here, we use bioinformatics-based integrative approach to identify and prioritize putative p53-regulated miRNAs, and unravel the miRNA-based microregulation of the p53 master regulatory network.Specifically, we identify putative microRNA regulators of a) transcription factors that are upstream or downstream to p53 and b) p53 interactants.Our predicted p53-miRNA-gene networks strongly suggest that coordinated transcriptional and p53-miR mediated networks could be integral to tumorigenesis and the underlying processes and pathways.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pediatrics, University of Cincinnati College of Medicine, Ohio, USA. sinhaam@ececs.uc.edu

ABSTRACT

Background: The master regulator p53 tumor-suppressor protein through coordination of several downstream target genes and upstream transcription factors controls many pathways important for tumor suppression. While it has been reported that some of the p53's functions are microRNA-mediated, it is not known as to how many other microRNAs might contribute to the p53-mediated tumorigenesis.

Results: Here, we use bioinformatics-based integrative approach to identify and prioritize putative p53-regulated miRNAs, and unravel the miRNA-based microregulation of the p53 master regulatory network. Specifically, we identify putative microRNA regulators of a) transcription factors that are upstream or downstream to p53 and b) p53 interactants. The putative p53-miRs and their targets are prioritized using current knowledge of cancer biology and literature-reported cancer-miRNAs.

Conclusion: Our predicted p53-miRNA-gene networks strongly suggest that coordinated transcriptional and p53-miR mediated networks could be integral to tumorigenesis and the underlying processes and pathways.

Show MeSH
Related in: MedlinePlus