Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer

Pro-MAP: a robust pipeline for the pre-processing of single channel protein microarray data

BACKGROUND

The central role of proteins in diseases has made them increasingly attractive as therapeutic targets and indicators of cellular processes. Protein microarrays are emerging as an important means of characterising protein activity. Their accurate downstream analysis to produce biologically significant conclusions is largely dependent on proper pre-processing of extracted signal intensities. However, existing computational tools are not specifically tailored to the nature of these data and lack unanimity.

RESULTS

Here, we present the single-channel Protein Microarray Analysis Pipeline, a tailored computational tool for analysis of single-channel protein microarrays enabling biomarker identification, implemented in R, and as an interactive web application. We compared four existing background correction and normalization methods as well as three array filtering techniques, applied to four real datasets with two microarray designs, extracted using two software programs. The normexp, cyclic loess, and array weighting methods were most effective for background correction, normalization, and filtering respectively.

CONCLUSIONS

Thus, here we provided a versatile and effective pre-processing and differential analysis workflow for single-channel protein microarray data in form of an R script and web application ( https://metaomics.uct.ac.za/shinyapps/Pro-MAP/ .) for those not well versed in the R programming language.

Development of high-resolution multiple-SNP arrays for genetic analyses and molecular breeding through genotyping by target sequencing and liquid chip

Genotyping platforms, as critical supports for genomics, genetics, and molecular breeding, have been well implemented at national institutions/universities in developed countries and multinational seed companies that possess high-throughput, automatic, large-scale, and shared facilities. In this study, we integrated an improved genotyping by target sequencing (GBTS) system with capture-in-solution (liquid chip) technology to develop a multiple single-nucleotide polymorphism (mSNP) approach in which mSNPs can be captured from a single amplicon. From one 40K maize mSNP panel, we developed three types of markers (40K mSNPs, 251K SNPs, and 690K haplotypes), and generated multiple panels with various marker densities (1K-40K mSNPs) by sequencing at different depths. Comparative genetic diversity analysis was performed with genic versus intergenic markers and di-allelic SNPs versus non-typical SNPs. Compared with the one-amplicon-one-SNP system, mSNPs and within-mSNP haplotypes are more powerful for genetic diversity detection, linkage disequilibrium decay analysis, and genome-wide association studies. The technologies, protocols, and application scenarios developed for maize in this study will serve as a model for the development of mSNP arrays and highly efficient GBTS systems in animals, plants, and microorganisms.

A perceptually optimised bivariate visualisation scheme for high-dimensional fold-change data

Visualising data as diagrams using visual attributes such as colour, shape, size, and orientation is challenging. In particular, large data sets demand graphical display as an essential step in the analysis. In order to achieve comprehension often different attributes need to be displayed simultaneously. In this work a comprehensible bivariate, perceptually optimised visualisation scheme for high-dimensional data is proposed and evaluated. It can be used to show fold changes together with confidence values within a single diagram. The visualisation scheme consists of two parts: a uniform, symmetric, two-sided colour scale and a patch grid representation. Evaluation of uniformity and symmetry of the two-sided colour scale was performed in comparison to a standard RGB scale by twenty-five observers. Furthermore, the readability of the generated map was validated and compared to a bivariate heat map scheme.

Genetic constraints on microevolutionary divergence of sex-biased gene expression

The evolution of sex-specific phenotypes is an important dimension of diversification and local adaptation. The sex-dependent regulation of gene expression is considered a key genomic mechanism facilitating sex-dependent adaptation. In many species, genes with male-biased expression evolve faster in DNA sequence and expression level than genes with female-biased or sexually monomorphic expression. While positive selection may be responsible for rapid DNA sequence evolution, why expression of male-biased genes also evolves rapidly remains unclear. Beyond sex differences in selection, some aspects of the genetic architecture of gene expression could contribute to the rapid evolution of male-biased gene expression. First, male-biased genes might simply have greater standing genetic variance than female-biased genes. Second, male-biased genes could be less constrained by pleiotropy, either within or between sexes. Here, we evaluate these alternative explanations on an intraspecific scale using a series of quantitative genetic experiments conducted on natural variation in male and female gene expression in the fly Drosophila serrata Male-biased genes had significantly higher genetic variance than female-biased genes and were generally more narrowly expressed across tissues, suggesting lower within-individual pleiotropy. However, consistent with stronger constraints due to between-sex pleiotropy, their between-sex genetic correlations, r_MF, were higher than for female-biased genes and more strongly negatively associated with sex bias. Using an extensive clinal dataset, we tested whether sex differences in gene expression divergence among populations have been shaped by pleiotropy. Here too, male-biased gene divergence was more strongly associated with between-sex pleiotropy than was female-biased gene divergence. Systematic differences in genetic variance and pleiotropy may be important factors influencing sex-specific adaptation arising through changes in gene expression.This article is part of the theme issue 'Linking local adaptation with the evolution of sex differences'.

