Expression Microarray Results Research Articles

Identification of shared disease marker genes and underlying mechanisms between major depression and rheumatoid arthritis

Both depression and rheumatoid arthritis (RA) have a very high comorbidity rate. A bilateral association is estimated to increase the mutual risk and the common denominator is inflammation being observed in both diseases. Previous studies have mainly focused on assessing peripheral blood's inflammatory and pro-inflammatory cytokines levels.We aimed to extend insights into the molecular mechanisms of depression based on hub RA genes. To do so, we prioritized RA-related genes using in-silico tools. We then investigated whether RA-related genes undergo altered expression in patients with unipolar and bipolar depression without a concurrent RA diagnosis and any exponents of active inflammation. In addition, we selected a homogeneous group of patients treated with lithium (Li), which has immunomodulatory properties.The study was performed on patients with bipolar depression (BD, n = 45; Li, n = 20), unipolar depression (UD, n = 27), and healthy controls (HC, n = 22) of both sexes. To identify DEGs in peripheral blood mononuclear cells (PBMCs), we used the SurePrint G3 Microarray and GeneSpring software.We selected a list of 180 hub genes whose altered expression we analyzed using the expression microarray results. In the entire study group, we identified altered expression of 93 of the 180 genes, including 35 down-regulated (OPRM1 gene with highest FC > 3) and 58 up-regulated (TLR4 gene with highest FC > 3). In UD patients, we observed maximally up-regulated expression of the TEK gene (FC > 3), and in BD of the CXCL8 gene (FC > 5). On the other hand, in lithium-treated patients, the gene with the most reduced expression was the TRPV1 gene.The study proved that depression and RA are produced by a partially shared “inflammatory interactome” in which the opioid and angiogenesis pathways are important.

The potential effect of Bruton’s tyrosine kinase in refractory periapical periodontitis

To determine the expression of Bruton’s tyrosine kinase (BTK) in refractory periapical periodontitis and analyze the relationship between BTK and bone resorption in refractory periapical periodontitis. The mechanism of bone resorption is also discussed. The OneArray Plus expression microarray was used to screen for genes related to refractory periapical periodontitis. Real-time PCR was used to detect the expression of BTK in refractory periapical periodontitis tissues. A model of periapical periodontitis was established by sealing E.faecalis into the pulp of rats. To establish a model of E.faecalis LTA infection of osteoclasts, the relationship between BTK and bone destruction during refractory periapical periodontitis was analyzed. OneArray Plus expression microarray results showed that we found that the expression of 1787 genes in the two samples was different. After validating these samples, we found that BTK was closely related to refractory periapical periodontitis. The results showed that the expression of BTK in refractory periapical periodontitis tissues was higher than that in normal tissues. Immunohistochemistry, enzyme histochemistry and real-time PCR showed that the BTK expression curve in the experimental model resembled a reverse V shape from week 1 to week 4. Osteoclasts were cultured in vitro and treated with E. faecalis LTA. The expression of BTK in the E. faecalis model was greater than that in the control group. BTK played an important role in the progression of refractory periapical periodontitis.

Interference of HMGB1 Lead to Increased Vulnerability of MM Cells to Dexamethasone and Doxorubicin

