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Results for “Gene expression profiling”

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PubMed2004

Quantitative gene expression profiling implicates genes for susceptibility and resistance to alveolar bone loss.

Periodontal disease is one of the most prevalent chronic inflammatory diseases. There is a genetic component to susceptibility and resistance to this disease. Using a mouse model, we investigated the progression of alveolar bone loss by gene expression profiling of susceptible and resistant mouse strains (BALB/cByJ and A/J, respectively). We employed a novel and sensitive quantitative real-time PCR method to compare basal RNA transcription of a 48-gene set in the gingiva and the spleen and the subsequent changes in gene expression due to Porphyromonas gingivalis oral infection. Basal expression of interleukin-1 beta (Il1b) and tumor necrosis factor alpha (Tnf) mRNA was higher in the gingiva of the susceptible BALB/cByJ mice than in the gingiva of resistant A/J mice. Gingival Il1b gene expression increased further and Stat6 gene expression was turned on after P. gingivalis infection in BALB/cByJ mice but not in A/J mice. The basal expression of interleukin-15 (Il15) in the gingiva and the basal expression of p-selectin (Selp) in the spleen were higher in the resistant A/J mice than in the susceptible BALB/cByJ mice. In the resistant A/J mice the expression of no genes detectably changed in the gingiva after infection. These results suggest a molecular phenotype in which discrete sets of differentially expressed genes are associated with genetically determined susceptibility (Il1b, Tnf, and Stat6) or resistance (Il15 and Selp) to alveolar bone loss, providing insight into the genetic etiology of this complex disease.

Alveolar Bone Loss↗
PubMed2004

Gene expression profiling in human insulinoma tissue: genes involved in the insulin secretion pathway and cloning of novel full-length cDNAs.

Insulinoma is a clinically common cause of organic hypoglycemia. The prominent characteristic of insulinoma is endogenous hyperinsulinism. Until now, the molecular biology of human insulinoma has been little understood. In this study, gene expression profiling of human insulinoma was established by expressed sequence tag (EST) sequencing and cDNA array. A total of 2063 clones were obtained, of these, 1589 clones were derived from EST sequencing, 975 clones were derived from cDNA array and 501 clones were shared by the two methods. G protein alpha-stimulating activity polypeptide (Gsalpha) and carboxypeptidase E (CPE) were the most highly expressed genes in human insulinoma, as derived by EST sequencing and cDNA array respectively. The genes involved in the protein/insulin secretion pathway were strongly expressed in human insulinoma tissue. Meanwhile, eight full-length cDNAs of novel genes were cloned and sequenced. The results demonstrated the molecular biology of human insulinoma tissue at the level of transcript abundance and validated the efficacy of EST sequencing combined with cDNA array in the construction of gene expression profiling. In conclusion, the predominance of the genes participating in the secretory pathway suggested that regulation of secretion might be a major mechanism by which insulin release is abnormally increased in patients with insulinomas. It was also concluded that overexpression of the Gsalpha gene played an important role in the pathogenesis of insulinoma.

Cloning, Molecular↗
PubMed1999

Classification of small cell lung cancer and pulmonary carcinoid by gene expression profiles.

Small cell lung cancer is a common type of lung cancer that is generally classified within the spectrum of neuroendocrine lung neoplasms. Using high-density cDNA arrays, we profiled gene expression of small cell lung cancers and compared these expression profiles to those of normal bronchial epithelial cells and pulmonary carcinoids, which are classified as benign neuroendocrine tumors. We found the overall expression profiles of two small cell lung cancer cell lines, two microdissected tissue samples of primary small cell lung cancer, and cultured bronchial epithelial cells to be relatively similar to one another, with an average Pearson correlation coefficient for these comparisons of 0.63. However, we found the expression profiles of small cell lung cancers (and bronchial epithelial cells) to be surprisingly dissimilar to those of two samples of pulmonary carcinoid tumors, with an average correlation coefficient for these comparisons of 0.20. We then compared the pulmonary carcinoid expression profiles to those of two samples of infiltrating astrocytic brain cancers (oligodendroglioma and high-grade astrocytoma) and found similarity of gene expression among these four samples (average correlation coefficient, 0.57). These gene expression profiles suggest that small cell lung cancers are closely related to (and possibly derived from) epithelial cells, and that pulmonary carcinoids are related to neural crest-derived brain tumors. More generally, our results suggest that broad profiles of gene expression may reveal similarities and differences between tumors that are not apparent by traditional morphological criteria.

