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The Gene Ontology (GO) project (http://www. geneontology.org/) provides structured, controlled vocabularies and classifications that cover several domains of molecular and cellular biology and are freely available for community use in the annotation of genes, gene products and sequences. Many model organism databases and genome annotation groups use the GO and contribute their annotation sets to the GO resource. The GO database integrates the vocabularies and contributed annotations and provides full access to this information in several formats. Members of the GO Consortium continually work collectively, involving outside experts as needed, to expand and update the GO vocabularies. The GO Web resource also provides access to extensive documentation about the GO project and links to applications that use GO data for functional analyses.

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The purpose of this study was to assess 65 pedigrees ascertained through a Bipolar I (BPI) proband for evidence of linkage, using nonparametric methods in a genome-wide scan and for possible parent of origin effect using several analytical methods. We identified 15 loci with nominally significant evidence for increased allele sharing among affected relative pairs. Eight of these regions, at 8q24, 18q22, 4q32, 13q12, 4q35, 10q26, 2p12, and 12q24, directly overlap with previously reported evidence of linkage to bipolar disorder. Five regions at 20p13, 2p22, 14q23, 9p13, and 1q41 are within several Mb of previously reported regions. We report our findings in rank order and the top five markers had an NPL>2.5. The peak finding in these regions were D8S256 at 8q24, NPL 3.13; D18S878 at 18q22, NPL 2.90; D4S1629 at 4q32, NPL 2.80; D2S99 at 2p12, NPL 2.54; and D13S1493 at 13q12, NPL 2.53. No locus produced statistically significant evidence for linkage at the genome-wide level. The parent of origin effect was studied and consistent with our previous findings, evidence for a locus on 18q22 was predominantly from families wherein the father or paternal lineage was affected. There was evidence consistent with paternal imprinting at the loci on 13q12 and 1q41.

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The most common human leukemia is B cell chronic lymphocytic leukemia (CLL), a malignancy of mature B cells with a characteristic clinical presentation but a variable clinical course. The rearranged immunoglobulin (Ig) genes of CLL cells may be either germ-line in sequence or somatically mutated. Lack of Ig mutations defined a distinctly worse prognostic group of CLL patients raising the possibility that CLL comprises two distinct diseases. Using genomic-scale gene expression profiling, we show that CLL is characterized by a common gene expression "signature," irrespective of Ig mutational status, suggesting that CLL cases share a common mechanism of transformation and/or cell of origin. Nonetheless, the expression of hundreds of other genes correlated with the Ig mutational status, including many genes that are modulated in expression during mitogenic B cell receptor signaling. These genes were used to build a CLL subtype predictor that may help in the clinical classification of patients with this disease.

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The dynamic instability of microtubules has long been understood to depend on the hydrolysis of GTP bound to beta-tubulin, an event stimulated by polymerization and necessary for depolymerization. Crystallographic studies of tubulin show that GTP is bound by beta-tubulin at the longitudinal dimer-dimer interface and contacts particular alpha-tubulin residues in the next dimer along the protofilament. This structural arrangement suggests that these contacts could account for assembly-stimulated GTP hydrolysis. As a test of this hypothesis, we examined, in yeast cells, the effect of mutating the alpha-tubulin residues predicted, on structural grounds, to be involved in GTPase activation. Mutation of these residues to alanine (i.e., D252A and E255A) created poisonous alpha-tubulins that caused lethality even as minor components of the alpha-tubulin pool. When the mutant alpha-tubulins were expressed from the galactose-inducible promoter of GAL1, cells rapidly acquired aberrant microtubule structures. Cytoplasmic microtubules were largely bundled, spindle assembly was inhibited, preexisting spindles failed to completely elongate, and occasional, stable microtubules were observed unattached to spindle pole bodies. Time-lapse microscopy showed that microtubule dynamics had ceased. Microtubules containing the mutant proteins did not depolymerize, even in the presence of nocodazole. These data support the view that alpha-tubulin is a GTPase-activating protein that acts, during microtubule polymerization, to stimulate GTP hydrolysis in beta-tubulin and thereby account for the dynamic instability of microtubules.

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The mineralocorticoid hormone, aldosterone, is known to play a role in sodium homeostasis. We serendipitously found, however, highly significant association between single-nucleotide polymorphisms in the aldosterone synthase gene and plasma glucose levels in a large population of Chinese and Japanese origin. Two polymorphisms--one in the putative promoter (T-344C) and another resulting in a lysine/arginine substitution at amino acid 173, which are in complete linkage disequilibrium in this population--were associated with fasting plasma glucose levels (P = 0.000017) and those 60 (P = 0.017) and 120 (P = 0.0019) min after an oral glucose challenge. A C/T variant in intron 1, between these polymorphisms, was not associated with glucose levels. Arg-173 and -344C homozygotes were most likely to be diabetic [odds ratio 2.51; 95% confidence interval (C.I.) 1.39-3.92; P = 0.0015] and have impaired fasting glucose levels (odds ratio 3.53; 95% C.I. 2.02-5.5; P = 0.0000036). These results suggest a new role for aldosterone in glucose homeostasis.

