Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation

Modeling cell line-specific recruitment of signaling proteins to the insulin-like growth factor 1 receptor

Receptor tyrosine kinases (RTKs) typically contain multiple autophosphorylation sites in their cytoplasmic domains. Once activated, these autophosphorylation sites can recruit downstream signaling proteins containing Src homology 2 (SH2) and phosphotyrosine-binding (PTB) domains, which recognize phosphotyrosine-containing short linear motifs (SLiMs). These domains and SLiMs have polyspecific or promiscuous binding activities. Thus, multiple signaling proteins may compete for binding to a common SLiM and vice versa. To investigate the effects of competition on RTK signaling, we used a rule-based modeling approach to develop and analyze models for ligand-induced recruitment of SH2/PTB domain-containing proteins to autophosphorylation sites in the insulin-like growth factor 1 (IGF1) receptor (IGF1R). Models were parameterized using published datasets reporting protein copy numbers and site-specific binding affinities. Simulations were facilitated by a novel application of model restructuration, to reduce redundancy in rule-derived equations. We compare predictions obtained via numerical simulation of the model to those obtained through simple prediction methods, such as through an analytical approximation, or ranking by copy number and/or K_D value, and find that the simple methods are unable to recapitulate the predictions of numerical simulations. We created 45 cell line-specific models that demonstrate how early events in IGF1R signaling depend on the protein abundance profile of a cell. Simulations, facilitated by model restructuration, identified pairs of IGF1R binding partners that are recruited in anti-correlated and correlated fashions, despite no inclusion of cooperativity in our models. This work shows that the outcome of competition depends on the physicochemical parameters that characterize pairwise interactions, as well as network properties, including network connectivity and the relative abundances of competitors.

Cells rely on networks of interacting biomolecules to sense and respond to environmental perturbations and signals. However, it is unclear how information is processed to generate appropriate and specific responses to signals, especially given that these networks tend to share many components. For example, receptors that detect distinct ligands and regulate distinct cellular activities commonly interact with overlapping sets of downstream signaling proteins. Here, to investigate the downstream signaling of a well-studied receptor tyrosine kinase (RTK), the insulin-like growth factor 1 (IGF1) receptor (IGF1R), we formulated and analyzed 45 cell line-specific mathematical models, which account for recruitment of 18 different binding partners to six sites of receptor autophosphorylation in IGF1R. The models were parameterized using available protein copy number and site-specific affinity measurements, and restructured to allow for network generation. We find that recruitment is influenced by the protein abundance profile of a cell, with different patterns of recruitment in different cell lines. Furthermore, in a given cell line, we find that pairs of IGF1R binding partners may be recruited in a correlated or anti-correlated fashion. We demonstrate that the simulations of the model have greater predictive power than protein copy number and/or binding affinity data, and that even a simple analytical model cannot reproduce the predicted recruitment ranking obtained via simulations. These findings represent testable predictions and indicate that the outputs of IGF1R signaling depend on cell line-specific properties in addition to the properties that are intrinsic to the biomolecules involved.

Scalable Inference of Ordinary Differential Equation Models of Biochemical Processes

Ordinary differential equation models have become a standard tool for the mechanistic description of biochemical processes. If parameters are inferred from experimental data, such mechanistic models can provide accurate predictions about the behavior of latent variables or the process under new experimental conditions. Complementarily, inference of model structure can be used to identify the most plausible model structure from a set of candidates, and, thus, gain novel biological insight. Several toolboxes can infer model parameters and structure for small- to medium-scale mechanistic models out of the box. However, models for highly multiplexed datasets can require hundreds to thousands of state variables and parameters. For the analysis of such large-scale models, most algorithms require intractably high computation times. This chapter provides an overview of the state-of-the-art methods for parameter and model inference, with an emphasis on scalability.

