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Find video protocols related to scientific articles indexed in Pubmed.
Identification of learning and memory genes in canine; promoter investigation and determining the selective pressure.
Mol. Biol. Rep.
PUBLISHED: 09-06-2014
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One of the important behaviors of dogs is trainability which is affected by learning and memory genes. These kinds of the genes have not yet been identified in dogs. In the current research, these genes were found in animal models by mining the biological data and scientific literatures. The proteins of these genes were obtained from the UniProt database in dogs and humans. Not all homologous proteins perform similar functions, thus comparison of these proteins was studied in terms of protein families, domains, biological processes, molecular functions, and cellular location of metabolic pathways in Interpro, KEGG, Quick Go and Psort databases. The results showed that some of these proteins have the same performance in the rat or mouse, dog, and human. It is anticipated that the protein of these genes may be effective in learning and memory in dogs. Then, the expression pattern of the recognized genes was investigated in the dog hippocampus using the existing information in the GEO profile. The results showed that BDNF, TAC1 and CCK genes are expressed in the dog hippocampus, therefore, these genes could be strong candidates associated with learning and memory in dogs. Subsequently, due to the importance of the promoter regions in gene function, this region was investigated in the above genes. Analysis of the promoter indicated that the HNF-4 site of BDNF gene and the transcription start site of CCK gene is exposed to methylation. Phylogenetic analysis of protein sequences of these genes showed high similarity in each of these three genes among the studied species. The dN/dS ratio for BDNF, TAC1 and CCK genes indicates a purifying selection during the evolution of the genes.
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Drug-target interaction prediction via chemogenomic space: learning-based methods.
Expert Opin Drug Metab Toxicol
PUBLISHED: 08-11-2014
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Identification of the interaction between drugs and target proteins is a crucial task in genomic drug discovery. The in silico prediction is an appropriate alternative for the laborious and costly experimental process of drug-target interaction prediction. Developing a variety of computational methods opens a new direction in analyzing and detecting new drug-target pairs.
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Informative Bayesian Model Selection: a method for identifying interactions in genome-wide data.
Mol Biosyst
PUBLISHED: 07-30-2014
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In high-dimensional genome-wide (GWA) data, a key challenge is to detect genomic variants that interact in a nonlinear fashion in their association with disease. Identifying such genomic interactions is important for elucidating the inheritance of complex phenotypes and diseases. In this paper, we introduce a new computational method called Informative Bayesian Model Selection (IBMS) that leverages correlation among variants in GWA data due to the linkage disequilibrium to identify interactions accurately in a computationally efficient manner. IBMS combines several statistical methods including canonical correlation analysis, logistic regression analysis, and a Bayesians statistical measure of evaluating interactions. Compared to BOOST and BEAM that are two widely used methods for detecting genomic interactions, IBMS had significantly higher power when evaluated on synthetic data. Furthermore, when applied to Alzheimer's disease GWA data, IBMS identified previously reported interactions. IBMS is a useful method for identifying variants in GWA data, and software that implements IBMS is freely available online from http://lbb.ut.ac.ir/Download/LBBsoft/IBMS.
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Cancer modeling and network biology: Accelerating toward personalized medicine.
Semin. Cancer Biol.
PUBLISHED: 06-28-2014
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The complexity of cancer progression can manifests itself on at least three scales that can be described using mathematical models, namely microscopic, mesoscopic and macroscopic scales. Multiscale cancer models have proven to be advantageous in this context because they can simultaneously incorporate the many different characteristics and scales of complex diseases such as cancer. This has driven the expansion of more predictive data-driven models, coupled to experimental and clinical data. These models are defining the foundations that facilitate the forthcoming design of patient specific cancer therapy. This should be considered as a great leap toward the era of personalized medicine. Consequently, further improvements in mathematical modeling of cancer will lead to the design of more sophisticated cancer therapy approaches.
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Distributed optical fiber dynamic magnetic field sensor based on magnetostriction.
Appl Opt
PUBLISHED: 06-13-2014
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A distributed optical fiber sensor is introduced which is capable of quantifying multiple magnetic fields along a 1 km sensing fiber with a spatial resolution of 1 m. The operation of the proposed sensor is based on measuring the magnetorestrictive induced strain of a nickel wire attached to an optical fiber. The strain coupled to the optical fiber was detected by measuring the strain-induced phase variation between the backscattered Rayleigh light from two segments of the sensing fiber. A magnetic field intensity resolution of 0.3 G over a bandwidth of 50-5000 Hz was demonstrated.
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Association of single nucleotide polymorphism of GHSR and TGFB2 genes with growth and body composition traits in sire and dam lines of a broiler chicken.
Anim. Biotechnol.
PUBLISHED: 06-06-2014
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Growth hormone secretagogue receptor (GHSR) modulates many physiological processes by binding to its ligand, as well as transforming growth factor-beta 2 (TGFB2) regulates cell growth and development in animals and, therefore, are candidate genes for performance in chickens. In the current study, single nucleotide polymorphisms of GHSR C3286 > T and TGFB2 T(-640) > C were genotyped in sire and dam lines of a broiler chicken to evaluate the association with the growth and body composition traits. Least squares means analysis showed that the GHSR C3286 > T SNP was significantly (P < 0.01) associated with growth (DFI and ADG) and body composition traits (AFW and %AFW). In addition, the TGFB2 T(-640) > C SNP was associated with ADG (P < 0.05) and DFI and body composition traits (DW, LBW, BAKWT, %BMW, %HNDWT and %CW) (P < 0.01). Significant associations of the single nucleotide polymorphisms (SNPs) on the traits reported in the present study might be the distinct usage of codons in avian, or relating to an enhancer element and modulating the expression of the gene in chicken. The data indicated that these SNPs could be valuable genetic elements for selection of chickens for better performance in the population.
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Association of neuropeptide Y gene rs16147 polymorphism with metabolic syndrome in patients with documented coronary artery disease.
Ann. Hum. Biol.
PUBLISHED: 06-05-2014
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Abstract Background and aims: There have been few epidemiological studies that have investigated genetic susceptibility to cardiovascular risk associated with the prevalence of metabolic syndrome (MetS). Neuropeptide Y (NPY) is a strong candidate gene for coronary artery disease (CAD). Therefore, the aim of this study was to investigate the association between the NPY gene rs16147 polymorphism and the presence of MetS in a well defined group of Iranian subjects with angiographically-defined CAD. Methods: A cross-sectional study design was used in which a total of 364 patients were recruited; 143 patients with MetS and 221 without MetS were genotyped using the ARMS-PCR technique. Logistic regression analyses were performed to determine the odds ratios (ORs) for the association of specific genotypes with the presence of MetS and related phenotypes. Results: The frequency of the variant G allele of the NPY gene was significantly higher in CAD patients without MetS (p?=?0.032). Compared to the AA genotype of the NPY gene, individuals carrying the GG genotype had a reduced risk of MetS (OR?=?0.51, 95% CI?=?0.27-0.95, p?=?0.034). Conclusion: The rs16147 polymorphism may be associated with presence of MetS among subjects with documented CAD. Carriage of NPY A allele in patients with CAD is associated with a higher prevalence of MetS.
