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A major challenge in DNA microarray analysis is to effectively dissociate actual gene expression values from experimental noise. We report here a detailed noise analysis for oligonuleotide-based microarray experiments involving reverse transcription, generation of labeled cRNA (target) through in vitro transcription, and hybridization of the target to the probe immobilized on the substrate. By designing sets of replicate experiments that bifurcate at different steps of the assay, we are able to separate the noise caused by sample preparation and the hybridization processes. We quantitatively characterize the strength of these different sources of noise and their respective dependence on the gene expression level. We find that the sample preparation noise is small, implying that the amplification process during the sample preparation is relatively accurate. The hybridization noise is found to have very strong dependence on the expression level, with different characteristics for the low and high expression values. The hybridization noise characteristics at the high expression regime are mostly Poisson-like, whereas its characteristics for the small expression levels are more complex, probably due to cross-hybridization. A method to evaluate the significance of gene expression fold changes based on noise characteristics is proposed.

Despite their similar spindle-shaped appearance, periodontal ligament fibroblasts (PDLF) and gingival fibroblasts (GF) appear to display distinct functional activities in the maintenance of tissue integrity and during inflammatory/immune responses. We postulated that different characteristics of PDLF and GF are defined by the differential expression of specific genes. To test this, we investigated the possible variance of gene expression profile between cultured PDLF and GF, using DNA microarray technology. One hundred sixty-three genes were found differentially expressed by at least three-fold between PDLF and GF. Genes encoding transmembrane proteins and cytoskeleton-related proteins tended to be up-regulated in PDLF, whereas genes encoding cell-cycle regulation proteins and metabolism-related proteins tended to be up-regulated in GF. We concluded that PDLF and GF appear to display different gene expression patterns that may reflect intrinsic functional differences of the two cell populations and may well coordinate with their tissue-specific activities.

Although DNA microarray analysis is presented as a revolution in gene expression studies, it is in fact based on the classic technique of Southern DNA hybridisation where a labelled DNA probe is hybridised to single stranded DNA that is bound to a solid support matrix. The truly revolutionary aspect of microarray analysis lies in the fact that, within a given cell population, the expression of tens of thousands of genes, and ultimately the entire genome, can be assayed simultaneously. This capability, when coupled with powerful data analysis software, allows researchers to rapidly compare gene expression between two cell populations. In the cancer field, this enables researchers to compare gene expression between normal and malignant cells and to identify genes that are differentially regulated during cancer development. Microarray data can also be used to categorize tumours on the basis of their molecular profile, which may provide important biological, diagnostic and prognostic information. As little as 5 years ago identifying even a few differentially expressed genes may have taken several years and cost tens of thousands of dollars. Today microarrays can identify ten times the number of candidate genes in just a few months and at a tenth of the cost. Even so, microarray analysis is still in its infancy and the technology is advancing rapidly. There is little doubt that microarrays will revolutionize our ability to quantify the complex changes that occur in gene expression during cancer development. The greatest challenge that lies ahead is how to translate this knowledge into clinically useful diagnostic and therapeutic tools. In this review, we describe the technical aspects of DNA microarray analysis and some of the current and future applications of this technology for analysing gene expression in ovarian cancer.

The work focuses on a new approach for DNA-chip image processing by using the cellular neural network (CNN) paradigm. In particular, the work aims to solve the main issues in automatic classification and spot validation that arise during an automatic DNA microarray analysis procedure. This contribution briefly presents the basic DNA micro-array technique and the state-of-the-art technologies, and introduces the idea of processing DNA chip data via CNNs, reporting suitable examples. The algorithm described has been developed using a high level language, dedicated to CNN universal machine (CNNUM) chip programming, and validated on a typical fluorescence image from a DNA microarray after hybridization.

Biomarker genes of human skin-derived cells were identified by new simple bioinformatic methods and DNA microarray analysis utilizing in vitro cultures of normal neonatal human epidermal keratinocytes, melanocytes, and dermal fibroblasts. A survey of 4405 human cDNAs was performed using DermArray DNA microarrays. Biomarkers were rank ordered by "likelihood ratio" algorithms and stringent selection criteria that have general applicability for analyzing a minimum of three RNA samples. Signature biomarker genes (up-regulated in one cell type) and anti-signature biomarker genes (down-regulated in one cell type) were determined for the three major skin cell types. Many of the signature genes are known biomarkers for these cell types. In addition, 17 signature genes were identified as ESTs, and 22 anti-signature biomarkers were discovered. Quantitative RT-PCR was used to verify nine signature biomarker genes. A total of 158 biomarkers of normal human skin cells were identified, many of which may be valuable in diagnostic applications and as molecular targets for drug discovery and therapeutic intervention.

To identify genes elicited by bisphenol A (BPA) in Sertoli cells, we carried out a microarray analysis of TTE3 cells (a mouse Sertoli cell line) treated with BPA. BPA (100, 200 and 400 microM) induced cell death concentration-dependently, with levels being 25%, 33% and 96%, respectively. Of the 1,081 genes analyzed, 3 genes showed decreased levels of expression while the remaining 10 genes showed increased levels in the cells treated with a subtoxic dose of BPA (200 microM). The expressions of six genes were confirmed by the TaqMan assay. These findings suggest that DNA microarray analysis is a useful tool for investigating the molecular mechanisms of the toxic effects of BPA in testicular cells.

Little is known about patterns of gene expression from cells populating the connective tissues. This study investigated the possible variance of gene expression profile between human osteoblasts (HO) and human fibroblasts (HF) in vitro, using DNA microarray technology. Clustering identification was used to compare expression patterns between HO and HF for biological significance. Our results showed that genes encoding the extracellular matrix or apoptosis-related proteins tended to be expressed in greater abundance in HO, while more proteolysis-related proteins were expressed in higher level in HF. Significant differences in expression were also noted with genes related to signaling pathways. To confirm the array results, three genes (periostin, MFG-E8, MMP-10) were selected and analyzed independently by RT-PCR and northern blot. The results were found consistent with the array data in HO and HF. The present findings suggest that HO and HF differ not only phenotypically but in the expression level of tissue specific genes to assure the turnover and homeostasis of their respective tissues.

