GABAergic and Cortical and Subcortical Glutamatergic Axon Terminals Contain CB1 Cannabinoid Receptors in the Ventromedial Nucleus of the Hypothalamus

Background: Type-1 cannabinoid receptors (CB1R) are enriched in the hypothalamus, particularly in the ventromedial hypothalamic nucleus (VMH) that participates in homeostatic and behavioral functions including food intake. Although CB1R activation modulates excitatory and inhibitory synaptic transmission in the brain, CB1R contribution to the molecular architecture of the excitatory and inhibitory synaptic terminals in the VMH is not known. Therefore, the aim of this study was to investigate the precise subcellular distribution of CB1R in the VMH to better understand the modulation exerted by the endocannabinoid system on the complex brain circuitries converging into this nucleus. Methodology/Principal Findings: Light and electron microscopy techniques were used to analyze CB1R distribution in the VMH of CB1R-WT, CB1R-KO and conditional mutant mice bearing a selective deletion of CB1R in cortical glutamatergic (Glu-CB1R-KO) or GABAergic neurons (GABA-CB1R-KO). At light microscopy, CB1R immunolabeling was observed in the VMH of CB1R-WT and Glu-CB1R-KO animals, being remarkably reduced in GABA-CB1R-KO mice. In the electron microscope, CB1R appeared in membranes of both glutamatergic and GABAergic terminals/preterminals. There was no significant difference in the percentage of CB1R immunopositive profiles and CB1R density in terminals making asymmetric or symmetric synapses in CB1R-WT mice. Furthermore, the proportion of CB1R immunopositive terminals/preterminals in CB1R-WT and Glu-CB1R-KO mice was reduced in GABA-CB1R-KO mutants. CB1R density was similar in all animal conditions. Finally, the percentage of CB1R labeled boutons making asymmetric synapses slightly decreased in Glu-CB1R-KO mutants relative to CB1R-WT mice, indicating that CB1R was distributed in cortical and subcortical excitatory synaptic terminals. Conclusions/Significance: Our anatomical results support the idea that the VMH is a relevant hub candidate in the endocannabinoid-mediated modulation of the excitatory and inhibitory neurotransmission of cortical and subcortical pathways regulating essential hypothalamic functions for the individual's survival such as the feeding behavior.

Neurotransmitter signaling in the pathophysiology of microglia

Erratun publicado en Frontiers in Cellular Neuroscience 7 : (2013) // Article ID 107

Contribution of Pannexin1 to Experimental Autoimmune Encephalomyelitis

Pannexin1 (Panx1) is a plasma membrane channel permeable to relatively large molecules, such as ATP. In the central nervous system (CNS) Panx1 is found in neurons and glia and in the immune system in macrophages and T-cells. We tested the hypothesis that Panx1-mediated ATP release contributes to expression of Experimental Autoimmune Encephalomyelitis (EAE), an animal model for multiple sclerosis, using wild-type (WT) and Panx1 knockout (KO) mice. Panx1 KO mice displayed a delayed onset of clinical signs of EAE and decreased mortality compared to WT mice, but developed as severe symptoms as the surviving WT mice. Spinal cord inflammatory lesions were also reduced in Panx1 KO EAE mice during acute disease. Additionally, pharmacologic inhibition of Panx1 channels with mefloquine (MFQ) reduced severity of acute and chronic EAE when administered before or after onset of clinical signs. ATP release and YoPro uptake were significantly increased in WT mice with EAE as compared to WT non-EAE and reduced in tissues of EAE Panx1 KO mice. Interestingly, we found that the P2X7 receptor was upregulated in the chronic phase of EAE in both WT and Panx1 KO spinal cords. Such increase in receptor expression is likely to counterbalance the decrease in ATP release recorded from Panx1 KO mice and thus contribute to the development of EAE symptoms in these mice. The present study shows that a Panx1 dependent mechanism (ATP release and/or inflammasome activation) contributes to disease progression, and that inhibition of Panx1 using pharmacology or gene disruption delays and attenuates clinical signs of EAE.

