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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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Schematic representation of p53-miR-mediated models downstream (A and C) and upstream (B and D) to p53.
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Figure 4: Schematic representation of p53-miR-mediated models downstream (A and C) and upstream (B and D) to p53.

Mentions: Alternately, a common TF (p53 in this case) can activate a downstream target gene and also a miRNA which then suppresses the downstream target gene. A well known example is the experimentally confirmed microregulatory network comprising miR-17-5p and E2F1 – both of which are transcriptionally activated by c-Myc in human cells [43]. We report a similar example of mir-122a, a putative p53-miR suppressing CCNG1, a downstream target of p53. CCNG1 is a known transcriptional target (activator) of the p53 tumor suppressor protein [44] and it was recently reported that mir-122a (predicted as p53-miR in our analysis) has an inverse correlation with cyclin G1 expression in primary liver carcinomas [45]. In other words, p53 activates CCNG1 and also mir-122a which in turn suppresses CCNG1. This is an incoherent model. Adding to this complexity further, it is also reported that CCNG1 negatively regulates the stabilization of p53 in a possible negative feedback loop [46] (Figure 4A). Thus, the regulatory network wherein p53 activates a downstream target and a miR (which target the downstream target) simultaneously appears "inefficient". However, feedforward loops have the potential to provide temporal control, because expression of the ultimate target may depend on the accumulation of adequate levels of master regulator and the secondary regulators [47]. Therefore, if we consider a sequential gap in the activation time of the target genes and the miRNA, then this downstream p53-gene and p53-miR regulation appears coherent. Feedforward loops may provide a form of multistep ultrasensitivity [48], as any deviation from the p53's steady state would drive the downstream targets and miRNA (p53-miR) away from their steady states in the same direction. p53-miRs could therefore tune the production rate of the downstream p53 target gene opposite to the direction of p53's fluctuation. Such noise buffering probably helps to maintain target protein homeostasis and ensures more uniform expression [49] of the p53 downstream target genes within a cell population. Additionally, since the level of p53-miR defines the p53 downstream target's translation rate, their coexpression may allow p53-miR to fine-tune the downstream target's steady state. Thus, p53-miRs acting on p53 downstream targets could significantly shorten the response delay, leading to more effective noise buffering, as well as precise definition and maintenance of steady states.


Dissecting microregulation of a master regulatory network.

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

Schematic representation of p53-miR-mediated models downstream (A and C) and upstream (B and D) to p53.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Schematic representation of p53-miR-mediated models downstream (A and C) and upstream (B and D) to p53.
Mentions: Alternately, a common TF (p53 in this case) can activate a downstream target gene and also a miRNA which then suppresses the downstream target gene. A well known example is the experimentally confirmed microregulatory network comprising miR-17-5p and E2F1 – both of which are transcriptionally activated by c-Myc in human cells [43]. We report a similar example of mir-122a, a putative p53-miR suppressing CCNG1, a downstream target of p53. CCNG1 is a known transcriptional target (activator) of the p53 tumor suppressor protein [44] and it was recently reported that mir-122a (predicted as p53-miR in our analysis) has an inverse correlation with cyclin G1 expression in primary liver carcinomas [45]. In other words, p53 activates CCNG1 and also mir-122a which in turn suppresses CCNG1. This is an incoherent model. Adding to this complexity further, it is also reported that CCNG1 negatively regulates the stabilization of p53 in a possible negative feedback loop [46] (Figure 4A). Thus, the regulatory network wherein p53 activates a downstream target and a miR (which target the downstream target) simultaneously appears "inefficient". However, feedforward loops have the potential to provide temporal control, because expression of the ultimate target may depend on the accumulation of adequate levels of master regulator and the secondary regulators [47]. Therefore, if we consider a sequential gap in the activation time of the target genes and the miRNA, then this downstream p53-gene and p53-miR regulation appears coherent. Feedforward loops may provide a form of multistep ultrasensitivity [48], as any deviation from the p53's steady state would drive the downstream targets and miRNA (p53-miR) away from their steady states in the same direction. p53-miRs could therefore tune the production rate of the downstream p53 target gene opposite to the direction of p53's fluctuation. Such noise buffering probably helps to maintain target protein homeostasis and ensures more uniform expression [49] of the p53 downstream target genes within a cell population. Additionally, since the level of p53-miR defines the p53 downstream target's translation rate, their coexpression may allow p53-miR to fine-tune the downstream target's steady state. Thus, p53-miRs acting on p53 downstream targets could significantly shorten the response delay, leading to more effective noise buffering, as well as precise definition and maintenance of steady states.

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