Accelerated neurodegeneration and neuroinflammation in transgenic mice expressing P301L tau mutant and tau-tubulin kinase 1.

Tau-tubulin kinase-1 (TTBK1) is a central nervous system (CNS)-specific protein kinase implicated in the pathological phosphorylation of tau. TTBK1-transgenic mice show enhanced neuroinflammation in the CNS. Double-transgenic mice expressing TTBK1 and frontotemporal dementia with parkinsonism-17-linked P301L (JNPL3) tau mutant (TTBK1/JNPL3) show increased accumulation of oligomeric tau protein in the CNS and enhanced loss of motor neurons in the ventral horn of the lumbar spinal cord. To determine the role of TTBK1-induced neuroinflammation in tauopathy-related neuropathogenesis, age-matched TTBK1/JNPL3, JNPL3, TTBK1, and non-transgenic littermates were systematically characterized. There was a striking switch in the activation phenotype and population of mononuclear phagocytes (resident microglia and infiltrating macrophages) in the affected spinal cord region: JNPL3 mice showed accumulation of alternatively activated microglia, whereas TTBK1 and TTBK1/JNPL3 mice showed accumulation of classically activated infiltrating peripheral monocytes. In addition, expression of chemokine ligand 2, a chemokine important for the recruitment of peripheral monocytes, was enhanced in TTBK1 and TTBK1/JNPL3 but not in other groups in the spinal cord. Furthermore, primary cultured mouse motor neurons showed axonal degeneration after transient expression of the TTBK1 gene or treatment with conditioned media derived from lipopolysaccharide-stimulated microglia; this was partially blocked by silencing of the endogenous TTBK1 gene in neurons. These data suggest that TTBK1 accelerates motor neuron neurodegeneration by recruiting proinflammatory monocytes and enhancing sensitivity to neurotoxicity in inflammatory conditions.

Statistical processing for gastric slow-wave identification.

Successful identification of gastric slow waves in canine gastric electrical activity (GEA) data was achieved using a statistical data-processing procedure based on the multiple linear regression (MLR) curve fitting technique. Both distal and proximal waveforms were identified, first by construction of separate orthonormal bases from pre-selected sets of representative distal and proximal gastric slow waves (GSWs). Respective basis matrices were used to fit proximal and distal data to an MLR data model. Residual waveforms were computed from the original and 'fitted' waveforms and used in identifying GSWs in the data. Canine GEA data were split into 1,800-point blocks, and each 245-point data segment in a block was processed to identify the GSWs. Gastric slow waves were located in the data using a residual mean-squared error (MSE) threshold and, for distal GEA data, the minimum value of the main distal waveform peak. All threshold values were determined empirically and were set to detect GSWs while limiting false matches. Identification rates of 95% and 99% for proximal and distal GSWs, respectively, represent a significant improvement over those obtained in a previous study in which the same data were analysed using linear signal-processing methods. The use of the method presented in this paper for real-time identification of GSWs in conjunction with an implantable gastric pacer unit appears promising. Because the technique is inherently customisable, results obtained in this study should also be applicable to human subjects.