Three microarray platforms: an analysis of their concordance in profiling gene expression

Glycolytic competence in gastric adenocarcinomas negatively impacts survival outcomes of patients treated with salvage paclitaxel-ramucirumab

INTRODUCTION

For energy production, cancer cells maintain a high rate of glycolysis instead of oxidative phosphorylation converting glucose into lactic acid. This metabolic shift is useful to survive in unfavorable microenvironments. We investigated whether a positive glycolytic profile (PGP) in gastric adenocarcinomas may be associated with unfavorable outcomes under an anticancer systemic therapy, including the anti-angiogenic ramucirumab.

MATERIALS AND METHODS

Normal mucosa (NM) and primary tumor (PT) of 40 metastatic gastric adenocarcinomas patients who received second-line paclitaxel-ramucirumab (PR) were analyzed for mRNA expression of the following genes: HK-1, HK-2, PKM-2, LDH-A, and GLUT-1. Patients were categorized with PGP when at least a doubling of mRNA expression (PT vs. NM) in all glycolytic core enzymes (HK-1 or HK-2, PKM-2, LDH-A) was observed. PGP was also related to TP53 mutational status.

RESULTS

Mean LDH-A, HK-2, PKM-2 mRNA expression levels were significantly higher in PT compared with NM. 18 patients were classified as PGP, which was associated with significantly worse progression-free and overall survival times. No significant association was observed between PGP and clinical-pathologic features, including TP53 positive mutational status, in 28 samples.

CONCLUSIONS

Glycolytic proficiency may negatively affect survival outcomes of metastatic gastric cancer patients treated with PR systemic therapy. TP53 mutational status alone does not seem to explain such a metabolic shift.

Inhibition of ABL1 tyrosine kinase reduces HTLV-1 proviral loads in peripheral blood mononuclear cells from patients with HTLV-1-associated myelopathy/tropical spastic paraparesis

Human T-cell leukemia virus type 1 (HTLV-1) causes incurable adult T-cell leukemia and HTLV-1-associated myelopathy/tropical spastic paraparesis (HAM/TSP). Patients with HAM/TSP have increased levels of HTLV-1-infected cells compared with asymptomatic HTLV-1 carriers. However, the roles of cellular genes in HTLV-1-infected CD4+ T cells await discovery. We performed microarray analysis of CD4+ T cells from HAM/TSP patients and found that the ABL1 is an important gene in HAM/TSP. ABL1 is a known survival factor for T- and B-lymphocytes and is part of the fused gene (BCR-ABL) known to be responsible for chronic myelogenous leukemia (CML). ABL1 tyrosine kinase inhibitors (TKIs), including imatinib, nilotinib, and dasatinib, are used clinically for treating CML. To evaluate whether ABL1 is indeed important for HAM/TSP, we investigated the effect of TKIs on HTLV-1-infected cells. We developed a propidium monoazide-HTLV-1 viability quantitative PCR assay, which distinguishes DNA from live cells and dead cells. Using this method, we were able to measure the HTLV-1 proviral load (PVL) in live cells alone when peripheral blood mononuclear cells (PBMCs) from HAM/TSP cases were treated with TKIs. Treating the PBMCs with nilotinib or dasatinib induced significant reductions in PVL (21.0% and 17.5%, respectively) in live cells. Furthermore, ABL1 siRNA transfection reduced cell viability in HTLV-1-infected cell lines, but not in uninfected cell lines. A retrospective survey based on our clinical records found a rare case of HAM/TSP who also suffered from CML. The patient showed an 84.2% PVL reduction after CML treatment with imatinib. We conclude that inhibiting the ABL1 tyrosine kinase specifically reduced the PVL in PBMCs from patients with HAM/TSP, suggesting that ABL1 is an important gene for the survival of HTLV-1-infected cells and that TKIs may be potential therapeutic agents for HAM/TSP.

Gene Expression Measurement Module (GEMM) for space application: Design and validation

In order to facilitate studies on the impact of the space environment on biological systems, we have developed a prototype of GEMM (Gene Expression Measurement Module) - an automated, miniaturized, integrated fluidic system for in-situ measurements of gene expression in microbial samples. The GEMM instrument is capable of (1) lysing bacterial cell walls, (2) extracting and purifying RNA released from cells, (3) hybridizing the RNA to probes attached to a microarray and (4) providing electrochemical readout, all in a microfluidics cartridge. To function on small, uncrewed spacecraft, the conventional, laboratory protocols for both sample preparation and hybridization required significant modifications. Biological validation of the instrument was carried out on Synechococcus elongatus, a photosynthetic cyanobacterium known for its metabolic diversity and resilience to adverse conditions. It was demonstrated that GEMM yielded reliable, reproducible gene expression profiles. GEMM is the only high throughput instrument that can be deployed in near future on space platforms other than the ISS to advance biological research in space. It can also prove useful for numerous terrestrial applications in the field.

Integrated multiomic predictors for ovarian cancer survival

Increasingly affordable high-throughput molecular profiling technologies have made feasible the measurement of omics-wide interindividual variations for the purposes of predicting cancer prognosis. While multiple types of genetic, epigenetic and expression changes have been implicated in ovarian cancer, existing prognostic biomarker strategies are constrained to analyzing a single class of molecular variations. The extra predictive power afforded by the integration of multiple omics types remains largely unexplored. In this study, we performed integrative analysis on tumor-based exome-, transcriptome- and methylome-wide molecular profiles from The Cancer Genome Atlas (TCGA) for variations in cancer-relevant genes to construct robust, cross-validated multiomic predictors for ovarian cancer survival. These integrated polygenic survival scores (PSSs) were able to predict 5-year overall (OS) and progression-free survival in the Caucasian subsample with high accuracy (AUROC = 0.87 and 0.81, respectively). These findings suggest that the PSSs are able to predict long-term OS in TCGA patients with accuracy beyond that of previously proposed protein-based biomarker strategies. Our findings reveal the promise of an integrated omics-based approach in enhancing existing prognostic strategies. Future investigations should be aimed toward prospective external validation, strategies for standardizing application and the integration of germline variants.

Mechanisms and Measurement of Changes in Gene Expression

Research on gene expression (GE) provides insights into the physiology of a cell or group of cells at a given point in time. Studies of changes in GE can be used to identify patients at higher risk for various medical conditions, a higher symptom burden, and/or the adverse consequences associated with various treatments. The aims of this article are as follows: (1) to describe the different types of RNA transcripts, (2) to describe the processes involved in GE (i.e., RNA transcription, epigenetics, and posttranscriptional modifications), (3) to describe common sources of variation in GE, (4) to describe the most common methods used to measure GE, and (5) to discuss factors to consider when choosing tissue for a GE study. This article begins with an overview of the mechanisms involved in GE. Then, the factors that can influence the findings from GE experiments (e.g., tissue specificity, host age, host gender, and time of sample collection) are described and potential solutions are presented. This article concludes with a discussion of how the types of tissue used in GE studies can affect study findings. Given that the costs associated with the measurement of changes in GE are decreasing and the methods to analyze GE data are becoming easier to use, nurse scientists need to understand the basic principles that underlie any GE study.