Background: Multiple myeloma (MM) is the second mostly diagnosed disease among hematological malignancies after lymphoma. With the novel agents, the survival of MM patients has been improved significantly but still remains incurable because of drug resistance. Studies have found that high-mobility group box 1 protein (HMGB1) was involved in inflammation, angiogenesis, and cancer invasion progression, metastasis, and drug resistance. Our research was aimed at exploring the role of HMGB1 in MM cell proliferation and drug resistance.Methods: First, semi-quantitative real time-polymerase chain (qRT-PCR) and western blot was used to determine the levels of HMGB1 mRNA and protein expression in MM cell lines (RPMI8226, CAG, and MM.1S) and primary MM samples. Second, MM cells were transfected with HMGB1-knockdown lentivirus and the Cell Counting Kit 8 (CCK8) assay was used to determine the proliferation of MM cells with or without chemotherapeutic drugs dexamethasone (Dex) and doxorubicin (ADM). Then cell apoptosis was detected by flow cytometry. Third, Affymetrix HTA 2.0 Array was used to compare changes in gene expression levels between HMGB1-knockdown cells and the control cells and qRT-PCR was used to verify the array results. Last, Western bolt was performed to analyze changes in signaling pathways after HMGB1 knockdown.Results: MM cell lines and primary MM samples expressed high levels of HMGB1 mRNA and protein. Although there was no difference in MM cell proliferation between HMGB1-knockdown group and the control group (P>0.05), HMGB1-knockdown significantly enhanced inhibitory effect of chemotherapy with Dex and ADM in comparison with the wildtype HMGB1 control (P<0.05). Flow cytometry results showed that apoptosis of MM cells induced by Dex and ADM was increased when HMGB1 expression was suppressed (P<0.05). Furthermore, gene array analysis on RPMI8226 and CAG cell lines showed that anti-apoptotic genes (bcl-2, HIAP1) and MM survival related genes (DEPTOR, CXCL12) were decreased and pro-apoptotic genes (TNFRSF1B, TRAIL, CXCL10) were increased in knockdown cells compared to the controls. In addition, expression levels of genes that play important roles in signaling pathways such as mTOR, NF-κB, PI3K-AKt, and p38-MAPK were also significantly changed. The gene expression microarray results were verified by qRT-PCR and Western blot demonstrated that phosphorylation of p70S6K (substrate of mTORC1 complex) and AKT-ser473 (substrate of mTORC2 complex) were both elevated in HMGB1-knockdown MM cells compared to that in the control cells.Conclusions: Our research showed that downregulation of HMGB1 increased sensitivity of MM cells to Dex and ADM through increasing apoptosis and regulating mTOR, NF-κB, PI3K-AKt, and p38-MAPK pathways. DisclosuresNo relevant conflicts of interest to declare.

Ecotoxicogenomics: Microarray interlaboratory comparability

Transcriptomic analysis can complement traditional ecotoxicology data by providing mechanistic insight, and by identifying sub-lethal organismal responses and contaminant classes underlying observed toxicity. Before transcriptomic information can be used in monitoring and risk assessment, it is necessary to determine its reproducibility and detect key steps impacting the reliable identification of differentially expressed genes. A custom 15K-probe microarray was used to conduct transcriptomics analyses across six laboratories with estuarine amphipods exposed to cyfluthrin-spiked or control sediments (10 days). Two sample types were generated, one consisted of total RNA extracts (Ex) from exposed and control samples (extracted by one laboratory) and the other consisted of exposed and control whole body amphipods (WB) from which each laboratory extracted RNA. Our findings indicate that gene expression microarray results are repeatable. Differentially expressed data had a higher degree of repeatability across all laboratories in samples with similar RNA quality (Ex) when compared to WB samples with more variable RNA quality. Despite such variability a subset of genes were consistently identified as differentially expressed across all laboratories and sample types. We found that the differences among the individual laboratory results can be attributed to several factors including RNA quality and technical expertise, but the overall results can be improved by following consistent protocols and with appropriate training.

Functional genomics annotation of a statistical epistasis network associated with bladder cancer susceptibility.

BackgroundSeveral different genetic and environmental factors have been identified as independent risk factors for bladder cancer in population-based studies. Recent studies have turned to understanding the role of gene-gene and gene-environment interactions in determining risk. We previously developed the bioinformatics framework of statistical epistasis networks (SEN) to characterize the global structure of interacting genetic factors associated with a particular disease or clinical outcome. By applying SEN to a population-based study of bladder cancer among Caucasians in New Hampshire, we were able to identify a set of connected genetic factors with strong and significant interaction effects on bladder cancer susceptibility.FindingsTo support our statistical findings using networks, in the present study, we performed pathway enrichment analyses on the set of genes identified using SEN, and found that they are associated with the carcinogen benzo[a]pyrene, a component of tobacco smoke. We further carried out an mRNA expression microarray experiment to validate statistical genetic interactions, and to determine if the set of genes identified in the SEN were differentially expressed in a normal bladder cell line and a bladder cancer cell line in the presence or absence of benzo[a]pyrene. Significant nonrandom sets of genes from the SEN were found to be differentially expressed in response to benzo[a]pyrene in both the normal bladder cells and the bladder cancer cells. In addition, the patterns of gene expression were significantly different between these two cell types.ConclusionsThe enrichment analyses and the gene expression microarray results support the idea that SEN analysis of bladder in population-based studies is able to identify biologically meaningful statistical patterns. These results bring us a step closer to a systems genetic approach to understanding cancer susceptibility that integrates population and laboratory-based studies.

