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PubMed2002

Development and application of new nucleic acid-based technologies for microbial community analyses in foods.

Several challenges still persist in the analysis of microorganisms in foods, particularly in studies of complex communities. Nucleic acid-based methods are promising tools in addressing new questions concerning microbial communities. We have developed several new methods in the field of nucleic acid-based microbial community analyses. These methods cover both sample preparation and detection approaches. The sample preparation method involves simplified DNA purification using paramagnetic beads. As an extension of this method, the same paramagnetic beads are used for both cell separation and DNA purification. This enables full automation. The separate detection of viable and dead bacteria is a major issue in nucleic acid-based diagnostics. We have applied a living/dead dye that binds covalently to DNA and inhibits the PCR from dead cells. In addition, a DNA array-based detection assay has been developed. The assay combines the specificity obtained by enzymatic labeling of DNA probes with the possibility of detecting several targets simultaneously by DNA array hybridization. In combination with 16S rDNA amplification, this is a promising tool for community analyses. Also, we have developed a novel approach for multiplex quantitative PCR. The multiplex PCR has been combined with our DNA array-based detection method. Finally, we are now in the process of adapting a system for monitoring microbial growth and death in real-time through the tagging of bacteria with green fluorescent protein (GFP) combined with fluorescence detection using a high-resolution confocal laser scanner.

Bacteria↗
PubMedDate not supplied

Analysis and visualization of gene expression data using self-organizing maps.

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by computing a SOM of yeast genes. The relationships of known functional classes of genes are investigated by analyzing their distribution on the SOM, the cluster structure is visualized by the U-matrix method, and the clusters are characterized in terms of the properties of the expression profiles of the genes. Finally, it is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.

Cluster Analysis↗
PubMed2001

Navigating gene expression using microarrays--a technology review.

Parallel quantification of large numbers of messenger RNA transcripts using microarray technology promises to provide detailed insight into cellular processes involved in the regulation of gene expression. This should allow new understanding of signalling networks that operate in the cell and of the molecular basis and classification of disease. But can the technology deliver such far-reaching promises?

Animals↗
PubMed2002

Amine-modified random primers to label probes for DNA microarrays.

DNA microarrays have been used to study the expression of thousands of genes at the same time in a variety of cells and tissues. The methods most commonly used to label probes for microarray studies require a minimum of 20 microg of total RNA or 2 microg of poly(A) RNA. This has made it difficult to study small and rare tissue samples. RNA amplification techniques and improved labeling methods have recently been described. These new procedures and reagents allow the use of less input RNA, but they are relatively time-consuming and expensive. Here we introduce a technique for preparing fluorescent probes that can be used to label as little as 1 microg of total RNA. The method is based on priming cDNA synthesis with random hexamer oligonucleotides, on the 5' ends of which are bases with free amino groups. These amine-modified primers are incorporated into the cDNA along with aminoallyl nucleotides, and fluorescent dyes are then chemically added to the free amines. The method is simple to execute, and amine-reactive dyes are considerably less expensive than dye-labeled bases or dendrimers.

3T3 Cells↗
PubMed2005

Integrative analysis of the cancer transcriptome.

DNA microarrays have been widely applied to the study of human cancer, delineating myriad molecular subtypes of cancer, many of which are associated with distinct biological underpinnings, disease progression and treatment response. These primary analyses have begun to decipher the molecular heterogeneity of cancer, but integrative analyses that evaluate cancer transcriptome data in the context of other data sources are often capable of extracting deeper biological insight from the data. Here we discuss several such integrative computational and analytical approaches, including meta-analysis, functional enrichment analysis, interactome analysis, transcriptional network analysis and integrative model system analysis.

Animals↗
PubMed2006

Use of microchip-based hydrodynamic focusing to measure the deformation-induced release of ATP from erythrocytes.

In order to understand the role that erythrocytes play in conditions such as pulmonary hypertension, in vitro mimics of the microcirculation are needed. This paper describes the use of microchip-based hydrodynamic focusing to develop a mimic that allows both mechanical deformation of erythrocytes and quantification of the adenosine triphosphate (ATP) that is subsequently released in response to this deformation. In this mimic, two sheathing streams of a luciferin/luciferase mixture are used to focus and deform a central fluid flow of an erythrocyte sample. The focusing width is changed by simply manipulating the sheath flow rate. This allows a variety of cross-sectional areas to be studied using single point chemiluminescent detection. It was shown that increasing the sheath flow rate does result in elevated levels of ATP release. For example, one sample of rabbit erythrocytes released 0.80 (+/- 0.13) microM ATP when focused to a cross-section of 3480 microm(2), while focusing the same sample to a smaller cross-section (1160 microm(2)) led to a release of 6.43 (+/- 0.40) microM ATP. In addition, two different inhibitors, diamide and glibenclamide, were used to ensure a lack of cell lysis. This approach can be used to examine a wide range of deformation forces in a high throughput fashion and will be of interest to researchers studying the mechanisms leading to vasodilation in the microvasculature.