Simultaneous RNA-seq based transcriptional profiling of intracellular Brucella abortus and B. abortus-infected murine macrophages

Brucella is a zoonotic pathogen that survives within macrophages; however the replicative mechanisms involved are not fully understood. We describe the isolation of sufficient Brucella abortus RNA from primary host cell environment using modified reported methods for RNA-seq analysis, and simultaneously characterize the transcriptional profiles of intracellular B. abortus and bone marrow-derived macrophages (BMM) from BALB/c mice at 24 h (replicative phase) post-infection. Our results revealed that 25.12% (801/3190) and 16.16% (515/3190) of the total B. abortus genes were up-regulated and down-regulated at >2-fold, respectively as compared to the free-living B. abortus. Among >5-fold differentially expressed genes, the up-regulated genes are mostly involved in DNA, RNA manipulations as well as protein biosynthesis and secretion while the down-regulated genes are mainly involved in energy production and metabolism. On the other hand, the host responses during B. abortus infection revealed that 14.01% (6071/43,346) of BMM genes were reproducibly transcribed at >5-fold during infection. Transcription of cytokines, chemokines and transcriptional factors, such as tumor necrosis factor (Tnf), interleukin-1α (Il1α), interleukin-1β (Il1β), interleukin-6 (Il6), interleukin-12 (Il12), chemokine C-X-C motif (CXCL) family, nuclear factor kappa B (Nf-κb), signal transducer and activator of transcription 1 (Stat1), that may contribute to host defense were markedly induced while transcription of various genes involved in cell proliferation and metabolism were suppressed upon B. abortus infection. In conclusion, these data suggest that Brucella modulates gene expression in hostile intracellular environment while simultaneously alters the host pathways that may lead to the pathogen's intracellular survival and infection.

Novel insights into human respiratory syncytial virus-host factor interactions through integrated proteomics and transcriptomics analysis

The lack of vaccine and limited antiviral options against respiratory syncytial virus (RSV) highlights the need for novel therapeutic strategies. One alternative is to develop drugs that target host factors required for viral replication. Several microarray and proteomics studies had been published to identify possible host factors that are affected during RSV replication. In order to obtain a comprehensive understanding of RSV-host interaction, we integrated available proteome and transcriptome datasets and used it to construct a virus-host interaction network. Then, we interrogated the network to identify host factors that are targeted by the virus and we searched for drugs from the DrugBank database that interact with these host factors, which may have potential applications in repositioning for future treatment options of RSV infection.

Blood-based identification of non-responders to anti-TNF therapy in rheumatoid arthritis

BACKGROUND

Faced with an increasing number of choices for biologic therapies, rheumatologists have a critical need for better tools to inform rheumatoid arthritis (RA) disease management. The ability to identify patients who are unlikely to respond to first-line biologic anti-TNF therapies prior to their treatment would allow these patients to seek alternative therapies, providing faster relief and avoiding complications of disease.

METHODS

We identified a gene expression classifier to predict, pre-treatment, which RA patients are unlikely to respond to the anti-TNF infliximab. The classifier was trained and independently evaluated using four published whole blood gene expression data sets, in which RA patients (n = 116 = 44 + 15 + 30 + 27) were treated with infliximab, and their response assessed 14-16 months post treatment according to the European League Against Rheumatism (EULAR) response criteria. For each patient, prior knowledge was used to group gene expression measurements into disease-relevant biological signaling mechanisms that were used as the input features for regularized logistic regression.

RESULTS

The classifier produced a substantial enrichment of non-responders (59 %, given by the cross validated test precision) compared to the full population (27 % non-responders), while identifying nearly a third of non-responders. Given this classifier performance, treatment of predicted non-responders with alternative biologics would decrease their chance of non-response by between a third and a half, substantially improving their odds of effective treatment and stemming further disease progression. The classifier consisted of 18 signaling mechanisms, which together indicated that higher inflammatory signaling mediated by TNF and other cytokines was present pre-treatment in the blood of patients who responded to infliximab treatment. In contrast, non-responders were classified by relatively higher levels of specific metabolic activities in the blood prior to treatment.

CONCLUSIONS

We were able to successfully produce a classifier to identify a population of RA patients significantly enriched in anti-TNF non-responders across four different patient cohorts. Additional prospective studies are needed to validate and refine the classifier for clinical use.

Integrative biological analysis for neuropsychopharmacology

Although advances in psychotherapy have been made in recent years, drug discovery for brain diseases such as schizophrenia and mood disorders has stagnated. The need for new biomarkers and validated therapeutic targets in the field of neuropsychopharmacology is widely unmet. The brain is the most complex part of human anatomy from the standpoint of number and types of cells, their interconnections, and circuitry. To better meet patient needs, improved methods to approach brain studies by understanding functional networks that interact with the genome are being developed. The integrated biological approaches--proteomics, transcriptomics, metabolomics, and glycomics--have a strong record in several areas of biomedicine, including neurochemistry and neuro-oncology. Published applications of an integrated approach to projects of neurological, psychiatric, and pharmacological natures are still few but show promise to provide deep biological knowledge derived from cells, animal models, and clinical materials. Future studies that yield insights based on integrated analyses promise to deliver new therapeutic targets and biomarkers for personalized medicine.

Genomic analysis of a hybridoma batch cell culture metabolic status in a standard laboratory 5 L bioreactor

Currently, there is a gap in the knowledge of the culture responses to controlled bioreactor environment during the course of batch cell culture from early exponential phase to stationary-phase. If available, such information could be used to designate gene transcripts for predicting cell status and as a quality predictor for a controlled bioreactor. In this study, we used oligonucleotide microarrays to obtain baseline gene expression profiles during the time-course of a hybridoma batch cell culture in a 5 L bench-scale bioreactor. Gene expression changes that were up or down modulated from early-to-late in batch culture, as well as invariant gene profiles with significant expression were identified using microarray. Typical cellular functions that seemed to be correlated with transcriptomics were oxidative stress response, DNA damage response, apoptosis, and cellular metabolism. As confirmatory evidence, microarray findings were verified with a more rigorous semiquantitative gene-specific Reverse transcriptase-polymerase chain reaction (RT-PCR). The results of this study suggest that under predefined bioreactor culture conditions, significant gene changes from lag to log to stationary phase could be identified, which could then be used to track the culture state.

Cardiovascular genomics: a biomarker identification pipeline

Genomic biomarkers are essential for understanding the underlying molecular basis of human diseases such as cardiovascular disease. In this review, we describe a biomarker identification pipeline for cardiovascular disease, which includes 1) high-throughput genomic data acquisition, 2) preprocessing and normalization of data, 3) exploratory analysis, 4) feature selection, 5) classification, and 6) interpretation and validation of candidate biomarkers. We review each step in the pipeline, presenting current and widely used bioinformatics methods. Furthermore, we analyze several publicly available cardiovascular genomics datasets to illustrate the pipeline. Finally, we summarize the current challenges and opportunities for further research.

Galectin-1, a gene preferentially expressed at the tumor margin, promotes glioblastoma cell invasion

Background

High-grade gliomas, including glioblastomas (GBMs), are recalcitrant to local therapy in part because of their ability to invade the normal brain parenchyma surrounding these tumors. Animal models capable of recapitulating glioblastoma invasion may help identify mediators of this aggressive phenotype.

Methods

Patient-derived glioblastoma lines have been propagated in our laboratories and orthotopically xenografted into the brains of immunocompromized mice. Invasive cells at the tumor periphery were isolated using laser capture microdissection. The mRNA expression profile of these cells was compared to expression at the tumor core, using normal mouse brain to control for host contamination. Galectin-1, a target identified by screening the resulting data, was stably over-expressed in the U87MG cell line. Sub-clones were assayed for attachment, proliferation, migration, invasion, and in vivo tumor phenotype.

