THE EFFECT OF GABAMIMETICS ON NEUROTRANSMITTER RECEPTOR BINDING AND FUNCTION IN RAT BRAIN

Gamma-aminobutyric acid (GABA) is a major inhibitory neurotransmitter in the central nervous system and alterations in central GABAergic transmission may contribute to the symptoms of a number of neurological and psychiatric disorders. Because of this relationship, numerous laboratories are attempting to develop agents which will selectively enhance GABA neurotransmission in brain. Due to these efforts, several promising compounds have recently been discovered. Should these drugs prove to be clinically effective, they will be used to treat chronic neuropsychiatric disabilities and, therefore, will be administered for long periods of time. Accordingly, the present investigation was undertaken to determine the neurochemical consequences of chronic activation of brain GABA systems in order to better define the therapeutic potential and possible side-effect liability of GABAmimetic compounds.^ Chronic (15 day) administration to rats of low doses of amino-oxyacetic acid (AOAA, 10 mg/kg, once daily), isonicotinic acid hydrazide (20 mg/kg, b.i.d.), two non-specific inhibitors of GABA-T, the enzyme which catabolizes GABA in brain, or (gamma)-acetylenic GABA (10 mg/kg, b.i.d.) a catalytic inhibitor of this enzyme, resulted in a significant elevation of brain and CSF GABA content throughout the course of treatment. In addition, chronic administration of these drugs, as well as the direct acting GABA receptor agonists THIP (8 mg/kg, b.i.d.) or kojic amine (18 mg/kg, b.i.d.) resulted in a significant increase in dopamine receptor number and a significant decrease in GABA receptor number in the corpus striatum of treated animals as determined by standard in vitro receptor binding techniques. Changes in the GABA receptor were limited to the corpus striatum and occurred more rapidly than did alterations in the dopamine receptor. The finding that dopamine-mediated stereotypic behavior was enhanced in animals treated chronically with AOAA suggested that the receptor binding changes noted in vitro have some functional consequence in vitro.^ Coadministration of atropine (a muscarinic cholinergic receptor antagonist) blocked the GABA-T inhibitor-induced increase in striatal dopamine receptors but was without effect on receptor alterations seen following chronic administration of direct acting GABA receptor agonists. Atropine administration failed to influence the drug-induced decreases in striatal GABA receptors.^ Other findings included the discovery that synaptosomal high affinity ('3)H-choline uptake, an index of cholinergic neuronal activity, was significantly increased in the corpus striatum of animals treated acutely, but not chronically, with GABAmimetics.^ It is suggested that the dopamine receptor supersensitivity observed in the corpus striatum of animals following long-term treatment with GABAmimetics is a result of the chronic inhibition of the nigrostriatal dopamine system by these drugs. Changes in the GABA receptor, on the other hand, are more likely due to a homospecific regulation of these receptors. An hypothesis based on the different sites of action of GABA-T inhibitors vis-a-vis the direct acting GABA receptor agonists is proposed to account for the differential effect of atropine on the response to these drugs.^ The results of this investigation provide new insights into the functional interrelationships that exist in the basal ganglia and suggest that chronic treatment with GABAmimetics may produce extrapyramidal side-effects in man. In addition, the constellation of neurochemical changes observed following administration of these drugs may be a useful guide for determining the GABAmimetic properties of neuropharmacological agents. ^

