Disease signatures are robust across tissues and experiments

Meta-analyses combining gene expression microarray experiments offer new insights into the molecular pathophysiology of disease not evident from individual experiments. Although the established technical reproducibility of microarrays serves as a basis for meta-analysis, pathophysiological reproducibility across experiments is not well established. In this study, we carried out a large-scale analysis of disease-associated experiments obtained from NCBI GEO, and evaluated their concordance across a broad range of diseases and tissue types. On evaluating 429 experiments, representing 238 diseases and 122 tissues from 8435 microarrays, we find evidence for a general, pathophysiological concordance between experiments measuring the same disease condition. Furthermore, we find that the molecular signature of disease across tissues is overall more prominent than the signature of tissue expression across diseases. The results offer new insight into the quality of public microarray data using pathophysiological metrics, and support new directions in meta-analysis that include characterization of the commonalities of disease irrespective of tissue, as well as the creation of multi-tissue systems models of disease pathology using public data

Analysis of multiplex gene expression maps obtained by voxelation

BackgroundGene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.ResultsTo analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum.ConclusionThe experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists

Glucocorticoids with different chemical structures but similar glucocorticoid receptor potency regulate subsets of common and unique genes in human trabecular meshwork cells

<p>Abstract</p> <p>Background</p> <p>In addition to their well-documented ocular therapeutic effects, glucocorticoids (GCs) can cause sight-threatening side-effects including ocular hypertension presumably via morphological and biochemical changes in trabecular meshwork (TM) cells. In the present study, we directly compared the glucocorticoid receptor (GR) potency for dexamethasone (DEX), fluocinolone acetonide (FA) and triamcinolone acetonide (TA), examined the expression of known GRα and GRβ isoforms, and used gene expression microarrays to compare the effects of DEX, FA, and TA on the complete transcriptome in two primary human TM cell lines.</p> <p>Methods</p> <p>GR binding affinity for DEX, FA, and TA was measured by a cell-free competitive radio-labeled GR binding assay. GR-mediated transcriptional activity was assessed using the GeneBLAzer beta-lactamase reporter gene assay. Levels of GRα and GRβ isoforms were assessed by Western blot. Total RNA was extracted from TM 86 and TM 93 cells treated with 1 μM DEX, FA, or TA for 24 hr and used for microarray gene expression analysis. The microarray experiments were repeated three times. Differentially expressed genes were identified by Rosetta Resolver Gene Expression Analysis System.</p> <p>Results</p> <p>The GR binding affinity (IC₅₀) for DEX, FA, and TA was 5.4, 2.0, and 1.5 nM, respectively. These values are similar to the GR transactivation EC₅₀of 3.0, 0.7, and 1.5 nM for DEX, FA, and TA, respectively. All four GRα translational isoforms (A-D) were expressed in TM 86 and TM 93 total cell lysates, however, the C and D isoforms were more highly expressed relative to A and B. All four GRβ isoforms (A-D) were also detected in TM cells, although GRβ-D isoform expression was lower compared to that of the A, B, or C isoforms. Microarray analysis revealed 1,968 and 1,150 genes commonly regulated by DEX, FA, and TA in TM 86 and TM 93, respectively. These genes included RGC32, OCA2, ANGPTL7, MYOC, FKBP5, SAA1 and ZBTB16. In addition, each GC specifically regulated a unique set of genes in both TM cell lines. Using Ingenuity Pathway Analysis (IPA) software, analysis of the data from TM 86 cells showed that DEX significantly regulated transcripts associated with RNA post-transcriptional modifications, whereas FA and TA modulated genes involved in lipid metabolism and cell morphology, respectively. In TM 93 cells, DEX significantly regulated genes implicated in histone methylation, whereas FA and TA altered genes associated with cell cycle and cell adhesion, respectively.</p> <p>Conclusion</p> <p>Human trabecular meshwork cells in culture express all known GRα and GRβ translational isoforms, and GCs with similar potency but subtly different chemical structure are capable of regulating common and unique gene subsets and presumably biologic responses in these cells. These GC structure-dependent effects appear to be TM cell-lineage dependent.</p