Results

Expression microarray data identified galectin-1 as the most potent marker (p-value 4.0 x 10^-8) to identify GBM cells between tumor-brain interface as compared to the tumor core. Over-expression of galectin-1 enhanced migration and invasion in vitro. In vivo, tumors expressing high galectin-1 levels showed enhanced invasion and decreased host survival.

Conclusions

In conclusion, cells at the margin of glioblastoma, in comparison to tumor core cells, have enhanced expression of mediators of invasion. Galectin-1 is likely one such mediator. Previous studies, along with the current one, have proven galectin-1 to be important in the migration and invasion of glioblastoma cells, in GBM neoangiogenesis, and also, potentially, in GBM immune privilege. Targeting this molecule may offer clinical improvement to the current standard of glioblastoma therapy, i.e. radiation, temozolomide, anti-angiogenic therapy, and vaccinotherapy.

Empirical comparison of cross-platform normalization methods for gene expression data

Background

Simultaneous measurement of gene expression on a genomic scale can be accomplished using microarray technology or by sequencing based methods. Researchers who perform high throughput gene expression assays often deposit their data in public databases, but heterogeneity of measurement platforms leads to challenges for the combination and comparison of data sets. Researchers wishing to perform cross platform normalization face two major obstacles. First, a choice must be made about which method or methods to employ. Nine are currently available, and no rigorous comparison exists. Second, software for the selected method must be obtained and incorporated into a data analysis workflow.

Results

Using two publicly available cross-platform testing data sets, cross-platform normalization methods are compared based on inter-platform concordance and on the consistency of gene lists obtained with transformed data. Scatter and ROC-like plots are produced and new statistics based on those plots are introduced to measure the effectiveness of each method. Bootstrapping is employed to obtain distributions for those statistics. The consistency of platform effects across studies is explored theoretically and with respect to the testing data sets.

Conclusions

Our comparisons indicate that four methods, DWD, EB, GQ, and XPN, are generally effective, while the remaining methods do not adequately correct for platform effects. Of the four successful methods, XPN generally shows the highest inter-platform concordance when treatment groups are equally sized, while DWD is most robust to differently sized treatment groups and consistently shows the smallest loss in gene detection. We provide an R package, CONOR, capable of performing the nine cross-platform normalization methods considered. The package can be downloaded at http://alborz.sdsu.edu/conor and is available from CRAN.

Meta-analysis of gene expression microarrays with missing replicates

Background

Many different microarray experiments are publicly available today. It is natural to ask whether different experiments for the same phenotypic conditions can be combined using meta-analysis, in order to increase the overall sample size. However, some genes are not measured in all experiments, hence they cannot be included or their statistical significance cannot be appropriately estimated in traditional meta-analysis. Nonetheless, these genes, which we refer to as incomplete genes, may also be informative and useful.

Results

We propose a meta-analysis framework, called "Incomplete Gene Meta-analysis", which can include incomplete genes by imputing the significance of missing replicates, and computing a meta-score for every gene across all datasets. We demonstrate that the incomplete genes are worthy of being included and our method is able to appropriately estimate their significance in two groups of experiments. We first apply the Incomplete Gene Meta-analysis and several comparable methods to five breast cancer datasets with an identical set of probes. We simulate incomplete genes by randomly removing a subset of probes from each dataset and demonstrate that our method consistently outperforms two other methods in terms of their false discovery rate. We also apply the methods to three gastric cancer datasets for the purpose of discriminating diffuse and intestinal subtypes.

Conclusions

Meta-analysis is an effective approach that identifies more robust sets of differentially expressed genes from multiple studies. The incomplete genes that mainly arise from the use of different platforms may also have statistical and biological importance but are ignored or are not appropriately involved by previous studies. Our Incomplete Gene Meta-analysis is able to incorporate the incomplete genes by estimating their significance. The results on both breast and gastric cancer datasets suggest that the highly ranked genes and associated GO terms produced by our method are more significant and biologically meaningful according to the previous literature.

Expression profiling of cassava storage roots reveals an active process of glycolysis/gluconeogenesis

Mechanisms related to the development of cassava storage roots and starch accumulation remain largely unknown. To evaluate genome-wide expression patterns during tuberization, a 60 mer oligonucleotide microarray representing 20 840 cassava genes was designed to identify differentially expressed transcripts in fibrous roots, developing storage roots and mature storage roots. Using a random variance model and the traditional twofold change method for statistical analysis, 912 and 3 386 upregulated and downregulated genes related to the three developmental phases were identified. Among 25 significantly changed pathways identified, glycolysis/gluconeogenesis was the most evident one. Rate-limiting enzymes were identified from each individual pathway, for example, enolase, L-lactate dehydrogenase and aldehyde dehydrogenase for glycolysis/gluconeogenesis, and ADP-glucose pyrophosphorylase, starch branching enzyme and glucan phosphorylase for sucrose and starch metabolism. This study revealed that dynamic changes in at least 16% of the total transcripts, including transcription factors, oxidoreductases/transferases/hydrolases, hormone-related genes, and effectors of homeostasis. The reliability of these differentially expressed genes was verified by quantitative real-time reverse transcription-polymerase chain reaction. These studies should facilitate our understanding of the storage root formation and cassava improvement.

A quantitative model of transcriptional regulation reveals the influence of binding location on expression

Understanding the mechanistic basis of transcriptional regulation has been a central focus of molecular biology since its inception. New high-throughput chromatin immunoprecipitation experiments have revealed that most regulatory proteins bind thousands of sites in mammalian genomes. However, the functional significance of these binding sites remains unclear. We present a quantitative model of transcriptional regulation that suggests the contribution of each binding site to tissue-specific gene expression depends strongly on its position relative to the transcription start site. For three cell types, we show that, by considering binding position, it is possible to predict relative expression levels between cell types with an accuracy approaching the level of agreement between different experimental platforms. Our model suggests that, for the transcription factors profiled in these cell types, a regulatory site's influence on expression falls off almost linearly with distance from the transcription start site in a 10 kilobase range. Binding to both evolutionarily conserved and non-conserved sequences contributes significantly to transcriptional regulation. Our approach also reveals the quantitative, tissue-specific role of individual proteins in activating or repressing transcription. These results suggest that regulator binding position plays a previously unappreciated role in influencing expression and blurs the classical distinction between proximal promoter and distal binding events.

Gene expression is controlled, in large part, by regulatory proteins called transcription factors that bind specific sites in the genome. A major focus of molecular biology has been understanding how these transcription factors interact with the cell's transcriptional machinery, the genome, and with each other to turn genes' expression on and off in various physiological contexts. Previous approaches for modeling transcriptional regulation have focused on the complex combinatorial interactions between groups of transcription factors at regulatory sites, or on the specific activating or repressive functions of individual proteins. In this work, we present a new modeling framework and demonstrate that an equally important, and previously overlooked, consideration in predicting the effect that a regulatory site has on gene expression is simply its location relative to the transcription start site of nearby genes. Our results show that, in general, the closer a binding event is to a gene's transcription start site, the more it influences expression. We also show that considering the particular proteins bound at a regulatory site helps predict the expression of nearby genes. However, considering the sequence conservation level of these sites does not lead to more accurate predictions.