1-42 beta-Amyloid peptide requires PDK1/nPKC/Rac 1 pathway to induce neuronal death

1-42 beta-Amyloid (A beta(1-42)) peptide is a key molecule involved in the development of Alzheimer's disease. Some of its effects are manifested at the neuronal morphological level. These morphological changes involve loss of neurites due to cytoskeleton alterations. However, the mechanism of A beta(1-42) peptide activation of the neurodegenerative program is still poorly understood. Here, A beta(1-42) peptide-induced transduction of cellular death signals through the phosphatidylinositol 3-kinase (PI3K)/phosphoinositol- dependent kinase (PDK)/novel protein kinase C (nPKC)/Rac 1 axis is described. Furthermore, pharmacological inhibition of PDK1 and nPKC activities blocks Rac 1 activation and neuronal cell death. Our results provide insights into an unsuspected connection between PDK1, nPKCs and Rac 1 in the same signal-transduction pathway and points out nPKCs and Rac 1 as potential therapeutic targets to block the toxic effects of A beta(1-42) peptide in neurons.

Classification of neocortical interneurons using affinity propagation

In spite of over a century of research on cortical circuits, it is still unknown how many classes of cortical neurons exist. Neuronal classification has been a difficult problem because it is unclear what a neuronal cell class actually is and what are the best characteristics are to define them. Recently, unsupervised classifications using cluster analysis based on morphological, physiological or molecular characteristics, when applied to selected datasets, have provided quantitative and unbiased identification of distinct neuronal subtypes. However, better and more robust classification methods are needed for increasingly complex and larger datasets. We explored the use of affinity propagation, a recently developed unsupervised classification algorithm imported from machine learning, which gives a representative example or exemplar for each cluster. As a case study, we applied affinity propagation to a test dataset of 337 interneurons belonging to four subtypes, previously identified based on morphological and physiological characteristics. We found that affinity propagation correctly classified most of the neurons in a blind, non-supervised manner. In fact, using a combined anatomical/physiological dataset, our algorithm differentiated parvalbumin from somatostatin interneurons in 49 out of 50 cases. Affinity propagation could therefore be used in future studies to validly classify neurons, as a first step to help reverse engineer neural circuits.

NMDA modulates oligodendrocyte differentiation of subventricular zone cells through PKC activation

7 p.

Role of G protein coupled inwardly rectifier K+ channels (GIRK) on the neurobiology depression

353 págs.

Oligodendrocyte differentiation from adult multipotent stem cells is modulated by glutamate

We used multipotent stem cells (MSCs) derived from the young rat subventricular zone (SVZ) to study the effects of glutamate in oligodendrocyte maturation. Glutamate stimulated oligodendrocyte differentiation from SVZ-derived MSCs through the activation of specific N-methyl-D-aspartate (NMDA) receptor subunits. The effect of glutamate and NMDA on oligodendrocyte differentiation was evident in both the number of newly generated oligodendrocytes and their morphology. In addition, the levels of NMDAR1 and NMDAR2A protein increased during differentiation, whereas NMDAR2B and NMDAR3 protein levels decreased, suggesting differential expression of NMDA receptor subunits during maturation. Microfluorimetry showed that the activation of NMDA receptors during oligodendrocyte differentiation elevated cytosolic calcium levels and promoted myelination in cocultures with neurons. Moreover, we observed that stimulation of MSCs by NMDA receptors induced the generation of reactive oxygen species (ROS), which were negatively modulated by the NADPH inhibitor apocynin, and that the levels of ROS correlated with the degree of differentiation. Taken together, these findings suggest that ROS generated by NADPH oxidase by the activation of NMDA receptors promotes the maturation of oligodendrocytes and favors myelination

The locus coeruleus Is Directly Implicated in L-DOPA-Induced Dyskinesia in Parkinsonian Rats: An Electrophysiological and Behavioural Study

