Multiple Microarray Platforms Research Articles

Integration of transcriptomic data identifies key hallmark genes in hypertrophic cardiomyopathy

BackgroundHypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated.MethodsThe significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor.ResultsOutstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein–protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A.ConclusionsThis study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level.

In more recent years, a significant increase in the number of available biological experiments has taken place due to the widespread use of massive sequencing data. Furthermore, the continuous developments in the machine learning and in the high performance computing areas, are allowing a faster and more efficient analysis and processing of this type of data. However, biological information about a certain disease is normally widespread due to the use of different sequencing technologies and different manufacturers, in different experiments along the years around the world. Thus, nowadays it is of paramount importance to attain a correct integration of biologically-related data in order to achieve genuine benefits from them. For this purpose, this work presents an integration of multiple Microarray and RNA-seq platforms, which has led to the design of a multiclass study by collecting samples from the main four types of leukemia, quantified at gene expression. Subsequently, in order to find a set of differentially expressed genes with the highest discernment capability among different types of leukemia, an innovative parameter referred to as coverage is presented here. This parameter allows assessing the number of different pathologies that a certain gen is able to discern. It has been evaluated together with other widely known parameters under assessment of an ANOVA statistical test which corroborated its filtering power when the identified genes are subjected to a machine learning process at multiclass level. The optimal tuning of gene extraction evaluated parameters by means of this statistical test led to the selection of 42 highly relevant expressed genes. By the use of minimum-Redundancy Maximum-Relevance (mRMR) feature selection algorithm, these genes were reordered and assessed under the operation of four different classification techniques. Outstanding results were achieved by taking exclusively the first ten genes of the ranking into consideration. Finally, specific literature was consulted on this last subset of genes, revealing the occurrence of practically all of them with biological processes related to leukemia. At sight of these results, this study underlines the relevance of considering a new parameter which facilitates the identification of highly valid expressed genes for simultaneously discerning multiple types of leukemia.

P53 Contributes to Differentiating Gene Expression Following Exposure to Acetaminophen and Its Less Hepatotoxic Regioisomer Both In Vitro and In Vivo.

The goal of the present study was to compare hepatic toxicogenomic signatures across in vitro and in vivo mouse models following exposure to acetaminophen (APAP) or its relatively nontoxic regioisomer 3′-hydroxyacetanilide (AMAP). Two different Affymetrix microarray platforms and one Agilent Oligonucleotide microarray were utilized. APAP and AMAP treatments resulted in significant and large changes in gene expression that were quite disparate, and likely related to their different toxicologic profiles. Ten transcripts, all of which have been implicated in p53 signaling, were identified as differentially regulated at all time-points following APAP and AMAP treatments across multiple microarray platforms. Protein-level quantification of p53 activity aligned with results from the transcriptomic analysis, thus supporting the implicated mechanism of APAP-induced toxicity. Therefore, the results of this study provide good evidence that APAP-induced p53 phosphorylation and an altered p53-driven transcriptional response are fundamental steps in APAP-induced toxicity.

English

Rapid diagnosis and treatment of disease is often based on the identification and characterization of causative agents derived from phenotypic characteristics. This can be laborious and time consuming, often requiring many skilled personnel and a large amount of lab space. However, the introduction of nucleic acid amplification techniques into molecular biology has transformed the laboratory detection of pathogens. The progression of the molecular diagnostic revolution currently relies on the ability to efficiently and accurately offer multiplex detection and characterization for a variety of infectious disease pathogens. DNA microarray analysis has the capability to offer robust multiplex detection. Multiple microarray platforms exist, including printed double-stranded DNA and oligonucleotide arrays, in situ-synthesized arrays, high-density bead arrays, electronic microarrays, and suspension bead arrays. The aim of this paper was to review DNA microarray technology, highlighting two major types: the oligonucleotide-based array and the PCR product-based array. Although, the use of microarrays to generate gene expression data has become routine, applications pertinent to microbiology continue to rapidly expand. This review highlights uses of microarray technology that impact diagnostic microbiology, including the detection and identification of pathogens, determination of antimicrobial resistance, epidemiological strain typing, and determination of virulence factors. Key words: DNA microarray, applications, microbiology.

