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Association of differentially expressed genes with activation of mouse hepatic stellate cells by high-density cDNA microarray.

ABSTRACT: AIM:To characterize the gene expression profiles associated with activation of mouse hepatic stellate cell (HSC) and provide novel insights into the pathogenesis of hepatic fibrosis. METHODS:Mice HSCs were isolated from BALB/c mice by in situ perfusion of collagenase and pronase and single-step density Nycodenz gradient. Total RNA and mRNA of quiescent HSC and culture-activated HSC were extracted, quantified and reversely transcribed into cDNA. cDNAs from activated HSC were labeled with Cy5 and cDNAs from the quiescent HSC were labeled with Cy3, which were mixed with equal quantity, then hybridized with cDNA chips containing 4000 genes. Chips were washed, scanned and analyzed. Increased expression of 4 genes and decreased expression of one gene in activated HSC were confirmed by reverse transcription-polymerase chain reaction (RT-PCR). RESULTS:A total of 835 differentially expressed genes were identified by cDNA chip between activated and quiescent HSC, and 465 genes were highly expressed in activated HSC. The differentially expressed genes included those involved in protein synthesis, cell-cycle regulation, apoptosis, and DNA damage response. CONCLUSION:Many genes implicated in intrahepatic inflammation, fibrosis and proliferation were up-regulated in activated HSC. cDNA microarray is an effective technique in screening for differentially expressed genes between two different situations of the HSC. Further analysis of the obtained genes will help understand the molecular mechanism of activation of HSC and hepatic fibrosis.

SUBMITTER: Liu XJ

PROVIDER: S-EPMC4572762 | biostudies-literature | 2004 Jun

REPOSITORIES: biostudies-literature

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Association of differentially expressed genes with activation of mouse hepatic stellate cells by high-density cDNA microarray.

Liu Xiao-Jing XJ Yang Li L Luo Feng-Ming FM Wu Hong-Bin HB Qiang Qu Q

World journal of gastroenterology 20040601 11

<h4>Aim</h4>To characterize the gene expression profiles associated with activation of mouse hepatic stellate cell (HSC) and provide novel insights into the pathogenesis of hepatic fibrosis.<h4>Methods</h4>Mice HSCs were isolated from BALB/c mice by in situ perfusion of collagenase and pronase and single-step density Nycodenz gradient. Total RNA and mRNA of quiescent HSC and culture-activated HSC were extracted, quantified and reversely transcribed into cDNA. cDNAs from activated HSC were labele ...[more]

PMID: 15162533

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Project description:Leptin and hypoxia are pro-fibrotic factors involved in fibrogenesis, however, the gene expression profiles remain to be fully elucidated. The aim of the present study was to investigate the regulatory roles of leptin and hypoxia on the L929 mouse fibroblast cell line. The cells were assigned to a normoxia, normoxia with leptin, hypoxia, and hypoxia with leptin group. The cDNA expression was detected using an Agilent mRNA array platform. The differentially expressed genes (DEGs) in response to leptin and hypoxia were identified using reverse transcription‑quantitative polymerase chain reaction analysis, followed by clustering analysis, Gene Ontology analysis and pathway analysis. As a result, 54, 1,507 and 1,502 DEGs were found in response to leptin, hypoxia and the two combined, respectively, among which 52 (96.30%), 467 (30.99%) and 495 (32.96%) of the DEGs were downregulated. The most significant functional terms in response to leptin were meiosis I for biological process (P=0.0041) and synaptonemal complex for cell component (P=0.0013). Only one significant pathway responded to leptin, which was axon guidance (P=0.029). Flow cytometry confirmed that leptin promoted L929 cell proliferation. The most significant functional terms in response to hypoxia were ion binding for molecular function (P=7.8621E‑05), glucose metabolic process for biological process (P=0.0008) and cell projection part for cell component (P=0.003). There were 12 pathways, which significantly responded to hypoxia (P<0.05) and the pathway with the highest significance was the chemokine signaling pathway (P=0.0001), which comprised 28 genes, including C‑C motif ligand (CCL)1, C‑X‑C motif ligand (CXCL)9, CXCL10, son of sevenless homolog 1, AKT serine/threonine kinase 2, Rho‑associated protein kinase 1, vav guanine nucleotide exchange factor 1, CCL17, arrestin β1 and C‑C motif chemokine receptor 2. In conclusion, the present study showed that leptin and hypoxia altered the proﬁles of gene expression in L929 cells. These findings not only extend the cell spectrum of leptin on cell proliferation, but also improve current understanding of hypoxia in fibroblast cells.