A cross-laboratory comparison of expression profiling data from normal human postmortem brain

Expression profiling of post-mortem human brain tissue has been widely used to study molecular changes associated with neuropsychiatric diseases as well as normal processes such as aging. Changes in expression associated with factors such as age, gender or postmortem interval are often more pronounced than changes associated with disease. Therefore in addition to being of interest in their own right, careful consideration of these effects are important in the interpretation of disease studies. We performed a large meta-analysis of genome-wide expression studies of normal human cortex to more fully catalogue the effects of age, gender, postmortem interval and brain pH, yielding a "meta-signature" of gene expression changes for each factor. We validated our results by showing a significant overlap with independent gene lists extracted from the literature. Importantly, meta-analysis identifies genes which are not significant in any individual study. Finally, we show that many schizophrenia candidate genes appear in the meta-signatures, reinforcing the idea that studies must be carefully controlled for interactions between these factors and disease. In addition to the inherent value of the meta-signatures, our results provide critical information for future studies of disease effects in the human brain.

Porcine aortic endothelial cell genes responsive to selected inflammatory stimulators

Use of porcine tissues has been suggested as a promising solution for severe shortage of transplantable human organs. The immediate hurdle for xenotransplantation is acute immune/inflammatory vascular rejection of the transplant. Because endothelial cells play a key role in the initiation and the amplification of inflammation, alteration of gene expression in human endothelial cells, by various inflammatory stimulators has been studied extensively. However, transcriptional changes induced by human and other inflammatory stimulators in porcine endothelial cells have thus far not been studied. In this study, we treated porcine endothelial cells with human tumor necrosis factor (TNF)-alpha, porcine interferon (IFN)-gamma, H(2)O(2) and lypopolysaccharide (LPS) and profiled transcriptional change at 1 hr, 6 hr and 24 hr, using pig oligonucleotide 13K microarray. We found that mRNA species such as chemokine (C-X-C motif) ligand 6 (CXCL6) and Cathepsin S were significantly induced in porcine endothelial cells, as was previously reported with human endothelial cell. We also found that mRNA species including secreted frizzled-related protein 2 (SFRP2), radical S-adenosyl methionine domain containing 2 (RSAD2), structure specific recognition protein 1 (SSRP1) also were highly overexpressed in porcine endothelial cells. This result shows clues to understand underlying mechanisms of xenotransplantation rejection and the highly responsive porcine genes may serve as novel targets to be regulated for improving the function of grafted porcine donor organs.

Microarray probes and probe sets

DNA microarrays have gained wide use in biomedical research by simultaneously monitoring the expression levels of a large number of genes. The successful implementation of DNA microarray technologies requires the development of methods and techniques for the fabrication of microarrays, the selection of probes to represent genes, the quantification of hybridization, and data analysis. In this paper, we concentrate on probes that are either spotted or synthesized on the glass slides through several aspects: sources of probes, the criteria for selecting probes, tools available for probe selections, and probes used in commercial microarray chips. We then provide a detailed review of one type of DNA microarray: Affymetrix GeneChips, discuss the need to re-annotate probes, review different methods for regrouping probes into probe sets, and compare various redefinitions through public available datasets.

Nanoliter high-throughput RT-qPCR: a statistical analysis and assessment

Biomarkers discovered from gene expression profiles using hybridization microarrays have made great inroads in the diagnosis and development of safer and efficacious drugs. The candidate gene set is biologically validated by quantitative measurement with reverse transcriptase quantitative PCR (RT-qPCR) and is an effective strategy when implemented with microplates if the number of candidate genes and samples is small. With the trend toward informative candidate gene panels increasing from tens to hundreds of genes and sample cohorts exceeding several hundred, an alternative fluidic approach is needed that preserves the intrinsic analytical precision, large dynamic range, and high sensitivity of RT-qPCR, yet is scalable to high throughputs. We have evaluated the performance of a nanoliter fluidic system that enables up to 3072 nanoliter RT-qPCR assays simultaneously in a high-density array format. We measured the transcription from two different adult human tissues to assess measurement reproducibility across replicates, measurement accuracy, precision, specificity, and sensitivity; determined the false positive rate (FPR) and false negative rate (FNR) of the expressed transcript copies; and determined differences in kinase gene expression reflecting tissue and dosage differences. Using our methodology, we confirm the potential of this technology in advancing pharmaceutical research and development.

Dysregulated molecular networks in head and neck carcinogenesis

Multiple genetic and epigenetic events, including the aberrant expression and function of molecules regulating cell signaling, growth, survival, motility, angiogenesis, and cell cycle control, underlie the progressive acquisition of a malignant phenotype in squamous carcinomas of the head and neck (HNSCC). In this regard, there has been a recent explosion in our understanding on how extracellular components, cell surface molecules, and a myriad of intracellular proteins and second messenger systems interact with each other, and are organized in pathways and networks to control cellular and tissue functions and cell fate decisions. This emerging ability to understand the basic mechanism controlling inter- and intra-cellular communication has provided an unprecedented opportunity to understand how their dysregulation contributes to the growth and dissemination of human cancers. Here, we will discuss the emerging information on how the use of modern technologies, including gene array and proteomic studies, combined with the molecular dissection of aberrant signaling networks, including the EGFR, ras, NFkappaB, Stat, Wnt/beta-catenin, TGF-beta, and PI3K-AKT-mTOR signaling pathways, can help elucidate the molecular mechanisms underlying HNSCC progression. Ultimately, we can envision that this knowledge may provide tremendous opportunities for the diagnosis of premalignant squamous lesions, and for the development of novel molecular-targeted strategies for the prevention and treatment of HNSCC.

Comparative analysis of methods for gene transcription profiling data derived from different microarray technologies in rat and mouse models of diabetes

Background

Microarray technologies are widely used to quantify the abundance of transcripts corresponding to thousands of genes. To maximise the robustness of transcriptome results, we have tested the performance and reproducibility of rat and mouse gene expression data obtained with Affymetrix, Illumina and Operon platforms.

Results

We present a thorough analysis of the degree of reproducibility provided by analysing the transcriptomic profile of the same animals of several experimental groups under different popular microarray technologies in different tissues. Concordant results from inter- and intra-platform comparisons were maximised by testing many popular computational methods for generating fold changes and significances and by only considering oligonucleotides giving high expression levels. The choice of Affymetrix signal extraction technique was shown to have the greatest effect on the concordance across platforms. In both species, when choosing optimal methods, the agreement between data generated on the Affymetrix and Illumina was excellent; this was verified using qRT-PCR on a selection of genes present on all platforms.

Conclusion

This study provides an extensive assessment of analytical methods best suited for processing data from different microarray technologies and can assist integration of technologically different gene expression datasets in biological systems.

