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At least 19 recordsLinked to original sources
PubMed2006

The promise and perils of microarray analysis.

Microarray analysis has provided a novel means of identifying clues into the mechanisms of disease development. As a methodology, microarray analysis holds the promise for genome-wide screening in which 2 tissues (diseased and normal) are compared, and molecular pathways that defined the phenotype of the disease could be precisely defined. Alternatively, microarray experiments can be used to differentially compare pathologically similar diseased tissues to predict response to chemotherapy and risk of recurrence. However, the clinician should be aware that various sources of error can influence microarray analysis results. Sources of error can be minimized but not eliminated, explaining why meticulously conducted experiments in different laboratories or using different platforms result in different lists of genes. Confirmation and validation of genome-wide microarray results using ancillary methods remains a critical step. With proper confirmatory studies and cautious interpretation, microarray analysis represents a powerful tool for molecular discovery.

Female↗
PubMed2001

Glioma cell motility is associated with reduced transcription of proapoptotic and proliferation genes: a cDNA microarray analysis.

Microarray analysis of complementary DNA (cDNA) allows large-scale, comparative, gene expression profiling of two different cell populations. This approach has the potential for elucidating the primary transcription events and genetic cascades responsible for increased glioma cell motility in vitro and invasion in vivo. These genetic determinants could become therapeutic targets. We compared cDNA populations of a glioma cell line (G112) exposed or not to a motility-inducing substrate of cell-derived extracellular matrix (ECM) proteins using two sets of cDNA microarrays of 5,700 and 7,000 gene sequences. The data were analyzed considering the level and consistency of differential expression (outliers) and whether genes involved in pathways of motility, apoptosis, and proliferation were differentially expressed when the motility behavior was engaged. Validation of differential expression of selected genes was performed on additional cell lines and human glioblastoma tissue using quantitative RT-PCR. Some genes involved in cell motility, like tenascin C, neuropilin 2, GAP43, PARG1 (an inhibitor of Rho), PLCy, and CD44, were over expressed; other genes, like adducin 3y and integrins, were down regulated in migrating cells. Many key cell cycle components, like cyclin A and B, and proliferation markers, like PCNA, were strongly down regulated on ECM. Interestingly, genes involved in apoptotic cascades, like Bcl-2 and effector caspases, were differentially expressed, suggesting the global down regulation of proapoptotic components in cells exposed to cell-derived ECM. Overall, our findings indicate a reduced proliferative and apoptotic activity of migrating cells. cDNA microarray analysis has the potential for uncovering genes linking the phenotypic aspects of motility, proliferation, and apoptosis.

Apoptosis↗
PubMed2006

Comparative microarray analysis.

Microarrays enable high-throughput parallel gene expression analysis, and their use has grown exponentially during the past decade. We are now in a position where individual experiments could benefit from using the swelling public data repositories to allow microarrays to progress from being a hypothesis-generating tool to a powerful resource that can be used to test hypothesis about biology. Comparative microarray analysis could better distinguish phenotypes from associated phenotypes; identify valid differentially expressed genes by combining many studies; test new hypothesis; and discover fundamental patterns of gene regulation. This review aims to describe the additional methodology needed for such comparative microarray analysis, and we identify and discuss a number of problems such as loss of published data, lack of annotations, and variable array quality, which need to be solved before comparative microarray analysis can be used in a more systematic and powerful manner.

Animals↗
PubMed2004

Expressions of hepatic genes, especially IGF-binding protein-1, correlating with serum corticosterone in microarray analysis.