Despite efforts to reduce mortality caused by stroke and perinatal asphyxia, these are still the 2nd largest cause of death worldwide in the age groups they affect. Furthermore, survivors of cerebral hypoxia-ischemia often suffer neurological morbidities. A better understanding of pathophysiological mechanisms in focal and global brain ischemia will contribute to the development of tailored therapeutic strategies. Similarly, insight into molecular pathways involved in preconditioning-induced brain protection will provide possibilities for future treatment. Microarray technology is a great tool for investigating large scale gene expression, and has been used in many experimental studies of cerebral ischemia and preconditioning to unravel molecular (patho-) physiology. However, the amount of data across microarray studies can be daunting and hard to interpret which is why we aim to provide a clear overview of available data in experimental rodent models. Findings for both injurious ischemia and preconditioning are reviewed under separate subtopics such as cellular stress, inflammation, cytoskeleton and cell signaling. Finally, we investigated the transcriptome signature of brain protection across preconditioning studies in search of transcripts that were expressed similarly across studies. Strikingly, when comparing genes discovered by single-gene analysis we observed only 15 genes present in two studies or more. We subjected these 15 transcripts to DAVID Annotation Clustering analysis to derive their shared biological meaning. Interestingly, the MAPK signaling pathway and more specifically the ERK1/2 pathway geared toward cell survival/proliferation was significantly enriched. To conclude, we advocate incorporating pathway analysis into all microarray data analysis in order to improve the detection of similarities between independently derived datasets.

A novel DNA microarray analysis targeting key functional genes involved in most nitrogen cycling reactions was developed to comprehensively analyze microbial populations associated with the nitrogen cycle. The developed microarray contained 876 oligonucleotide probes based on the nucleotide sequences of the nif, amo, hao/hzo, nap, nar, nirK, nirS, nrf, cnor, qnor and nos genes. An analytical method combining detection by the designed microarray with whole community genome amplification was then applied to monitor the nitrogen cycling microorganisms in river water and wastewater treatment sludge samples. The developed method revealed that nitrogen cycling microorganisms in river water appeared to become less diverse in response to input of effluent from municipal wastewater treatment plants. Additionally, the nitrogen cycling community associated with anaerobic ammonium oxidation and partial nitrification reactors could be reasonably analyzed by the developed method. However, the results obtained for two activated sludge samples from municipal wastewater treatment plants with almost equivalent wastewater treatment performance differed greatly from each other. These results suggested that the developed method is useful for comprehensive analysis of nitrogen cycling microorganisms, although its applicability to complex samples with abundant untargeted populations should be further examined.

It has recently been shown through DNA microarray analysis of Bacillus subtilis two-component regulatory systems (DegS-DegU, ComP-ComA, and PhoR-PhoP) that overproduction of a response regulator of the two-component systems in the background of a deficiency of its cognate sensor kinase affects the regulation of genes, including its target ones. The genome-wide effect on gene expression caused by the overproduction was revealed by DNA microarray analysis. In the present work, we newly analyzed 24 two-component systems by means of this strategy, leaving out 8 systems to which it was unlikely to be applicable. This analysis revealed various target gene candidates for these two-component systems. It is especially notable that interesting interactions appeared to take place between several two-component systems. Moreover, the probable functions of some unknown two-component systems were deduced from the list of their target gene candidates. This work is heuristic but provides valuable information for further study toward a comprehensive understanding of the B. subtilis two-component regulatory systems. The DNA microarray data obtained in this work are available at the KEGG Expression Database website ( http://www.genome.ad.jp/kegg/expression ).

The recently developed DNA microarray technology provides a powerful and efficient tool to rapidly compare the differential expression of a large number of genes. Using the DNA microarray approach, we investigated gene expression profiles in cultured human aortic endothelial cells (HAECs) in response to 24 h of laminar shear stress at 12 dyn/cm(2). This relatively long-term shearing of cultured HAECs led to the modulation of the expression of a number of genes. Several genes related to inflammation and EC proliferation were downregulated, suggesting that 24-h shearing may keep ECs in a relatively noninflammatory and nonproliferative state compared with static cells. Some genes were significantly upregulated by the 24-h shear stress; these includes genes involved in EC survival and angiogenesis (Tie2 and Flk-1) and vascular remodeling (matrix metalloproteinase 1). These results provide information on the profile of gene expression in shear-adapted ECs, which is the case for the native ECs in the straight part of the aorta in vivo.

The differential display reverse transcriptional polymerase chain reaction (DD-RT-PCR) was used to hunt for cDNA fragments specifically expressed by taxol treatment of HeLa cells. Forty-eight cDNA clones were differentially displayed through the experiments. The cDNA fragments obtained were separately spotted onto glass slides to prepare a tailor-made DNA chip. The gene expression pattern of differentially displayed cDNA fragments were checked by DNA microarray analysis.

Prognostic and predictive factors are indispensable tools in the treatment of patients with neoplastic disease. For the most part, such factors rely on a few specific cell surface, histological, or gross pathologic features. Gene expression assays have the potential to supplement what were previously a few distinct features with many thousands of features. We have developed Bayesian regression models that provide predictive capability based on gene expression data derived from DNA microarray analysis of a series of primary breast cancer samples. These patterns have the capacity to discriminate breast tumors on the basis of estrogen receptor status and also on the categorized lymph node status. Importantly, we assess the utility and validity of such models in predicting the status of tumors in crossvalidation determinations. The practical value of such approaches relies on the ability not only to assess relative probabilities of clinical outcomes for future samples but also to provide an honest assessment of the uncertainties associated with such predictive classifications on the basis of the selection of gene subsets for each validation analysis. This latter point is of critical importance in the ability to apply these methodologies to clinical assessment of tumor phenotype.

The future direction of post-genomic nematode parasitology should focus on the function of the genes that are defined by large-scale expressed sequence tag sequencing and on broader questions about the genetic basis of parasitism. Functional characterisation will require the application of high throughput technologies that have been developed in other fields, including genome mapping strategies and DNA microarray analysis. These will be greatly aided by the development and application of appropriate model organisms. It is crucial that the field make the transition from a narrow focus on one or a few genes at a time to a focus on whole genomes in order to fully realise the potential of the expressed sequence tag and other genomic projects currently under way.

