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PubMed2003

Monitoring expression profiles of Arabidopsis gene expression during rehydration process after dehydration using ca 7000 full-length cDNA microarray.

Plants respond and adapt to drought stress in order to survive under stress conditions. Several genes that respond to drought at the transcriptional level have been described, but there are few reports on genes involved in the recovery from dehydration. Analysis of rehydration-inducible genes should help not only to understand the molecular mechanisms of stress responses in higher plants, but also to improve the stress tolerance of crops by gene manipulation. We used a full-length cDNA microarray containing ca. 7000 Arabidopsis full-length cDNAs and identified 152 rehydration-inducible genes. Venn diagram analysis showed relationship of the rehydration-inducible genes to proline-inducible and water-treatment-inducible genes. Among the 152 rehydration-inducible genes, 58 genes contained the ACTCAT sequence involved in proline- and hypoosmolarity-inducible gene expression in their promoter regions, suggesting that ACTCAT sequence is a major cis-acting element involved in rehydration-inducible gene expression, and that some novel cis-acting elements are involved in rehydration-inducible gene expression. Functional analysis of rehydration-inducible and rehydration-repressed genes revealed their functions not only in the release from a stressed status but also in the recovery of growth in plants.

Arabidopsis↗

PubMed2004

Gene expression profiles highlight adaptive brain mechanisms in corticotropin releasing factor overexpressing mice.

Corticotropin-releasing factor (CRF) plays an important role in mediating central and peripheral responses to stress. Alterations in CRF system activity have been linked to a number of psychiatric disorders, including anxiety and depression. Aim of this study was to elucidate homeostatic mechanisms induced by lifelong elevated CRF levels in the brain. We therefore profiled gene expression in several brain areas of transgenic mice overexpressing CRF (CRF-OE), a model for chronic stress. Several genes showed altered expression levels in CRF-OE mice when compared to their wild type littermates and were confirmed by quantitative PCR. Differences in gene expression profiles revealed the presence of previously unrecognized homeostatic mechanisms in CRF-OE animals. These included changes in glucocorticoid signaling, as exemplified by changes in 11beta-hydroxysteroid dehydrogenase type 1, FK506 binding protein 5 and serum/glucocorticoid kinase. Alterations in expression of genes involved in myelination (myelin, myelin-associated glycoprotein), cell proliferation and extracellular matrix formation (Edg2, Fgfr2, decorin, brevican) suggest changes in the dynamics of neurogenesis in CRF-OE. Pronounced changes in neurotensin (NT) receptors 1 and 2 mRNA were identified. Overall downregulation of NT receptors in CRF-OE animal was substantiated by receptor binding studies. Pronounced neurotensin receptor downregulation was observed for NT type 1 receptors in limbic brain areas, suggesting that NT could be implicated in some of the effects attributed to CRF overexpression. These data show that lifelong exposure to excessive CRF leads to adaptive changes in the brain which could play a role in some of the behavioral and physiological alterations seen in these animals.

Animals↗

PubMed2006

Gene expression profiles revealing the mechanisms of anti-androgen- and estrogen-induced feminization in fish.

