SNARE-fusion mediated inserton of membrane proteins into native and artificial membranes

Membrane proteins carry out functions such as nutrient uptake, ATP synthesis or transmembrane signal transduction. An increasing number of reports indicate that cellular processes are underpinned by regulated interactions between these proteins. Consequently, functional studies of these networks at a molecular level require co-reconstitution of the interacting components. Here, we report a SNARE-protein based method for incorporation of multiple membrane proteins into membranes, and for delivery of large water-soluble substrates into closed membrane vesicles. The approach is used for in vitro reconstruction of a fully functional bacterial respiratory chain from purified components. Furthermore, the method is used for functional incorporation of the entire F1F0-ATP synthase complex into native bacterial membranes from which this component had been genetically removed. The novel methodology offers a tool to investigate complex interaction networks between membrane-bound proteins at a molecular level, which is expected to generate functional insights into key cellular functions.

Diversity of neuronal connexins in the mammalian retina

Many neurons in the mammalian retina are electrically coupled by intercellular channels or gap junctions, which are assembled from a family of proteins called connexins. Numerous studies indicate that gap junctions differ in properties such as conductance and tracer permeability. For example, A-type horizontal cell gap junctions are permeable to Lucifer Yellow, but B-type horizontal cell gap junctions are not. This suggests the two cell types express different connexins. My hypothesis is that multiple neuronal connexins are expressed in the mammalian retina in a cell type specific manner. Immunohistochemical techniques and confocal microscopy were used to localize certain connexins within well-defined neuronal circuits. The results of this study can be summarized as follows: AII amacrine cells, which receive direct input from rod bipolar cells, are well-coupled to neighboring AIIs. In addition, AII amacrine cells also form gap junctions with ON cone bipolar cells. This is a complex heterocellular network. In both rabbit and primate retina, connexin36 occurs at dendritic crossings in the AII matrix as well as between AIIs and ON cone bipolar cells. Coupling in the AII network is thought to reduce noise in the rod pathway while AII/bipolar gap junctions are required for the transmission of rod signals to ON ganglion cells. In the outer plexiform layer, connexin36 forms gap junctions between cones and between rods and cones via cone telodendria. Cone to cone coupling is thought to reduce noise and is partly color selective. Rod to cone coupling forms an alternative rod pathway thought to operate at intermediate light intensity. A-type horizontal cells in the rabbit retina are strongly coupled via massive low resistance gap junctions composed from Cx50. Coupling dramatically extends the receptive field of horizontal cells and the modulation of coupling is thought to change the strength of the feedback signal from horizontal cells to cones. Finally, there are other coupled networks, such as B-type horizontal cells and S1/S2 amacrine cells, which do not use either connexin36 or Cx50. These results confirm the hypothesis that multiple neuronal connexins are expressed in the mammalian retina and these connexins are localized to particular retinal circuits. ^

Pyramidal cells in prefrontal cortex: comparative observations reveal unparalleled specializations in neuronal structure among primate species

The most ubiquitous neuron in the cerebral cortex, the pyramidal cell, is characterized by markedly different dendritic structure among different cortical areas. The complex pyramidal cell phenotype in granular prefrontal cortex (gPFC) of higher primates endows specific biophysical properties and patterns of connectivity, which differ from those in other cortical regions. However, within the gPFC, data have been sampled from only a select few cortical areas. The gPFC of species such as human and macaque monkey includes more than 10 cortical areas. It remains unknown as to what degree pyramidal cell structure may vary among these cortical areas. Here we undertook a survey of pyramidal cells in the dorsolateral, medial, and orbital gPFC of cercopithecid primates. We found marked heterogeneity in pyramidal cell structure within and between these regions. Moreover, trends for gradients in neuronal complexity varied among species. As the structure of neurons determines their computational abilities, memory storage capacity and connectivity, we propose that these specializations in the pyramidal cell phenotype are an important determinant of species-specific executive cortical functions in primates.