Interpreting the gene expression microarray results: a user-based experience.

In recent years many tools have been developed to cope with the interpretation of gene expression results from microarray experiments. The effectiveness of these tools largely depends on their ease of use by biomedical researchers. Tools based on effective computational methods, indeed, cannot be fully exploited by users if they are not supported by an intuitive interface, a large set of utilities and effective outputs. In this paper, 10 tools for the interpretation of gene expression microarray results have been tested on 11 microarray datasets and evaluated according to eight assessment criteria: 1. interface design and usability, 2. easiness of input submission, 3. effectiveness of output representation and 4. of the downloaded outputs, 5. possibility to submit multiple gene IDs, 6. sources of information, 7. provision of different statistical tests and 8. of multiple test correction methods. Strengths and weaknesses of each tool are highlighted to: a. provide useful tips to users dealing with the biological interpretation of microarray results; b. draw the attention of software developers on the usability of their tools.

Interpreting the gene expression microarray results: a user-based evaluation of 10 bioinformatics tools

In recent years many tools have been developed to cope with the interpretation of gene expression results from microarray experiments. The effectiveness of these tools largely depends on their ease of use by biomedical researchers. Tools based on effective computational methods, indeed, cannot be fully exploited by users if they are not supported by an intuitive interface, a large set of utilities and effective outputs. In this paper, ten tools for the interpretation of gene expression microarray results have been tested on eleven microarray datasets and evaluated according to eight assessment criteria: 1. interface design and usability, 2. easiness of input submission, 3. effectiveness of output representation, 4. efficacy of the downloaded outputs, 5. possibility to submit multiple gene IDs, 6. sources of information, 7. provision of different statistical tests and 8. supply of multiple test correction methods. Strengths and weaknesses of each tool are highlighted: a. to provide useful tips to users dealing with the biological interpretation of microarray results; b. to draw the attention of software developers on the usability of their tools.

Abstract 5065: A 22-gene signature predicts response to chemotherapy in a broad range of breast cancer subtypes

Abstract Statement of Purpose: To test the utility of a novel, multifaceted 22-gene signature to predict response to chemotherapy in different molecular subtypes of breast cancer. Background: Only 15-25% of breast cancer patients from all histopathological subtypes currently respond to primary chemotherapy with a complete pathological response (pCR) and there is currently no test in widespread clinical use to predict whether a patient will respond. Here we present results describing a 22-gene signature that can identify breast cancer patients who will respond to taxol-combination chemotherapy in a wide range of molecular subtypes. The signature was identified using a biology-driven approach. We hypothesized that genes involved in normal human mammary acinar morphogenesis represent biomarkers capable of stratifying breast cancers. Methods: To determine whether genes with differential expression during acinar morphogenesis predict response to chemotherapy, we have analyzed gene expression microarray results. Three preexisting microarray datasets were used. 1) The dataset of Fournier et al (Cancer Res, 2006. 66:7095) obtained from a time course of two different non-malignant human mammary epithelial cell types grown in a laminin-rich extracellular matrix culture (lrECM). 2) The dataset of Hess et al (J Clin Oncol, 2006. 24:4236) including 133 patients with stage I-III breast cancer obtained before primary taxol-combination chemotherapy (TFAC). 3) The dataset of Popovici et al (Breast Cancer Res, 2010. 12:R5) including 278 patient samples that overlaps with the previous dataset. The assignment of molecular subtype of tumor samples was performed using the intrinsic gene set of 300 genes (Hu et al., BMC Genomics, 2006.7:96). Results: The majority of genes (12 out 22) down regulated upon growth arrest and polarity formation during acinar morphogenesis in lrECM were significantly over expressed in patients who responded to chemotherapy with pCR (p&amp;lt;0.05, Student's T-test and Fisher's Exact test). These 22 genes are referred to here as the 22-gene signature. To test this signature's ability to predict response in different molecular subtypes of breast cancer we applied logistic regression with three-fold cross validation. The 22-gene signature accurately predicted response in all samples with AUC 0.75 and in specific breast cancer molecular subtypes including ER-positive, ER-negative, luminal B, Her2-positive, and basal-like with average AUC 0.69. Conclusions: This study is the first example of a single gene signature to predict chemotherapy response in different molecular subtypes of breast cancer. Its predictive value in ER-negative and basal-like breast cancers is notable. Patients identified as non-responsive have the potential to benefit from adding an alternative treatment early in their care or from improved quality of life in forgoing an ineffective treatment. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5065. doi:10.1158/1538-7445.AM2011-5065