We analyzed changes in gene expression in Caco-2 cells cocultured with THP-1 cells over time periods of 0, 1, 3, 6, 24, and 48 h using a DNA microarray. Differentially expressed genes extracted with maSigPro indicated that an early defense response and cell death occured at the same time, causing an inflammatory condition.

DNA microarrays have the ability to analyze the expression of thousands of the same set of genes under at least two different experimental conditions. However, DNA microarrays require substantial amounts of RNA to generate the probes, especially when bacterial RNA is used for hybridization (50 microg of bacterial total RNA contains approximately 2 microg of mRNA). We have developed a computer-based algorithm for prediction of the minimal number of primers to specifically anneal to all genes in a given genome. The algorithm predicts, for example, that 37 oligonucleotides should prime all genes in the Mycobacterium tuberculosis genome. We tested the usefulness of the genome-directed primers (GDPs) in comparison to random primers for gene expression profiling using DNA microarrays. Both types of primers were used to generate fluorescent-labeled probes and to hybridize to an array of 960 mycobacterial genes. Compared to random-primer probes, the GDP probes were more sensitive and more specific, especially when mammalian RNA samples were spiked with mycobacterial RNA. The GDPs were used for gene expression profiling of mycobacterial cultures grown to early log or stationary growth phases. This approach could be useful for accurate genome-wide expression analysis, especially for in vivo gene expression profiling, as well as directed amplification of sequenced genomes.

To examine the transcriptional responses of rat bone cells continuously exposed to cadmium, we performed DNA microarray analysis. Cadmium increased levels of expression of 13 genes, including genes for Spp1, and reduced those of 10 genes.

Since its introduction, the DNA microarray platform has experienced a tremendous growth and currently it is a powerful tool used in various biological applications. However, it has not been embraced in the molecular diagnostic market as much as it was anticipated in the early days. The challenge comes from the skepticism on the reproducibility of the microarray data and on the reliability of the biological interpretations inferred. In addition, the microarray technology is suffering from strong competition by other diagnostic techniques. On the other hand, integration of various steps of the microarray assay into a harmonized and miniaturized handheld device suitable for point-of-care (POC) has been a goal for the microarray community. In this respect, significant progress has been achieved in coupling the DNA microarrays with efficient liquid manipulation microsystems as well as in developing novel technologies of supporting subsystems that well suits future POC microarray devices.

The microflora in environmental water consists of a high density and diversity of bacterial species that form the foundation of the water ecosystem. Because the majority of these species cannot be cultured in vitro, a different approach is needed to identify prokaryotes in environmental water. A novel DNA microarray was developed as a simplified detection protocol. Multiple DNA probes were designed against each of the 97,927 sequences in the DNA Data Bank of Japan and mounted on a glass chip in duplicate. Evaluation of the microarray was performed using the DNA extracted from one liter of environmental water samples collected from seven sites in Japan. The extracted DNA was uniformly amplified using whole genome amplification (WGA), labeled with Cy3-conjugated 16S rRNA specific primers and hybridized to the microarray. The microarray successfully identified soil bacteria and environment-specific bacteria clusters. The DNA microarray described herein can be a useful tool in evaluating the diversity of prokaryotes and assessing environmental changes such as global warming.

Background: The aim of this study was to identify key genes and novel potential therapeutic targets related to gastric cancer (GC) by comparing cancer tissue samples and healthy control samples using DNA microarray analysis. Methods: Microarray data set GSE19804 was downloaded from Gene Expression Omnibus. Preprocessing and differential analysis were conducted with of R statistical software packages, and a number of differentially expressed genes (DEGs) were obtained. Cluster analysis was also done with gene expression values. Functional enrichment analysis was performed for all the DEGs with DAVID tools. The significantly up- and downregulated genes were selected out and their interactors were retrieved with STRING and HitPredict, followed by construction of networks. For all the genes in the two networks, GeneCodis was chosen for gene function annotation. Results: A total of 638 DEGs were identified, and we found that SPP1 and FABP4 were the markedly up- and downregulated genes, respectively. Cell cycle and regulation of proliferation were the most significantly overrepresented functional terms in up- and downregulated genes. In addition, extracellular matrix–receptor interaction was found to be significant in the SPP1-included interaction network. Conclusions: A range of DEGs were obtained for GC. These genes not only provided insights into the pathogenesis of GC but also could develop into biomarkers for diagnosis or treatment.

Cadmium (Cd) is a toxic heavy metal and chronic exposure causes kidney injury. This study used DNA microarray analysis to examine gene expression in the kidney of mice chronically exposed to Cd. Female C57BL/6J mice were fed a 300 ppm Cd-containing diet or a control diet for 12 months. In comparison with control mice, the expression levels of 32 genes, including Hmox1 and Mt2, were elevated more than 2.0-fold, whereas 113 genes, including transport- and ubiquitination-related genes, were reduced less than 0.5-fold.

Background Analysis of global gene expression by DNA microarrays is widely used in experimental molecular biology. However, the complexity of such high-dimensional data sets makes it difficult to fully understand the underlying biological features present in the data. The aim of this study is to introduce a method for DNA microarray analysis that provides an intuitive interpretation of data through dimension reduction and pattern recognition. We present the first “Archetypal Analysis” of global gene expression. The analysis is based on microarray data from five integrated studies of Pseudomonas aeruginosa isolated from the airways of cystic fibrosis patients. Results Our analysis clustered samples into distinct groups with comprehensible characteristics since the archetypes representing the individual groups are closely related to samples present in the data set. Significant changes in gene expression between different groups identified adaptive changes of the bacteria residing in the cystic fibrosis lung. The analysis suggests a similar gene expression pattern between isolates with a high mutation rate (hypermutators) despite accumulation of different mutations for these isolates. This suggests positive selection in the cystic fibrosis lung environment, and changes in gene expression for these isolates are therefore most likely related to adaptation of the bacteria. Conclusions Archetypal analysis succeeded in identifying adaptive changes of P. aeruginosa. The combination of clustering and matrix factorization made it possible to reveal minor similarities among different groups of data, which other analytical methods failed to identify. We suggest that this analysis could be used to supplement current methods used to analyze DNA microarray data.

