Unknown,Transcriptomics,Genomics,Proteomics

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Bladder cancer microarrays

ABSTRACT: Background: Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays. Purpose: Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays. Methodology/Principal Findings: BC samples and controls, both experimental as well as publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually as well as in each tumor group, respectively. A dual analysis strategy was followed: First, experimental samples were analyzed and conclusions were extracted; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search for common DE genes. The experimental dataset, identified 831 genes that were differentially expressed in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of all available samples revealed 17 common genes (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) four of which participate in numerous pathways. Conclusions/Significance: The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers. Oligos microarray chips (~57k genes) were obtained from GE HealthCare (IL) and AppliedMicroarrays (MA) (former Amersham Biosciences) (CodeLink 57k Human Whole Genome). Hybridization was performed with the CodeLink RNA amplification and Labeling kit as described by the manufacturer, utilizing the Cy5 fluorescent dye. Slides were scanned with a microarray scanner (ScanArray 4000XL). Images were generated with ScanArray microarray acquisition software (GSI Lumonics, USA). cRNAs from three experimental setups were used in single experiments with internal spikes as controls. The experimental setups consisted of 10 urinary BC samples of different histology and 5 control samples. The scanned images were further processed with the CodeLink Expression Analysis Software v5.0 from Amersham Biosciences (presently GE Health Care Inc.). The experimental setup was analyzed based on the reference-design as described previously. All tumor samples were compared against the mean value of the control samples. Raw microarray data are available as supplementary data and at the GEO microarray database. All microarray data are MIAME compliant. We used the following publicly available microarray datasets in our analysis: 1) GSE89 dataset (GDS183), comprised of 40 BC samples; 2) GSE3167 dataset (GDS1479), comprised of 60 samples (9 controls and 51 BC samples); 3) GSE7476 dataset, composed of 12 samples (3 controls and 9 BC samples) and 4) GSE12630 dataset, comprised of 19 BC samples. In total, our pooled microarray analysis was composed of 17 control samples (n=5, for the CodeLink platform; and n=12, for the rest microarray platforms) and 129 BC samples (n=10, for the CodeLink platform; and n=119, for the rest microarray platforms). Public data were used in their available normalized form, since background correction and normalization had already been performed.

ORGANISM(S): Homo sapiens

SUBMITTER: George Lambrou

PROVIDER: E-GEOD-27448 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Background: Current diagnosis and treatment of urinary bladder cancer (BC) has shown great progress with the utilization of microarrays. Purpose: Our goal was to identify common differentially expressed (DE) genes among clinically relevant subclasses of BC using microarrays. Methodology/Principal Findings: BC samples and controls, both experimental as well as publicly available datasets, were analyzed by whole genome microarrays. We grouped the samples according to their histology and defined the DE genes in each sample individually as well as in each tumor group, respectively. A dual analysis strategy was followed: First, experimental samples were analyzed and conclusions were extracted; and second, experimental sets were combined with publicly available microarray datasets and were further analyzed in search for common DE genes. The experimental dataset, identified 831 genes that were differentially expressed in all tumor samples, simultaneously. Moreover, 33 genes were up-regulated and 85 genes were down-regulated in all 10 BC samples compared to the 5 normal tissues, simultaneously. Hierarchical clustering partitioned tumor groups in accordance to their histology. K-means clustering of all genes and all samples, as well as clustering of tumor groups, presented 49 clusters. K-means clustering of common DE genes in all samples revealed 24 clusters. Genes manifested various differential patterns of expression, based on PCA. YY1 and NFκB were among the most common transcription factors that regulated the expression of the identified DE genes. Chromosome 1 contained 32 DE genes, followed by chromosomes 2 and 11, which contained 25 and 23 DE genes, respectively. Chromosome 21 had the least number of DE genes. GO analysis revealed the prevalence of transport and binding genes in the common down-regulated DE genes; the prevalence of RNA metabolism and processing genes in the up-regulated DE genes; as well as the prevalence of genes responsible for cell communication and signal transduction in the DE genes that were down-regulated in T1-Grade III tumors and up-regulated in T2/T3-Grade III tumors. Combination of all available samples revealed 17 common genes (BMP4, CRYGD, DBH, GJB1, KRT83, MPZ, NHLH1, TACR3, ACTC1, MFAP4, SPARCL1, TAGLN, TPM2, CDC20, LHCGR, TM9SF1 and HCCS) four of which participate in numerous pathways. Conclusions/Significance: The identification of the common DE genes among BC samples of different histology can provide further insight into the discovery of new putative markers.

