Evaluation of methods for oligonucleotide array data via quantitative real-time PCR

BACKGROUND: There are currently many different methods for processing and summarizing probe-level data from Affymetrix oligonucleotide arrays. It is of great interest to validate these methods and identify those that are most effective. There is no single best way to do this validation, and a variety of approaches is needed. Moreover, gene expression data are collected to answer a variety of scientific questions, and the same method may not be best for all questions. Only a handful of validation studies have been done so far, most of which rely on spike-in datasets and focus on the question of detecting differential expression. Here we seek methods that excel at estimating relative expression. We evaluate methods by identifying those that give the strongest linear association between expression measurements by array and the "gold-standard" assay. Quantitative reverse-transcription polymerase chain reaction (qRT-PCR) is generally considered the "gold-standard" assay for measuring gene expression by biologists and is often used to confirm findings from microarray data. Here we use qRT-PCR measurements to validate methods for the components of processing oligo array data: background adjustment, normalization, mismatch adjustment, and probeset summary. An advantage of our approach over spike-in studies is that methods are validated on a real dataset that was collected to address a scientific question. RESULTS: We initially identify three of six popular methods that consistently produced the best agreement between oligo array and RT-PCR data for medium- and high-intensity genes. The three methods are generally known as MAS5, gcRMA, and the dChip mismatch mode. For medium- and high-intensity genes, we identified use of data from mismatch probes (as in MAS5 and dChip mismatch) and a sequence-based method of background adjustment (as in gcRMA) as the most important factors in methods' performances. However, we found poor reliability for methods using mismatch probes for low-intensity genes, which is in agreement with previous studies. CONCLUSION: We advocate use of sequence-based background adjustment in lieu of mismatch adjustment to achieve the best results across the intensity spectrum. No method of normalization or probeset summary showed any consistent advantages

Evaluation of Microarray Preprocessing Algorithms Based on Concordance with RT-PCR in Clinical Samples

BACKGROUND Several preprocessing algorithms for Affymetrix gene expression microarrays have been developed, and their performance on spike-in data sets has been evaluated previously. However, a comprehensive comparison of preprocessing algorithms on samples taken under research conditions has not been performed. METHODOLOGY/PRINCIPAL FINDINGS We used TaqMan RT-PCR arrays as a reference to evaluate the accuracy of expression values from Affymetrix microarrays in two experimental data sets: one comprising 84 genes in 36 colon biopsies, and the other comprising 75 genes in 29 cancer cell lines. We evaluated consistency using the Pearson correlation between measurements obtained on the two platforms. Also, we introduce the log-ratio discrepancy as a more relevant measure of discordance between gene expression platforms. Of nine preprocessing algorithms tested, PLIER+16 produced expression values that were most consistent with RT-PCR measurements, although the difference in performance between most of the algorithms was not statistically significant. CONCLUSIONS/SIGNIFICANCE Our results support the choice of PLIER+16 for the preprocessing of clinical Affymetrix microarray data. However, other algorithms performed similarly and are probably also good choices

A Practical Platform for Blood Biomarker Study by Using Global Gene Expression Profiling of Peripheral Whole Blood

Background: Although microarray technology has become the most common method for studying global gene expression, a plethora of technical factors across the experiment contribute to the variable of genome gene expression profiling using peripheral whole blood. A practical platform needs to be established in order to obtain reliable and reproducible data to meet clinical requirements for biomarker study. Methods and Findings: We applied peripheral whole blood samples with globin reduction and performed genome-wide transcriptome analysis using Illumina BeadChips. Real-time PCR was subsequently used to evaluate the quality of array data and elucidate the mode in which hemoglobin interferes in gene expression profiling. We demonstrated that, when applied in the context of standard microarray processing procedures, globin reduction results in a consistent and significant increase in the quality of beadarray data. When compared to their pre-globin reduction counterparts, post-globin reduction samples show improved detection statistics, lowered variance and increased sensitivity. More importantly, gender gene separation is remarkably clearer in post-globin reduction samples than in pre-globin reduction samples. Our study suggests that the poor data obtained from pre-globin reduction samples is the result of the high concentration of hemoglobin derived from red blood cells either interfering with target mRNA binding or giving the pseudo binding background signal. Conclusion: We therefore recommend the combination of performing globin mRNA reduction in peripheral whole blood samples and hybridizing on Illumina BeadChips as the practical approach for biomarker study

Function Annotation of an SBP-box Gene in Arabidopsis Based on Analysis of Co-expression Networks and Promoters

The SQUAMOSA PROMOTER BINDING PROTEIN–LIKE (SPL) gene family is an SBP-box transcription family in Arabidopsis. While several physiological responses to SPL genes have been reported, their biological role remains elusive. Here, we use a combined analysis of expression correlation, the interactome, and promoter content to infer the biological role of the SPL genes in Arabidopsis thaliana. Analysis of the SPL-correlated gene network reveals multiple functions for SPL genes. Network analysis shows that SPL genes function by controlling other transcription factor families and have relatives with membrane protein transport activity. The interactome analysis of the correlation genes suggests that SPL genes also take part in metabolism of glucose, inorganic salts, and ATP production. Furthermore, the promoters of the correlated genes contain a core binding cis-element (GTAC). All of these analyses suggest that SPL genes have varied functions in Arabidopsis

Expression of estrogen, estrogen related and androgen receptors in adrenal cortex of intact adult male and female rats