Collective Organization of Complex Networks: Emergent Scales, Vulnerability, and Targeting of Desired Dynamics

Cuando una colectividad de sistemas dinámicos acoplados mediante una estructura irregular de interacciones evoluciona, se observan dinámicas de gran complejidad y fenómenos emergentes imposibles de predecir a partir de las propiedades de los sistemas individuales. El objetivo principal de esta tesis es precisamente avanzar en nuestra comprensión de la relación existente entre la topología de interacciones y las dinámicas colectivas que una red compleja es capaz de mantener. Siendo este un tema amplio que se puede abordar desde distintos puntos de vista, en esta tesis se han estudiado tres problemas importantes dentro del mismo que están relacionados entre sí. Por un lado, en numerosos sistemas naturales y artificiales que se pueden describir mediante una red compleja la topología no es estática, sino que depende de la dinámica que se desarrolla en la red: un ejemplo son las redes de neuronas del cerebro. En estas redes adaptativas la propia topología emerge como consecuencia de una autoorganización del sistema. Para conocer mejor cómo pueden emerger espontáneamente las propiedades comúnmente observadas en redes reales, hemos estudiado el comportamiento de sistemas que evolucionan según reglas adaptativas locales con base empírica. Nuestros resultados numéricos y analíticos muestran que la autoorganización del sistema da lugar a dos de las propiedades más universales de las redes complejas: a escala mesoscópica, la aparición de una estructura de comunidades, y, a escala macroscópica, la existencia de una ley de potencias en la distribución de las interacciones en la red. El hecho de que estas propiedades aparecen en dos modelos con leyes de evolución cuantitativamente distintas que siguen unos mismos principios adaptativos sugiere que estamos ante un fenómeno que puede ser muy general, y estar en el origen de estas propiedades en sistemas reales. En segundo lugar, proponemos una medida que permite clasificar los elementos de una red compleja en función de su relevancia para el mantenimiento de dinámicas colectivas. En concreto, estudiamos la vulnerabilidad de los distintos elementos de una red frente a perturbaciones o grandes fluctuaciones, entendida como una medida del impacto que estos acontecimientos externos tienen en la interrupción de una dinámica colectiva. Los resultados que se obtienen indican que la vulnerabilidad dinámica es sobre todo dependiente de propiedades locales, por tanto nuestras conclusiones abarcan diferentes topologías, y muestran la existencia de una dependencia no trivial entre la vulnerabilidad y la conectividad de los elementos de una red. Finalmente, proponemos una estrategia de imposición de una dinámica objetivo genérica en una red dada e investigamos su validez en redes con diversas topologías que mantienen regímenes dinámicos turbulentos. Se obtiene como resultado que las redes heterogéneas (y la amplia mayora de las redes reales estudiadas lo son) son las más adecuadas para nuestra estrategia de targeting de dinámicas deseadas, siendo la estrategia muy efectiva incluso en caso de disponer de un conocimiento muy imperfecto de la topología de la red. Aparte de la relevancia teórica para la comprensión de fenómenos colectivos en sistemas complejos, los métodos y resultados propuestos podrán dar lugar a aplicaciones en sistemas experimentales y tecnológicos, como por ejemplo los sistemas neuronales in vitro, el sistema nervioso central (en el estudio de actividades síncronas de carácter patológico), las redes eléctricas o los sistemas de comunicaciones. ABSTRACT The time evolution of an ensemble of dynamical systems coupled through an irregular interaction scheme gives rise to dynamics of great of complexity and emergent phenomena that cannot be predicted from the properties of the individual systems. The main objective of this thesis is precisely to increase our understanding of the interplay between the interaction topology and the collective dynamics that a complex network can support. This is a very broad subject, so in this thesis we will limit ourselves to the study of three relevant problems that have strong connections among them. First, it is a well-known fact that in many natural and manmade systems that can be represented as complex networks the topology is not static; rather, it depends on the dynamics taking place on the network (as it happens, for instance, in the neuronal networks in the brain). In these adaptive networks the topology itself emerges from the self-organization in the system. To better understand how the properties that are commonly observed in real networks spontaneously emerge, we have studied the behavior of systems that evolve according to local adaptive rules that are empirically motivated. Our numerical and analytical results show that self-organization brings about two of the most universally found properties in complex networks: at the mesoscopic scale, the appearance of a community structure, and, at the macroscopic scale, the existence of a power law in the weight distribution of the network interactions. The fact that these properties show up in two models with quantitatively different mechanisms that follow the same general adaptive principles suggests that our results may be generalized to other systems as well, and they may be behind the origin of these properties in some real systems. We also propose a new measure that provides a ranking of the elements in a network in terms of their relevance for the maintenance of collective dynamics. Specifically, we study the vulnerability of the elements under perturbations or large fluctuations, interpreted as a measure of the impact these external events have on the disruption of collective motion. Our results suggest that the dynamic vulnerability measure depends largely on local properties (our conclusions thus being valid for different topologies) and they show a non-trivial dependence of the vulnerability on the connectivity of the network elements. Finally, we propose a strategy for the imposition of generic goal dynamics on a given network, and we explore its performance in networks with different topologies that support turbulent dynamical regimes. It turns out that heterogeneous networks (and most real networks that have been studied belong in this category) are the most suitable for our strategy for the targeting of desired dynamics, the strategy being very effective even when the knowledge on the network topology is far from accurate. Aside from their theoretical relevance for the understanding of collective phenomena in complex systems, the methods and results here discussed might lead to applications in experimental and technological systems, such as in vitro neuronal systems, the central nervous system (where pathological synchronous activity sometimes occurs), communication systems or power grids.