OvMark: a user-friendly system for the identification of prognostic biomarkers in publically available ovarian cancer gene expression datasets

BackgroundOvarian cancer has the lowest survival rate of all gynaecologic cancers and is characterised by a lack of early symptoms and frequent late stage diagnosis. There is a paucity of robust molecular markers that are independent of and complementary to clinical parameters such as disease stage and tumour grade.MethodsWe have developed a user-friendly, web-based system to evaluate the association of genes/miRNAs with outcome in ovarian cancer. The OvMark algorithm combines data from multiple microarray platforms (including probesets targeting miRNAs) and correlates them with clinical parameters (e.g. tumour grade, stage) and outcomes (disease free survival (DFS), overall survival). In total, OvMark combines 14 datasets from 7 different array platforms measuring the expression of ~17,000 genes and 341 miRNAs across 2,129 ovarian cancer samples.ResultsTo demonstrate the utility of the system we confirmed the prognostic ability of 14 genes and 2 miRNAs known to play a role in ovarian cancer. Of these genes, CXCL12 was the most significant predictor of DFS (HR = 1.42, p-value = 2.42x10−6). Surprisingly, those genes found to have the greatest correlation with outcome have not been heavily studied in ovarian cancer, or in some cases in any cancer. For instance, the three genes with the greatest association with survival are SNAI3, VWA3A and DNAH12.Conclusions/ImpactOvMark is a powerful tool for examining putative gene/miRNA prognostic biomarkers in ovarian cancer (available at http://glados.ucd.ie/OvMark/index.html). The impact of this tool will be in the preliminary assessment of putative biomarkers in ovarian cancer, particularly for research groups with limited bioinformatics facilities.Electronic supplementary materialThe online version of this article (doi:10.1186/1476-4598-13-241) contains supplementary material, which is available to authorized users.

Abstract 4150: Quantitative sequencing following PCR-driven library preparation with internal standard mixtures has improved analytical performance and lower cost.

Abstract Background: Next-generation sequencing (NGS) is amenable to a multitude of clinical applications by virtue of its automated and highly parallelized analysis of nucleic acid templates. However, prior studies have identified non-systematic biases introduced during preparation of NGS libraries as the primary source of technical variation preventing immediate application for measuring nucleic acid abundance in the clinical setting. We reasoned that a PCR-based NGS library preparation protocol that incorporated competitive internal amplification control (IAC) mixtures (i.e. internal standards) would control for the majority of bias introduced during NGS library preparation, enabling clinical laboratories to offer cost effective moderately complex diagnostic panels from quantitative NGS data. Methods: In order to test this approach, we obtained reference material RNA titration pools used in the FDA-sponsored Sequencing Quality Control (SEQC) project that have been characterized for nucleic acid abundance by multiple qPCR, Microarray and NGS platforms. Using Multiplex-PCR with primers and competitive IAC for 150-gene targets we prepared NGS libraries from: 1) gDNA to test general analytical performance, and 2) cDNA from reverse transcribed SEQC project reference material to determine accuracy in detecting fold change. Using gDNA mixed with serially titrated IAC mixtures as input, we observed a linear dynamic range over 106 orders of magnitude, with an average R2 = 0.995 (0.993 – 0.997; 95% CI). There was a high correlation coefficient (R2= 0.96) between measured values for copies of nucleic acid abundance in two separate library preparations (separate reverse transcriptions and Multiplex PCR-based library preparations) from the same reference RNA material (FDA SEQC project Sample A). Because the SEQC project RNA Samples C and D represent a known cross titration between SEQC project RNA Samples A and B, by comparing measured to expected values for expression of each gene in Samples C and D it is possible to determine accuracy of the method. In preliminary studies, the correlation coefficient of expected versus observed for Sample C was R2 = 0.96, with an ROC curve-determined accuracy to detect a 3-fold change of 97% (95 – 99%; 95% CI). Inter-platform and inter-laboratory comparisons are ongoing. Conclusion: The approach described here overcomes key sources of non-systematic bias introduced during NGS library preparation. This should enable reproducible inter-laboratory and inter-platform quantitative NGS results, and a clear path to regulatory approval for clinical diagnostic applications. Citation Format: Thomas Blomquist, Erin L. Crawford, James C. Willey. Quantitative sequencing following PCR-driven library preparation with internal standard mixtures has improved analytical performance and lower cost. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 4150. doi:10.1158/1538-7445.AM2013-4150