Sex-biased transcriptome divergence along a latitudinal gradient

Sex-dependent gene expression is likely an important genomic mechanism that allows sex-specific adaptation to environmental changes. Among Drosophila species, sex-biased genes display remarkably consistent evolutionary patterns; male-biased genes evolve faster than unbiased genes in both coding sequence and expression level, suggesting sex differences in selection through time. However, comparatively little is known of the evolutionary process shaping sex-biased expression within species. Latitudinal clines offer an opportunity to examine how changes in key ecological parameters also influence sex-specific selection and the evolution of sex-biased gene expression. We assayed male and female gene expression in Drosophila serrata along a latitudinal gradient in eastern Australia spanning most of its endemic distribution. Analysis of 11 631 genes across eight populations revealed strong sex differences in the frequency, mode and strength of divergence. Divergence was far stronger in males than females and while latitudinal clines were evident in both sexes, male divergence was often population specific, suggesting responses to localized selection pressures that do not covary predictably with latitude. While divergence was enriched for male-biased genes, there was no overrepresentation of X-linked genes in males. By contrast, X-linked divergence was elevated in females, especially for female-biased genes. Many genes that diverged in D. serrata have homologs also showing latitudinal divergence in Drosophila simulans and Drosophila melanogaster on other continents, likely indicating parallel adaptation in these distantly related species. Our results suggest that sex differences in selection play an important role in shaping the evolution of gene expression over macro- and micro-ecological spatial scales.

Transcriptional Reprogramming of the Mycoparasitic Fungus Ampelomyces quisqualis During the Powdery Mildew Host-Induced Germination

Ampelomyces quisqualis is a mycoparasite of a diverse range of phytopathogenic fungi associated with the powdery mildew disease. Among them are several Erysiphaceae species with great economic impact on high-value crops such as grape. Due to its ability to parasitize and prevent the spread of powdery mildews, A. quisqualis has received considerable attention for its biocontrol potential. However, and in sharp contrast to the extensively studied biocontrol species belonging to the genus Trichoderma, little is known about the biology of A. quisqualis at the molecular and genetic levels. We present the first genome-wide transcription profiling in A. quisqualis during host-induced germination. A total of 1,536 putative genes showed significant changes in transcription during the germination of A. quisqualis. This finding denotes an extensive transcriptional reprogramming of A. quisqualis induced by the presence of the host. Several upregulated genes were predicted to encode for putative mycoparasitism-related proteins such as secreted proteases, virulence factors, and proteins related to toxin biosynthesis. Our data provide the most comprehensive sequence resource currently available for A. quisqualis in addition to offering valuable insights into the biology of A. quisqualis and its mycoparasitic lifestyle. Eventually, this may improve the biocontrol capacity of this mycoparasite.

Bifidobacterium bifidum PRL2010 modulates the host innate immune response

Here, we describe data obtained from transcriptome profiling of human cell lines and intestinal cells of a murine model upon exposure and colonization, respectively, with Bifidobacterium bifidum PRL2010. Significant changes were detected in the transcription of genes that are known to be involved in innate immunity. Furthermore, results from enzyme-linked immunosorbent assays (ELISAs) showed that exposure to B. bifidum PRL2010 causes enhanced production of interleukin 6 (IL-6) and IL-8 cytokines, presumably through NF-κB activation. The obtained global transcription profiles strongly suggest that Bifidobacterium bifidum PRL2010 modulates the innate immune response of the host.

Resolving Sample Traces in Complex Mixtures with Microarray Analyses

This chapter reviews the microarray technology and deal with the majority of aspects regarding microarrays. It focuses on today’s knowledge of separation techniques and methodologies of complex signal, i.e. samples. Overall, the chapter reviews the current knowledge on the topic of microarrays and presents the analyses and techniques used, which facilitate such approaches. It starts with the theoretical framework on microarray technology; second, the chapter gives a brief review on statistical methods used for microarray analyses, and finally, it contains a detailed review of the methods used for discriminating traces of nucleic acids within a complex mixture of samples.

