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Results for “Gene Expression Profiling”

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At least 19 recordsLinked to original sources
PubMed2001

Analysis of host responses to microbial infection using gene expression profiling.

Gene expression profiling offers new opportunities for understanding host-cell responses to microbial pathogens and their products. Current strategies involve either first identifying mRNAs that differ in their expression status under different experimental conditions and later defining the identity of the respective genes (for example, differential display or serial analysis of gene expression), or alternatively assessing changes in the expression of already defined genes (for example, cDNA or oligonucleotide microarrays). Early studies indicate the power of gene expression profiling for providing new insights into groups of genes whose expression is altered during the course of host-microbe interactions, and for the discovery of cellular genes that were not previously recognized to be regulated by infection.

Gene Expression Profiling↗
PubMed2006

Multiple dysregulated pathways in nasopharyngeal carcinoma revealed by gene expression profiling.

Gene expression profiling was conducted using primary human nasopharyngeal carcinoma (NPC) biopsy samples to improve the understanding of the molecular pathways defining NPC and to identify novel potential therapeutic targets. RNA samples were extracted from 36 patients suspected to have NPC and hybridized onto the Affymetrix U133A chip. NPC was diagnosed in 19 patients, 11 had lymphoid hyperplasia (LH), and 6 were "normal" biopsies. Clinical stages for these NPC patients ranged from I-IV, including one M1. All NPC patients (except the M1) were treated with curative intent, which included radiotherapy alone (4 patients), or combined with chemotherapy (14 patients). Unsupervised clustering demonstrated a distinct NPC expression pattern, compared to normal biopsies. Subsequent Significance Analysis of Microarrays (SAM) derived from 14 NPC and 6 normal samples discovered 1,089 differentially regulated genes. Pathway analyses revealed novel insights into the mechanisms leading to NPC, whereby upregulation of NFkappaB2 and survivin play central roles in increasing resistance to apoptosis, and changes in integrin and WNT/beta-catenin signaling leading to uncontrolled proliferation. The role of survivin in resisting apoptosis in NPC was confirmed by RNA interference. Our data provide novel insights into the development and progression of NPC, and suggest survivin as a novel therapeutic target for NPC.

Apoptosis↗
PubMed2002

Molecular diagnosis of lymphoid malignancies by gene expression profiling.

Gene expression profiling using DNA microarrays has great potential to improve the understanding, diagnosis, and management of lymphomas, leukemias, and other malignancies. Gene expression profiling studies of diffuse large B-cell lymphoma (DLBCL) have shown that this diagnostic category encompasses at least two molecularly distinct diseases, differing in differentiation stage (cell of origin), oncogenic mechanisms, and clinical outcome. Gene expression profiling revealed that the antiapoptotic NF-kappaB pathway is constitutively active in one DLBCL subgroup, termed activated B cell-like DLBCL, and subsequent studies validated NF-kappaB as a therapeutic target in this type of lymphoma. DNA microarray studies of chronic lymphocytic leukemia (CLL) have led to a gene expression-based predictor that identifies two subtypes of CLL that differ with respect to clinical course and presence of immunoglobulin gene mutations in the CLL cells. These findings underscore the value of gene expression profiling in defining subtypes within the lymphoid malignancies that are molecularly and clinically distinct and argue that this genomic technology should become an integral part of prospective clinical trials.

DNA, Neoplasm↗
PubMed2005

The biology of human lymphoid malignancies revealed by gene expression profiling.

Gene expression profiling provides a quantitative molecular framework for the study of human lymphomas. This genomic technology has revealed that existing diagnostic categories are comprised of multiple molecularly and clinically distinct diseases. Diffuse large B-cell lymphoma (DLBCL), for example, consists of three gene expression subgroups, termed germinal center B-cell-like (GCB) DLBCL, activated B-cell-like (ABC) DLBCL, and primary mediastinal B-cell lymphoma (PMBL). These DLBCL subgroups arise from different stages of normal B-cell differentiation, utilize distinct oncogenic mechanisms, and differ in their ability to be cured by chemotherapy. Key regulatory factors and their target genes are differentially expressed among these subgroups, including BCL-6, Blimp-1, and XBP1. ABC DLBCL and PMBL depend upon constitutive activation of the NF-kappaB pathway for their survival but GCB DLBCL does not, demonstrating that this pathway is a potential therapeutic target for certain DLBCL subgroups. In DLBCL, mantle cell lymphoma, and follicular lymphoma, gene expression profiling has also been used to create gene expression-based models of survival, which have identified the biological characteristics of the tumors that influence their clinical behavior. In mantle cell lymphoma, the length of survival following diagnosis is primarily influenced by the tumor proliferation rate, which can be quantitatively measured by a proliferation gene expression "signature." Based on this accurate measure, the proliferation rate can now be viewed as an integration of several oncogenic lesions that each increase progression from the G1 to the S phase of the cell cycle. In DLBCL and follicular lymphoma, gene expression profiling has revealed that the molecular characteristics of non-malignant tumor-infiltrating immune cells have a major influence on the length of survival. The implications of these insights for the diagnosis and treatment of non-Hodgkin lymphomas are discussed.

