Neuronal Pathfinding and Recognition: Roles of Cell Adhesion Molecules

Neurons contact their neighbors through a diverse array of cell adhesion and other surface molecules. These molecules can exhibit highly regulated patterns of expression, underscoring their multiple roles in establishing specific interactions between neurons and their environment. Recent studies are beginning to ask how these membrane-bound neural recognition molecules interact with each other and intracellular signaling pathways within an individual neuronal growth cone, and direct the formation of neural connections during development.     

Parkinson's Disease: Mechanisms of Neuronal Death

Based on the published data, the authors analyze in detail events resulting in the death of neurons in the substantia nigra (according to the apoptosis scenario) and in the development of Parkinson's disease (idiopathic parkinsonism).     

The Relationship Between Adhesion Molecules and Neuronal Plasticity

1. It is presently widely assumed that structural reorganization of synaptic architectures subserves the functional gains that define certain neuronal plasticities.2. While target molecules thought to participate in such morphological dynamics are not well defined, growing evidence suggests a pivotal role for cell adhesion molecules.3. Herein, brief discussions are presented on (i) the history of how adhesion molecules became implicated in plasticity and memory processes, (ii) the general biology of some of the major classes of such molecules, and (iii) the future of the adhesion molecule/plasticity relationship.     

Different Mechanisms of NMDA-Mediated Protection Against Neuronal Apoptosis: A Stimuli-Dependent Effect

The mechanisms of protective effect of N-methyl-D-aspartate (NMDA) receptor stimulation on apoptosis of neurons at their early stage of development are poorly understood. In the present study, we investigated the effects of NMDA on staurosporine (St)- and low-potassium (LP)-evoked apoptotic cell death in primary cerebellar granule cell (CGC) cultures at 7 days in vitro (DIV). We found that NMDA (200 μM) attenuated the St (0.5 μM)- and LP (5 mM KCl)-induced neuronal cell death in 7 but not 12 DIV CGC as confirmed by LDH release and MTT reduction assays. Moreover, NMDA attenuated St-and LP-evoked DNA fragmentation and cytosolic apoptosis inducing factor (AIF) protein level but not caspase-3 activation induced by both pro-apoptotic factors. Neuroprotective effects of NMDA on St-induced apoptosis in CGC were attenuated by inhibitors of ERK/MAPK-signaling, PD 98059 and U0126 but not by NMDA receptor antagonists, AP-5 (100 μM) and MK-801 (1 μM) or by inhibitors of PI3-K/Akt pathway (LY 294002 and wortmannin). In contrast to staurosporine model of apoptosis, AP-5 and MK-801 but not inhibitors of PI3-K/Akt and MAPK/ERK1/2 prevented the NMDA-mediated neuroprotection in LP-induced apoptosis of CGC. In separate experiments, we observed also the anti-apoptotic action of NMDA on St (0.5 μM)- and salsolinol (250 μM)-evoked cell death in human neuroblastoma SH-SY5Y cells without its influence on caspase-3 activity, induced by these pro-apoptotic factors. These data indicate that neuroprotection evoked by NMDA in CGC strongly depends on used pro-apoptotic agent and could engage NMDA channel function or be connected with the activation of pro-survival MAPK/ERK1/2 pathway. It is also suggested that anti-apoptotic effects of NMDA is connected with inhibition of fragmentation of DNA via caspase-3-independent mechanism.     

Long-Lasting Maintenance of Learning-Induced Enhanced Neuronal Excitability: Mechanisms and Functional Significance

Pyramidal neurons in the piriform cortex of olfactory discrimination trained rats show enhanced intrinsic neuronal excitability that lasts for several days after learning. Such enhanced intrinsic excitability is mediated by long-term reduction in the postburst after hyperpolarization which is generated by repetitive spike firing. The molecular machinery underlying such long-lasting modulation of intrinsic excitability, as well as its exceptional durability, is yet to be fully described. In this review, we present recent advancements that reveal the identity of the current that is modulated after learning and the second messenger system by which enhanced excitability is maintained. We also discuss the significance of such long-lasting modulation to the local network’s sensitivity to noradrenaline, a major learning-relevant neuromodulator.     

