Levetiracetam Reverses Synaptic Deficits Produced by Overexpression of SV2A

Levetiracetam is an FDA-approved drug used to treat epilepsy and other disorders of the nervous system. Although it is known that levetiracetam binds the synaptic vesicle protein SV2A, how drug binding affects synaptic functioning remains unknown. Here we report that levetiracetam reverses the effects of excess SV2A in autaptic hippocampal neurons. Expression of an SV2A-EGFP fusion protein produced a ∼1.5-fold increase in synaptic levels of SV2, and resulted in reduced synaptic release probability. The overexpression phenotype parallels that seen in neurons from SV2 knockout mice, which experience severe seizures. Overexpression of SV2A also increased synaptic levels of the calcium-sensor protein synaptotagmin, an SV2-binding protein whose stability and trafficking are regulated by SV2. Treatment with levetiracetam rescued normal neurotransmission and restored normal levels of SV2 and synaptotagmin at the synapse. These results indicate that changes in SV2 expression in either direction impact neurotransmission, and suggest that levetiracetam may modulate SV2 protein interactions

Comparing Artificial Neural Networks, General Linear Models and Support Vector Machines in Building Predictive Models for Small Interfering RNAs

Exogenous short interfering RNAs (siRNAs) induce a gene knockdown effect in cells by interacting with naturally occurring RNA processing machinery. However not all siRNAs induce this effect equally. Several heterogeneous kinds of machine learning techniques and feature sets have been applied to modeling siRNAs and their abilities to induce knockdown. There is some growing agreement to which techniques produce maximally predictive models and yet there is little consensus for methods to compare among predictive models. Also, there are few comparative studies that address what the effect of choosing learning technique, feature set or cross validation approach has on finding and discriminating among predictive models.Three learning techniques were used to develop predictive models for effective siRNA sequences including Artificial Neural Networks (ANNs), General Linear Models (GLMs) and Support Vector Machines (SVMs). Five feature mapping methods were also used to generate models of siRNA activities. The 2 factors of learning technique and feature mapping were evaluated by complete 3x5 factorial ANOVA. Overall, both learning techniques and feature mapping contributed significantly to the observed variance in predictive models, but to differing degrees for precision and accuracy as well as across different kinds and levels of model cross-validation.The methods presented here provide a robust statistical framework to compare among models developed under distinct learning techniques and feature sets for siRNAs. Further comparisons among current or future modeling approaches should apply these or other suitable statistically equivalent methods to critically evaluate the performance of proposed models. ANN and GLM techniques tend to be more sensitive to the inclusion of noisy features, but the SVM technique is more robust under large numbers of features for measures of model precision and accuracy. Features found to result in maximally predictive models are not consistent across learning techniques, suggesting care should be taken in the interpretation of feature relevance. In the models developed here, there are statistically differentiable combinations of learning techniques and feature mapping methods where the SVM technique under a specific combination of features significantly outperforms all the best combinations of features within the ANN and GLM techniques

Thermal effects of carbonated hydroxyapatite modified by glycine and albumin

In this work calcium phosphate powders were obtained by precipitation method from simulated solutions of synovial fluid containing glycine and albumin. X-ray diffraction and IR spectroscopy determined that all samples are single-phase and are presented by carbonate containing hydroxyapatite (CHA). The thermograms of solid phases of CHA were obtained and analyzed; five stages of transformation in the temperature range of 25-1000°C were marked. It is shown that in this temperature range dehydration, decarboxylation and thermal degradation of amino acid and protein connected to the surface of solid phase occur. The tendency of temperature lowering of the decomposition of powders synthesized from a medium containing organic substances was determined. Results demonstrate a direct dependence between the concentration of the amino acid in a model solution and its content in the solid phase

Reconsideration of In-Silico siRNA Design Based on Feature Selection: A Cross-Platform Data Integration Perspective