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Microarray gene expression analysis of the human airway in patients exposed to sulfur mustard.
J. Recept. Signal Transduct. Res.
PUBLISHED: 05-13-2014
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There is much data about the acute effects of sulfur mustard gas on humans, animals and cells. But less is known regarding the molecular basics of chronic complications in humans. Basically, mustard gas, as an alkylating agent, causes several chronic problems in the eyes, skin and more importantly in the pulmonary system which is the main cause of death. Although recent proteomic research has been carried out on bronchoalveolar lavage (BAL) and serum, but high-throughput transcriptomics have not yet been applied to chronic airway remodeling. This is the first cDNA-microarray report on the chronic human mustard lung disease, 25 years after exposure during the Iran-Iraq war. Microarray transcriptional profiling indicated that a total of 122 genes were significantly dysregulated in tissues located in the airway of patients. These genes are associated with the extracellular matrix components, apoptosis, stress response, inflammation and mucus secretion.
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Cancer systems biology and modeling: Microscopic scale and multiscale approaches.
Semin. Cancer Biol.
PUBLISHED: 03-11-2014
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Cancer has become known as a complex and systematic disease on macroscopic, mesoscopic and microscopic scales. Systems biology employs state-of-the-art computational theories and high-throughput experimental data to model and simulate complex biological procedures such as cancer, which involves genetic and epigenetic, in addition to intracellular and extracellular complex interaction networks. In this paper, different systems biology modeling techniques such as systems of differential equations, stochastic methods, Boolean networks, Petri nets, cellular automata methods and agent-based systems are concisely discussed. We have compared the mentioned formalisms and tried to address the span of applicability they can bear on emerging cancer modeling and simulation approaches. Different scales of cancer modeling, namely, microscopic, mesoscopic and macroscopic scales are explained followed by an illustration of angiogenesis in microscopic scale of the cancer modeling. Then, the modeling of cancer cell proliferation and survival are examined on a microscopic scale and the modeling of multiscale tumor growth is explained along with its advantages.
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Predictive genomics: A cancer hallmark network framework for predicting tumor clinical phenotypes using genome sequencing data.
Semin. Cancer Biol.
PUBLISHED: 03-10-2014
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Tumor genome sequencing leads to documenting thousands of DNA mutations and other genomic alterations. At present, these data cannot be analyzed adequately to aid in the understanding of tumorigenesis and its evolution. Moreover, we have little insight into how to use these data to predict clinical phenotypes and tumor progression to better design patient treatment. To meet these challenges, we discuss a cancer hallmark network framework for modeling genome sequencing data to predict cancer clonal evolution and associated clinical phenotypes. The framework includes: (1) cancer hallmarks that can be represented by a few molecular/signaling networks. 'Network operational signatures' which represent gene regulatory logics/strengths enable to quantify state transitions and measures of hallmark traits. Thus, sets of genomic alterations which are associated with network operational signatures could be linked to the state/measure of hallmark traits. The network operational signature transforms genotypic data (i.e., genomic alterations) to regulatory phenotypic profiles (i.e., regulatory logics/strengths), to cellular phenotypic profiles (i.e., hallmark traits) which lead to clinical phenotypic profiles (i.e., a collection of hallmark traits). Furthermore, the framework considers regulatory logics of the hallmark networks under tumor evolutionary dynamics and therefore also includes: (2) a self-promoting positive feedback loop that is dominated by a genomic instability network and a cell survival/proliferation network is the main driver of tumor clonal evolution. Surrounding tumor stroma and its host immune systems shape the evolutionary paths; (3) cell motility initiating metastasis is a byproduct of the above self-promoting loop activity during tumorigenesis; (4) an emerging hallmark network which triggers genome duplication dominates a feed-forward loop which in turn could act as a rate-limiting step for tumor formation; (5) mutations and other genomic alterations have specific patterns and tissue-specificity, which are driven by aging and other cancer-inducing agents. This framework represents the logics of complex cancer biology as a myriad of phenotypic complexities governed by a limited set of underlying organizing principles. It therefore adds to our understanding of tumor evolution and tumorigenesis, and moreover, potential usefulness of predicting tumors' evolutionary paths and clinical phenotypes. Strategies of using this framework in conjunction with genome sequencing data in an attempt to predict personalized drug targets, drug resistance, and metastasis for cancer patients, as well as cancer risks for healthy individuals are discussed. Accurate prediction of cancer clonal evolution and clinical phenotypes will have substantial impact on timely diagnosis, personalized treatment and personalized prevention of cancer.
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Genome scale modeling in systems biology: algorithms and resources.
Curr. Genomics
PUBLISHED: 02-16-2014
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In recent years, in silico studies and trial simulations have complemented experimental procedures. A model is a description of a system, and a system is any collection of interrelated objects; an object, moreover, is some elemental unit upon which observations can be made but whose internal structure either does not exist or is ignored. Therefore, any network analysis approach is critical for successful quantitative modeling of biological systems. This review highlights some of most popular and important modeling algorithms, tools, and emerging standards for representing, simulating and analyzing cellular networks in five sections. Also, we try to show these concepts by means of simple example and proper images and graphs. Overall, systems biology aims for a holistic description and understanding of biological processes by an integration of analytical experimental approaches along with synthetic computational models. In fact, biological networks have been developed as a platform for integrating information from high to low-throughput experiments for the analysis of biological systems. We provide an overview of all processes used in modeling and simulating biological networks in such a way that they can become easily understandable for researchers with both biological and mathematical backgrounds. Consequently, given the complexity of generated experimental data and cellular networks, it is no surprise that researchers have turned to computer simulation and the development of more theory-based approaches to augment and assist in the development of a fully quantitative understanding of cellular dynamics.
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WITHDRAWN: Cancer modeling: The holonic agent-based approach.
Semin. Cancer Biol.
PUBLISHED: 02-14-2014
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This article has been withdrawn at the request of the author(s) and/or editor. The Publisher apologizes for any inconvenience this may cause. The full Elsevier Policy on Article Withdrawal can be found at http://www.elsevier.com/locate/withdrawalpolicy.