Análisis de la actividad neuronal y cerebral mediante técnicas de redes complejas

El cerebro humano es probablemente uno de los sistemas más complejos a los que nos enfrentamos en la actualidad, si bien es también uno de los más fascinantes. Sin embargo, la compresión de cómo el cerebro organiza su actividad para llevar a cabo tareas complejas es un problema plagado de restos y obstáculos. En sus inicios la neuroimagen y la electrofisiología tenían como objetivo la identificación de regiones asociadas a activaciones relacionadas con tareas especificas, o con patrones locales que variaban en el tiempo dada cierta actividad. Sin embargo, actualmente existe un consenso acerca de que la actividad cerebral tiene un carácter temporal multiescala y espacialmente extendido, lo que lleva a considerar el cerebro como una gran red de áreas cerebrales coordinadas, cuyas conexiones funcionales son continuamente creadas y destruidas. Hasta hace poco, el énfasis de los estudios de la actividad cerebral funcional se han centrado en la identidad de los nodos particulares que forman estas redes, y en la caracterización de métricas de conectividad entre ellos: la hipótesis subyacente es que cada nodo, que es una representación mas bien aproximada de una región cerebral dada, ofrece a una única contribución al total de la red. Por tanto, la neuroimagen funcional integra los dos ingredientes básicos de la neuropsicología: la localización de la función cognitiva en módulos cerebrales especializados y el rol de las fibras de conexión en la integración de dichos módulos. Sin embargo, recientemente, la estructura y la función cerebral han empezado a ser investigadas mediante la Ciencia de la Redes, una interpretación mecánico-estadística de una antigua rama de las matemáticas: La teoría de grafos. La Ciencia de las Redes permite dotar a las redes funcionales de una gran cantidad de propiedades cuantitativas (robustez, centralidad, eficiencia, ...), y así enriquecer el conjunto de elementos que describen objetivamente la estructura y la función cerebral a disposición de los neurocientíficos. La conexión entre la Ciencia de las Redes y la Neurociencia ha aportado nuevos puntos de vista en la comprensión de la intrincada anatomía del cerebro, y de cómo las patrones de actividad cerebral se pueden sincronizar para generar las denominadas redes funcionales cerebrales, el principal objeto de estudio de esta Tesis Doctoral. Dentro de este contexto, la complejidad emerge como el puente entre las propiedades topológicas y dinámicas de los sistemas biológicos y, específicamente, en la relación entre la organización y la dinámica de las redes funcionales cerebrales. Esta Tesis Doctoral es, en términos generales, un estudio de cómo la actividad cerebral puede ser entendida como el resultado de una red de un sistema dinámico íntimamente relacionado con los procesos que ocurren en el cerebro. Con este fin, he realizado cinco estudios que tienen en cuenta ambos aspectos de dichas redes funcionales: el topológico y el dinámico. De esta manera, la Tesis está dividida en tres grandes partes: Introducción, Resultados y Discusión. En la primera parte, que comprende los Capítulos 1, 2 y 3, se hace un resumen de los conceptos más importantes de la Ciencia de las Redes relacionados al análisis de imágenes cerebrales. Concretamente, el Capitulo 1 está dedicado a introducir al lector en el mundo de la complejidad, en especial, a la complejidad topológica y dinámica de sistemas acoplados en red. El Capítulo 2 tiene como objetivo desarrollar los fundamentos biológicos, estructurales y funcionales del cerebro, cuando éste es interpretado como una red compleja. En el Capítulo 3, se resumen los objetivos esenciales y tareas que serán desarrolladas a lo largo de la segunda parte de la Tesis. La segunda parte es el núcleo de la Tesis, ya que contiene los resultados obtenidos a lo largo