Microarray technology was evaluated for usefulness in assessing relationships between serum corticosterone and hepatic gene expression. Nine pairs of female Swiss mice were chosen to provide a wide range of serum corticosterone ratios; cDNA microarray analysis (approximately 8000 genes) was performed on their livers. A statistical method based on calculation of 99% confidence intervals discovered 32 genes which varied significantly among the livers. Five of these ratios correlated significantly with serum corticosterone ratio, including tyrosine aminotransferase, stress-induced protein, pleiotropic regulator 1 and insulin-like growth factor-binding protein-1; the latter has a potential role in cancer development. Secondly, linear regression of gene expression vs corticosterone ratios was screened for those with r> or =0.8 (P<0.01), yielding 141 genes, including some known to be corticosterone regulated and others of interest as possible glucocorticoid targets. Half of these significant correlations involved data sets where no microarray ratio exceeded +/- 1.5. These results showed that microarray may be used to survey tissues for changes in gene expression related to serum hormones, and that even small changes in expression can be of statistical significance in a study with adequate numbers of replicate samples.

Animals↗
PubMed2005

Comments on selected fundamental aspects of microarray analysis.

Microarrays are becoming a ubiquitous tool of research in life sciences. However, the working principles of microarray-based methodologies are often misunderstood or apparently ignored by the researchers who actually perform and interpret experiments. This in turn seems to lead to a common over-expectation regarding the explanatory and/or knowledge-generating power of microarray analyses. In this note we intend to explain basic principles of five (5) major groups of analytical techniques used in studies of microarray data and their interpretation: the principal component analysis (PCA), the independent component analysis (ICA), the t-test, the analysis of variance (ANOVA), and self organizing maps (SOM). We discuss answers to selected practical questions related to the analysis of microarray data. We also take a closer look at the experimental setup and the rules, which have to be observed in order to exploit microarrays efficiently. Finally, we discuss in detail the scope and limitations of microarray-based methods. We emphasize the fact that no amount of statistical analysis can compensate for (or replace) a well thought through experimental setup. We conclude that microarrays are indeed useful tools in life sciences but by no means should they be expected to generate complete answers to complex biological questions. We argue that even well posed questions, formulated within a microarray-specific terminology, cannot be completely answered with the use of microarray analyses alone.

Computational Biology↗
PubMed2000

A concise guide to cDNA microarray analysis.

Microarray expression analysis has become one of the most widely used functional genomics tools. Efficient application of this technique requires the development of robust and reproducible protocols. We have optimized all aspects of the process, including PCR amplification of target cDNA clones, microarray printing, probe labeling and hybridization, and have developed strategies for data normalization and analysis.

DNA, Complementary↗
PubMedDate not supplied

Clinical applications of DNA microarray analysis.

Microarray technology provides a revolutionary macro-genetic and bioinformatic-rich platform for understanding human diseases. DNA microarrays facilitate the study of complex diseases, enabling several observations simultaneously that can become foundations for newer hypotheses-shifting us towards a non-reductionist approach to biological phenomenon. This appears of particular value for scientific and clinical dissection of tumor pathologies. Despite the tremendous potential presented by microarray technology for the investigation of disease, concrete insights and advances that translate to the clinical setting are only recently beginning to be tapped. Here, we discuss specific examples of how microarray technology is being integrated into our ever-evolving approach to clinical disease. We focus on molecular strategies for (a) disease classification, (b) disease outcome, and (c) disease mechanisms.

Gene Expression Profiling↗
PubMed2003

Plac8 and Plac9, novel placental-enriched genes identified through microarray analysis.

Microarray expression profiling of a collection of 15,000 mouse genes with placental and embryonic RNAs revealed candidates for placental-enriched genes, three of which we have confirmed and further characterized. One, Plac1, strongly expressed in all trophoblast-derived cells in the placenta, has been described earlier (Genomics 68 (2000) 305). Here we report that of the other two, Plac8 expression is restricted to the spongiotrophoblast layer during development, whereas Plac9 is weakly expressed though highly enriched in placenta. For both, cDNAs with complete open reading frames were recovered and exon-intron structures inferred from comparisons of mouse cDNA and genomic sequence. The predicted proteins (112 and 108 amino acids) both contain putative signal peptides, with a coiled-coil segment of mPLAC9 as the only other detected motif. Genomic sequence comparisons reveal that in addition to an apparent pseudogene on chromosome 1, Plac8 is expressed at mouse cytoband 5e3. It is tightly conserved in human in a syntenically equivalent ortholog at 4q21.23. Plac9 is present in a single copy on chromosome 14, with a syntenically equivalent human ortholog at 10q22.3. Putative promoter regions up to 10 kb 5' of the transcription units for Plac1, Plac8, and Plac9 contain sites for widely-expressed transcription factors which, by analogy to other instances, may be sufficient to explain placental enrichment.