Adenosine Triphosphate↗
PubMed2002

Global gene expression patterns during neural differentiation of P19 embryonic carcinoma cells.

The nervous system is composed of many different types of neurons and glia cells. Differentiation of these cell types is regulated by various intrinsic transcriptional programs as well as extrinsic signals. Studies of neural differentiation have been focused on the roles of individual factors. Here we profiled global gene expression patterns during neural differentiation of P19 embryonic carcinoma cells. Grouping of the genes induced during P19 neural differentiation into functional categories reveals a set of important transcription factors and extracellular signaling pathways, many of which are also involved in neural development in vivo. In addition, clustering of the induced genes according to their temporal expression pattern reveals 6 groups of genes, each with distinct kinetics, suggesting the existence of different phases in P19 neural differentiation. Our studies provide a temporal array of global pictures of the gene expression patterns used during neural differentiation. The results of this study provide the framework for subsequent analysis of the effects of various intrinsic and extrinsic factors on neural differentiation.

Animals↗
PubMed2003

Gene expression profile of tissue engineered skin subjected to acute barrier disruption.

The main function of the skin is to protect the body from infection, dehydration, and other environmental insults by creating an impermeable barrier of cornified cell layers, the stratum corneum. In contrast to cells in culture, tissue-engineered skin equivalents contain well-developed basal, spinous, granular, and cornified cell layers providing an excellent model to study the tissue response to barrier disruption. After 7 d of culture at the air-liquid interface the barrier of the tissues was disrupted by short exposure to acetone and the global gene expression profile of the tissues was evaluated using DNA microarrays. We found that tissue-engineered skin responds to barrier disruption by a two-wave dynamic response. Early on, the cells upregulate signal transducing, stress, proliferation, and inflammation genes to protect the tissue and possibly to communicate the damage to the immune system and neighboring tissues. At later times, pro-inflammatory cytokines and some growth-related genes are significantly reduced but enzymes that participate in lipid synthesis increase, suggesting that the epidermal cells attempt to restore the lost barrier. Quantitative immunostaining for the proliferation antigen Ki67 revealed that barrier disruption by acetone increased proliferation by 4-fold in agreement with the microarray data and previous in vivo studies. Our work suggests that functional genomics may be used in tissue engineering to understand tissue development, wound regeneration, and response to environmental stimuli. A better understanding of engineered tissues at the molecular level may facilitate their application in the clinic and as biosensors for toxicologic testing.

Acetone↗
PubMed2006

Homozygosity mapping with SNP arrays identifies TRIM32, an E3 ubiquitin ligase, as a Bardet-Biedl syndrome gene (BBS11).

The identification of mutations in genes that cause human diseases has largely been accomplished through the use of positional cloning, which relies on linkage mapping. In studies of rare diseases, the resolution of linkage mapping is limited by the number of available meioses and informative marker density. One recent advance is the development of high-density SNP microarrays for genotyping. The SNP arrays overcome low marker informativity by using a large number of markers to achieve greater coverage at finer resolution. We used SNP microarray genotyping for homozygosity mapping in a small consanguineous Israeli Bedouin family with autosomal recessive Bardet-Biedl syndrome (BBS; obesity, pigmentary retinopathy, polydactyly, hypogonadism, renal and cardiac abnormalities, and cognitive impairment) in which previous linkage studies using short tandem repeat polymorphisms failed to identify a disease locus. SNP genotyping revealed a homozygous candidate region. Mutation analysis in the region of homozygosity identified a conserved homozygous missense mutation in the TRIM32 gene, a gene coding for an E3 ubiquitin ligase. Functional analysis of this gene in zebrafish and expression correlation analyses among other BBS genes in an expression quantitative trait loci data set demonstrate that TRIM32 is a BBS gene. This study shows the value of high-density SNP genotyping for homozygosity mapping and the use of expression correlation data for evaluation of candidate genes and identifies the proteasome degradation pathway as a pathway involved in BBS.

Animals↗
PubMed2001

Cascades of transcriptional induction during human lymphocyte activation.

Lymphocyte activation is known to be associated with the induction of genes implicated in cytokine signaling and cellular proliferation. High-density microarrays offer the means to monitor global cellular expression profiles, temporal relationships between classes of transcripts, and alterations associated with human disease or immunosuppression. We sought to determine whether microarray analysis would accurately reflect the normal pattern of gene expression following human T cell activation, and whether the complex expression patterns identified could be analyzed to produce a functional profile of lymphocyte activation. We examined a time course of sequential expression profiles for 6,800 cellular transcripts in human lymphocytes activated with concanavalin A. Expression patterns were grouped using clustering analysis and validated using Northern blotting. Genes known to be induced following T cell activation were accurately identified, and the qualitative patterns of gene expression were well correlated between Northern and microarray analyses. Quantitative differences in gene expression levels were less well correlated between these two techniques. Expression profile analysis revealed the sequential induction of groups of functionally similar genes, whose temporal coregulation underscores known cellular events during T cell activation. This functional "fingerprint" of lymphocyte activation may prove useful for comparisons of lymphocyte responses under experimental conditions and in disease states.

