CCN1 (CYR61) and CCN3 (NOV) signaling drives human trophoblast cells into senescence and stimulates migration properties

<p>During placental development, continuous invasion of trophoblasts into the maternal compartment depends on the support of proliferating extravillous trophoblasts (EVTs). Unlike tumor cells, EVTs escape from the cell cycle before invasion into the decidua and spiral arteries. This study focused on the regulation properties of glycosylated and non-glycosylated matricellular CCN1 and CCN3, primarily for proliferation control in the benign SGHPL-5 trophoblast cell line, which originates from the first-trimester placenta. Treating SGHPL-5 trophoblast cells with the glycosylated forms of recombinant CCN1 and CCN3 decreased cell proliferation by bringing about G0/G1 cell cycle arrest, which was accompanied by the upregulation of activated Notch-1 and its target gene p21. Interestingly, both CCN proteins increased senescence-associated β-galactosidase activity and the expression of the senescence marker p16. The migration capability of SGHPL-5 cells was mostly enhanced in response to CCN1 and CCN3, by the activation of FAK and Akt kinase but not by the activation of ERK1/2. In summary, both CCN proteins play a key role in regulating trophoblast cell differentiation by inducing senescence and enhancing migration properties. Reduced levels of CCN1 and CCN3, as found in early-onset preeclampsia, could contribute to a shift from invasive to proliferative EVTs and may explain their shallow invasion properties in this disease.</p

Time course of Reelin stimulation in neurons.

<p>Activation and degradation of core components of lipoprotein receptor-dependent Reelin signaling were measured by use of quantitative western blotting. Representative blots of tyrosine-phosphorylation of the neuronal adaptor protein Dab1, total Dab1, total VLDLR and total ApoER2, activation of Src family kinases as well as activation of the PI3 kinase targeting molecule Akt/PKB in primary cortical neurons prepared from E15.5 mice are shown for (A) 0–30 min and (B) 0–240 min of Reelin stimulation.</p

Overview of model reduction steps.

<p>(A) Parameter profile likelihood of the Akt deactivation. (B) Dependency of Akt activation and deactivation leading to a coupling with phosphorylated Dab1. (C) Parameter profile of Dab1 trans-phosphorylation that does not exceed the 95% threshold for high values. (D) Remaining model parameters are unchanged in the upper limit. (E) Parameter profile of SFK degradation, which is non-identifiable towards zero. (F) Remaining model parameters re-optimized in turn.</p

Mathematical model of early Reelin-induced Src family kinase-mediated signaling

<div><p>Reelin is a large glycoprotein with a dual role in the mammalian brain. It regulates the positioning and differentiation of postmitotic neurons during brain development and modulates neurotransmission and memory formation in the adult brain. Alterations in the Reelin signaling pathway have been described in different psychiatric disorders. Reelin mainly signals by binding to the lipoprotein receptors Vldlr and ApoER2, which induces tyrosine phosphorylation of the adaptor protein Dab1 mediated by Src family kinases (SFKs). In turn, phosphorylated Dab1 activates downstream signaling cascades, including PI3-kinase-dependent signaling. In this work, a mechanistic model based on ordinary differential equations was built to model early dynamics of the Reelin-mediated signaling cascade. Mechanistic models are frequently used to disentangle the highly complex mechanisms underlying cellular processes and obtain new biological insights. The model was calibrated on time-resolved data and a dose-response measurement of protein concentrations measured in cortical neurons treated with Reelin. It focusses on the interplay between Dab1 and SFKs with a special emphasis on the tyrosine phosphorylation of Dab1, and their role for the regulation of Reelin-induced signaling. Model selection was performed on different model structures and a comprehensive mechanistic model of the early Reelin signaling cascade is provided in this work. It emphasizes the importance of Reelin-induced lipoprotein receptor clustering for SFK-mediated Dab1 trans-phosphorylation and does not require co-receptors to describe the measured data. The model is freely available within the open-source framework Data2Dynamics (<a href="http://www.data2dynamics.org" target="_blank">www.data2dynamics.org</a>). It can be used to generate predictions that can be validated experimentally, and provides a platform for model extensions both to downstream targets such as transcription factors and interactions with other transmembrane proteins and neuronal signaling pathways.</p></div

Time-course of tyrosine-phosphorylated Dab1 after Reelin stimulation in neurons from WT, Vldlr -/- and ApoER2 -/- knockout mice, measured on a single Western blot.