Project description:Goal of the experiment: To examine differential gene expression in granulosa cells of women undergoing ovarian stimulation with either recombinant human follicle stimulating hormone or purified human menopausal gonadotropin for in vitro fertilization. Brief description of the experiment: The study was designed to test the hypothesis that human granulosa cell (GC) gene expression response differs between pure recombinant FSH and human menopausal gonadotropin (hMG) stimulation regimens. Women < 35 years-old undergoing in vitro fertilization for tubal or male factor infertility were prospectively randomized to one of two stimulation protocols, Gonadotropin releasing hormone (GnRH) agonist long protocol plus individualized dosages of (1) recombinant follicle stimulating hormone (rFSH) (Gonal-F®) (n=4) or (2) purified human menopausal gonadotropin (hMG; Menopur®; 75 IU FSH/75 IU LH per vial) (n=3). Follicle development was monitored by ultrasound and serum estradiol levels. When two follicles were ≥ 17mm, human chorionic gonadotropin (hCG) (10,000 IU; Novarel®) was administered. Oocytes were retrieved 35 h post-hCG. Following oocyte recovery, GC were immediately collected in RNALater®. Pooled GC from individual patients were washed, centrifuged over 40% Percoll®, resuspended in RNALater, and snap-frozen in LN. Total RNA was extracted and stored at -70C. Biotinylated cRNA was synthesized from total RNA and each sample was run individually on CodeLink Whole Human Genome Bioarrays (Applied Microarrays). Unnamed genes and genes with <2-fold difference in expression were excluded from further analysis. After exclusions, 1736 genes exhibited differential expression between groups. Over 400 were categorized as signal transduction genes, ~180 as transcriptional regulators, and ~175 as enzymes/metabolic genes. Expression of selected genes was confirmed by RT-PCR. Differentially expressed genes included A kinase anchor protein 11 (AKAP11), bone morphogenic protein receptor II (BMPR2), epidermal growth factor (EGF), insulin-like growth factor binding protein (IGFBP)-4, IGFBP-5, basigin, and hypoxia-inducible factor (HIF)-1 alpha. The results suggest that major differences exist in the mechanism by which pure FSH alone versus FSH/LH regulate gene expression in preovulatory GC that could impact oocyte maturity and developmental competence. Experimental design: Women < 35 years-old undergoing in vitro fertilization for tubal or male factor infertility were prospectively randomized to one of two stimulation protocols, Gonadotropin releasing hormone (GnRH) agonist long protocol plus individualized dosages of (1) recombinant follicle stimulating hormone (rFSH) (Gonal-F®) (n=4) or (2) purified human menopausal gonadotropin (hMG; Menopur®; 75 IU FSH/75 IU LH per vial) (n=3). Follicle development was monitored by ultrasound and serum estradiol levels. When two follicles were ≥ 17mm, human chorionic gonadotropin (hCG) (10,000 IU; Novarel®) was administered. Oocytes were retrieved 35 h post-hCG. Following oocyte recovery, GC were immediately collected in RNALater®. Pooled GC from individual patients were washed, centrifuged over 40% Percoll®, resuspended in RNALater, and snap-frozen in LN. Total RNA was extracted and stored at -70C. Biotinylated cRNA was synthesized from total RNA and each sample was run individually on CodeLink Whole Human Genome Bioarrays (Applied Microarrays Quality control steps: The cRNA that was synthesized from the granulosa cells from individual patients was used for hybridization to a single CodeLink (Applied Microarrays) Whole Human Genome microarray. Only one sample was hybridized with each slide and only one dye (Alexa 647) was used so no dye swaps were necessary. Bacterial control spikes were used as per manufacturer's instructions. Samples used, extract preparation and labelling: The origin of each biological sample: Granulosa cells from women < 35 years-old undergoing in vitro fertilization. Manipulations of biological samples and protocols used: Female patients were treated with gonadotropin releasing hormone (GnRH) agonist long protocol plus individualized dosages of (1) recombinant follicle stimulating hormone (rFSH) (Gonal-F®) (n=4) or (2) purified human menopausal gonadotropin (hMG; Menopur®; 75 IU FSH/75 IU LH per vial) (n=3). Follicle development was monitored by ultrasound and serum estradiol levels. When two follicles were ≥ 17mm, human chorionic gonadotropin (hCG) (10,000 IU; Novarel®) was administered. Oocytes were retrieved 35 h post-hCG. Following oocyte recovery, granulosa cells were immediately collected in RNALater®. Pooled GC from individual patients were washed, centrifuged over 40% Percoll®, resuspended in RNALater, and snap-frozen in LN. Experimental factor: treatment with recombinant FSH versus purified human menopausal gonadotropin Technical protocols: Granulosa cells were collected into RNAlater (Ambion) and stored at -80C until processed. The granulosa cells were pelleted by centrifugation to remove the RNAlater. Each cell pellet was homogenized in TRI reagent (Molecular Research Centers). Bromochloropropane and sodium acetate were added, and the samples were centrifuged to separate the phases. The RNA-containing layer was removed and the RNA purified on an RNeasy extraction column (Qiagen). The sample was treated with an on-column DNase treatment (RNase-free DNase, Qiagen). The purity and quantity were evaluated by an Agilent Bioanalyzer using the RNA 6000 Nanoassay LabChip. Labelled cRNA was prepared using the manufacturer's Instruction Protocol (www1.amershambiosciences.com, CodeLink Gene Expression System: Manual Labelled cRNA Target Preparation). The source of reagents was the CodeLink Expression Assay Reagent Kit, Manual Prep, except where specified otherwise. 0.285 micrograms of total granulosa cell RNA was mixed with bacterial control RNA spikes and primed with T7 oligo(dT) primer for 10 min at 70C. (The bacterial control spikes included araB, entF, fixB, gnd, hisB, and leuB.) The first strand of cDNA was synthesized with first strand buffer, dNTP mix, RNase inhibitor, and reverse transcriptase for 2 h at 42C. The second strand cDNA synthesis reaction was prepared using second strand buffer, dNTP mix, DNA polymerase mix, and RNase H; the reaction was carried out for 2h at 16C. The double-stranded cDNA was purified on QIAquick columns (Qiagen) and the eluent was dried down in a SpeedVac concentrator. The double-stranded cDNA was resuspended in a mixture containing T7 reaction buffer, T7 ATP, T7 GTP, T7 UTP, T7 CTP, biotin-11-UTP, and T7 enzyme mix for the synthesis of cRNA. The cRNA synthesis reaction was terminated after 14h at 37C by purifying the cRNA on RNeasy columns (Qiagen). The concentration of cRNA was determined by spectrophotometry. Hybridization procedures and parameters: 10 micrograms of cRNA was mixed with fragmentation buffer and heated to 94C for 20 min. The fragmented cRNA was mixed with CodeLink hybridization buffer, loaded on the microarray slides, and hybridized for 18 hours at 37C. The slides were washed in 0.75x TNT (Tris-HCl, NaCl, Tween-20) at 46C for 1h then incubated with streptavidin-Alexa 647 fluorescent dye for 30 min at room temperature. The Alexa fluor was prepared in TNB blocking buffer (0.1M Tris-HCl, 0.15M NaCL, 0.5% NEN Blocking Reagent-PerkinElmer) The slides were then washed 4 times for 5 min each in 1x TNT and once in 0.05% Tween 20 for 5 sec. The slides were dried by centrifugation and scanned in an Axon GenePix 4000B scanner.

