Project description:BackgroundSeveral high throughput technologies have been employed to identify differentially regulated genes that may be molecular targets for drug discovery. Here we compared the sets of differentially regulated genes discovered using two experimental approaches: a subtracted suppressive hybridization (SSH) cDNA library methodology and Affymetrix GeneChip technology. In this "case study" we explored the transcriptional pattern changes during the in vitro differentiation of human monocytes to myeloid dendritic cells (DC), and evaluated the potential for novel gene discovery using the SSH methodology.ResultsThe same RNA samples isolated from peripheral blood monocyte precursors and immature DC (iDC) were used for GeneChip microarray probing and SSH cDNA library construction. 10,000 clones from each of the two-way SSH libraries (iDC-monocytes and monocytes-iDC) were picked for sequencing. About 2000 transcripts were identified for each library from 8000 successful sequences. Only 70% to 75% of these transcripts were represented on the U95 series GeneChip microarrays, implying that 25% to 30% of these transcripts might not have been identified in a study based only on GeneChip microarrays. In addition, about 10% of these transcripts appeared to be "novel", although these have not yet been closely examined. Among the transcripts that are also represented on the chips, about a third were concordantly discovered as differentially regulated between iDC and monocytes by GeneChip microarray transcript profiling. The remaining two thirds were either not inferred as differentially regulated from GeneChip microarray data, or were called differentially regulated but in the opposite direction. This underscores the importance both of generating reciprocal pairs of SSH libraries, and of real-time RT-PCR confirmation of the results.ConclusionsThis study suggests that SSH could be used as an alternative and complementary transcript profiling tool to GeneChip microarrays, especially in identifying novel genes and transcripts of low abundance.

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:The transport and catabolic machinery involved in carbohydrate utilization by Lactobacillus acidophilus was characterized genetically by using whole-genome cDNA microarrays. Global transcriptional profiles were determined for growth on glucose, fructose, sucrose, lactose, galactose, trehalose, raffinose, and fructooligosaccharides. Hybridizations were carried out by using a round-robin design, and microarray data were analyzed with a two-stage mixed model ANOVA. Differentially expressed genes were visualized by hierarchical clustering, volcano plots, and contour plots. Overall, only 63 genes (3% of the genome) showed a >4-fold induction. Specifically, transporters of the phosphoenolpyruvate:sugar transferase system were identified for uptake of glucose, fructose, sucrose, and trehalose, whereas ATP-binding cassette transporters were identified for uptake of raffinose and fructooligosaccharides. A member of the LacS subfamily of galactoside-pentose hexuronide translocators was identified for uptake of galactose and lactose. Saccharolytic enzymes likely involved in the metabolism of monosaccharides, disaccharides, and polysaccharides into substrates of glycolysis were also found, including enzymatic machinery of the Leloir pathway. The transcriptome appeared to be regulated by carbon catabolite repression. Although substrate-specific carbohydrate transporters and hydrolases were regulated at the transcriptional level, genes encoding regulatory proteins CcpA, Hpr, HprK/P, and EI were consistently highly expressed. Genes central to glycolysis were among the most highly expressed in the genome. Collectively, microarray data revealed that coordinated and regulated transcription of genes involved in sugar uptake and metabolism is based on the specific carbohydrate provided. L. acidophilus's adaptability to environmental conditions likely contributes to its competitive ability for limited carbohydrate sources available in the human gastrointestinal tract.

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. 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.

Project description:BackgroundSpotted cDNA microarrays generally employ co-hybridization of fluorescently-labeled RNA targets to produce gene expression ratios for subsequent analysis. Direct comparison of two RNA samples in the same microarray provides the highest level of accuracy; however, due to the number of combinatorial pair-wise comparisons, the direct method is impractical for studies including large number of individual samples (e.g., tumor classification studies). For such studies, indirect comparisons using a common reference standard have been the preferred method. Here we evaluated the precision and accuracy of reconstructed ratios from three indirect methods relative to ratios obtained from direct hybridizations, herein considered as the gold-standard.ResultsWe performed hybridizations using a fixed amount of Cy3-labeled reference oligonucleotide (RefOligo) against distinct Cy5-labeled targets from prostate, breast and kidney tumor samples. Reconstructed ratios between all tissue pairs were derived from ratios between each tissue sample and RefOligo. Reconstructed ratios were compared to (i) ratios obtained in parallel from direct pair-wise hybridizations of tissue samples, and to (ii) reconstructed ratios derived from hybridization of each tissue against a reference RNA pool (RefPool). To evaluate the effect of the external references, reconstructed ratios were also calculated directly from intensity values of single-channel (One-Color) measurements derived from tissue sample data collected in the RefOligo experiments. We show that the average coefficient of variation of ratios between intra- and inter-slide replicates derived from RefOligo, RefPool and One-Color were similar and 2 to 4-fold higher than ratios obtained in direct hybridizations. Correlation coefficients calculated for all three tissue comparisons were also similar. In addition, the performance of all indirect methods in terms of their robustness to identify genes deemed as differentially expressed based on direct hybridizations, as well as false-positive and false-negative rates, were found to be comparable.ConclusionRefOligo produces ratios as precise and accurate as ratios reconstructed from a RNA pool, thus representing a reliable alternative in reference-based hybridization experiments. In addition, One-Color measurements alone can reconstruct expression ratios without loss in precision or accuracy. We conclude that both methods are adequate options in large-scale projects where the amount of a common reference RNA pool is usually restrictive.

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Gene discovery in bladder cancer progression using cDNA microarrays.

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Gene discovery in bladder cancer progression using cDNA microarrays.

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