Amino Acid Sequence↗
PubMed2006

Random data set generation to support microarray analysis.

As microarray analyses become increasingly routine, involving the simultaneous investigation of huge numbers of genes, researchers can easily search for and uncover what appear to be promising patterns in their data. In such circumstances tools are needed to help decide the extent to which these patterns are meaningful or can be explained by chance alone. The purpose of this chapter is to describe examples of the use of microarray analysis for inferential purposes and how validation of inference is addressed by Monte-Carlo techniques, which essentially amounts to investigation of statistical methods on synthetic or random data sets.

Animals↗
PubMed2003

Automated high-throughput probe production for DNA microarray analysis.

DNA microarrays have become an established tool for gene expression profiling. Construction of these microarrays using immobilized cDNAs is a common experimental strategy. However, this is extremely laborious, requiring the preparation of hundreds or thousands of cDNA probes. To minimize this initial bottleneck, we developed a comprehensive high-throughput robotic system to prepare DNA probes suitable for microarray analysis with minimal user intervention. We describe an automated system using the MultiPROBE Nucleic Acid Purification Workstation to provide the liquid handling and other functions needed to optimize this process. We were able to carry out fully automated plasmid cDNA isolation, PCR assay setup, and PCR purification and also to direct the characterization and tracking of DNA probes during processing. Protocols began with the initial preparation of a plasmid DNA archive of bacterial stocks in parallel 96-well plates (192 samples/run) and continued through to the dilution and reformatting of chip-ready DNA probes in 384-well format. These and other probe production procedures and additional instrument systems were used to process fully a set of mouse cDNA clones that were then validated by differential gene expression analysis.

Animals↗
PubMed2005

Tumor heterogeneity affects the precision of microarray analysis.

Microarray-based analysis of global gene expression patterns defines groups of genes that correlate with specific tumor types and prognosis, but the identified genes may not all be of equal clinical utility due to technical factors that affect the precision of their measurement. To analyze how technical variability in measured expression levels may impact microarray-based analysis in a clinical setting, we used Ewing sarcoma/peripheral neuroectodermal tumor (EWS/PNET) in a model system that replicates the clinical scenario in which microarray-based analysis of gene expression will likely occur, namely analysis of a fresh tumor sample by a single chip. By comparing variability of measured expression due to purely technical factors with variability due to biologic factors, we confirm that variability is dependent on the level of gene expression. We also demonstrate that the variability in expression level from either cell line or tumor samples is significantly higher than can be attributed to specific probe sets that have an intrinsically poor performance. These results have significant impact on the application of cDNA microarray chip for molecular analysis performed in a clinical setting.

Bone Neoplasms↗
PubMed1999

Informatic selection of a neural crest-melanocyte cDNA set for microarray analysis.

With cDNA microarrays, it is now possible to compare the expression of many genes simultaneously. To maximize the likelihood of finding genes whose expression is altered under the experimental conditions, it would be advantageous to be able to select clones for tissue-appropriate cDNA sets. We have taken advantage of the extensive sequence information in the dbEST expressed sequence tag (EST) database to identify a neural crest-derived melanocyte cDNA set for microarray analysis. Analysis of characterized genes with dbEST identified one library that contained ESTs representing 21 neural crest-expressed genes (library 198). The distribution of the ESTs corresponding to these genes was biased toward being derived from library 198. This is in contrast to the EST distribution profile for a set of control genes, characterized to be more ubiquitously expressed in multiple tissues (P < 1 x 10(-9)). From library 198, a subset of 852 clustered ESTs were selected that have a library distribution profile similar to that of the 21 neural crest-expressed genes. Microarray analysis demonstrated the majority of the neural crest-selected 852 ESTs (Mel1 array) were differentially expressed in melanoma cell lines compared with a non-neural crest kidney epithelial cell line (P < 1 x 10(-8)). This was not observed with an array of 1,238 ESTs that was selected without library origin bias (P = 0.204). This study presents an approach for selecting tissue-appropriate cDNAs that can be used to examine the expression profiles of developmental processes and diseases.