Project description:BackgroundMicroarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how many replicates are required in a typical experimental system needs to be addressed. Of particular interest is the difference in required sample sizes for similar experiments in inbred vs. outbred populations (e.g. mouse and rat vs. human).ResultsWe hypothesize that if all other factors (assay protocol, microarray platform, data pre-processing) were equal, fewer individuals would be needed for the same statistical power using inbred animals as opposed to unrelated human subjects, as genetic effects on gene expression will be removed in the inbred populations. We apply the same normalization algorithm and estimate the variance of gene expression for a variety of cDNA data sets (humans, inbred mice and rats) comparing two conditions. Using one sample, paired sample or two independent sample t-tests, we calculate the sample sizes required to detect a 1.5-, 2-, and 4-fold changes in expression level as a function of false positive rate, power and percentage of genes that have a standard deviation below a given percentile.ConclusionsFactors that affect power and sample size calculations include variability of the population, the desired detectable differences, the power to detect the differences, and an acceptable error rate. In addition, experimental design, technical variability and data pre-processing play a role in the power of the statistical tests in microarrays. We show that the number of samples required for detecting a 2-fold change with 90% probability and a p-value of 0.01 in humans is much larger than the number of samples commonly used in present day studies, and that far fewer individuals are needed for the same statistical power when using inbred animals rather than unrelated human subjects.

Project description:BACKGROUND: This paper presents a unified framework for finding differentially expressed genes (DEGs) from the microarray data. The proposed framework has three interrelated modules: (i) gene ranking, ii) significance analysis of genes and (iii) validation. The first module uses two gene selection algorithms, namely, a) two-way clustering and b) combined adaptive ranking to rank the genes. The second module converts the gene ranks into p-values using an R-test and fuses the two sets of p-values using the Fisher's omnibus criterion. The DEGs are selected using the FDR analysis. The third module performs three fold validations of the obtained DEGs. The robustness of the proposed unified framework in gene selection is first illustrated using false discovery rate analysis. In addition, the clustering-based validation of the DEGs is performed by employing an adaptive subspace-based clustering algorithm on the training and the test datasets. Finally, a projection-based visualization is performed to validate the DEGs obtained using the unified framework. RESULTS: The performance of the unified framework is compared with well-known ranking algorithms such as t-statistics, Significance Analysis of Microarrays (SAM), Adaptive Ranking, Combined Adaptive Ranking and Two-way Clustering. The performance curves obtained using 50 simulated microarray datasets each following two different distributions indicate the superiority of the unified framework over the other reported algorithms. Further analyses on 3 real cancer datasets and 3 Parkinson's datasets show the similar improvement in performance. First, a 3 fold validation process is provided for the two-sample cancer datasets. In addition, the analysis on 3 sets of Parkinson's data is performed to demonstrate the scalability of the proposed method to multi-sample microarray datasets. CONCLUSION: This paper presents a unified framework for the robust selection of genes from the two-sample as well as multi-sample microarray experiments. Two different ranking methods used in module 1 bring diversity in the selection of genes. The conversion of ranks to p-values, the fusion of p-values and FDR analysis aid in the identification of significant genes which cannot be judged based on gene ranking alone. The 3 fold validation, namely, robustness in selection of genes using FDR analysis, clustering, and visualization demonstrate the relevance of the DEGs. Empirical analyses on 50 artificial datasets and 6 real microarray datasets illustrate the efficacy of the proposed approach. The analyses on 3 cancer datasets demonstrate the utility of the proposed approach on microarray datasets with two classes of samples. The scalability of the proposed unified approach to multi-sample (more than two sample classes) microarray datasets is addressed using three sets of Parkinson's Data. Empirical analyses show that the unified framework outperformed other gene selection methods in selecting differentially expressed genes from microarray data.

Project description:BackgroundMicroarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles. In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes.ResultsIn this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms. We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with approximately 80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR.ConclusionIn this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.

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Association of differentially expressed genes with activation of mouse hepatic stellate cells by high-density cDNA microarray.

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Association of differentially expressed genes with activation of mouse hepatic stellate cells by high-density cDNA microarray.

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