IGF-1 stimulated upregulation of cyclin D1 is mediated via STAT5 signaling pathway in neuronal cells

Signal Transducer and Activator of Transcription (STATs) regulate various target genes such as cyclin D1, MYC, and BCL2 in nonneuronal cells which contribute towards progression as well as prevention of apoptosis and are involved in differentiation and cell survival. However, in neuronal cells, the role of STATs in the activation and regulation of these target genes and their signaling pathways are still not well established. In this study, a robust cyclin D1 expression was observed following IGF-1 stimulation in SY5Y cells as well as neurospheres. JAK/STAT pathway was shown to be involved in this upregulation. A detailed promoter analysis revealed that the specific STAT involved was STAT5, which acted as a positive regulatory element for cyclin D1 expression. Overexpression studies confirmed increase in cyclin D1 expression in response to STAT5a and STAT5b constructs when compared to dominant-negative STAT5. siRNA targeting STAT5, diminished the cyclin D1 expression, further confirming that STAT5 specifically regulated cyclin D1 in neuronal cells. Together, these findings shed new light on the mechanism of IGF-1 mediated upregulation of cyclin D1 expression in neural cell lines as well as in neural stem cells via the JAK/STAT5 signaling cascade.

Molecule Modeling of Human Pentameric alpha(7) Neuronal Nicotinic Acetylcholine Receptor and Its Interaction with its Agonist and Competitive Antagonist

The nicotinic Acetylcholine Receptor (nAChR) is the major class of neurotransmitter receptors that is involved in many neurodegenerative conditions such as schizophrenia, Alzheimer's and Parkinson's diseases. The N-terminal region or Ligand Binding Domain (LBD) of nAChR is located at pre- and post-synaptic nervous system, which mediates synaptic transmission. nAChR acts as the drug target for agonist and competitive antagonist molecules that modulate signal transmission at the nerve terminals. Based on Acetylcholine Binding Protein (AChBP) from Lymnea stagnalis as the structural template, the homology modeling approach was carried out to build three dimensional model of the N-terminal region of human alpha(7)nAChR. This theoretical model is an assembly of five alpha(7) subunits with 5 fold axis symmetry, constituting a channel, with the binding picket present at the interface region of the subunits. alpha-netlrotoxin is a potent nAChR competitive antagonist that readily blocks the channel resulting in paralysis. The molecular interaction of alpha-Bungarotoxin, a long chain alpha-neurotoxin from (Bungarus multicinctus) and human alpha(7)nAChR seas studied. Agonists such as acetylcholine, nicotine, which are used in it diverse array of biological activities, such as enhancements of cognitive performances, were also docked with the theoretical model of human alpha(7)nAChR. These docked complexes were analyzed further for identifying the crucial residues involved i interaction. These results provide the details of interaction of agonists and competitive antagonists with three dimensional model of the N-terminal region of human alpha(7)nAChR and thereby point to the design of novel lead compounds.

Fluorescent labelling of strychnine: A novel approach for recognition of strychnine binding sites on neuronal membrane

trychnine was coupled to fluorescein isothiocyanate to mark strychnine binding sites in spinal cord of rat. Specific binding of strychnine could be demonstrated in synaptosomal fraction. Addition of glycine to the strychninised membrane led to a decrease in fluorescence indicating same receptor loci.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Studies on bicuculline binding sites on neuronal membrane using fluorescent antibody technique: Comparative binding of gaba and bicuculline

Highly purified fluorescent labelled anti-bicuculline antibodies were used to mark bicuculline binding sites in cerebral cortex of monkey brain. Specific binding of bicuculline could be demonstrated in the synaptosomal fraction, when bicuculline was added both Image and Image . Addition of γ-aminobutyric acid (GABA) to the bicucullinised membrane led to a decrease in fluorescence indicating same receptor loci and establishing GABA-bicuculline antagonism at a molecular level.