Calibration↗
PubMed2005

Comparative phylogenomics of pathogenic bacteria by microarray analysis.

DNA microarrays represent a powerful technology that enables whole-scale comparison of bacterial genomes. This, coupled with new methods to model DNA microarray data, is facilitating the development of robust comparative phylogenomics analyses. Such studies have dramatically increased our ability to differentiate between bacteria, highlighting previously undetected genetic differences and population structures and providing new insight into virulence and evolution of bacterial pathogens. Recent results from such studies have generated insights into the evolution of bacterial pathogens, the levels of diversity and plasticity in the genome of a species, as well as the differences in virulence amongst pathogenic bacteria.

Bacteria↗
PubMed2001

Rapid and diverse changes of gene expression in the kidneys of protein-overload proteinuria mice detected by microarray analysis.

BACKGROUND: Microarray is a method that allows the analysis of a large number of genes at the same time. We applied this method to show the difference of gene expression in the kidney caused by proteinuria. METHODS: An experimental mouse model of protein overload was prepared by bovine serum albumin injection. The mRNAs of kidneys isolated after 0, 1, 2, 3 and 4 weeks loading were analysed by Northern blotting. We analysed about 18000 genes by microarray. The expression patterns of the microarray were displayed on control, 1 and 3 weeks of protein overload using the clustering procedure. A clone showing the greatest changes of up-regulation in the kidney was cloned and analysed by in situ hybridization and immunohistochemistry. RESULTS: Over 1600 kinds of gene expression were confirmed in control kidneys. Proteinuria caused systematic changes of gene expression demonstrated by the cluster analysis. The up-regulation of osteopontin mRNA was shown and confirmed by Northern blot analysis. One of the clones showing the largest changes, AA275245, was isolated and characterized. It revealed that AA275245 was an unreported 3' non-coding region of vinculin mRNA which was associated with cytoskeleton proteins (e.g. alpha-actinin, talin, F-actin). Immunohistochemistry and in situ hybridization showed that this clone was identified in glomeruli as a mesangial pattern. The detected signal intensity using both methods, however, was virtually identical in control and disease kidney models. All data including images and analysed signal intensities are accessible on the web site. CONCLUSION: The microarray analysis revealed that the renal gene expression pattern was changed dynamically in mice with experimentally induced proteinuria within a few weeks.

Actins↗
PubMed2005

Identification of molecular apocrine breast tumours by microarray analysis.

Previous microarray studies on breast cancer identified multiple tumour classes, of which the most prominent, named luminal and basal, differ in expression of the oestrogen receptor alpha gene (ER). We report here the identification of a group of breast tumours with increased androgen signalling and a 'molecular apocrine' gene expression profile. Tumour samples from 49 patients with large operable or locally advanced breast cancers were tested on Affymetrix U133A gene expression microarrays. Principal components analysis and hierarchical clustering split the tumours into three groups: basal, luminal and a group we call molecular apocrine. All of the molecular apocrine tumours have strong apocrine features on histological examination (P=0.0002). The molecular apocrine group is androgen receptor (AR) positive and contains all of the ER-negative tumours outside the basal group. Kolmogorov-Smirnov testing indicates that oestrogen signalling is most active in the luminal group, and androgen signalling is most active in the molecular apocrine group. ERBB2 amplification is commoner in the molecular apocrine than the other groups. Genes that best split the three groups were identified by Wilcoxon test. Correlation of the average expression profile of these genes in our data with the expression profile of individual tumours in four published breast cancer studies suggest that molecular apocrine tumours represent 8-14% of tumours in these studies. Our data show that it is possible with microarray data to divide mammary tumour cells into three groups based on steroid receptor activity: luminal (ER+ AR+), basal (ER- AR-) and molecular apocrine (ER- AR+).