BioTile, A Perl based tool for the identification of differentially enriched regions in tiling microarray data

BackgroundGenome-wide tiling array experiments are increasingly used for the analysis of DNA methylation. Because DNA methylation patterns are tissue and cell type specific, the detection of differentially methylated regions (DMRs) with small effect size is a necessary feature of tiling microarray ‘peak’ finding algorithms, as cellular heterogeneity within a studied tissue may lead to a dilution of the phenotypically relevant effects. Additionally, the ability to detect short length DMRs is necessary as biologically relevant signal may occur in focused regions throughout the genome.ResultsWe present a free open-source Perl application, Binding Intensity Only Tile array analysis or “BioTile”, for the identification of differentially enriched regions (DERs) in tiling array data. The application of BioTile to non-smoothed data allows for the identification of shorter length and smaller effect-size DERs, while correcting for probe specific variation by inversely weighting on probe variance through a permutation corrected meta-analysis procedure employed at identified regions. BioTile exhibits higher power to identify significant DERs of low effect size and across shorter genomic stretches as compared to other peak finding algorithms, while not sacrificing power to detect longer DERs.ConclusionBioTile represents an easy to use analysis option applicable to multiple microarray platforms, allowing for its integration into the analysis workflow of array data analysis.

A Microarray Platform-Independent Classification Tool for Cell of Origin Class Allows Comparative Analysis of Gene Expression in Diffuse Large B-cell Lymphoma

Cell of origin classification of diffuse large B-cell lymphoma (DLBCL) identifies subsets with biological and clinical significance. Despite the established nature of the classification existing studies display variability in classifier implementation, and a comparative analysis across multiple data sets is lacking. Here we describe the validation of a cell of origin classifier for DLBCL, based on balanced voting between 4 machine-learning tools: the DLBCL automatic classifier (DAC). This shows superior survival separation for assigned Activated B-cell (ABC) and Germinal Center B-cell (GCB) DLBCL classes relative to a range of other classifiers. DAC is effective on data derived from multiple microarray platforms and formalin fixed paraffin embedded samples and is parsimonious, using 20 classifier genes. We use DAC to perform a comparative analysis of gene expression in 10 data sets (2030 cases). We generate ranked meta-profiles of genes showing consistent class-association using ≥6 data sets as a cut-off: ABC (414 genes) and GCB (415 genes). The transcription factor ZBTB32 emerges as the most consistent and differentially expressed gene in ABC-DLBCL while other transcription factors such as ARID3A, BATF, and TCF4 are also amongst the 24 genes associated with this class in all datasets. Analysis of enrichment of 12323 gene signatures against meta-profiles and all data sets individually confirms consistent associations with signatures of molecular pathways, chromosomal cytobands, and transcription factor binding sites. We provide DAC as an open access Windows application, and the accompanying meta-analyses as a resource.

Pathway-based visualization of cross-platform microarray datasets

Traditionally, microarrays were almost exclusively used for the genome-wide analysis of differential gene expression. But nowadays, their scope of application has been extended to various genomic features, such as microRNAs (miRNAs), proteins and DNA methylation (DNAm). Most available methods for the visualization of these datasets are focused on individual platforms and are not capable of integratively visualizing multiple microarray datasets from cross-platform studies. Above all, there is a demand for methods that can visualize genomic features that are not directly linked to protein-coding genes, such as regulatory RNAs (e.g. miRNAs) and epigenetic alterations (e.g. DNAm), in a pathway-centred manner. We present a novel pathway-based visualization method that is especially suitable for the visualization of high-throughput datasets from multiple different microarray platforms that were used for the analysis of diverse genomic features in the same set of biological samples. The proposed methodology includes concepts for linking DNAm and miRNA expression datasets to canonical signalling and metabolic pathways. We further point out strategies for displaying data from multiple proteins and protein modifications corresponding to the same gene. Ultimately, we show how data from four distinct platform types (messenger RNA, miRNA, protein and DNAm arrays) can be integratively visualized in the context of canonical pathways. The described method is implemented as part of the InCroMAP application that is freely available at www.cogsys.cs.uni-tuebingen.de/software/InCroMAP. clemens.wrzodek@uni-tuebingen.de or andreas.zell@uni-tuebingen.de.

