A gene signature for post-infectious chronic fatigue syndrome
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At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition.Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7).Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significanceDifferential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment.Keywords:
Gene chip analysis
Human genetics
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At present, there are no clinically reliable disease markers for chronic fatigue syndrome. DNA chip microarray technology provides a method for examining the differential expression of mRNA from a large number of genes. Our hypothesis was that a gene expression signature, generated by microarray assays, could help identify genes which are dysregulated in patients with post-infectious CFS and so help identify biomarkers for the condition.Human genome-wide Affymetrix GeneChip arrays (39,000 transcripts derived from 33,000 gene sequences) were used to compare the levels of gene expression in the peripheral blood mononuclear cells of male patients with post-infectious chronic fatigue (n = 8) and male healthy control subjects (n = 7).Patients and healthy subjects differed significantly in the level of expression of 366 genes. Analysis of the differentially expressed genes indicated functional implications in immune modulation, oxidative stress and apoptosis. Prototype biomarkers were identified on the basis of differential levels of gene expression and possible biological significanceDifferential expression of key genes identified in this study offer an insight into the possible mechanism of chronic fatigue following infection. The representative biomarkers identified in this research appear promising as potential biomarkers for diagnosis and treatment.
Gene chip analysis
Human genetics
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Microarray can parallel quantification of large numbers of genes and promises to provide detailed insight into cellular processes involved in the regulation of gene expression. In plants, microarray for gene expression profiling should provide an important way for understanding of gene functions and signaling networks that operate plants growth and development, respond to biotic and abiotic stresses, important agronomic characters. This review focuses on the application of microarray for gene expression profiling in plants. Moreover, development and application of tobacco microarray is also summarized. This review will provide useful information for better application of plant microarray in studies of gene function and regulation mechanism.
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The expression profiles of rice (Oryza sativa) near isogenic line (NIL) Zhongzao 14R (resistance) and Zhongzao 9S (susceptible) in response to Xanthomonas oryzae pv. oyzae (Xoo) were studied using cDNA microarray containing 2200 expression sequence tags (ESTs) and ~(33)P hybridization. Results showed that the expression of 315 genes were u-regulated while 176 genes down-regulated among the valid data of 1336 genes after induced for 3 H with Xoo at 4-leaves stage. Further studies of the 78 induced genes with known functions indicated that the changes of gene expression pattern not only reflected coordinating gene regulation in plant defense but also showed the basic model of plant in response to pathogen attack. Some genes never reported to related to the plant defense response while induced by Xoo were reported here by microarray analysis. Novel genes induced by Xoo were also found by using this high-throughput cDNA microarray method.
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This study aimed to use gene chips to investigate differential gene expression profiles in the occurrence and development of acute myocardial infarction (AMI). The study included 12 AMI patients and 12 healthy individuals. Total mRNA of peripheral bloodwas extracted and reversed-transcribed to cDNA for microarray analysis. After establishing two pools with three subjects each (3 AMI patients and 3 healthy individuals), the remaining samples were used for RT-qPCR to confirm the microarray data. From the microarray results, seven genes were randomly selected for RT-qPCR. RT-qPCR results were analyzed by the 2-ΔΔCt method. Microarray analysis showed that 228 genes were up- regulated and 271 were down-regulated (p ≤ 0.05, |logFC| > 1). Gene ontology showed that these genes belong to 128 cellular components, 521 biological processes, and 151 molecular functions. KEGG pathway analysis showed that these genes are involved in 107 gene pathways. RT-qPCR results for the seven genes showed expression levels consistent with those obtained by microarray. Thus, microarray data could be used to select the pathogenic genes for AMI. Investigating the abnormal expression of these differentially expressed genes might suggest efficient strategies for the prevention, diagnosis, and treatment of AMI.
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Microarray technology has become an indispensable tool for monitoring the levels of gene expression in a given organism through organization, analysis, interpretation, and utilization of biological sequences.Importantly, preliminary microarray gene expression differs from experimentally validated gene expression.Generally, microarray analysis of gene expression in microglial cells is used to identify genes in the brain and spinal cord that are responsible for the onset of neurodegenerative diseases; these genes are either upregulated or downregulated.In the present study, 770 genes identified in prior publications, including experimental studies, were analyzed to determine whether these genes encode novel disease genes.Among the genes published, 340 genes were matched among multiple publications, whereas 430 genes were mismatched; the matched genes were presumed to have the greatest likelihood of contributing to neurodegenerative diseases and thus to be potentially useful target genes for treatment of neurodegenerative diseases.In protein and mRNA expression studies, matched and mismatched genes showed 99% and 97% potentiality, respectively.In addition, some genes identified in microarray analyses were significantly different from those in experimentally validated expression patterns.This study identified novel genes in microglial cells through comparative analysis of published microarray and experimental data on neurodegenerative diseases.