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Comparison of Culture and PCR Methods for Diagnosis of Mycobacterium tuberculosis in Different Clinical Specimens.
Jundishapur J Microbiol
PUBLISHED: 02-01-2014
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Tuberculosis remains a global epidemic, especially in developing countries, including Iran. Rapid diagnosis of active Mycobacterium tuberculosis infection plays a critical role in controlling the spread of tuberculosis. Conventional methods may take up to several weeks or longer to produce results. In addition to multiplicity of steps involved in conventional detection, including isolation, identification and drug susceptibility testing, the slow growth rate of M. tuberculosis is also responsible for this lengthy time.
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Metabolic Cancer Biology: Structural-based analysis of cancer as a metabolic disease, new sights and opportunities for disease treatment.
Semin. Cancer Biol.
PUBLISHED: 01-15-2014
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The cancer cell metabolism or the Warburg effect discovery goes back to 1924 when, for the first time Otto Warburg observed, in contrast to the normal cells, cancer cells have different metabolism. With the initiation of high throughput technologies and computational systems biology, cancer cell metabolism renaissances and many attempts were performed to revise the Warburg effect. The development of experimental and analytical tools which generate high-throughput biological data including lots of information could lead to application of computational models in biological discovery and clinical medicine especially for cancer. Due to the recent availability of tissue-specific reconstructed models, new opportunities in studying metabolic alteration in various kinds of cancers open up. Structural approaches at genome-scale levels seem to be suitable for developing diagnostic and prognostic molecular signatures, as well as in identifying new drug targets. In this review, we have considered these recent advances in structural-based analysis of cancer as a metabolic disease view. Two different structural approaches have been described here: topological and constraint-based methods. The ultimate goal of this type of systems analysis is not only the discovery of novel drug targets but also the development of new systems-based therapy strategies.
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Computational Analysis of Reciprocal Association of Metabolism and Epigenetics in the Budding Yeast: A Genome-Scale Metabolic Model (GSMM) Approach.
PLoS ONE
PUBLISHED: 01-01-2014
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Metaboloepigenetics is a newly coined term in biological sciences that investigates the crosstalk between epigenetic modifications and metabolism. The reciprocal relation between biochemical transformations and gene expression regulation has been experimentally demonstrated in cancers and metabolic syndromes. In this study, we explored the metabolism-histone modifications crosstalk by topological analysis and constraint-based modeling approaches in the budding yeast. We constructed nine models through the integration of gene expression data of four mutated histone tails into a genome-scale metabolic model of yeast. Accordingly, we defined the centrality indices of the lowly expressed enzymes in the undirected enzyme-centric network of yeast by CytoHubba plug-in in Cytoscape. To determine the global effects of histone modifications on the yeast metabolism, the growth rate and the range of possible flux values of reactions, we used constraint-based modeling approach. Centrality analysis shows that the lowly expressed enzymes could affect and control the yeast metabolic network. Besides, constraint-based modeling results are in a good agreement with the experimental findings, confirming that the mutations in histone tails lead to non-lethal alterations in the yeast, but have diverse effects on the growth rate and reveal the functional redundancy.
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Genome-scale co-expression network comparison across Escherichia coli and Salmonella enterica serovar Typhimurium reveals significant conservation at the regulon level of local regulators despite their dissimilar lifestyles.
PLoS ONE
PUBLISHED: 01-01-2014
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Availability of genome-wide gene expression datasets provides the opportunity to study gene expression across different organisms under a plethora of experimental conditions. In our previous work, we developed an algorithm called COMODO (COnserved MODules across Organisms) that identifies conserved expression modules between two species. In the present study, we expanded COMODO to detect the co-expression conservation across three organisms by adapting the statistics behind it. We applied COMODO to study expression conservation/divergence between Escherichia coli, Salmonella enterica, and Bacillus subtilis. We observed that some parts of the regulatory interaction networks were conserved between E. coli and S. enterica especially in the regulon of local regulators. However, such conservation was not observed between the regulatory interaction networks of B. subtilis and the two other species. We found co-expression conservation on a number of genes involved in quorum sensing, but almost no conservation for genes involved in pathogenicity across E. coli and S. enterica which could partially explain their different lifestyles. We concluded that despite their different lifestyles, no significant rewiring have occurred at the level of local regulons involved for instance, and notable conservation can be detected in signaling pathways and stress sensing in the phylogenetically close species S. enterica and E. coli. Moreover, conservation of local regulons seems to depend on the evolutionary time of divergence across species disappearing at larger distances as shown by the comparison with B. subtilis. Global regulons follow a different trend and show major rewiring even at the limited evolutionary distance that separates E. coli and S. enterica.
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Pathway reconstruction of airway remodeling in chronic lung diseases: a systems biology approach.
PLoS ONE
PUBLISHED: 01-01-2014
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Airway remodeling is a pathophysiologic process at the clinical, cellular, and molecular level relating to chronic obstructive airway diseases such as chronic obstructive pulmonary disease (COPD), asthma and mustard lung. These diseases are associated with the dysregulation of multiple molecular pathways in the airway cells. Little progress has so far been made in discovering the molecular causes of complex disease in a holistic systems manner. Therefore, pathway and network reconstruction is an essential part of a systems biology approach to solve this challenging problem. In this paper, multiple data sources were used to construct the molecular process of airway remodeling pathway in mustard lung as a model of airway disease. We first compiled a master list of genes that change with airway remodeling in the mustard lung disease and then reconstructed the pathway by generating and merging the protein-protein interaction and the gene regulatory networks. Experimental observations and literature mining were used to identify and validate the master list. The outcome of this paper can provide valuable information about closely related chronic obstructive airway diseases which are of great importance for biologists and their future research. Reconstructing the airway remodeling interactome provides a starting point and reference for the future experimental study of mustard lung, and further analysis and development of these maps will be critical to understanding airway diseases in patients.
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Discovery of Inhibitors of Burkholderia pseudomallei Methionine Aminopeptidase with Antibacterial Activity.
ACS Med Chem Lett
PUBLISHED: 12-31-2013
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Evaluation of a series of MetAP inhibitors in an in vitro enzyme activity assay led to the first identification of potent molecules that show significant growth inhibition against Burkholderia pseudomallei. Nitroxoline analogs show excellent inhibition potency in the BpMetAP1 enzyme activity assay with the lowest IC50 of 30 nM, and inhibit the growth of B. pseudomallei and B. thailandensis at concentrations ? 31 ?M.
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Distributed dynamic large strain optical fiber sensor based on the detection of spontaneous Brillouin scattering.