B-Lymphocytes↗
PubMed2005

Comparison of non-human primate and human whole blood tissue gene expression profiles.

Gene expression profiling is an important tool in the development of medical countermeasures against chemical warfare agents (CWAs). Non-human primates (NHPs), specifically the rhesus macaque (Macaca mulatta), the cynomologus macaque (Macaca fascicularis), and the African green monkey (Chlorocebus aethiops), are vital models in the development of CWA prophylactics, therapeutics, and diagnostics. However, gene expression profiling of these NHPs is complicated by the fact their genomes are not completely sequenced, and that no commercially available oligonucleotide microarrays (genechips) exist. We, therefore, sought to determine whether gene expression profiling of NHPs could be performed using human genechips. Whole blood RNA was isolated from each species and used to generate genechip probes. Hybridization of the NHP samples to human genechips (Affymetrix Human U133 Plus 2.0) resulted in comparable numbers of transcripts detected compared with human samples. Statistical analysis revealed intraspecies reproducibility of genechip quality control metrics; interspecies comparison between NHPs and humans showed little significant difference in the quality and reproducibility of data generated using human genechips. Expression profiles of each species were compared using principal component analysis (PCA) and hierarchical clustering to determine the similarity of the expression profiles within and across the species. The cynomologus group showed the least intraspecies variability, and the human group showed the greatest intraspecies variability. Intraspecies comparison of the expression profiles identified probe sets that were reproducibly detected within each species. Each NHP species was found to be dissimilar to humans; the cynomologus group was the most dissimilar. Interspecies comparison of the expression profiles revealed probe sets that were reproducibly detected in all species examined. These results show that human genechips can be used for expression profiling of NHP samples and provide a foundation for the development of tools for comparing human and NHP gene expression profiles.

Animals↗
PubMed2006

Quantification of N-(deoxyguanosin-8-yl)-4-aminobiphenyl adducts in human lymphoblastoid TK6 cells dosed with N-hydroxy-4-acetylaminobiphenyl and their relationship to mutation, toxicity, and gene expression profiling.

Gene expression profiles that are anchored to phenotypic endpoints may lead to the identification of signatures that predict mutagenicity or carcinogenicity. The study presented here describes the analysis of DNA adducts in the human TK6 lymphoblastoid cell line after exposure to N-hydroxy-4-aminobiphenyl, a mutagenic metabolite of 4-aminobiphenyl. A validated nano-LC microelectrospray mass spectrometry assay is reported for the detection and quantification of N-(deoxyguanosin-8-yl)-4-aminobiphenyl (dG-C8-ABP), the principal DNA adduct of 4-aminobiphenyl. Limits of quantification, based on a signal-to-noise ratio of 10:1, are determined to correspond to approximately 27 fg of dG-C8-ABP injected on-column. The assay has been used to measure the steady-state levels of the adduct in the human TK6 lymphoblastoid cell line as a function of dose (0.5, 1.0, and 10.0 microM) and time (2, 6, and 27 h) after exposure to N-hydroxy-4-aminobiphenyl. The levels of dG-C8-ABP adducts in the cells, ranging from 18 to 500 adducts in 10(9) nucleotides, were then correlated to cell toxicity, induced mutation at the TK (thymidine kinase) and HPRT loci, and gene expression profiling through microarray analysis. Cell cultures were evaluated for toxicity by growth curve extrapolation, mutation assays were performed on the HPRT and TK loci, and gene expression profiles were generated by analyses using microarray technology. In the mutation assay analysis, as the toxicant concentration increased, there was an increase in mutation fraction, indicating a direct correlation to metabolite dosing level and mutations occurring at these two loci. Statistical analysis of the gene expression data determined that a total of 2250 genes exhibited statistically significant changes in expression after treatment with N-OH-AABP (P < 0.05). Among the genes identified, 2245 were up-regulated, whereas 5 genes that had functions in cell survival and cell growth and, hence, could be indicators of toxicity, were down-regulated relative to controls. The results demonstrate the value of anchoring gene expression patterns to phenotypic markers, such as DNA adduct levels, toxicity, and mutagenicity.