Washing scaling of GeneChip microarray expression

BACKGROUND Post-hybridization washing is an essential part of microarray experiments. Both the quality of the experimental washing protocol and adequate consideration of washing in intensity calibration ultimately affect the quality of the expression estimates extracted from the microarray intensities. RESULTS We conducted experiments on GeneChip microarrays with altered protocols for washing, scanning and staining to study the probe-level intensity changes as a function of the number of washing cycles. For calibration and analysis of the intensity data we make use of the 'hook' method which allows intensity contributions due to non-specific and specific hybridization of perfect match (PM) and mismatch (MM) probes to be disentangled in a sequence specific manner. On average, washing according to the standard protocol removes about 90% of the non-specific background and about 30-50% and less than 10% of the specific targets from the MM and PM, respectively. Analysis of the washing kinetics shows that the signal-to-noise ratio doubles roughly every ten stringent washing cycles. Washing can be characterized by time-dependent rate constants which reflect the heterogeneous character of target binding to microarray probes. We propose an empirical washing function which estimates the survival of probe bound targets. It depends on the intensity contribution due to specific and non-specific hybridization per probe which can be estimated for each probe using existing methods. The washing function allows probe intensities to be calibrated for the effect of washing. On a relative scale, proper calibration for washing markedly increases expression measures, especially in the limit of small and large values. CONCLUSIONS Washing is among the factors which potentially distort expression measures. The proposed first-order correction method allows direct implementation in existing calibration algorithms for microarray data. We provide an experimental 'washing data set' which might be used by the community for developing amendments of the washing correction.This publication is supported by the Leipzig Interdisciplinary Research Cluster of Genetic Factors, Clinical Phenotypes and Environment (LIFE Center, Universität Leipzig) and an Australian Academy of Science Visits to Europe grant. LIFE is funded by means of the European Union, by the European Regional Development Fund (ERFD) and by means of the Free State of Saxony within the framework of the excellence initiative

Gene expression profiles in human gastric cancer: expression of maspin correlates with lymph node metastasis

To seek for a candidate gene that would regulate tumour progression and metastasis in gastric cancer, we investigated gene expression profiles by using DNA microarray. Tumour tissue and adjacent normal tissue were obtained from 21 patients with gastric cancer and then examined for their gene expression profiles by the Gene Chip® Human U95Av2 array, which includes 12 000 human genes and EST sequences. A total of 25 genes were upregulated and two genes were downregulated by at least four-fold in the tumour tissue. In a further analysis according to lymph node metastasis, the expressed levels of maspin, as well as carcinoembryonic antigen and nonspecific crossreacting antigen were significantly higher in tumours with lymph node metastasis than in those without it. Maspin expression in 85 gastric cancer patients was further investigated by using immunohistochemistry. Maspin expression was not observed in normal gastric epithelia without intestinal metaplasia. In contrast, maspin was expressed in 74 of 85 tumour tissues. There was a significant correlation between the incidence of maspin-positive tumour staining and lymph node metastasis. These results suggest that maspin has a potential role for tumour metastasis in gastric cancer

Differential gene expression in mouse primary hepatocytes exposed to the peroxisome proliferator-activated receptor α agonists

BACKGROUND: Fibrates are a unique hypolipidemic drugs that lower plasma triglyceride and cholesterol levels through their action as peroxisome proliferator-activated receptor alpha (PPARα) agonists. The activation of PPARα leads to a cascade of events that result in the pharmacological (hypolipidemic) and adverse (carcinogenic) effects in rodent liver. RESULTS: To understand the molecular mechanisms responsible for the pleiotropic effects of PPARα agonists, we treated mouse primary hepatocytes with three PPARα agonists (bezafibrate, fenofibrate, and WY-14,643) at multiple concentrations (0, 10, 30, and 100 μM) for 24 hours. When primary hepatocytes were exposed to these agents, transactivation of PPARα was elevated as measured by luciferase assay. Global gene expression profiles in response to PPARα agonists were obtained by microarray analysis. Among differentially expressed genes (DEGs), there were 4, 8, and 21 genes commonly regulated by bezafibrate, fenofibrate, and WY-14,643 treatments across 3 doses, respectively, in a dose-dependent manner. Treatments with 100 μM of bezafibrate, fenofibrate, and WY-14,643 resulted in 151, 149, and 145 genes altered, respectively. Among them, 121 genes were commonly regulated by at least two drugs. Many genes are involved in fatty acid metabolism including oxidative reaction. Some of the gene changes were associated with production of reactive oxygen species, cell proliferation of peroxisomes, and hepatic disorders. In addition, 11 genes related to the development of liver cancer were observed. CONCLUSION: Our results suggest that treatment of PPARα agonists results in the production of oxidative stress and increased peroxisome proliferation, thus providing a better understanding of mechanisms underlying PPARα agonist-induced hepatic disorders and hepatocarcinomas

An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays

<p>Abstract</p> <p>Background</p> <p>Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches.</p> <p>Results</p> <p>In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA) will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay.</p> <p>Conclusions</p> <p>By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at <url>http://www.laurenzi.net</url>.</p

Pressure Load: The Main Factor for Altered Gene Expression in Right Ventricular Hypertrophy in Chronic Hypoxic Rats

