A comprehensive functional analysis of tissue specificity of human gene expression.

BACKGROUND: In recent years, the maturation of microarray technology has allowed the genome-wide analysis of gene expression patterns to identify tissue-specific and ubiquitously expressed ('housekeeping') genes. We have performed a functional and topological analysis of housekeeping and tissue-specific networks to identify universally necessary biological processes, and those unique to or characteristic of particular tissues. RESULTS: We measured whole genome expression in 31 human tissues, identifying 2374 housekeeping genes expressed in all tissues, and genes uniquely expressed in each tissue. Comprehensive functional analysis showed that the housekeeping set is substantially larger than previously thought, and is enriched with vital processes such as oxidative phosphorylation, ubiquitin-dependent proteolysis, translation and energy metabolism. Network topology of the housekeeping network was characterized by higher connectivity and shorter paths between the proteins than the global network. Ontology enrichment scoring and network topology of tissue-specific genes were consistent with each tissue's function and expression patterns clustered together in accordance with tissue origin. Tissue-specific genes were twice as likely as housekeeping genes to be drug targets, allowing the identification of tissue 'signature networks' that will facilitate the discovery of new therapeutic targets and biomarkers of tissue-targeted diseases. CONCLUSION: A comprehensive functional analysis of housekeeping and tissue-specific genes showed that the biological function of housekeeping and tissue-specific genes was consistent with tissue origin. Network analysis revealed that tissue-specific networks have distinct network properties related to each tissue's function. Tissue 'signature networks' promise to be a rich source of targets and biomarkers for disease treatment and diagnosis.

Gene expression signature in peripheral blood detects thoracic aortic aneurysm.

BACKGROUND: Thoracic aortic aneurysm (TAA) is usually asymptomatic and associated with high mortality. Adverse clinical outcome of TAA is preventable by elective surgical repair; however, identifying at-risk individuals is difficult. We hypothesized that gene expression patterns in peripheral blood cells may correlate with TAA disease status. Our goal was to identify a distinct gene expression signature in peripheral blood that may identify individuals at risk for TAA. METHODS AND FINDINGS: Whole genome gene expression profiles from 94 peripheral blood samples (collected from 58 individuals with TAA and 36 controls) were analyzed. Significance Analysis of Microarray (SAM) identified potential signature genes characterizing TAA vs. normal, ascending vs. descending TAA, and sporadic vs. familial TAA. Using a training set containing 36 TAA patients and 25 controls, a 41-gene classification model was constructed for detecting TAA status and an overall accuracy of 78+/-6% was achieved. Testing this classifier on an independent validation set containing 22 TAA samples and 11 controls yielded an overall classification accuracy of 78%. These 41 classifier genes were further validated by TaqMan real-time PCR assays. Classification based on the TaqMan data replicated the microarray results and achieved 80% classification accuracy on the testing set. CONCLUSIONS: This study identified informative gene expression signatures in peripheral blood cells that can characterize TAA status and subtypes of TAA. Moreover, a 41-gene classifier based on expression signature can identify TAA patients with high accuracy. The transcriptional programs in peripheral blood leading to the identification of these markers also provide insights into the mechanism of development of aortic aneurysms and highlight potential targets for therapeutic intervention. The classifier genes identified in this study, and validated by TaqMan real-time PCR, define a set of promising potential diagnostic markers, setting the stage for a blood-based gene expression test to facilitate early detection of TAA.

Effect of various normalization methods on Applied Biosystems expression array system data.

BACKGROUND: DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays. RESULTS: We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists. CONCLUSION: Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System.

Evaluation of DNA microarray results with quantitative gene expression platforms.

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

Distinct molecular mechanisms underlying clinically relevant subtypes of breast cancer: gene expression analyses across three different platforms.

