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PubMedDate not supplied

Evaluation of an in vitro model of hepatic inflammatory response by gene expression profiling.

The body's response to biochemical stress involves coordinated changes in the expression of several sets of genes that regulate its return to homeostasis. Although several cell culture systems have been utilized for studying such complex physiological events in vitro, their assessment has been limited to biochemical assays on individual genes and proteins, limiting interpretation of the results in a systems context. Advances in genomics provide an opportunity to provide a more comprehensive assessment. In this study, we have used DNA microarrays to profile gene expression dynamics during interleukin 6-stimulated inflammation in hepatocytes maintained in a stable, collagen double-gel in vitro model system. The observed expression profile was also compared with that obtained from rat liver tissue after burn injury to determine the extent and nature of responses captured by the in vitro system. Our results indicate that several aspects of the in vivo hepatic inflammatory response can be captured by the in vitro system at the molecular systems level. Statistical analysis of the mRNA profiles was also used to characterize the temporal response in each model system and demonstrate similar behavior. A small panel of molecules involved in the hepatic acute-phase response was also profiled, using quantitative kinetic polymerase chain reaction, to confirm these observations. These results indicate the utility of the stable hepatocyte culture system for expression profiling of inflammatory states and for providing insights into the interplay of changes in gene expression during complex physiological states.

Acute-Phase Reaction↗
PubMed2003

Gene expression profiling in ecotoxicology.

Gene expression profiling is a powerful new end point for ecotoxicology and a means for bringing the genomics revolution to this field. We review the usefulness of gene expression profiling as an end point in ecotoxicology and describe methods for applying this approach to non-model organisms. Since genomes contain thousands of genes representing hundreds of pathways, it is possible to identify toxicant-specific responses from this wide array of possibilities. Stressor-specific signatures in gene expression profiles can be used to diagnose which stressors are impacting populations in the field. Screening for stress-induced genes requires special techniques in organisms without sequenced genomes. These techniques include differential display polymerase chain reaction (DD PCR), suppressive subtractive hybridization PCR (SSH PCR), and representational difference analysis. Gene expression profiling in model organisms like yeast has identified hundreds of genes that are up-regulated in response to various stressors, including several that are well characterized (e.g., hsp78, metallothionein, superoxide dismutase). Using consensus PCR primers from several animal sequences, it is possible to amplify some of these well characterized stress-induced genes from organisms of interest in ecotoxicology. We describe how several stress-induced genes can be grouped into cDNA arrays for rapidly screening samples.

Animals↗
PubMed2004

Microarray gene expression profiling and analysis in renal cell carcinoma.

BACKGROUND: Renal cell carcinoma (RCC) is the most common cancer in adult kidney. The accuracy of current diagnosis and prognosis of the disease and the effectiveness of the treatment for the disease are limited by the poor understanding of the disease at the molecular level. To better understand the genetics and biology of RCC, we profiled the expression of 7,129 genes in both clear cell RCC tissue and cell lines using oligonucleotide arrays. METHODS: Total RNAs isolated from renal cell tumors, adjacent normal tissue and metastatic RCC cell lines were hybridized to affymatrix HuFL oligonucleotide arrays. Genes were categorized into different functional groups based on the description of the Gene Ontology Consortium and analyzed based on the gene expression levels. Gene expression profiles of the tissue and cell line samples were visualized and classified by singular value decomposition. Reverse transcription polymerase chain reaction was performed to confirm the expression alterations of selected genes in RCC. RESULTS: Selected genes were annotated based on biological processes and clustered into functional groups. The expression levels of genes in each group were also analyzed. Seventy-four commonly differentially expressed genes with more than five-fold changes in RCC tissues were identified. The expression alterations of selected genes from these seventy-four genes were further verified using reverse transcription polymerase chain reaction (RT-PCR). Detailed comparison of gene expression patterns in RCC tissue and RCC cell lines shows significant differences between the two types of samples, but many important expression patterns were preserved. CONCLUSIONS: This is one of the initial studies that examine the functional ontology of a large number of genes in RCC. Extensive annotation, clustering and analysis of a large number of genes based on the gene functional ontology revealed many interesting gene expression patterns in RCC. Most notably, genes involved in cell adhesion were dominantly up-regulated whereas genes involved in transport were dominantly down-regulated. This study reveals significant gene expression alterations in key biological pathways and provides potential insights into understanding the molecular mechanism of renal cell carcinogenesis.