BACKGROUND: The present study investigated whether changes in gene expression in the right ventricle following pulmonary hypertension can be attributed to hypoxia or pressure loading. METHODOLOGY/PRINCIPAL FINDINGS: To distinguish hypoxia from pressure-induced alterations, a group of rats underwent banding of the pulmonary trunk (PTB), sham operation, or the rats were exposed to normoxia or chronic, hypobaric hypoxia. Pressure measurements were performed and the right ventricle was analyzed by Affymetrix GeneChip, and selected genes were confirmed by quantitative PCR and immunoblotting. Right ventricular systolic blood pressure and right ventricle to body weight ratio were elevated in the PTB and the hypoxic rats. Expression of the same 172 genes was altered in the chronic hypoxic and PTB rats. Thus, gene expression of enzymes participating in fatty acid oxidation and the glycerol channel were downregulated. mRNA expression of aquaporin 7 was downregulated, but this was not the case for the protein expression. In contrast, monoamine oxidase A and tissue transglutaminase were upregulated both at gene and protein levels. 11 genes (e.g. insulin-like growth factor binding protein) were upregulated in the PTB experiment and downregulated in the hypoxic experiment, and 3 genes (e.g. c-kit tyrosine kinase) were downregulated in the PTB and upregulated in the hypoxic experiment. CONCLUSION/SIGNIFICANCE: Pressure load of the right ventricle induces a marked shift in the gene expression, which in case of the metabolic genes appears compensated at the protein level, while both expression of genes and proteins of importance for myocardial function and remodelling are altered by the increased pressure load of the right ventricle. These findings imply that treatment of pulmonary hypertension should also aim at reducing right ventricular pressure

Differentially Expressed RNA from Public Microarray Data Identifies Serum Protein Biomarkers for Cross-Organ Transplant Rejection and Other Conditions

Serum proteins are routinely used to diagnose diseases, but are hard to find due to low sensitivity in screening the serum proteome. Public repositories of microarray data, such as the Gene Expression Omnibus (GEO), contain RNA expression profiles for more than 16,000 biological conditions, covering more than 30% of United States mortality. We hypothesized that genes coding for serum- and urine-detectable proteins, and showing differential expression of RNA in disease-damaged tissues would make ideal diagnostic protein biomarkers for those diseases. We showed that predicted protein biomarkers are significantly enriched for known diagnostic protein biomarkers in 22 diseases, with enrichment significantly higher in diseases for which at least three datasets are available. We then used this strategy to search for new biomarkers indicating acute rejection (AR) across different types of transplanted solid organs. We integrated three biopsy-based microarray studies of AR from pediatric renal, adult renal and adult cardiac transplantation and identified 45 genes upregulated in all three. From this set, we chose 10 proteins for serum ELISA assays in 39 renal transplant patients, and discovered three that were significantly higher in AR. Interestingly, all three proteins were also significantly higher during AR in the 63 cardiac transplant recipients studied. Our best marker, serum PECAM1, identified renal AR with 89% sensitivity and 75% specificity, and also showed increased expression in AR by immunohistochemistry in renal, hepatic and cardiac transplant biopsies. Our results demonstrate that integrating gene expression microarray measurements from disease samples and even publicly-available data sets can be a powerful, fast, and cost-effective strategy for the discovery of new diagnostic serum protein biomarkers

Design, Validation and Annotation of Transcriptome-Wide Oligonucleotide Probes for the Oligochaete Annelid Eisenia fetida

High density oligonucleotide probe arrays have increasingly become an important tool in genomics studies. In organisms with incomplete genome sequence, one strategy for oligo probe design is to reduce the number of unique probes that target every non-redundant transcript through bioinformatic analysis and experimental testing. Here we adopted this strategy in making oligo probes for the earthworm Eisenia fetida, a species for which we have sequenced transcriptome-scale expressed sequence tags (ESTs). Our objectives were to identify unique transcripts as targets, to select an optimal and non-redundant oligo probe for each of these target ESTs, and to annotate the selected target sequences. We developed a streamlined and easy-to-follow approach to the design, validation and annotation of species-specific array probes. Four 244K-formatted oligo arrays were designed using eArray and were hybridized to a pooled E. fetida cRNA sample. We identified 63,541 probes with unsaturated signal intensities consistently above the background level. Target transcripts of these probes were annotated using several sequence alignment algorithms. Significant hits were obtained for 37,439 (59%) probed targets. We validated and made publicly available 63.5K oligo probes so the earthworm research community can use them to pursue ecological, toxicological, and other functional genomics questions. Our approach is efficient, cost-effective and robust because it (1) does not require a major genomics core facility; (2) allows new probes to be easily added and old probes modified or eliminated when new sequence information becomes available, (3) is not bioinformatics-intensive upfront but does provide opportunities for more in-depth annotation of biological functions for target genes; and (4) if desired, EST orthologs to the UniGene clusters of a reference genome can be identified and selected in order to improve the target gene specificity of designed probes. This approach is particularly applicable to organisms with a wealth of EST sequences but unfinished genome