Despite being the most effective treatment for Parkinson's disease, L-DOPA causes a development of dyskinetic movements in the majority of treated patients. L-DOPA-induced dyskinesia is attributed to a dysregulated dopamine transmission within the basal ganglia, but serotonergic and noradrenergic systems are believed to play an important modulatory role. In this study, we have addressed the role of the locus coeruleus nucleus (LC) in a rat model of L-DOPA-induced dyskinesia. Single-unit extracellular recordings in vivo and behavioural and immunohistochemical approaches were applied in rats rendered dyskinetic by the destruction of the nigrostriatal dopamine neurons followed by chronic treatment with L-DOPA. The results showed that L-DOPA treatment reversed the change induced by 6-hydroxydopamine lesions on LC neuronal activity. The severity of the abnormal involuntary movements induced by L-DOPA correlated with the basal firing parameters of LC neuronal activity. Systemic administration of the LC-selective noradrenergic neurotoxin N-(2-chloroethyl)-N-ethyl-2-bromobenzylamine did not modify axial, limb, and orolingual dyskinesia, whereas chemical destruction of the LC with ibotenic acid significantly increased the abnormal involuntary movement scores. These results are the first to demonstrate altered LC neuronal activity in 6-OHDA lesioned rats treated with L-DOPA, and indicate that an intact noradrenergic system may limit the severity of this movement disorder.

GABA release by hippocampal astrocytes

10 p.

Intracellular Ca2+ release through ryanodine receptors contributes to AMPA receptor-mediated mitochondrial dysfunction and ER stress in oligodendrocytes

Overactivation of ionotropic glutamate receptors in oligodendrocytes induces cytosolic Ca2+ overload and excitotoxic death, a process that contributes to demyelination and multiple sclerosis. Excitotoxic insults cause well-characterized mitochondrial alterations and endoplasmic reticulum (ER) dysfunction, which is not fully understood. In this study, we analyzed the contribution of ER-Ca2+ release through ryanodine receptors (RyRs) and inositol triphosphate receptors (IP(3)Rs) to excitotoxicity in oligodendrocytes in vitro. First, we observed that oligodendrocytes express all previously characterized RyRs and IP(3)Rs. Blockade of Ca2+-induced Ca2+ release by TMB-8 following alpha-amino-3-hydroxyl-5-methyl-4-isoxazole-propionate (AMPA) receptor-mediated insults attenuated both oligodendrocyte death and cytosolic Ca2+ overload. In turn, RyR inhibition by ryanodine reduced as well the Ca2+ overload whereas IP3R inhibition was ineffective. Furthermore, AMPA-triggered mitochondrial membrane depolarization, oxidative stress and activation of caspase-3, which in all instances was diminished by RyR inhibition. In addition, we observed that AMPA induced an ER stress response as revealed by alpha subunit of the eukaryotic initiation factor 2 alpha phosphorylation, overexpression of GRP chaperones and RyR-dependent cleavage of caspase-12. Finally, attenuating ER stress with salubrinal protected oligodendrocytes from AMPA excitotoxicity. Together, these results show that Ca2+ release through RyRs contributes to cytosolic Ca2+ overload, mitochondrial dysfunction, ER stress and cell death following AMPA receptor-mediated excitotoxicity in oligodendrocytes. Cell Death and Disease (2010) 1, e54; doi:10.1038/cddis.2010.31; published online 15 July 2010

Electronic spectroscopy of propofol, propofol homomers ad their hydratated clusters

538 p.

Psychobiophysics of transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a technique that stimulates the brain using a magnetic coil placed on the scalp. Since it is applicable to humans non-invasively, directly interfering with neural electrical activity, it is potentially a good tool to study the direct relationship between perceptual experience and neural activity. However, it has been difficult to produce a clear perceptible phenomenon with TMS of sensory areas, especially using a single magnetic pulse. Also, the biophysical mechanisms of magnetic stimulation of single neurons have been poorly understood.

In the psychophysical part of this thesis, perceptual phenomena induced by TMS of the human visual cortex are demonstrated as results of the interactions with visual inputs. We first introduce a method to create a hole, or a scotoma, in a flashed, large-field visual pattern using single-pulse TMS. Spatial aspects of the interactions are explored using the distortion effect of the scotoma depending on the visual pattern, which can be luminance-defined or illusory. Its similarity to the distortion of afterimages is also discussed. Temporal interactions are demonstrated in the filling-in of the scotoma with temporally adjacent visual features, as well as in the effective suppression of transient visual features. Also, paired-pulse TMS is shown to lead to different brightness modulations in transient and sustained visual stimuli.