Adenocarcinoma, Clear Cell↗
PubMed2001

Muscular expressions: profiling genes in complex tissues.

Gene-expression profiling has yielded important information about simple systems, but complex tissues have not yet been widely profiled. Four recent studies of mammalian skeletal muscles have added to the catalogs of their gene expression differences, but have yet to lead to better understanding of the molecular processes underlying their physiological differences.

Animals↗
PubMed2004

Gene expression profiles post Roux-en-Y gastric bypass.

BACKGROUND: The hypothalamus is involved in regulation of food intake (FI) and fat deposition. Molecular mechanisms of weight loss after Roux-en-Y gastric bypass (RYGB) were studied by correlating changes in gene expression profiles in hypothalamic arcuate nucleus (ARC) and subcutaneous abdominal fat (SAF). METHOD: Diet-induced obese rats were divided into RYGB, sham-operated (SO-Obese), and sham-operated pair-fed (PF) groups. A non-obese group on a regular chow diet served as control. Body weight (BW) and FI were measured. Rats were killed 10 days after the operation. Plasma was analyzed for biochemical indices, ARC and SAF were analyzed for gene expression profiles. Body SAF was also weighed. Data were analyzed by ANOVA and factor analysis. RESULTS: BW and FI decreased in RYGB versus SO-Obese, as reflected by decreased SAF (53%). Genes similarly expressed in ARC and SAF after RYGB were limited to several genes that predominantly related to metabolic pathways of carbohydrate, fat, neuropeptide, and cytokines. These expression profiles were similar to those seen in chow control and to those seen in a comparison of PF and SO-Obese. CONCLUSIONS: RYGB-induced weight loss is associated with changes in gene profile expressions that could influence metabolic changes, contributing to weight loss.

Anastomosis, Roux-en-Y↗
PubMed2003

Profiling gene expression ratios of paired cancerous and normal tissue predicts relapse of esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma has heterogeneous clinical outcomes that cannot be predicted well using any existing clinical or molecular prognostic factors. Gene expression profiling may enable more precise prediction of the clinical outcome of these patients. We developed a new approach using gene expression ratios of paired cancerous and normal tissue specimens from the same patient to reduce the effects of variation among individuals. Using oligonucleotide microarrays, we analyzed total RNA expression levels corresponding to 12,600 transcript sequences in 24 paired cancerous and normal tissue operative specimens from 12 patients with esophageal squamous cell carcinoma. Hierarchical clustering analysis using gene expression ratios (cancer:normal) divided the 12 patients into two groups; all 7 patients in the first cluster survived without relapse (median follow-up, 483 days), whereas all 5 patients in the second cluster relapsed (median relapse-free survival time, 280 days; log-rank test, P = 0.006). In contrast, clustering either with cancerous tissue alone or with normal tissue alone did not show significant differences in the outcomes. The expressions of a variety of genes related with cell cycle, gene-repair, apoptosis and chemoradiotherpay resistance were up-regulated in the poor prognostic cluster. These results suggest that ratios of paired gene expression profiles may more efficiently predict relapse-free survival of esophageal squamous cell carcinoma than existing prognostic factors or than gene expression profiling with cancerous tissue alone.

Aged↗
PubMed2006

Characterization of a method for profiling gene expression in cells recovered from intact human prostate tissue using RNA linear amplification.