Reelin immunoreactivity in dissociated cultures of the postnatal hippocampus

Reelin is an extracellular matrix glycoprotein expressed in different nerve cell populations in the developing, early postnatal and adult central nervous system. During histogenesis of the neocortex and hippocampus, reelin is present in Cajal-Retzius cells and other early neurons and contributes to correct layering of these regions. During early postnatal life, pioneer neurons disappear and reelin expression establishes in a subpopulation of cortical and hippocampal GABAergic interneurons, where it is maintained throughout adult life. We studied the developmental distribution pattern of reelin in dissociated cultures obtained from the early postnatal hippocampus to verify whether or not such a maturation phenomenon is maintained in vitro. Reelin is expressed both in Cajal-Retzius cells and multipolar and pyramidal neurons in younger cultures. The density of reelin-positive Cajal-Retzius cells dropped drastically by about 84% in 4-week-old cultures. Multipolar and pyramidal neurons containing reelin represented 12% of the total cell population in younger cultures and decreased by about 25% after 3 to 4 weeks of cultivation. Their density was significantly lower in cultures of the same age treated with glutamate receptor antagonists. These reelin-positive multipolar and pyramidal neurons were heterogeneous, including a larger amount of non-GABAergic, and 30-40% of GABAergic neurons. Cells double labeled for reelin and the GABA synthesizing enzyme glutamic acid decarboxylase represented about 4% of the total neuron population in culture and their density remained constant with age. It is thus possible that the decrease in the total reelin population may selectively be of importance to the larger non-GABAergic fraction of reelin cells. This study shows that reelin-expressing neurons are maintained in dissociated cultures of the neonatal hippocampus and their distribution and age-dependent changes in density resemble those of the early postnatal hippocampus in vivo.

Ionotropic Glutamate Receptors Show Unique Distribution and Localization in the Dorsal Cochlear Nucleus of the Rhesus Monkey

The dorsal cochlear nucleus (DCN) receives auditory information via the auditory nerve coming from the cochlea. It is responsible for much of the integration of auditory information, and it projects this auditory information to higher auditory brain centers for further processing. This study focuses on the DCN of adult Rhesus monkeys to characterize two specific cell types, the fusiform and cartwheel cell, based on morphometric parameters and type of glutamate receptor they express. The fusiform cell is the main projection neuron, while the cartwheel cell is the main inhibitory interneuron. Expression of AMPA glutamate receptor subunits is localized to certain cell types. The activity of the CN depends on the AMPA receptor subunit composition and expression. Immunocytochemistry, using specific antibodies for AMPA glutamate receptor subunits GluR1, GluR2/3 and GluR4, was used in conjunction with morphometry to determine the location, morphological characteristics and expression of AMPA receptor subunits in fusiform and cartwheel cells in the primate DCN. Qualitative as well as quantitative data indicates that there are important morphological differences in cell location and expression of AMPA glutamate receptor subunits between the rodent DCN and that of primates. GluR2/3 is widely expressed in the primate DCN. GluR1 is also widely expressed in the primate DCN. GluR4 is diffusely expressed. Expression of GluR2/3 and GluR4 in the primate is similar to that of the rodent. However, expression of GluR1 is different. GluR1 is only expressed by cartwheel cells in the rodent DCN, but is expressed by a variety of cells, including fusiform cells, in the DCN of the primate.

La estimulación gabaérgica del núcleo septal lateral del cerebro de ratas macho incrementa la respuesta agresiva frente a estímulos neutros

El núcleo septal lateral forma parte de estructuras subcorticales del cerebro. La destrucción de dicho núcleo genera lo que se conoce como síndrome de furia septal. En este trabajo demostramos que el agonista GABAérgico muscimol, en dosis no sedativas, indujo una “inhibición del miedo" en ratas macho de la cepa Sprague- Dawley, asociada a un aumento de respuestas agresivas a objetos habitualmente neutros. Estos resultados, sumados al hecho de que el núcleo septal lateral participa en diversas entidades psiquiátricas, hace que sea interesante aportar al conocimiento de su función apelando a moduladores que se sabe están presentes en el en dicha estructura del sistema nervioso central.