Opt Lett
PUBLISHED: 08-31-2013
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A Brillouin-based distributed optical fiber dynamic strain sensor is described which converts strain-induced Brillouin frequency shift into optical intensity variations by using an imbalanced Mach-Zhender interferometer. A 3×3 coupler is used at the output of this interferometer to permit differentiate and cross multiply demodulation. The demonstrated sensor is capable of probing dynamic strain disturbances over 2 km of sensing length every 0.5 s up to a strain of 10 m? with an accuracy of ±50 ?? and spatial resolution of 1.3 m.
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Computational structure analysis of biomacromolecule complexes by interface geometry.
Comput Biol Chem
PUBLISHED: 06-11-2013
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The ability to analyze and compare protein-nucleic acid and protein-protein interaction interface has critical importance in understanding the biological function and essential processes occurring in the cells. Since high-resolution three-dimensional (3D) structures of biomacromolecule complexes are available, computational characterizing of the interface geometry become an important research topic in the field of molecular biology. In this study, the interfaces of a set of 180 protein-nucleic acid and protein-protein complexes are computed to understand the principles of their interactions. The weighted Voronoi diagram of the atoms and the Alpha complex has provided an accurate description of the interface atoms. Our method is implemented in the presence and absence of water molecules. A comparison among the three types of interaction interfaces show that RNA-protein complexes have the largest size of an interface. The results show a high correlation coefficient between our method and the PISA server in the presence and absence of water molecules in the Voronoi model and the traditional model based on solvent accessibility and the high validation parameters in comparison to the classical model.
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Discovering dominant pathways and signal-response relationships in signaling networks through nonparametric approaches.
Genomics
PUBLISHED: 06-10-2013
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A signaling pathway is a sequence of proteins and passenger molecules that transmits information from the cell surface to target molecules. Understanding signal transduction process requires detailed description of the involved pathways. Several methods and tools resolved this problem by incorporating genomic and proteomic data. However, the difficulty of obtaining prior knowledge of complex signaling networks limited the applicability of these tools. In this study, based on the simulation of signal flow in signaling network, we introduce a method for determining dominant pathways and signal response to stimulations. The model uses topology-weighted transit compartment approach and comprises four main steps which include weighting the edges, simulating signal transduction in the network (weighting the nodes), finding paths between initial and target nodes, and assigning a significance score to each path. We applied the proposed model to eighty-three signaling networks by using biologically derived source and sink molecules. The recovered dominant paths matched many known signaling pathways and suggesting a promising index to analyze the phenotype essentiality of molecule encoding paths. We also modeled the stimulus-response relations in long and short-term synaptic plasticity based on the dominant signaling pathway concept. We showed that the proposed method not only accurately determines dominant signaling pathways, but also identifies effective points of intervention in signal transduction.
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Human RNAi pathway: crosstalk with organelles and cells.
Funct. Integr. Genomics
PUBLISHED: 05-06-2013
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Understanding gene regulation mechanisms has been a serious challenge in biology. As a novel mechanism, small non-coding RNAs are an alternative means of gene regulation in a specific and efficient manner. There are growing reports on regulatory roles of these RNAs including transcriptional gene silencing/activation and post-transcriptional gene silencing events. Also, there are several known small non-coding RNAs which all work through RNA interference pathway. Interestingly, these small RNAs are secreted from cells toward targeted cells presenting new communication approach in cell-cell or cell-organ signal transduction. In fact, understanding cellular and molecular basis of these pathways will strongly improve developing targeted therapies and potent and specific regulatory tools. This study will review some of the most recent findings in this subject and will introduce a super-pathway RNA interference-based small RNA silencing network.
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Chasing acyl carrier protein through a catalytic cycle of lipid A production.
Nature
PUBLISHED: 04-15-2013
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Acyl carrier protein represents one of the most highly conserved proteins across all domains of life and is natures way of transporting hydrocarbon chains in vivo. Notably, type II acyl carrier proteins serve as a crucial interaction hub in primary cellular metabolism by communicating transiently between partner enzymes of the numerous biosynthetic pathways. However, the highly transient nature of such interactions and the inherent conformational mobility of acyl carrier protein have stymied previous attempts to visualize structurally acyl carrier protein tied to an overall catalytic cycle. This is essential to understanding a fundamental aspect of cellular metabolism leading to compounds that are not only useful to the cell, but also of therapeutic value. For example, acyl carrier protein is central to the biosynthesis of the lipid A (endotoxin) component of lipopolysaccharides in Gram-negative microorganisms, which is required for their growth and survival, and is an activator of the mammalian hosts immune system, thus emerging as an important therapeutic target. During lipid A synthesis (Raetz pathway), acyl carrier protein shuttles acyl intermediates linked to its prosthetic 4-phosphopantetheine group among four acyltransferases, including LpxD. Here we report the crystal structures of three forms of Escherichia coli acyl carrier protein engaging LpxD, which represent stalled substrate and liberated products along the reaction coordinate. The structures show the intricate interactions at the interface that optimally position acyl carrier protein for acyl delivery and that directly involve the pantetheinyl group. Conformational differences among the stalled acyl carrier proteins provide the molecular basis for the association-dissociation process. An unanticipated conformational shift of 4-phosphopantetheine groups within the LpxD catalytic chamber shows an unprecedented role of acyl carrier protein in product release.
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Glucosamine alleviates scopolamine induced spatial learning and memory deficits in rats.
Pathophysiology
PUBLISHED: 04-14-2013
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Beneficial effects of glucosamine in spatial learning and memory impairment induced by scopolamine has been evaluated in rats by using Morris water maze.
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PPIevo: protein-protein interaction prediction from PSSM based evolutionary information.
Genomics
PUBLISHED: 01-26-2013
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Protein-protein interactions regulate a variety of cellular processes. There is a great need for computational methods as a complement to experimental methods with which to predict protein interactions due to the existence of many limitations involved in experimental techniques. Here, we introduce a novel evolutionary based feature extraction algorithm for protein-protein interaction (PPI) prediction. The algorithm is called PPIevo and extracts the evolutionary feature from Position-Specific Scoring Matrix (PSSM) of protein with known sequence. The algorithm does not depend on the protein annotations, and the features are based on the evolutionary history of the proteins. This enables the algorithm to have more power for predicting protein-protein interaction than many sequence based algorithms. Results on the HPRD database show better performance and robustness of the proposed method. They also reveal that the negative dataset selection could lead to an acute performance overestimation which is the principal drawback of the available methods.
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Modified frontalis sling procedure with lid crease formation.
