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

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 (www.data2dynamics.org). 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.

Loss of Reelin protects mice against arterial thrombosis by impairing integrin activation and thrombus formation under high shear conditions.

Reelin is a secreted glycoprotein and essential for brain development and plasticity. Recent studies provide evidence that Reelin modifies platelet actin cytoskeletal dynamics. In this study we sought to dissect the contribution of Reelin in arterial thrombus formation. Here we analyzed the impact of Reelin in arterial thrombosis ex vivo and in vivo using Reelin deficient (reeler) and wildtype mice. We found that Reelin is secreted upon platelet activation and mediates signaling via glycoprotein (GP)Ib, the amyloid precursor protein (APP) and apolipoprotein E receptor 2 (ApoER2) to induce activation of Akt, extracellular signal-regulated kinase (Erk), SYK and Phospholipase Cγ2. Moreover, our data identifies Reelin as first physiological ligand for platelet APP. Platelets from reeler mice displayed attenuated platelet adhesion and significantly reduced thrombus formation under high shear conditions indicating an important role for Reelin in GPIb-dependent integrin αIIbß3 activation. Accordingly, adhesion to immobilized vWF as well as integrin activation and the phosphorylation of Erk and Akt after GPIb engagement was reduced in Reelin deficient platelets. Defective Reelin signaling translated into protection from arterial thrombosis and cerebral ischemia/reperfusion injury beside normal hemostasis. Furthermore, treatment with an antagonistic antibody specific for Reelin protects wildtype mice from occlusive thrombus formation. Mechanistically, GPIb co-localizes to the major Reelin receptor APP in platelets suggesting that Reelin-induced effects on GPIb signaling are mediated by APP-GPIb interaction. These results indicate that Reelin is an important regulator of GPIb-mediated platelet activation and may represent a new therapeutic target for the prevention and treatment of cardio- and cerebrovascular diseases.

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

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 ß-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.

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

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 http://www.data2dynamics.org/, as well as the R packages dMod/cOde available at https://github.com/dkaschek/. Moreover, the concept is generally applicable and can readily be used with any software capable of calculating the profile likelihood.