Altered neurotransmitter function in CO2-exposed stickleback (Gasterosteus aculeatus): a temperate model species for ocean acidification research

Studies on the consequences of ocean acidification for the marine ecosystem have revealed behavioural changes in coral reef fishes exposed to sustained near-future CO2 levels. The changes have been linked to altered function of GABAergic neurotransmitter systems, because the behavioural alterations can be reversed rapidly by treatment with the GABAA receptor antagonist gabazine. Characterization of the molecular mechanisms involved would be greatly aided if these can be examined in a well-characterized model organism with a sequenced genome. It was recently shown that CO2-induced behavioural alterations are not confined to tropical species, but also affect the three-spined stickleback, although an involvement of the GABAA receptor was not examined. Here, we show that loss of lateralization in the stickleback can be restored rapidly and completely by gabazine treatment. This points towards a worrying universality of disturbed GABAA function after high-CO2 exposure in fishes from tropical to temperate marine habitats. Importantly, the stickleback is a model species with a sequenced and annotated genome, which greatly facilitates future studies on underlying molecular mechanisms.

ESPINA: a tool for the automated segmentation and counting of synapses in large stacks of electron microscopy images

The synapses in the cerebral cortex can be classified into two main types, Gray’s type I and type II, which correspond to asymmetric (mostly glutamatergic excitatory) and symmetric (inhibitory GABAergic) synapses, respectively. Hence, the quantification and identification of their different types and the proportions in which they are found, is extraordinarily important in terms of brain function. The ideal approach to calculate the number of synapses per unit volume is to analyze 3D samples reconstructed from serial sections. However, obtaining serial sections by transmission electron microscopy is an extremely time consuming and technically demanding task. Using focused ion beam/scanning electron microscope microscopy, we recently showed that virtually all synapses can be accurately identified as asymmetric or symmetric synapses when they are visualized, reconstructed, and quantified from large 3D tissue samples obtained in an automated manner. Nevertheless, the analysis, segmentation, and quantification of synapses is still a labor intensive procedure. Thus, novel solutions are currently necessary to deal with the large volume of data that is being generated by automated 3D electron microscopy. Accordingly, we have developed ESPINA, a software tool that performs the automated segmentation and counting of synapses in a reconstructed 3D volume of the cerebral cortex, and that greatly facilitates and accelerates these processes.