Reproducible chemical-induced changes in gene expression profiles in human hepatoma HepaRG cells under various experimental conditions

The use of in vitro human liver cell models is an attractive approach in toxicogenomic studies designed to analyze gene expression changes induced by a toxic chemical. However, in such studies, reliability, reproducibility and interlaboratory concordance of microarrays, as well as the choice of the most suitable cell model, remain a matter of debate. This work was aimed at evaluating the robustness of microarray technologies and the suitability of the highly differentiated human HepaRG cell line in the investigation of gene expression changes induced by a toxic compound in human liver. The influence of various experimental conditions including cell cultures grown at different test sites, different generations of microarrays, RNA analysis platforms and softwares, was tested on gene expression profiles induced by a 20h treatment with an 8mM concentration of phenobarbital as the toxic compound. As many as 1099 genes (p-value<0.01 and 1.5-fold-change), representing 74% and 30% of the signature genes detected with Agilent 22 and 44K pangenomic microarrays, respectively, were shown to be modulated in common in six independently performed experiments. The most modulated genes included both those known to be regulated by phenobarbital, such as cytochromes P450 and membrane transporters, and those involved in oxidative stress, inflammation and apoptosis, typifying a toxic insult. These data provide strong support for the use of a toxicogenomic approach for the in vitro prediction of chemical toxicity, and for the choice of human HepaRG cells as a promising model system for human hepatotoxicity testing.

What would you do if you could sequence everything?

It could be argued that the greatest transformative aspect of the Human Genome Project has been not the sequencing of the genome itself, but the resultant development of new technologies. A host of new approaches has fundamentally changed the way we approach problems in basic and translational research. Now, a new generation of high-throughput sequencing technologies promises to again transform the scientific enterprise, potentially supplanting array-based technologies and opening up many new possibilities. By allowing DNA/RNA to be assayed more rapidly than previously possible, these next-generation platforms promise a deeper understanding of genome regulation and biology. Significantly enhancing sequencing throughput will allow us to follow the evolution of viral and bacterial resistance in real time, to uncover the huge diversity of novel genes that are currently inaccessible, to understand nucleic acid therapeutics, to better integrate biological information for a complete picture of health and disease at a personalized level and to move to advances that we cannot yet imagine.

Meta-analysis of microarray studies reveals a novel hematopoietic progenitor cell signature and demonstrates feasibility of inter-platform data integration

Microarray-based studies of global gene expression (GE) have resulted in a large amount of data that can be mined for further insights into disease and physiology. Meta-analysis of these data is hampered by technical limitations due to many different platforms, gene annotations and probes used in different studies. We tested the feasibility of conducting a meta-analysis of GE studies to determine a transcriptional signature of hematopoietic progenitor and stem cells. Data from studies that used normal bone marrow-derived hematopoietic progenitors was integrated using both RefSeq and UniGene identifiers. We observed that in spite of variability introduced by experimental conditions and different microarray platforms, our meta-analytical approach can distinguish biologically distinct normal tissues by clustering them based on their cell of origin. When studied in terms of disease states, GE studies of leukemias and myelodysplasia progenitors tend to cluster with normal progenitors and remain distinct from other normal tissues, further validating the discriminatory power of this meta-analysis. Furthermore, analysis of 57 normal hematopoietic stem and progenitor cell GE samples was used to determine a gene expression signature characteristic of these cells. Genes that were most uniformly expressed in progenitors and at the same time differentially expressed when compared to other normal tissues were found to be involved in important biological processes such as cell cycle regulation and hematopoiesis. Validation studies using a different microarray platform demonstrated the enrichment of several genes such as SMARCE, Septin 6 and others not previously implicated in hematopoiesis. Most interestingly, alpha-integrin, the only common stemness gene discovered in a recent comparative murine analysis (Science 302(5644):393) was also enriched in our dataset, demonstrating the usefulness of this analytical approach.

Gene expression patterns of sulfur starvation in Synechocystis sp. PCC 6803

BACKGROUND

The unicellular cyanobacterium Synechocystis sp. PCC 6803 is a model microbe for studying biochemistry, genetics and molecular biology of photobiological processes. Importance of this bacterium in basic and applied research calls for a systematic, genome-wide description of its transcriptional regulatory capacity. Characteristic transcriptional responses to changes in the growth environment are expected to provide a scaffold for describing the Synechocystis transcriptional regulatory network as well as efficient means for functional annotation of genes in the genome.

RESULTS

We designed, validated and used Synechocystis genome-wide oligonucleotide (70-mer) microarray (representing 96.7% of all chromosomal ORFs annotated at the time of the beginning of this project) to study transcriptional activity of the cyanobacterial genome in response to sulfur (S) starvation. The microarray data were verified by quantitative RT-PCR. We made five main observations: 1) Transcriptional changes upon sulfate starvation were relatively moderate, but significant and consistent with growth kinetics; 2) S acquisition genes encoding for a high-affinity sulfate transporter were significantly induced, while decreased transcription of genes for phycobilisome, photosystems I and II, cytochrome b6/f, and ATP synthase indicated reduced light-harvesting and photosynthetic activity; 3) S starvation elicited transcriptional responses associated with general growth arrest and stress; 4) A large number of genes regulated by S availability encode hypothetical proteins or proteins of unknown function; 5) Hydrogenase structural and maturation accessory genes were not identified as differentially expressed, even though increased hydrogen evolution was observed.

CONCLUSION

The expression profiles recorded by using this oligonucleotide-based microarray platform revealed that during transition from the condition of plentiful S to S starvation, Synechocystis undergoes coordinated transcriptional changes, including changes in gene expression whose products are involved in sensing nutrient limitations and tuning bacterial metabolism. The transcriptional profile of the nutrient starvation was dominated by a decrease in abundances of many transcripts. However, these changes were unlikely due to the across-the-board, non-specific shut down of transcription in a condition of growth arrest. Down-regulation of transcripts encoding proteins whose function depends on a cellular S status indicated that the observed repression has a specific regulatory component. The repression of certain S-related genes was paralleled by activation of genes involved in internal and external S scavenging.

Anaplasma phagocytophilum-induced gene expression in both human neutrophils and HL-60 cells

Anaplasma phagocytophilum (Ap), the etiologic agent of the tick-borne disease human granulocytic anaplasmosis, is an obligate intracellular pathogen unique in its ability to target and replicate within neutrophils. We define and compare the spectra of host gene expression in response to Ap infection of human neutrophils and of HL-60 cells using long (70-mer)-oligonucleotide array technology. In addition to apoptosis-related genes, genes involved in signaling pathways, transcriptional regulation, immune response, host defense, cell adhesion, and cytoskeleton were modulated in neutrophils infected with Ap. Ap infection affected the same pathways in HL-60 cells but transcriptional changes occurred more slowly and in a reduced spectrum of genes. Gene expression changes detected by microarray were confirmed for randomly selected genes by QRT-PCR and Western blot studies. These studies demonstrate for the first time that the ERK pathway is activated in Ap-infected neutrophils and also define multiple pathways that are activated during intracellular Ap infection, which together serve to prolong the cell survival that is needed to allow bacterial replication and survival in neutrophils, which otherwise would rapidly apoptose.

Persistence of regenerative myogenesis in spite of down-regulation of activity-dependent genes in long-term denervated rat muscle

Contrary to general expectation, in humans, we have recently shown that after complete conus cauda lesion, the lower motoneuron denervated myofibers may survive several years. In adult rats, the sciatectomized muscle progresses in 4-6 months from severe atrophy to a dystrophic stage and undergoes a dramatic weight loss; during this process, myofiber death/regeneration processes maintain a decreasing population of very small, but vital myofibers. At the same time, in vitro electrophysiologic recordings show that denervated fibers can maintain membrane excitability longer than they can retain contractile properties. A certain level of myofiber regeneration seems to have a role in the process, with the early re-expression of embryonic subunits of integrins and acetylcholine receptor subunits. In the present work, using the reliable real-time quantitative PCR, we confirm the long-lasting occurrence of myoblast proliferation-dependent events and their focal nature. In fact, we show here that in sciatectomized muscle, the expression of 12 selected genes was differentially regulated after 3 and 9 month denervation. At both time points, indexes of muscle activity/inactivity and tissue remodeling (proteolysis, energy usage and angiogenic factors) were down-regulated, while indexes of regenerative myogenesis (Myogenin, MyoD, MRF4 and MHCemb) were up-regulated. Immunohistochemistry with anti-MHCemb and anti-NCAM monoclonal antibodies show that such regeneration events were focally distributed. We conclude that myofiber regeneration is a non-compensatory mechanism, which prolongs the chance of reinnervation during long-lasting denervation. It may also contribute to muscle recovery in paraplegic patients, even when rehabilitation strategies based on functional electric stimulation start late after spinal cord injury (SCI).