Microarray analysis of cytoplasmic versus whole cell RNA reveals a considerable number of missed and false positive mRNAs

With no known exceptions, every published microarray study to determine differential mRNA levels in eukaryotes used RNA extracted from whole cells. It is assumed that the use of whole cell RNA in microarray gene expression analysis provides a legitimate profile of steady-state mRNA. Standard labeling methods and the prevailing dogma that mRNA resides almost exclusively in the cytoplasm has led to the long-standing belief that the nuclear RNA contribution is negligible. We report that unadulterated cytoplasmic RNA uncovers differentially expressed mRNAs that otherwise would not have been detected when using whole cell RNA and that the inclusion of nuclear RNA has a large impact on whole cell gene expression microarray results by distorting the mRNA profile to the extent that a substantial number of false positives are generated. We conclude that to produce a valid profile of the steady-state mRNA population, the nuclear component must be excluded, and to arrive at a more realistic view of a cell's gene expression profile, the nuclear and cytoplasmic RNA fractions should be analyzed separately.

Learning from microarray interlaboratory studies: measures of precision for gene expression

BackgroundThe ability to demonstrate the reproducibility of gene expression microarray results is a critical consideration for the use of microarray technology in clinical applications. While studies have asserted that microarray data can be "highly reproducible" under given conditions, there is little ability to quantitatively compare amongst the various metrics and terminology used to characterize and express measurement performance. Use of standardized conceptual tools can greatly facilitate communication among the user, developer, and regulator stakeholders of the microarray community. While shaped by less highly multiplexed systems, measurement science (metrology) is devoted to establishing a coherent and internationally recognized vocabulary and quantitative practice for the characterization of measurement processes.ResultsThe two independent aspects of the metrological concept of "accuracy" are "trueness" (closeness of a measurement to an accepted reference value) and "precision" (the closeness of measurement results to each other). A carefully designed collaborative study enables estimation of a variety of gene expression measurement precision metrics: repeatability, several flavors of intermediate precision, and reproducibility. The three 2004 Expression Analysis Pilot Proficiency Test collaborative studies, each with 13 to 16 participants, provide triplicate microarray measurements on each of two reference RNA pools. Using and modestly extending the consensus ISO 5725 documentary standard, we evaluate the metrological precision figures of merit for individual microarray signal measurement, building from calculations appropriate to single measurement processes, such as technical replicate expression values for individual probes on a microarray, to the estimation and display of precision functions representing all of the probes in a given platform.ConclusionWith only modest extensions, the established metrological framework can be fruitfully used to characterize the measurement performance of microarray and other highly multiplexed systems. Precision functions, summarizing routine precision metrics estimated from appropriately repeated measurements of one or more reference materials as functions of signal level, are demonstrated and merit further development for characterizing measurement platforms, monitoring changes in measurement system performance, and comparing performance among laboratories or analysts.

SpliceCenter: A suite of web-based bioinformatic applications for evaluating the impact of alternative splicing on RT-PCR, RNAi, microarray, and peptide-based studies