Contributions to Bayesian network learning with applications to neuroscience

Neuronal morphology is a key feature in the study of brain circuits, as it is highly related to information processing and functional identification. Neuronal morphology affects the process of integration of inputs from other neurons and determines the neurons which receive the output of the neurons. Different parts of the neurons can operate semi-independently according to the spatial location of the synaptic connections. As a result, there is considerable interest in the analysis of the microanatomy of nervous cells since it constitutes an excellent tool for better understanding cortical function. However, the morphologies, molecular features and electrophysiological properties of neuronal cells are extremely variable. Except for some special cases, this variability makes it hard to find a set of features that unambiguously define a neuronal type. In addition, there are distinct types of neurons in particular regions of the brain. This morphological variability makes the analysis and modeling of neuronal morphology a challenge. Uncertainty is a key feature in many complex real-world problems. Probability theory provides a framework for modeling and reasoning with uncertainty. Probabilistic graphical models combine statistical theory and graph theory to provide a tool for managing domains with uncertainty. In particular, we focus on Bayesian networks, the most commonly used probabilistic graphical model. In this dissertation, we design new methods for learning Bayesian networks and apply them to the problem of modeling and analyzing morphological data from neurons. The morphology of a neuron can be quantified using a number of measurements, e.g., the length of the dendrites and the axon, the number of bifurcations, the direction of the dendrites and the axon, etc. These measurements can be modeled as discrete or continuous data. The continuous data can be linear (e.g., the length or the width of a dendrite) or directional (e.g., the direction of the axon). These data may follow complex probability distributions and may not fit any known parametric distribution. Modeling this kind of problems using hybrid Bayesian networks with discrete, linear and directional variables poses a number of challenges regarding learning from data, inference, etc. In this dissertation, we propose a method for modeling and simulating basal dendritic trees from pyramidal neurons using Bayesian networks to capture the interactions between the variables in the problem domain. A complete set of variables is measured from the dendrites, and a learning algorithm is applied to find the structure and estimate the parameters of the probability distributions included in the Bayesian networks. Then, a simulation algorithm is used to build the virtual dendrites by sampling values from the Bayesian networks, and a thorough evaluation is performed to show the model’s ability to generate realistic dendrites. In this first approach, the variables are discretized so that discrete Bayesian networks can be learned and simulated. Then, we address the problem of learning hybrid Bayesian networks with different kinds of variables. Mixtures of polynomials have been proposed as a way of representing probability densities in hybrid Bayesian networks. We present a method for learning mixtures of polynomials approximations of one-dimensional, multidimensional and conditional probability densities from data. The method is based on basis spline interpolation, where a density is approximated as a linear combination of basis splines. The proposed algorithms are evaluated using artificial datasets. We also use the proposed methods as a non-parametric density estimation technique in Bayesian network classifiers. Next, we address the problem of including directional data in Bayesian networks. These data have some special properties that rule out the use of classical statistics. Therefore, different distributions and statistics, such as the univariate von Mises and the multivariate von Mises–Fisher distributions, should be used to deal with this kind of information. In particular, we extend the naive Bayes classifier to the case where the conditional probability distributions of the predictive variables given the class follow either of these distributions. We consider the simple scenario, where only directional predictive variables are used, and the hybrid case, where discrete, Gaussian and directional distributions are mixed. The classifier decision functions and their decision surfaces are studied at length. Artificial examples are used to illustrate the behavior of the classifiers. The proposed classifiers are empirically evaluated over real datasets. We also study the problem of interneuron classification. An extensive group of experts is asked to classify a set of neurons according to their most prominent anatomical features. A web application is developed to retrieve the experts’ classifications. We compute agreement measures to analyze the consensus between the experts when classifying the neurons. Using Bayesian networks and clustering algorithms on the resulting data, we investigate the suitability of the anatomical terms and neuron types commonly used in the literature. Additionally, we apply supervised learning approaches to automatically classify interneurons using the values of their morphological measurements. Then, a methodology for building a model which captures the opinions of all the experts is presented. First, one Bayesian network is learned for each expert, and we propose an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts is induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts is built. A thorough analysis of the consensus model identifies different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types can be defined by performing inference in the Bayesian multinet. These findings are used to validate the model and to gain some insights into neuron morphology. Finally, we study a classification problem where the true class label of the training instances is not known. Instead, a set of class labels is available for each instance. This is inspired by the neuron classification problem, where a group of experts is asked to individually provide a class label for each instance. We propose a novel approach for learning Bayesian networks using count vectors which represent the number of experts who selected each class label for each instance. These Bayesian networks are evaluated using artificial datasets from supervised learning problems. Resumen La morfología neuronal es una característica clave en el estudio de los circuitos cerebrales, ya que está altamente relacionada con el procesado de información y con los roles funcionales. La morfología neuronal afecta al proceso de integración de las señales de entrada y determina las neuronas que reciben las salidas de otras neuronas. Las diferentes partes de la neurona pueden operar de forma semi-independiente de acuerdo a la localización espacial de las conexiones sinápticas. Por tanto, existe un interés considerable en el