Genome-wide gene expression patterns and growth requirements suggest that Pelobacter carbinolicus reduces Fe(III) indirectly via sulfide production

Although Pelobacter species are closely related to Geobacter species, recent studies suggested that Pelobacter carbinolicus may reduce Fe(III) via a different mechanism because it lacks the outer-surface c-type cytochromes that are required for Fe(III) reduction by Geobacter sulfurreducens. Investigation into the mechanisms for Fe(III) reduction demonstrated that P. carbinolicus had growth yields on both soluble and insoluble Fe(III) consistent with those of other Fe(III)-reducing bacteria. Comparison of whole-genome transcript levels during growth on Fe(III) versus fermentative growth demonstrated that the greatest apparent change in gene expression was an increase in transcript levels for four contiguous genes. These genes encode two putative periplasmic thioredoxins; a putative outer-membrane transport protein; and a putative NAD(FAD)-dependent dehydrogenase with homology to disulfide oxidoreductases in the N terminus, rhodanese (sulfurtransferase) in the center, and uncharacterized conserved proteins in the C terminus. Unlike G. sulfurreducens, transcript levels for cytochrome genes did not increase in P. carbinolicus during growth on Fe(III). P. carbinolicus could use sulfate as the sole source of sulfur during fermentative growth, but required elemental sulfur or sulfide for growth on Fe(III). The increased expression of genes potentially involved in sulfur reduction, coupled with the requirement for sulfur or sulfide during growth on Fe(III), suggests that P. carbinolicus reduces Fe(III) via an indirect mechanism in which (i) elemental sulfur is reduced to sulfide and (ii) the sulfide reduces Fe(III) with the regeneration of elemental sulfur. This contrasts with the direct reduction of Fe(III) that has been proposed for Geobacter species.

The T7-primer is a source of experimental bias and introduces variability between microarray platforms

Eberwine(-like) amplification of mRNA adds distinct 6–10 bp nucleotide stretches to the 5′ end of amplified RNA transcripts. Analysis of over six thousand microarrays reveals that probes containing motifs complementary to these stretches are associated with aberrantly high signals up to a hundred fold the signal observed in unaffected probes. This is not observed when total RNA is used as target source. Different T7 primer sequences are used in different laboratories and platforms and consequently different T7 primer bias is observed in different datasets. This will hamper efforts to compare data sets across platforms.

Technical variables in high-throughput miRNA expression profiling: much work remains to be done

MicroRNA (miRNA) gene expression profiling has provided important insights into plant and animal biology. However, there has not been ample published work about pitfalls associated with technical parameters in miRNA gene expression profiling. One source of pertinent information about technical variables in gene expression profiling is the separate and more well-established literature regarding mRNA expression profiling. However, many aspects of miRNA biochemistry are unique. For example, the cellular processing and compartmentation of miRNAs, the differential stability of specific miRNAs, and aspects of global miRNA expression regulation require specific consideration. Additional possible sources of systematic bias in miRNA expression studies include the differential impact of pre-analytical variables, substrate specificity of nucleic acid processing enzymes used in labeling and amplification, and issues regarding new miRNA discovery and annotation. We conclude that greater focus on technical parameters is required to bolster the validity, reliability, and cultural credibility of miRNA gene expression profiling studies.

Can subtle changes in gene expression be consistently detected with different microarray platforms?

Background

The comparability of gene expression data generated with different microarray platforms is still a matter of concern. Here we address the performance and the overlap in the detection of differentially expressed genes for five different microarray platforms in a challenging biological context where differences in gene expression are few and subtle.

Results

Gene expression profiles in the hippocampus of five wild-type and five transgenic δC-doublecortin-like kinase mice were evaluated with five microarray platforms: Applied Biosystems, Affymetrix, Agilent, Illumina, LGTC home-spotted arrays. Using a fixed false discovery rate of 10% we detected surprising differences between the number of differentially expressed genes per platform. Four genes were selected by ABI, 130 by Affymetrix, 3,051 by Agilent, 54 by Illumina, and 13 by LGTC. Two genes were found significantly differentially expressed by all platforms and the four genes identified by the ABI platform were found by at least three other platforms. Quantitative RT-PCR analysis confirmed 20 out of 28 of the genes detected by two or more platforms and 8 out of 15 of the genes detected by Agilent only. We observed improved correlations between platforms when ranking the genes based on the significance level than with a fixed statistical cut-off. We demonstrate significant overlap in the affected gene sets identified by the different platforms, although biological processes were represented by only partially overlapping sets of genes. Aberrances in GABA-ergic signalling in the transgenic mice were consistently found by all platforms.

Conclusion

The different microarray platforms give partially complementary views on biological processes affected. Our data indicate that when analyzing samples with only subtle differences in gene expression the use of two different platforms might be more attractive than increasing the number of replicates. Commercial two-color platforms seem to have higher power for finding differentially expressed genes between groups with small differences in expression.

Constitutional polymorphisms of prostate cancer: prognostic and diagnostic implications

Prostate cancer is the most common cancer diagnosis in men. While often perceived as a slow, indolent malignancy, prostate cancer trails only lung cancer among cancer-related mortality in men. Current diagnosis and treatment algorithms are plagued by overdiagnosis of non-lethal indolent prostate cancer with no proven means to predict, detect, and prevent aggressive lethal prostate cancer in men most at risk. These challenges are particularly concerning for African-American men who demonstrate increased rates of prostate cancer incidence and mortality when compared to other ethnic groups. With the completion of the human genome project, technology and techniques now exist to differentiate cancer from normal tissues based on the expression patterns of thousands of genes assessed simultaneously on a single microarray gene 'chip'. This platform has greatly improved our understanding of genes that regulate tumor behavior once cancer is established. Microarrays can also be utilized in patients without cancer to determine which patients are at high risk for tumor development and in need of rational prevention strategies. Constitutional single nucleotide polymorphisms (SNPs) are one source of genetic variation and may serve as a means to identify these high-risk individuals. SNPs are single nucleotide base pair changes within a gene which occur in one percent or more of the population. SNPs can contribute to a disease state by altering the function of a protein encoded by a gene without affecting gene expression. This review will examine the current understanding of constitutional SNPs associated with prostate cancer carcinogenesis, highlight two current diagnostic array platforms and discuss implications for future prevention and screening programs.

Characterization of a newly developed chicken 44K Agilent microarray

Background

The development of microarray technology has greatly enhanced our ability to evaluate gene expression. In theory, the expression of all genes in a given organism can be monitored simultaneously. Sequencing of the chicken genome has provided the crucial information for the design of a comprehensive chicken transcriptome microarray. A long oligonucleotide microarray has been manually curated and designed by our group and manufactured using Agilent inkjet technology. This provides a flexible and powerful platform with high sensitivity and specificity for gene expression studies.