BACKGROUND: Gene expression profiling has been used to define molecular phenotypes of complex diseases such as breast cancer. The luminal A and basal-like subtypes have been repeatedly identified and validated as the two main subtypes out of a total of five molecular subtypes of breast cancer. These two are associated with distinctly different gene expression patterns and more importantly, a significant difference in clinical outcome. To further validate and more thoroughly characterize these two subtypes at the molecular level in tumors at an early stage, we report a gene expression profiling study using three different DNA microarray platforms. RESULTS: Expression data from 20 tumor biopsies of early stage breast carcinomas were generated on three different DNA microarray platforms; Applied Biosystems Human Genome Survey Microarrays, Stanford cDNA Microarrays and Agilent's Whole Human Genome Oligo Microarrays, and the resulting gene expression patterns were analyzed. Both unsupervised and supervised analyses identified the different clinically relevant subtypes of breast tumours, and the results were consistent across all three platforms. Gene classification and biological pathway analyses of the genes differentially expressed between the two main subtypes revealed different molecular mechanisms descriptive of the two expression-based subtypes: Signature genes of the luminal A subtype were over-represented by genes involved in fatty acid metabolism and steroid hormone-mediated signaling pathways, in particular estrogen receptor signaling, while signature genes of the basal-like subtype were over-represented by genes involved in cell proliferation and differentiation, p21-mediated pathway, and G1-S checkpoint of cell cycle-signaling pathways. A minimal set of 54 genes that best discriminated the two subtypes was identified using the combined data sets generated from the three different array platforms. These predictor genes were further verified by TaqMan Gene Expression assays. CONCLUSION: We have identified and validated the two main previously defined clinically relevant subtypes, luminal A and basal-like, in a small set of early stage breast carcinomas. Signature genes characterizing these two subtypes revealed that distinct molecular mechanisms might have been pre-programmed at an early stage in different subtypes of the disease. Our results provide further evidence that these breast tumor subtypes represent biologically distinct disease entities and may require different therapeutic strategies. Finally, validated by multiple gene expression platforms, including quantitative PCR, the set of 54 predictor genes identified in this study may define potential prognostic molecular markers for breast cancer.

Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays.

BACKGROUND: DNA microarrays are rapidly becoming a fundamental tool in discovery-based genomic and biomedical research. However, the reliability of the microarray results is being challenged due to the existence of different technologies and non-standard methods of data analysis and interpretation. In the absence of a "gold standard"/"reference method" for the gene expression measurements, studies evaluating and comparing the performance of various microarray platforms have often yielded subjective and conflicting conclusions. To address this issue we have conducted a large scale TaqMan Gene Expression Assay based real-time PCR experiment and used this data set as the reference to evaluate the performance of two representative commercial microarray platforms. RESULTS: In this study, we analyzed the gene expression profiles of three human tissues: brain, lung, liver and one universal human reference sample (UHR) using two representative commercial long-oligonucleotide microarray platforms: (1) Applied Biosystems Human Genome Survey Microarrays (based on single-color detection); (2) Agilent Whole Human Genome Oligo Microarrays (based on two-color detection). 1,375 genes represented by both microarray platforms and spanning a wide dynamic range in gene expression levels, were selected for TaqMan Gene Expression Assay based real-time PCR validation. For each platform, four technical replicates were performed on the same total RNA samples according to each manufacturer's standard protocols. For Agilent arrays, comparative hybridization was performed using incorporation of Cy5 for brain/lung/liver RNA and Cy3 for UHR RNA (common reference). Using the TaqMan Gene Expression Assay based real-time PCR data set as the reference set, the performance of the two microarray platforms was evaluated focusing on the following criteria: (1) Sensitivity and accuracy in detection of expression; (2) Fold change correlation with real-time PCR data in pair-wise tissues as well as in gene expression profiles determined across all tissues; (3) Sensitivity and accuracy in detection of differential expression. CONCLUSION: Our study provides one of the largest "reference" data set of gene expression measurements using TaqMan Gene Expression Assay based real-time PCR technology. This data set allowed us to use an alternative gene expression technology to evaluate the performance of different microarray platforms. We conclude that microarrays are indeed invaluable discovery tools with acceptable reliability for genome-wide gene expression screening, though validation of putative changes in gene expression remains advisable. Our study also characterizes the limitations of microarrays; understanding these limitations will enable researchers to more effectively evaluate microarray results in a more cautious and appropriate manner.

An efficient and high-throughput approach for experimental validation of novel human gene predictions.

A highly automated RT-PCR-based approach has been established to validate novel human gene predictions with no prior experimental evidence of mRNA splicing (ab initio predictions). Ab initio gene predictions were selected for high-throughput validation using predicted protein classification, sequence similarity to other genomes, colocalization with an MPSS tag, or microarray expression. Initial microarray prioritization followed by RT-PCR validation was the most efficient combination, resulting in approximately 35% of the ab initio predictions being validated by RT-PCR. Of the 7252 novel genes that were prioritized and processed, 796 constituted real transcripts. In addition, high-throughput RACE successfully extended the 5' and/or 3' ends of >60% of RT-PCR-validated genes. Reevaluation of these transcripts produced 574 novel transcripts using RefSeq as a reference. RT-PCR sequencing in combination with RACE on ab initio gene predictions could be used to define the transcriptome across all species.

The External RNA Controls Consortium: a progress report.

Standard controls and best practice guidelines advance acceptance of data from research, preclinical and clinical laboratories by providing a means for evaluating data quality. The External RNA Controls Consortium (ERCC) is developing commonly agreed-upon and tested controls for use in expression assays, a true industry-wide standard control.