de los últimos cuatro años. Esta parte está dividida en cinco Capítulos, que contienen una versión detallada de las publicaciones llevadas a cabo durante esta Tesis. El Capítulo 4 está relacionado con la topología de las redes funcionales y, específicamente, con la detección y cuantificación de los nodos mas importantes: aquellos denominados “hubs” de la red. En el Capítulo 5 se muestra como las redes funcionales cerebrales pueden ser vistas no como una única red, sino más bien como una red-de-redes donde sus componentes tienen que coexistir en una situación de balance funcional. De esta forma, se investiga cómo los hemisferios cerebrales compiten para adquirir centralidad en la red-de-redes, y cómo esta interacción se mantiene (o no) cuando se introducen fallos deliberadamente en la red funcional. El Capítulo 6 va un paso mas allá al considerar las redes funcionales como sistemas vivos. En este Capítulo se muestra cómo al analizar la evolución de la topología de las redes, en vez de tratarlas como si estas fueran un sistema estático, podemos caracterizar mejor su estructura. Este hecho es especialmente relevante cuando se quiere tratar de encontrar diferencias entre grupos que desempeñan una tarea de memoria, en la que las redes funcionales tienen fuertes fluctuaciones. En el Capítulo 7 defino cómo crear redes parenclíticas a partir de bases de datos de actividad cerebral. Este nuevo tipo de redes, recientemente introducido para estudiar las anormalidades entre grupos de control y grupos anómalos, no ha sido implementado nunca en datos cerebrales y, en este Capítulo explico cómo hacerlo cuando se quiere evaluar la consistencia de la dinámica cerebral. Para concluir esta parte de la Tesis, el Capítulo 8 se centra en la relación entre las propiedades topológicas de los nodos dentro de una red y sus características dinámicas. Como mostraré más adelante, existe una relación entre ellas que revela que la posición de un nodo dentro una red está íntimamente correlacionada con sus propiedades dinámicas. Finalmente, la última parte de esta Tesis Doctoral está compuesta únicamente por el Capítulo 9, el cual contiene las conclusiones y perspectivas futuras que pueden surgir de los trabajos expuestos. En vista de todo lo anterior, espero que esta Tesis aporte una perspectiva complementaria sobre uno de los más extraordinarios sistemas complejos frente a los que nos encontramos: El cerebro humano. ABSTRACT The human brain is probably one of the most complex systems we are facing, thus being a timely and fascinating object of study. Characterizing how the brain organizes its activity to carry out complex tasks is highly non-trivial. While early neuroimaging and electrophysiological studies typically aimed at identifying patches of task-specific activations or local time-varying patterns of activity, there has now been consensus that task-related brain activity has a temporally multiscale, spatially extended character, as networks of coordinated brain areas are continuously formed and destroyed. Up until recently, though, the emphasis of functional brain activity studies has been on the identity of the particular nodes forming these networks, and on the characterization of connectivity metrics between them, the underlying covert hypothesis being that each node, constituting a coarse-grained representation of a given brain region, provides a unique contribution to the whole. Thus, functional neuroimaging initially integrated the two basic ingredients of early neuropsychology: localization of cognitive function into specialized brain modules and the role of connection fibres in the integration of various modules. Lately, brain structure and function have started being investigated using Network Science, a statistical mechanics understanding of an old branch of pure mathematics: graph theory. Network Science allows endowing networks with a great