BackgroundOver 60% of protein-coding genes in vertebrates express mRNAs that undergo alternative splicing. The resulting collection of transcript isoforms poses significant challenges for contemporary biological assays. For example, RT-PCR validation of gene expression microarray results may be unsuccessful if the two technologies target different splice variants. Effective use of sequence-based technologies requires knowledge of the specific splice variant(s) that are targeted. In addition, the critical roles of alternative splice forms in biological function and in disease suggest that assay results may be more informative if analyzed in the context of the targeted splice variant.ResultsA number of contemporary technologies are used for analyzing transcripts or proteins. To enable investigation of the impact of splice variation on the interpretation of data derived from those technologies, we have developed SpliceCenter. SpliceCenter is a suite of user-friendly, web-based applications that includes programs for analysis of RT-PCR primer/probe sets, effectors of RNAi, microarrays, and protein-targeting technologies. Both interactive and high-throughput implementations of the tools are provided. The interactive versions of SpliceCenter tools provide visualizations of a gene's alternative transcripts and probe target positions, enabling the user to identify which splice variants are or are not targeted. The high-throughput batch versions accept user query files and provide results in tabular form. When, for example, we used SpliceCenter's batch siRNA-Check to process the Cancer Genome Anatomy Project's large-scale shRNA library, we found that only 59% of the 50,766 shRNAs in the library target all known splice variants of the target gene, 32% target some but not all, and 9% do not target any currently annotated transcript.ConclusionSpliceCenter provides unique, user-friendly applications for assessing the impact of transcript variation on the design and interpretation of RT-PCR, RNAi, gene expression microarrays, antibody-based detection, and mass spectrometry proteomics. The tools are intended for use by bench biologists as well as bioinformaticists.

Exploratory Visual Analysis of Statistical Results from Microarray Experiments Comparing High and Low Grade Glioma

The biological interpretation of gene expression microarray results is a daunting challenge. For complex diseases such as cancer, wherein the body of published research is extensive, the incorporation of expert knowledge provides a useful analytical framework. We have previously developed the Exploratory Visual Analysis (EVA) software for exploring data analysis results in the context of annotation information about each gene, as well as biologically relevant groups of genes. We present EVA as a flexible combination of statistics and biological annotation that provides a straightforward visual interface for the interpretation of microarray analyses of gene expression in the most commonly occuring class of brain tumors, glioma. We demonstrate the utility of EVA for the biological interpretation of statistical results by analyzing publicly available gene expression profiles of two important glial tumors. The results of a statistical comparison between 21 malignant, high-grade glioblastoma multiforme (GBM) tumors and 19 indolent, low-grade pilocytic astrocytomas were analyzed using EVA. By using EVA to examine the results of a relatively simple statistical analysis, we were able to identify tumor class-specific gene expression patterns having both statistical and biological significance. Our interactive analysis highlighted the potential importance of genes involved in cell cycle progression, proliferation, signaling, adhesion, migration, motility, and structure, as well as candidate gene loci on a region of Chromosome 7 that has been implicated in glioma. Because EVA does not require statistical or computational expertise and has the flexibility to accommodate any type of statistical analysis, we anticipate EVA will prove a useful addition to the repertoire of computational methods used for microarray data analysis. EVA is available at no charge to academic users and can be found at http://www.epistasis.org .

Immunohistochemical Validation of Expression Microarray Results

Expression microarrays, which determine the level of expression of tens of thousands of mRNAs by a specific cell or tissue type, are powerful and increasingly more widely used investigative, diagnostic, and prognostic molecular biological tools.1,2 However, there are technical aspects to using expression microarrays that can produce results erroneously representing either under- or overexpression of specific genes. Chuaqui et al3 and Simon et al4 have discussed some of these pitfalls. For example, false negativity can result from low expression levels, transcript drop-out (attributable to inefficient priming of specific mRNA(s)), poor adhesion of DNA to the slide, and splice variants with sequences not included on the array. Conversely, sources of false positivity include repetitive nucleotide elements, poly(A) tails, and sequence homology between functionally different transcripts, an inappropriately chosen reference standard, and high background levels due to nonspecific binding of nucleotides to the microarray slides. Ways to minimize the sources of error continue to be developed. For example, use of multiple different sequences of a given gene provides a way not to under-represent a given gene. Conversely, more rigorous attention to minimizing sources of background binding of detection nucleotides can minimize over-representation of highly expressed genes.

Optimal Amounts of Fluorescent Dye Improve Expression Microarray Results in Tumor Specimens

Expression microarrays have great potential for clinical use but variability of the results represents a challenge for reliable practical application. The amount of fluorescent dye used in microarray experiments is a significant source of variability that has not been systematically studied. Here we demonstrate that the quantity of Cy3 dye affects microarray results performed on tumor specimens. Signal-to-noise ratios and coefficients of variation are significantly improved by increasing Cy3 to 150-180 pmol, but any further increase does not improve the data. In conclusion, optimal amounts of dye reduce variability and improve reliability of expression microarray experiments.