RNA interference via exogenous short interference RNAs (siRNA) is increasingly more widely employed as a tool in gene function studies, drug target discovery and disease treatment. Currently there is a strong need for rational siRNA design to achieve more reliable and specific gene silencing; and to keep up with the increasing needs for a wider range of applications. While progress has been made in the ability to design siRNAs with specific targets, we are clearly at an infancy stage towards achieving rational design of siRNAs with high efficacy. Among the many obstacles to overcome, lack of general understanding of what sequence features of siRNAs may affect their silencing efficacy and of large-scale homogeneous data needed to carry out such association analyses represents two challenges. To address these issues, we investigated a feature-selection based in-silico siRNA design from a novel cross-platform data integration perspective. An integration analysis of 4,482 siRNAs from ten meta-datasets was conducted for ranking siRNA features, according to their possible importance to the silencing efficacy of siRNAs across heterogeneous data sources. Our ranking analysis revealed for the first time the most relevant features based on cross-platform experiments, which compares favorably with the traditional in-silico siRNA feature screening based on the small samples of individual platform data. We believe that our feature ranking analysis can offer more creditable suggestions to help improving the design of siRNA with specific silencing targets. Data and scripts are available at http://csbl.bmb.uga.edu/publications/materials/qiliu/siRNA.html

Improved siRNA/shRNA Functionality by Mismatched Duplex

siRNA (small interfering RNA) and shRNA (small hairpin RNA) are powerful and commonly used tools in biomedical research. Currently, siRNAs are generally designed as two 21 nt strands of RNA that include a 19 nt completely complementary part and a 2 nt overhang. However, since the si/shRNAs use the endogenous miRNA machinery for gene silencing and the miRNAs are generally 22 nt in length and contain multiple internal mismatches, we tested if the functionality can be increased by designing the si/shRNAs to mimic a miRNA structure. We systematically investigated the effect of single or multiple mismatches introduced in the passenger strand at different positions on siRNA functionality. Mismatches at certain positions could significantly increase the functionality of siRNAs and also, in some cases decreased the unwanted passenger strand functionality. The same strategy could also be used to design shRNAs. Finally, we showed that both si and miRNA structured oligos (siRNA with or without mismatches in the passenger strand) can repress targets in all individual Ago containing cells, suggesting that the Ago proteins do not differentiate between si/miRNA-based structure for silencing activity

BKV Agnoprotein Interacts with α-Soluble N-Ethylmaleimide-Sensitive Fusion Attachment Protein, and Negatively Influences Transport of VSVG-EGFP

Background: The human polyomavirus BK (BKV) infects humans worldwide and establishes a persistent infection in the kidney. The BK virus genome encodes three regulatory proteins, large and small tumor-antigen and the agnoprotein, as well as the capsid proteins VP1 to VP3. Agnoprotein is conserved among BKV, JC virus (JCV) and SV40, and agnoprotein-deficient mutants reveal reduced viral propagation. Studies with JCV and SV40 indicate that their agnoproteins may be involved in transcription, replication and/or nuclear and cellular release of the virus. However, the exact function(s) of agnoprotein of BK virus remains elusive. Principal Findings: As a strategy of exploring the functions of BKV agnoprotein, we decided to look for cellular interaction partners for the viral protein. Several partners were identified by yeast two-hybrid assay, among them a-SNAP which is involved in disassembly of vesicles during secretion. BKV agnoprotein and a-SNAP were found to partially co-localize in cells, and a complex consisting of agnoprotein and a-SNAP could be co-immunoprecipitated from cells ectopically expressing the proteins as well as from BKV-transfected cells. The N-terminal part of the agnoprotein was sufficient for the interaction with a-SNAP. Finally, we could show that BKV agnoprotein negatively interferes with secretion of VSVG-EGFP reporter suggesting that agnoprotein may modulate exocytosis. Conclusions: We have identified the first cellular interaction partner for BKV agnoprotein. The most N-terminal part of BKV agnoprotein is involved in the interaction with a-SNAP. Presence of BKV agnoprotein negatively interferes with secretion of VSVG-EGFP reporter