Aminobiphenyl Compounds↗
PubMed2003

Serial analysis of gene expression in circulating gamma delta T cell subsets defines distinct immunoregulatory phenotypes and unexpected gene expression profiles.

Gene expression profiles were compared in circulating bovine GD3.5+ (CD8-) and GD3.5- (predominantly CD8+) gammadelta T cells using serial analysis of gene expression (SAGE). Approximately 20,000 SAGE tags were generated from each library. A comparison of the two libraries demonstrated 297 and 173 tags representing genes with 5-fold differential expression in GD3.5+ and GD3.5- gammadelta T cells, respectively. Consistent with their localization into sites of inflammation, GD3.5+ gammadelta T cells appeared transcriptionally and translationally more active than GD3.5- gammadelta cells. GD3.5- gammadelta T cells demonstrated higher expression of the cell proliferation inhibitor BAP 37, which was associated with their less activated gene expression phenotype. The immune regulatory and apoptosis-inducing molecule, galectin-1, was identified as a highly abundant molecule and was higher in GD3.5+gammadelta T cells. Surface molecules attributed to myeloid cells, such as CD14, CD68, and scavenger receptor-1, were identified in both populations. Furthermore, expression of B lymphocyte-induced maturation protein, a master regulator of B cell and myeloid cell differentiation, was identified by SAGE analysis and was confirmed at the RNA level to be selectively expressed in gammadelta T cells vs alphabeta T cells. These results provide new insights into the inherent differences between circulating gammadelta T cell subsets.

Animals↗
PubMed2004

Sister grouping of chimpanzees and humans as revealed by genome-wide phylogenetic analysis of brain gene expression profiles.

Gene expression profiles from the anterior cingulate cortex (ACC) of human, chimpanzee, gorilla, and macaque samples provide clues about genetic regulatory changes in human and other catarrhine primate brains. The ACC, a cerebral neocortical region, has human-specific histological features. Physiologically, an individual's ACC displays increased activity during that individual's performance of cognitive tasks. Of approximately 45,000 probe sets on microarray chips representing transcripts of all or most human genes, approximately 16,000 were commonly detected in human ACC samples and comparable numbers, 14,000-15,000, in gorilla and chimpanzee ACC samples. Phylogenetic results obtained from gene expression profiles contradict the traditional expectation that the non-human African apes (i.e., chimpanzee and gorilla) should be more like each other than either should be like humans. Instead, the chimpanzee ACC profiles are more like the human than like the gorilla; these profiles demonstrate that chimpanzees are the sister group of humans. Moreover, for those unambiguous expression changes mapping to important biological processes and molecular functions that statistically are significantly represented in the data, the chimpanzee clade shows at least as much apparent regulatory evolution as does the human clade. Among important changes in the ancestry of both humans and chimpanzees, but to a greater extent in humans, are the up-regulated expression profiles of aerobic energy metabolism genes and neuronal function-related genes, suggesting that increased neuronal activity required increased supplies of energy.

Animals↗
PubMed2006

Molecular diagnosis of chronic liver disease and hepatocellular carcinoma: the potential of gene expression profiling.

Gene expression profile analysis through DNA microarrays and other high-throughput technologies permit simultaneous investigation of all genes within a biologic sample, providing a snapshot of the transcriptional state of healthy or diseased tissue. Although most of the current applications are still geared toward research (study of disease mechanisms/signaling pathways involved, identification of novel oncogenes/tumor suppressor genes), clinical diagnostic applications of this approach are beginning to enter more routine molecular usage. There are clear examples where a group of genes, or "signature," can provide clinically useful information (e.g., cancer prognosis and response to treatment). Importantly, the identification of genes that are up-regulated during tumorigenesis is heralding a new era of targeted molecular therapies in oncology. In addition, the high capacity of the technologies available allow for the identification of new biomarkers for early diagnosis. In the future, gene expression profiling ("disease fingerprinting") is likely to complement liver biopsy in the molecular differential diagnosis of chronic liver diseases and hepatocellular carcinoma (HCC). There has already been some success in the identification of subtypes of HCC based on derived sets of signature gene clusters. Data recently reported have been able to provide the first molecular classification of HCC, although more comprehensive studies are required to confirm these results and translate them into the clinical practice. In fact, variations in gene expression in normal and diseased livers, as well technical factors, are obstacles to the routine use of microarray-based methods in the liver clinic. Continued progress is anticipated in the practical application of gene array methods to refine diagnosis and therapy of HCC.