A total of 34 Staphylococcus aureus isolates from flock-wise pooled chicken neck skin samples collected at two abattoirs during slaughter were characterized with DNA microarray analysis and spa typing. The 20 isolates from abattoir A all belonged to clonal complex (CC) 12 and spa type t160. Of the 14 isolates from abattoir B, 7 belonged to CC5-t3478, 5 to CC12-t160, 1 to CC45-t040, and 1 to CC101-t056. Of the various resistance-associated genes tested, only blaZ/R/I (6 isolates of CC12 and CC101 from abattoir B), sdrM (n = 34), fosB (n = 33), and qacC (n = 22) were detected. None of the isolates harbored genes conferring methicillin resistance. In terms of genes encoding enterotoxins, seb (all isolates of CC12), egc (seg, sei, selm, seln, selo, selu; all isolates of CC5 and CC45), and sea (14 isolates of CC12 and 1 isolate of CC5) were found. In addition, all isolates harbored genes for intracellular adhesion proteins (icaA/C/D) and were positive for cap5 or cap8 (capsule type 5 or 8). Comparison of DNA microarray profiles identified four categories comprising (i) all isolates of CC12, (ii) all isolates of CC5, (iii) the CC45 isolate, and (iv) the CC101 isolate. The high similarity of the isolates from abattoir A could indicate contamination of chicken carcasses with S. aureus persisting on the slaughter equipment, but further investigations are required to elucidate potential contamination routes.

Pufferfish accumulate tetrodotoxin (TTX) at high levels in liver and ovary through the food chain. However, the mechanisms underlying TTX toxification in pufferfish have been poorly understood. In order to search gene candidates involved in TTX accumulation in the torafugu pufferfish Takifugu rubripes, a custom 4x44k oligonucleotide microarray slide was designed by the Agilent eArray program using oligonucleotide probes of 60 bp in length referring to 42,724 predicted transcripts in the publicly available Fugu genome database. DNA microarray analysis was performed with total RNA samples from the livers of two toxic wild specimens in comparison with those from a nontoxic wild specimen and two nontoxic cultured specimens. The mRNA levels of 1108 transcripts were more than 2-fold higher in the toxic specimens than in the nontoxic specimens. The levels of 613 transcripts were remarkably high, and 16 transcripts encoded by 9 genes were up-regulated more than 10-fold. These genes included those encoding forming structural filaments (keratins) and those related to vitamin D metabolism and immunity. It was also noted that the levels of the transcripts encoding serpin peptidase inhibitor clade C member 1, coagulation factor X precursor, complement C2, C3, C5, C8 precursors, and interleukin-6 receptor were high in the toxic liver samples.

Although cadmium causes hepatotoxicity, its molecular mechanism is unclear. In the present study, transcriptional responses in the liver of C57BL/6J mice given 50 ppm cadmium as a drinking water for 30 days were evaluated with DNA microarray. Serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities were not elevated following the administration of cadmium. Cadmium increased the expressions of 2 genes and reduced those of 15 genes in the liver of mice before the leading to hepatotoxicity.

Recent advances in homologous recombination in Escherichia coli have enabled improved genome engineering by multiplex recombineering. In this chapter, we present trackable multiplex recombineering (TRMR), a method for gene-trait mapping which creates simulated knockdown and overexpression mutants for virtually all genes in the E. coli genome. The method combines oligonucleotide synthesis with multiplex recombineering to create two libraries comprising of over 8,000 E. coli strains in total that can be selected for traits of interest via high-throughput screening or selection. DNA barcodes included in the recombineering cassette allow for rapid characterization of a naive or selected population via DNA microarray analysis. Important considerations for oligonucleotide design, DNA library construction, recombineering, strain characterization, and selection are discussed.

The characteristics of plant growth enhancement effect and the mechanism of the enhancement induced by plasma irradiation are investigated using various active species in plasma. Active oxygen species in oxygen plasma are effective for growth enhancement of plants. DNA microarray analysis of Arabidopsis thaliana indicates that the genes coding proteins that counter oxidative stresses by eliminating active oxygen species are expressed at significantly high levels. The size of plant cells increases owing to oxygen plasma irradiation. The increases in gene expression levels and cell size suggest that the increase in the expression level of the expansin protein is essential for plant growth enhancement phenomena.

Aberrant DNA methylation has shown promise as a biomarker for the early detection of cancer. To discover novel genes frequently methylated at an early stage in colorectal cancer (CRC), DNA microarray analysis coupled with enriched methylated DNA was performed in primary tumors and compared with adjacent nontumor tissues of 12 patients with CRC at stages I to IV. Stepwise filtering for candidate selection in microarray data analysis yielded a set of genes that are highly methylated across all CRC tumors and that can be used as a composite biomarker for CRC detection. Verification assay identified the SDC2 gene as a potential methylation biomarker for early CRC detection. In clinical validation in tissues from 139 CRC patients, a much higher level of aberrant SDC2 methylation was measured in most primary tumors (97.8%), compared with corresponding nontumor tissue of CRC patients, irrespective of clinical stage. Clinical validation of SDC2 methylation in serum DNA from CRC patients (n = 131) at stages I to IV and from healthy individuals (n = 125) by quantitative methylation-specific PCR demonstrated a high sensitivity of 87.0% (95% CI, 80.0% to 92.3%) in detecting cancers, with a specificity of 95.2% (95% CI, 89.8% to 98.2%). Importantly, sensitivity at stage I was 92.3%, indicating the potential of SDC2 methylation as a blood-based DNA test for early detection of CRC.