Bifidobacterium asteroides PRL2011 genome analysis reveals clues for colonization of the insect gut

Bifidobacteria are known as anaerobic/microaerophilic and fermentative microorganisms, which commonly inhabit the gastrointestinal tract of various animals and insects. Analysis of the 2,167,301 bp genome of Bifidobacterium asteroides PRL2011, a strain isolated from the hindgut of Apis mellifera var. ligustica, commonly known as the honey bee, revealed its predicted capability for respiratory metabolism. Conservation of the latter gene clusters in various B. asteroides strains enforces the notion that respiration is a common metabolic feature of this ancient bifidobacterial species, which has been lost in currently known mammal-derived Bifidobacterium species. In fact, phylogenomic based analyses suggested an ancient origin of B. asteroides and indicates it as an ancestor of the genus Bifidobacterium. Furthermore, the B. asteroides PRL2011 genome encodes various enzymes for coping with toxic products that arise as a result of oxygen-mediated respiration.

Deep sequencing and microarray hybridization identify conserved and species-specific microRNAs during somatic embryogenesis in hybrid yellow poplar

BACKGROUND

To date, several studies have indicated a major role for microRNAs (miRNAs) in regulating plant development, but miRNA-mediated regulation of the developing somatic embryo is poorly understood, especially during early stages of somatic embryogenesis in hardwood plants. In this study, Solexa sequencing and miRNA microfluidic chips were used to discover conserved and species-specific miRNAs during somatic embryogenesis of hybrid yellow poplar (Liriodendron tulipifera×L. chinense).

METHODOLOGY/PRINCIPAL FINDINGS

A total of 17,214,153 reads representing 7,421,623 distinct sequences were obtained from a short RNA library generated from small RNAs extracted from all stages of somatic embryos. Through a combination of deep sequencing and bioinformatic analyses, we discovered 83 sequences with perfect matches to known miRNAs from 33 conserved miRNA families and 273 species-specific candidate miRNAs. MicroRNA microarray results demonstrated that many conserved and species-specific miRNAs were expressed in hybrid yellow poplar embryos. In addition, the microarray also detected another 149 potential miRNAs, belonging to 29 conserved families, which were not discovered by deep sequencing analysis. The biological processes and molecular functions of the targets of these miRNAs were predicted by carrying out BLAST search against Arabidopsis thaliana GenBank sequences and then analyzing the results with Gene Ontology.

CONCLUSIONS

Solexa sequencing and microarray hybridization were used to discover 232 candidate conserved miRNAs from 61 miRNA families and 273 candidate species-specific miRNAs in hybrid yellow poplar. In these predicted miRNAs, 64 conserved miRNAs and 177 species-specific miRNAs were detected by both sequencing and microarray hybridization. Our results suggest that miRNAs have wide-ranging characteristics and important roles during all stages of somatic embryogenesis in this economically important species.

Array-based transcriptional analysis of Clostridium sporogenes UC9000 during germination, cell outgrowth and vegetative life

The members of the genus Clostridium, including the spore-forming anaerobic bacteria, have a complex and strictly regulated life cycle, but very little is known about the genetic pathways involved in the different stages of their life cycle. Clostridium sporogenes, a Gram-positive bacterium usually involved in food spoilage and frequently isolated from late blowing cheese, is genetically indistinguishable from the proteolytic Clostridium botulinum. As the non-neurotoxic counterpart, it is often used as an exemplar for the toxic subtypes. In this work, we performed a microscopic study combined with a custom array-based analysis of the C. sporogenes cycle, from dormant spores to the early stationary phase. We identified a total of 211 transcripts in spores, validating the hypothesis that mRNAs are abundant in spores and the pattern of mRNA expression is strikingly different from that present in growing cells. The spore transcripts included genes responsible for different life-sustaining functions, suggesting there was transcript entrapment or basic poly-functional gene activation for future steps. In addition, 3 h after the beginning of the germination process, 20% of the total up-regulated genes were temporally expressed in germinating spores. The vegetative condition appeared to be more active in terms of gene transcription and protein synthesis than the spore, and genes coding for germination and sporulation factors seemed to be expressed at this point. These results suggest that spores are not silent entities, and a broader knowledge of the genetic pathways involved in the Clostridium life cycle could provide a better understanding of pathogenic clostridia types.