Coupling array technology to laser capture microdissection (LCM) has the potential to yield gene expression profiles of specific cell populations within tissue. However, remaining problems with linear amplification preclude accurate expression profiling when using the low nanogram amounts of RNA recovered after LCM of human tissue. We describe a novel robust method to reliably amplify RNA after LCM, allowing direct probing of 12K gene arrays. The fidelity of amplification was demonstrated by comparing the ability of amplified RNA (aRNA) versus that of native RNA to identify differentially expressed genes between two different cell lines, demonstrating a 99.3% concordance between observations. Array findings were validated by quantitative polymerase chain reaction analysis of a randomly selected subset of 32 genes. Using LCM to recover normal (N=5 subjects) or cancer (N=3) cell populations from intact human prostate tissue, three differentially expressed genes were identified. Independent investigators have previously identified differential expression of two of these three genes, hepsin and beta-microseminoprotein, in prostate cancer. Taken together, the current study demonstrates that accurate gene expression profiling can readily be performed on specific cell populations present within complex tissue. It also demonstrates that this approach efficiently identifies biologically relevant genes.

Cell Line, Tumor↗
PubMed2004

Gene-expression profiling reveals distinct expression patterns for Classic versus Variant Merkel cell phenotypes and new classifier genes to distinguish Merkel cell from small-cell lung carcinoma.

Merkel cell carcinoma (MCC) is a rare aggressive skin tumor which shares histopathological and genetic features with small-cell lung carcinoma (SCLC), both are of neuroendocrine origin. Comparable to SCLC, MCC cell lines are classified into two different biochemical subgroups designated as 'Classic' and 'Variant'. With the aim to identify typical gene-expression signatures associated with these phenotypically different MCC cell lines subgroups and to search for differentially expressed genes between MCC and SCLC, we used cDNA arrays to profile 10 MCC cell lines and four SCLC cell lines. Using significance analysis of microarrays, we defined a set of 76 differentially expressed genes that allowed unequivocal identification of Classic and Variant MCC subgroups. We assume that the differential expression levels of some of these genes reflect, analogous to SCLC, the different biological and clinical properties of Classic and Variant MCC phenotypes. Therefore, they may serve as useful prognostic markers and potential targets for the development of new therapeutic interventions specific for each subgroup. Moreover, our analysis identified 17 powerful classifier genes capable of discriminating MCC from SCLC. Real-time quantitative RT-PCR analysis of these genes on 26 additional MCC and SCLC samples confirmed their diagnostic classification potential, opening opportunities for new investigations into these aggressive cancers.

Biomarkers, Tumor↗
PubMed2002

Gene expression profiling: methodological challenges, results, and prospects for addiction research.

This review describes the current methods used to profile gene expression. These methods include microarrays, spotted arrays, serial analysis of gene expression (SAGE), and massive parallel signature sequencing (MPSS). Methodological and statistical problems in interpreting microarray and spotted array experiments are also discussed. Methods and formats such as minimum information about microarray experiments (MIAME) needed to share gene expression data are described. The last part of the review provides an overview of the application of gene-expression profiling technology to substance abuse research and discusses future directions.

Animals↗
PubMed2004

Monitoring of gene expression profiles and isolation of candidate genes involved in pollination and fertilization in rice ( Oryza sativa L.) with a 10K cDNA microarray.

To monitor gene expression profiles during pollination and fertilization in rice at a genome scale, we generated 73,424 high-quality expressed sequence tags (ESTs) derived from the green/etiolated shoot and pistil (0-5 h after pollination, 5hP) of rice, which were subsequently used to construct a cDNA microarray containing ca. 10 000 unique rice genes. This microarray was used to analyze gene expression in pistil unpollinated (UP), 5hP and 5DAP(5 days after pollination), anther, shoot, root, 10-day-old embryo (10EM) and 10-day-old endosperm (10EN). Clustering analysis revealed that the anther has a gene-expression profile more similar to root than to pistil and most pistil-preferentially expressed genes respond to pollination and/or fertilization. There are 253 ESTs exhibiting differential expression (e +/- 2-fold changes) during pollination and fertilization, and about 70% of them can be assigned a putative function. We also recovered 20 genes similar to pollination-related and/or fertility-related genes previously identified as well as genes that were not implicated previously. Microarray and real-time PCR analyses showed that the array sensitivity was estimated at 1-5 copies of mRNA per cell, and the differentially expressed genes showed a high correlation between the two methods. Our results indicated that this cDNA microarray constructed here is reliable and can be used for monitoring gene expression profiles in rice. In addition, the genes that differentially expressed during pollination represent candidate genes for dissecting molecular mechanism of this important biological process in rice.