análisis de la microanatomía de las células nerviosas, ya que constituye una excelente herramienta para comprender mejor el funcionamiento de la corteza cerebral. Sin embargo, las propiedades morfológicas, moleculares y electrofisiológicas de las células neuronales son extremadamente variables. Excepto en algunos casos especiales, esta variabilidad morfológica dificulta la definición de un conjunto de características que distingan claramente un tipo neuronal. Además, existen diferentes tipos de neuronas en regiones particulares del cerebro. La variabilidad neuronal hace que el análisis y el modelado de la morfología neuronal sean un importante reto científico. La incertidumbre es una propiedad clave en muchos problemas reales. La teoría de la probabilidad proporciona un marco para modelar y razonar bajo incertidumbre. Los modelos gráficos probabilísticos combinan la teoría estadística y la teoría de grafos con el objetivo de proporcionar una herramienta con la que trabajar bajo incertidumbre. En particular, nos centraremos en las redes bayesianas, el modelo más utilizado dentro de los modelos gráficos probabilísticos. En esta tesis hemos diseñado nuevos métodos para aprender redes bayesianas, inspirados por y aplicados al problema del modelado y análisis de datos morfológicos de neuronas. La morfología de una neurona puede ser cuantificada usando una serie de medidas, por ejemplo, la longitud de las dendritas y el axón, el número de bifurcaciones, la dirección de las dendritas y el axón, etc. Estas medidas pueden ser modeladas como datos continuos o discretos. A su vez, los datos continuos pueden ser lineales (por ejemplo, la longitud o la anchura de una dendrita) o direccionales (por ejemplo, la dirección del axón). Estos datos pueden llegar a seguir distribuciones de probabilidad muy complejas y pueden no ajustarse a ninguna distribución paramétrica conocida. El modelado de este tipo de problemas con redes bayesianas híbridas incluyendo variables discretas, lineales y direccionales presenta una serie de retos en relación al aprendizaje a partir de datos, la inferencia, etc. En esta tesis se propone un método para modelar y simular árboles dendríticos basales de neuronas piramidales usando redes bayesianas para capturar las interacciones entre las variables del problema. Para ello, se mide un amplio conjunto de variables de las dendritas y se aplica un algoritmo de aprendizaje con el que se aprende la estructura y se estiman los parámetros de las distribuciones de probabilidad que constituyen las redes bayesianas. Después, se usa un algoritmo de simulación para construir dendritas virtuales mediante el muestreo de valores de las redes bayesianas. Finalmente, se lleva a cabo una profunda evaluaci ón para verificar la capacidad del modelo a la hora de generar dendritas realistas. En esta primera aproximación, las variables fueron discretizadas para poder aprender y muestrear las redes bayesianas. A continuación, se aborda el problema del aprendizaje de redes bayesianas con diferentes tipos de variables. Las mixturas de polinomios constituyen un método para representar densidades de probabilidad en redes bayesianas híbridas. Presentamos un método para aprender aproximaciones de densidades unidimensionales, multidimensionales y condicionales a partir de datos utilizando mixturas de polinomios. El método se basa en interpolación con splines, que aproxima una densidad como una combinación lineal de splines. Los algoritmos propuestos se evalúan utilizando bases de datos artificiales. Además, las mixturas de polinomios son utilizadas como un método no paramétrico de estimación de densidades para clasificadores basados en redes bayesianas. Después, se estudia el problema de incluir información direccional en redes bayesianas. Este tipo de datos presenta una serie de características especiales que impiden el uso de las técnicas estadísticas clásicas. Por ello, para manejar este tipo de información se deben usar estadísticos y distribuciones de probabilidad específicos, como la distribución univariante von Mises y la distribución multivariante von Mises–Fisher. En concreto, en esta tesis extendemos el clasificador naive Bayes al caso en el que las distribuciones de probabilidad condicionada de las variables predictoras dada la clase siguen alguna de estas distribuciones. Se estudia el caso base, en el que sólo se utilizan variables direccionales, y el caso híbrido, en el que variables discretas, lineales y direccionales aparecen mezcladas. También se estudian los clasificadores desde un punto de vista teórico, derivando sus funciones de decisión y las superficies de decisión asociadas. El comportamiento de los clasificadores se ilustra utilizando bases de datos artificiales. Además, los clasificadores son evaluados empíricamente utilizando bases de datos reales. También se estudia el problema de la clasificación de interneuronas. Desarrollamos una aplicación web que permite a un grupo de expertos clasificar un conjunto de neuronas de acuerdo a sus características morfológicas más destacadas. Se utilizan medidas de concordancia para analizar el consenso entre los expertos a la hora de clasificar las neuronas. Se investiga la idoneidad de los términos anatómicos y de los tipos neuronales utilizados frecuentemente en la literatura a través del análisis de redes bayesianas y la aplicación de algoritmos de clustering. Además, se aplican técnicas de aprendizaje supervisado con el objetivo de clasificar de forma automática las interneuronas a partir de sus valores morfológicos. A continuación, se presenta una metodología para construir un modelo que captura las opiniones de todos los expertos. Primero, se genera una red bayesiana para cada experto y se propone un algoritmo para agrupar las redes bayesianas que se corresponden con expertos con comportamientos similares. Después, se induce una red bayesiana que modela la opinión de cada grupo de expertos. Por último, se construye una multired bayesiana que modela las opiniones del conjunto completo de expertos. El análisis del modelo consensuado permite identificar diferentes comportamientos entre los expertos a la hora de clasificar las neuronas. Además, permite extraer un conjunto de características morfológicas relevantes para cada uno de los tipos neuronales mediante inferencia con la multired bayesiana. Estos descubrimientos se utilizan para validar el modelo y constituyen información relevante acerca de la morfología neuronal. Por último, se estudia un problema de clasificación en el que la etiqueta de clase de los datos de entrenamiento es incierta. En cambio, disponemos de un conjunto de etiquetas para cada instancia. Este problema está inspirado en el problema de la clasificación de neuronas, en el que un grupo de expertos proporciona una etiqueta de clase para cada instancia de manera individual. Se propone un método para aprender redes bayesianas utilizando vectores de cuentas, que representan el número de expertos que seleccionan cada etiqueta de clase para cada instancia. Estas redes bayesianas se evalúan utilizando bases de datos artificiales de problemas de aprendizaje supervisado.

