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. 2008 Dec 16;105(50):19678-83.
doi: 10.1073/pnas.0811166106. Epub 2008 Dec 9.

MicroRNA regulation of a cancer network: consequences of the feedback loops involving miR-17-92, E2F, and Myc

Baltazar D Aguda  1 , Yangjin Kim, Melissa G Piper-Hunter, Avner Friedman, Clay B Marsh
Affiliations

MicroRNA regulation of a cancer network: consequences of the feedback loops involving miR-17-92, E2F, and Myc

Baltazar D Aguda et al. Proc Natl Acad Sci U S A. .

Abstract

The transcription factors E2F and Myc participate in the control of cell proliferation and apoptosis, and can act as oncogenes or tumor suppressors depending on their levels of expression. Positive feedback loops in the regulation of these factors are predicted-and recently shown experimentally-to lead to bistability, which is a phenomenon characterized by the existence of low and high protein levels ("off" and "on" levels, respectively), with sharp transitions between levels being inducible by, for example, changes in growth factor concentrations. E2F and Myc are inhibited at the posttranscriptional step by members of a cluster of microRNAs (miRs) called miR-17-92. In return, E2F and Myc induce the transcription of miR-17-92, thus forming a negative feedback loop in the interaction network. The consequences of the coupling between the E2F/Myc positive feedback loops and the E2F/Myc/miR-17-92 negative feedback loop are analyzed using a mathematical model. The model predicts that miR-17-92 plays a critical role in regulating the position of the off-on switch in E2F/Myc protein levels, and in determining the on levels of these proteins. The model also predicts large-amplitude protein oscillations that coexist with the off steady state levels. Using the concept and model prediction of a "cancer zone," the oncogenic and tumor suppressor properties of miR-17-92 is demonstrated to parallel the same properties of E2F and Myc.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
A cancer network. This network is part of the mammalian G1-S regulatory network involving oncogenes (black boxes) and tumor suppressor genes (gray boxes). Dashed arrows mean induction of gene expression. For example, the E2F protein induces the expression of its own gene. Solid arrow means activation; for example, the Cdc25A phosphatase activates Cdk2 by catalyzing the removal of an inhibitory phosphate. Hammerheads mean inhibition. Details of the mechanism are discussed in ref. . Note that E2F and Myc are labeled as both oncogene and tumor suppressor genes (see text for explanation). Not shown in the figure are pathways downstream of E2F and Myc that lead to caspase-mediated apoptosis (reviewed in ref. 9). Cdk, cyclin-dependent kinase; pRb, retinoblastoma protein.
Fig. 2.
Fig. 2.
Reduction of the complex Myc/E2F/miR-17-92 network to an abstract model. (A) Summary of the interactions among the transcription factors Myc, E2F1, E2F2, E2F3, and members of the miR-17-92 cluster (reviewed in ref. 14). All arrows refer to induction of gene expression. The hammerheads from miR-17-92 members to Myc and to the E2Fs refer to inhibition of translation or degradation of mRNAs. (B) First stage in the reduction of the model. (C) The final network model that abstracts the essential structure of the network in A.
Fig. 3.
Fig. 3.
The miR-17-92 cluster as an oncogene or as a tumor suppressor. As the transcriptional activities of E2F or Myc increase, cells transit through quiescence, cell cycle, and apoptosis. It is postulated that there exists a range of E2F or Myc levels—called the cancer zone—that leads to hyperproliferation because cell division is not appropriately balanced by apoptosis (cell death). As shown at the top of the figure, there are two ways that miR-17-92 can keep levels of E2F or Myc in the cancer zone (i.e., acting as an oncogene). (case a) Increasing miR levels drives E2F or Myc levels to enter the cancer zone. (case b) Increasing miR inhibition of E2F or Myc translation suppresses exit from the cancer zone. (cases c and d) The two ways that miR-17-92 can suppress entry into the cancer zone (i.e., acting as a tumor suppressor) are: increasing miR inhibition of E2F or Myc translation suppresses entry into the cancer zone (case c), and increasing miR levels drives E2F or Myc levels to exit the cancer zone and enter apoptosis (case d). It is shown in this article that cases a and c are concomitant, as are cases b and d.
Fig. 4.
Fig. 4.
Steady-state bifurcation diagrams. Steady states of the variable φ (dimensionless protein concentration) as a function of the parameter α′ for different values of the parameter Γ′2 (all parameters are dimensionless, according to Eq. 5). Values of other parameters: κ = 5, Γ′1 = 1 (specific value of ε not required for steady-state calculations). Note that α′ does not extend to infinity, but only to a maximum value dictated by cell physiology (a maximum of α′ = 0.4 is assumed only for illustrative purposes).
Fig. 5.
Fig. 5.
Model dynamics. (A) Phase plane trajectories from different initial conditions: φ (0) = 0.13, μ (0) = 0.340, 0.343, 0.345, 0.350, 0.355. Parameter values: ε = 0.02, α′ = 0.1, κ = 5, Γ′1 = 1, Γ′2 = 1.8. The empty circle represents a steady state of the system. (B) Time courses for 2 very close initial conditions φ (0) = 0.13, μ (0) = 0.345 (black curve) and φ (0) = 0.13, μ (0) = 0.350 (gray curve). (C) Same as A except α′ = 0.2 and μ (0) = 1, 2, 3. The empty circle represents a steady state of the system. (D) Temporal course of φ for parameters and initial conditions identical to C.
Fig. 6.
Fig. 6.
In and out of the cancer zone. (A) Decrease in Γ′2 from 2.0 to 1.0 corresponds to a decrease in the miR inhibition efficiency of protein expression (see Fig. 3C) and to an increase in the steady-state level of the miR cluster (Eq. 8; see also Fig. 3A)—leading to entry into the cancer zone. (B) A further decrease in Γ′2 from 1.0 to 0.5 corresponds to a decrease in miR inhibition efficiency (Fig. 3B) and to an increase in miR steady state level (Fig. 3D)—thus driving the exit from the cancer zone and into apoptosis.
Fig. 7.
Fig. 7.
Model dynamics with time delay. Eqs. 4 and 6 are numerically integrated, along with slowly changing α′ (with rate dα′/dτ = 0.001) in the increasing (α′ →) and decreasing (← α′) directions, without time delay (Δ = 0, Left) and with time delay (Δ = 0.2, Right). Other parameter values: ε = 0.02, Γ′1 = 1, Γ′2 = 1.8, κ = 5.
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