The node of Ranvier in CNS pathology.

Healthy nodes of Ranvier are crucial for action potential propagation along myelinated axons, both in the central and in the peripheral nervous system. Surprisingly, the node of Ranvier has often been neglected when describing CNS disorders, with most pathologies classified simply as being due to neuronal defects in the grey matter or due to oligodendrocyte damage in the white matter. However, recent studies have highlighted changes that occur in pathological conditions at the node of Ranvier, and at the associated paranodal and juxtaparanodal regions where neurons and myelinating glial cells interact. Lengthening of the node of Ranvier, failure of the electrically resistive seal between the myelin and the axon at the paranode, and retraction of myelin to expose voltage-gated K(+) channels in the juxtaparanode, may contribute to altering the function of myelinated axons in a wide range of diseases, including stroke, spinal cord injury and multiple sclerosis. Here, we review the principles by which the node of Ranvier operates and its molecular structure, and thus explain how defects at the node and paranode contribute to neurological disorders

The expression of TAG-1 in glial cells is sufficient for the formation of the juxtaparanodal complex and the phenotypic rescue of Tag-1 homozygous mutants in the CNS

Myelinated fibers are organized into specialized domains that ensure the rapid propagation of action potentials and are characterized by protein complexes underlying axoglial interactions. TAG-1 (Transient Axonal Glycoprotein-1), a cell adhesion molecule of the Ig superfamily, is expressed by neurons as well as by myelinating glia. It is essential for the molecular organization of myelinated fibers as it maintains the integrity of the juxtaparanodal region through its interactions with Caspr2 and the voltage-gated potassium channels (VGKCs) on the axolemma. Since TAG-1 is the only known component of the juxtaparanodal complex expressed by the glial cell, it is important to clarify its role in the molecular organization of juxtaparanodes. For this purpose, we generated transgenic mice that exclusively express TAG-1 in oligodendrocytes and lack endogenous gene expression (Tag-1 -/-;plpTg(rTag-1)). Phenotypic analysis clearly demonstrates that glial TAG-1 is sufficient for the proper organization and maintenance of the juxtaparanodal domain in the CNS. Biochemical analysis shows that glial TAG-1 physically interacts with Caspr2 and VGKCs. Ultrastructural and behavioral analysis of Tag-1-/-;plpTg(rTag-1) mice shows that the expression of glial TAG-1 is sufficient to restore the axonal and myelin deficits as well as the behavioral defects observed in Tag-1 -/- animals. Together, these data highlight the pivotal role of myelinating glia on axonal domain differentiation and organization. Copyright © 2010 the authors

UniProt-Related Documents (UniReD): assisting wet lab biologists in their quest on finding novel counterparts in a protein network

The in-depth study of protein–protein interactions (PPIs) is of key importance for understanding how cells operate. Therefore, in the past few years, many experimental as well as computational approaches have been developed for the identification and discovery of such interactions. Here, we present UniReD, a user-friendly, computational prediction tool which analyses biomedical literature in order to extract known protein associations and suggest undocumented ones. As a proof of concept, we demonstrate its usefulness by experimentally validating six predicted interactions and by benchmarking it against public databases of experimentally validated PPIs succeeding a high coverage. We believe that UniReD can become an important and intuitive resource for experimental biologists in their quest for finding novel associations within a protein network and a useful tool to complement experimental approaches (e.g. mass spectrometry) by producing sorted lists of candidate proteins for further experimental validation. UniReD is available at http://bioinformatics.med.uoc.gr/unired/. © The Author(s) 2020. Published by Oxford University Press on behalf of NAR Genomics and Bioinformatics

Brain-Computer Interface with Corrupted EEG Data: a Tensor Completion Approach

One of the current issues in brain-computer interface (BCI) is how to deal with noisy electroencephalography (EEG) measurements organized as multidimensional datasets (tensors). On the other hand, recently, significant advances have been made in multidimensional signal completion algorithms that exploit tensor decomposition models to capture the intricate relationship among entries in a multidimensional signal. We propose to use tensor completion applied to EEG data for improving the classification performance in a motor imagery BCI system with corrupted measurements. Noisy measurements (electrode misconnections, subject movements, etc.) are considered as unknowns (missing samples) that are inferred from a tensor decomposition model (tensor completion). We evaluate the performance of four recently proposed tensor completion algorithms, CP-WOPT (Acar et al. Chemom Intell Lab Syst. 106:41-56, 2011), 3DPB-TC (Caiafa et al. 2013), BCPF (Zhao et al. IEEE Trans Pattern Anal Mach Intell. 37(9):1751-1763, 2015), and HaLRT (Liu et al. IEEE Trans Pattern Anal Mach Intell. 35(1):208-220, 2013), plus a simple interpolation strategy, first with random missing entries and then with missing samples constrained to have a specific structure (random missing channels), which is a more realistic assumption in BCI applications. We measured the ability of these algorithms to reconstruct the tensor from observed data. Then, we tested the classification accuracy of imagined movement in a BCI experiment with missing samples. We show that for random missing entries, all tensor completion algorithms can recover missing samples increasing the classification performance compared to a simple interpolation approach. For the random missing channels case, we show that tensor completion algorithms help to reconstruct missing channels, significantly improving the accuracy in the classification of motor imagery (MI), however, not at the same level as clean data. Summarizing, compared to the interpolation case, all tensor completion algorithms succeed to increase the classification performance by 7–9% (LDA–SVD) for random missing entries and 15–8% (LDA–SVD) for random missing channels. Tensor completion algorithms are useful in real BCI applications. The proposed strategy could allow using motor imagery BCI systems even when EEG data is highly affected by missing channels and/or samples, avoiding the need of new acquisitions in the calibration stage.Fil: Solé Casals, J.. Universitat de Vic; EspañaFil: Caiafa, César Federico. Indiana University; Estados Unidos. Provincia de Buenos Aires. Gobernación. Comisión de Investigaciones Científicas. Instituto Argentino de Radioastronomía. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto Argentino de Radioastronomía; ArgentinaFil: Zhao, Q.. Guangdong University Of Technology; China. Riken; JapónFil: Cichocki, A.. Skolkovo Institute Of Science And Technology; Rusia. Hangzhou Dianzi University; Chin