When the whole human genome sequence was determined by the Human Genome Project, the number of identified genes was fewer than expected. However, recent studies suggest that undiscovered transcripts still exist in the human genome. Furthermore, a new technology, the DNA microarray, which can simultaneously characterize huge amounts of genome sequence data, has become a useful tool for analyzing genetic changes in various diseases. A version of this tool, the tiling DNA microarray, was designed to search all the transcripts of the entire human genome, and provides huge amounts of data, including both exon and intron sequences, by a simple process. Although some previous studies using tiling DNA microarray analysis have indicated that numerous novel transcripts can be found in the human genome, none of them has reported any novel full-length human genes. Here, to find novel genes, we analyzed all the transcripts expressed in normal human prostate cells using this microarray. Because the optimal analytical parameters for using tiling DNA microarray data for this purpose had not been established, we established parameters for extracting the most likely regions for novel transcripts. The three parameters we optimized were the threshold for positive signal intensity, the Max gap, and the Min run, which we set to detect all transcriptional regions that were above the average length of known exons and had a signal intensity in the top 5%. We succeeded in obtaining the full-length sequence of one novel gene, located on chromosome 12q24.13. We named the novel gene “POTAGE”. Its 5841-bp mRNA consists of 26 exons. We detected part of exon 2 in the tiling data analysis. The full-length sequence was then obtained by RT-PCR and RACE. Although the function of POTAGE is unclear, its sequence showed high homology with genes in other species, suggesting it might have an important or essential function. This study demonstrates that the tiling DNA microarray can be useful for identifying novel human genes.

The growing crisis of multidrug-resistant (MDR) Gram-negative bacteria requires that current technologies permit the rapid detection of extended-spectrum β-lactamase ( bla ESBL ) and Klebsiella pneumoniae carbapenemase ( bla KPC ) genes. In the present study, we assessed the performance characteristics of a commercially available nucleic acid microarray system for the detection of bla ESBL and bla KPC genes directly from positive blood cultures. Using blood cultures (BCs) that contained Gram-negative bacilli identified by Gram staining, we isolated bacterial DNA using spin columns (BC-C) and rapid water lysis (BC-W). Twenty ESBL/KPC-positive and 20 ESBL/KPC-negative blood culture samples, as well as 20 non-lactose-fermenting organisms, were tested. The 20 isolates that were ESBL positive by phenotypic testing were also evaluated on solid medium (SM), and the DNA was extracted by use of a spin column (SM-C). The resulting 140 DNA extractions were assessed for DNA quantity and quality using 260/280-nm absorbance ratios, and DNA microarray analysis was performed in a blinded fashion. Microarray and phenotypic results were concordant for 98.3% of BC-W, 90% of BC-C, and 95% of SM-C samples. Compared to phenotypic testing, the sensitivity and specificity for BC-C samples were 88.9% and 100%, respectively, and for BC-W samples, the sensitivity and specificity were 94.4% and 100%, respectively. BC-W samples yielded the highest concordance with phenotypic results. Nucleic acid microarrays offer promise in the identification of bla ESBL and bla KPC genes directly from blood cultures, thereby reducing the time to identification of these important pathogens.

Induced pluripotent stem cells (iPSCs) obtained by the ectopic expression of defined transcription factors have tremendous promise and therapeutic potential for regenerative medicine. Many studies have highlighted important differences between iPSCs and embryonic stem cells (ESCs). In this work, we used meta-analysis to compare the global transcriptional profiles of human iPSCs from various cellular origins and induced by different methods. The induction strategy affected the quality of iPSCs in terms of transcriptional signatures. The iPSCs generated by non-integrating methods were closer to ESCs in terms of transcriptional distance than iPSCs generated by integrating methods. Several pathways that could be potentially useful for studying the molecular mechanisms underlying transcription factor-mediated reprogramming leading to pluripotency were also identified. These pathways were mostly associated with the maintenance of ESC pluripotency and cancer regulation. Numerous genes that are up-regulated during the induction of reprogramming also have an important role in the success of human preimplantation embryonic development. Our results indicate that hiPSCs maintain their pluripotency through mechanisms similar to those of hESCs.

Uno de los retos más importantes de este siglo en la neurología genómica es construir mapas de expresión espacial de genes a lo largo de las distintas estructuras cerebrales con el fin de correlacionarlos con ciertas neuropatologías. Se analizaron los perfiles de transcripción de ocho genes HAS21 localizados en la región crítica del síndrome de Down en diferentes estructuras del cerebro humano normal. Se tomaron como referencia los valores de expresión de ocho genes HAS21/DSCR provenientes de experimentos de micromatrices de ADN de cerebros humanos normales y cuyos valores están disponibles en la base de datos del proyecto cerebro humano del Atlas del Cerebro del Allen Institute for Brain Sciences en Seattle, Washington (http://www.brainmap.org). Se determinó una expresión diferencial de estos genes HAS21/DSCR a lo largo de las estructuras localizadas en el lóbulo frontal, el lóbulo límbico y en los núcleos centrales. En el putamen, el núcleo caudado, el giro parahipocampal y en las áreas centrales se registraron los mayores niveles de transcripción global; estas áreas del cerebro parecen estar asociadas con diversos procesos de aprendizaje y de memoria. Se correlacionó la transcripción diferencial de genes DSCR con la localización cerebral y su potencial papel funcional. One of the most important challenges of the 21st Century Neurology is to build gene expression profiles along the different structures of human brain trying to correlate them with some neuropathologies. The expression profiles of eight HAS21 genes located on the Down syndrome critical region in different structures of the normal human brain was analyzed. From DNA microarray experiments of normal human brains which are available in the free access human brain database of the Brain Atlas project of the Allen Institute for Brain Sciences in Seattle, Washington (http://www.brainmap.org) expression levels data of eight HSA21/DSCR genes along different structures of normal human brain were statistically analyzed. A differential expression of these genes HSA21/DSCR in some anatomic structures located in the frontal lobe, limbic lobe and cerebral central nuclei was registered. Putamen, caudate nucleus, parahipocampal gyro and central areas, showed high levels of transcription for those HSA21/DSCR genes included in the study; these areas of the brain appear to be associated with some processes of learning and memory. This study allowed us to correlate the differential transcription of DSCR genes, their structural localization and functional role in brain function. Um dos maiores desafio deste século na neurologia genômica é construir mapas de expressão espacial de genes ao longo das diferentes estruturas cerebrais com o fim de correlacionálos com certas neuropatologias. Foram analisados os perfis de transcrição de oito genes HAS21 localizados na região crítica da síndrome de Down em diferentes estruturas do cérebro humano normal. Foram usados como referência os valores de expressão de oito genes HAS21/DSCR provenientes de experimentos de micromatrizes de ADN de cérebros humanos normais e cujos valores estão disponíveis no bando de dados do projeto cérebro humano do Atlas do Cérebro do Allen Institute for Brain Sciences em Seattle, Washington (http://www.brainmap.org). Determinouse uma expressão diferencial destes genes HAS21/DSCR ao longo das estruturas localizadas no lóbulo frontal, o lóbulo límbico e nos núcleos centrais. No putâmen, o núcleo caudado, o giro parahipocampal e nas áreas centrais foram registrados os maiores níveis de transcrição global; estas áreas do cérebro parecem estar associadas com diversos processos de aprendizagem e de memória. Correlacionouse a transcrição diferencial de genes DSCR com a localização cerebral e seu potencial papel funcional.