Bayesian network modeling of the consensus between experts: an application to neuron classification

Neuronal morphology is hugely variable across brain regions and species, and their classification strategies are a matter of intense debate in neuroscience. GABAergic cortical interneurons have been a challenge because it is difficult to find a set of morphological properties which clearly define neuronal types. A group of 48 neuroscience experts around the world were asked to classify a set of 320 cortical GABAergic interneurons according to the main features of their three-dimensional morphological reconstructions. A methodology for building a model which captures the opinions of all the experts was proposed. First, one Bayesian network was learned for each expert, and we proposed an algorithm for clustering Bayesian networks corresponding to experts with similar behaviors. Then, a Bayesian network which represents the opinions of each group of experts was induced. Finally, a consensus Bayesian multinet which models the opinions of the whole group of experts was built. A thorough analysis of the consensus model identified different behaviors between the experts when classifying the interneurons in the experiment. A set of characterizing morphological traits for the neuronal types was defined by performing inference in the Bayesian multinet. These findings were used to validate the model and to gain some insights into neuron morphology.

The role of the synthetic enzyme GAD65 in the control of neuronal γ-aminobutyric acid release

We have studied GABAergic synaptic transmission in retinal ganglion cells and hippocampal pyramidal cells to determine, at a cellular level, what is the effect of the targeted disruption of the gene encoding the synthetic enzyme GAD65 on the synaptic release of γ-aminobutyric acid (GABA). Neither the size nor the frequency of GABA-mediated spontaneous inhibitory postsynaptic currents (IPSCs) were reduced in retina or hippocampus in GAD65−/− mice. However, the release of GABA during sustained synaptic activation was substantially reduced. In the retina both electrical- and K+-induced increases in IPSC frequency were depressed without a change in IPSC amplitude. In the hippocampus the transient increase in the probability of inhibitory transmitter release associated with posttetanic potentiation was absent in the GAD65−/− mice. These results indicate that during and immediately after sustained stimulation the increase in the probability of transmitter release is not maintained in GAD65−/− mice. Such a finding suggests a decrease in the size or refilling kinetics of the releasable pool of vesicles, and various mechanisms are discussed that could account for such a defect.