Global genome transcription profiling of Bifidobacterium bifidum PRL2010 under in vitro conditions and identification of reference genes for quantitative real-time PCR

Bifidobacteria have attracted significant scientific attention due to their perceived role as health-promoting microorganisms, although the genetics of the bacterial group is still underexplored. In this study, we investigated the transcriptome of Bifidobacterium bifidum PRL2010 during in vitro growth by microarray technology. When B. bifidum PRL2010 was grown in liquid broth, 425 of the 1,644 PRL2010 genes represented on the array were expressed in at least one of the three investigated growth phases, i.e., the lag, exponential, and stationary phases. These transcriptional analyses identified a core in vitro transcriptome encompassing 150 genes that are expressed in all phases. A proportion of these genes were further investigated as potential reference genes by quantitative real-time reverse transcription-PCR (qRT-PCR) assays. Their expression stability was evaluated under different growth conditions, which included cultivation on different carbon sources, exposure to environmental stresses (thermal, acidic, and osmotic), and growth phases. Our analyses validated six reference genes suitable for normalizing mRNA expression levels in qRT-PCR experiments applied to bifidobacteria.

Temporal profiling of the coding and noncoding murine cytomegalovirus transcriptomes

The global transcriptional program of murine cytomegalovirus (MCMV), involving coding, noncoding, and antisense transcription, remains unknown. Here we report an oligonucleotide custom microarray platform capable of measuring both coding and noncoding transcription on a genome-wide scale. By profiling MCMV wild-type and immediate-early mutant strains in fibroblasts, we found rapid activation of the transcriptome by 6.5 h postinfection, with absolute dependency on ie3, but not ie1 or ie2, for genomic programming of viral gene expression. Evidence is also presented to show, for the first time, genome-wide noncoding and bidirectional transcription at late stages of MCMV infection.

Assessment of comparative genomic hybridization experiment by an in situ synthesized CombiMatrix microarray with Yersinia pestis vaccine strain EV76 DNA

OBJECTIVE

The quality of microarray data influences the accuracy of comparative genomic analyses to a large extent. To ensure that the results obtained by using an in situ synthesized microarray are accurate, data quality is to be assessed by evaluating the melting temperature (Tm) of probes, probability of false synthesis rates, and fragmentation of labeled targets.

METHODS

DNA from the Yersinia pestis vaccine strain EV76 was used for microarray analyses. Microarray results were confirmed by PCR. Statistical and bioinformatics methods were employed to perform microarray data analyses and evaluation.

RESULTS

Correlation coefficients of the three datasets were above 0.95 after two-time stripping and hybridization with a labeled DNA with the size of fragmentation being 200 bp - 2 kb, which showed that the hybridization results were highly reproducible. Correlation coefficients were lower with the values ranging from 0.87 to 0.92 between the datasets generated from hybridization with different sizes of the labeled DNA fragment. For the relationship between Tm and signal intensity, there was a different distribution of Tm in the lowest 300 or 3,000 probes with a range of 70 °C-72 °C and the highest 300 or 3,000 probes with a range of 72 °C-74 °C.

CONCLUSION

The results of this study suggest that the initial microarray design may affect the accuracy of final analyses and that the probe Tm and the size of the labeled fragment may be the two factors of the greatest importance.

ANAIS: analysis of NimbleGen arrays interface

ANAIS is a user-friendly web-based tool for the processing of NimbleGen expression data. The interface reads single-channel microarray files generated by NimbleGen platforms and produces easily interpretable graphical and numerical results. It provides biologists six turnkey analysis modules-normalization, probe to gene, quality controls, differential expression, detection, queries and clustering-to explore quickly, freely and without the need for computer programming, NimbleGen transcriptome data.

AVAILABILITY

http://anais.versailles.inra.fr.

Characterization of the serpin-encoding gene of Bifidobacterium breve 210B

Members of the serpin (serine protease inhibitor) superfamily have been identified in higher multicellular eukaryotes, as well as in bacteria, although examination of available genome sequences has indicated that homologs of the bacterial serpin-encoding gene (ser) are not widely distributed. In members of the genus Bifidobacterium this gene appears to be present in at least 5, and perhaps up to 9, of the 30 species tested. Moreover, phylogenetic analysis using available bacterial and eukaryotic serpin sequences revealed that bifidobacteria produce serpins that form a separate clade. We characterized the ser(210B) locus of Bifidobacterium breve 210B, which encompasses a number of genes whose deduced protein products display significant similarity to proteins encoded by corresponding loci found in several other bifidobacteria. Northern hybridization, primer extension, microarray, reverse transcription-PCR (RT-PCR), and quantitative real-time PCR (qRT-PCR) analyses revealed that a 3.5-kb polycistronic mRNA encompassing the ser(210B) operon with a single transcriptional start site is strongly induced following treatment of B. breve 210B cultures with some proteases. Interestingly, transcription of other bifidobacterial ser homologs appears to be triggered by different proteases.

