Phosphorylation of the actin binding protein Drebrin at S647 and is regulated by neuronal activity and PTEN

Defects in actin dynamics affect activity-dependent modulation of synaptic transmission and neuronal plasticity, and can cause cognitive impairment. A salient candidate actin-binding protein linking synaptic dysfunction to cognitive deficits is Drebrin (DBN). However, the specific mode of how DBN is regulated at the central synapse is largely unknown. In this study we identify and characterize the interaction of the PTEN tumor suppressor with DBN. Our results demonstrate that PTEN binds DBN and that this interaction results in the dephosphorylation of a site present in the DBN C-terminus - serine 647. PTEN and pS647-DBN segregate into distinct and complimentary compartments in neurons, supporting the idea that PTEN negatively regulates DBN phosphorylation at this site. We further demonstrate that neuronal activity increases phosphorylation of DBN at S647 in hippocampal neurons in vitro and in ex vivo hippocampus slices exhibiting seizure activity, potentially by inducing rapid dissociation of the PTEN:DBN complex. Our results identify a novel mechanism by which PTEN is required to maintain DBN phosphorylation at dynamic range and signifies an unusual regulation of an actin-binding protein linked to cognitive decline and degenerative conditions at the CNS synapse

Brain Endothelial- and Epithelial-Specific Interferon Receptor Chain 1 Drives Virus-Induced Sickness Behavior and Cognitive Impairment

Sickness behavior and cognitive dysfunction occur frequently by unknown mechanisms in virus-infected individuals with malignancies treated with type I interferons (IFNs) and in patients with autoimmune disorders. We found that during sickness behavior, single-stranded RNA viruses, double-stranded RNA ligands, and IFNs shared pathways involving engagement of melanoma differentiation-associated protein 5 (MDA5), retinoic acid-inducible gene 1 (RIG-I), and mitochondrial antiviral signaling protein (MAVS), and subsequently induced IFN responses specifically in brain endothelia and epithelia of mice. Behavioral alterations were specifically dependent on brain endothelial and epithelial IFN receptor chain 1 (IFNAR). Using gene profiling, we identified that the endothelia-derived chemokine ligand CXCL10 mediated behavioral changes through impairment of synaptic plasticity. These results identified brain endothelial and epithelial cells as natural gatekeepers for virus-induced sickness behavior, demonstrated tissue specific IFNAR engagement, and established the CXCL10-CXCR3 axis as target for the treatment of behavioral changes during virus infection and type I IFN therapy

Neuroscience Scaffolded by Informatics: A Raging Interdisciplinary Field

Following breakthrough achievements in molecular neurosciences, the current decade witnesses a trend toward interdisciplinary and multimodal development. Supplementation of neurosciences with tools from computer science solidifies previous knowledge and sets the ground for new research on “big data” and new hypothesis-free experimental models. In this Special Issue, we set the focus on informatics-supported interdisciplinary neuroscience accomplishments symmetrically combining wet-lab and clinical routines. Video-tracking and automated mitosis detection in vitro, the macromolecular modeling of kinesin motion, and the unsupervised classification of the brain’s macrophage activation status share a common denominator: they are energized by machine and deep learning. Essential clinical neuroscience questions such as the estimated risk of brain aneurysm rupture and the surgical outcome of facial nerve transplantation are addressed in this issue as well. Precise and rapid evaluation of complex clinical data by deep learning and data mining dives deep to reveal symmetrical and asymmetrical features beyond the abilities of human perception or the limits of linear algebraic modeling. This editorial opts to motivate researchers from the wet lab, computer science, and clinical environments to join forces in reshaping scientific platforms, share and converge high-quality data on public platforms, and use informatics to facilitate interdisciplinary information exchange

Neuroscience Scaffolded by Informatics: A Raging Interdisciplinary Field

Following breakthrough achievements in molecular neurosciences, the current decade witnesses a trend toward interdisciplinary and multimodal development. Supplementation of neurosciences with tools from computer science solidifies previous knowledge and sets the ground for new research on “big data” and new hypothesis-free experimental models. In this Special Issue, we set the focus on informatics-supported interdisciplinary neuroscience accomplishments symmetrically combining wet-lab and clinical routines. Video-tracking and automated mitosis detection in vitro, the macromolecular modeling of kinesin motion, and the unsupervised classification of the brain’s macrophage activation status share a common denominator: they are energized by machine and deep learning. Essential clinical neuroscience questions such as the estimated risk of brain aneurysm rupture and the surgical outcome of facial nerve transplantation are addressed in this issue as well. Precise and rapid evaluation of complex clinical data by deep learning and data mining dives deep to reveal symmetrical and asymmetrical features beyond the abilities of human perception or the limits of linear algebraic modeling. This editorial opts to motivate researchers from the wet lab, computer science, and clinical environments to join forces in reshaping scientific platforms, share and converge high-quality data on public platforms, and use informatics to facilitate interdisciplinary information exchange