DRPLA transgenic mouse substrains carrying single copy of full-length mutant human DRPLA gene with variable sizes of expanded CAG repeats exhibit CAG repeat length- and age-dependent changes in behavioral abnormalities and gene expression profiles

Dentatorubral-pallidoluysian atrophy (DRPLA) is an autosomal dominant progressive neurodegenerative disorder with intellectual deterioration and various motor deficits including ataxia, choreoathetosis, and myoclonus, caused by an abnormal expansion of CAG repeats in the DRPLA gene. Longer expanded CAG repeats contribute to an earlier age of onset, faster progression, and more severe neurological symptoms in DRPLA patients. In this study, we have established DRPLA transgenic mouse lines (sublines) harboring a single copy of the full-length mutant human DRPLA gene carrying various lengths of expanded CAG repeats (Q76, Q96, Q113, and Q129), which have clearly shown motor deficits and memory disturbance whose severity increases with the length of expanded CAG repeats and age, and successfully replicated the CAG repeat length- and age-dependent features of DRPLA patients. Neuronal intranuclear accumulation of the mutant DRPLA protein has been suggested to cause transcriptional dysregulation, leading to alteration in gene expression and neuronal dysfunction. In this study, we have conducted a comprehensive analysis of gene expression profiles in the cerebrum and cerebellum of transgenic mouse lines at 4, 8, and 12weeks using multiple microarray platforms, and demonstrated that both the number and expression levels of the altered genes are highly dependent on CAG repeat length and age in both brain regions. Specific groups of genes and their function categories were identified by further agglomerative cluster analysis and gene functional annotation analysis. Calcium signaling and neuropeptide signaling, among others, were implicated in the pathophysiology of DRPLA. Our study provides unprecedented CAG-repeat-length-dependent mouse models of DRPLA, which are highly valuable not only for elucidating the CAG-repeat-length-dependent pathophysiology of DRPLA but also for developing therapeutic strategies for DRPLA.

Probe mapping across multiple microarray platforms

Access to gene expression data has become increasingly common in recent years; however, analysis has become more difficult as it is often desirable to integrate data from different platforms. Probe mapping across microarray platforms is the first and most crucial step for data integration. In this article, we systematically review and compare different approaches to map probes across seven platforms from different vendors: U95A, U133A and U133 Plus 2.0 from Affymetrix, Inc.; HT-12 v1, HT-12v2 and HT-12v3 from Illumina, Inc.; and 4112A from Agilent, Inc. We use a unique data set, which contains 56 lung cancer cell line samples-each of which has been measured by two different microarray platforms-to evaluate the consistency of expression measurement across platforms using different approaches. Based on the evaluation from the empirical data set, the BLAST alignment of the probe sequences to a recent revision of the Transcriptome generated better results than using annotations provided by Vendors or from Bioconductor's Annotate package. However, a combination of all three methods (deemed the 'Consensus Annotation') yielded the most consistent expression measurement across platforms. To facilitate data integration across microarray platforms for the research community, we develop a user-friendly web-based tool, an API and an R package to map data across different microarray platforms from Affymetrix, Illumina and Agilent. Information on all three can be found at http://qbrc.swmed.edu/software/probemapper/.

