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DNA microarray for discrimination between pathogenic 0157:H7 EDL933 and non-pathogenic Escherichia coli strains.

The primary technique currently used to detect biological agents is based on immunoassays. Although sensitive and specific, currently employed immunoassays generally rely on the detection of a single epitope, and therefore often cannot discriminate subtle strain-specific differences. Since DNA microarrays can hybridize hundreds to thousands of genomic targets simultaneously and do not rely on phenotypic expression of these genetic features for identification purposes, they have enormous potential to provide inexpensive, flexible and specific strain-specific detection and identification of pathogens. In this study, pathogenic Escherichia coli O157:H7-specific genes, non-pathogenic K12-specific genes, common E. coli genes, and negative control genes were polymerase chain reaction-amplified and spotted onto the surface of treated glass slides. After labeled bacterial cDNA samples were hybridized with probes on the microarray, specific fluorescence patterns were obtained, enabling identification of pathogenic E. coli O157:H7 and non-pathogenic E. coli K12. To test the utility of this microarray device to detect genetically engineered bacteria, E. coli BL21 (a B strain derivative with antibiotic resistance gene, ampR) and E. coli JM107 (a K12 strain derivative lacking the gene ompT) were also employed. The array successfully confirmed the strain genotypes and demonstrated that antibiotic resistance can also be detected. The ability to assess multiple data points makes this array method more efficient and accurate than a typical immunoassay, which detects a single protein product.

Colony Count, Microbial↗

Using ANOVA for gene selection from microarray studies of the nervous system.

Methods are presented for detecting differential expression using statistical hypothesis testing methods including analysis of variance (ANOVA). Practicalities of experimental design, power, and sample size are discussed. Methods for multiple testing correction and their application are described. Instructions for running typical analyses are given in the R programming environment. R code and the sample data set used to generate the examples are available at http://microarray.cpmc.columbia.edu/pavlidis/pub/aovmethods/.

Analysis of Variance↗

Combining hierarchical clustering and self-organizing maps for exploratory analysis of gene expression patterns.

Self-organizing maps (SOM) constitute an alternative to classical clustering methods because of its linear run times and superior performance to deal with noisy data. Nevertheless, the clustering obtained with SOM is dependent on the relative sizes of the clusters. Here, we show how the combination of SOM with hierarchical clustering methods constitutes an excellent tool for exploratory analysis of massive data like DNA microarray expression patterns.

Cluster Analysis↗

A highly parallel nanoliter dispenser for microarray fabrication.

We report about the correlation between satellite free droplet release and liquid viscosity in a highly parallel, pressure driven nanoliter dispenser. In extensive studies, we found that for liquids of different viscosities the duration of the pressure pulse is the predominant effect compared to pressure amplitude. This result is of essential importance when actuation parameters have to be adopted for different media like oligonucleotide, DNA or protein solutions as it is the case for the non-contact high throughput fabrication of microarrays (Ducree et al., 2000). Experiments with oligonucleotides as well as with different proteins showed ascertained carry-over and cross-contamination free printing of DNA and protein microarrays. With it a prime critical point of microarray production is solved, leading to high quality whilst high throughput microarray fabrication. For oligonucleotides printing, we found CVs to be better than 1% within one single dispensing channel and 1.5% within all 24 channels of a 24 channel printhead for each used printing buffer. By optimizing the protein printing buffer the CVs for protein printing were reduced to about 1% within all 24 channels. As a serious practical application test oligonucleotides microarrays were produced using our nanoliter dispenser system. With it a full DNA hybridization experiment was performed. Clear positive signals one hand and no signals in the negative controls on the other hand showed that our system is suited for microarray production.

Adsorption↗

Molecular genetic analysis of central nervous system germ cell tumors with comparative genomic hybridization.