Evaluating the performance of oligonucleotide microarrays for bacterial strains with increasing genetic divergence from the reference strain

DNA oligonucleotide microarrays (oligoarrays) are being developed continuously; however, several issues regarding the applicability of these arrays for whole-genome DNA-DNA strain comparisons (genomotyping) have not been investigated. For example, the extent of false negatives (i.e., no hybridization signal is observed when the amino acid sequence is conserved but the nucleotide sequence has diverged to a level that does not allow hybridization) remains speculative. To provide quantitative answers to such questions, we performed competitive DNA-DNA oligoarray (60-mer) hybridizations with several fully sequenced (tester) strains and a reference strain (whose genome was used to design the oligoarray probes) of the genus Burkholderia and compared the experimental results obtained to the results predicted based on bioinformatic modeling of the probe-target pair using the available sequences. Our comparisons revealed that the fraction of the total probes that provided experimental results consistent with the predicted results decreased substantially with increasing divergence of the tester strain from the reference strain. The fractions were 90.8%, 84.3%, and 77.4% for tester strains showing 96% 89%, and 80% genome-aggregate average nucleotide identity (ANI) to the reference strain, respectively. New approaches to determine gene presence or absence based on the hybridization signal, which outperformed previous approaches (e.g., 92.9% accuracy versus 86.0% accuracy) and to normalize across different array experiments are also described. Collectively, our results suggest that the performance of oligoarrays is acceptable for tester strains showing >90% ANI to the reference strain and provide useful guidelines for using oligoarray applications in environmental gene detection and gene expression studies with strains other than the reference strain.

Combining next-generation pyrosequencing with microarray for large scale expression analysis in non-model species

Background

The next generation sequencing technologies provide new options to characterize the transcriptome and to develop affordable tools for functional genomics. We describe here an innovative approach for this purpose and demonstrate its potential also for non-model species.

Results

The method we developed is based on 454 sequencing of 3' cDNA fragments from a normalized library constructed from pooled RNAs to generate, through de novo reads assembly, a large catalog of unique transcripts in organisms for which a comprehensive collection of transcripts or the complete genome sequence, is not available. This "virtual transcriptome" provides extensive coverage depth, and can be used for the setting up of a comprehensive microarray based expression analysis. We evaluated the potential of this approach by monitoring gene expression during berry maturation in Vitis vinifera as if no other sequence information was available for this species. The microarray designed on the berries' transcriptome derived from half of a 454 run detected the expression of 19,609 genes, and proved to be more informative than one of the most comprehensive grape microarrays available to date, the GrapeArray 1.2 developed by the Italian-French Public Consortium for Grapevine Genome Characterization, which could detect the expression of 15,556 genes in the same samples.

Conclusion

This approach provides a powerful method to rapidly build up an extensive catalog of unique transcripts that can be successfully used to develop a microarray for large scale analysis of gene expression in any species, without the need for prior sequence knowledge.

Evaluation of cDNA microarray data by multiple clones mapping to the same transcript

Although novel technologies are rapidly emerging, the cDNA microarray data accumulated is still and will be an important source for bioinformatics and biological studies. Thus, the reliability and applicability of the cDNA microarray data warrants further evaluation. In cDNA microarrays, multiple clones are measured for a transcript, which can be exploited to evaluate the consistency of microarray data. We show that even for pairs of RCs, the average Pearson correlation coefficient of their measurements is not high. However, this low consistency could largely be explained by random noise signals for a fraction of unexpressed genes and/or low signal-to-noise ratios for low abundance transcripts. Encouragingly, a large fraction of inconsistent data will be filtered out in the procedure of selecting differentially expressed genes (DEGs). Therefore, although cDNA microarray data are of low consistency, applications based on DEGs selections could still reach correct biological results, especially at the functional modules level.

Signal deconvolution based expression-detection and background adjustment for microarray data

Background adjustment is an essential stage in analyzing DNA microarrays. Discriminating expressed genes from unexpressed ones (expression detection), and estimating the expression levels of weakly expressed genes, critically depend on accurate treatment of the background intensity. Current methods for background adjustment either do not deal with nonspecific hybridization or strongly depend on the reliability of control probes. Existing model-based methods have limited accuracy. A new platform-independent background adjustment algorithm is presented. The algorithm relies on the deconvoluted experimental signal distribution for evaluating the expression probability and adjusting the background of each probe. Considering expression detection, it is shown, for two-channels cDNA arrays and for the Affymetrix GeneChip platform, that the algorithm performs at least as good or better than control-probes-based algorithms. For the Affymetrix GeneChip arrays, it is further shown that the algorithm outperforms the robust multiarray (RMA) expression measure in estimating genomewide expression levels.