Identification of a Predominant Co-Regulation among Kinetochore Genes, Prospective Regulatory Elements, and Association with Genomic Instability

The NCI-60 cell line panel is the most extensively characterized set of cells in existence, and has been used extensively as a screening tool for drug discovery. Previously, the potential of this panel has not been applied to the fundamental cellular processes of chromosome segregation. In the current study, we used data from multiple microarray platforms accumulated for the NCI-60 to characterize an expression pattern of genes involved in kinetochore assembly. This analysis revealed that 17 genes encoding the constitutive centromere associated network of the kinetochore core (the CCAN complex) plus four additional genes with established importance in kinetochore maintenance (CENPE, CENPF, INCENP, and MIS12) exhibit similar patterns of expression in the NCI-60, suggesting a mechanism for co-regulated transcription of these genes which is maintained despite the multiple genetic and epigenetic rearrangements accumulated in these cells (such as variations in DNA copy number and karyotypic complexity). A complex group of potential regulatory influences are identified for these genes, including the transcription factors CREB1, E2F1, FOXE1, and FOXM1, DNA copy number variation, and microRNAs has-miR-200a, 23a, 23b, 30a, 30c, 27b, 374b, 365. Thus, our results provide a template for experimental studies on the regulation of genes encoding kinetochore proteins, the process that, when aberrant, leads to the aneuploidy that is a hallmark of many cancers. We propose that the comparison of expression profiles in the NCI-60 cell line panel could be a tool for the identification of other gene groups whose products are involved in the assembly of organelle protein complexes.

Abstract 3074: Identification of a predominant pattern of co-regulation among kinetochore genes; prospective regulatory elements, and association with genomic instability

Abstract The NCI-60 cell line panel is the most extensively characterized set of cells in existence. It provides multiple forms of data on different cancer types, and has been used extensively as a screening tool for drug discovery. Previously, the potential of this panel has not been applied to the fundamental cellular processes of chromosome segregation. In the current study, we used data from multiple microarray platforms accumulated for the NCI-60 to characterize an expression pattern of genes involved in kinetochore assembly. This analysis revealed that 17 genes encoding the constitutive centromere associated network of the kinetochore core (the CCAN complex) plus four additional genes with established importance in kinetochore maintenance (CENPE, CENPF, INCENP, and MIS12) exhibit similar patterns of expression in the NCI-60, suggesting a mechanism for co-regulated transcription of these genes which is maintained despite the multiple genetic and epigenetic rearrangements accumulated in these cells (such as variations in DNA copy number and karyotypic complexity). Several potential regulatory influences are identified for these genes, including transcription factors and microRNAs. Thus, our results provide a template for experimental studies on regulation of genes encoding kinetochore proteins, the process that, when aberrant, leads to the aneuploidy that is a hallmark of many cancers. We propose that the comparison of expression profiles in the NCI-60 cell line panel could be a tool for the identification of other genes groups whose products are involved in the assembly of organelle protein complexes. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 3074. doi:10.1158/1538-7445.AM2011-3074

FISH Oracle: a web server for flexible visualization of DNA copy number data in a genomic context

BackgroundThe rapidly growing amount of array CGH data requires improved visualization software supporting the process of identifying candidate cancer genes. Optimally, such software should work across multiple microarray platforms, should be able to cope with data from different sources and should be easy to operate.ResultsWe have developed a web-based software FISH Oracle to visualize data from multiple array CGH experiments in a genomic context. Its fast visualization engine and advanced web and database technology supports highly interactive use. FISH Oracle comes with a convenient data import mechanism, powerful search options for genomic elements (e.g. gene names or karyobands), quick navigation and zooming into interesting regions, and mechanisms to export the visualization into different high quality formats. These features make the software especially suitable for the needs of life scientists.ConclusionsFISH Oracle offers a fast and easy to use visualization tool for array CGH and SNP array data. It allows for the identification of genomic regions representing minimal common changes based on data from one or more experiments. FISH Oracle will be instrumental to identify candidate onco and tumor suppressor genes based on the frequency and genomic position of DNA copy number changes. The FISH Oracle application and an installed demo web server are available at http://www.zbh.uni-hamburg.de/fishoracle.

Bimodal gene expression patterns in breast cancer

We identified a set of genes with an unexpected bimodal distribution among breast cancer patients in multiple studies. The property of bimodality seems to be common, as these genes were found on multiple microarray platforms and in studies with different end-points and patient cohorts. Bimodal genes tend to cluster into small groups of four to six genes with synchronised expression within the group (but not between the groups), which makes them good candidates for robust conditional descriptors. The groups tend to form concise network modules underlying their function in cancerogenesis of breast neoplasms.

