A dynamic analysis of IRS-PKR signaling in liver cells: a discrete modeling approach

Abstract

A major challenge in systems biology is to develop a detailed dynamic understanding of the functions and behaviors in a particular cellular system, which depends on the elements and their inter-relationships in a specific network. Computational modeling plays an integral part in the study of network dynamics and uncovering the underlying mechanisms. Here we proposed a systematic approach that incorporates discrete dynamic modeling and experimental data to reconstruct a phenotype-specific network of cell signaling. A dynamic analysis of the insulin signaling system in liver cells provides a proof-of-concept application of the proposed methodology. Our group recently identified that double-stranded RNA-dependent protein kinase (PKR) plays an important role in the insulin signaling network. The dynamic behavior of the insulin signaling network is tuned by a variety of feedback pathways, many of which have the potential to cross talk with PKR. Given the complexity of insulin signaling, it is inefficient to experimentally test all possible interactions in the network to determine which pathways are functioning in our cell system. Our discrete dynamic model provides an in silico model framework that integrates potential interactions and assesses the contributions of the various interactions on the dynamic behavior of the signaling network. Simulations with the model generated testable hypothesis on the response of the network upon perturbation, which were experimentally evaluated to identify the pathways that function in our particular liver cell system. The modeling in combination with the experimental results enhanced our understanding of the insulin signaling dynamics and aided in generating a context-specific signaling network.

Figure 1. Signaling network of insulin signaling and its feedbacks.

Each arc is assigned an attribute—either activation or inhibition.

Figure 2. The simulation process of our discrete dynamic model.

Figure 3. The simulated and experimental results of the PKR and IRS phosphorylation.

A) Model simulations with or without insulin stimulation. The simulation is on the initial model including the potential interactions and components from the literature and our experiments. The interactions emphasized are the level of IRS serine phosphorylation (IRSS), IRS tyrosine phosphorylation (IRST), and the PKR phosphorylation. B) Time series of the PKR and IRS phosphorylation upon insulin stimulation at time 0. HepG2 cells were exposed to 1 nM of insulin for 5, 10, 15, 30, or 60 minutes. After treatment, the cells were harvested, and western blot analysis was performed to detect the total and phosphorylated levels of PKR and IRS1 . The phosphorylation levels of PKR (blue) and IRS1 (red for p-IRS1 at Ser312 and green for p-IRS1 at Tyr941) were quantified and normalized to the total protein levels of PKR and IRS1, respectively, and are expressed as the average of four samples±SD from four independent experiments.

Figure 4. Breakdown of the Akt-PKR loop.

A) In silico knock-out. For each subgraph, an interaction is deleted from the model and the simulation is performed on the knock-out model. The interaction being knocked out is labeled at the top of each subgraph in the form of “no”-regulator-target. Red line: IRS serine phosphorylation, green line: IRS tyrosine phosphorylation, blue line: PKR phosphorylation. B) Western blot: effect of PP2A inhibitor (OA, 2 nM [56]) on the phosphorylation of Akt at Thr308. OA: Okadaic Acid.

Figure 5. Identification of essential feedback pathways through in silico perturbation study.

Each column represents a different architecture containing. A) Original ERK+JNK+IKK feedback pathways, B) Only with IKK+JNK feedback pathways, C) Only with ERK+JNK feedback pathways, and D) Only with ERK+IKK feedback pathways. Row 1 the model simulation is performed without any perturbations. Row 2 the model simulation is performed on IKK inhibition. Row 3 the model simulation is performed on JNK inhibition. Row 4 the model simulation is performed on ERK inhibition. E) Western blot: effects of JNK, IKK and ERK inhibitors on the phosphorylation of IRS1 upon simulation with 1 nM insulin for 15 mins.

Figure 6. Application of the simulation model in PKR over-expression.

A) In silico perturbation. Simulation of PKR over-expression and IKK or JNK inhibition with PKR over-expressed. Simulations are based upon the model including the JNK and IKK pathways but without the ERK feedback. B) Western blot results of the tyrosine and serine phosphorylation of IRS1 and the phosphorylation of PKR, after 15 mins of 1 nM insulin treatment, when PKR is over-expressed in HepG2 cells, with or without the IKK/JNK inhibition. IKK and JNK restores Tyr and reduces Ser phosphorylation of IRS1 (red box), as compared to the control with PKR over-expressed (blue box).

Figure 7. Essential signaling network of insulin signaling and its feedbacks.

The direct interaction between PP2A and AKT is removed. The ERK feedback is removed, together with the downstream factors in the ERK pathway that do not have an effect on the regulation of insulin signaling.

Figure 8. Robust dynamics against noise.

In row 1 the noise is represented by the variations over the distribution of the initial states of each component. Simulation without or with stimulus are shown in row 2 and row 3, respectively. In column 1, 70% of the initial protein activity levels are at the control state, indicating minor noise; in column 2, 50% are at the control state; and in column 2, the components are equally likely to be assigned to any of the three states, indicating a higher level of noise. Simulations are based upon the essential pathway model that excludes the ERK pathway. Colors on the distribution: light grey: control state (1), dark grey: higher than control (2), black: lower than control (0).