number of quantitative properties, thus vastly enriching the set of objective descriptors of brain structure and function at neuroscientists’ disposal. The link between Network Science and Neuroscience has shed light about how the entangled anatomy of the brain is, and how cortical activations may synchronize to generate the so-called functional brain networks, the principal object under study along this PhD Thesis. Within this context, complexity appears to be the bridge between the topological and dynamical properties of biological systems and, more specifically, the interplay between the organization and dynamics of functional brain networks. This PhD Thesis is, in general terms, a study of how cortical activations can be understood as the output of a network of dynamical systems that are intimately related with the processes occurring in the brain. In order to do that, I performed five studies that encompass both the topological and the dynamical aspects of such functional brain networks. In this way, the Thesis is divided into three major parts: Introduction, Results and Discussion. In the first part, comprising Chapters 1, 2 and 3, I make an overview of the main concepts of Network Science related to the analysis of brain imaging. More specifically, Chapter 1 is devoted to introducing the reader to the world of complexity, specially to the topological and dynamical complexity of networked systems. Chapter 2 aims to develop the biological, topological and functional fundamentals of the brain when it is seen as a complex network. Next, Chapter 3 summarizes the main objectives and tasks that will be developed along the forthcoming Chapters. The second part of the Thesis is, in turn, its core, since it contains the results obtained along these last four years. This part is divided into five Chapters, containing a detailed version of the publications carried out during the Thesis. Chapter 4 is related to the topology of functional networks and, more specifically, to the detection and quantification of the leading nodes of the network: the hubs. In Chapter 5 I will show that functional brain networks can be viewed not as a single network, but as a network-of-networks, where its components have to co-exist in a trade-off situation. In this way, I investigate how the brain hemispheres compete for acquiring the centrality of the network-of-networks and how this interplay is maintained (or not) when failures are introduced in the functional network. Chapter 6 goes one step beyond by considering functional networks as living systems. In this Chapter I show how analyzing the evolution of the network topology instead of treating it as a static system allows to better characterize functional networks. This fact is especially relevant when trying to find differences between groups performing certain memory tasks, where functional networks have strong fluctuations. In Chapter 7 I define how to create parenclitic networks from brain imaging datasets. This new kind of networks, recently introduced to study abnormalities between control and anomalous groups, have not been implemented with brain datasets and I explain in this Chapter how to do it when evaluating the consistency of brain dynamics. To conclude with this part of the Thesis, Chapter 8 is devoted to the interplay between the topological properties of the nodes within a network and their dynamical features. As I will show, there is an interplay between them which reveals that the position of a node in a network is intimately related with its dynamical properties. Finally, the last part of this PhD Thesis is composed only by Chapter 9, which contains the conclusions and future perspectives that may arise from the exposed results. In view of all, I hope that reading this Thesis will give a complementary perspective of one of the most extraordinary complex systems: The human brain.