Wnt Signaling Prognosticates Outcome in Breast Cancer Cohorts.

Abstract Background: Wnt signaling is highly conserved in Metazoan species. Wnt activation and inactivation have been well studied for their roles in development and tumorigenesis, and has emerged as one the most important core pathways in cancer biology. Wnt activation has long been associated with breast cancer, but has only recently been shown to be specifically upregulated in triple negative (TN) breast cancers.Material and Methods: FFPE specimens were obtained from St. Mary's Hospital, Montreal, QC (Quebec cohort) and Grady Hospital, Atlanta, GA (Georgia cohort). RNA was extracted from FFPE sections or cores using the RNA High Pure Kit (Roche) and quality analyzed as described previously (Abramovitz et al. Biotechniques, 2008). Quebec and Georgia cohort mRNAs were quantified using DASL assays, including a 502-gene panel of cancer related genes and a custom 512-gene panel targeting breast cancer pathologies. After quality control the Quebec cohort composed 97 patients and the Georgia cohort 142. Differential mRNA regulation was assessed by Significance Analysis of Microarrays. Meta-analysis included a cohort from Memorial Sloan-Kettering Cancer Center (MSKCC; GEO Series GSE2603), two cohorts from the UNC Lineberger Cancer Center (UNCCC; GSE6128 and GSE10886) and a study from the Netherlands Cancer Institute (NKI). Survival analysis utilized Kaplan-Meier curves and p-values derived from log-rank tests.Results: In the annotation of differentially expressed genes between TN tumors and other subtypes, a significant number of Wnt transcriptional targets and transducers were overexpressed, whereas Wnt attenuators were downregulated in the TN tumors. Meta-analysis in MSKCC and UNCCC cohorts yielded 9 Wnt-related mRNAs differentially regulated between TN (basal-like in the UNCCC case) and other subtypes. To determine if this 9-gene Wnt signature was a driver of breast cancer pathology, we developed a bioinformatic discriminator to bifurcate patients into high (Wnt+) and low (Wnt-) Wnt-expressing categories and applied this to MSKCC, UNCCC, and NKI published data sets composing 625 tumor profiles. Patients classified as Wnt+ exhibited worse outcome in bone metastasis free survival (BMFS), lung metastasis free survival (LMFS), recurrence free survival (RFS) and overall survival (OS). LMFS (p ≤ 0.001), RFS (p ≤ 0.001), and OS (p ≤ 0.01) were statistically significant, whereas BMFS was not. Furthermore, reapplying this bioinformatic discriminator on ER+ patients yielded similar results with Wnt+ patients experiencing worse metastasis-free survival (MFS) and OS (p ≤ 0.025).Discussion: Characterization of mRNAs in breast tumors from 864 patients across multiple microarray platforms indicated Wnt signaling as upregulated in TN breast cancers. A 9-gene Wnt signature was found to be indicative of outcome with respect to LMFS, RFS, and OS. A separate analysis of luminal patients will be presented which indicates that Wnt signaling, as a key mediator of breast cancer pathogenesis, segregates ER+ patients into good and poor prognoses. Citation Information: Cancer Res 2009;69(24 Suppl):Abstract nr 109.

Mitochondrial Gene Profiling: Translational Perspectives

The last decade has witnessed the development of multiple microarray platforms designed to study, in a comprehensive fashion, the expression and sequence of both mitochondrial and nuclear genes that encode mitochondrial proteins. Mitochondrial dysfunction has been implicated in a number of severe medical conditions including cancer, metabolic diseases (i.e., cardiovascular, diabetes and obesity) and neurodegenerative disorders and it is responsible for the adverse effects of numerous drugs. Profiling of the genetic and genomic status of mitochondria with focused microarrays offers the promise of rapidly and robustly identifying novel biomarkers for early disease diagnoses and prognoses, predicting of drug safety, liability, and selecting and stratifying of patients in clinical trials.