Impaired long-term potentiation induction in dentate gyrus of calretinin-deficient mice

Calretinin (Cr) is a Ca2+ binding protein present in various populations of neurons distributed in the central and peripheral nervous systems. We have generated Cr-deficient (Cr−/−) mice by gene targeting and have investigated the associated phenotype. Cr−/− mice were viable, and a large number of morphological, biochemical, and behavioral parameters were found unaffected. In the normal mouse hippocampus, Cr is expressed in a widely distributed subset of GABAergic interneurons and in hilar mossy cells of the dentate gyrus. Because both types of cells are part of local pathways innervating dentate granule cells and/or pyramidal neurons, we have explored in Cr−/− mice the synaptic transmission between the perforant pathway and granule cells and at the Schaffer commissural input to CA1 pyramidal neurons. Cr−/− mice showed no alteration in basal synaptic transmission, but long-term potentiation (LTP) was impaired in the dentate gyrus. Normal LTP could be restored in the presence of the GABAA receptor antagonist bicuculline, suggesting that in Cr−/− dentate gyrus an excess of γ-aminobutyric acid (GABA) release interferes with LTP induction. Synaptic transmission and LTP were normal in CA1 area, which contains only few Cr-positive GABAergic interneurons. Cr−/− mice performed normally in spatial memory task. These results suggest that expression of Cr contributes to the control of synaptic plasticity in mouse dentate gyrus by indirectly regulating the activity of GABAergic interneurons, and that Cr−/− mice represent a useful tool to understand the role of dentate LTP in learning and memory.

Importance of newly generated neurons in the adult olfactory bulb for odor discrimination

In adult rodents, neurons are continually generated in the subventricular zone of the forebrain, from where they migrate tangentially toward the olfactory bulb, the only known target for these neuronal precursors. Within the main olfactory bulb, they ascend radially into the granule and periglomerular cell layers, where they differentiate mainly into local interneurons. The functional consequences of this permanent generation and integration of new neurons into existing circuits are unknown. To address this question, we used neural cell adhesion molecule-deficient mice that have documented deficits in the migration of olfactory-bulb neuron precursors, leading to about 40% size reduction of this structure. Our anatomical study reveals that this reduction is restricted to the granule cell layer, a structure that contains exclusively γ-aminobutyric acid (GABA)ergic interneurons. Furthermore, mutant mice were subjected to experiments designed to examine the behavioral consequences of such anatomical alteration. We found that the specific reduction in the newly generated interneuron population resulted in an impairment of discrimination between odors. In contrast, both the detection thresholds for odors and short-term olfactory memory were unaltered, demonstrating that a critical number of bulbar granule cells is crucial only for odor discrimination but not for general olfactory functions.

Dopamine receptor subtypes modulate olfactory bulb γ-aminobutyric acid type A receptors

The γ-aminobutyric acid type A (GABAA) receptor is the predominant Cl− channel protein mediating inhibition in the olfactory bulb and elsewhere in the mammalian brain. The olfactory bulb is rich in neurons containing both GABA and dopamine. Dopamine D1 and D2 receptors are also highly expressed in this brain region with a distinct and complementary distribution pattern. This distribution suggests that dopamine may control the GABAergic inhibitory processing of odor signals, possibly via different signal-transduction mechanisms. We have observed that GABAA receptors in the rat olfactory bulb are differentially modulated by dopamine in a cell-specific manner. Dopamine reduced the currents through GABA-gated Cl- channels in the interneurons, presumably granule cells. This action was mediated via D1 receptors and involved phosphorylation of GABAA receptors by protein kinase A. Enhancement of GABA responses via activation of D2 dopamine receptors and phosphorylation of GABAA receptors by protein kinase C was observed in mitral/tufted cells. Decreasing or increasing the binding affinity for GABA appears to underlie the modulatory effects of dopamine via distinct receptor subtypes. This dual action of dopamine on inhibitory GABAA receptor function in the rat olfactory bulb could be instrumental in odor detection and discrimination, olfactory learning, and ultimately odotopic memory formation.