Results

A chicken 60-mer oligonucleotide microarray consisting of 42,034 features including the entire Marek's disease virus, two avian influenza virus (H5N2 and H5N3), and 150 chicken microRNAs has been designed and tested. In an important validation study, total RNA isolated from four major chicken tissues: cecal tonsil (C), ileum (I), liver (L), and spleen (S) were used for comparative hybridizations. More than 95% of spots had high signal noise ratio (SNR > 10). There were 2886, 2660, 358, 3208, 3355, and 3710 genes differentially expressed between liver and spleen, spleen and cecal tonsil, cecal tonsil and ileum, liver and cecal tonsil, liver and ileum, spleen and ileum (P < 10^-7), respectively. There were a number of tissue-selective genes for cecal tonsil, ileum, liver, and spleen identified (95, 71, 535, and 108, respectively; P < 10^-7). Another highlight of these data revealed that the antimicrobial peptides GAL1, GAL2, GAL6 and GAL7 were highly expressed in the spleen compared to other tissues tested.

Conclusion

A chicken 60-mer oligonucleotide 44K microarray was designed and validated in a comprehensive survey of gene expression in diverse tissues. The results of these tissue expression analyses have demonstrated that this microarray has high specificity and sensitivity, and will be a useful tool for chicken functional genomics. Novel data on the expression of putative tissue specific genes and antimicrobial peptides is highlighted as part of this comprehensive microarray validation study. The information for accessing and ordering this 44K chicken array can be found at

Gene expression microarray data analysis demystified

The increasing use of gene expression microarrays, and depositing of the resulting data into public repositories, means that more investigators are interested in using the technology either directly or through meta analysis of the publicly available data. The tools available for data analysis have generally been developed for use by experts in the field, making them difficult to use by the general research community. For those interested in entering the field, especially those without a background in statistics, it is difficult to understand why experimental results can be so variable. The purpose of this review is to go through the workflow of a typical microarray experiment, to show that decisions made at each step, from choice of platform through statistical analysis methods to biological interpretation, are all sources of this variability.

Disease-specific gene expression profiling in multiple models of lung disease

RATIONALE

Microarray technology is widely employed for studying the molecular mechanisms underlying complex diseases. However, analyses of individual diseases or models of diseases frequently yield extensive lists of differentially expressed genes with uncertain relationships to disease pathogenesis.

OBJECTIVES

To compare gene expression changes in a heterogeneous set of lung disease models in order to identify common gene expression changes seen in diverse forms of lung pathology, as well as relatively small subsets of genes likely to be involved in specific pathophysiological processes.

METHODS

We profiled lung gene expression in 12 mouse models of infection, allergy, and lung injury. A linear model was used to estimate transcript expression changes for each model, and hierarchical clustering was used to compare expression patterns between models. Selected expression changes were verified by quantitative polymerase chain reaction.

MEASUREMENTS AND MAIN RESULTS

A total of 24 transcripts, including many involved in inflammation and immune activation, were differentially expressed in a substantial majority (9 or more) of the models. Expression patterns distinguished three groups of models: (1) bacterial infection (n = 5), with changes in 89 transcripts, including many related to nuclear factor-kappaB signaling, cytokines, chemokines, and their receptors; (2) bleomycin-induced diseases (n = 2), with changes in 53 transcripts, including many related to matrix remodeling and Wnt signaling; and (3) T helper cell type 2 (allergic) inflammation (n = 5), with changes in 26 transcripts, including many encoding epithelial secreted molecules, ion channels, and transporters.

CONCLUSIONS

This multimodel dataset highlights novel genes likely involved in various pathophysiological processes and will be a valuable resource for the investigation of molecular mechanisms underlying lung disease pathogenesis.

Application of a correlation correction factor in a microarray cross-platform reproducibility study

BACKGROUND

Recent research examining cross-platform correlation of gene expression intensities has yielded mixed results. In this study, we demonstrate use of a correction factor for estimating cross-platform correlations.

RESULTS

In this paper, three technical replicate microarrays were hybridized to each of three platforms. The three platforms were then analyzed to assess both intra- and cross-platform reproducibility. We present various methods for examining intra-platform reproducibility. We also examine cross-platform reproducibility using Pearson's correlation. Additionally, we previously developed a correction factor for Pearson's correlation which is applicable when X and Y are measured with error. Herein we demonstrate that correcting for measurement error by estimating the "disattenuated" correlation substantially improves cross-platform correlations.

CONCLUSION

When estimating cross-platform correlation, it is essential to thoroughly evaluate intra-platform reproducibility as a first step. In addition, since measurement error is present in microarray gene expression data, methods to correct for attenuation are useful in decreasing the bias in cross-platform correlation estimates.

Profiling studies in ovarian cancer: a review

Ovarian cancer is a heterogeneous disease with respect to histopathology, molecular biology, and clinical outcome. In advanced stages, surgery and chemotherapy result in an approximately 25% overall 5-year survival rate, pointing to a strong need to identify subgroups of patients that may benefit from targeted innovative molecular therapy. This review summarizes: (a) microarray research identifying gene-expression profiles in ovarian cancer; (b) the methodological flaws in the available microarray studies; and (c) applications of pathway analysis to define new molecular subgroups. Microarray technology now permits the analysis of expression levels of thousands of genes. So far seven studies have aimed to identify a genetic profile that can predict survival/clinical outcome and/or response to platinum-based therapy. To date, the clinical evidence of prognostic microarray studies has only reached the level of small retrospective studies, and there are other issues that may explain the nonreproducibility among the reported prognostic profiles, such as overfitting, technical platform differences, and accuracy of measurements. We consider pathway analysis a promising new strategy. The accumulation of small differential expressions within a meaningful molecular regulatory network might lead to a critical threshold level, resulting in ovarian cancer. Microarray technologies have already provided valuable expression data for classifying ovarian cancer and the first clues about which molecular changes in ovarian cancer could be exploited in new treatment strategies. Further improvements in technology as well as in study design, combined with pathway analysis, will allow us to detect even more subtle tumor expression differences among subgroups of ovarian cancer patients. Disclosure of potential conflicts of interest is found at the end of this article.

A detailed transcript-level probe annotation reveals alternative splicing based microarray platform differences

BACKGROUND

Microarrays are a popular tool used in experiments to measure gene expression levels. Improving the reproducibility of microarray results produced by different chips from various manufacturers is important to create comparable and combinable experimental results. Alternative splicing has been cited as a possible cause of differences in expression measurements across platforms, though no study to this point has been conducted to show its influence in cross-platform differences.

RESULTS

Using probe sequence data, a new microarray probe/transcript annotation was created based on the AceView Aug05 release that allowed for the categorization of genes based on their expression measurements' susceptibility to alternative splicing differences across microarray platforms. Examining gene expression data from multiple platforms in light of the new categorization, genes unsusceptible to alternative splicing differences showed higher signal agreement than those genes most susceptible to alternative splicing differences. The analysis gave rise to a different probe-level visualization method that can highlight probe differences according to transcript specificity.

CONCLUSION

The results highlight the need for detailed probe annotation at the transcriptome level. The presence of alternative splicing within a given sample can affect gene expression measurements and is a contributing factor to overall technical differences across platforms.

