Identifying a Kinase Network Regulating FGF14:Nav1.6 Complex Assembly Using Split-Luciferase Complementation

Kinases play fundamental roles in the brain. Through complex signaling pathways, kinases regulate the strength of protein:protein interactions (PPI) influencing cell cycle, signal transduction, and electrical activity of neurons. Changes induced by kinases on neuronal excitability, synaptic plasticity and brain connectivity are linked to complex brain disorders, but the molecular mechanisms underlying these cellular events remain for the most part elusive. To further our understanding of brain disease, new methods for rapidly surveying kinase pathways in the cellular context are needed. The bioluminescence-based luciferase complementation assay (LCA) is a powerful, versatile toolkit for the exploration of PPI. LCA relies on the complementation of two firefly luciferase protein fragments that are functionally reconstituted into the full luciferase enzyme by two interacting binding partners. Here, we applied LCA in live cells to assay 12 kinase pathways as regulators of the PPI complex formed by the voltage-gated sodium channel, Nav1.6, a transmembrane ion channel that elicits the action potential in neurons and mediates synaptic transmission, and its multivalent accessory protein, the fibroblast growth factor 14 (FGF14). Through extensive dose-dependent validations of structurally-diverse kinase inhibitors and hierarchical clustering, we identified the PI3K/Akt pathway, the cell-cycle regulator Wee1 kinase, and protein kinase C (PKC) as prospective regulatory nodes of neuronal excitability through modulation of the FGF14:Nav1.6 complex. Ingenuity Pathway Analysis shows convergence of these pathways on glycogen synthase kinase 3 (GSK3) and functional assays demonstrate that inhibition of GSK3 impairs excitability of hippocampal neurons. This combined approach provides a versatile toolkit for rapidly surveying PPI signaling, allowing the discovery of new modular pathways centered on GSK3 that might be the basis for functional alterations between the normal and diseased brain.     

Long noncoding RNA PTENP1 affects the recovery of spinal cord injury by regulating the expression of miR-19b and miR-21

Exosomes derived from differentiated P12 cells and MSCs were proved to suppress apoptosis of neuron cells, and phosphatase and tensin homolog pseudogene 1 (PTENP1) was reported to inhibit cell proliferation. In this study, we aimed to investigate the role of PTENP1 in the process of post-spinal cord injury (SCI) recovery, so as to evaluate the therapeutic effects of exosomes derived from MSCs transfected with PTENP1 short hairpin RNA (shRNA), as a type of novel biomarkers in the treatment of SCI. Electron microscopy was used to observe the morphology of different exosomes. Real-time polymerase chain reaction and western blot, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide assays, flow cytometry, Nissl staining, immunohistochemistry assay, and terminal deoxynucleotidyl transferase dUTP nick end labeling assay were conducted to investigate and validate the underlying molecular signaling pathway. PTENP1-shRNA downregulated PTENP1 and PTEN while upregulating miR-21 and miR-19b. PTENP1-shRNA also accelerated cell apoptosis and reduced cell viability. In addition, PTENP1 reduced the miR-21 and miR-19b expression by directly targeting miR-21 and miR-19b. Meanwhile, both miR-21 and miR-19b reduced the expression of PTEN by directly targeting the 3′-untranslated region of PTEN. Furthermore, PTEN level and apoptosis index of neuron cells was the highest in the SCI group, while the treatment with exosomes+PTENP1-shRNA reduced the PTEN expression to a level similar to that in the sham group. Finally, PTENP1 inhibited miR-21 and miR-19b expression but upregulated PTEN expression. The upregulation of miR-21/miR-19b also suppressed the apoptosis of neuron cells by downregulating the PTEN expression. PTENP1 is involved in the recovery of SCI by regulating the expression of miR-19b and miR-21, and exosomes from PTENP1-shRNA-transfected cells may be used as a novel biomarker in SCI treatment.     

Boolean Combinations of Implicit Functions for Model Clipping in Computer-Assisted Surgical Planning

This paper proposes an interactive method of model clipping for computer-assisted surgical planning. The model is separated by a data filter that is defined by the implicit function of the clipping path. Being interactive to surgeons, the clipping path that is composed of the plane widgets can be manually repositioned along the desirable presurgical path, which means that surgeons can produce any accurate shape of the clipped model. The implicit function is acquired through a recursive algorithm based on the Boolean combinations (including Boolean union and Boolean intersection) of a series of plane widgets’ implicit functions. The algorithm is evaluated as highly efficient because the best time performance of the algorithm is linear, which applies to most of the cases in the computer-assisted surgical planning. Based on the above stated algorithm, a user-friendly module named SmartModelClip is developed on the basis of Slicer platform and VTK. A number of arbitrary clipping paths have been tested. Experimental results of presurgical planning for three types of Le Fort fractures and for tumor removal demonstrate the high reliability and efficiency of our recursive algorithm and robustness of the module.