Project description:Background: The combination of Proteasome inhibitor with Glucocorticoid is one of the most promising antileukemic treatments. Both agents act through pluripotent signal mediators. The two types of agents inhibit key signals that are considered crucial to their effects on malignant cells. Methodology/Principal Findings: Combined use of the two reagents on the lymphoblastic leukemia cell line CCRF-CEM has a range of effects on the viability that depend on the dose and the time used. Even though both reagents are capable of enhancing cell death on the CEM cell line, no combinatorial increase in cell death is detectable, until after the first 120 hours of treatment. In contrast, there are a number of combinatorial effects on the cell cycle phase distribution of treated cells, which indicates a potential for mutual signal disruption between glucocorticoid and proteasome inhibitor at multiple levels. Microarray analysis indicates that Prednisolone and MG132 elicit highly divergent, early-response molecular signatures on this cell line. We assayed levels of the antiapoptotic protein Mcl-1 as a potential model of late, downstream target regulation by both glucocorticoid and proteasome. Synchronized use of Prednisolone with MG132 results in temporary stabilization of Mcl-1 in CEM cells. Stabilization is not the result of a common mechanism of action on the genome, as Prednisolone and MG132 elicit nonreduntant molecular signatures, and it occurs also when the cells are treated at different timepoints with either agent. Conclusions/Significance: Our results show that this glucocorticoid-resistant ALL lymphoblast line is highly sensitive to proteasome inhibitor, and suggest that the proteasome inhibitor and the glucocorticoid regulate different direct target genes. This difference in immediate targets, when the two agents are applied in combination, may lead to negative or positive interference with mechanisms regulating viability of the leukemic lymphoblast. Signal interference between glucocorticoid Prednisolone and the proteasome inhibitor MG132 is expected to occur at more than one level, resulting in complex effects on intracellular signal transduction pathways. The net result depends on the development of individual downstream effects and interactions between key signal mediators. Elucidation of the conditions of interference between glucocorticoid and proteasome targets on leukemic cell fate is expected to improve effects of applied treatment combinations. Experimental setups consisted of the three following samples obtained after 4 h treatment: control, 10nM prednisolone, 1uM prednisolone, 10uM prednisolone, 100M-NM-<M prednisolone, 700M-NM-<M prednisolone, 200nM MG132, 2M-NM-<M MG132 and 20M-NM-<M MG132. Untreated cells were used as reference.

