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Gene Ontology (gene + ontology)
Terms modified by Gene Ontology Selected AbstractsExpressed sequence tag analysis of the diapausing queen of the bumblebee Bombus ignitusENTOMOLOGICAL RESEARCH, Issue 4 2006Yeon-Ju KIM Abstract We constructed a full-length cDNA library from diapausing queens of the bumblebee Bombus ignitus. A total of 480 randomly selected clones was sequenced by single-run 5,-end sequencing. Of these, there were 437 high quality clones, 23 poor quality clones and 20 read-fail clones. Each high quality clone sequence was searched against a public protein database. The most frequently found matching genes were ribosomal proteins (12.5%), p10 (3.58%), cytochrome P450 monooxygenase (3.13%) and sensory appendage protein (2.9%). Sequence similarity analysis between bumblebees and other insect species showed that 72 out of 437 (16.5%) bumblebee expressed sequence tags (EST) matched sequences of Apis mellifera, with matches to Drosophila melanogaster (6.6%), Caenorhabditis briggsae (6.2%), Lysiphlebus testaceipes (4.8%), Periplaneta americana (3.7%) and Anopheles gambiae (3.4%) following, suggesting that sequence similarity of bumblebee EST is closest to that of A. mellifera. Functional classification of EST based on Gene Ontology showed that most genes found by sequencing are associated with physiological processes in the bumblebee. The results of sequencing and analysis of our 437 cDNA demonstrated that high-throughput EST sequencing and data analysis are powerful means for identifying novel genes and for expression profiling. Our bumblebee EST collection could be a useful platform for further studies of gene expression in diapausing bumblebees. [source] Comparative gene expression profiling of olfactory ensheathing glia and Schwann cells indicates distinct tissue repair characteristics of olfactory ensheathing gliaGLIA, Issue 12 2008Elske H.P. Franssen Abstract Olfactory ensheathing glia (OEG) are a specialized type of glia that support the growth of primary olfactory axons from the neuroepithelium in the nasal cavity to the brain. Transplantation of OEG in the injured spinal cord promotes sprouting of injured axons and results in reduced cavity formation, enhanced axonal and tissue sparing, remyelination, and angiogenesis. Gene expression analysis may help to identify the molecular mechanisms underlying the ability of OEG to recreate an environment that supports regeneration in the central nervous system. Here, we compared the transcriptome of cultured OEG (cOEG) with the transcriptomes of cultured Schwann cells (cSCs) and of OEG directly obtained from their natural environment (nOEG), the olfactory nerve layer of adult rats. Functional data mining by Gene Ontology (GO)-analysis revealed a number of overrepresented GO-classes associated with tissue repair. These classes include "response to wounding," "blood vessel development," "cell adhesion," and GO-classes related to the extracellular matrix and were overrepresented in the set of differentially expressed genes between both comparisons. The current screening approach combined with GO-analysis has identified distinct molecular properties of OEG that may underlie their efficacy and interaction with host tissue after implantation in the injured spinal cord. These observations can form the basis for studies on the function of novel target molecules for therapeutic intervention after neurotrauma. © 2008 Wiley-Liss, Inc. [source] Access to immunology through the Gene OntologyIMMUNOLOGY, Issue 2 2008Ruth C. Lovering Summary The Gene Ontology (GO) is widely recognized as the premier tool for the organization and functional annotation of molecular aspects of cellular systems. However, for many immunologists the use of GO is a very foreign concept. Indeed, as a controlled vocabulary, GO can almost be considered a new language, and it can be difficult to appreciate the use and value of this approach for understanding the immune system. This review reflects on the application of GO to the field of immunology and explains the process of GO annotation. Finally, this review hopes to inspire immunologists to invest time and energy in improving both the content of the GO and the quality of GO annotations associated with genes of immunological interest. [source] A network analysis of the single nucleotide polymorphisms in acute allergic diseasesALLERGY, Issue 1 2010J. Renkonen Abstract Background:, Genetics of acute allergies has focused on identifying single nucleotide polymorphisms (SNPs) within genes relevant in the pathogenesis. In this study, we begin a systems biology analysis of the interconnectivity and biological functions of these genes, their transcripts and their corresponding proteins. Methods:, The literature (Pubmed) was searched for SNPs within genes relevant in acute allergic diseases. The SNP-modified genes were converted to corresponding proteins and their protein,protein interactions were searched from six different databases. This interaction network was analysed with annotated vocabularies (ontologies), such as Gene Ontology, Reactome and Nature pathway interaction database. Time-series transcriptomics was performed with nasal epithelial cells obtained from allergic patients and their healthy control subjects. Results:, A total of 39 genes with SNPs related to acute allergic diseases were found from a literature search. The corresponding proteins were then hooked into a large protein,protein interaction network with the help of various databases. Twenty-five SNP-related proteins had more than one interacting protein and a network contained 95 proteins, and 182 connections could be generated. This network was 10-fold enriched with protein kinases and proteins involved in the host,virus interaction compared with background human proteome. Finally, eight of the 95 nodes on our network displayed nasal epithelial transcriptomal regulation in a time-series analysis collected from birch allergic patients during the spring pollen season. Conclusions:, Signal transduction with special reference to host,virus interactions dominated in the allergy-related protein interaction network. Systems level analysis of allergy-related mutation can provide new insights into pathogenetic mechanisms of the diseases. [source] Increased expression of aquaporin 3 in atopic eczemaALLERGY, Issue 9 2006M. Olsson Background:, Dry skin in atopic eczema depends on increased water loss. The mechanisms behind this are poorly understood. The aim of this work was to identify genes that may contribute to water loss in eczema. Methods:, Affymetrix DNA microarrays U133A were used to analyse gene expression in skin biopsies from 10 patients with atopic eczema and 10 healthy controls. Results:, DNA microarray analysis showed up-regulation of 262 genes and down-regulation of 129 genes in atopic eczema. The known functions of these genes were analysed using Gene Ontology to identify genes that could contribute to increased water loss. This led to identification of aquaporin 3 (AQP3), which has a key role in hydrating healthy epidermis. Increased expression of AQP3 was found in eczema compared with healthy skin. This was confirmed with real-time polymerase chain reaction (P < 0.001). In healthy skin, epidermal AQP3 immunoreactivity was weak and mainly found in the stratum basale. A gradient was formed with decreasing AQP3 staining in the lower layers of the stratum spinosum. By contrast, in acute and chronic atopic eczema strong AQP3 staining was found in both the stratum basale and the stratum spinosum. Conclusions:, Aquaporin 3 is the predominant aquaporin in human skin. Increased expression and altered cellular distribution of AQP3 is found in eczema and this may contribute to water loss. [source] Pilot Study Examining the Utility of Microarray Data to Identify Genes Associated with Weight in Transplant RecipientsNURSING & HEALTH SCIENCES, Issue 2 2006Ann Cashion Purpose/Methods:, Obesity, a complex, polygenic disorder and a growing epidemic in transplant recipients, is a risk factor for chronic diseases. This secondary data analysis identified if microarray technologies and bioinformatics could find differences in gene expression profiles between liver transplant recipients with low Body Mass Index (BMI < 29; n = 5) vs. high (BMI > 29; n = 7). Blood was hybridized on Human U133 Plus 2 GeneChip (Affymetrix) and analyzed using GeneSpring Software. Results:, Groups were similar in age and race, but not gender. Expression levels of 852 genes were different between the low and high BMI groups (P < 0.05). The majority (562) of the changes associated with high BMI were decreases in transcript levels. Among the 852 genes associated with BMI, 263 and 14 genes were affected greater than 2- or 5-fold, respectively. Following functionally classification using Gene Ontology (GO), we found that 19 genes (P < 0.00008) belonged to defense response and 15 genes (P < 0.00006) belonged to immune response. Conclusion:, These data could point the way toward therapeutic interventions and identify those at-risk. These results demonstrate that we can (1) extract high quality RNA from immunosuppressed patients; (2) manage large datasets and perform statistical and functional analysis. [source] Altered gene expression in the brain and liver of female fathead minnows Pimephales promelas Rafinesque exposed to fadrozoleJOURNAL OF FISH BIOLOGY, Issue 9 2008D. L. Villeneuve The fathead minnow Pimephales promelas is a small fish species widely used for ecotoxicology research and regulatory