A decrease of reelin expression as a putative vulnerability factor in schizophrenia

Postmortem prefrontal cortices (PFC) (Brodmann’s areas 10 and 46), temporal cortices (Brodmann’s area 22), hippocampi, caudate nuclei, and cerebella of schizophrenia patients and their matched nonpsychiatric subjects were compared for reelin (RELN) mRNA and reelin (RELN) protein content. In all of the brain areas studied, RELN and its mRNA were significantly reduced (≈50%) in patients with schizophrenia; this decrease was similar in patients affected by undifferentiated or paranoid schizophrenia. To exclude possible artifacts caused by postmortem mRNA degradation, we measured the mRNAs in the same PFC extracts from γ-aminobutyric acid (GABA)A receptors α1 and α5 and nicotinic acetylcholine receptor α7 subunits. Whereas the expression of the α7 nicotinic acetylcholine receptor subunit was normal, that of the α1 and α5 receptor subunits of GABAA was increased when schizophrenia was present. RELN mRNA was preferentially expressed in GABAergic interneurons of PFC, temporal cortex, hippocampus, and glutamatergic granule cells of cerebellum. A protein putatively functioning as an intracellular target for the signal-transduction cascade triggered by RELN protein released into the extracellular matrix is termed mouse disabled-1 (DAB1) and is expressed at comparable levels in the neuroplasm of the PFC and hippocampal pyramidal neurons, cerebellar Purkinje neurons of schizophrenia patients, and nonpsychiatric subjects; these three types of neurons do not express RELN protein. In the same samples of temporal cortex, we found a decrease in RELN protein of ≈50% but no changes in DAB1 protein expression. We also observed a large (up to 70%) decrease of GAD67 but only a small decrease of GAD65 protein content. These findings are interpreted within a neurodevelopmental/vulnerability “two-hit” model for the etiology of schizophrenia.

An astroglia-linked dopamine D2-receptor action in prefrontal cortex

Typical neuroleptic drugs elicit their antipsychotic effects mainly by acting as antagonists at dopamine D2 receptors. Much of this activity is thought to occur in the cerebral cortex, where D2 receptors are found largely in inhibitory GABAergic neurons. Here we confirm this localization at the electron microscopic level, but additionally show that a subset of cortical interneurons with low or undetectable expression of D2 receptor isoforms are surrounded by astrocytic processes that strongly express D2 receptors. Ligand binding of isolated astrocyte preparations indicate that cortical astroglia account for approximately one-third of the total D2 receptor binding sites in the cortex, a proportion that we found conserved among rodent, monkey, and human tissues. Further, we show that the D2 receptor-specific agonist, quinpirole, can induce Ca2+ elevation in isolated cortical astrocytes in a pharmacologically reversible manner, thus implicating this receptor in the signaling mechanisms by which astrocytes communicate with each other as well as with neurons. The discovery of D2 receptors in astrocytes with a selective anatomical relationship to interneurons represents a neuron/glia substrate for cortical dopamine action in the adult cerebral cortex and a previously unrecognized site of action for antipsychotic drugs with affinities at the D2 receptor.

Genetically altered AMPA-type glutamate receptor kinetics in interneurons disrupt long-range synchrony of gamma oscillation

Gamma oscillations synchronized between distant neuronal populations may be critical for binding together brain regions devoted to common processing tasks. Network modeling predicts that such synchrony depends in part on the fast time course of excitatory postsynaptic potentials (EPSPs) in interneurons, and that even moderate slowing of this time course will disrupt synchrony. We generated mice with slowed interneuron EPSPs by gene targeting, in which the gene encoding the 67-kDa form of glutamic acid decarboxylase (GAD67) was altered to drive expression of the α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) glutamate receptor subunit GluR-B. GluR-B is a determinant of the relatively slow EPSPs in excitatory neurons and is normally expressed at low levels in γ-aminobutyric acid (GABA)ergic interneurons, but at high levels in the GAD-GluR-B mice. In both wild-type and GAD-GluR-B mice, tetanic stimuli evoked gamma oscillations that were indistinguishable in local field potential recordings. Remarkably, however, oscillation synchrony between spatially separated sites was severely disrupted in the mutant, in association with changes in interneuron firing patterns. The congruence between mouse and model suggests that the rapid time course of AMPA receptor-mediated EPSPs in interneurons might serve to allow gamma oscillations to synchronize over distance.