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:Diabetes Mellitus (DM) is a chronic, severe disease rapidly increasing in incidence and prevalence and is associated with numerous complications. Patients with DM are at high risk of developing diabetic foot ulcers (DFU) that often lead to lower limb amputations, long term disability, and a shortened lifespan. Despite this, the effects of DM on human foot skin biology are largely unknown. Thus, the focus of this study was to determine whether DM changes foot skin biology predisposing it for healing impairment and development of DFU. Foot skin samples were collected from 20 patients receiving corrective foot surgery and, using a combination of multiple molecular and cellular approaches we performed comparative analyses of non-ulcerated non-neuropathic diabetic foot skin (DFS) and healthy non-diabetic foot skin (NFS). MicroRNA (miR) profiling of laser captured epidermis and primary dermal fibroblasts from both DFS and NFS samples identified 5 miRs de-regulated in the epidermis of DFS though none reached statistical significance. MiR-31-5p and miR-31-3p were most profoundly induced. Although none were significantly regulated in diabetic fibroblasts, miR-29c-3p showed a trend of up-regulation, which was confirmed by qPCR in a prospective set of 20 skin samples. Gene expression profiling of full thickness biopsies identified 36 de-regulated genes in DFS (>2 fold-change, unadjusted p-value ≤ 0.05). Of this group, three out of seven tested genes were confirmed by qPCR: SERPINB3 was up-regulated whereas OR2A4 and LGR5 were down-regulated in DFS. However no morphological differences in histology, collagen deposition, and number of blood vessels or lymphocytes were found. No difference in proliferative capacity was observed by quantification of Ki67 positive cells in epidermis. These findings suggest DM causes only subtle changes to foot skin. Since morphology, mRNA and miR levels were not affected in a major way, additional factors, such as neuropathy, vascular complications, or duration of DM, may further compromise tissue’s healing ability leading to development of DFUs.

Project description:a salmonid microarray was used to characterize environmentally-regulated shifts in gene expression between ocean and river habitats in gill and liver tissues of wild migrating adult Pacific sockeye salmon (Oncorhynchus nerka). To correlate gene expression with survival, non-lethal biopsy sampling of gill tissue and microarray-based profiling was combined with biotelemetry and genetic stock identification so that transcriptomic profiles could be compared between fish reaching spawning grounds and presumed mortalities. Fish were captured fish at two marine sampling sites, one within Johnstone Strait (JS), BC. Canada and one within Juan De Fuca Strait (JDFS), BC Canada. Ocean sites were contrasted to fish sampled within the Fraser River at Whonnock (W), BC, Canada. Gill and liver tissues were dissected at each of these sites. Non-lethal biopsy sampling was performed on migrating sockeye salmon intercepted within the Fraser River at Mission, BC, Canada and genetically-based stock ID was used to determine the stock-specific spawning grounds for each fish, giving an intended end-point of migration for each of the stocks investigated in this study.Gene expression levels were determined by comparing the amount of mRNA transcript in the experimental samples relative to a reference sample. A total of 123 microarrays were used to generate the dataset, corresponding to individual hybridizations of both gill and liver samples collected from JS (gill n=14; liver n=15), JDFS (gill n=15; liver n=13), W (gill n=11; liver n=10), and biopsy sampled gill tissue collected at Mission (n=45).Total RNA was amplified (1 round) with MessageAmpTMII-96 kit (Ambion, TX, USA), and reverse transcribed to cDNA before labelling with ALEXA dyes using the Invitrogen Indirect Labelling Kit. The reference contained the combined aRNA of all individuals used in the experiment, excluding bioposy sampled fish. Individual samples were labelled with Alexa 555 and the reference control with Alexa 647, and no dye swaps were perfromed.

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Bladder cancer microarrays

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