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The global gene expression profiles for 67 human lung tumors representing 56 patients were examined by using 24,000-element cDNA microarrays. Subdivision of the tumors based on gene expression patterns faithfully recapitulated morphological classification of the tumors into squamous, large cell, small cell, and adenocarcinoma. The gene expression patterns made possible the subclassification of adenocarcinoma into subgroups that correlated with the degree of tumor differentiation as well as patient survival. Gene expression analysis thus promises to extend and refine standard pathologic analysis.

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The kidney, by regulating the volume of fluid in the body, plays a key role in regulating blood pressure (BP). The kidney uses primarily sodium and, to a lesser extent, urea to maintain the appropriate volume of fluid. Genetic variation in proteins that determine sodium reabsorption and excretion is known to significantly influence BP. However, the influence of genetic variation in urea transporters on BP has not been examined. We determined therefore whether nucleotide variation in the kidney-specific human urea transporter, HUT2, is associated with variation in BP. After determining the genomic structure of the coding sequence, seven single nucleotide polymorphisms (SNPs) were identified. Two of the SNPs result in Val/Ile and Ala/Thr amino acid substitutions at positions 227 and 357 in the HUT2 open reading frame, respectively. Another SNP is silent and four others are in introns or the 3' untranslated region. Over 1000 hypertensive and low-normotensive individuals of Chinese origin were typed for five of these SNPs using a high-throughput genotyping method. The Ile227 and Ala357 alleles were associated with low diastolic BP in men but not women, with odds ratios 2.1 [95% confidence interval (CI) 1.5-2.7, P < 0.001] and 1.5 (95% CI 1.2-1.8, P < 0.001), respectively. There was a similar trend for systolic BP, and odds ratios for the Ile227 and Ala357 alleles were 1.7 (95% CI 1.2-2.3, P = 0.002) and 1.3 (95% CI 1.1-1.6, P = 0.007), respectively, in men.

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Analysis of iron-regulated gene expression in Saccharomyces cerevisiae using cDNA microarrays has identified three putative cell wall proteins that are directly regulated by Aft1p, the major iron-dependent transcription factor in yeast. FIT1, FIT2, and FIT3 (for facilitator of iron transport) were more highly expressed in strains grown in low concentrations of iron and in strains in which AFT1-1(up), a constitutively active allele of AFT1, was expressed. Northern blot analysis confirmed that FIT1, FIT2, and FIT3 mRNA transcript levels were increased 60-230-fold in response to iron deprivation in an Aft1p-dependent manner. Fit1p was localized exclusively to the cell wall by indirect immunofluorescence. Deletion of the FIT genes, individually or in combination, resulted in diminished uptake of iron bound to the siderophores ferrioxamine B and ferrichrome, without diminishing the uptake of ferric iron salts, or the siderophores triacetylfusarinine C and enterobactin. FIT-deletion strains exhibited increased expression of Aft1p target genes as measured by a FET3-lacZ reporter gene or by Arn1p Western blotting, indicating that cells respond to the absence of FIT genes by up-regulating systems of iron uptake. Aft1p activation in FIT-deleted strains occurred when either ferrichrome or ferric salts were used as sources of iron during growth, suggesting that the FIT genes enhance uptake of iron from both sources. Enzymatic digestion of the cell wall resulted in the release of significant amounts of iron from cells, and the relative quantity of iron released was reduced in FIT-deletion strains. Fit1p, Fit2p, and Fit3p may function by increasing the amount of iron associated with the cell wall and periplasmic space.

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Eukaryotic cells respond to DNA damage by arresting the cell cycle and modulating gene expression to ensure efficient DNA repair. The human ATR kinase and its homolog in yeast, MEC1, play central roles in transducing the damage signal. To characterize the role of the Mec1 pathway in modulating the cellular response to DNA damage, we used DNA microarrays to observe genomic expression in Saccharomyces cerevisiae responding to two different DNA-damaging agents. We compared the genome-wide expression patterns of wild-type cells and mutants defective in Mec1 signaling, including mec1, dun1, and crt1 mutants, under normal growth conditions and in response to the methylating-agent methylmethane sulfonate (MMS) and ionizing radiation. Here, we present a comparative analysis of wild-type and mutant cells responding to these DNA-damaging agents, and identify specific features of the gene expression responses that are dependent on the Mec1 pathway. Among the hundreds of genes whose expression was affected by Mec1p, one set of genes appears to represent an MEC1-dependent expression signature of DNA damage. Other aspects of the genomic responses were independent of Mec1p, and likely independent of DNA damage, suggesting the pleiotropic effects of MMS and ionizing radiation. The complete data set as well as supplemental materials is available at http://www-genome.stanford.edu/mec1.