Carcinoma, Hepatocellular↗
PubMed2001

Arrays of arrays for high-throughput gene expression profiling.

Gene expression profiling using DNA arrays is rapidly becoming an essential tool for research and drug discovery and may soon play a central role in disease diagnosis. Although it is possible to make significant discoveries on the basis of a relatively small number of expression profiles, the full potential of this technology is best realized through more extensive collections of expression measurements. The generation of large numbers of expression profiles can be a time-consuming and labor-intensive process with current one-at-a-time technology. We have developed the ability to obtain expression profiles in a highly parallel yet straightforward format using glass wafers that contain 49 individual high-density oligonucleotide arrays. This arrays of arrays concept is generalizable and can be adapted readily to other types of arrays, including spotted cDNA microarrays. It is also scalable for use with hundreds and even thousands of smaller arrays on a single piece of glass. Using the arrays of arrays approach and parallel preparation of hybridization samples in 96-well plates, we were able to determine the patterns of gene expression in 27 ovarian carcinomas and 4 normal ovarian tissue samples, along with a number of control samples, in a single experiment. This new approach significantly increases the ease, efficiency, and throughput of microarray-based experiments and makes possible new applications of expression profiling that are currently impractical.

Carcinoma↗
PubMed2005

A quality-controlled microarray method for gene expression profiling.

Gene expression profiling on microarrays is widely used to measure the expression of large numbers of genes in a single experiment. Because of the high cost of this method, feasible numbers of replicates are limited, thus impairing the power of statistical analysis. As a step toward reducing technically induced variation, we developed a procedure of sample preparation and analysis that minimizes the number of sample manipulation steps, introduces quality control before array hybridization, and allows recovery of the prepared mRNA for independent validation of results. Sample preparation is based on mRNA separation using oligo(dT) magnetic beads, which are subsequently used for first-strand cDNA synthesis on the beads. cDNA covalently bound to the magnetic beads is used as template for second-strand cDNA synthesis, leaving the intact mRNA in solution for further analysis. The quality of the synthesized cDNA can be assessed by quantitative polymerase chain reaction using 3'- and 5'-specific primer pairs for housekeeping genes such as glyceraldehyde-3-phosphate dehydrogenase. Second-strand cDNA is chemically labeled with fluorescent dyes to avoid dye bias in enzymatic labeling reactions. After hybridization of two differently labeled samples to microarray slides, arrays are scanned and images analyzed automatically with high reproducibility. Quantile-normalized data from five biological replica display a coefficient of variation 45% for 90% of profiled genes, allowing detection of twofold changes with false positive and false negative rates of 10% each. We demonstrate successful application of the procedure for expression profiling in plant leaf tissue. However, the method could be easily adapted for samples from animal including human or from microbial origin.

DNA, Complementary↗
PubMed2000

General method for HPLC purification and sequencing of selected dsDNA gene fragments from complex PCRs generated during gene expression profiling.

Gene expression profiling using an AFLP-based technique generates a large number of gene fragments that require identification by sequencing. The DNA fragments vary in length from about 50-500 bp. Ion-pair reversed-phase HPLC can be used to purify selected double-stranded DNA fragments that represent differentially expressed genes. The gene fragments are sequenced directly after vacuum drying of the collected HPLC fractions.

Animals↗
PubMed2006

Tumor-specific gene expression patterns with gene expression profiles.

Gene expression profiles of 14 common tumors and their counterpart normal tissues were analyzed with machine learning methods to address the problem of selection of tumor-specific genes and analysis of their differential expressions in tumor tissues. First, a variation of the Relief algorithm, "RFE_Relief algorithm" was proposed to learn the relations between genes and tissue types. Then, a support vector machine was employed to find the gene subset with the best classification performance for distinguishing cancerous tissues and their counterparts. After tissue-specific genes were removed, cross validation experiments were employed to demonstrate the common deregulated expressions of the selected gene in tumor tissues. The results indicate the existence of a specific expression fingerprint of these genes that is shared in different tumor tissues, and the hallmarks of the expression patterns of these genes in cancerous tissues are summarized at the end of this paper.

Algorithms↗
PubMed2003

MLL-rearranged leukemias: insights from gene expression profiling.