In the biophysical part, we first develop a biophysical theory to simulate the effect of magnetic stimulation on arbitrary neuronal structure. Computer simulations are performed on cortical neuron models with realistic structure and channels, combined with the current injection that simulates magnetic stimulation. The simulation results account for general and basic characteristics of the macroscopic effects of TMS including our psychophysical findings, such as a long inhibitory effect, dependence on the background activity, and dependence on the direction of the induced electric field.

The perceptual effects and the cortical neuron model presented here provide foundations for the study of the relationship between perception and neural activity. Further insights would be obtained from extension of our model to neuronal networks and psychophysical studies based on predictions of the biophysical model.

Neural representation of auditory temporal structure

Neurons in the songbird forebrain nucleus HVc are highly sensitive to auditory temporal context and have some of the most complex auditory tuning properties yet discovered. HVc is crucial for learning, perceiving, and producing song, thus it is important to understand the neural circuitry and mechanisms that give rise to these remarkable auditory response properties. This thesis investigates these issues experimentally and computationally.

Extracellular studies reported here compare the auditory context sensitivity of neurons in HV c with neurons in the afferent areas of field L. These demonstrate that there is a substantial increase in the auditory temporal context sensitivity from the areas of field L to HVc. Whole-cell recordings of HVc neurons from acute brain slices are described which show that excitatory synaptic transmission between HVc neurons involve the release of glutamate and the activation of both AMPA/kainate and NMDA-type glutamate receptors. Additionally, widespread inhibitory interactions exist between HVc neurons that are mediated by postsynaptic GABA_A receptors. Intracellular recordings of HVc auditory neurons in vivo provides evidence that HV c neurons encode information about temporal structure using a variety of cellular and synaptic mechanisms including syllable-specific inhibition, excitatory post-synaptic potentials with a range of different time courses, and burst-firing, and song-specific hyperpolarization.

The final part of this thesis presents two computational approaches for representing and learning temporal structure. The first method utilizes comput ational elements that are analogous to temporal combination sensitive neurons in HVc. A network of these elements can learn using local information and lateral inhibition. The second method presents a more general framework which allows a network to discover mixtures of temporal features in a continuous stream of input.

Latent variable models for neural data analysis

The brain is perhaps the most complex system to have ever been subjected to rigorous scientific investigation. The scale is staggering: over 10^11 neurons, each making an average of 10^3 synapses, with computation occurring on scales ranging from a single dendritic spine, to an entire cortical area. Slowly, we are beginning to acquire experimental tools that can gather the massive amounts of data needed to characterize this system. However, to understand and interpret these data will also require substantial strides in inferential and statistical techniques. This dissertation attempts to meet this need, extending and applying the modern tools of latent variable modeling to problems in neural data analysis.

It is divided into two parts. The first begins with an exposition of the general techniques of latent variable modeling. A new, extremely general, optimization algorithm is proposed - called Relaxation Expectation Maximization (REM) - that may be used to learn the optimal parameter values of arbitrary latent variable models. This algorithm appears to alleviate the common problem of convergence to local, sub-optimal, likelihood maxima. REM leads to a natural framework for model size selection; in combination with standard model selection techniques the quality of fits may be further improved, while the appropriate model size is automatically and efficiently determined. Next, a new latent variable model, the mixture of sparse hidden Markov models, is introduced, and approximate inference and learning algorithms are derived for it. This model is applied in the second part of the thesis.

The second part brings the technology of part I to bear on two important problems in experimental neuroscience. The first is known as spike sorting; this is the problem of separating the spikes from different neurons embedded within an extracellular recording. The dissertation offers the first thorough statistical analysis of this problem, which then yields the first powerful probabilistic solution. The second problem addressed is that of characterizing the distribution of spike trains recorded from the same neuron under identical experimental conditions. A latent variable model is proposed. Inference and learning in this model leads to new principled algorithms for smoothing and clustering of spike data.