Environmental anti-androgens are increasingly being recognized as potential contributing factors in the chemically induced feminization of wild fish because, by blocking androgen action, they can produce phenotypic effects similar to environmental estrogens. The molecular mechanisms by which anti-androgens and estrogens exert feminizing effects, however, have not been systematically compared. Using a targeted approach, we profiled the expression responses of a suite of 22 genes involved in reproduction, growth and development (processes controlled by androgens and estrogens) in the liver and gonad in adult male and female fathead minnow (Pimephales promelas) exposed to the model anti-androgen flutamide and the model synthetic estrogen 17alpha-ethinylestradiol (EE(2)). Both flutamide (320 microg/L) and EE(2) (10ng/L) produced phenotypic effects indicative of feminization (induction of plasma vitellogenin, reduced gonadosomatic index, and reduced secondary sex characters), although for the chosen test concentrations EE(2) was the more potent. For the genes studied, flutamide and EE(2) produced distinct expression profiles, suggesting that they largely operate via distinct molecular mechanisms. As examples, in liver EE(2) (but not flutamide) exposure up-regulated estrogen receptor (ER) alpha mRNA, whereas flutamide exposure increased ERbeta and ERgamma mRNAs in males and resulted in decreased androgen receptor (AR) mRNA in females. In the testis, flutamide up-regulated genes coding for enzymes involved in androgen biosynthesis (cytochrome P450 17 [CYP17] and 11beta-hydroxysteroid dehydrogenase [11beta-HSD]) implying an inhibitory action on androgen negative feedback pathways. EE(2), in contrast, inhibited the expression of enzymes involved in androgen biosynthesis (CYP17, 11beta-HSD and 17beta-hydroxysteroid dehydrogenase [17beta-HSD]). There were also some commonalities in the molecular mechanisms of flutamide and EE(2) action, including the down-regulation of gonadal sex steroid receptor expression (gonadal AR and ovarian ERalpha), increased expression of genes coding for estrogen-producing enzymes (cytochrome P450 19A and B [CYP19A and CYP19B]), decreased expression of genes involved in testis differentiation (anti-Mullerian hormone [AMH] and doublesex and mab-3 related transcription factor 1 [DMRT1]), and decreased expression of hepatic genes which mediate wider physiological processes such as somatic growth (growth hormone [GH], GH receptor [GHR], insulin-like growth factor-I [IGF-I], IGF-I receptor [IGF-IR], thyroid hormone receptor alpha [TRalpha] and beta [TRbeta]).

Androgen Antagonists↗

PubMed2005

Gene discovery and expression profile analysis through sequencing of expressed sequence tags from different developmental stages of the chytridiomycete Blastocladiella emersonii.

Blastocladiella emersonii is an aquatic fungus of the chytridiomycete class which diverged early from the fungal lineage and is notable for the morphogenetic processes which occur during its life cycle. Its particular taxonomic position makes this fungus an interesting system to be considered when investigating phylogenetic relationships and studying the biology of lower fungi. To contribute to the understanding of the complexity of the B. emersonii genome, we present here a survey of expressed sequence tags (ESTs) from various stages of the fungal development. Nearly 20,000 cDNA clones from 10 different libraries were partially sequenced from their 5' end, yielding 16,984 high-quality ESTs. These ESTs were assembled into 4,873 putative transcripts, of which 48% presented no matches with existing sequences in public databases. As a result of Gene Ontology (GO) project annotation, 1,680 ESTs (35%) were classified into biological processes of the GO structure, with transcription and RNA processing, protein biosynthesis, and transport as prevalent processes. We also report full-length sequences, useful for construction of molecular phylogenies, and several ESTs that showed high similarity with known proteins, some of which were not previously described in fungi. Furthermore, we analyzed the expression profile (digital Northern analysis) of each transcript throughout the life cycle of the fungus using Bayesian statistics. The in silico approach was validated by Northern blot analysis with good agreement between the two methodologies.

Amino Acid Sequence↗

PubMed2006

Alternative isoform discrimination by the next generation of expression profiling microarrays.

Microarray expression profiling has revolutionised the way that many therapeutic targets have been identified over the past 10 years. High-density microarrays have allowed scientists to simultaneously scrutinise the expression of all genes encoded on a given genome. Although the data collected from classically designed microarrays greatly enriched the pool of information available to help guide the selection and design of new therapeutic strategies, they were unable to tell the complete story. The major limitation with most array designs is that they can only produce a global expression value for all transcripts produced from a specific locus and cannot monitor each individual alternative isoform produced from the interrogated locus. Recently, new array designs have been described, and become commercially available, that can efficiently monitor individual alternatively spliced isoforms produced from a single locus, allowing the research community to get a more accurate picture of the biological landscape of the expressed transcripts.

Alternative Splicing↗

PubMed2006

Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles.