testing in North America. This study used a 2000 gene oligonucleotide microarray to evaluate the effects of the aromatase inhibitor, fadrozole, on gene expression in the liver and brain tissue of exposed females. Reproductive measures, plasma vitellogenin and gene expression data for the brain isoform of aromatase (cytP19B), vitellogenin precursors and transferrin provided evidence supporting the efficacy of the fadrozole exposure. Unsupervised analysis of the microarray results identified 20 genes in brain and 41 in liver as significantly up-regulated and seven genes in brain and around 45 in liver as significantly down-regulated. Differentially expressed genes were associated with a broad spectrum of biological functions, many with no obvious relationship to aromatase inhibition. However, in brain, fadrozole exposure elicited significant up-regulation of several genes involved in the cholesterol synthesis, suggesting it as a potentially affected pathway. Gene ontology-based analysis of expression changes in liver suggested overall down-regulation of protein biosynthesis. While real-time polymerase chain reaction analyses supported some of the microarray responses, others could not be verified. Overall, results of this study provide a foundation for developing novel hypotheses regarding the system-wide effects of fadrozole, and other chemical stressors with similar modes of action, on fish biology. [source] A domain level interaction network of amyloid precursor protein and A, of Alzheimer's diseasePROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 12 2010Victoria M. Perreau Abstract The primary constituent of the amyloid plaque, ,-amyloid (A,), is thought to be the causal "toxic moiety" of Alzheimer's disease. However, despite much work focused on both A, and its parent protein, amyloid precursor protein (APP), the functional roles of APP and its cleavage products remain to be fully elucidated. Protein,protein interaction networks can provide insight into protein function, however, high-throughput data often report false positives and are in frequent disagreement with low-throughput experiments. Moreover, the complexity of the CNS is likely to be under represented in such databases. Therefore, we curated the published work characterizing both APP and A, to create a protein interaction network of APP and its proteolytic cleavage products, with annotation, where possible, to the level of APP binding domain and isoform. This is the first time that an interactome has been refined to domain level, essential for the interpretation of APP due to the presence of multiple isoforms and processed fragments. Gene ontology and network analysis were used to identify potentially novel functional relationships among interacting proteins. [source] Microarray data classification using inductive logic programming and gene ontology background informationJOURNAL OF CHEMOMETRICS, Issue 5 2010Einar Ryeng Abstract There exists many databases containing information on genes that are useful for background information in machine learning analysis of microarray data. The gene ontology and gene ontology annotation projects are among the most comprehensive of these. We demonstrate how inductive logic programming (ILP) can be used to build classification rules for microarray data which naturally incorporates the gene ontology and annotations to it as background knowledge without removing the inherent graph structure of the ontology. The ILP rules generated are parsimonious and easy to interpret. Copyright © 2010 John Wiley & Sons, Ltd. [source] Prediction of interactiveness between small molecules and enzymes by combining gene ontology and compound similarityJOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 8 2010Lei Chen Abstract Determination of whether a small organic molecule interacts with an enzyme can help to understand the molecular and cellular functions of organisms, and the metabolic pathways. In this research, we present a prediction model, by combining compound similarity and enzyme similarity, to predict the interactiveness between small molecules and enzymes. A dataset consisting of 2859 positive couples of small molecule and enzyme and 286,056 negative couples was employed. Compound similarity is a measurement of how similar two small molecules are, proposed by Hattori et al., J Am Chem Soc 2003, 125, 11853 which can be availed at http://www.genome.jp/ligand-bin/search_compound, while enzyme similarity was obtained by three ways, they are blast method, using gene ontology items and functional domain composition. Then a new distance between a pair of couples was established and nearest neighbor algorithm (NNA) was employed to predict the interactiveness of enzymes and small molecules. A data distribution strategy was adopted to get a better data balance between the positive samples and the negative samples during training the prediction model, by singling out one-fourth couples as testing samples and dividing the rest data into seven training datasets,the rest positive samples were added into each training dataset while only the negative samples were divided. In this way, seven NNAs were built. Finally, simple majority voting system was applied to integrate these seven models to predict the testing dataset, which was demonstrated to have better prediction results than using any single prediction model. As a result, the highest overall prediction accuracy achieved 97.30%. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010 [source] Incorporating gene functional annotations in detecting differential gene expressionJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 3 2006Wei Pan Summary., The importance of incorporating existing biological knowledge, such as gene functional annotations in gene ontology, in analysing high throughput genomic and proteomic data is being increasingly recognized. In the context of detecting differential gene expression, however, the current practice of using gene annotations is limited primarily to validations. Here we take a direct approach to incorporating gene annotations into mixture models for analysis. First, in contrast with a standard mixture model assuming that each gene of the genome has the same distribution, we study stratified mixture models allowing genes with different annotations to have different distributions, such as prior probabilities. Second, rather than treating parameters in stratified mixture models independently, we propose a hierarchical model to take advantage of the hierarchical structure of most gene annotation systems, such as gene ontology. We consider a simplified implementation for the proof of concept. An application to a mouse microarray data set and a simulation study demonstrate the improvement of the two new approaches over the standard mixture model. [source] Transcription profile in mouse four-cell, morula, and blastocyst: Genes implicated in compaction and blastocoel formationMOLECULAR REPRODUCTION & DEVELOPMENT, Issue 2 2007Xiang-Shun Cui Abstract To gain insight into early embryo development, we utilized microarray technology to compare gene expression profiles in four-cell (4C), morula (MO), and blastocyst (BL) stage embryos. Differences in spot intensities were normalized, and grouped by using Avadis Prophetic software platform (version 3.3, Strand Genomics Ltd.) and categories were based on the PANTHER and gene ontology (GO) classification system. This technique identified 622 of 7,927 genes as being more highly expressed in MO when compared to 4C (P,<,0.05); similarly, we identified 654 of 9,299 genes as being more highly expressed in BL than in MO (P,<,0.05). Upregulation of genes for cytoskeletal, cell adhesion, and cell junction proteins were identified in the MO as compared to the 4C stage embryos, this means they could be involved in the cell compaction necessary for the development to the MO. Genes thought to be involved in ion channels, membrane traffic, transfer/carrier proteins, and lipid metabolism were also identified as being expressed at a higher level in the BL stage embryos than in the MO. Real-time RT-PCR was performed to confirm differential expression of selected genes. The identification of the genes being expressed in here will provide insight into the complex gene regulatory networks effecting compaction and blastocoel formation. Mol. Reprod. Dev. © 2006 Wiley-Liss, Inc. [source] Protein Kinase Target Discovery From Genome-Wide Messenger RNA Expression ProfilingMOUNT SINAI JOURNAL OF MEDICINE: A JOURNAL OF PERSONALIZED AND TRANSLATIONAL MEDICINE, Issue 4 2010Avi Ma'ayan Abstract Genome-wide messenger RNA profiling provides a snapshot of the global state of the cell under different experimental conditions such as diseased versus normal cellular states. However, because measurements are in the form of quantitative changes in messenger RNA levels, such experimental data does not provide direct understanding of the regulatory molecular mechanisms responsible for the observed changes. Identifying potential cell signaling regulatory mechanisms responsible for changes in gene expression under different experimental conditions or in different tissues has been the focus of many computational systems biology studies. Most popular approaches include promoter analysis, gene ontology, or pathway enrichment analysis, as well as reverse engineering of networks from messenger RNA expression data. Here we present a rational approach for identifying and ranking protein kinases that are likely responsible for observed changes in gene expression. By