The limited information available to date regarding the genetic alterations in germ cell tumors of the central nervous system has raised concerns about their biologic relationship to other germ cell tumor entities. We investigated fresh-frozen or archival tumor samples from 19 patients with central nervous system germ cell tumors (CNS-GCTs), including seven germinomas, eight malignant nongerminomatous germ cell tumors and four teratomas, using chromosomal comparative genomic hybridization to determine recurrent chromosomal imbalances. All 15 malignant CNS-GCTs and two of four teratomas showed multiple chromosomal imbalances. Chromosomal gains (median: 4 gains/tumor, range: 0-9 gains/tumor) were observed more frequently than losses (median: 1.6 losses/tumor, range: 0-6 losses/tumor). Gain of 12p, which is considered characteristic for germ cell tumors of the adult testis, was detected in 11 of 19 tumors and 10 of 15 malignant CNS-GCTs. In one tumor, gain of 12p was confined to an amplicon at 12p12, corresponding to the commonly amplified region on 12p. Other common gains were found on chromosome arms 1q and 8q (n = 9, each). Among the chromosomal losses, parts of chromosome 11 (n = 5), 18 (n = 4), and 13 (n = 3) were deleted most frequently. Notably, we observed no difference in the genetic profiles of germinomatous and nongerminomatous CNS-GCTs; however, the average number of imbalances was higher in the latter group. A meta-analysis comparing 116 malignant gonadal and extragonadal germ cell tumors revealed that the genomic alterations in CNS-GCTs are virtually indistinguishable from those found in their gonadal or other extragonadal counterparts of the corresponding age group. These data strongly argue in favor of common pathogenetic mechanisms in gonadal and extragonadal germ cell tumors.

Adolescent↗

Molecular cytogenetic analyses of breakpoints in apparently balanced reciprocal translocations carried by phenotypically normal individuals.

To test the hypothesis that translocation breakpoints in normal individuals are simple and do not disrupt genes, we characterised the breakpoints in 13 phenotypically normal individuals incidentally ascertained with an apparently balanced reciprocal translocation. Cases were karyotyped, and the breakpoints were refined by fluorescence in situ hybridisation until breakpoint-spanning clones were identified. 1 Mb array-CGH was performed as a whole genome analysis tool to detect any imbalances in chromatin not directly involved in the breakpoints. Breakpoint-associated imbalances were not found in any of the patients analysed in this study. However, breakpoints which disrupted known genes were identified in two patients, with RYR2 disrupted in one patient and COL13A1 in the other. In a further eight patients, Ensembl mapping data suggested that a gene might be disrupted by a breakpoint. In one further patient, the translocation was shown to be nonreciprocal. This study shows that apparently balanced reciprocal translocations in phenotypically normal patients do not have imbalances at the breakpoints, in contrast to phenotypically abnormal patients where the translocation breakpoints are often associated with cryptic imbalances. However, phenotypically normal individuals, and phenotypically abnormal individuals may have genes disrupted and therefore inactivated by one of the breakpoints. The significance of these disruptions remains to be determined.

Cell Line↗

A cluster of differentially expressed signal transduction genes identified by microarray analysis in a rat genetic model of alcoholism.

Analyzing gene expression patterns in genetic models of alcoholism may uncover previously unknown susceptibility genes, and point to novel targets for drug development. Here, we compared expression profiles in alcohol-preferring AA rats with the alcohol-avoiding counterpart ANA line, and unselected Wistar rats. Cingulate cortex, Nc. accumbens, amygdala and hippocampus of each line were analyzed using the Afymetrix RN U34 arrays and dChip 1.1 software. Analysis of line-specific expression revealed 48 differentially expressed genes between AA and ANA rats. Elevated hippocampal neuropeptide Y (NPY) was found in ANA rats in agreement with previous studies. A cluster of MAP-kinases indicating altered signal transduction was upregulated within the Nc. Accumbens of the AA line, and is of particular functional interest. Within the amygdala, a more loosely inter-related cluster of cytoskeleton-associated genes may point to structural abnormalities. The observed dysregulations may contribute to the alcohol-preferring phenotype.

Alcoholism↗

Disposable real-time microPCR device: lab-on-a-chip at a low cost.

We have designed, fabricated and tested a real-time micro polymerase chain reaction (microPCR) system. It consists of a microscope glass cover slip placed on top of a micromachined silicon chip integrated with a heater and a temperature sensor. A single microL of a sample containing DNA was placed on the glass and encapsulated with mineral oil to prevent the evaporation of water, thus forming a virtual reaction chamber (VRC). The PCR chip required half a second to heat up from 72 to 94 degrees C and two seconds to cool from 94 to 55 degrees C, corresponding to a cooling rate of -20 K s(-1). The real-time PCR yield was determined by a fluorescence method. The melting curve analysis method as well as capillary electrophoresis was performed to determine the purity of the PCR product. As the glass slip is disposable, cross-contamination from sample to sample is eliminated. The total cost of running the PCR is given by the value of the cover slip and its treatment.