The coordinated effects of Apatinib and Tripterine on the proliferation, invasiveness and apoptosis of human hepatoma Hep3B cells

As a novel vascular endothelial growth factor receptor-2 (VEGFR-2) tyrosine kinase inhibitor, Apatinib has exhibited antitumor effects in a variety of solid tumors. Extracts of Chinese herbal medicines have emerged as a promising alternative option to increase the sensitivity of patients to chemotherapeutics while alleviating side effects. The present study aimed to investigate the effects of Apatinib and the traditional Chinese herb Tripterine on the proliferation, invasion and apoptosis of human hepatoma Hep3B cells. The expression of VEGFR-2 in Hep3B cells was detected by western blotting and immunofluorescence assays. Hep3B cells were then divided into four different groups: Control group, Apatinib group, Tripterine group and Apatinib plus Tripterine group. The proliferation, invasion and apoptosis of these four groups of Hep3B cells were assessed by MTS, wound healing and Transwell assays, and flow cytometry, respectively. Finally, the levels of the proliferation-associated proteins phosphorylated protein kinase B (p-Akt) and phosphorylated extracellular signal-regulated kinase (p-ERK) and the apoptosis-associated proteins cleaved Caspase-3 and B-cell lymphoma-associated X protein (Bax) were detected by western blotting. The proliferation, migration and invasion of Hep3B cells were significantly inhibited by Apatinib and Tripterine, compared with the control group (P<0.01). The inhibitory effect of the combination group was markedly stronger than that of the Apatinib and Tripterine groups. The downregulation of p-Akt and p-ERK induced by Apatinib and Tripterine was further inhibited in the combination group (P<0.05), and the expression levels of Caspase-3 and Bax were also significantly increased in the combination group (P<0.05). The combination of Apatinib and Tripterine significantly inhibited the proliferation, migration and invasion ability and promoted the apoptosis of Hep3B cells by downregulating the expression of p-Akt and p-ERK, and upregulating the expression of Caspase-3 and Bax.

The extracellular matrix as a key regulator of intracellular signalling networks

The extracellular matrix (ECM) is a salient feature of all solid tissues within the body. This complex, acellular entity is composed of hundreds of individual molecules whose assembly, architecture and biomechanical properties are critical to controlling the behaviour and phenotype of the different cell types residing within tissues. Cells are the basic unit of life and the core building block of tissues and organs. At their simplest, they follow a set of rules, governed by their genetic code and effected through the complex protein signalling networks that these genes encode. These signalling networks assimilate and process the information received by the cell to control cellular decisions that govern cell fate. The ECM is the biggest provider of external stimuli to cells and as such is responsible for influencing intracellular signalling dynamics. In this review, we discuss the inclusion of ECM as a central regulatory signalling sub-network in computational models of cellular decision making, with a focus on its role in diseases such as cancer. LINKED ARTICLES: This article is part of a themed section on Translating the Matrix. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v176.1/issuetoc.

The Differential Effects of Repetitive Magnetic Stimulation in an In Vitro Neuronal Model of Ischemia/Reperfusion Injury

Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive therapy that has been implicated in treatment of serious neurological disorders. However, the neurobiological mechanisms underlying the effects of rTMS remain unclear. Therefore, this study examined the differential effects of repetitive magnetic stimulation (rMS) in an in vitro neuronal model of ischemia/reperfusion (I/R) injury, depending on low and high frequency. Neuro-2a cells were differentiated with retinoic acid and established for in vitro neuronal model of I/R injury under a subsequent 3 h of oxygen and glucose deprivation/reoxygenation (OGD/R) condition. After the I/R injury, the differentiated neuronal cells were stimulated with rMS on day 1 and randomly divided into three groups: OGD/R+sham, OGD/R+low-frequency, and OGD/R+high-frequency groups. High-frequency rMS increases cell proliferation through activation of extracellular signal-regulated kinases and AKT-signaling pathway and inhibits apoptosis in OGD/R-injured cells. Furthermore, high-frequency rMS increases Ca²⁺–calmodulin-dependent protein kinase II (CaMKII)-cAMP-response element binding protein (CREB) signaling pathway, further leading to alternation of brain-derived neurotrophic factor expression and synaptic plasticity in OGD/R injured cells. These results verified the neurobiological mechanisms of frequency-dependent rMS in I/R injury-treated neuronal cells. These mechanisms will help develop more powerful and credible rTMS stimulation treatment protocols.