Energy substrates that fuel fast neuronal network oscillations

Fast neuronal network oscillations in the gamma-frequency band (30-100 Hz) provide a fundamental mechanism of complex neuronal information processing in the hippocampus and neocortex of mammals. Gamma oscillations have been implicated in higher brain functions such as sensory perception, motor activity and memory formation. The oscillations emerge from precise synapse interactions between excitatory principal neurons such as pyramidal cells and inhibitory GABAergic interneurons, and they are associated with high energy expenditure. However, both energy substrates and metabolic pathways that are capable to power cortical gamma oscillations have been less defined. Here, we investigated the energy sources fueling persistent gamma oscillations in the CA3 subfield of organotypic hippocampal slice cultures of the rat. This preparation permits superior oxygen supply as well as fast application of glucose, glycolytic metabolites or drugs such as glycogen phosphorylase inhibitor during extracellular recordings of the local field potential. Our findings are: (i) gamma oscillations persist in the presence of glucose (10 mmol/L) for greater than 60 minutes in slice cultures while (ii) lowering glucose levels (2.5 mmol/L) significantly reduces the amplitude of the oscillation. (iii) Gamma oscillations are absent at low concentration of lactate (2 mmol/L). (iv) Gamma oscillations persist at high concentration (20 mmol/L) of either lactate or pyruvate, albeit showing significant reductions in the amplitude. (v) The breakdown of glycogen significantly delays the decay of gamma oscillations during glucose deprivation. However, when glucose is present, the turnover of glycogen is not essential to sustain gamma oscillations. Our study shows that fast neuronal network oscillations can be fueled by different energy-rich substrates, with glucose being most effective

High-Frequency Stimulation of the Subthalamic Nucleus Counteracts Cortical Expression of Major Histocompatibility Complex Genes in a Rat Model of Parkinson’s Disease

<div><p>High-frequency stimulation of the subthalamic nucleus (STN-HFS) is widely used as therapeutic intervention in patients suffering from advanced Parkinson’s disease. STN-HFS exerts a powerful modulatory effect on cortical motor control by orthodromic modulation of basal ganglia outflow and via antidromic activation of corticofugal fibers. However, STN-HFS-induced changes of the sensorimotor cortex are hitherto unexplored. To address this question at a genomic level, we performed mRNA expression analyses using Affymetrix microarray gene chips and real-time RT-PCR in sensorimotor cortex of parkinsonian and control rats following STN-HFS. Experimental parkinsonism was induced in Brown Norway rats by bilateral nigral injections of 6-hydroxydopamine and was assessed histologically, behaviorally, and electrophysiologically. We applied prolonged (23h) unilateral STN-HFS in awake and freely moving animals, with the non-stimulated hemisphere serving as an internal control for gene expression analyses. Gene enrichment analysis revealed strongest regulation in major histocompatibility complex (MHC) related genes. STN-HFS led to a cortical downregulation of several MHC class II (RT1-Da, Db1, Ba, and Cd74) and MHC class I (RT1CE) encoding genes. The same set of genes showed increased expression levels in a comparison addressing the effect of 6-hydroxydopamine lesioning. Hence, our data suggest the possible association of altered microglial activity and synaptic transmission by STN-HFS within the sensorimotor cortex of 6-hydroxydopamine treated rats.</p></div

Spectral analysis of cortical and subthalamic local field potentials.

<p>Representative examples (A) of oscillatory activity in the raw local field potentials (LFP) recorded from STN (upper row) and frontal ECoG (lower row) of controls (left column) and 6-OHDA treated parkinsonian rats (right column). Time-frequency plots (B) of spectral power during 180 s of quiet rest from all PD animals included in gene expression analyses. Power spectra (C) calculated from frontal ECoG and STN-LFP data of parkinsonian rats (light gray lines/shadings) and controls (dark gray lines/shadings): (i) low-frequency range (1–45 Hz) and (ii) high-frequency range (45–80 Hz) of ECoG-power spectra pooled across all hemispheres (n = 6). (iii) Low-frequency power spectrum of STN-LFP ipsilateral to the side of HFS and (iv) contralateral to the side of HFS (n = 3 each). (v) High-frequency power spectrum of STN-LFP pooled across all hemispheres. Asterisks indicate significantly different frequency bands. Note the dampening at 50 Hz that resulted from bandpass filtering of line noise. LFP, local field potential; STN, subthalamic nucleus; ECoG, electrocorticogram; 6-OHDA, 6-hydroxydopamine, HFS, high-frequency stimulation.</p