Costs and Cost Analysis↗

Co-ordinated programme of gene expression during asexual intraerythrocytic development of the human malaria parasite Plasmodium falciparum revealed by microarray analysis.

Plasmodium falciparum is a protozoan parasite responsible for the most severe forms of human malaria. All the clinical symptoms and pathological changes seen during human infection are caused by the asexual blood stages of Plasmodium. Within host red blood cells, the parasite undergoes enormous developmental changes during its maturation. In order to analyse the expression of genes during intraerythrocytic development, DNA microarrays were constructed and probed with stage-specific cDNA. Developmental upregulation of specific mRNAs was found to cluster into functional groups and revealed a co-ordinated programme of gene expression. Those involved in protein synthesis (ribosomal proteins, translation factors) peaked early in development, followed by those involved in metabolism, most dramatically glycolysis genes. Adhesion/invasion genes were turned on later in the maturation process. At the end of intraerythrocytic development (late schizogony), there was a general shut-off of gene expression, although a small set of genes, including a number of protein kinases, were turned on at this stage. Nearly all genes showed some regulation over the course of development. A handful of genes remained constant and should be useful for normalizing mRNA levels between stages. These data will facilitate functional analysis of the P. falciparum genome and will help to identify genes with a critical role in parasite progression and multiplication in the human host.

Animals↗

High-resolution mapping of genotype-phenotype relationships in cri du chat syndrome using array comparative genomic hybridization.

We have used array comparative genomic hybridization to map DNA copy-number changes in 94 patients with cri du chat syndrome who had been carefully evaluated for the presence of the characteristic cry, speech delay, facial dysmorphology, and level of mental retardation (MR). Most subjects had simple deletions involving 5p (67 terminal and 12 interstitial). Genotype-phenotype correlations localized the region associated with the cry to 1.5 Mb in distal 5p15.31, between bacterial artificial chromosomes (BACs) containing markers D5S2054 and D5S676; speech delay to 3.2 Mb in 5p15.32-15.33, between BACs containing D5S417 and D5S635; and the region associated with facial dysmorphology to 2.4 Mb in 5p15.2-15.31, between BACs containing D5S208 and D5S2887. These results overlap and refine those reported in previous publications. MR depended approximately on the 5p deletion size and location, but there were many cases in which the retardation was disproportionately severe, given the 5p deletion. All 15 of these cases, approximately two-thirds of the severely retarded patients, were found to have copy-number aberrations in addition to the 5p deletion. Restriction of consideration to patients with only 5p deletions clarified the effect of such deletions and suggested the presence of three regions, MRI-III, with differing effect on retardation. Deletions including MRI, a 1.2-Mb region overlapping the previously defined cri du chat critical region but not including MRII and MRIII, produced a moderate level of retardation. Deletions restricted to MRII, located just proximal to MRI, produced a milder level of retardation, whereas deletions restricted to the still-more proximal MRIII produced no discernible phenotype. However, MR increased as deletions that included MRI extended progressively into MRII and MRIII, and MR became profound when all three regions were deleted.

Adult↗

Microarray analyses of gene expression during chondrocyte differentiation identifies novel regulators of hypertrophy.

Ordered chondrocyte differentiation and maturation is required for normal skeletal development, but the intracellular pathways regulating this process remain largely unclear. We used Affymetrix microarrays to examine temporal gene expression patterns during chondrogenic differentiation in a mouse micromass culture system. Robust normalization of the data identified 3300 differentially expressed probe sets, which corresponds to 1772, 481, and 249 probe sets exhibiting minimum 2-, 5-, and 10-fold changes over the time period, respectively. GeneOntology annotations for molecular function show changes in the expression of molecules involved in transcriptional regulation and signal transduction among others. The expression of identified markers was confirmed by RT-PCR, and cluster analysis revealed groups of coexpressed transcripts. One gene that was up-regulated at later stages of chondrocyte differentiation was Rgs2. Overexpression of Rgs2 in the chondrogenic cell line ATDC5 resulted in accelerated hypertrophic differentiation, thus providing functional validation of microarray data. Collectively, these analyses provide novel information on the temporal expression of molecules regulating endochondral bone development.