Shaping Neuronal Network Activity by Presynaptic Mechanisms

Neuronal microcircuits generate oscillatory activity, which has been linked to basic functions such as sleep, learning and sensorimotor gating. Although synaptic release processes are well known for their ability to shape the interaction between neurons in microcircuits, most computational models do not simulate the synaptic transmission process directly and hence cannot explain how changes in synaptic parameters alter neuronal network activity. In this paper, we present a novel neuronal network model that incorporates presynaptic release mechanisms, such as vesicle pool dynamics and calcium-dependent release probability, to model the spontaneous activity of neuronal networks. The model, which is based on modified leaky integrate-and-fire neurons, generates spontaneous network activity patterns, which are similar to experimental data and robust under changes in the model''s primary gain parameters such as excitatory postsynaptic potential and connectivity ratio. Furthermore, it reliably recreates experimental findings and provides mechanistic explanations for data obtained from microelectrode array recordings, such as network burst termination and the effects of pharmacological and genetic manipulations. The model demonstrates how elevated asynchronous release, but not spontaneous release, synchronizes neuronal network activity and reveals that asynchronous release enhances utilization of the recycling vesicle pool to induce the network effect. The model further predicts a positive correlation between vesicle priming at the single-neuron level and burst frequency at the network level; this prediction is supported by experimental findings. Thus, the model is utilized to reveal how synaptic release processes at the neuronal level govern activity patterns and synchronization at the network level.     

Intracellular Cholesterol and Phospholipid Trafficking: Comparable Mechanisms in Macrophages and Neuronal Cells

During the past ten years considerable evidences have accumulated that in addition to monocytes/macrophages, that are implicated in innate immunity and atherogenesis, neuronal cells also exhibit an extensive cellular metabolism. The present study focuses on the major protein players that establish cellular distribution of cholesterol and phospholipids. Evidences are provided that neuronal cells and monocytes/macrophages are equipped with comparable intracellular lipid trafficking mechanisms. Selected examples are presented that trafficking dysfunctions lead to disease development, such as Tangier disease and Niemann-Pick disease type C, or contribute to the pathogenesis of diseases such as Alzheimer disease and atherosclerosis.     

Neuronal Mechanisms Encoding Global-to-Fine Information in Inferior-Temporal Cortex

Sugase et al. found that global information is represented at the initial transient firing of a single face-responsive neuron in inferior-temporal (IT) cortex, and that finer information is represented at the subsequent sustained firing. A feed-forward model and an attractor network are conceivable models to reproduce this dynamics. The attractor network, specifically an associative memory model, is employed to elucidate the neuronal mechanisms producing the dynamics. The results obtained by computer simulations show that a state of neuronal population initially approaches to a mean state of similar memory patterns, and that it finally converges to a memory pattern. This dynamics qualitatively coincides with that of face-responsive neurons. The dynamics of a single neuron in the model also coincides with that of a single face-responsive neuron. Furthermore, we propose two physiological experiments and predict the results from our model. Both predicted results are not explainable by the feed-forward model. Therefore, if the results obtained by actual physiological experiments coincide with our predicted results, the attractor network might be the neuronal mechanisms producing the dynamics of face-responsive neurons.     

A Proteomic Screen of Neuronal Cell-Surface Molecules Reveals IgLONs as Structurally Conserved Interaction Modules at the Synapse

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Supralinear and Supramodal Integration of Visual and Tactile Signals in Rats: Psychophysics and Neuronal Mechanisms

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A Primer on Metagenomics

Metagenomics is a discipline that enables the genomic study of uncultured microorganisms. Faster, cheaper sequencing technologies and the ability to sequence uncultured microbes sampled directly from their habitats are expanding and transforming our view of the microbial world. Distilling meaningful information from the millions of new genomic sequences presents a serious challenge to bioinformaticians. In cultured microbes, the genomic data come from a single clone, making sequence assembly and annotation tractable. In metagenomics, the data come from heterogeneous microbial communities, sometimes containing more than 10,000 species, with the sequence data being noisy and partial. From sampling, to assembly, to gene calling and function prediction, bioinformatics faces new demands in interpreting voluminous, noisy, and often partial sequence data. Although metagenomics is a relative newcomer to science, the past few years have seen an explosion in computational methods applied to metagenomic-based research. It is therefore not within the scope of this article to provide an exhaustive review. Rather, we provide here a concise yet comprehensive introduction to the current computational requirements presented by metagenomics, and review the recent progress made. We also note whether there is software that implements any of the methods presented here, and briefly review its utility. Nevertheless, it would be useful if readers of this article would avail themselves of the comment section provided by this journal, and relate their own experiences. Finally, the last section of this article provides a few representative studies illustrating different facets of recent scientific discoveries made using metagenomics.