J Ophthalmic Vis Res
PUBLISHED: 01-15-2013
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To report the results of a modified frontalis sling procedure using Mersilene mesh for correction of upper lid ptosis associated with poor levator muscle function.
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Controllability in cancer metabolic networks according to drug targets as driver nodes.
PLoS ONE
PUBLISHED: 01-01-2013
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Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.
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C-element: a new clustering algorithm to find high quality functional modules in PPI networks.
PLoS ONE
PUBLISHED: 01-01-2013
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Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithms result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.
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QuateXelero: an accelerated exact network motif detection algorithm.
PLoS ONE
PUBLISHED: 01-01-2013
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Finding motifs in biological, social, technological, and other types of networks has become a widespread method to gain more knowledge about these networks structure and function. However, this task is very computationally demanding, because it is highly associated with the graph isomorphism which is an NP problem (not known to belong to P or NP-complete subsets yet). Accordingly, this research is endeavoring to decrease the need to call NAUTY isomorphism detection method, which is the most time-consuming step in many existing algorithms. The work provides an extremely fast motif detection algorithm called QuateXelero, which has a Quaternary Tree data structure in the heart. The proposed algorithm is based on the well-known ESU (FANMOD) motif detection algorithm. The results of experiments on some standard model networks approve the overal superiority of the proposed algorithm, namely QuateXelero, compared with two of the fastest existing algorithms, G-Tries and Kavosh. QuateXelero is especially fastest in constructing the central data structure of the algorithm from scratch based on the input network.
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Reconstruction of an integrated genome-scale co-expression network reveals key modules involved in lung adenocarcinoma.
PLoS ONE
PUBLISHED: 01-01-2013
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Our goal of this study was to reconstruct a "genome-scale co-expression network" and find important modules in lung adenocarcinoma so that we could identify the genes involved in lung adenocarcinoma. We integrated gene mutation, GWAS, CGH, array-CGH and SNP array data in order to identify important genes and loci in genome-scale. Afterwards, on the basis of the identified genes a co-expression network was reconstructed from the co-expression data. The reconstructed network was named "genome-scale co-expression network". As the next step, 23 key modules were disclosed through clustering. In this study a number of genes have been identified for the first time to be implicated in lung adenocarcinoma by analyzing the modules. The genes EGFR, PIK3CA, TAF15, XIAP, VAPB, Appl1, Rab5a, ARF4, CLPTM1L, SP4, ZNF124, LPP, FOXP1, SOX18, MSX2, NFE2L2, SMARCC1, TRA2B, CBX3, PRPF6, ATP6V1C1, MYBBP1A, MACF1, GRM2, TBXA2R, PRKAR2A, PTK2, PGF and MYO10 are among the genes that belong to modules 1 and 22. All these genes, being implicated in at least one of the phenomena, namely cell survival, proliferation and metastasis, have an over-expression pattern similar to that of EGFR. In few modules, the genes such as CCNA2 (Cyclin A2), CCNB2 (Cyclin B2), CDK1, CDK5, CDC27, CDCA5, CDCA8, ASPM, BUB1, KIF15, KIF2C, NEK2, NUSAP1, PRC1, SMC4, SYCE2, TFDP1, CDC42 and ARHGEF9 are present that play a crucial role in cell cycle progression. In addition to the mentioned genes, there are some other genes (i.e. DLGAP5, BIRC5, PSMD2, Src, TTK, SENP2, PSMD2, DOK2, FUS and etc.) in the modules.
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Genome-scale computational analysis of DNA curvature and repeats in Arabidopsis and rice uncovers plant-specific genomic properties.
BMC Genomics
PUBLISHED: 05-06-2011
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Due to its overarching role in genome function, sequence-dependent DNA curvature continues to attract great attention. The DNA double helix is not a rigid cylinder, but presents both curvature and flexibility in different regions, depending on the sequence. More in depth knowledge of the various orders of complexity of genomic DNA structure has allowed the design of sophisticated bioinformatics tools for its analysis and manipulation, which, in turn, have yielded a better understanding of the genome itself. Curved DNA is involved in many biologically important processes, such as transcription initiation and termination, recombination, DNA replication, and nucleosome positioning. CpG islands and tandem repeats also play significant roles in the dynamics and evolution of genomes.
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Emerging roles of epigenetic mechanisms in Parkinsons disease.
Funct. Integr. Genomics
PUBLISHED: 04-30-2011
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Epigenetic mechanisms have emerged as important components of a variety of human diseases, including cancer and central nervous system disorders. Despite recent studies highlighting the role of epigenetic mechanisms in several neurodegenerative and neuropsychiatric disorders, to date, there has been a paucity of studies exploring the role of epigenetic factors in Parkinsons disease (PD). PD is a progressive neurological disorder with characteristic motor and non-motor symptoms, including a range of neuropsychiatric features, for which neither preventative nor effective long-term treatment strategies are available. It is one of the most common neurodegenerative disorders and the second most prevalent after Alzheimers disease. In this review, we present several lines of evidence suggesting that epigenetic factors may play an important role in the pathogenesis of PD and propose on this basis a framework to guide future investigations into epigenetic mechanisms and systems biology of PD. These notions, together with technical advances in the ability to perform genome-wide analysis of epigenomic states, and newly available small-molecule probes targeting chromatin-modifying enzymes, may help design new treatment strategies for PD and other human diseases involving epigenetic dysregulation.
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RNAi pathway integration in Caenorhabditis elegans development.
Funct. Integr. Genomics
PUBLISHED: 04-30-2011
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In this review, the pathways involving small RNAs are provided followed by a new and updated network that illustrates their interplay with diverse cellular mechanisms in Caenorhabditis elegans. The RNA silencing pathways are now recognized as key factors that connect together the many variations in biological processes, including transcriptional gene regulation, post-transcriptional gene silencing, translational gene silencing, apoptosis, meiosis, and antiviral defense. The utilization of small RNAs represents a specific, energy conserving, and fast mechanism of gene regulation via a core system known as RNA interference.
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Computational analysis of RNA-protein interaction interfaces via the Voronoi diagram.
J. Theor. Biol.
PUBLISHED: 03-01-2011
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Cellular functions are mediated by various biological processes including biomolecular interactions, such as protein-protein, DNA-protein and RNA-protein interactions in which RNA-Protein interactions are indispensable for many biological processes like cell development and viral replication. Unlike the protein-protein and protein-DNA interactions, accurate mechanisms and structures of the RNA-Protein complexes are not fully understood. A large amount of theoretical evidence have shown during the past several years that computational geometry is the first pace in understanding the binding profiles and plays a key role in the study of intricate biological structures, interactions and complexes. In this paper, RNA-Protein interaction interface surface is computed via the weighted Voronoi diagram of atoms. Using two filter operations provides a natural definition for interface atoms as classic methods. Unbounded parts of Voronoi facets that are far from the complex are trimmed using modified convex hull of atom centers. This algorithm is implemented to a database with different RNA-Protein complexes extracted from Protein Data Bank (PDB). Afterward, the features of interfaces have been computed and compared with classic method. The results show high correlation coefficients between interface size in the Voronoi model and the classical model based on solvent accessibility, as well as high accuracy and precision in comparison to classical model.