Inferring neuronal network connectivity from spike data: A temporal data mining approach

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent d evelopments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computational problem. This task involves finding significant temporal patterns from vast amounts of symbolic time series data. In this paper we show that the frequent episode mining methods from the field of temporal data mining can be very useful in this context. In the frequent episode discovery framework, the data is viewed as a sequence of events, each of which is characterized by an event type and its time of occurrence and episodes are certain types of temporal patterns in such data. Here we show that, using the set of discovered frequent episodes from multi-neuronal data, one can infer different types of connectivity patterns in the neural system that generated it. For this purpose, we introduce the notion of mining for frequent episodes under certain temporal constraints; the structure of these temporal constraints is motivated by the application. We present algorithms for discovering serial and parallel episodes under these temporal constraints. Through extensive simulation studies we demonstrate that these methods are useful for unearthing patterns of neuronal network connectivity.

A lower bound to the survival probability and an approximate first passage time distribution for Markovian and non-Markovian dynamics in phase space

We derive a very general expression of the survival probability and the first passage time distribution for a particle executing Brownian motion in full phase space with an absorbing boundary condition at a point in the position space, which is valid irrespective of the statistical nature of the dynamics. The expression, together with the Jensen's inequality, naturally leads to a lower bound to the actual survival probability and an approximate first passage time distribution. These are expressed in terms of the position-position, velocity-velocity, and position-velocity variances. Knowledge of these variances enables one to compute a lower bound to the survival probability and consequently the first passage distribution function. As examples, we compute these for a Gaussian Markovian process and, in the case of non-Markovian process, with an exponentially decaying friction kernel and also with a power law friction kernel. Our analysis shows that the survival probability decays exponentially at the long time irrespective of the nature of the dynamics with an exponent equal to the transition state rate constant.

Characterization of mouse neuronal Ca2+/calmodulin kinase II inhibitor alpha

We have overexpressed an 8.5-kDa mouse Ca2+/calmodulin kinase II inhibitor a protein (mCaMKIIN alpha) in Escherichia coli and demonstrate that the recombinant protein is a potent inhibitor of Ca2+/calmodulin kinase 11 (CaMKII) in vitro. However, antibodies raised against recombinant mCaMKIIN alpha. react with an similar to 37-kDa protein present in mouse brain. The pattern of expression of the similar to 37-kDa protein is similar to that of mCaMKIIN alpha mRNA as both are expressed in normal but not Japanese encephalitis virus (JEV)-infected mouse brain. Subcellular localization studies indicate that the similar to 37-kDa protein is present in the post-synaptic density (PSD) where mCaMKII alpha is known to perform key regulatory functions. We conclude that the similar to 37-kDa protein identified in this study is mCaMKIIN alpha. and its localization in the PSD indicates a novel role for this protein in the regulation of neuronal CaMKII alpha. (c) 2007 Elsevier B.V. All rights reserved.

Generalized survival in step fluctuations

The properties of the generalized survival probability, that is, the probability of not crossing an arbitrary location R during relaxation, have been investigated experimentally (via scanning tunneling microscope observations) and numerically. The results confirm that the generalized survival probability decays exponentially with a time constant tau(s)(R). The distance dependence of the time constant is shown to be tau(s)(R)=tau(s0)exp[-R/w(T)], where w(2)(T) is the material-dependent mean-squared width of the step fluctuations. The result reveals the dependence on the physical parameters of the system inherent in the prior prediction of the time constant scaling with R/L-alpha, with L the system size and alpha the roughness exponent. The survival behavior is also analyzed using a contrasting concept, the generalized inside survival S-in(t,R), which involves fluctuations to an arbitrary location R further from the average. Numerical simulations of the inside survival probability also show an exponential time dependence, and the extracted time constant empirically shows (R/w)(lambda) behavior, with lambda varying over 0.6 to 0.8 as the sampling conditions are changed. The experimental data show similar behavior, and can be well fit with lambda=1.0 for T=300 K, and 0.5

Analysis of connectivity map: Control to glutamate injured and phenobarbital treated neuronal network

We study the responses of a cultured neural network when it is exposed to epileptogenesis glutamate injury causing epilepsy and subsequent treatment with phenobarbital by constructing connectivity map of neurons using correlation matrix. This study is particularly useful in understanding the pharmaceutical drug induced changes in the neuronal network properties with insights into changes at the systems biology level. (C) 2010 American Institute of Physics. [doi:10.1063/1.3398025]

Spatial survival probability for one-dimensional fluctuating interfaces in the steady state