MOTIVATION: Gene expression profiling experiments in cell lines and animal models characterized by specific genetic or molecular perturbations have yielded sets of genes annotated by the perturbation. These gene sets can serve as a reference base for interrogating other expression datasets. For example, a new dataset in which a specific pathway gene set appears to be enriched, in terms of multiple genes in that set evidencing expression changes, can then be annotated by that reference pathway. We introduce in this paper a formal statistical method to measure the enrichment of each sample in an expression dataset. This allows us to assay the natural variation of pathway activity in observed gene expression data sets from clinical cancer and other studies. RESULTS: Validation of the method and illustrations of biological insights gleaned are demonstrated on cell line data, mouse models, and cancer-related datasets. Using oncogenic pathway signatures, we show that gene sets built from a model system are indeed enriched in the model system. We employ ASSESS for the use of molecular classification by pathways. This provides an accurate classifier that can be interpreted at the level of pathways instead of individual genes. Finally, ASSESS can be used for cross-platform expression models where data on the same type of cancer are integrated over different platforms into a space of enrichment scores. AVAILABILITY: Versions are available in Octave and Java (with a graphical user interface). Software can be downloaded at http://people.genome.duke.edu/assess.

Algorithms↗

PubMed2005

Survival trees for analyzing clinical outcome in lung adenocarcinomas based on gene expression profiles: identification of neogenin and diacylglycerol kinase alpha expression as critical factors.

We present survival trees as an exploratory tool for revealing new insights into gene expression profiles in combination with clinical patient data. Survival trees partition the patient data studied into groups with similar survival outcomes and identify characteristic genetic profiles within these groups. We demonstrate the application of survival trees in a study involving the expression profiles of 3,588 genes in 211 lung adenocarcinoma patients. The survival tree identified a group of early-stage cancer patients with relatively low survival rates and another group of advanced-stage patients with remarkably good survival outcome. For both groups, the tree identified characteristic expression profiles of genes that might play a role in cancerogenesis and disease progression, notably the genes for the netrin receptor neogenin and the Ras/Rho kinase modulator diacylglycerol kinase alpha.

Adenocarcinoma↗

PubMed2001

Gene expression profiling in postmortem Rett Syndrome brain: differential gene expression and patient classification.

The identification of mutations in the transcriptional repressor methyl-CpG-binding protein 2 (MECP2) gene in Rett Syndrome (RTT) suggests that an inappropriate release of transcriptional silencing may give rise to RTT neuropathology. Despite this progress, the molecular basis of RTT neuropathogenesis remains unclear. Using multiple cDNA microarray technologies, subtractive hybridization, and conventional biochemistry, we generated comprehensive gene expression profiles of postmortem brain tissue from RTT patients and matched controls. Many glial transcripts involved in known neuropathological mechanisms were found to have increased expression in RTT brain, while decreases were observed in the expression of multiple neuron-specific mRNAs. Dramatic and consistent decreases in transcripts encoding presynaptic markers indicated a specific deficit in presynaptic development. Employing multiple clustering algorithms, it was possible to accurately segregate RTT from control brain tissue samples based solely on gene expression profile. Although previously achieved in cancers, our results constitute the first report of human disease classification using gene expression profiling in a complex tissue source such as brain.

Adolescent↗

PubMed2002

[Preliminary analysis on the gene expression profiles of different types of gliomas with cDNA array].

BACKGROUND & OBJECTIVE: There is great signification to classify the molecular pathogenesis of gliomas. Gene chip is able to profile the gene expression of tumors, which may become a new method for studying molecular pathology of tumors. The cDNA was used in order to detect the differences in gene expression profiles of different types of gliomas. METHODS: cDNA probes tagged with 32P were converted from total RNA extracted from 4 samples of 3 different pathological types, including oligodendrogliomas, anaplastic astrocytomas and ependymomas, and one sample of normal human brain tissue as well, which were then hybridized to the Atlas array. After autoradiography, the gene expression profiles were analyzed with the special software and by the cluster analysis. RESULTS: As compared to the normal brain tissue, the number of differential expressed genes in these tumor samples ranging from 11 to 118 was found. These 4 samples could be divided into 3 kinds of types when 107 differential expressed genes were selected to be as the cluster analysis index. This result was in accordance with the neuropathological diagnosis. CONCLUSION: cDNA array is able to profile gene expression in different types of gliomas. Combined with the bioinformational method, we may further obtain useful information about molecular pathology of gliomas.

