Non-canonical Activation of Akt in Serum-Stimulated Fibroblasts, Revealed by Comparative Modeling of Pathway Dynamics

Tri Hieu Nim, Le Luo, Jacob K White, Marie-Véronique Clément and Lisa Tucker-Kellogg

PLOS Computational Biology, 2015, vol. 11, issue 11, 1-27

Abstract: The dynamic behaviors of signaling pathways can provide clues to pathway mechanisms. In cancer cells, excessive phosphorylation and activation of the Akt pathway is responsible for cell survival advantages. In normal cells, serum stimulation causes brief peaks of extremely high Akt phosphorylation before reaching a moderate steady-state. Previous modeling assumed this peak and decline behavior (i.e., “overshoot”) was due to receptor internalization. In this work, we modeled the dynamics of the overshoot as a tool for gaining insight into Akt pathway function. We built an ordinary differential equation (ODE) model describing pathway activation immediately upstream of Akt phosphorylation at Thr308 (Aktp308). The model was fit to experimental measurements of Aktp308, total Akt, and phosphatidylinositol (3,4,5)-trisphosphate (PIP3), from mouse embryonic fibroblasts with serum stimulation. The canonical Akt activation model (the null hypothesis) was unable to recapitulate the observed delay between the peak of PIP3 (at 2 minutes), and the peak of Aktp308 (at 30–60 minutes). From this we conclude that the peak and decline behavior of Aktp308 is not caused by PIP3 dynamics. Models for alternative hypotheses were constructed by allowing an arbitrary dynamic curve to perturb each of 5 steps of the pathway. All 5 of the alternative models could reproduce the observed delay. To distinguish among the alternatives, simulations suggested which species and timepoints would show strong differences. Time-series experiments with membrane fractionation and PI3K inhibition were performed, and incompatible hypotheses were excluded. We conclude that the peak and decline behavior of Aktp308 is caused by a non-canonical effect that retains Akt at the membrane, and not by receptor internalization. Furthermore, we provide a novel spline-based method for simulating the network implications of an unknown effect, and we demonstrate a process of hypothesis management for guiding efficient experiments.Author Summary: Influential pathways of cell signalling (such as PI3K/Akt) are routinely communicated using simple textbook-like diagrams that show only the most widely-accepted steps of the pathway. At the same time, there are countless other molecular influences relevant to each pathway, documented in the published literature, and more are being published every week. It should perhaps come as little surprise that during a routine observation of the Akt activation pathway, a simulation of the canonical model was mathematically incompatible with our observed dynamics. To progress beyond the standard, simplified model without testing an unreasonable number of molecular candidates individually, we employed computational modeling to analyze the dynamics of pathway activation. We asked when and where a non-canonical deviation could occur, relative to the canonical pathway. We used the timing of downstream activation to solve for the possible times of upstream initiation. By categorizing unknown effects by their dynamics, we were able to prune away implausible hypotheses using an efficient number of in vitro experiments. At the end we had a single plausible explanation for non-canonical Akt activation in our cells, and we confirmed experimentally that Akt is retained at the membrane after PIP3 is no longer present.

Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004505 (text/html)
https://journals.plos.org/ploscompbiol/article/fil ... 04505&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004505

DOI: 10.1371/journal.pcbi.1004505

Access Statistics for this article

More articles in PLOS Computational Biology from Public Library of Science
Bibliographic data for series maintained by ploscompbiol (ploscompbiol@plos.org).