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Objective:To study the different gene expression of glioblastomas and the tissue around the glioblastomas by gene microarray analysis in order to reveal the biological behaviour of glioblastoma in the level of gene. Methods: The human gene microarray chip (SBC-R-HC-100-20) manufacted by the Biotechnology Limited Company of Shanghai have 13824 gene situs and was adopt to detected the glioblastoma in the level of WHOIII and WHOIV and the tissue of the glioblastomas.Results: Between the glioblastomas and the tissue around the glioblastomas ,there were 275 genes differential about DNA- binding,transcription and transcription factor, proteo- translation,cell cycle,metachoresis,in which 143 genes up-regulated,132 genes down-regulated. Conclusion:There are many changes about gene expression in the development of the glioblastoma. Application of the cDNA microarray in the research of glioblastoma can conveniently and quickly deflate the study circumscription, effectually select objective-gene to research deeply.
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Objective To analyze the differential gene expression profiling of liver in rats subjected to hemorrhagic shock(HS) and sham hemorrhage shock(SHAM) by gene chip technology, thus to evaluate the possible molecular pathogenesis of HS. Method 20 male Wistar rats were randomly divided into a SHAM group and a HS group, with 10 rats in each group. Hepatic gene expression profiles were detected by oligonucleotide microarrays of 5705 mouse genes in two groups for three times. Genes with ratio(R) > 2 were identified as up-regulated and R < 0.5 were identified as down-regulated. Biological function of differentially expressed genes was analyzed and 9 genes were selected to undergo semi-quantitative RT-PCR. Results Among the total 5705 probes detected,86 genes showed differential expression in HS group comparison with SHAM group. The expression levels of 72 genes were up-regulated while those of 14 genes were down-regulated significantly. Differentially expressed genes were classified according to their biological function: transport genes, transcription regulator genes, signaling genes, response to stress genes, metabolic genes, development genes and cell adhesion genes. Conclusions cDNA microarray is an efficient and high-throughout method to survey gene expression profiles in HS.The variation of those gene expressions might be a potential pathogenic mechanism for HS that may offer a novel target for further study of therapeutic strategies of HS.
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Hemorrhagic shock; DNA chip; Gene expression; liver
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As part of the civil aviation safety program to define the adverse effects of ethanol on flying performance, we performed a DNA microarray analysis of human whole blood samples from a five-time point study of subjects administered ethanol orally, followed by breathalyzer analysis, to monitor blood alcohol concentration (BAC) to discover significant gene expression changes in response to the ethanol exposure. Subjects were administered either orange juice or orange juice with ethanol. Blood samples were taken based on BAC and total RNA was isolated from PaxGene™ blood tubes. The amplified cDNA was used in microarray and quantitative real-time polymerase chain reaction (RT-qPCR) analyses to evaluate differential gene expression. Microarray data was analyzed in a pipeline fashion to summarize and normalize and the results evaluated for relative expression across time points with multiple methods. Candidate genes showing distinctive expression patterns in response to ethanol were clustered by pattern and further analyzed for related function, pathway membership and common transcription factor binding within and across clusters. RT-qPCR was used with representative genes to confirm relative transcript levels across time to those detected in microarrays. Microarray analysis of samples representing 0%, 0.04%, 0.08%, return to 0.04%, and 0.02% wt/vol BAC showed that changes in gene expression could be detected across the time course. The expression changes were verified by qRT-PCR. The candidate genes of interest (GOI) identified from the microarray analysis and clustered by expression pattern across the five BAC points showed seven coordinately expressed groups. Analysis showed function-based networks, shared transcription factor binding sites and signaling pathways for members of the clusters. These include hematological functions, innate immunity and inflammation functions, metabolic functions expected of ethanol metabolism, and pancreatic and hepatic function. Five of the seven clusters showed links to the p38 MAPK pathway. The results of this study provide a first look at changing gene expression patterns in human blood during an acute rise in blood ethanol concentration and its depletion because of metabolism and excretion, and demonstrate that it is possible to detect changes in gene expression using total RNA isolated from whole blood. The analysis approach for this study serves as a workflow to investigate the biology linked to expression changes across a time course and from these changes, to identify target genes that could serve as biomarkers linked to pilot performance.
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