Anomalous Consistency in Mild Cognitive Impairment: A Complex Networks Approach

Increased variability in performance has been associated with the emergence of several neurological and psychiatric pathologies. However, whether and how consistency of neuronal activity may also be indicative of an underlying pathology is still poorly understood. Here we propose a novel method for evaluating consistency from non-invasive brain recordings. We evaluate the consistency of the cortical activity recorded with magnetoencephalography in a group of subjects diagnosed with Mild Cognitive Impairment (MCI), a condition sometimes prodromal of dementia, during the execution of a memory task. We use metrics coming from nonlinear dynamics to evaluate the consistency of cortical regions. A representation known as parenclitic networks is constructed, where atypical features are endowed with a network structure, the topological properties of which can be studied at various scales. Pathological conditions correspond to strongly heterogeneous networks, whereas typical or normative conditions are characterized by sparsely connected networks with homogeneous nodes. The analysis of this kind of networks allows identifying the extent to which consistency is affected in the MCI group and the focal points where MCI is especially severe. To the best of our knowledge, these results represent the first attempt at evaluating the consistency of brain functional activity using complex networks theory.

Src interacts with dynamin and synapsin in neuronal cells

The nonreceptor tyrosine kinase Src is expressed at a high level in cells that are specialized for regulated secretion, such as the neuron, and is concentrated on secretory vesicles or at the site of exocytosis. To investigate the possibility that Src may play a role in regulating membrane traffic, we searched for neuronal proteins that will interact with Src. The SH3 domain of Src, but not that of the splice variant N-Src, bound to three proteins from mouse synaptosomes or PC12 cells: dynamin, synapsin Ia, and synapsin Ib. Dynamin and the synapsins coprecipitated with Src from PC12 cell extracts, and they colocalized with a subset of Src in the PC12 cell by immunofluorescence. Neither dynamin nor the synapsins were phosphorylated by Src, suggesting that the interaction of these proteins serves to direct the kinase activity of Src toward other proteins in the vesicle population. In immunoprecipitates containing Src and dynamin, the clathrin adaptor protein α-adaptin was also found. The association of Src and synapsin suggests a role for Src in the life cycle of the synaptic vesicle. The identification of a complex containing Src, dynamin, and α-adaptin indicates that Src may play a more general role in membrane traffic as well.

Neuronal type information encoded in the basic-helix–loop–helix domain of proneural genes

The proneural genes encode basic-helix–loop–helix (bHLH) proteins and promote the formation of distinct types of sensory organs. In Drosophila, two sets of proneural genes, atonal (ato) and members of the achaete–scute complex (ASC), are required for the formation of chordotonal (ch) organs and external sensory (es) organs, respectively. We assayed the production of sensory organs in transgenic flies expressing chimeric genes of ato and scute (sc), a member of ASC, and found that the information that specifies ch organs resides in the bHLH domain of ato; chimeras containing the b domain of ato and the HLH domain of sc also induced ch organ formation, but to a lesser extent than those containing the bHLH domain of ato. The b domains of ato and sc differ in seven residues. Mutations of these seven residues in the b domain of ato suggest that most or perhaps all of these residues are required for induction of ch organs. None of these seven residues is predicted to contact DNA directly by computer simulation using the structure of the myogenic factor MyoD as a model, implying that interaction of ato with other cofactors is likely to be involved in neuronal type specification.

Neural restrictive silencer factor recruits mSin3 and histone deacetylase complex to repress neuron-specific target genes