Animals↗

Unsupervised pattern recognition: an introduction to the whys and wherefores of clustering microarray data.

Clustering has become an integral part of microarray data analysis and interpretation. The algorithmic basis of clustering -- the application of unsupervised machine-learning techniques to identify the patterns inherent in a data set -- is well established. This review discusses the biological motivations for and applications of these techniques to integrating gene expression data with other biological information, such as functional annotation, promoter data and proteomic data.

Algorithms↗

AMADA: analysis of microarray data.

SUMMARY: AMADA is a Windows program for identifying co-expressed genes from microarray data. It performs data transformation, principal component analysis, a variety of cluster analyses and extensive graphic functions for visualizing expression profiles.

Algorithms↗

Partial least squares proportional hazard regression for application to DNA microarray survival data.

MOTIVATION: Microarrays are increasingly used in cancer research. When gene transcription data from microarray experiments also contains patient survival information, it is often of interest to predict the survival times based on the gene expression. In this paper we consider the well-known proportional hazard (PH) regression model for survival analysis. Ordinarily, the PH model is used with a few covariates and many observations (subjects). We consider here the case that the number of covariates, p, exceeds the number of samples, N, a setting typical of gene expression data from DNA microarrays. RESULTS: For a given vector of response values which are survival times and p gene expressions (covariates) we examine the problem of how to predict the survival probabilities, when N << p. The approach taken to cope with the high dimensionality is to reduce the dimension using partial least squares with the response variable as the vector of survival times. After dimension reduction, the extracted PLS gene components are then used as covariates in a PH regression to predict the survival probabilities. We demonstrate the use of the methodology on two cDNA gene expression data sets, both containing survival data. The first data set contains 40 diffuse large B-cell lymphoma (DLBCL) tissue samples and the second data set contains 49 tissue samples from patients with locally advanced breast cancer in a prospective study.

Breast Neoplasms↗

Multi-class cancer classification via partial least squares with gene expression profiles.

MOTIVATION: Discrimination between two classes such as normal and cancer samples and between two types of cancers based on gene expression profiles is an important problem which has practical implications as well as the potential to further our understanding of gene expression of various cancer cells. Classification or discrimination of more than two groups or classes (multi-class) is also needed. The need for multi-class discrimination methodologies is apparent in many microarray experiments where various cancer types are considered simultaneously. RESULTS: Thus, in this paper we present the extension to the classification methodology proposed earlier Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50) to classify cancer samples from multiple classes. The methodologies proposed in this paper are applied to four gene expression data sets with multiple classes: (a) a hereditary breast cancer data set with (1) BRCA1-mutation, (2) BRCA2-mutation and (3) sporadic breast cancer samples, (b) an acute leukemia data set with (1) acute myeloid leukemia (AML), (2) T-cell acute lymphoblastic leukemia (T-ALL) and (3) B-cell acute lymphoblastic leukemia (B-ALL) samples, (c) a lymphoma data set with (1) diffuse large B-cell lymphoma (DLBCL), (2) B-cell chronic lymphocytic leukemia (BCLL) and (3) follicular lymphoma (FL) samples, and (d) the NCI60 data set with cell lines derived from cancers of various sites of origin. In addition, we evaluated the classification algorithms and examined the variability of the error rates using simulations based on randomization of the real data sets. We note that there are other methods for addressing multi-class prediction recently and our approach is along the line of Nguyen and Rocke (2002b; Bioinformatics, 18, 39-50). CONTACT: dnguyen@stat.tamu.edu; dmrocke@ucdavis.edu

Algorithms↗

mdclust--exploratory microarray analysis by multidimensional clustering.

MOTIVATION: Unsupervised clustering of microarray data may detect potentially important, but not obvious characteristics of samples, for instance subgroups of diagnoses with distinct gene profiles or systematic errors in experimentation. RESULTS: Multidimensional clustering (mdclust) is a method, which identifies sets of sample clusters and associated genes. It applies iteratively two-means clustering and score-based gene selection. For any phenotype variable best matching sets of clusters can be selected. This provides a method to identify gene-phenotype associations, suited even for settings with a large number of phenotype variables. An optional model based discriminant step may reduce further the number of selected genes.

Cluster Analysis↗