2,555 research outputs found

    The effect of column purification on cDNA indirect labelling for microarrays

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    <p>Abstract</p> <p>Background</p> <p>The success of the microarray reproducibility is dependent upon the performance of standardized procedures. Since the introduction of microarray technology for the analysis of global gene expression, reproducibility of results among different laboratories has been a major problem. Two of the main contributors to this variability are the use of different microarray platforms and different laboratory practices. In this paper, we address the latter question in terms of how variation in one of the steps of a labelling procedure affects the cDNA product prior to microarray hybridization.</p> <p>Results</p> <p>We used a standard procedure to label cDNA for microarray hybridization and employed different types of column chromatography for cDNA purification. After purifying labelled cDNA, we used the Agilent 2100 Bioanalyzer and agarose gel electrophoresis to assess the quality of the labelled cDNA before its hybridization onto a microarray platform. There were major differences in the cDNA profile (i.e. cDNA fragment lengths and abundance) as a result of using four different columns for purification. In addition, different columns have different efficiencies to remove rRNA contamination. This study indicates that the appropriate column to use in this type of protocol has to be experimentally determined. Finally, we present new evidence establishing the importance of testing the method of purification used during an indirect labelling procedure. Our results confirm the importance of assessing the quality of the sample in the labelling procedure prior to hybridization onto a microarray platform.</p> <p>Conclusion</p> <p>Standardization of column purification systems to be used in labelling procedures will improve the reproducibility of microarray results among different laboratories. In addition, implementation of a quality control check point of the labelled samples prior to microarray hybridization will prevent hybridizing a poor quality sample to expensive micorarrays.</p

    Genome-wide estimation of gender differences in the gene expression of human livers: Statistical design and analysis

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    BACKGROUND: Gender differences in gene expression were estimated in liver samples from 9 males and 9 females. The study tested 31,110 genes for a gender difference using a design that adjusted for sources of variation associated with cDNA arrays, normalization, hybridizations and processing conditions. RESULTS: The genes were split into 2,800 that were clearly expressed (expressed genes) and 28,310 that had expression levels in the background range (not expressed genes). The distribution of p-values from the 'not expressed' group was consistent with no gender differences. The distribution of p-values from the 'expressed' group suggested that 8 % of these genes differed by gender, but the estimated fold-changes (expression in males / expression in females) were small. The largest observed fold-change was 1.55. The 95 % confidence bounds on the estimated fold-changes were less than 1.4 fold for 79.3 %, and few (1.1%) exceed 2-fold. CONCLUSION: Observed gender differences in gene expression were small. When selecting genes with gender differences based upon their p-values, false discovery rates exceed 80 % for any set of genes, essentially making it impossible to identify any specific genes with a gender difference

    Comparison Between Expression Microarrays and RNA-Sequencing Using UKBEC Dataset Identified a trans-eQTL Associated with MPZ Gene in Substantia Nigra

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    In recent years, the advantages of RNA-sequencing (RNA-Seq) have made it the platform of choice for measuring gene expression over traditional microarrays. However, RNA-Seq comes with bioinformatical challenges and higher computational costs. Therefore, this study set out to assess whether the increased depth of transcriptomic information facilitated by RNA-Seq is worth the increased computation over microarrays, specifically at three levels: absolute expression levels, differentially expressed genes identification, and expression QTL (eQTL) mapping in regions of the human brain. Using the United Kingdom Brain Expression Consortium (UKBEC) dataset, there is high agreement of gene expression levels measured by microarrays and RNA-seq when quantifying absolute expression levels and when identifying differentially expressed genes. These findings suggest that depending on the aims of a study, the relative ease of working with microarray data may outweigh the computational time and costs of RNA-Seq pipelines. On the other, there was low agreement when mapping eQTLs. However, a number of eQTLs associated with genes that play important roles in the brain were found in both platforms. For example, a trans-eQTL was mapped that is associated with the MPZ gene in the substantia nigra. These eQTLs that we have highlighted are extremely promising candidates that merit further investigation