Accumulative evidence suggests that more than 20 neuron-specific genes are regulated by a transcriptional cis-regulatory element known as the neural restrictive silencer (NRS). A trans-acting repressor that binds the NRS, NRSF [also designated RE1-silencing transcription factor (REST)] has been cloned, but the mechanism by which it represses transcription is unknown. Here we show evidence that NRSF represses transcription of its target genes by recruiting mSin3 and histone deacetylase. Transfection experiments using a series of NRSF deletion constructs revealed the presence of two repression domains, RD-1 and RD-2, within the N- and C-terminal regions, respectively. A yeast two-hybrid screen using the RD-1 region as a bait identified a short form of mSin3B. In vitro pull-down assays and in vivo immunoprecipitation-Western analyses revealed a specific interaction between NRSF-RD1 and mSin3 PAH1-PAH2 domains. Furthermore, NRSF and mSin3 formed a complex with histone deacetylase 1, suggesting that NRSF-mediated repression involves histone deacetylation. When the deacetylation of histones was inhibited by tricostatin A in non-neuronal cells, mRNAs encoding several neuronal-specific genes such as SCG10, NMDAR1, and choline acetyltransferase became detectable. These results indicate that NRSF recruits mSin3 and histone deacetylase 1 to silence neural-specific genes and suggest further that repression of histone deacetylation is crucial for transcriptional activation of neural-specific genes during neuronal terminal differentiation.

Neuronal calcium sparks and intracellular calcium “noise”

Intracellular calcium ions are involved in many forms of cellular function. To accommodate so many control functions, a complex spatiotemporal organization of calcium signaling has developed. In both excitable and nonexcitable cells, calcium signaling was found to fluctuate. Sudden localized increases in the intracellular calcium concentration—or calcium sparks—were found in heart, striated and smooth muscle, Xenopus Laevis oocytes, and HeLa and P12 cells. In the nervous system, intracellular calcium ions were found important in key processes such as transmitter release, repetitive firing, and gene expression. Hence, we examined whether calcium sparks also exist in neurons. Using confocal laser-scanning microscopy and fluorescent probes, we found that calcium sparks exist in two types of neuronal preparations: the presynaptic boutons of the lizard neuromuscular junction and rat hippocampal neurons in cell culture. Control experiments exclude the possibility that these calcium sparks originate from instrumental or biological artifacts. Calcium sparks seem to be just the tip of the iceberg of a more general phenomenon of intracellular calcium “noise.” We speculate that calcium sparks and calcium noise may be of key importance in calcium signaling in the nervous system.

Characterization of a Novel Yeast SNARE Protein Implicated in Golgi Retrograde Traffic

The protein trafficking machinery of eukaryotic cells is employed for protein secretion and for the localization of resident proteins of the exocytic and endocytic pathways. Protein transit between organelles is mediated by transport vesicles that bear integral membrane proteins (v-SNAREs) which selectively interact with similar proteins on the target membrane (t-SNAREs), resulting in a docked vesicle. A novel Saccharomyces cerevisiae SNARE protein, which has been termed Vti1p, was identified by its sequence similarity to known SNAREs. Vti1p is a predominantly Golgi-localized 25-kDa type II integral membrane protein that is essential for yeast viability. Vti1p can bind Sec17p (yeast SNAP) and enter into a Sec18p (NSF)-sensitive complex with the cis-Golgi t-SNARE Sed5p. This Sed5p/Vti1p complex is distinct from the previously described Sed5p/Sec22p anterograde vesicle docking complex. Depletion of Vti1p in vivo causes a defect in the transport of the vacuolar protein carboxypeptidase Y through the Golgi. Temperature-sensitive mutants of Vti1p show a similar carboxypeptidase Y trafficking defect, but the secretion of invertase and gp400/hsp150 is not significantly affected. The temperature-sensitive vti1 growth defect can be rescued by the overexpression of the v-SNARE, Ykt6p, which physically interacts with Vti1p. We propose that Vti1p, along with Ykt6p and perhaps Sft1p, acts as a retrograde v-SNARE capable of interacting with the cis-Golgi t-SNARE Sed5p.