combining promoter analysis; data from various chromatin immunoprecipitation studies such as chromatin immunoprecipitation sequencing, chromatin immunoprecipitation coupled with paired-end ditag, and chromatin immunoprecipitation-on-chip; protein-protein interactions; and kinase-protein phosphorylation reactions collected from the literature, we can identify and rank candidate protein kinases for knock-down, or other types of functional validations, based on genome-wide changes in gene expression. We describe how protein kinase candidate identification and ranking can be made robust by cross-validation with phosphoproteomics data as well as through a literature-based text-mining approach. In conclusion, data integration can produce robust candidate rankings for understanding cell regulation through identification of protein kinases responsible for gene expression changes, and thus rapidly advancing drug target discovery and unraveling drug mechanisms of action. Mt Sinai J Med 77:345,349, 2010. © 2010 Mount Sinai School of Medicine [source] SPLASH: Systematic proteomics laboratory analysis and storage hubPROTEINS: STRUCTURE, FUNCTION AND BIOINFORMATICS, Issue 6 2006Siaw Ling Lo Abstract In the field of proteomics, the increasing difficulty to unify the data format, due to the different platforms/instrumentation and laboratory documentation systems, greatly hinders experimental data verification, exchange, and comparison. Therefore, it is essential to establish standard formats for every necessary aspect of proteomics data. One of the recently published data models is the proteomics experiment data repository [Taylor, C. F., Paton, N. W., Garwood, K. L., Kirby, P. D. et,al., Nat. Biotechnol. 2003, 21, 247,254]. Compliant with this format, we developed the systematic proteomics laboratory analysis and storage hub (SPLASH) database system as an informatics infrastructure to support proteomics studies. It consists of three modules and provides proteomics researchers a common platform to store, manage, search, analyze, and exchange their data. (i),Data maintenance includes experimental data entry and update, uploading of experimental results in batch mode, and data exchange in the original PEDRo format. (ii),The data search module provides several means to search the database, to view either the protein information or the differential expression display by clicking on a gel image. (iii),The data mining module contains tools that perform biochemical pathway, statistics-associated gene ontology, and other comparative analyses for all the sample sets to interpret its biological meaning. These features make SPLASH a practical and powerful tool for the proteomics community. [source] Using gene chips to identify organ-specific, smooth muscle responses to experimental diabetes: potential applications to urological diseasesBJU INTERNATIONAL, Issue 2 2007Jason D. Hipp OBJECTIVE To identify early diabetes-related alterations in gene expression in bladder and erectile tissue that would provide novel diagnostic and therapeutic treatment targets to prevent, delay or ameliorate the ensuing bladder and erectile dysfunction. MATERIALS AND METHODS The RG-U34A rat GeneChip® (Affymetrix Inc., Sunnyvale, CA, USA) oligonucleotide microarray (containing ,8799 genes) was used to evaluate gene expression in corporal and male bladder tissue excised from rats 1 week after confirmation of a diabetic state, but before demonstrable changes in organ function in vivo. A conservative analytical approach was used to detect alterations in gene expression, and gene ontology (GO) classifications were used to identify biological themes/pathways involved in the aetiology of the organ dysfunction. RESULTS In all, 320 and 313 genes were differentially expressed in bladder and corporal tissue, respectively. GO analysis in bladder tissue showed prominent increases in biological pathways involved in cell proliferation, metabolism, actin cytoskeleton and myosin, as well as decreases in cell motility, and regulation of muscle contraction. GO analysis in corpora showed increases in pathways related to ion channel transport and ion channel activity, while there were decreases in collagen I and actin genes. CONCLUSIONS The changes in gene expression in these initial experiments are consistent with the pathophysiological characteristics of the bladder and erectile dysfunction seen later in the diabetic disease process. Thus, the observed changes in gene expression might be harbingers or biomarkers of impending organ dysfunction, and could provide useful diagnostic and therapeutic targets for a variety of progressive urological diseases/conditions (i.e. lower urinary tract symptoms related to benign prostatic hyperplasia, erectile dysfunction, etc.). [source] | ||||||||||||||||