Comparing two microarray platforms for identifying mutated genes in barley (Hordeum vulgare L.)

We have previously described the evaluation of a cDNA microarray platform to identify and clone mutated barley (Hordeum vulgare L.) genes, using their transcriptionally defective mutant alleles (S. Zakhrabekova, C.G. Kannangara, D. von Wettstein, M. Hansson, A microarray approach for identification of mutated genes, Plant Physiol. Biochem. 40 (2002) 189-197). It was concluded that competitive hybridization between phenotypically similar mutants could specifically highlight an arrayed clone, corresponding to the mutated gene. In this study we evaluate whether the Affymetrix microarray platform can be used for the same purpose. The Affymetrix barley microarray contains a large number of clones (22,792 probe sets). In this and the previous study we used two barley mutant strains, xantha-h.57 and xantha-f.27, with known mutations in different subunit genes of the chlorophyll biosynthetic enzyme magnesium chelatase (EC 6.6.1.1). Mutant xantha-h.57 produces no Xantha-h mRNA whereas in xantha-f.27 the nonsense mutation in the last exon of the gene, results in nonsense-mediated decay of Xantha-f mRNA. We conclude that the Affymetrix platform meets our requirements and that our approach successfully highlighted the arrayed Xantha-h clone and that Xantha-f was among the top fourteen candidates.

Benefits of in-situ synthesized microarrays for analysis of gene expression in understudied microorganisms

Although the genome sequences of many microorganisms are now known, whole-genome DNA microarray platforms consisting of PCR amplicon, or oligonucleotide elements printed onto glass slides have been readily available for only a relatively few, highly studied microorganisms. For those microorganisms more recently cultured or studied by fewer investigators it has been difficult to justify the initial time and expense of developing such array platforms especially if only a limited number of gene expression studies are envisioned. However, in-situ synthesized oligonucleotide (ISO) arrays can be inexpensively fabricated on an 'as needed' basis with a reduced initial investment in time, personnel, resources, and costs. To evaluate the performance of one ISO array platform, gene expression patterns in Geobacter sulfurreducens under nitrogen-fixing conditions were compared with results from quantitative reverse transcriptase PCR (qRT-PCR) and previously published data from a similar experiment using spotted PCR amplicon arrays. There were strong correlations between the results of the ISO arrays and the results from qRT-PCR (r(2)=0.762) and spotted array (r(2)=0.744) analyses. After initial use the ISO arrays could be successfully stripped and reused. The increased flexibility in array design and reusability coupled with a lower initial investment in terms of fabrication time and cost for the ISO arrays suggest that they may be the preferred approach when investigating gene expression in microorganisms, especially when only a few expression studies are required.

Autocorrelation analysis reveals widespread spatial biases in microarray experiments

Background

DNA microarrays provide the ability to interrogate multiple genes in a single experiment and have revolutionized genomic research. However, the microarray technology suffers from various forms of biases and relatively low reproducibility. A particular source of false data has been described, in which non-random placement of gene probes on the microarray surface is associated with spurious correlations between genes.

Results

In order to assess the prevalence of this effect and better understand its origins, we applied an autocorrelation analysis of the relationship between chromosomal position and expression level to a database of over 2000 individual yeast microarray experiments. We show that at least 60% of these experiments exhibit spurious chromosomal position-dependent gene correlations, which nonetheless appear in a stochastic manner within each experimental dataset. Using computer simulations, we show that large spatial biases caused in the microarray hybridization step and independently of printing procedures can exclusively account for the observed spurious correlations, in contrast to previous suggestions. Our data suggest that such biases may generate more than 15% false data per experiment. Importantly, spatial biases are expected to occur regardless of microarray design and over a wide range of microarray platforms, organisms and experimental procedures.

Conclusions

Spatial biases comprise a major source of noise in microarray studies; revision of routine experimental practices and normalizations to account for these biases may significantly and comprehensively improve the quality of new as well as existing DNA microarray data.

Application of toxicogenomics to genetic toxicology risk assessment

Based on the assumption that compounds having similar toxic modes of action induce specific gene expression changes, the toxicity of unknown compounds can be predicted after comparison of their molecular fingerprints with those obtained with compounds of known toxicity. These predictive models will therefore rely on the characterization of marker genes. Toxicogenomics (TGX) also provides mechanistic insight into the mode of toxicity, and can therefore be used as an adjunct to the standard battery of genotoxicity tests. Promising results, highlighting the ability of TGX to differentiate genotoxic from non-genotoxic carcinogens, as well as DNA-reactive from non-DNA reactive genotoxins, have been reported. Additional data suggested the possibility of ranking genotoxins according to the nature of their interactions with DNA. This new approach could contribute to the improvement of risk assessment. TGX could be applied as a follow-up testing strategy in case of positive in vitro genotoxicity findings, and could contribute to improve our ability to identify the molecular mechanism of action and to possibly better assess dose-response curves. TGX has been found to be less sensitive than the standard genotoxicity end-points, probably because it measures the whole cell population response, when compared with standard tests designed to detect rare events in a small number of cells. Further validation will be needed (1) to better link the profiles obtained with TGX to the established genotoxicity end-points, (2) to improve the gene annotation tools, and (3) to standardise study design and data analysis and to better evaluate the impact of variability between platforms and bioinformatics approaches.

Review of the literature examining the correlation among DNA microarray technologies

DNA microarray technologies are used in a variety of biological disciplines. The diversity of platforms and analytical methods employed has raised concerns over the reliability, reproducibility and correlation of data produced across the different approaches. Initial investigations (years 2000-2003) found discrepancies in the gene expression measures produced by different microarray technologies. Increasing knowledge and control of the factors that result in poor correlation among the technologies has led to much higher levels of correlation among more recent publications (years 2004 to present). Here, we review the studies examining the correlation among microarray technologies. We find that with improvements in the technology (optimization and standardization of methods, including data analysis) and annotation, analysis across platforms yields highly correlated and reproducible results. We suggest several key factors that should be controlled in comparing across technologies, and are good microarray practice in general.

Long oligonucleotide microarrays for African green monkey gene expression profile analysis

Nonhuman primates, including African green monkey (AGM), are important models for biomedical research. The information on monkey genomes is still limited and no versatile gene expression screening tool is available. We tested human whole genome microarrays for cross-species reactivity with AGM transcripts using both long oligonucleotide arrays (60-mer probes) and short oligonucleotide arrays (25-mer). Using the long oligonucleotide arrays, we detected 4-fold more AGM transcripts than with the short oligonucleotide technology. The number of detected transcripts was comparable to that detected using human RNA, with 87% of the detected genes being shared between both species. The specificity of the signals obtained with the long oligonucleotide arrays was determined by analyzing the transcriptome of concanavalin A-activated CD4+ T cells vs. nonactivated T cells of two monkey species AGM and macaque. For both species, the genes showing the most significant changes in expression, such as IL-2R, were those known to be regulated in human CD4+ T cell activation. Finally, tissue specificity of the signals was established by comparing the transcription profiles of AGM brain and tonsil cells. In conclusion, the ABI human microarray platform provides a highly valuable tool for the assessment of AGM gene expression profiles.

Cross platform microarray analysis for robust identification of differentially expressed genes

BACKGROUND

Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles. In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes.

RESULTS

In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms. We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with approximately 80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR.

CONCLUSION

In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.