<p>(A) Data is shown as dots with their respective error bars from N = 4 biological replicates. Model trajectories as lines from the complex model that is calibrated on all available data. Representative Westernblots for all conditions are shown on the right. Detailed information about the knockout mice and antibodies are given in Section Materials and Methods. (B) Parameter profile of the amount of ApoER2 within the sum of all receptors, which is compatible with 100% denoting that no Vldl receptors need to be involved in the signaling.</p

Model validation.

<p>(A) Prediction bands for an EC50 concentration of Reelin for both monomer (red dashed lines) and complex (black solid lines) models. Data is shown as open circles with errors. The shaded areas correspond to the standard deviation of the model predictions. (B) Model trajectories for Reelin stimulus after SFK inhibition, if SFK-dependent trans-phosphorylation between Dab1 proteins in the cluster is prohibited.</p

Quantification of the absolute Reelin amount in supernatants used for stimulation.

<p>Standard curve of recombinant Reelin protein and several dilutions of Reelin-conditioned supernatant, measured by Western blotting. Lanes ‘undiluted’ and ‘1:10’ were omitted from calculation. Measurements comprise three biological replicates.</p

Scheme of the monomer (A) and complex (B) signaling models.

<p>In the monomer model variant (A), Dab1 is activated by tyrosine phosphorylation after binding of Reelin to the receptor. The initial phosphorylation of Dab1 initiates phosphorylation of SFKs, which results in a positive feedback. Subsequently, p-Dab1 induces activation of downstream targets, such as Akt. Signaling is negatively regulated by degradation of phosphorylated Dab1, which is further increased through the Reelin-dependent activation of SFKs. In the complex model (B), clusters of the lipoprotein receptors are formed. These bind the adaptor protein Dab1, which is phosphorylated by SFKs. As a feed-forward loop, SFKs activated in a p-Dab1/SFK complex can trans-phosphorylate other Dab1 proteins bound to the receptor complex. The pathway is regulated by ubiquitination and degradation of phosphorylated Dab1.</p

Driving the Model to Its Limit: Profile Likelihood Based Model Reduction

<div><p>In systems biology, one of the major tasks is to tailor model complexity to information content of the data. A useful model should describe the data and produce well-determined parameter estimates and predictions. Too small of a model will not be able to describe the data whereas a model which is too large tends to overfit measurement errors and does not provide precise predictions. Typically, the model is modified and tuned to fit the data, which often results in an oversized model. To restore the balance between model complexity and available measurements, either new data has to be gathered or the model has to be reduced. In this manuscript, we present a data-based method for reducing non-linear models. The profile likelihood is utilised to assess parameter identifiability and designate likely candidates for reduction. Parameter dependencies are analysed along profiles, providing context-dependent suggestions for the type of reduction. We discriminate four distinct scenarios, each associated with a specific model reduction strategy. Iterating the presented procedure eventually results in an identifiable model, which is capable of generating precise and testable predictions. Source code for all toy examples is provided within the freely available, open-source modelling environment Data2Dynamics based on MATLAB available at <a href="http://www.data2dynamics.org/" target="_blank">http://www.data2dynamics.org/</a>, as well as the R packages dMod/cOde available at <a href="https://github.com/dkaschek/" target="_blank">https://github.com/dkaschek/</a>. Moreover, the concept is generally applicable and can readily be used with any software capable of calculating the profile likelihood.</p></div

Typical flow-chart for model reduction based on the profile likelihood.

<p>The depicted steps are to be applied for each parameter individually, starting with the calculation of the respective profile likelihood. After detection, the procedure resolves non-identifiabilities by fixing parameters, removing reactions, performing algebraic substitutions, or context-specific reductions. The method terminates when all parameters of interest are identifiable.</p