    The role of gene expression in ecological speciation

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    Ecological speciation is the process by which barriers to gene flow between populations evolve due to adaptive divergence via natural selection. A relatively unexplored area in ecological speciation is the role of gene expression. Gene expression may be associated with ecologically important phenotypes not evident from morphology and play a role during colonization of new environments. Here we review two potential roles of gene expression in ecological speciation: (1) its indirect role in facilitating population persistence and (2) its direct role in contributing to genetically based reproductive isolation. We find indirect evidence that gene expression facilitates population persistence, but direct tests are lacking. We also find clear examples of gene expression having effects on phenotypic traits and adaptive genetic divergence, but links to the evolution of reproductive isolation itself remain indirect. Gene expression during adaptive divergence seems to often involve complex genetic architectures controlled by gene networks, regulatory regions, and “eQTL hotspots.” Nonetheless, we review how approaches for isolating the functional mutations contributing to adaptive divergence are proving to be successful. The study of gene expression has promise for increasing our understanding ecological speciation, particularly when integrative approaches are applied

    A qualitative assessment of direct-labeled cDNA products prior to microarray analysis

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    BACKGROUND: The success of the microarray process in determining differential gene expression of thousands of genes is dependent upon the quality and integrity of the starting RNA, this being particularly true of direct labeling via a reverse transcription procedure. Furthermore, an RNA of reasonable quality still may not yield reliable hybridization data if the labeling efficiency was poor. RESULTS: Here we present a novel assay for assessing the quality of directly labeled fluorescent cDNA prior to microarray hybridization utilizing the Agilent 2100 Bioanalyzer, which employs microfluidic technology for the analysis of nucleic acids and proteins. Using varying amounts of RNase to simulate RNA degradation, we show the strength of this un-advertised assay in determining the relative amounts of cDNA obtained from a direct labeling reaction. CONCLUSION: Utilization of this method in the lab will help to prevent the costly mistake of hybridizing poor quality direct labeled products to expensive arrays

    Analyzing the dose-dependence of the Saccharomyces cerevisiae global transcriptional response to methyl methanesulfonate and ionizing radiation

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    BACKGROUND: One of the most crucial tasks for a cell to ensure its long term survival is preserving the integrity of its genetic heritage via maintenance of DNA structure and sequence. While the DNA damage response in the yeast Saccharomyces cerevisiae, a model eukaryotic organism, has been extensively studied, much remains to be elucidated about how the organism senses and responds to different types and doses of DNA damage. We have measured the global transcriptional response of S. cerevisiae to multiple doses of two representative DNA damaging agents, methyl methanesulfonate (MMS) and gamma radiation. RESULTS: Hierarchical clustering of genes with a statistically significant change in transcription illustrated the differences in the cellular responses to MMS and gamma radiation. Overall, MMS produced a larger transcriptional response than gamma radiation, and many of the genes modulated in response to MMS are involved in protein and translational regulation. Several clusters of coregulated genes whose responses varied with DNA damaging agent dose were identified. Perhaps the most interesting cluster contained four genes exhibiting biphasic induction in response to MMS dose. All of the genes (DUN1, RNR2, RNR4, and HUG1) are involved in the Mec1p kinase pathway known to respond to MMS, presumably due to stalled DNA replication forks. The biphasic responses of these genes suggest that the pathway is induced at lower levels as MMS dose increases. The genes in this cluster with a threefold or greater transcriptional response to gamma radiation all showed an increased induction with increasing gamma radiation dosage. CONCLUSION: Analyzing genome-wide transcriptional changes to multiple doses of external stresses enabled the identification of cellular responses that are modulated by magnitude of the stress, providing insights into how a cell deals with genotoxicity

    Microarray resources for genetic and genomic studies in chicken: a review

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    Advent of microarray technologies revolutionized the nature and scope of genetic and genomic research in human and other species by allowing massively parallel analysis of thousands of genomic sites. They have been used for diverse purposes such as for transcriptome analysis, CNV detection, SNP and CNV genotyping, studying DNA-protein interaction, and detection of genome methylation. Microarrays have also made invaluable contributions to research in chicken which is an important model organism for studying embryology, immunology, oncology, virology, evolution, genetics, and genomics and also for other avian species. Despite their huge contributions in life science research, the future of microarrays is now being questioned with the advent of massively parallel next generation sequencing (NGS) technologies, which promise to overcome some of the limitations of microarray platforms. In this article we review the various microarray resources developed for chicken and their past and potential future applications. We also discuss about the future of microarrays in the NGS era particularly in the context of livestock genetics. We argue that even though NGS promises some major advantages-in particular, offers the opportunity to discover novel elements in the genome-microarrays will continue to be major tools for research and practice in the field of livestock genetics/genomics due to their affordability, high throughput nature, mature established technologies and ease of application. Moreover, with advent of new microarray technologies like capture arrays, the NGS and microarrays are expected to complement each other in future research in life science