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Escherichia coli mutants that synthesize dephosphorylated lipid A molecules.
Biochemistry
PUBLISHED: 08-28-2010
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The lipid A moiety of Escherichia coli lipopolysaccharide is a hexaacylated disaccharide of glucosamine that is phosphorylated at the 1 and 4 positions. Expression of the Francisella novicida lipid A 1-phosphatase FnLpxE in E. coli results in dephosphorylation of the lipid A proximal unit. Coexpression of FnLpxE and the Rhizobium leguminosarum lipid A oxidase RlLpxQ in E. coli converts much of the proximal glucosamine to 2-amino-2-deoxygluconate. Expression of the F. novicida lipid A 4-phosphatase FnLpxF in wild-type E. coli has no effect because FnLpxF cannot dephosphorylate hexaacylated lipid A. However, expression of FnLpxF in E. coli lpxM mutants, which synthesize pentaacylated lipid A lacking the secondary 3-myristate chain, causes extensive 4-dephosphorylation. Coexpression of FnLpxE and FnLpxF in lpxM mutants results in massive accumulation of lipid A species lacking both phosphate groups, and introduction of RlLpxQ generates phosphate-free lipid A variants containing 2-amino-2-deoxygluconate. The proposed lipid A structures were confirmed by electrospray ionization mass spectrometry. Strains with 4-dephosphorylated lipid A display increased polymyxin resistance. Heptose-deficient mutants of E. coli lacking both the 1- and 4-phosphate moieties are viable on plates but sensitive to CaCl(2). Our methods for reengineering lipid A structure may be useful for generating novel vaccines and adjuvants.
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Molecular typing of Epidermophyton floccosum isolated from patients with dermatophytosis by RAPD-PCR.
J. Basic Microbiol.
PUBLISHED: 03-10-2010
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We evaluated the ability of randomly amplified polymorphic DNA (RAPD) to type Epidermophyton floccosum isolates recovered from patients with dermatophytosis originating from different regions of Iran. A total of 13 clinical isolates of E. floccosum obtained from Iranian patients were analyzed by RAPD with 7 arbitrary primers (OPN16, OPD18 OPU15, OPX19, R28, OPA04 and OPAA17). Among the applied primers, OPN16 produced banding patterns from all the isolates. In addition, some of the isolates had very close relation. The phenon line which represented the mean similarities was at the value of 73%. At this level, 4 groups were characterized. Two isolates of a patient had different molecular patterns, suggesting infection transmission from different sources in the case of a single patient. RAPD-PCR provided a rapid and practical tool for identification of E. floccosum isolates, which was independent of morphological characteristics, and enhanced laboratory diagnosis of dermatophytosis.
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Exclusion of NEU1 and PPGB from candidate genes for a lysosomal storage disease in Japanese Black cattle.
Anim. Sci. J.
PUBLISHED: 12-13-2009
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A case of lysosomal storage disease has been reported in a calf of Japanese Black cattle. Lysosomal storage diseases are hereditary diseases caused by deficiency of lysosomal hydrolases. The clinical and pathological features and accumulated substrates of the affected animal indicated a possibility of sialidosis or galactosialidosis caused by deficiency of neuraminidase (NEU1) or protective protein for beta-galactosidase (PPGB). In the present study, we investigated nucleotide sequences of the genes encoding these two proteins to evaluate whether mutation of these genes is involved in this disease. We determined cattle genomic sequences of these two genes by using bovine EST sequences and the nucleotide sequences of all exons of these genes were compared between affected and normal animals. The results showed several nucleotide substitutions, but none of them was a functional mutation or specific to the affected animal. Furthermore, genotyping of the microsatellite markers in the vicinity of these two genes revealed no homozygosity of the chromosomal regions including these genes in the affected animal. These findings indicated that neither NEU1 nor PPGB gene is responsible for the lysosomal storage disease of Japanese Black cattle and therefore the disease is neither sialidosis nor galactosialidosis.
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N4: a precise and highly sensitive promoter predictor using neural network fed by nearest neighbors.
Genes Genet. Syst.
PUBLISHED: 09-06-2009
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Promoters, the genomic regions proximal to the transcriptional start sites (TSSs) play pivotal roles in determining the rate of transcription initiation by serving as direct docking platforms for the RNA polymerase II complex. In the post-genomic era, correct gene prediction has become one of the biggest challenges in genome annotation. Species-independent promoter prediction tools could also be useful in meta-genomics, since transcription data will not be available for micro-organisms which are not cultivated. Promoter prediction in prokaryotic genomes presents unique challenges owing to their organizational properties. Several methods have been developed to predict the promoter regions of genomes in prokaryotes, including algorithms for recognition of sequence motifs, artificial neural networks, and algorithms based on genomes structure. However, none of them satisfies both criteria of sensitivity and precision. In this work, we present a modified artificial neural network fed by nearest neighbors based on DNA duplex stability, named N4, which can predict the transcription start sites of Escherichia coli with sensitivity and precision both above 94%, better than most of the existed algorithms.
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MODA: an efficient algorithm for network motif discovery in biological networks.
Genes Genet. Syst.
PUBLISHED: 08-27-2009
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In recent years, interest has been growing in the study of complex networks. Since Erdös and Rényi (1960) proposed their random graph model about 50 years ago, many researchers have investigated and shaped this field. Many indicators have been proposed to assess the global features of networks. Recently, an active research area has developed in studying local features named motifs as the building blocks of networks. Unfortunately, network motif discovery is a computationally hard problem and finding rather large motifs (larger than 8 nodes) by means of current algorithms is impractical as it demands too much computational effort. In this paper, we present a new algorithm (MODA) that incorporates techniques such as a pattern growth approach for extracting larger motifs efficiently. We have tested our algorithm and found it able to identify larger motifs with more than 8 nodes more efficiently than most of the current state-of-the-art motif discovery algorithms. While most of the algorithms rely on induced subgraphs as motifs of the networks, MODA is able to extract both induced and non-induced subgraphs simultaneously. The MODA source code is freely available at: http://LBB.ut.ac.ir/Download/LBBsoft/MODA/
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Reconstruction of Arabidopsis thaliana fully integrated small RNA pathway.