Carcinoid Tumor↗
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↗
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↗
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↗
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↗
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↗
PubMed2004

Profiling gene expression in Coccidioides posadasii.

Coccidioides posadasii is a dimorphic fungal pathogen which grows as a filamentous saprobe in the soil and multicellular parasitic form in host lung tissue. Studies of gene expression profiles during saprobic and parasitic phase development can provide clues about morphogenetic regulation and may lead to the discovery of molecular targets for novel antifungal drugs. Suppression-subtractive hybridization (SSH) and quantitative real-time polymerase chain reaction (QRT-PCR) were used to identify and quantify differential gene expression during in vitro growth of Coccidioides. DNA fragments obtained from the subtraction of cDNA pools derived from the saprobic and parasitic phase RNA preparations were each cloned into an appropriate vector and subjected to sequence analysis. Semi-quantitative, reverse transcription polymerase chain reaction (RT-PCR) experiments were first conducted to assess whether these inserts represented differentially expressed genes. Nucleotide sequences of the partial and full-length genes selected by RT-PCR were obtained by genome walking and rapid amplification of cDNA ends (RACE) methods. QRT-PCR analysis of the expression of these genes during saprobic and parasitic cell growth was then conducted using DNA standard curves normalized to a constitutively expressed control gene. Four C. posadasii genes whose expression is essentially restricted to the parasitic cycle were discovered using this approach. These genes include homologues of OPS1 (encodes opsin-related protein), MDR1 (multidrug resistance protein), ALDR1 (aldehyde reductase), and PSP1 (hypothetical lipid transporter/flippase protein). The combined applications of SSH and QRT-PCR permit global analysis of gene expression patterns in C. posadasii.

Coccidioides↗
PubMed2002

Temporal gene expression profile of human precursor B leukemia cells induced by adhesion receptor: identification of pathways regulating B-cell survival.

The physical interactions between B cells and stromal cells from the lymphoid tissue microenvironment are critical to the survival of normal and malignant B cells. They are principally mediated by integrins expressed on B cells and counterreceptors on stromal cells. Specifically, alpha4beta1 integrin engagement rescues B cells from physiological or drug-induced apoptosis. Therefore, in order to understand the mechanisms by which integrins prevent apoptosis in leukemia B cells, we compared the temporal gene expression profiles induced by beta1-integrin ligation with fibronectin (Fn) or adhesion by poly-L-Lysine in serum-starved precursor B leukemia cells. Among the 38 selected differentially expressed genes, 6 genes involved in adhesion (VAV2, EPB41L1, CORO1A), proliferation (FRAP1, CCT4), and intercellular communication (GJB3) were validated by real-time quantitative polymerase chain reaction (RT-Q-PCR). Gene expression modulation could also be validated at the protein level for 5 other genes. We show that integrin stimulation up-regulated FBI-1 expression but inhibited CD79a, Requiem, c-Fos, and caspase 7 induction when the cells underwent apoptosis. We further demonstrate that Fn stimulation also inhibits caspase 3 activation but increases XIAP and survivin expression. Moreover, integrin stimulation also prevents caspase activation induced by doxorubicin. Therefore, we identified genes modulated by adhesion of human precursor B leukemia cells that regulate proliferation and apoptosis, highlighting new pathways that might provide insights into future therapy aiming at targeting apoptosis of leukemia cells.

Apoptosis↗
PubMed2003

Profiling gene expression in kidney development.

Insights into the normal development of the kidney have been derived from metanephric organ cultures, knockout mice and the later definition of the responsible single genetic abnormalities in some human congenital renal tract malformations. Deciphering of the human genome and the ability to examine multiple messenger RNA has opened new vistas and the capacity to begin to understand the complex kidney developmental process. In this review we focus on recent large-scale gene expression profiles performed during normal murine and human nephrogenesis and suggest how they might provide insights into normal kidney development as well as aberrations of this normal process that may result in renal dysplasia or pediatric renal malignancy (Wilms' tumor).

Animals↗