Cluster Analysis↗
PubMed2001

Gene expression profiling using a novel method: amplified differential gene expression (ADGE).

Amplified differential gene expression (ADGE) is a novel technique, designed to profile gene expression of the whole transcriptome or to compare expression of a set of genes between two samples. ADGE employs hybridization to quadratically amplify the ratio of an expressed gene between control and tester samples before displaying. The subtle structures of adapters and primers are designed for displaying the amplified ratio of an expressed gene between two samples. Four selective nucleotides at the 3' end of primers are used to increase PCR efficiency for targeted molecules and to improve detection of PCR products. Double PCR with the same pair of primers expands the detection range, especially for genes of low abundance. Integration of these steps makes ADGE sensitive and accurate. Application to drug resistant human tumor cell lines showed that ADGE accurately profiled expression levels for induced, repressed or unchanged genes. The qualitative expression patterns for ADGE were verified with RT-PCR.

DNA Primers↗
PubMed2005

Gene expression profile reveals deregulation of genes with relevant functions in the different subclasses of acute myeloid leukemia.

Bone marrow samples from 43 adult patients with de novo diagnosed acute myeloid leukemia (AML)--10 acute promyelocytic leukemias (APL) with t(15;17), four AML with inv(16), seven monocytic leukemias and 22 nonmonocytic leukemias--were analyzed using high-density oligonucleotide microarrays. Hierarchical clustering analysis segregated APL, AML with inv(16), monocytic leukemias and the remaining AML into separate groups. A set of only 21 genes was able to assign AML to one of these three classes: APL, inv(16) and other AML subtype without a specific translocation. Quantitative RT-PCR performed for 18 out of these predictor genes confirmed microarray results. APL expressed high levels of FGF13 and FGFR1 as well as two potent angiogenic factors, HGF and VEGF. AML with inv(16) showed an upregulation of MYH11 and a downregulation of a gene encoding a core-binding factor protein, RUNX3. Genes involved in cell adhesion represented the most altered functional category in monocytic leukemias. Two major groups emerged from the remaining 22 AML: cluster A with 10 samples and cluster B with 12. All the eight leukemias that were either refractory to treatment or that relapsed afterwards were assigned to cluster B. In the latter cluster, CD34 upregulation and serine proteases downregulation is consistent with a maturation arrest and lack of granulocytic differentiation.

Adolescent↗
PubMed2003

A Bayesian missing value estimation method for gene expression profile data.

MOTIVATION: Gene expression profile analyses have been used in numerous studies covering a broad range of areas in biology. When unreliable measurements are excluded, missing values are introduced in gene expression profiles. Although existing multivariate analysis methods have difficulty with the treatment of missing values, this problem has received little attention. There are many options for dealing with missing values, each of which reaches drastically different results. Ignoring missing values is the simplest method and is frequently applied. This approach, however, has its flaws. In this article, we propose an estimation method for missing values, which is based on Bayesian principal component analysis (BPCA). Although the methodology that a probabilistic model and latent variables are estimated simultaneously within the framework of Bayes inference is not new in principle, actual BPCA implementation that makes it possible to estimate arbitrary missing variables is new in terms of statistical methodology. RESULTS: When applied to DNA microarray data from various experimental conditions, the BPCA method exhibited markedly better estimation ability than other recently proposed methods, such as singular value decomposition and K-nearest neighbors. While the estimation performance of existing methods depends on model parameters whose determination is difficult, our BPCA method is free from this difficulty. Accordingly, the BPCA method provides accurate and convenient estimation for missing values. AVAILABILITY: The software is available at http://hawaii.aist-nara.ac.jp/~shige-o/tools/.