    Integrating bioinformatics and physiology to describe genetic effects in complex polygenic diseases

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    Type 2 diabetes mellitus (T2DM) results from interaction between genetic and environmental factors. The worldwide prevalence of T2DM is increasing rapidly due to reduction in physical activity, increase in dietary intake, and the aging of the population. This thesis has focused on dissecting the genetic contribution in T2DM using largescale genomic approaches with a particular emphasis on analysis of gene transcripts in different tissues, predominantly muscle. In paper I, we identified TXNIP as a gene whose expression is powerfully suppressed by insulin yet stimulated by glucose. In healthy individuals, its expression was inversely correlated to total body measures of glucose uptake. Forced expression of TXNIP in cultured adipocytes significantly reduced glucose uptake, while silencing with RNA interference in adipocytes and in skeletal muscle enhanced glucose uptake, confirming that the gene product is also a regulator of glucose uptake. TXNIP expression is consistently elevated in the muscle of pre-diabetics and diabetics, although in a panel of 4,450 Scandinavian individuals, we found no evidence for association between common genetic variation in the TXNIP gene and T2DM. TXNIP regulates both insulindependent and insulin-independent pathways of glucose uptake in human skeletal muscle. Combined with recent studies that have implicated TXNIP in pancreatic β-cell glucose toxicity, our data suggest that TXNIP might play a key role in defective glucose homeostasis preceding overt T2DM. In paper II, we investigated molecular mechanisms associated with insulin sensitivity in skeletal muscle by relating global skeletal muscle gene expression to physiological measures of the insulin sensitivity. We identified 70 genes positively and 110 genes inversely correlated with insulin sensitivity in human skeletal muscle. Most notably, genes involved in a mammalian target-of-rapamycin signaling pathway were positively whereas genes encoding extracellular matrix structural constituent such as extracellular matrix-receptor, cell communication, and focal adhesion pathways were inversely correlated with insulin sensitivity. More specifically, expression of CPT1B was positively and that of LEO1 inversely correlated with insulin sensitivity, a finding which was replicated in an independent study of 9 non-diabetic men. These data suggest that a high capacity of fat oxidation in mitochondria is reflected by a high expression of CPT1B which is a marker of insulin sensitivity. In paper III, we investigated molecular mechanisms associated with maximal oxygen uptake (VO2max) and type 1 fibers in human skeletal muscle. We identified 66 genes positively and 83 genes inversely correlated with VO2max and 171 genes positively and 217 genes inversely correlated with percentage of type 1 fibers in human skeletal muscle. Genes involved in oxidative phosphorylation (OXPHOS) showed high expression in individuals with high VO2max, whereas the opposite was not the case in individuals with low VO2max. Instead, genes such as AHNAK and BCL6 were associated with low VO2max. Also, expression of the OXPHOS genes, NDUFB5 and ATP5C1, increased with exercise training and decreased with aging. In contrast, expression of AHNAK in skeletal muscle decreased with exercise training and increased with aging. These findings indicate that VO2max closely reflects expression of OXPHOS genes, particularly that of NDUFB5 and ATP5C1 in skeletal muscle and high expression of these genes suggest good muscle fitness. In contrast, a high expression of AHNAK was associated with a low VO2max and poor muscle fitness. In paper IV, we combined results from the Diabetes Genetics Initiative (DGI) and the Wellcome Trust Case Control Consortium (WTCCC) genome-wide association (GWA) studies with genome-wide expression profiling in pancreas, adipose tissue, liver, and skeletal muscle in patients with or without T2DM or animal models thereof to identify novel T2DM susceptibility loci. We identified 453 single nucleotide polymorphisms (SNPs) associated with T2DM with P < 0.01 in at least one of the GWA studies and 150 genes that were located in vicinity of these SNPs. Out of these 150 genes, we identified 41 genes differentially expressed using publicly available gene expression profiling data. Most notably, we were able to identify four genes namely IGF2BP2, CDKAL1, TSPAN8, and NOTCH2 for which SNPs located in vicinity of these genes have shown association with T2DM in different populations. In addition, we identified a SNP (rs27582) in the CAST gene which was associated with future risk of T2DM (odds ratio (OR) = 1.10, 95% CI: 1.00-1.20, P < 0.05) in a prospective study of 16,061 Swedish individuals followed for more than 25 years; this association was stronger in lean individuals (OR = 1.19, 95% CI: 1.03-1.36, P = 0.024). Moreover in the Botnia Prospective Study (BPS) involving 2,770 individuals followed for more than 7 years, carriers of the A-allele were more insulin resistant than carriers of the G-allele as indicated by higher fasting insulin concentrations (regression coefficient (β) = 0.048, P = 0.017) and higher HOMA-IR index (β = 0.044, P = 0.025) as well as lower insulin sensitivity index during OGTT (β = -0.039, P = 0.039) at follow-up. In conclusion, using gene expression in different tissues from patients with T2DM and animal models is a powerful tool for prioritizing SNPs from GWA studies for replication studies. We thereby identified association of a variant (rs27582) in the CAST gene with T2DM and insulin resistance