MysiRNA-designer: a workflow for efficient siRNA design

The design of small interfering RNA (siRNA) is a multi factorial problem that has gained the attention of many researchers in the area of therapeutic and functional genomics. MysiRNA score was previously introduced that improves the correlation of siRNA activity prediction considering state of the art algorithms. In this paper, a new program, MysiRNA-Designer, is described which integrates several factors in an automated work-flow considering mRNA transcripts variations, siRNA and mRNA target accessibility, and both near-perfect and partial off-target matches. It also features the MysiRNA score, a highly ranked correlated siRNA efficacy prediction score for ranking the designed siRNAs, in addition to top scoring models Biopredsi, DISR, Thermocomposition21 and i-Score, and integrates them in a unique siRNA score-filtration technique. This multi-score filtration layer filters siRNA that passes the 90% thresholds calculated from experimental dataset features. MysiRNA-Designer takes an accession, finds conserved regions among its transcript space, finds accessible regions within the mRNA, designs all possible siRNAs for these regions, filters them based on multi-scores thresholds, and then performs SNP and off-target filtration. These strict selection criteria were tested against human genes in which at least one active siRNA was designed from 95.7% of total genes. In addition, when tested against an experimental dataset, MysiRNA-Designer was found capable of rejecting 98% of the false positive siRNAs, showing superiority over three state of the art siRNA design programs. MysiRNA is a freely accessible (Microsoft Windows based) desktop application that can be used to design siRNA with a high accuracy and specificity. We believe that MysiRNA-Designer has the potential to play an important role in this area

Computational Design of Artificial RNA Molecules For Gene Regulation

This volume provides an overview of RNA bioinformatics methodologies, including basic strategies to predict secondary and tertiary structures, and novel algorithms based on massive RNA sequencing. Interest in RNA bioinformatics has rapidly increased thanks to the recent high-throughput sequencing technologies allowing scientists to investigate complete transcriptomes at single nucleotide resolution. Adopting advanced computational technics, scientists are now able to conduct more in-depth studies and present them to you in this book. Written in the highly successful Methods of Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and equipment, step-by-step, readily reproducible bioinformatics protocols, and key tips to avoid known pitfalls.Authoritative and practical, RNA Bioinformatics seeks to aid scientists in the further study of bioinformatics and computational biology of RNA

Expression Patterns of Protein Kinases Correlate with Gene Architecture and Evolutionary Rates

Protein kinase (PK) genes comprise the third largest superfamily that occupy ∼2% of the human genome. They encode regulatory enzymes that control a vast variety of cellular processes through phosphorylation of their protein substrates. Expression of PK genes is subject to complex transcriptional regulation which is not fully understood.Our comparative analysis demonstrates that genomic organization of regulatory PK genes differs from organization of other protein coding genes. PK genes occupy larger genomic loci, have longer introns, spacer regions, and encode larger proteins. The primary transcript length of PK genes, similar to other protein coding genes, inversely correlates with gene expression level and expression breadth, which is likely due to the necessity to reduce metabolic costs of transcription for abundant messages. On average, PK genes evolve slower than other protein coding genes. Breadth of PK expression negatively correlates with rate of non-synonymous substitutions in protein coding regions. This rate is lower for high expression and ubiquitous PKs, relative to low expression PKs, and correlates with divergence in untranslated regions. Conversely, rate of silent mutations is uniform in different PK groups, indicating that differing rates of non-synonymous substitutions reflect variations in selective pressure. Brain and testis employ a considerable number of tissue-specific PKs, indicating high complexity of phosphorylation-dependent regulatory network in these organs. There are considerable differences in genomic organization between PKs up-regulated in the testis and brain. PK genes up-regulated in the highly proliferative testicular tissue are fast evolving and small, with short introns and transcribed regions. In contrast, genes up-regulated in the minimally proliferative nervous tissue carry long introns, extended transcribed regions, and evolve slowly.PK genomic architecture, the size of gene functional domains and evolutionary rates correlate with the pattern of gene expression. Structure and evolutionary divergence of tissue-specific PK genes is related to the proliferative activity of the tissue where these genes are predominantly expressed. Our data provide evidence that physiological requirements for transcription intensity, ubiquitous expression, and tissue-specific regulation shape gene structure and affect rates of evolution

Analysis of the structure of postoperative recerrence of varicose veins of the lower extremities and the choice of tactics for their correction

The purpose of the study was to study the structure of recurrences of varicose veins of the lower extremities after endovasal laser coagulation and tactics of management of patients with recurrences.Цель исследования – изучить структуру рецидивов варикозной болезни нижних конечностей после проведения эндовазальной лазерной коагуляции и тактику ведения пациентов с рецидивами