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Current development in molecular techniques has extended the opportunities to explore genetic alterations in malignant tissue. There is a need to improve prognostication and, in particular, to understand the mechanisms of treatment resistance in different tumours. Gene analyses by microarrays allow concomitant analyses of several genes in concert, providing new opportunities for tumour classification and understanding of key biological disturbances. This paper outlines our continuing studies exploring prognostic and, we hope, predictive factors in breast cancer therapy.

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The purpose of this study was to classify breast carcinomas based on variations in gene expression patterns derived from cDNA microarrays and to correlate tumor characteristics to clinical outcome. A total of 85 cDNA microarray experiments representing 78 cancers, three fibroadenomas, and four normal breast tissues were analyzed by hierarchical clustering. As reported previously, the cancers could be classified into a basal epithelial-like group, an ERBB2-overexpressing group and a normal breast-like group based on variations in gene expression. A novel finding was that the previously characterized luminal epithelial/estrogen receptor-positive group could be divided into at least two subgroups, each with a distinctive expression profile. These subtypes proved to be reasonably robust by clustering using two different gene sets: first, a set of 456 cDNA clones previously selected to reflect intrinsic properties of the tumors and, second, a gene set that highly correlated with patient outcome. Survival analyses on a subcohort of patients with locally advanced breast cancer uniformly treated in a prospective study showed significantly different outcomes for the patients belonging to the various groups, including a poor prognosis for the basal-like subtype and a significant difference in outcome for the two estrogen receptor-positive groups.

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Using microarray analysis, we identified a unique ras superfamily gene, termed RERG (ras-related and estrogen-regulated growth inhibitor), whose expression was decreased or lost in a significant percentage of primary human breast tumors that show a poor clinical prognosis. Importantly, high RERG expression correlated with expression of a set of genes that define a breast tumor subtype that is estrogen receptor-positive and associated with a slow rate of tumor cell proliferation and a favorable prognosis for these cancer patients. RERG mRNA expression was induced rapidly in MCF-7 cells stimulated by beta-estradiol and repressed by tamoxifen treatment. Like Ras, RERG protein exhibited intrinsic GDP/GTP binding and GTP hydrolysis activity. Unlike Ras proteins, RERG lacks a known recognition signal for COOH-terminal prenylation and was localized primarily in the cytoplasm. Expression of RERG protein in MCF-7 breast carcinoma cells resulted in a significant inhibition of both anchorage-dependent and anchorage-independent growth in vitro and inhibited tumor formation in nude mice. These features of RERG are strikingly different from most Ras superfamily GTP-binding pro-teins and suggest that the loss of RERG expression may contribute to breast tumorigenesis.

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To make large-scale association studies a reality, automated high-throughput methods for genotyping with single-nucleotide polymorphisms (SNPs) are needed. We describe PCR conditions that permit the use of the TaqMan or 5' nuclease allelic discrimination assay for typing large numbers of individuals with any SNP and computational methods that allow genotypes to be assigned automatically. To demonstrate the utility of these methods, we typed >1600 individuals for a G-to-T transversion that results in a glutamate-to-aspartate substitution at position 298 in the endothelial nitric oxide synthase gene, and a G/C polymorphism (newly identified in our laboratory) in intron 8 of the 11-beta hydroxylase gene. The genotyping method is accurate-we estimate an error rate of fewer than 1 in 2000 genotypes, rapid-with five 96-well PCR machines, one fluorescent reader, and no automated pipetting, over one thousand genotypes can be generated by one person in one day, and flexible-a new SNP can be tested for association in less than one week. Indeed, large-scale genotyping has been accomplished for 23 other SNPs in 13 different genes using this method. In addition, we identified three "pseudo-SNPs" (WIAF1161, WIAF2566, and WIAF335) that are probably a result of duplication.

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We determined the distribution of repressor-activator protein 1 (Rap1) and the accessory silencing proteins Sir2, Sir3 and Sir4 in vivo on the entire yeast genome, at a resolution of 2 kb. Rap1 is central to the cellular economy during rapid growth, targeting 294 loci, about 5% of yeast genes, and participating in the activation of 37% of all RNA polymerase II initiation events in exponentially growing cells. Although the DNA sequence recognized by Rap1 is found in both coding and intergenic sequences, the binding of Rap1 to the genome was highly specific to intergenic regions with the potential to act as promoters. This global phenomenon, which may be a general characteristic of sequence-specific transcriptional factors, indicates the existence of a genome-wide molecular mechanism for marking promoter regions.