Gene expression analysis of human leukemias has provided insight into disease classification and mechanisms of oncogenesis. Its success is particularly evident for acute leukemias with rearrangement of the mixed lineage leukemia (MLL) gene on chromosome 11q23. Unlike most other recurrent translocations, MLL rearrangements are found in leukemias classified as acute myelogenous leukemia (AML) or acute lymphoblastic leukemia (ALL). In addition, MLL-rearranged leukemias often express both myeloid- and lymphoid-associated genes. These unusual characteristics have generated much interest in the cell of origin and the mechanism of transformation by MLL rearrangements. Here we review insights gained from characterization of MLL-rearranged human leukemias by genome-wide expression profiling and compare these to data from model systems.

Cell Lineage↗
PubMed2005

Gene function classification using NCI-60 cell line gene expression profiles.

Gene expression patterns from NCI's panel of 60 cell lines were used to train a Neural Network model for classifying genes to pathways. The model assigns probabilities to each gene for each of the 21 modeled pathways assigned by the Kyoto Encyclopedia of Genes and Genomes. Cross-validation of the model showed that 10 of the 21 pathways exhibited good performance in statistical significance and accuracy. The model was designed to output gene probabilities that could be screened for higher probabilities resulting in higher confidence in classification though yielding fewer genes per pathway. The model was deployed on 5798 genes and our approach allowed us to ascertain the most relevant genes above an estimated background. Eight pathways were identified with both good cross-validation and significant numbers above background, TCA Cycle, Oxidative Phosphorylation, Porphyrin Biosynthesis, Ribosome, Polymerases, Proteasome, Cell Cycle, and Cell Adhesion. Gene Ontology (GO) annotation was used for additional validation of gene classification results. A total of 551 GO annotated genes and 468 unannotated genes were classified to the 8 pathways. The primary and secondary classifications of genes revealed known pathway relationships and provide the potential for discovering new pathway relationships.

Cell Line↗
PubMed2003

Evaluation of techniques using amplified nucleic acid probes for gene expression profiling.

Gene expression analyses using spotted cDNA microarrays typically require relatively large quantities of total RNA (up to 100 microg) or polyA+RNA (1-5 microg). However, samples obtained by microdissection, patient biopsies, or embryonic samples often are small and yield an insufficient amount of RNA. Methods such as linear RNA amplification by in vitro transcription (IVT) or cDNA amplification by PCR are currently being used to circumvent these limitations. In the present study, labeled probes from mouse liver and kidney were generated with two amplification methods and were analyzed in terms of reproducibility of intensity values from repeated experiments. In addition, the reliability of differential gene expression detection among the different types of amplified and non-amplified probes was assessed. Data derived from IVT-amplified RNA, as well as from PCR-amplified cDNA probes were reproducible with correlation coefficients of 0.89 and 0.91, respectively. 88-92% of the strongly differentially expressed genes detected with non-amplified probes were also detected as being at least two-folds differentially expressed with the amplified probes. Both the PCR-amplified probe and the IVT-amplified probe were comparable in reproducibility and reliability.

Animals↗
PubMed2001

Molecular approaches in the study of Biomphalaria glabrata--Schistosoma mansoni interactions: linkage analysis and gene expression profiling.

Gene mapping and the generation of linkage groups are fundamental to an understanding of the organization and relationships of genes and marker sequences, providing a framework with which to investigate their association with traits of interest. The abundance of techniques available for generating polymorphic molecular markers, and recent advances in high throughput screening, have allowed the extension of map analysis to the tropical freshwater snail Biomphalaria glabrata, an important intermediate host for Schistosoma mansoni. Direct comparison of gene expression by differential display screening, without prior identification of candidate genes, can be combined with mapping to quantify the involvement of specific sequences in the schistosome resistance response, and other important host-parasite interactions. Here we discuss the application of current and emergent technologies to gene characterization and linkage analysis in snail-schistosome interactions. Preliminary results from the analysis of comparative gene expression in resistant and susceptible snails are also presented.

Animals↗
PubMed1997

The significance of digital gene expression profiles.

Genes differentially expressed in different tissues, during development, or during specific pathologies are of foremost interest to both basic and pharmaceutical research. "Transcript profiles" or "digital Northerns" are generated routinely by partially sequencing thousands of randomly selected clones from relevant cDNA libraries. Differentially expressed genes can then be detected from variations in the counts of their cognate sequence tags. Here we present the first systematic study on the influence of random fluctuations and sampling size on the reliability of this kind of data. We establish a rigorous significance test and demonstrate its use on publicly available transcript profiles. The theory links the threshold of selection of putatively regulated genes (e.g., the number of pharmaceutical leads) to the fraction of false positive clones one is willing to risk. Our results delineate more precisely and extend the limits within which digital Northern data can be used.

Actins↗