Heterogeneous kinetics of AKT signaling in individual cells are accounted for by variable protein concentration

Abstract

In most solid cancers, cells harboring oncogenic mutations represent only a sub-fraction of the entire population. Within this sub-fraction the expression level of mutated proteins can vary significantly due to cellular variability limiting the efficiency of targeted therapy. To address the causes of the heterogeneity, we performed a systematic analysis of one of the most frequently mutated pathways in cancer cells, the phosphatidylinositol 3 kinase (PI3K) signaling pathway. Among others PI3K signaling is activated by the hepatocyte growth factor (HGF) that regulates proliferation of hepatocytes during liver regeneration but also fosters tumor cell proliferation. HGF-mediated responses of PI3K signaling were monitored both at the single cell and cell population level in primary mouse hepatocytes and in the hepatoma cell line Hepa1_6. Interestingly, we observed that the HGF-mediated AKT responses at the level of individual cells is rather heterogeneous. However, the overall average behavior of the single cells strongly resembled the dynamics of AKT activation determined at the cell population level. To gain insights into the molecular cause for the observed heterogeneous behavior of individual cells, we employed dynamic mathematical modeling in a stochastic framework. Our analysis demonstrated that intrinsic noise was not sufficient to explain the observed kinetic behavior, but rather the importance of extrinsic noise has to be considered. Thus, distinct from gene expression in the examined signaling pathway fluctuations of the reaction rates has only a minor impact whereas variability in the concentration of the various signaling components even in a clonal cell population is a key determinant for the kinetic behavior.

Modeling Cellular Noise Underlying Heterogeneous Cell Responses in the Epidermal Growth Factor Signaling Pathway

“…Therefore, a mammalian cell has a huge number of molecules even if the same concentrations of proteins are present in yeast and bacterial cells. For example, the EGF signaling pathway consists of 10 3 –10 7 molecules [40] and the HGF signaling pathway consists of 10 4 –10 7 molecules [9]. On the other hand, E .…”

“…In other signaling pathways, ranges of measured CV of signaling proteins were 8%– 75% in the hepatocyte growth factor (HGF) signaling pathway [9], 21%– 28% in the apoptotic intrinsic pathway [8], and 15%– 30% in normally cycling human cells [42]. Thus, estimated CV was included in the range of observed protein variability, indicating that our method is useful for estimating or predicting the variability of all signaling proteins.…”

“…The intrinsic noise was defined by fluctuations in reactions, which were realized by a stochastic simulation method (Gillespie algorithm) [41,53]. The extrinsic noise was defined by protein variability between individual cells, which was represented by independently sampling initial values of each protein from log-normal distributed random variables at various CV [8,9,18]. We implemented four types of simulation: 1) without either type of noise; 2) with only intrinsic noise; 3) with only extrinsic noise; 4) with both types of noise.…”

Quantitative Analysis of Receptor Tyrosine Kinase-Effector Coupling at Functionally Relevant Stimulus Levels

“…In particular, it has been shown that in single cells pERK levels do not vary continuously, but instead they manifest bistable fluctuations between minimal and maximal pERK, with higher levels of stimulus causing an increase in the frequency and duration of these spikes of ERK activation (37,69). The same does not appear to be the case for Akt, however; it has been shown that pAkt levels in individual cells are distributed stochastically around the mean (72). Nevertheless, because of the limitations arising from averaging over many cells, we focus here on identifying quantitative, empirical relationships between key factors and events, rather than proposing detailed mechanistic or network models.…”

Unraveling liver complexity from molecular to organ level: Challenges and perspectives

“…In the liver, hepatocyte growth factor (HGF)-induced activation of the phosphatidylinositol 3 kinase (PI3K) signaling pathway contributes to hepatocyte proliferation and consequently to liver regeneration in response to damage. A comparison of PI3K pathway activation upon HGF stimulation at the cell population and single cell level was performed in primary mouse hepatocytes and in the hepatoma cell line Hepa1-6 (Meyer et al, 2012). At the cell population level, the activation kinetics of the PI3K pathway were analyzed by combining experimental data with a deterministic coupled ODE based model.…”