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MOTIVATION: Gene expression microarray experiments can generate data sets with multiple missing expression values. Unfortunately, many algorithms for gene expression analysis require a complete matrix of gene array values as input. For example, methods such as hierarchical clustering and K-means clustering are not robust to missing data, and may lose effectiveness even with a few missing values. Methods for imputing missing data are needed, therefore, to minimize the effect of incomplete data sets on analyses, and to increase the range of data sets to which these algorithms can be applied. In this report, we investigate automated methods for estimating missing data. RESULTS: We present a comparative study of several methods for the estimation of missing values in gene microarray data. We implemented and evaluated three methods: a Singular Value Decomposition (SVD) based method (SVDimpute), weighted K-nearest neighbors (KNNimpute), and row average. We evaluated the methods using a variety of parameter settings and over different real data sets, and assessed the robustness of the imputation methods to the amount of missing data over the range of 1--20% missing values. We show that KNNimpute appears to provide a more robust and sensitive method for missing value estimation than SVDimpute, and both SVDimpute and KNNimpute surpass the commonly used row average method (as well as filling missing values with zeros). We report results of the comparative experiments and provide recommendations and tools for accurate estimation of missing microarray data under a variety of conditions.

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BACKGROUND: We propose a new method for supervised learning from gene expression data. We call it 'tree harvesting'. This technique starts with a hierarchical clustering of genes, then models the outcome variable as a sum of the average expression profiles of chosen clusters and their products. It can be applied to many different kinds of outcome measures such as censored survival times, or a response falling in two or more classes (for example, cancer classes). The method can discover genes that have strong effects on their own, and genes that interact with other genes. RESULTS: We illustrate the method on data from a lymphoma study, and on a dataset containing samples from eight different cancers. It identified some potentially interesting gene clusters. In simulation studies we found that the procedure may require a large number of experimental samples to successfully discover interactions. CONCLUSIONS: Tree harvesting is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worthy of further investigation.

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The Saccharomyces cerevisiae Yap1p transcription factor is required for the H2O2-dependent activation of many antioxidant genes including the TRX2 gene encoding thioredoxin 2. To identify factors that regulate Yap1p activity, we carried out a genetic screen for mutants that show elevated expression of a TRX2-HIS3 fusion in the absence of H2O2. Two independent mutants isolated in this screen carried mutations in the TRR1 gene encoding thioredoxin reductase. Northern blot and whole-genome expression analysis revealed that the basal expression of most Yap1p targets and many other H2O2-inducible genes is elevated in Deltatrr1 mutants in the absence of external stress. In Deltatrr1 mutants treated with H2O2, the Yap1p targets, as well as genes comprising a general environmental stress response and genes encoding protein-folding chaperones, are hyperinduced. However, despite the elevated expression of genes encoding antioxidant enzymes, Deltatrr1 mutants are extremely sensitive to H2O2. The results suggest that cells lacking thioredoxin reductase have diminished capacity to detoxify oxidants and/or to repair oxidative stress-induced damage and that the thioredoxin system is involved in the redox regulation of Yap1p transcriptional activity.

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Proteins interact with genomic DNA to bring the genome to life; and these interactions also define many functional features of the genome. SBF and MBF are sequence-specific transcription factors that activate gene expression during the G1/S transition of the cell cycle in yeast. SBF is a heterodimer of Swi4 and Swi6, and MBF is a heterodimer of Mbpl and Swi6 (refs 1, 3). The related Swi4 and Mbp1 proteins are the DNA-binding components of the respective factors, and Swi6 mayhave a regulatory function. A small number of SBF and MBF target genes have been identified. Here we define the genomic binding sites of the SBF and MBF transcription factors in vivo, by using DNA microarrays. In addition to the previously characterized targets, we have identified about 200 new putative targets. Our results support the hypothesis that SBF activated genes are predominantly involved in budding, and in membrane and cell-wall biosynthesis, whereas DNA replication and repair are the dominant functions among MBF activated genes. The functional specialization of these factors may provide a mechanism for independent regulation of distinct molecular processes that normally occur in synchrony during the mitotic cell cycle.

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Upon the completion of the SACCHAROMYCES: cerevisiae genomic sequence in 1996 [Goffeau,A. et al. (1997) NATURE:, 387, 5], several creative and ambitious projects have been initiated to explore the functions of gene products or gene expression on a genome-wide scale. To help researchers take advantage of these projects, the SACCHAROMYCES: Genome Database (SGD) has created two new tools, Function Junction and Expression Connection. Together, the tools form a central resource for querying multiple large-scale analysis projects for data about individual genes. Function Junction provides information from diverse projects that shed light on the role a gene product plays in the cell, while Expression Connection delivers information produced by the ever-increasing number of microarray projects. WWW access to SGD is available at genome-www.stanford. edu/Saccharomyces/.

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