Gene Expression Profiling↗

PubMed2025

Global lncRNA expression profiles in medulloblastoma reveal crucial lncRNA-oncogene interactions in Sonic hedgehog and Group 4.

BACKGROUND: Advances in multi-omic studies have improved medulloblastoma (MB) characterization, yet novel molecular biomarkers are needed to refine tumor biology and therapeutic strategies. Current profiling mainly targets the protein-coding genome, while the potential of noncoding regions remains unexplored. This study aims to identify long noncoding RNAs (lncRNAs), emerging as crucial regulators in MB, as potential key biomarkers specific to molecular group, enhancing understanding of MB's genomic landscape. METHODS: RNA-seq data from 54 Spanish MB patients (C1) and 207 public samples (C2) were analyzed to profile lncRNAs. Expression and Weighted Gene Coexpression Network (WGCNA) analyses were performed to identify lncRNA-oncogene interactions. Group-specific interactions were examined to infer their role in MB pathogenesis and highlight potential lncRNA involvement in disease mechanisms. RESULTS: LncRNA expression profiles identified 4 clusters corresponding to the MB molecular groups, confirming their potential as biomarkers. Expression and WGCNA analyses revealed group-specific lncRNAs for Sonic hedgehog (SHH), Group 3 (Gr3), and Group 4 (Gr4) MB. Lnc-SMARCA2 was exclusively upregulated in SHH MB, and associated with ATOH1 and PDLIM3, key cilium regulators of this group's cell of origin. In Gr4 MB, MGC32805 and LOC107986446 were upregulated and linked to SNCAIP, potentially influencing PRDM6 activation via enhancer hijacking. Additionally, a 5-lncRNA signature linked to phototransduction was exclusive to Gr3, offering insights into its lineage switch and molecular regulation. CONCLUSIONS: Lnc-SMARCA2 and, MGC32805 and LOC107986446, are exclusively deregulated in SHH and Gr4 MB, respectively, and directly associated with group-specific MB oncogenes, representing promising novel biomarkers and therapeutic targets in MB.

cancer biomarkers↗

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↗

PubMed2000

Expression profiles and biological function.

Expression arrays facilitate the monitoring of changes in expression patterns of large collections of genes. It is generally expected that genes with similar expression patterns would correspond to proteins of common biological function. We assess this common assumption by comparing levels of similarity of expression patterns and statistical significance of biological terms that describe the corresponding protein functions. Terms are automatically obtained by mining large collections of Medline abstracts. We propose that the combined use of the tools for expression profiles clustering and automatic function retrieval, can be useful tools for the detection of biologically relevant associations between genes in complex gene expression experiments. The results obtained using publicly available experimental data show how, in general, an increase in the similarity of the expression patterns is accompanied by an enhancement of the amount of specific functional information or, in other words, how the selected terms became more specific following an increase in the specificity of the expression patterns. Particularly interesting are the discrepancies from this general trend, i.e. groups of genes with similar expression patterns but very little in common at the functional level. In these cases the similarity of their expression profiles becomes the first link between previously unrelated genes.

Cluster Analysis↗

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↗

PubMed1995

High-density cDNA filter analysis of the expression profiles of the genes preferentially expressed in human brain.

We previously established a method, called high-density cDNA filter analysis (HDCFA), for analyzing the expression profiles of a large number of genes in a systematic manner. In the present study, we constructed a cDNA filter of about 8300 cDNAs from a human cerebral cortex cDNA library and quantitatively analyzed their expression in human adult brain, fetal brain, kidney and liver using HDCFA. Using a comparison of the relative amount of expression of each clone in different tissues and following (partial) sequence analysis, about 200 clones were selected as those preferentially expressed in adult or fetal brain, one half of which may be unknown. Their expression was further analyzed in human neuroblastoma cell lines, a human glioma cell line, human cerebral cortex, cerebellum and kidney. Finally, eight clones were selected and sequenced as characteristically expressed genes (cDNAs). A homology search revealed that three clones were human homologues of the rat genes preferentially expressed in brain and five clones were unknown. The full-length cDNA sequence of one of the unknown clones was determined.