Project description:Cold acclimation in conifers is a complex process, the timing and extent of which reflects local adaptation and varies widely along latitudinal gradients for many temperate and boreal tree species. In spite of their ecological and economic importance, little is known about the global changes in gene expression that accompany autumn cold acclimation in conifers. Using three populations of Sitka spruce (Picea sitchensis) spanning the species range, and a Picea cDNA microarray with 21,840 unique elements, we monitored within and among-population gene expression during the fall. Microarray data were validated for selected genes using real-time PCR. Similar numbers of genes were significantly two-fold upregulated (1,257) and downregulated (967) between late summer and early winter. Among those upregulated were dehydrins, pathogenesis-related/antifreeze genes, carbohydrate and lipid metabolism genes, and genes involved in signal transduction and transcriptional regulation. Among-population microarray hybridizations at early and late autumn time points revealed substantial variation in the autumn transcriptome, some of which may reflect local adaptation. Our results demonstrate the complexity of cold acclimation in conifers, highlight similarities and differences to cold tolerance in annual plants, and provide a solid foundation for functional and genetic studies of this important adaptive process in conifers. Keywords: Time course Foliage for RNA extraction was obtained from four-year-old Sitka spruce seedlings, which were grown from seed collected across the species range in a raised-bed outdoor common garden in Vancouver, BC, Canada (49Âº N) (Mimura and Aitken, 2007, Heredity). Needle tissues from current year upper lateral shoots, separated from stem and bark tissue, were collected at five time points between late summer and early winter, 2004. Three populations were chosen for sampling: Valdez, Alaska, USA (61Âº N) (AK), Prince Rupert, British Columbia, Canada (54Âº N) (BC) and Redwood, California, USA (41Âº N) (CA) (Figure 1a). Needle samples (~1g) were taken from eight individuals in the BC population on each of the five dates (Aug. 30, Oct. 18, Nov. 24, Dec. 1, and Dec. 13). Eight individuals from the AK and CA populations were also sampled on the second and fourth dates, as well as eight additional individuals from the BC population. Tissues were flash frozen in an N2 vapor tank immediately upon collection, and subsequently stored at -80Â°C until processing. To decrease the effects of biological variance among individual seedlings within populations, equal amounts of foliar tissue were pooled from four individuals prior to RNA extraction. Two pools were collected at each time point for each population, and total RNA was extracted following a previously published protocol (Kolosova et al., 2004). Slides were scanned and spot intensity quantified using ImaGene software (BioDiscovery, Inc., El Segundo, CA). To correct for background intensity, the lowest 10% of median foreground intensities was subtracted from the median foreground intensities. Data were then normalized by variance stabilizing normalization (VSN) to compensate for nonlinearity of intensity distributions (Huber et al., 2002). To identify significant changes in gene expression, a linear mixed-effects model was fit to the normalized intensities in the Cy3 and Cy5 channels of the 32 microarray slides. The model contained an adjustment for dye bias, an array effect indicating which Cy5/Cy3 pair was on each array, a treatment effect indicating sample population and time point, and a random effect to adjust for repeated measures on the same biological sample (Kerr et al., 2000). P-values were computed for each gene-by-treatment effect and Q-values were calculated to adjust for the false discovery rate (FDR) (Storey & Tibshirani, 2003).

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Bladder cancer microarrays

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