A comparison of background correction methods for two-colour microarrays

MOTIVATION

Microarray data must be background corrected to remove the effects of non-specific binding or spatial heterogeneity across the array, but this practice typically causes other problems such as negative corrected intensities and high variability of low intensity log-ratios. Different estimators of background, and various model-based processing methods, are compared in this study in search of the best option for differential expression analyses of small microarray experiments.

RESULTS

Using data where some independent truth in gene expression is known, eight different background correction alternatives are compared, in terms of precision and bias of the resulting gene expression measures, and in terms of their ability to detect differentially expressed genes as judged by two popular algorithms, SAM and limma eBayes. A new background processing method (normexp) is introduced which is based on a convolution model. The model-based correction methods are shown to be markedly superior to the usual practice of subtracting local background estimates. Methods which stabilize the variances of the log-ratios along the intensity range perform the best. The normexp+offset method is found to give the lowest false discovery rate overall, followed by morph and vsn. Like vsn, normexp is applicable to most types of two-colour microarray data.

AVAILABILITY

The background correction methods compared in this article are available in the R package limma (Smyth, 2005) from http://www.bioconductor.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available from http://bioinf.wehi.edu.au/resources/webReferences.html.

Identification of human pathogens isolated from blood using microarray hybridisation and signal pattern recognition

Background

Pathogen identification in clinical routine is based on the cultivation of microbes with subsequent morphological and physiological characterisation lasting at least 24 hours. However, early and accurate identification is a crucial requisite for fast and optimally targeted antimicrobial treatment. Molecular biology based techniques allow fast identification, however discrimination of very closely related species remains still difficult.

Results

A molecular approach is presented for the rapid identification of pathogens combining PCR amplification with microarray detection. The DNA chip comprises oligonucleotide capture probes for 25 different pathogens including Gram positive cocci, the most frequently encountered genera of Enterobacteriaceae, non-fermenter and clinical relevant Candida species. The observed detection limits varied from 10 cells (e.g. E. coli) to 10⁵ cells (S. aureus) per mL artificially spiked blood. Thus the current low sensitivity for some species still represents a barrier for clinical application. Successful discrimination of closely related species was achieved by a signal pattern recognition approach based on the k-nearest-neighbour method. A prototype software providing this statistical evaluation was developed, allowing correct identification in 100 % of the cases at the genus and in 96.7 % at the species level (n = 241).

Conclusion

The newly developed molecular assay can be carried out within 6 hours in a research laboratory from pathogen isolation to species identification. From our results we conclude that DNA microarrays can be a useful tool for rapid identification of closely related pathogens particularly when the protocols are adapted to the special clinical scenarios.

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.

Quantitative high-throughput measurement of gene expression with sub-zeptomole sensitivity by capillary electrophoresis

Microarray technologies have provided the ability to monitor the expression of whole genomes rapidly. However, concerns persist with regard to quantitation and reproducibility, and the detection limits for individual genes in particular arrays are generally unknown. This article describes a semiautomated PCR-based technology, Q-RAGE, which rapidly provides measurements of mRNA abundance with extremely high sensitivity using fluorescent detection of specific products separated by capillary electrophoresis. A linear relationship between template concentration and fluorescent signal can be demonstrated down to template concentrations in the low aM region, corresponding to approximately 0.04 zmol (24 molecules) per reaction. The technique is shown to be quantitative over five orders of magnitude of template concentration, and average mRNA abundances of approximately 0.01 molecule per cell can be detected. A single predefined set of 320 primers provides 90-95% coverage of all eukaryotic genomes. Analysis of a set of 19 p53-regulated genes in untreated cultures of normal human epithelial cells, derived from three different tissues, revealed a 600-fold range of apparent constitutive expression levels. For most of the genes assayed, good correlations were observed among the expression levels in normal mammary, bronchial, and epidermal epithelial cells.