Basic Concepts of Microarrays and Potential Applications in Clinical Microbiology

The introduction of in vitro nucleic acid amplification techniques, led by real-time PCR, into the clinical microbiology laboratory has transformed the laboratory detection of viruses and select bacterial pathogens. However, the progression of the molecular diagnostic revolution currently relies on the ability to efficiently and accurately offer multiplex detection and characterization for a variety of infectious disease pathogens. Microarray analysis has the capability to offer robust multiplex detection but has just started to enter the diagnostic microbiology laboratory. Multiple microarray platforms exist, including printed double-stranded DNA and oligonucleotide arrays, in situ-synthesized arrays, high-density bead arrays, electronic microarrays, and suspension bead arrays. One aim of this paper is to review microarray technology, highlighting technical differences between them and each platform's advantages and disadvantages. Although the use of microarrays to generate gene expression data has become routine, applications pertinent to clinical microbiology continue to rapidly expand. This review highlights uses of microarray technology that impact diagnostic microbiology, including the detection and identification of pathogens, determination of antimicrobial resistance, epidemiological strain typing, and analysis of microbial infections using host genomic expression and polymorphism profiles.

Assessing and selecting gene expression signals based upon the quality of the measured dynamics

BackgroundOne of the challenges with modeling the temporal progression of biological signals is dealing with the effect of noise and the limited number of replicates at each time point. Given the rising interest in utilizing predictive mathematical models to describe the biological response of an organism or analysis such as clustering and gene ontology enrichment, it is important to determine whether the dynamic progression of the data has been accurately captured despite the limited number of replicates, such that one can have confidence that the results of the analysis are capturing important salient dynamic features.ResultsBy pre-selecting genes based upon quality before the identification of differential expression via algorithm such as EDGE, it was found that the percentage of statistically enriched ontologies (p < .05) was improved. Furthermore, it was found that a majority of the genes found via the proposed technique were also selected via an EDGE selection though the reverse was not necessarily true. It was also found that improvements offered by the proposed algorithm are anti-correlated with improvements in the various microarray platforms and the number of replicates. This is illustrated by the fact that newer arrays and experiments with more replicates show less improvement when the filtering for quality is first run before the selection of differentially expressed genes. This suggests that the increase in the number of replicates as well as improvements in array technologies are increase the confidence one has in the dynamics obtained from the experiment.ConclusionWe have developed an algorithm that quantifies the quality of temporal biological signal rather than whether the signal illustrates a significant change over the experimental time course. Because the use of these temporal signals, whether it is in mathematical modeling or clustering, focuses upon the entire time series, it is necessary to develop a method to quantify and select for signals which conform to this ideal. By doing this, we have demonstrated a marked and consistent improvement in the results of a clustering exercise over multiple experiments, microarray platforms, and experimental designs.

Refinement of Light-Responsive Transcript Lists Using Rice Oligonucleotide Arrays: Evaluation of Gene-Redundancy

Studies of gene function are often hampered by gene-redundancy, especially in organisms with large genomes such as rice (Oryza sativa). We present an approach for using transcriptomics data to focus functional studies and address redundancy. To this end, we have constructed and validated an inexpensive and publicly available rice oligonucleotide near-whole genome array, called the rice NSF45K array. We generated expression profiles for light- vs. dark-grown rice leaf tissue and validated the biological significance of the data by analyzing sources of variation and confirming expression trends with reverse transcription polymerase chain reaction. We examined trends in the data by evaluating enrichment of gene ontology terms at multiple false discovery rate thresholds. To compare data generated with the NSF45K array with published results, we developed publicly available, web-based tools (www.ricearray.org). The Oligo and EST Anatomy Viewer enables visualization of EST-based expression profiling data for all genes on the array. The Rice Multi-platform Microarray Search Tool facilitates comparison of gene expression profiles across multiple rice microarray platforms. Finally, we incorporated gene expression and biochemical pathway data to reduce the number of candidate gene products putatively participating in the eight steps of the photorespiration pathway from 52 to 10, based on expression levels of putatively functionally redundant genes. We confirmed the efficacy of this method to cope with redundancy by correctly predicting participation in photorespiration of a gene with five paralogs. Applying these methods will accelerate rice functional genomics.