Tsutomu Sakai1, Kenichi Kohzaki1, Akira Watanabe1, Hiroshi Tsuneoka1, Mitsunobu Shimadzu21Department of Ophthalmology, Jikei University School of Medicine, 2Mitsubishi Chemical Medience Corporation, Tokyo, JapanBackground: To examine the utility of DNA microarray analysis for identifying causative microorganisms in endophthalmitis.Methods: Thirteen samples of vitreous fluid (VF) were obtained from 13 patients during vitrectomy. Vitreous fluids from three patients with suspected endophthalmitis and ten controls without infection were subjected to testing for the presence of bacteria and fungi in culture tests, polymerase chain reaction (PCR) analysis, and DNA microarray analysis.Results: No control sample was positive for bacteria or fungi in the culture test, PCR, or microarray analysis. Specimens from two patients (Cases 1 and 2) with suspected endophthalmitis were positive for bacteria in PCR, and a specimen from one patient (Case 3) was positive for fungi in PCR. Klebsiella pneumonia (Case 1), Streptococcus agalactiae (Case 2), and Candida parapsilosis (Case 3) in the PCR-positive specimens were identified by DNA microarray analysis within 24 hours. Culture results were also positive for K. pneumonia in Case 1, S. agalactiae in Case 2, and C. parapsilosis in Case 3, but required 3 to 4 days to obtain.Conclusions: Microarray analysis is complementary to routine cultures for identifying causative microorganisms and is likely to be a useful tool in patients with suspected endophthalmitis who require rapid diagnosis and early antibiotic treatment.Keywords: DNA microarray, endophthalmitis, microorganism, vitreous fluidCorrigendum for this paper has been published

PREMISE OF THE STUDY To capture molecular markers that are applicable to environmental risk assessment of genetically modified oilseed rape, and to streamline their development, we screened variations in nucleotide sequences of three Brassica species by DNA microarray analysis. METHODS AND RESULTS Using the Affymetrix GeneChip Arabidopsis ATH1 Genome Array, we monitored gene expression at 22810 loci among the Brassica species and picked out 192 putative polymorphic loci. We sequenced 25 of these and successfully aligned them among all three species. All 25 loci possessed some interspecific and at times intraspecific nucleotide variation. CONCLUSIONS DNA microarray analysis effectively detected a large number of nucleotide sequence variations among closely related Brassica species. The polymorphic regions will allow the subsequent development of functional gene markers.

DNA microarray analysis represents a relevant technology in genetic research to explore and recognize possible genomic features of many diseases. Since it is a high-throughput technology, it requires advanced tools for a dimensional reduction in massive data sets. Clustering is among the most appropriate tools for mining these data, although it suffers from the following problems: instability of the results, large number of genes compared with the number of samples, high noise level, complexity of initialization, and grouping genes and samples simultaneously. Almost all these problems can be positively addressed by using novel techniques, such as biclustering. In this paper, a new biclustering algorithm is proposed, hereafter denoted as combinatorial biclustering algorithm (CBA), that addresses the problems listed above. The algorithm analyzes the data finding biclusters of the desired size and allowable error. CBA performances are compared with the ones of other bicluster algorithms by discussing the output of different methods once running them on a synthetic data set. CBA seems to perform better, and for this reason, it has been applied to study a real data set as well. In particular, CBA has analyzed the transcriptional profile of 38 gastric cancer tissues with microsatellite instability (MSI) and without MSS. The results show clearly a much coherent behavior in gene expression of normal tissues versus tumoral ones. The high level of gene misregulation in tumoral tissues affects any further bicluster analysis, and it is only partially smoothed in the MSI/MSS study even admitting much higher level on initial admissible error.

To isolate genes responsive to N-acetylchitooligosaccharide elicitor in suspension-cultured rice cells, we screened rice ESTs by DNA microarray analysis, and were able to identify novel elicitor-responsive genes. Sequencing analysis of three ESTs revealed that the up-regulated genes include a transcription factor, Myb, small G-protein, Rac, and calmodulin. The results indicated the genome-wide change of gene expression in response to N-acetylchitooligosaccharide elicitor.

Although all three texts address the topic of microarrays, each has a different focus. The first two are edited by Mark Schena, one of the pioneers in the field of DNA microarrays, whereas the last, and newest, text is edited by Jang Rampal of the Beckman Coulter microarray group. DNA Microarrays: A Practical Approach focuses on methods and procedures that would be used by someone just entering the field of DNA microarrays. It joins the well-known “Practical Approach Series” of publications familiar to many in biomedical sciences. By contrast, Microarray Biochip Technology discusses the technology behind the major instruments and procedures used for DNA microarray analysis and is from Eaton, the publisher of BioTechniques . Finally, DNA Arrays: Methods and Protocols is the most recent publication and joins a rapidly expanding series, “Methods in Molecular Biology”. Although this text contains both procedures and technology assessment, most chapters are not as extensive as those in the books edited by Schena. Although all three books offer insights into this rapidly expanding field, each provides the reader with a different perspective that would satisfy unique needs. Several workers who have made substantial advances in DNA microarrays contributed to this 10-chapter book. Its major theme is providing practical information (some in a recipe format) that illustrates how to perform DNA microarray analyses. The first chapter is an overview of selected techniques and recipes and is cowritten by the Editor and R.W. Davis, another pioneer in the use of microarrays. Chapter 2, authored by an early developer of confocal scanners, addresses issues with confocal laser fluorescence scanning of arrays prepared on glass slides. The next three chapters discuss less-recognized applications of microarrays, such as representational differential analysis, use of oligonucleotide arrays for detection of single nucleotide polymorphisms, and selection of antisense oligomers (the latter two …