We report numerical and analytic results for the spatial survival probability for fluctuating one-dimensional interfaces with Edwards-Wilkinson or Kardar-Parisi-Zhang dynamics in the steady state. Our numerical results are obtained from analysis of steady-state profiles generated by integrating a spatially discretized form of the Edwards-Wilkinson equation to long times. We show that the survival probability exhibits scaling behavior in its dependence on the system size and the "sampling interval" used in the measurement for both "steady-state" and "finite" initial conditions. Analytic results for the scaling functions are obtained from a path-integral treatment of a formulation of the problem in terms of one-dimensional Brownian motion. A "deterministic approximation" is used to obtain closed-form expressions for survival probabilities from the formally exact analytic treatment. The resulting approximate analytic results provide a fairly good description of the numerical data.

Imaging in vivo Neuronal Transport in Genetic Model Organisms Using Microfluidic Devices

Microfluidic devices have been developed for imaging behavior and various cellular processes in Caenorhabditis elegans, but not subcellular processes requiring high spatial resolution. In neurons, essential processes such as axonal, dendritic, intraflagellar and other long-distance transport can be studied by acquiring fast time-lapse images of green fluorescent protein (GFP)-tagged moving cargo. We have achieved two important goals in such in vivo studies namely, imaging several transport processes in unanesthetized intact animals and imaging very early developmental stages. We describe a microfluidic device for immobilizing C. elegans and Drosophila larvae that allows imaging without anesthetics or dissection. We observed that for certain neuronal cargoes in C. elegans, anesthetics have significant and sometimes unexpected effects on the flux. Further, imaging the transport of certain cargo in early developmental stages was possible only in the microfluidic device. Using our device we observed an increase in anterograde synaptic vesicle transport during development corresponding with synaptic growth. We also imaged Q neuroblast divisions and mitochondrial transport during early developmental stages of C. elegans and Drosophila, respectively. Our simple microfluidic device offers a useful means to image high-resolution subcellular processes in C. elegans and Drosophila and can be readily adapted to other transparent or translucent organisms.

A Ten-microRNA Expression Signature Predicts Survival in Glioblastoma

Glioblastoma (GBM) is the most common and aggressive primary brain tumor with very poor patient median survival. To identify a microRNA (miRNA) expression signature that can predict GBM patient survival, we analyzed the miRNA expression data of GBM patients (n = 222) derived from The Cancer Genome Atlas (TCGA) dataset. We divided the patients randomly into training and testing sets with equal number in each group. We identified 10 significant miRNAs using Cox regression analysis on the training set and formulated a risk score based on the expression signature of these miRNAs that segregated the patients into high and low risk groups with significantly different survival times (hazard ratio HR] = 2.4; 95% CI = 1.4-3.8; p < 0.0001). Of these 10 miRNAs, 7 were found to be risky miRNAs and 3 were found to be protective. This signature was independently validated in the testing set (HR = 1.7; 95% CI = 1.1-2.8; p = 0.002). GBM patients with high risk scores had overall poor survival compared to the patients with low risk scores. Overall survival among the entire patient set was 35.0% at 2 years, 21.5% at 3 years, 18.5% at 4 years and 11.8% at 5 years in the low risk group, versus 11.0%, 5.5%, 0.0 and 0.0% respectively in the high risk group (HR = 2.0; 95% CI = 1.4-2.8; p < 0.0001). Cox multivariate analysis with patient age as a covariate on the entire patient set identified risk score based on the 10 miRNA expression signature to be an independent predictor of patient survival (HR = 1.120; 95% CI = 1.04-1.20; p = 0.003). Thus we have identified a miRNA expression signature that can predict GBM patient survival. These findings may have implications in the understanding of gliomagenesis, development of targeted therapy and selection of high risk cancer patients for adjuvant therapy.

Survival of the fittest and zero sum games

Competition for available resources is natural amongst coexisting species, and the fittest contenders dominate over the rest in evolution. The. dynamics of this selection is studied using a simple linear model. It has similarities to features of quantum computation, in particular conservation laws leading to destructive interference. Compared to an altruistic scenario, competition introduces instability and eliminates the weaker species in a finite time.