Rapid analysis of gene expression changes caused by liver carcinogens and chemopreventive agents using a newly developed three‐dimensional microarray system
Naomi HokaiwadoMakoto AsamotoKazunari TsujimuraTakeshi HirotaToshio IchiharaTakatomo SatohTomoyuki Shirai
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We investigated changes of gene expression in livers of rats treated with carcinogens and tumor promoters using a novel three‐dimensional microarray system developed by Olympus Optical Co., Ltd., to assess the feasibility of predicting modifying effects on hepatocarcinogenesis on the basis of changes in the patterns. For this purpose, two genotoxic carcinogens, two nongenotoxic carcinogens (promoters) and seven candidate chemopreventive agents were examined. Six‐week‐old male F344 rats were treated for 2 weeks with the 11 chemicals (0.05% phenobarbital, 0.3% clofibrate, 0.01% N‐diethylnitrosamine (DEN), 0.01% 2‐amino‐3, 8‐dimethylimidazo[4,5‐f]quinoxaline (MeIQx), 1% catechol, 1% caffeic acid, 0.05% nobiletin, 0.05% garcinol, 0.05% auraptene, 0.05% zermbone and 0.05% 1′‐acetoxychavicol acetate (ACA). Test chemicals were mixed in food with the exception of DEN, which was administered in drinking water. RNAs from liver were then analyzed using two kinds of customized microarrays (PamChip® microarray A spotted for 28 genes of drugmetabolizing enzymes in duplicate, and PamChip® microarray B spotted for 131 genes which are known to be up‐ or down‐regulated in hepatocarcinoma cells). Hybridization and subsequent analysis were usually completed within 2 h and the data obtained were highly reproducible. Carcinogens were classified into genotoxic and nongenotoxic substances by clustering analysis. We could also divide test chemicals into carcinogens and chemopreventive agents from their effects on gene expression. In this study, we have thus shown that it is feasible to predict the modifying effects of chemicals on the basis of changes of gene expression patterns after only 2 weeks of exposure, using our novel three‐dimensional microarrays.Keywords:
Nobiletin
To study the technology for establishing DNA microarrays for the diagnosis of HPV. HPV6, 11, 16 and 18 gene fragments were isolated and printed onto aminosilane-coated glass slides by PixSys 5500 microarray printer as probes to prepare the HPV. HPV samples, after labeled with Cy3 or Cy5, were hybridaized with the microarray followed by scanning for analysis. The experimental condition for preparing the HPV gene chips was investigated and the possibility of HPV genotying using DNA microarrays was discussed. The technique established in this study for preparing HPV DNA microarrays is applicable and has potential clinical application significance.
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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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High‐throughput microarray technologies have the potential to impact significantly on the practice of histopathology over the coming years. Global gene expression profiling allows for a systematic search of all human genes for novel diagnostic and prognostic markers and for potential therapeutic targets. Likewise, gene copy number changes can be determined on a gene‐by‐gene basis using microarrays. Tissue microarrays are an efficient method to extend and validate the findings obtained from the initial ‘discovery’ phase of the research, done using cDNA microarrays. In addition, tissue microarrays can be used for quality assurance for immunohistochemical and in situ hybridization procedures. In this review we give a brief overview of microarray technology and research uses, and discuss potential applications of microarrays in the practice of diagnostic histopathology.
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Abstract More than 110 000 publications have used microarrays to decipher phenotype-associated genes, clinical biomarkers and gene functions. Microarrays rely on digital assaying the fluorescence signals of arrays. In this study, we retrospectively constructed raw images for 37 724 published microarray data, and developed deep learning algorithms to automatically detect systematic defects. We report that an alarming amount of 26.73% of the microarray-based studies are affected by serious imaging defects. By literature mining, we found that publications associated with these affected microarrays have reported disproportionately more biological discoveries on the genes in the contaminated areas compared to other genes. 28.82% of the gene-level conclusions reported in these publications were based on measurements falling into the contaminated area, indicating severe, systematic problems caused by such contaminations. We provided the identified published, problematic datasets, affected genes and the imputed arrays as well as software tools for scanning such contamination that will become essential to future studies to scrutinize and critically analyze microarray data.
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Large genomes contain families of highly similar genes that cannot be individually identified by microarray probes. This limitation is due to thermodynamic restrictions and cannot be resolved by any computational method. Since gene annotations are updated more frequently than microarrays, another common issue facing microarray users is that existing microarrays must be routinely reanalyzed to determine probes that are still useful with respect to the updated annotations. PICKY 2.0 can design shared probes for sets of genes that cannot be individually identified using unique probes. PICKY 2.0 uses novel algorithms to track sharable regions among genes and to strictly distinguish them from other highly similar but nontarget regions during thermodynamic comparisons. Therefore, PICKY does not sacrifice the quality of shared probes when choosing them. The latest PICKY 2.1 includes the new capability to reanalyze existing microarray probes against updated gene sets to determine probes that are still valid to use. In addition, more precise nonlinear salt effect estimates and other improvements are added, making PICKY 2.1 more versatile to microarray users. Shared probes allow expressed gene family members to be detected; this capability is generally more desirable than not knowing anything about these genes. Shared probes also enable the design of cross-genome microarrays, which facilitate multiple species identification in environmental samples. The new nonlinear salt effect calculation significantly increases the precision of probes at a lower buffer salt concentration, and the probe reanalysis function improves existing microarray result interpretations.
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