Micro RNAs (miRNA) are an abundant class of small RNAs that regulate the stability and translation of cognate mRNAs. MiRNAs are potential diagnostic markers, moreover, they play an essential role in the development of various heart disesases. In case of limited tissue material, such as, e.g. human biopsies, purification of miRNAs with sufficient yield is critical. Reproducible expression analysis of miRNAs is highly dependent on the quality of the RNA, which is often difficult to achieve from fibrous tissue such as the heart. Several companies developed general purification kits for miRNAs, however, none of them are specialized to fibrotic tissues. Here we describe an optimized miRNA purification protocol that results in high miRNA yield as compared to other methods including trizol-based and column-based protocols. By using our improved protocol, miRNA obtained from heart tissue gave more reproducible results in QRT-PCR analysis and obtained more significant calls (172 vs. 118) during DNA microarray analysis when compared to the commercially available kit. In addition to the heart tissue, the present protocol can be applied to other fibrotic tissues, such as lung or skeletal muscle to isolate high-purity miRNA.

Recently, it has been shown by the ENCODE consortium that more than 90% of the human genome might be transcribed. While only about 1.5% of these transcripts correspond to mRNAs, it was proposed that the majority of them (i.e., 88.5%) might correspond to regulatory noncoding RNAs (ncRNAs). Numerous protocols dedicated to the generation of cDNA libraries coupled to next-generation sequencing (NGS) technologies are currently available to identify novel ncRNA species, and we have recently developed a novel procedure for the generation of ribonucleoprotein (RNP) libraries. To validate differential expression of ncRNAs identified using our or any library generation approach, we describe an innovative ncRNA profiling approach based on microarray technology. Employing LNA probes, dedicated to the analysis of small/microRNAs, and DNA probes, dedicated to the study of longer ncRNAs, our platform enables the study of most ncRNAs independently of their length in a single experiment. Detailed methodological solution description includes the automated design of probes to be spotted on the array, optimization of spotting and labeling of probes, as well as hybridization conditions. All the steps have been improved for the analysis of ncRNAs, which are generally difficult to study owing to their peculiarities in terms of secondary structure or abundance.

DNA microarray analysis of gene expression has become a valuable tool for bioprocessing research aimed at improving therapeutic protein yields. The highly parallel nature of DNA microarray technology allows researchers to assess hundreds of gene simultaneously, essentially enabling genome-wide snapshots. The quality and amount of therapeutic proteins produced by cultured mammalian cells rely heavily on the culture environment. In order to implement beneficial changes to the culture environment, a better understanding of the relationship between the product quality and culture environment must be developed. By analyzing gene expression levels under various environmental conditions, light can be shed on the underlying mechanisms. This paper describes a method for evaluating gene expression changes for cultured NS0 cells, a mouse-derived myeloma cell line, under culture environment conditions, such as ammonia buildup, known to affect product quality. These procedures can be easily adapted to other environmental conditions and any mammalian cell lines cultured in suspension, so long as a sufficient number of gene sequences are publicly available.

To elucidate the molecular mechanism involved in methylmercury-induced cerebellar disorder, we performed DNA microarray analysis of the cerebellum of methylmercury-treated mice. The expression levels of 21 genes were elevated 2-fold or higher in response to methylmercury, including many genes encoding proteins involved in inflammatory reactions associated with chemokines. The expression levels of 11 genes were reduced by half or more in response to methylmercury.

Microarray technology has become a widely used tool for analyzing the expression of tens of thousands of genes simultaneously on high-density microarrays in a single experiment, together with the availability of the complete nucleotide sequence of the human genome. DNA microarray technologies are powerful tools in the laboratory setting for scientific research, hypothesis generation, and the production of leads for subsequent validation through more sophisticated technologies. Since 2002, and particularly in the past few years, the utility of various DNA microarray technologies in endometriosis has rapidly and tremendously evolved; DNA microarray studies provide extremely important information that enable a better understanding of pathophysiology and disease etiology. This is a review of the literature focused on new findings of endometriosis pathophysiology obtained using various microarray technologies: gene expression profiling, microarray-based comparative genomic hybridization (CGH) (array CGH) single nucleotide polymorphism (SNP) arrays, the combination of chromatin immunoprecipitation (ChIP) with hybridization of microarrays (ChIP-on-chip). The present review discusses the results of a decade of DNA microarray technology experience, with emphasis on the pathophysiology of endometriosis in the context of clinical studies.

Cadmium (Cd) is a toxic heavy metal that has been shown to induce vascular diseases, such as atherosclerosis. We used DNA microarray to monitor the transcriptional response of human coronary artery endothelial cells (HCAEC) to a non-lethal dose of Cd (10 µM). Out of 35,035 human genes, Cd enhanced the expression of 3 metallothionein (MT)-I subisoform genes, including MT1E, MT1H and MT1B, and reduced the expression of 12 genes, including ISG20 and TK1, 2-fold or greater.