Algorithms↗
PubMed2006

Temporal gene expression profiles of target-ablated olfactory epithelium in mice with disrupted expression of scavenger receptor A: impact on macrophages.

Target ablation [removal of the olfactory bulb (OBX)] induces apoptotic death of olfactory sensory neurons (OSNs) and an immune response in which activation and recruitment of macrophages (ms) into the olfactory epithelium (OE) occupy a central role. Ms phagocytose apoptotic neurons and secrete cytokines/growth factors that regulate subsequent progenitor cell proliferation and neurogenesis. Scavenger receptor A (SR-A) is a pattern recognition receptor that mediates binding of ms to apoptotic cells and other relevant immune response functions. The aim of this study was to determine the impact of the absence of SR-A on the immune response to OBX. The immune response to OBX was evaluated in mice in which functional expression of the m scavenger receptor (MSR) was eliminated by gene disruption (MSR-/-) and wild-type (wt) mice of the same genetic background. OBX induced significant apoptotic death of mature OSNs in the two strains. However, subsequent m infiltration and activation and progenitor cell proliferation were significantly reduced in MSR-/- vs. wt mice. Gene expression profiling at short intervals after OBX demonstrated significant differences in temporal patterns of expression of several gene categories, including immune response genes. Many immune response genes that showed different temporal patterns of expression are related to m function, including cytokine and chemokine secretion, phagocytosis, and m maturation and activation. These studies suggest that impairment of the immune response to OBX in the OE of MSR-/- mice most likely resulted from decreased m adhesion and subsequent reduced infiltration and activation, with a resultant decrease in neurogenesis.

Animals↗
PubMed2000

Tissue classification with gene expression profiles.

Constantly improving gene expression profiling technologies are expected to provide understanding and insight into cancer-related cellular processes. Gene expression data is also expected to significantly aid in the development of efficient cancer diagnosis and classification platforms. In this work we examine three sets of gene expression data measured across sets of tumor(s) and normal clinical samples: The first set consists of 2,000 genes, measured in 62 epithelial colon samples (Alon et al., 1999). The second consists of approximately equal to 100,000 clones, measured in 32 ovarian samples (unpublished extension of data set described in Schummer et al. (1999)). The third set consists of approximately equal to 7,100 genes, measured in 72 bone marrow and peripheral blood samples (Golub et al, 1999). We examine the use of scoring methods, measuring separation of tissue type (e.g., tumors from normals) using individual gene expression levels. These are then coupled with high-dimensional classification methods to assess the classification power of complete expression profiles. We present results of performing leave-one-out cross validation (LOOCV) experiments on the three data sets, employing nearest neighbor classifier, SVM (Cortes and Vapnik, 1995), AdaBoost (Freund and Schapire, 1997) and a novel clustering-based classification technique. As tumor samples can differ from normal samples in their cell-type composition, we also perform LOOCV experiments using appropriately modified sets of genes, attempting to eliminate the resulting bias. We demonstrate success rate of at least 90% in tumor versus normal classification, using sets of selected genes, with, as well as without, cellular-contamination-related members. These results are insensitive to the exact selection mechanism, over a certain range.

Cluster Analysis↗
PubMed2005

Microarray-based gene expression profiling of benign, atypical and anaplastic meningiomas identifies novel genes associated with meningioma progression.

To identify gene expression profiles associated with human meningiomas of different World Health Organization (WHO) malignancy grades, we analyzed 30 tumors (13 benign meningiomas, WHO grade I; 12 atypical meningiomas, WHO grade II; 5 anaplastic meningiomas, WHO grade III) for the expression of 2,600 genes using cDNA-microarray technology. Receiver operator curve (ROC) analysis with a cutoff value of 45% selection probability identified 37 genes with decreased and 27 genes with increased expression in atypical and anaplastic meningiomas, compared to benign meningiomas. Supervised classification of the tumors did not reveal specific expression patterns representative of each WHO grade. However, anaplastic meningiomas could be distinguished from benign meningiomas by differential expression of a distinct set of genes, including several ones associated with cell cycle regulation and proliferation. Investigation of potential correlations between microarray expression data and genomic aberrations, detected by comparative genomic hybridization (CGH), demonstrated that losses on chromosomes 10 and 14 were associated with distinct expression profiles, including increased expression of several genes related to the insulin-like growth factor (IGF) (IGF2, IGFBP3 and AKT3) or wingless (WNT) (CTNNB1, CDK5R1, ENC1 and CCND1) pathways. Taken together, our microarray-based expression profiling revealed interesting novel candidate genes and pathways that may contribute to meningioma progression.