Mechanisms of erythrocyte development and regeneration: implications for regenerative medicine and beyond

Hemoglobin-expressing erythrocytes (red blood cells) act as fundamental metabolic regulators by providing oxygen to cells and tissues throughout the body. Whereas the vital requirement for oxygen to support metabolically active cells and tissues is well established, almost nothing is known regarding how erythrocyte development and function impact regeneration. Furthermore, many questions remain unanswered relating to how insults to hematopoietic stem/progenitor cells and erythrocytes can trigger a massive regenerative process termed 'stress erythropoiesis' to produce billions of erythrocytes. Here, we review the cellular and molecular mechanisms governing erythrocyte development and regeneration, and discuss the potential links between these events and other regenerative processes.

Model Based Targeting of IL-6-Induced Inflammatory Responses in Cultured Primary Hepatocytes to Improve Application of the JAK Inhibitor Ruxolitinib

IL-6 is a central mediator of the immediate induction of hepatic acute phase proteins (APP) in the liver during infection and after injury, but increased IL-6 activity has been associated with multiple pathological conditions. In hepatocytes, IL-6 activates JAK1-STAT3 signaling that induces the negative feedback regulator SOCS3 and expression of APPs. While different inhibitors of IL-6-induced JAK1-STAT3-signaling have been developed, understanding their precise impact on signaling dynamics requires a systems biology approach. Here we present a mathematical model of IL-6-induced JAK1-STAT3 signaling that quantitatively links physiological IL-6 concentrations to the dynamics of IL-6-induced signal transduction and expression of target genes in hepatocytes. The mathematical model consists of coupled ordinary differential equations (ODE) and the model parameters were estimated by a maximum likelihood approach, whereas identifiability of the dynamic model parameters was ensured by the Profile Likelihood. Using model simulations coupled with experimental validation we could optimize the long-term impact of the JAK-inhibitor Ruxolitinib, a therapeutic compound that is quickly metabolized. Model-predicted doses and timing of treatments helps to improve the reduction of inflammatory APP gene expression in primary mouse hepatocytes close to levels observed during regenerative conditions. The concept of improved efficacy of the inhibitor through multiple treatments at optimized time intervals was confirmed in primary human hepatocytes. Thus, combining quantitative data generation with mathematical modeling suggests that repetitive treatment with Ruxolitinib is required to effectively target excessive inflammatory responses without exceeding doses recommended by the clinical guidelines.

Protein abundance of AKT and ERK pathway components governs cell type-specific regulation of proliferation

Signaling through the AKT and ERK pathways controls cell proliferation. However, the integrated regulation of this multistep process, involving signal processing, cell growth and cell cycle progression, is poorly understood. Here, we study different hematopoietic cell types, in which AKT and ERK signaling is triggered by erythropoietin (Epo). Although these cell types share the molecular network topology for pro‐proliferative Epo signaling, they exhibit distinct proliferative responses. Iterating quantitative experiments and mathematical modeling, we identify two molecular sources for cell type‐specific proliferation. First, cell type‐specific protein abundance patterns cause differential signal flow along the AKT and ERK pathways. Second, downstream regulators of both pathways have differential effects on proliferation, suggesting that protein synthesis is rate‐limiting for faster cycling cells while slower cell cycles are controlled at the G1‐S progression. The integrated mathematical model of Epo‐driven proliferation explains cell type‐specific effects of targeted AKT and ERK inhibitors and faithfully predicts, based on the protein abundance, anti‐proliferative effects of inhibitors in primary human erythroid progenitor cells. Our findings suggest that the effectiveness of targeted cancer therapy might become predictable from protein abundance.