Apocrine Glands↗
PubMed2006

Evaluation and comparison of gene clustering methods in microarray analysis.

MOTIVATION: Microarray technology has been widely applied in biological and clinical studies for simultaneous monitoring of gene expression in thousands of genes. Gene clustering analysis is found useful for discovering groups of correlated genes potentially co-regulated or associated to the disease or conditions under investigation. Many clustering methods including hierarchical clustering, K-means, PAM, SOM, mixture model-based clustering and tight clustering have been widely used in the literature. Yet no comprehensive comparative study has been performed to evaluate the effectiveness of these methods. RESULTS: In this paper, six gene clustering methods are evaluated by simulated data from a hierarchical log-normal model with various degrees of perturbation as well as four real datasets. A weighted Rand index is proposed for measuring similarity of two clustering results with possible scattered genes (i.e. a set of noise genes not being clustered). Performance of the methods in the real data is assessed by a predictive accuracy analysis through verified gene annotations. Our results show that tight clustering and model-based clustering consistently outperform other clustering methods both in simulated and real data while hierarchical clustering and SOM perform among the worst. Our analysis provides deep insight to the complicated gene clustering problem of expression profile and serves as a practical guideline for routine microarray cluster analysis.

Algorithms↗
PubMed2001

Optimized T7 amplification system for microarray analysis.

Glass cDNA microarray technologies offer a highly parallel approach for profiling expressed gene sequences in disease-relevant tissues. However, standard hybridization and detection protocols are insufficient for milligram quantities of tissue, such as those derived from needle biopsies. Amplification systems utilizing T7 RNA polymerase can provide multiple cRNA copies from mRNA transcripts, permitting microarray studies with reduced sample inputs. Here, we describe an optimized T7-based amplification system for microarray analysis that yields between 200- and 700-fold amplification. This system was evaluated with both mRNA and total RNA samples and provided microarray sensitivity and precision that are comparable to our standard production process without amplification. The size distributions of amplified cRNA ranged from 200 bp to 4 kb and were similar to original mRNA profiles. These amplified cRNA samples were fluorescently labeled by reverse transcription and hybridized to microarrays comprising approximately 10,000 cDNA targets using a dual-channel format. Replicate hybridization experiments were conducted with the same and different tissues in each channel to assess the sensitivity and precision of differential expression ratios. Statistical analysis of differential expression ratios showed the lower limit of detection to be about 2-fold within and between amplified data sets, and about 3-fold when comparing amplified data to unamplified data (99.5% confidence).

Biotechnology↗
PubMed2002

PRIMEGENS: robust and efficient design of gene-specific probes for microarray analysis.

MOTIVATION: DNA microarray is a powerful high-throughput tool for studying gene function and regulatory networks. Due to the problem of potential cross hybridization, using full-length genes for microarray construction is not appropriate in some situations. A bioinformatic tool, PRIMEGENS, has recently been developed for the automatic design of PCR primers using DNA fragments that are specific to individual open reading frames (ORFs). RESULTS: PRIMEGENS first carries out a BLAST search for each target ORF against all other ORFs of the genome to quickly identify possible homologous sequences. Then it performs optimal sequence alignment between the target ORF and each of its homologous ORFs using dynamic programming. PRIMEGENS uses the sequence alignments to select gene- specific fragments, and then feeds the fragments to the Primer3 program to design primer pairs for PCR amplification. PRIMEGENS can be run from the command line on Unix/Linux platforms as a stand-alone package or it can be used from a Web interface. The program runs efficiently, and it takes a few seconds per sequence on a typical workstation. PCR primers specific to individual ORFs from Shewanella oneidensis MR-1 and Deinococcus radiodurans R1 have been designed. The PCR amplification results indicate that this method is very efficient and reliable for designing specific probes for microarray analysis.

Algorithms↗