Evolution of genomic content in the stepwise emergence of Escherichia coli O157:H7

Genome comparisons have demonstrated that dramatic genetic change often underlies the emergence of new bacterial pathogens. Evolutionary analysis of Escherichia coli O157:H7, a pathogen that has emerged as a worldwide public health threat in the past two decades, has posited that this toxin-producing pathogen evolved in a series of steps from O55:H7, a recent ancestor of a nontoxigenic pathogenic clone associated with infantile diarrhea. We used comparative genomic hybridization with 50-mer oligonucleotide microarrays containing probes from both pathogenic and nonpathogenic genomes to infer when genes were acquired and lost. Many ancillary virulence genes identified in the O157 genome were already present in an O55:H7-like progenitor, with 27 of 33 genomic islands of >5 kb and specific for O157:H7 (O islands) that were acquired intact before the split from this immediate ancestor. Most (85%) of variably absent or present genes are part of prophages or phage-like elements. Divergence in gene content among these closely related strains was approximately 140 times greater than divergence at the nucleotide sequence level. A >100-kb region around the O-antigen gene cluster contained highly divergent sequences and also appears to be duplicated in its entirety in one lineage, suggesting that the whole region was cotransferred in the antigenic shift from O55 to O157. The beta-glucuronidase-positive O157 variants, although phylogenetically closest to the Sakai strain, were divergent for multiple adherence factors. These observations suggest that, in addition to gains and losses of phage elements, O157:H7 genomes are rapidly diverging and radiating into new niches as the pathogen disseminates.

Interaction of amifostine and ionizing radiation on transcriptional patterns of apoptotic genes expressed in human microvascular endothelial cells (HMEC)

PURPOSE

Amifostine is a prodrug that requires dephosphorylation by alkaline phosphatase to become activated. This process occurs rapidly within the bloodstream after its i.v. administration to patients undergoing cancer treatment with selected radiation and chemotherapies. Vascular endothelial cells will, therefore, represent a normal cell system that is among the first to experience the radioprotective effects of this agent. Amifostine's active free thiol WR-1065 was investigated to determine its effect on radiation-induced changes in transcriptional patterns and subsequent apoptosis in human microvascular endothelial cells (HMEC) growing in vitro.

METHODS AND MATERIALS

Human microvascular endothelial cells were grown to confluency and then exposed to WR-1065 at a concentration of 4 mM for 30 min, radiation doses that ranged from 0 to 6 Gy, and WR-1065 at a concentration of 4 mM for 30 min before exposure to ionizing radiation. Cell survival was assessed by clonogenic assay, cell cycle phase was analyzed by flow cytometry, apoptosis was also assessed by flow cytometry in which Anexin V staining and sub-G1 fraction analysis were applied, and gene expression was analyzed by the Clontech Atlas Human cDNA array to identify synergistic and antagonistic effects as a function of amifostine and radiation exposure conditions with a focus on apoptotic-related factors.

RESULTS

Exposure of HMEC to 4 mM WR-1065 30 min before irradiation resulted in a protection enhancement factor of 2.0; that is, D(O-IRR) of 1.25 Gy and D(O-IRR+WR) of 2.56 Gy. Expression profiling revealed 29 genes that were synergistically activated by the combined action of WR-1065 and ionizing radiation, and an additional 12 genes were synergistically or additively suppressed. In particular, a subset of apoptosis-related genes that included caspases 2, 4, and 9 and different members of the bcl family, along with apoptosis-related receptors, were identified as being significantly affected by the combined treatment of WR-1065 and radiation exposure. In addition, a number of cell cycle-related genes that express cyclins A, G1, G2, and D3 and DNA damage/check point proteins ATM, DNA-PK and RAD23B were also found to be significantly affected. Functional assays of apoptosis were also performed that demonstrated the ability of WR-1065 to protect against radiation-induced apoptosis.

CONCLUSIONS

WR-1065, the active thiol form of amifostine, is an effective radioprotector of HMEC as determined by use of clonogenic and apoptotic assays for cell survival. Expression profiling successfully defined the transcriptional response of HMEC to both WR-1065 and ionizing radiation exposure, either alone or in combination, and demonstrated both synergistic and antagonistic effects on the expression of different cellular genes, along with corresponding functional responses. The radioprotective effects of amifostine are not limited to its well-characterized physiochemical properties, which include free-radical scavenging, auto-oxidation leading to intracellular hypoxia, and chemical repair by hydrogen atom donation, but include its ability to modulate the complex transcriptional regulation of genes that are involved in apoptosis, cell cycle, and DNA repair.

Reliability analysis of microarray data using fuzzy c-means and normal mixture modeling based classification methods

MOTIVATION

A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. Therefore, the elimination of unreliable signal intensities will enhance reproducibility and reliability of gene expression ratios produced from microarray data. In this study, we applied fuzzy c-means (FCM) and normal mixture modeling (NMM) based classification methods to separate microarray data into reliable and unreliable signal intensity populations.