Chromosome Mapping↗
PubMed2005

Validation and application of a high fidelity mRNA linear amplification procedure for profiling gene expression.

The need for microgram quantities of RNA for microarray experiments has hindered application of this novel technology in cell types/tissue samples with limited abundance of RNA. In this study, potential application of T7-based linear RNA amplification was investigated for use in gene expression profiling experiments where starting material is limited. Yield and integrity of amplified antisense RNA (aaRNA), microarray hybridization intensities, and fidelity of differential gene expression detected were determined for arrays generated for unamplified versus amplified RNA from the same homogenous starting pools. Total RNA was extracted from bovine spleen and fetal ovary, serially diluted to concentrations ranging from 2 microg to 500 pg and amplified. Quality and quantity of total input RNA and aaRNA were assessed by spectrophotometry, gel electrophoresis and bioanalyzer. In experiment 1, we determined the optimal amounts of aaRNA generated from 20, 40, 200 ng and 2 microg input total RNA for use in cDNA synthesis, labeling and array hybridization that would yield robust and consistent hybridization signals on a bovine oocyte cDNA microarray. In experiment 2, comparison of microarray hybridization intensities and fidelity of differential gene expression between aaRNA generated from 2, 20 and 40 ng input total RNA versus unamplified RNA (uRNA) were conducted. The hybridization intensities for each of the 7000 spots per slide for microarrays conducted using aaRNA versus uRNA were highly correlated (2 ng = 0.84, 20 ng = 0.88, 40 ng = 0.90; P < 0.01). The false positive rate was low and similar (4.0% versus 4.4%) for arrays done with uRNA and aaRNA. Ninety-seven ESTs were detected as differentially expressed in the fetal ovary versus spleen at > 1.5- or < 0.5-fold using uRNA (P < 0.05). However, the number of genes detected in arrays using aaRNA was approximately 1.5-2.5 times greater than with uRNA. Approximately, 65-70% of differentially expressed genes were common between uRNA and aaRNA arrays. Relative fold-expression (Cy3/Cy5 ratios) for 25 overlapping abundant genes was comparable for uRNA versus aaRNA arrays with 2 and 20 ng total RNA as input. Results demonstrate that T7-based linear amplification of small amounts of input RNA and use of aaRNA in microarray experiments retains fidelity of detection of differential gene expression that is relatively comparable to experiments done with uRNA and provides a potentially viable approach to facilitate gene expression profiling using limited amounts of starting material.

Animals↗
PubMed2001

Statistical estimation of cluster boundaries in gene expression profile data.

MOTIVATION: Gene expression profile data are rapidly accumulating due to advances in microarray techniques. The abundant data are analyzed by clustering procedures to extract the useful information about the genes inherent in the data. In the clustering analyses, the systematic determination of the boundaries of gene clusters, instead of by visual inspection and biological knowledge, still remains challenging. RESULTS: We propose a statistical procedure to estimate the number of clusters in the hierarchical clustering of the expression profiles. Following the hierarchical clustering, the statistical property of the profiles at the node in the dendrogram is evaluated by a statistics-based value: the variance inflation factor in the multiple regression analysis. The evaluation leads to an automatic determination of the cluster boundaries without any additional analyses and any biological knowledge of the measured genes. The performance of the present procedure is demonstrated on the profiles of 2467 yeast genes, with very promising results. AVAILABILITY: A set of programs will be electronically sent upon request. CONTACT: horimoto@post.saga-med.ac.jp; toh@beri.co.jp

Analysis of Variance↗