The mixed blessing of AMPK signaling in Cancer treatments

Abstract

Background: Nutrient acquisition and metabolism pathways are altered in cancer cells to meet bioenergetic and biosynthetic demands. A major regulator of cellular metabolism and energy homeostasis, in normal and cancer cells, is AMP-activated protein kinase (AMPK). AMPK influences cell growth via its modulation of the mechanistic target of Rapamycin (mTOR) pathway, specifically, by inhibiting mTOR complex mTORC1, which facilitates cell proliferation, and by activating mTORC2 and cell survival. Given its conflicting roles, the effects of AMPK activation in cancer can be counter intuitive. Prior to the establishment of cancer, AMPK acts as a tumor suppressor. However, following the onset of cancer, AMPK has been shown to either suppress or promote cancer, depending on cell type or state.

Methods: To unravel the controversial roles of AMPK in cancer, we developed a computational model to simulate the effects of pharmacological maneuvers that target key metabolic signalling nodes, with a specific focus on AMPK, mTORC, and their modulators. Specifically, we constructed an ordinary differential equation-based mechanistic model of AMPK-mTORC signaling, and parametrized the model based on existing experimental data.

Results: Model simulations were conducted to yield the following predictions: (i) increasing AMPK activity has opposite effects on mTORC depending on the nutrient availability; (ii) indirect inhibition of AMPK activity through inhibition of sirtuin 1 (SIRT1) only has an effect on mTORC activity under conditions of low nutrient availability; (iii) the balance between cell proliferation and survival exhibits an intricate dependence on DEP domain-containing mTOR-interacting protein (DEPTOR) abundance and AMPK activity; (iv) simultaneous direct inhibition of mTORC2 and activation of AMPK is a potential strategy for suppressing both cell survival and proliferation.

Conclusions: Taken together, model simulations clarify the competing effects and the roles of key metabolic signalling pathways in tumorigenesis, which may yield insights on innovative therapeutic strategies.

Keywords: AMPK; Cancer; Dynamical system; Metabolism; mTORC.

Fig. 1

Model metabolic signalling network. A schematic diagram depicting the interactions and feedback loops within the DEPTOR-mTOR network and their connections with external inhibitors and activators AMPK, ULK1, and insulin receptor substrate (IRS). Normal, blunt and dashed arrows denote activation, inhibition, and complex formation, respectively

Fig. 2

Model parameters are fitted against experimental data. A-C, oscillatory model solution under energetic stress. Predicted time profiles are shown for pmTORC1, pAMPK, and pULK1, together with the corresponding experimental data points [38]. D, predicted phosphorylated-to-unphosphorylated mTORC1 ratio as a function of AMPK activation, compared with data from [39]. E, predicted activation of AMPK and AKT following AICAR administration, compared with data from [29, 40]. F, predicted abundance of phosphorylated mTORC1 at different insulin receptor levels, corresponding to healthy and diabetic conditions. Diabetes values are normalized by the respective non-diabetes values. Experimental data [44] are shown in red dots with error bars

Fig. 3

Top panel, heat map that illustrates the global sensitivity of key model outputs (horizontal axis) to variations in selected model parameters (vertical axis). Definition of the parameters can be found in the Supplemental Materials. Bottom panel, parallel coordinate plot showing the oscillations-inducing sets returned from a 7D analysis where the abundances of the model species are randomly sampled

Fig. 4

The effect of AMPK activation and nutrient levels on cell proliferation and survival, given by the sum of phosphorylated mTORC1 and mTORC2. A, results shown for two nutritional (V_IR) levels and three AMPK values. B, the full dependence of pmTORC1 + pmTORC2 on V_IR and AMPK. The data points corresponding to those shown in panel A are indicated by red diamonds (V_IR = 0.00737) and blue squares (V_IR = 0.2)

Fig. 5

Effect of SIRT1 inhibition on AMPK and mTORC dynamics. A1--A3, V_IR = 0.1, which corresponds to high nutrient levels, the model predicts a time-independent steady-state solution. B1--B3, V_IR = 0.005, damped oscillations. C1--C3, V_IR = 0.00269, sustained oscillations

Fig. 6

The shift between cell proliferation (pmTORC1) and cell survival (pmTORC2) as one moves around the DEPTOR and AMPK parameter space. A, V_IR = 0.002687. B, V_IR = 0.01. Insets show ratios of pmTORC1/pmTORC2 as functions of AMPK, at specific DEPTOR values. Inset in A shows that oscillations are predicted for V_IR = 0.002687, DEPTOR = 0, and low AMPK values

Fig. 7

The effect of mTORC2 inhibition and AMPK abundance on cell proliferation and survival. The two surfaces represent the envelope of an oscillatory quantity