Adult↗

PubMed2003

Gene expression profile of leiomyoma and myometrium and the effect of gonadotropin releasing hormone analogue therapy.

OBJECTIVE: To determine the profile of differentially expressed genes in leiomyoma and matched unaffected myometrium and to identify the genes whose expression are altered after gonadotropin releasing hormone analogue (GnRHa) therapy. METHODS: Using total RNA isolated from untreated and GnRHa-treated leiomyoma and myometrium subjected to Clontech Atlas 1.2 K cancer microarray containing 1176 known genes, we found transcripts for many cytokines, growth factors and their receptors, adhesion molecules, extracellular matrix, proteases, signaling intermediates, and transcription factors in both tissues. Based on the overall normalization and reproducibility of the results, 328 differentially expressed and regulated genes with at least threefold change in their expression in untreated and GnRHa-treated tissues were chosen for further analysis. RESULTS: The expression value of the 328 genes subjected to significance analysis of microarray showed a total of 100 genes with relative significant change in expression. Of these genes, the expression of 18 was up-regulated and 82 down-regulated in untreated leiomyoma compared with myometrium. In GnRHa-treated tissues, the expression of 34 genes showed a significant decrease in leiomyoma, whereas 27 genes increased and 15 decreased in myometrium compared with their respective untreated tissues. There was no difference in the expression profile of these genes when comparing leiomyoma and myometrium from the GnRHa-treated group. Hierarchical cluster analysis revealed that these differentially expressed or regulated genes, classified based on their biologic functions, are involved in regulation of cell growth/cycle, signal transduction, transcription factors, and cell and tissue structure. CONCLUSION: Microarray analysis allowed us to identify the profile expression of several specific genes in leiomyoma and myometrium whose expression appears to be differentially regulated after GnRHa therapy.

Cell Cycle↗

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↗

PubMed2003

Comparing the continuous representation of time-series expression profiles to identify differentially expressed genes.

We present a general algorithm to detect genes differentially expressed between two nonhomogeneous time-series data sets. As increasing amounts of high-throughput biological data become available, a major challenge in genomic and computational biology is to develop methods for comparing data from different experimental sources. Time-series whole-genome expression data are a particularly valuable source of information because they can describe an unfolding biological process such as the cell cycle or immune response. However, comparisons of time-series expression data sets are hindered by biological and experimental inconsistencies such as differences in sampling rate, variations in the timing of biological processes, and the lack of repeats. Our algorithm overcomes these difficulties by using a continuous representation for time-series data and combining a noise model for individual samples with a global difference measure. We introduce a corresponding statistical method for computing the significance of this differential expression measure. We used our algorithm to compare cell-cycle-dependent gene expression in wild-type and knockout yeast strains. Our algorithm identified a set of 56 differentially expressed genes, and these results were validated by using independent protein-DNA-binding data. Unlike previous methods, our algorithm was also able to identify 22 non-cell-cycle-regulated genes as differentially expressed. This set of genes is significantly correlated in a set of independent expression experiments, suggesting additional roles for the transcription factors Fkh1 and Fkh2 in controlling cellular activity in yeast.

Algorithms↗

PubMed2005

Gene expression profiling and multiple myeloma.

Gene expression profiling is a powerful tool through which the biology of multiple myeloma can be dissected. We will describe in this chapter how early studies using this technology have provided meaningful insights into myeloma biology, have led to the identification of new therapeutic targets, and have identified powerful prognostic and pharmacogenomic markers. Specifically, we will demonstrate that gene expression profiling can be used to segregate myeloma patients into prognostic categories within which known IgH translocation signatures can be readily defined. We also show that expression signatures can identify patients with chromosome 13 deletion. Finally, we demonstrate that global gene expression signatures can be distilled to short lists of three genes or more which together impart clinical outcome information, which is significantly more powerful than any previously defined prognostic tool. Expression profiling has also led to the identification of a number of new therapeutic targets not only in myeloma cell survival but also in the pathogenesis of the osteolysis which is a hallmark of this disease.

Bone Diseases↗