Conformational rearrangements in the transmembrane domain of CNGA1 channels revealed by single-molecule force spectroscopy

Cyclic nucleotide-gated (CNG) channels are activated by binding of cyclic nucleotides. Although structural studies have identified the channel pore and selectivity filter, conformation changes associated with gating remain poorly understood. Here we combine single-molecule force spectroscopy (SMFS) with mutagenesis, bioinformatics and electrophysiology to study conformational changes associated with gating. By expressing functional channels with SMFS fingerprints in Xenopus laevis oocytes, we were able to investigate gating of CNGA1 in a physiological-like membrane. Force spectra determined that the S4 transmembrane domain is mechanically coupled to S5 in the closed state, but S3 in the open state. We also show there are multiple pathways for the unfolding of the transmembrane domains, probably caused by a different degree of \u3b1-helix folding. This approach demonstrates that CNG transmembrane domains have dynamic structure and establishes SMFS as a tool for probing conformational change in ion channels

Early phase of plasticity-related gene regulation and SRF dependent transcription in the hippocampus

Hippocampal organotypic cultures are a highly reliable in vitro model for studying neuroplasticity: in this paper, we analyze the early phase of the transcriptional response induced by a 20 \ub5M gabazine treatment (GabT), a GABA-Ar antagonist, by using Affymetrix oligonucleotide microarray, RT-PCR based time-course and chromatin-immuno-precipitation. The transcriptome profiling revealed that the pool of genes up-regulated by GabT, besides being strongly related to the regulation of growth and synaptic transmission, is also endowed with neuro-protective and pro-survival properties. By using RT-PCR, we quantified a time-course of the transient expression for 33 of the highest up-regulated genes, with an average sampling rate of 10 minutes and covering the time interval [10 3690] minutes. The cluster analysis of the time-course disclosed the existence of three different dynamical patterns, one of which proved, in a statistical analysis based on results from previous works, to be significantly related with SRF-dependent regulation (p-value<0.05). The chromatin immunoprecipitation (chip) assay confirmed the rich presence of working CArG boxes in the genes belonging to the latter dynamical pattern and therefore validated the statistical analysis. Furthermore, an in silico analysis of the promoters revealed the presence of additional conserved CArG boxes upstream of the genes Nr4a1 and Rgs2. The chip assay confirmed a significant SRF signal in the Nr4a1 CArG box but not in the Rgs2 CArG box

The brain's neural classifiers considering both the posterior probabilities and generalities to control the mechanism underlying decision making; An evidence for computational Bayesian classifiers

To study a cognitive neural model of decision making, we analyzed the neural and behavioral data recorded in Shadlen Neuroscience Lab [9] from the monkeys performing motion-discrimination reaction-time task with consideration of six coherence levels. Two uncorrelated principal components of the timing sequences of each trial's action potential have been further extracted to examine the existing information from the spike trains. The trials corresponding to right and wrong choices were analyzed independently to determine whether the Bays' rule can describe the decision making mechanism or not. The result demonstrates that the brain generates the spikes to temporally extract two principal components leading to making a decision: posterior probabilities and generalities. At the end, the temporal model of Bayesian decision making has been theoretically described and verified through examination of above data. © 2013 IEEE

The brain's neural classifiers considering both the posterior probabilities and generalities to control the mechanism underlying decision making; An evidence for computational Bayesian classifiers

To study a cognitive neural model of decision making, we analyzed the neural and behavioral data recorded in Shadlen Neuroscience Lab [9] from the monkeys performing motion-discrimination reaction-time task with consideration of six coherence levels. Two uncorrelated principal components of the timing sequences of each trial's action potential have been further extracted to examine the existing information from the spike trains. The trials corresponding to right and wrong choices were analyzed independently to determine whether the Bays' rule can describe the decision making mechanism or not. The result demonstrates that the brain generates the spikes to temporally extract two principal components leading to making a decision: posterior probabilities and generalities. At the end, the temporal model of Bayesian decision making has been theoretically described and verified through examination of above data. © 2013 IEEE

Classification of low and high schizotypy levels via evaluation of brain connectivity

Schizotypy is a latent cluster of personality traits that denote a vulnerability for schizophrenia or a type of spectrum disorder. The aim of the study is to investigate parametric effective brain connectivity features for classifying high versus low schizotypy (LS) status. Electroencephalography (EEG) signals are recorded from 13 high schizotypy (HS) and 11 LS participants during an emotional auditory odd-ball task. The brain connectivity signals for machine learning are taken after the settlement of event-related potentials. A multivariate autoregressive (MVAR)-based connectivity measure is estimated from the EEG signals using the directed transfer functions (DTFs) method. The values of DTF power in five standard frequency bands are used as features. The support vector machines (SVMs) revealed significant differences between HS and LS. The accuracy, specificity, and sensitivity of the results using SVM are as high as 89.21%, 90.3%, and 88.2%, respectively. Our results demonstrate that the effective brain connectivity in prefrontal/parietal and prefrontal/frontal brain regions considerably changes according to schizotypal status. These findings prove that the brain connectivity indices offer valuable biomarkers for detecting schizotypal personality. Further monitoring of the changes in DTF following the diagnosis of schizotypy may lead to the early identification of schizophrenia and other spectrum disorders