DNA microarray allows molecular profiling of rheumatoid arthritis and identification of pathophysiological targets
Valérie DevauchelleSandrine MarionNicolas CagnardSylvie MistouGéraldine FalgaroneMaxime BrébanFranck LetourneurAmandine PitavalOlivier AlibertCarlo LucchesiPhilippe AnractMoussa HamadoucheX AyralMaxime DougadosXavier GidrolCatherine FournierGilles Chiocchia
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Objective:To screen and identify functional gene sets in aged kidney-Yang deficiency syndrome by cDNA microarray assay.Methods:Two aged kidney-Yang deficiency patients and two healthy peoples were selected as samples during the epidemiologic survey of the kidney-Yang deficiency syndrome.The differentially expressed genes were screened by applying HG-u133Plus2.0 of Affymetrix GeneChip.The total RNAs were isolated from leukocyte of 4 samples by TR Izolmethod and then purified,reversely transcribed to cDNA with incorporating biotin labeling probe and hybridizated with GeneChip.Picture signals of fluorescence in gene array were scanned and compared differential expression of genes by computer analysis.Results:680 and 503 differentially expressed genes were obtained.The immune response-associated gene sets were found with significance in GO annotated biological processes.Conclusion:Immune factor play an important role in occurrence and development of the kidney-Yang deficiency.
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The introduction of microarray technology has dramatically changed the way that researchers address many biomedical questions. DNA microarrays can measure expression of thousands of genes simultaneously, providing extensive information on gene interaction and function. Microarray technology is a powerful tool for identifying novel molecular drug targets and for elucidating mechanisms of drug action. Furthermore, microarrays can monitor the global profile of gene expression in response to specific pharmacologic agents, providing information on drug efficacy and toxicity. Over the last several years, dramatic advancements have occurred in array technology. In this review we describe basic aspects of microarray instrumentation and experimentation. Each of the major array formats including oligonucleotides arrays, spotted arrays, and macroarrays are examined, and advantages and options for using each format are presented. Important factors in the design and analysis of microarray experiments are also discussed. Most importantly, we explore recent developments in microarray technology that are relevant to pharmacogenomics and the discovery of gene function.
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Although microarray analysis is a highly promising technology in the genome era, its application for gene expression profiling to characterize various phenomes, including genetic phenotypes, diseases, responses to chemicals and clinical annotations, is far from being a real use. One of the obstacles is the quality of the data, which needs to be enough to be able to solely use microarrays for these purposes. For this, selecting a set of genes as a molecular signature, based on transcriptomics, proteomics or metabolomics, and the use of the selected set of genes in focused microarrays has great merits. Here, we summarize how sets of genes were selected, what types of genes were used and what kind of statistics will be needed for focused microarrays, to distinguish them from genome-wide microarrays and to explain why focused microarray analysis is advantageous in gene expression profiling. Keywords: cDNA array, genome-wide microarray, Replicate Assays, EUROSTERONE microarray, Signaling Pathways
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Murine transplantation models are used extensively to research immunological rejection and tolerance. Here we studied both murine heart and liver allograft models using microarray technology. We had difficulty in identifying genes related to acute rejections expressed in both heart and liver transplantation models using two standard methodologies: Student's t test and linear models for microarray data (Limma). Here we describe a new method, standardized fold change (SFC), for differential analysis of microarray data. We estimated the performance of SFC, the t test and Limma by generating simulated microarray data 100 times. SFC performed better than the t test and showed a higher sensitivity than Limma where there is a larger value for fold change of expression. SFC gave better reproducibility than Limma and the t test with real experimental data from the MicroArray Quality Control platform and expression data from a mouse cardiac allograft. Eventually, a group of significant overlapping genes was detected by SFC in the expression data of mouse cardiac and hepatic allografts and further validated with the quantitative RT-PCR assay. The group included genes for important reactions of transplantation rejection and revealed functional changes of the immune system in both heart and liver of the mouse model. We suggest that SFC can be utilized to stably and effectively detect differential gene expression and to explore microarray data in further studies.
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