Pentameric assembly of a neuronal glutamate transporter

Freeze-fracture electron microscopy was used to study the structure of a human neuronal glutamate transporter (EAAT3). EAAT3 was expressed in Xenopus laevis oocytes, and its function was correlated with the total number of transporters in the plasma membrane of the same cells. Function was assayed as the maximum charge moved in response to a series of transmembrane voltage pulses. The number of transporters in the plasma membrane was determined from the density of a distinct 10-nm freeze-fracture particle, which appeared in the protoplasmic face only after EAAT3 expression. The linear correlation between EAAT3 maximum carrier-mediated charge and the total number of the 10-nm particles suggested that this particle represented functional EAAT3 in the plasma membrane. The cross-sectional area of EAAT3 in the plasma membrane (48 ± 5 nm2) predicted 35 ± 3 transmembrane α-helices in the transporter complex. This information along with secondary structure models (6–10 transmembrane α-helices) suggested an oligomeric state for EAAT3. EAAT3 particles were pentagonal in shape in which five domains could be identified. They exhibited fivefold symmetry because they appeared as equilateral pentagons and the angle at the vertices was 110°. Each domain appeared to contribute to an extracellular mass that projects ≈3 nm into the extracellular space. Projections from all five domains taper toward an axis passing through the center of the pentagon, giving the transporter complex the appearance of a penton-based pyramid. The pentameric structure of EAAT3 offers new insights into its function as both a glutamate transporter and a glutamate-gated chloride channel.

Experimentally determined chaotic phase synchronization in a neuronal system

Mathematical analysis of the subthreshold oscillatory properties of inferior olivary neurons in vitro indicates that the oscillation is nonlinear and supports low dimensional chaotic dynamics. This property leads to the generation of complex functional states that can be attained rapidly via phase coherence that conform to the category of “generalized synchronization.” Functionally, this translates into neuronal ensemble properties that can support maximum functional permissiveness and that rapidly can transform into robustly determined multicellular coherence.

Selective Formation of Sed5p-containing SNARE Complexes Is Mediated by Combinatorial Binding Interactions

Sed5p is the only syntaxin family member required for protein transport through the yeast Golgi and it is known to bind up to nine other soluble N-ethylmaleimide-sensitive factor attachment receptor (SNARE) proteins in vivo. We describe in vitro binding experiments in which we identify ternary and quaternary Sed5p-containing SNARE complexes. The formation of SNARE complexes among these endoplasmic reticulum- and Golgi-localized proteins requires Sed5p and is syntaxin-selective. In addition, Sed5p-containing SNARE complexes form selectively and this selectivity is mediated by Sed5p-containing intermediates that discriminate among subsequent binding partners. Although many of these SNAREs have overlapping distributions in vivo, the SNAREs that form complexes with Sed5p in vitro reflect their functionally distinct locales. Although SNARE–SNARE interactions are promiscuous and a single SNARE protein is often found in more than one complex, both the biochemical as well as genetic analyses reported here suggest that this is not a result of nonselective direct substitution of one SNARE for another. Rather our data are consistent with the existence of multiple (perhaps parallel) trafficking pathways where Sed5p-containing SNARE complexes play overlapping and/or distinct functional roles.

Mice with disrupted GM2/GD2 synthase gene lack complex gangliosides but exhibit only subtle defects in their nervous system.

Gangliosides, sialic acid-containing glycosphingolipids, are abundant in the vertebrate (mammalian) nervous system. Their composition is spatially and developmentally regulated, and gangliosides have been widely believed to lay essential roles in establishment of the nervous system, especially in neuritogenesis and synaptogenesis. However, this has never been tested directly. Here we report the generation of mice with a disrupted beta 1,4-N-acetylgalactosaminyltransferase (GM2/GD2 synthase; EC 2.4.1.92) gene. The mice lacked all complex gangliosides. Nevertheless, they did not show any major histological defects in their nervous systems or in gross behavior. Just a slight reduction in the neural conduction velocity from the tibial nerve to the somatosensory cortex, but not to the lumbar spine, was detected. These findings suggest that complex gangliosides are required in neuronal functions but not in the morphogenesis and organogenesis of the brain. The higher levels of GM3 and GD3 expressed in the brains of these mutant mice may be able to compensate for the lack of complex gangliosides.