    Gene expression profiling of head and neck cancer

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    MDThe purpose of this study was to classify oral squamous cell carcinomas (OSCCs) based on their gene expression profiles, to identify differentially expressed genes in these cancers, and to correlate genetic deregulation with clinical-histopathological data and patient outcome. After conducting proof of principle experiments utilizing six head and neck squamous cell carcinomas (HNSCCs) cell lines, the gene expression profiles of 20 OSCCs and subsequently an additional 8 OSCCs were determined using cDNA microarrays containing 19,200 sequences and the Binary Tree-Structured Vector Quantization (BTSVQ) method of data analysis. Two sample clusters were identified in the group of 20 tumors that correlated with T3-T4 category of disease (P=0.035) and nodal metastasis( p=0.035). Samplec lustering of 28 OSCCsa nd the 6 cell lines revealed a correlation with disease free survival. BTSVQ analysis identified a subset of 23 differentially expressed genes with the lowest quantization error scores in the cluster containing more advanceds taget umors from the 20 OSCC dataset.T he expressiono f six of these differentially expressedg enesw as validated by quantitative real-time RT-PCR. Statistical analysis of quantitative real-time RT-PCR data was performed and, after Bonferroni correction, CLDNI (p = 0.007) over-expressionw as significantly correlated with the cluster containing more advanced stage tumors. Despite the clinical heterogeneity of OSCC, molecular subtyping by cDNA microarray analysis was able to identify distinct patternso f genee xpressiona ssociatedw ith relevant clinical parameters. The application of this methodology represents an advance in the classification of oral cavity tumors, and may ultimately aid in the development of more tailored therapies for oral carcinoma

    Combining transcriptional datasets using the generalized singular value decomposition

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    Background Both microarrays and quantitative real-time PCR are convenient tools for studying the transcriptional levels of genes. The former is preferable for large scale studies while the latter is a more targeted technique. Because of platform-dependent systematic effects, simple comparisons or merging of datasets obtained by these technologies are difficult, even though they may often be desirable. These difficulties are exacerbated if there is only partial overlap between the experimental conditions and genes probed in the two datasets. Results We show here that the generalized singular value decomposition provides a practical tool for merging a small, targeted dataset obtained by quantitative real-time PCR of specific genes with a much larger microarray dataset. The technique permits, for the first time, the identification of genes present in only one dataset co-expressed with a target gene present exclusively in the other dataset, even when experimental conditions for the two datasets are not identical. With the rapidly increasing number of publically available large scale microarray datasets the latter is frequently the case. The method enables us to discover putative candidate genes involved in the biosynthesis of the (1,3;1,4)-β-D-glucan polysaccharide found in plant cell walls. Conclusion We show that the generalized singular value decomposition provides a viable tool for a combined analysis of two gene expression datasets with only partial overlap of both gene sets and experimental conditions. We illustrate how the decomposition can be optimized self-consistently by using a judicious choice of genes to define it. The ability of the technique to seamlessly define a concept of "co-expression" across both datasets provides an avenue for meaningful data integration. We believe that it will prove to be particularly useful for exploiting large, publicly available, microarray datasets for species with unsequenced genomes by complementing them with more limited in-house expression measurements.Andreas W Schreiber, Neil J Shirley, Rachel A Burton and Geoffrey B Finche
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