Funct. Integr. Genomics
PUBLISHED: 07-28-2009
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Small non-protein-coding RNAs (ncRNAs) have acquired diverse functions in plant and animals development. These riboregulators have been confirmed to cooperate in plant growth and development in the processes of moderating the levels of specific mRNAs and their translation products through to chromatin remodeling and silencing. According to the specific needs for interpreting and annotating small-RNA-based regulatory networks, it is evident that many additional components remain to be discovered and functionally characterized. Shedding light on the remaining components and uncovering the relation of the small-RNA-generating pathways with the synthesis of other components such as hormones and transcription factors define future challenges as more complicated mechanisms are expected to be characterized. In this review, we summarize our current understanding of the molecular and biological roles of ncRNAs in gene regulation/expression, describe in detail the specific and shared components for each pathway and highlight the advances in currently identified relationships and interactions between small ncRNAs. We present a fully integrated pathway connecting ncRNA entities such as miRNAs, siRNAs, ta-siRNAs, and nat-siRNAs, to each other within Arabidopsis thaliana.
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Kavosh: a new algorithm for finding network motifs.
BMC Bioinformatics
PUBLISHED: 03-17-2009
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Complex networks are studied across many fields of science and are particularly important to understand biological processes. Motifs in networks are small connected sub-graphs that occur significantly in higher frequencies than in random networks. They have recently gathered much attention as a useful concept to uncover structural design principles of complex networks. Existing algorithms for finding network motifs are extremely costly in CPU time and memory consumption and have practically restrictions on the size of motifs.
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HomoTarget: A new algorithm for prediction of microRNA targets in Homo sapiens.
Genomics
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MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates.
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Modeling of tumor progression in NSCLC and intrinsic resistance to TKI in loss of PTEN expression.
PLoS ONE
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EGFR signaling plays a very important role in NSCLC. It activates Ras/ERK, PI3K/Akt and STAT activation pathways. These are the main pathways for cell proliferation and survival. We have developed two mathematical models to relate to the different EGFR signaling in NSCLC and normal cells in the presence or absence of EGFR and PTEN mutations. The dynamics of downstream signaling pathways vary in the disease state and activation of some factors can be indicative of drug resistance. Our simulation denotes the effect of EGFR mutations and increased expression of certain factors in NSCLC EGFR signaling on each of the three pathways where levels of pERK, pSTAT and pAkt are increased. Over activation of ERK, Akt and STAT3 which are the main cell proliferation and survival factors act as promoting factors for tumor progression in NSCLC. In case of loss of PTEN, Akt activity level is considerably increased. Our simulation results show that in the presence of erlotinib, downstream factors i.e. pAkt, pSTAT3 and pERK are inhibited. However, in case of loss of PTEN expression in the presence of erlotinib, pAkt level would not decrease which demonstrates that these cells are resistant to erlotinib.
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CytoKavosh: a cytoscape plug-in for finding network motifs in large biological networks.
PLoS ONE
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Network motifs are small connected sub-graphs that have recently gathered much attention to discover structural behaviors of large and complex networks. Finding motifs with any size is one of the most important problems in complex and large networks. It needs fast and reliable algorithms and tools for achieving this purpose. CytoKavosh is one of the best choices for finding motifs with any given size in any complex network. It relies on a fast algorithm, Kavosh, which makes it faster than other existing tools. Kavosh algorithm applies some well known algorithmic features and includes tricky aspects, which make it an efficient algorithm in this field. CytoKavosh is a Cytoscape plug-in which supports us in finding motifs of given size in a network that is formerly loaded into the Cytoscape work-space (directed or undirected). High performance of CytoKavosh is achieved by dynamically linking highly optimized functions of Kavoshs C++ to the Cytoscape Java program, which makes this plug-in suitable for analyzing large biological networks. Some significant attributes of CytoKavosh is efficiency in time usage and memory and having no limitation related to the implementation in motif size. CytoKavosh is implemented in a visual environment Cytoscape that is convenient for the users to interact and create visual options to analyze the structural behavior of a network. This plug-in can work on any given network and is very simple to use and generates graphical results of discovered motifs with any required details. There is no specific Cytoscape plug-in, specific for finding the network motifs, based on original concept. So, we have introduced for the first time, CytoKavosh as the first plug-in, and we hope that this plug-in can be improved to cover other options to make it the best motif-analyzing tool.
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Candidate gene prioritization.
Mol. Genet. Genomics
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Candidate gene identification is typically labour intensive, involving laboratory experiments required to corroborate or disprove any hypothesis for a nominated candidate gene being considered the causative gene. The traditional approach to reduce the number of candidate genes entails fine-mapping studies using markers and pedigrees. Gene prioritization establishes the ranking of candidate genes based on their relevance to the biological process of interest, from which the most promising genes can be selected for further analysis. To date, many computational methods have focused on the prediction of candidate genes by analysis of their inherent sequence characteristics and similarity with respect to known disease genes, as well as their functional annotation. In the last decade, several computational tools for prioritizing candidate genes have been proposed. A large number of them are web-based tools, while others are standalone applications that install and run locally. This review attempts to take a close look at gene prioritization criteria, as well as candidate gene prioritization algorithms, and thus provide a comprehensive synopsis of the subject matter.
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Nonparametric simulation of signal transduction networks with semi-synchronized update.
PLoS ONE
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Simulating signal transduction in cellular signaling networks provides predictions of network dynamics by quantifying the changes in concentration and activity-level of the individual proteins. Since numerical values of kinetic parameters might be difficult to obtain, it is imperative to develop non-parametric approaches that combine the connectivity of a network with the response of individual proteins to signals which travel through the network. The activity levels of signaling proteins computed through existing non-parametric modeling tools do not show significant correlations with the observed values in experimental results. In this work we developed a non-parametric computational framework to describe the profile of the evolving process and the time course of the proportion of active form of molecules in the signal transduction networks. The model is also capable of incorporating perturbations. The model was validated on four signaling networks showing that it can effectively uncover the activity levels and trends of response during signal transduction process.
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Bioinformatics study of the 3-hydroxy-3-methylglotaryl-coenzyme A reductase (HMGR) gene in Gramineae.
Mol. Biol. Rep.