Introduction. Adrenocortical activity in various species is sensitive to androgens and estrogens. They may affect adrenal cortex growth and functioning either via central pathways (CRH and ACTH) or directly, via specific receptors expressed in the cortex and/or by interfering with adrenocortical enzymes, among them those involved in steroidogenesis. Only limited data on expression of androgen and estrogen receptors in adrenal glands are available. Therefore the present study aimed to characterize, at the level of mRNA, expression of these receptors in specific components of adrenal cortex of intact adult male and female rats. Material and methods. Studies were performed on adult male and female (estrus) Wistar rats. Total RNA was isolated from adrenal zona glomerulosa (ZG) and fasciculate/reticularis (ZF/R). Expression of genes were evaluated by means of Affymetrix® Rat Gene 1.1 ST Array Strip and QPCR. Results. By means of Affymetrix® Rat Gene 1.1 ST Array we examined adrenocortical sex differences in the expression of nearly 30,000 genes. All data were analyzed in relation to the adrenals of the male rats. 32 genes were differentially expressed in ZG, and 233 genes in ZF/R. In the ZG expression levels of 24 genes were lower and 8 higher in female rats. The more distinct sex differences were observed in the ZF/R, in which expression levels of 146 genes were lower and 87 genes higher in female rats. Performed analyses did not reveal sex differences in the expression levels of both androgen (AR) and estrogen (ER) receptor genes in the adrenal cortex of male and female rats. Therefore matrix data were validated by QPCR. QPCR revealed higher expression levels of AR gene both in ZG and ZF/R of male than female rats. On the other hand, QPCR did not reveal sex-related differences in the expression levels of ERα, ERβ and non-genomic GPR30 (GPER-1) receptor. Of those genes expression levels of ERα genes were the highest. In studied adrenal samples the relative expression of ERα mRNA was higher than ERβ mRNA. In adrenals of adult male and female rats expression levels of estrogen-related receptors ERRα and ERRβ were similar, and only in the ZF/R of female rats ERRγ expression levels were significantly higher than in males. We also analyzed expression profile of three isoforms of steroid 5α-reductase (Srd5a1, Srd5a2 and Srd5a3) and aromatase (Cyp19a1) and expression levels of all these genes were similar in ZG and ZF/R of male and female rats. Conclusions. In contrast to Affymetrix microarray data QPCR revealed higher expression levels of AR gene in adrenal glands of the male rats. In adrenals of both sexes expression levels of ERa, ERb, non-genomic GPR30 (GPER-1), ERR α and ERRβ receptors were comparable. The obtained results suggest that acute steroidogenic effect of estrogens on corticosteroid secretion may be mediated by non-genomic GPR30

Context-Specific Metabolic Networks Are Consistent with Experiments

Reconstructions of cellular metabolism are publicly available for a variety of different microorganisms and some mammalian genomes. To date, these reconstructions are “genome-scale” and strive to include all reactions implied by the genome annotation, as well as those with direct experimental evidence. Clearly, many of the reactions in a genome-scale reconstruction will not be active under particular conditions or in a particular cell type. Methods to tailor these comprehensive genome-scale reconstructions into context-specific networks will aid predictive in silico modeling for a particular situation. We present a method called Gene Inactivity Moderated by Metabolism and Expression (GIMME) to achieve this goal. The GIMME algorithm uses quantitative gene expression data and one or more presupposed metabolic objectives to produce the context-specific reconstruction that is most consistent with the available data. Furthermore, the algorithm provides a quantitative inconsistency score indicating how consistent a set of gene expression data is with a particular metabolic objective. We show that this algorithm produces results consistent with biological experiments and intuition for adaptive evolution of bacteria, rational design of metabolic engineering strains, and human skeletal muscle cells. This work represents progress towards producing constraint-based models of metabolism that are specific to the conditions where the expression profiling data is available

Predictions Generated from a Simulation Engine for Gene Expression Micro-arrays for use in Research Laboratories

In this paper we introduce the technical components, the biology and data science involved in the use of microarray technology in biological and clinical research. We discuss how laborious experimental protocols involved in obtaining this data used in laboratories could benefit from using simulations of the data. We discuss the approach used in the simulation engine from [7]. We use this simulation engine to generate a prediction tool in Power BI, a Microsoft, business intelligence tool for analytics and data visualization [22]. This tool could be used in any laboratory using micro-arrays to improve experimental design by comparing how predicted signal intensity compares to observed signal intensity. Signal intensity in micro-arrays is a proxy for level of gene expression in cells. We suggest further development avenues for the prediction tool

iGentifier: indexing and large-scale profiling of unknown transcriptomes

Development and refinement of methods to analyse differential gene expression has been essential in the progress of molecular biology. A novel approach called iGentifier is presented for profiling known and unknown transcriptomes, thus bypassing a major limitation in microarray analysis. The iGentifier technology combines elements of fragment display (e.g. Differential Display or RMDD) and tag sequencing (e.g. SAGE, MPSS) and allows for analysis of samples in high throughput using current capillary electrophoresis equipment. Application to epidermal tissue of wild-type and mlo5 barley (Hordeum vulgare) plants, infected with powdery mildew [Blumeria graminis (DC.) E.O. Speer f.sp.hordei], led to the identification of several 100 genes induced or repressed upon infection with many well known for their response to fungal pathogens or other stressors. Ten of these genes are suggested to be classified as marker genes for durable resistance mediated by the mlo5 resistance gene