RESULTS

We compared the results of FCM classification with those of classification based on NMM. Both approaches were validated against reference sets of biological data consisting of only true positives and true negatives. We observed that both methods performed equally well in terms of sensitivity and specificity. Although a comparison of the computation times indicated that the fuzzy approach is computationally more efficient, other considerations support the use of NMM for the reliability analysis of microarray data.

AVAILABILITY

The classification approaches described in this paper and sample microarray data are available as Matlab( TM ) (The MathWorks Inc., Natick, MA) programs (mfiles) and text files, respectively, at http://rc.kfshrc.edu.sa/bssc/staff/MusaAsyali/Downloads.asp. The programs can be run/tested on many different computer platforms where Matlab is available.

CONTACT

asyali@kfshrc.edu.sa.

Assessment of reliability of microarray data and estimation of signal thresholds using mixture modeling

DNA microarray is an important tool for the study of gene activities but the resultant data consisting of thousands of points are error-prone. A serious limitation in microarray analysis is the unreliability of the data generated from low signal intensities. Such data may produce erroneous gene expression ratios and cause unnecessary validation or post-analysis follow-up tasks. In this study, we describe an approach based on normal mixture modeling for determining optimal signal intensity thresholds to identify reliable measurements of the microarray elements and subsequently eliminate false expression ratios. We used univariate and bivariate mixture modeling to segregate the microarray data into two classes, low signal intensity and reliable signal intensity populations, and applied Bayesian decision theory to find the optimal signal thresholds. The bivariate analysis approach was found to be more accurate than the univariate approach; both approaches were superior to a conventional method when validated against a reference set of biological data that consisted of true and false gene expression data. Elimination of unreliable signal intensities in microarray data should contribute to the quality of microarray data including reproducibility and reliability of gene expression ratios.

Determining a detectable threshold of signal intensity in cDNA microarray based on accumulated distribution

In microarray data mining, one of the key problems is how to handle weak signals. Based on a bent piecewise linear accumulated distribution generally found in the microarray data, a new detectable threshold finding method is proposed to filter genes with unreliable information in this paper. More reliable and reproducible data is produced for the subsequent data mining.

Genomewide transcriptional changes associated with genetic alterations and nutritional supplementation affecting tryptophan metabolism in Bacillus subtilis

DNA microarrays comprising approximately 95% of the Bacillus subtilis annotated protein coding ORFs were deployed to generate a series of snapshots of genomewide transcriptional changes that occur when cells are grown under various conditions that are expected to increase or decrease transcription of the trp operon segment of the aromatic supraoperon. Comparisons of global expression patterns were made between cells grown in the presence of indole acrylic acid, a specific inhibitor of tRNA(Trp) charging; cells deficient in expression of the mtrB gene, which encodes the tryptophan-activated negative regulatory protein, TRAP; WT cells grown in the presence or absence of two or three of the aromatic amino acids; and cells harboring a tryptophanyl tRNA synthetase mutation conferring temperature-sensitive tryptophan-dependent growth. Our findings validate expected responses of the tryptophan biosynthetic genes and presumed regulatory interrelationships between genes in the different aromatic amino acid pathways and the histidine biosynthetic pathway. Using a combination of supervised and unsupervised statistical methods we identified approximately 100 genes whose expression profiles were closely correlated with those of the genes in the trp operon. This finding suggests that expression of these genes is influenced directly or indirectly by regulatory events that affect or are a consequence of altered tryptophan metabolism.

Receiver operating characteristic analysis: a general tool for DNA array data filtration and performance estimation

A critical step for DNA array analysis is data filtration, which can reduce thousands of detected signals to limited sets of genes. Commonly accepted rules for such filtration are still absent. We present a rational approach, based on thresholding of intensities with cutoff levels that are estimated by receiver operating characteristic (ROC) analysis. The technique compares test results with known distributions of positive and negative signals. We apply the method to Atlas cDNA arrays, GeneFilters, and Affymetrix GeneChip. ROC analysis demonstrates similarities in the distribution of false and true positive data for these different systems. We illustrate the estimation of an optimal cutoff level for intensity-based filtration, providing the highest ratio of true to false signals. For GeneChip arrays, we derived filtration thresholds consistent with the reported data based on replicate hybridizations. Intensity-based filtration optimized with ROC combined with other types of filtration (for example, based on significances of differences and/or ratios), should improve DNA array analysis. ROC methodology is also demonstrated for comparison of the performance of different types of arrays, imagers, and analysis software.