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Isoprenoids or terpenoids are synthesized by two important units including dimethylallyl diphosphate and isopentenyl diphosphate (IPP). Plants use two different methods for formation of IPP, which is a cytosolic and a plastidial method. The 3-hydroxy-3-methylglutaryl-CoA reductase (HMGR, EC 1.1.1.34) catalyzes the conversion of HMG-CoA to mevalonate, which is the first stage in the cytosolic pathway for biosynthesis of isoprenoid in plants. In this study, a total of fifty HMGR protein sequences from Gramineae and three animal samples including human, mouse and fruit fly were aligned and analyzed by computational tools to predict the protein properties, such as molecular mass, pI, signal peptide, transmembrane and conserved domains, secondary and spatial structures. Sequence comparison analysis revealed that there is high identity between plants and animals. Three catalytic regions including L domain, N domain and S domain were detected by structural modeling of HMGR. The tertiary structure model of Oryza sativa HMGR (Accession Number: NP_001063541) was further checked by PROCHECK algorithm, and showed that 90.3 % of the amino acid residues were located in the most favored regions in Ramachandran plot, indicating that the simulated three-dimensional structure was reliable. Phylogenetic analysis indicated that there is a relationship among species of Gramineae and other organisms. According to these results, HMGRs should be derived from a common ancestor.
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Molecular epidemiology of hepatitis C virus among injection drug users in Iran: a slight change in prevalence of HCV genotypes over time.
Arch. Virol.
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Injecting drug users (IDUs) are the main at-risk population for hepatitis C virus (HCV) transmission. We studied HCV infection, risk factors, and genotype distribution in relation to the year of first injection among Iranian IDUs. Of a total of 126 specimens positive for HCV antibody, 93 (74 %) had detectible HCV RNA, and the NS5B gene was sequenced for 83, with genotype 3a (n = 48, 58 %) being predominant, followed by 1a (n = 35, 42 %). Tattooing was an independent predictor for HCV infection. No significant difference was found between HCV genotypes and IDU characteristics. Although there was no change in the distribution of prevalent genotypes before and after 1997, a slight variation in the prevalence was observed (p = 0.71). The difference in the prevalence of subtypes 1a and 3a (9.1 % in the period 1984-1996 and 18.2 % in the period 1997-2009) during 25 years was 9.1 %. These findings indicate a high prevalence of HCV infection among Iranian IDUs and highlights HCV-3a as the most prevalent subtype for the past 25 years. Harm-reduction strategies appear to be the most important measures to reduce the transmission of HCV in Iran.
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LPS remodeling is an evolved survival strategy for bacteria.
Proc. Natl. Acad. Sci. U.S.A.
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Maintenance of membrane function is essential and regulated at the genomic, transcriptional, and translational levels. Bacterial pathogens have a variety of mechanisms to adapt their membrane in response to transmission between environment, vector, and human host. Using a well-characterized model of lipid A diversification (Francisella), we demonstrate temperature-regulated membrane remodeling directed by multiple alleles of the lipid A-modifying N-acyltransferase enzyme, LpxD. Structural analysis of the lipid A at environmental and host temperatures revealed that the LpxD1 enzyme added a 3-OH C18 acyl group at 37 °C (host), whereas the LpxD2 enzyme added a 3-OH C16 acyl group at 18 °C (environment). Mutational analysis of either of the individual Francisella lpxD genes altered outer membrane (OM) permeability, antimicrobial peptide, and antibiotic susceptibility, whereas only the lpxD1-null mutant was attenuated in mice and subsequently exhibited protection against a lethal WT challenge. Additionally, growth-temperature analysis revealed transcriptional control of the lpxD genes and posttranslational control of the LpxD1 and LpxD2 enzymatic activities. These results suggest a direct mechanism for LPS/lipid A-level modifications resulting in alterations of membrane fluidity, as well as integrity and may represent a general paradigm for bacterial membrane adaptation and virulence-state adaptation.
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Patterns Prediction of Chemotherapy Sensitivity in Cancer Cell lines Using FTIR Spectrum, Neural Network and Principal Components Analysis.
Iran J Pharm Res
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Drug resistance enables cancer cells to break away from cytotoxic effect of anticancer drugs. Identification of resistant phenotype is very important because it can lead to effective treatment plan. There is an interest in developing classifying models of resistance phenotype based on the multivariate data. We have investigated a vibrational spectroscopic approach in order to characterize a sensitive human ovarian cell line, A2780, and its cisplatin-resistant derivative, A2780-cp. In this study FTIR method have been evaluated via the use of principal components analysis (PCA), ANN (artificial neuronal network) and LDA (linear discriminate analysis). FTIR spectroscopy on these cells in the range of 400-4000 cm(-1) showed alteration in the secondary structure of proteins and a CH stretching vibration. We have found that the ANN models correctly classified more than 95% of the cell lines, while the LDA models with the same data sets could classify 85% of cases. In the process of different ranges of spectra, the best classification of data set in the range of 1000-2000 cm(-1) was done using ANN model, while the data set between 2500-3000 cm(-1) was more correctly classified with the LDA model. PCA of the spectral data also provide a good separation for representing the variety of cell line spectra. Our work supports the promise of ANN analysis of FTIR spectrum as a supervised powerful approach and PCA as unsupervised modeling for the development of automated methods to determine the resistant phenotype of cancer classification.
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Neuropeptide Y Leu7Pro polymorphism associated with the metabolic syndrome and its features in patients with coronary artery disease.
Angiology
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The metabolic syndrome (MetS) is characterized by constellation of clinical and biochemical features that increase the risk of cardiovascular disease. Neuropeptide Y (NPY) is a neurotransmitter and enhances the development of obesity and other aspects of MetS. We determined the association between NPY Leu7Pro polymorphism and features of MetS in Iranian patients with coronary artery disease (CAD). A total of 550 patients with CAD including individuals with (n = 184) and without MetS (n = 366) were genotyped by polymerase chain reaction-restriction fragment length polymorphism. A significantly higher frequency of the Leu7Pro polymorphism was found in patients with MetS compared with the non-MetS patients (P = .001). Furthermore, there was a significant difference in Pro7 frequency between diabetics versus nondiabetics (P = .005), dyslipidemic versus nondyslipidemic (P = .04), and obese versus nonobese (P = .001) in this population. Leu7Pro polymorphism is associated with the MetS in patients with CAD.
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High frequency of Neuropeptide Y Leu7Pro polymorphism in an Iranian population and its association with coronary artery disease.
Gene
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Neuropeptide Y (NPY), a 36-amino acid peptide, is widely expressed in the central and peripheral nervous systems as well as in the heart. A relationship has been reported between NPY gene variants and coronary artery disease (CAD) in some populations. However, there are few data on the NPY gene polymorphism and CAD in the Persian population. In the current study we have investigated the relationship between the NPY Leu7Pro polymorphism and CAD within a population from Iran.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.