This PhD thesis explains the recent issue concerning the resolution of high-dimensional problems. We present methods designed to solve them, and their applications for feature selection problems, in the data mining field. In the first part of this thesis, we introduce the stakes of solving high-dimensional problems. We mainly investigate line search methods, because we consider them to be particularly suitable for solving such problems. Then, we present the methods we developed, based on this principle : CUS, EUS and EM323. We emphasize, in particular, the very high convergence speed of CUS and EUS, and their simplicity of implementation. The EM323 method is based on an hybridization between EUS and a one-dimensional optimization algorithm developed by F. Glover : the 3-2-3 algorithm. We show that the results of EM323 are more accurate, especially for non-separable problems, which are the weakness of line search based methods. In the second part, we focus on data mining problems, and especially those concerning microarray data analysis. The objectives are to classify data and to predict the behavior of new samples. A collaboration with the Tenon Hospital in Paris allows us to analyze their private breast cancer data. To this end, we develop an exact method, called delta-test, enhanced by a method designed to automatically select the optimal number of variables. In a second time, we develop an heuristic, named ABEUS, based on the optimization of the DLDA classifier performances. The results obtained from publicly available data show that our methods manage to select very small subsets of variables, which is an important criterion to avoid overfitting, Cette thèse expose la problématique récente concernant la résolution de problèmes de grande dimension. Nous présentons les méthodes permettant de les résoudre ainsi que leurs applications, notamment pour la sélection de variables dans le domaine de la fouille de données. Dans la première partie de cette thèse, nous exposons les enjeux de la résolution de problèmes de grande dimension. Nous nous intéressons principalement aux méthodes de recherche linéaire, que nous jugeons particulièrement adaptées pour la résolution de tels problèmes. Nous présentons ensuite les méthodes que nous avons développées, basées sur ce principe : CUS, EUS et EM323. Nous soulignons en particulier la très grande vitesse de convergence de CUS et EUS, ainsi que leur simplicité de mise en oeuvre. La méthode EM323 est issue d'une hybridation entre la méthode EUS et un algorithme d'optimisation unidimensionnel développé par F. Glover : l'algorithme 3-2-3. Nous montrons que ce dernier algorithme obtient des résultats d'une plus grande précision, notamment pour les problèmes non séparables, qui sont le point faible des méthodes issues de la recherche linéaire. Dans une deuxième partie, nous nous intéressons aux problèmes de fouille de données, et plus particulièrement l'analyse de données de puces à ADN. Le but est de classer ces données et de prédire le comportement de nouveaux exemples. Dans un premier temps, une collaboration avec l'hôpital Tenon nous permet d'analyser des données privées concernant le cancer du sein. Nous développons alors une méthode exacte, nommée delta-test, enrichie par la suite d'une méthode permettant la sélection automatique du nombre de variables. Dans un deuxième temps, nous développons une méthode heuristique de sélection de variables, nommée ABEUS, basée sur l'optimisation des performances du classifieur DLDA. Les résultats obtenus sur des données publiques montrent que nos méthodes permettent de sélectionner des sous-ensembles de variables de taille très faible,ce qui est un critère important permettant d'éviter le sur-apprentissage

This PhD thesis explains the recent issue concerning the resolution of high-dimensional problems. We present methods designed to solve them, and their applications for feature selection problems, in the data mining field. In the first part of this thesis, we introduce the stakes of solving high-dimensional problems. We mainly investigate line search methods, because we consider them to be particularly suitable for solving such problems. Then, we present the methods we developed, based on this principle : CUS, EUS and EM323. We emphasize, in particular, the very high convergence speed of CUS and EUS, and their simplicity of implementation. The EM323 method is based on an hybridization between EUS and a one-dimensional optimization algorithm developed by F. Glover : the 3-2-3 algorithm. We show that the results of EM323 are more accurate, especially for non-separable problems, which are the weakness of line search based methods. In the second part, we focus on data mining problems, and especially those concerning microarray data analysis. The objectives are to classify data and to predict the behavior of new samples. A collaboration with the Tenon Hospital in Paris allows us to analyze their private breast cancer data. To this end, we develop an exact method, called delta-test, enhanced by a method designed to automatically select the optimal number of variables. In a second time, we develop an heuristic, named ABEUS, based on the optimization of the DLDA classifier performances. The results obtained from publicly available data show that our methods manage to select very small subsets of variables, which is an important criterion to avoid overfitting, Cette thèse expose la problématique récente concernant la résolution de problèmes de grande dimension. Nous présentons les méthodes permettant de les résoudre ainsi que leurs applications, notamment pour la sélection de variables dans le domaine de la fouille de données. Dans la première partie de cette thèse, nous exposons les enjeux de la résolution de problèmes de grande dimension. Nous nous intéressons principalement aux méthodes de recherche linéaire, que nous jugeons particulièrement adaptées pour la résolution de tels problèmes. Nous présentons ensuite les méthodes que nous avons développées, basées sur ce principe : CUS, EUS et EM323. Nous soulignons en particulier la très grande vitesse de convergence de CUS et EUS, ainsi que leur simplicité de mise en oeuvre. La méthode EM323 est issue d'une hybridation entre la méthode EUS et un algorithme d'optimisation unidimensionnel développé par F. Glover : l'algorithme 3-2-3. Nous montrons que ce dernier algorithme obtient des résultats d'une plus grande précision, notamment pour les problèmes non séparables, qui sont le point faible des méthodes issues de la recherche linéaire. Dans une deuxième partie, nous nous intéressons aux problèmes de fouille de données, et plus particulièrement l'analyse de données de puces à ADN. Le but est de classer ces données et de prédire le comportement de nouveaux exemples. Dans un premier temps, une collaboration avec l'hôpital Tenon nous permet d'analyser des données privées concernant le cancer du sein. Nous développons alors une méthode exacte, nommée delta-test, enrichie par la suite d'une méthode permettant la sélection automatique du nombre de variables. Dans un deuxième temps, nous développons une méthode heuristique de sélection de variables, nommée ABEUS, basée sur l'optimisation des performances du classifieur DLDA. Les résultats obtenus sur des données publiques montrent que nos méthodes permettent de sélectionner des sous-ensembles de variables de taille très faible,ce qui est un critère important permettant d'éviter le sur-apprentissage

Isolating mitochondria by subcellular fractionation is a well-established method for retrieving intact and functional mitochondria. This procedure has been used to identify proteins of the mitochondria and to explore import mechanisms. Using the same method, it was shown that mitochondria can be purified along with cytoplasmic ribosomes and nuclear-encoded mRNAs attached to the outer membrane. Combining this procedure with DNA microarray analysis allows for global identification of the mRNAs associated with mitochondria, and hence a better understanding of the underlying molecular mechanisms. In this chapter, we will describe a procedure for the isolation of mitochondria from yeast and RNA purification. We will then describe the process of labeling and hybridization to DNA microarrays, and comment on a few aspects of the data analysis.

To investigate the in vivo effects of cobalt chloride on gene expression at early time points, DNA microarray analysis was performed on the liver of mice injected subcutaneously with cobalt chloride. The liver tissue samples were taken 0.5, 1, and 3 hr after injection. Of the 14 genes up-regulated at 0.5 hr after injection, 7 are related to immunological responses, and 4 of the 7 were found to be involved in the activation of interferon.