Cluster Analysis↗
PubMed2005

Statistical analysis of microarray data.

Microarrays promise dynamic snapshots of cell activity, but microarray results are unfortunately not straightforward to interpret. This article aims to distill the most useful practical results from the vast body of literature available on microarray data analysis. Topics covered include: experimental design issues, normalization, quality control, exploratory analysis, and tests for differential expression. Special attention is paid to the peculiarities of low-level analysis of Affymetrix chips, and the multiple testing problem in determining differential expression. The aim of this article is to provide useful answers to the most common practical issues in microarray data analysis. The main topics are pre-processing (normalization), and detecting differential expression. Subsidiary topics include experimental design, and exploratory analysis. Further discussion is found at the author's web page (http://discover.nci.nih.gov --> Notes on Microarray Data Analysis).

Cluster Analysis↗
PubMed2004

Evaluating methods for classifying expression data.

An attractive application of expression technologies is to predict drug efficacy or safety using expression data of biomarkers. To evaluate the performance of various classification methods for building predictive models, we applied these methods on six expression datasets. These datasets were from studies using microarray technologies and had either two or more classes. From each of the original datasets, two subsets were generated to simulate two scenarios in biomarker applications. First, a 50-gene subset was used to simulate a candidate gene approach when it might not be practical to measure a large number of genes/biomarkers. Next, a 2000-gene subset was used to simulate a whole genome approach. We evaluated the relative performance of several classification methods by using leave-one-out cross-validation and bootstrap cross-validation. Although all methods perform well in both subsets for a relative easy dataset with two classes, differences in performance do exist among methods for other datasets. Overall, partial least squares discriminant analysis (PLS-DA) and support vector machines (SVM) outperform all other methods. We suggest a practical approach to take advantage of multiple methods in biomarker applications.

Algorithms↗
PubMed2001

Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

MOTIVATION: Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied. RESULTS: We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies.

Algorithms↗
PubMed2002

Tumor classification by partial least squares using microarray gene expression data.

MOTIVATION: One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. The use of microarrays allows simultaneous monitoring of thousands of genes expressions per sample. This ability to measure gene expression en masse has resulted in data with the number of variables p(genes) far exceeding the number of samples N. Standard statistical methodologies in classification and prediction do not work well or even at all when N < p. Modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. RESULTS: We propose a novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions. This procedure involves dimension reduction using Partial Least Squares (PLS) and classification using Logistic Discrimination (LD) and Quadratic Discriminant Analysis (QDA). We compare PLS to the well known dimension reduction method of Principal Components Analysis (PCA). Under many circumstances PLS proves superior; we illustrate a condition when PCA particularly fails to predict well relative to PLS. The proposed methods were applied to five different microarray data sets involving various human tumor samples: (1) normal versus ovarian tumor; (2) Acute Myeloid Leukemia (AML) versus Acute Lymphoblastic Leukemia (ALL); (3) Diffuse Large B-cell Lymphoma (DLBCLL) versus B-cell Chronic Lymphocytic Leukemia (BCLL); (4) normal versus colon tumor; and (5) Non-Small-Cell-Lung-Carcinoma (NSCLC) versus renal samples. Stability of classification results and methods were further assessed by re-randomization studies.

Colonic Neoplasms↗
PubMed2003

Machaon CVE: cluster validation for gene expression data.

UNLABELLED: This paper presents a cluster validation tool for gene expression data. Machaon CVE (Clustering and Validation Environment) system aims to partition samples or genes into groups characterized by similar expression patterns, and to evaluate the quality of the clusters obtained. AVAILABILITY: The program is freely available for non-profit use on request at http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html SUPPLEMENTARY INFORMATION: http://www.cs.tcd.ie/Nadia.Bolshakova/Machaon.html

Algorithms↗
PubMed2004

Accurate detection of aneuploidies in array CGH and gene expression microarray data.

MOTIVATION: Chromosomal copy number changes (aneuploidies) are common in cell populations that undergo multiple cell divisions including yeast strains, cell lines and tumor cells. Identification of aneuploidies is critical in evolutionary studies, where changes in copy number serve an adaptive purpose, as well as in cancer studies, where amplifications and deletions of chromosomal regions have been identified as a major pathogenetic mechanism. Aneuploidies can be studied on whole-genome level using array CGH (a microarray-based method that measures the DNA content), but their presence also affects gene expression. In gene expression microarray analysis, identification of copy number changes is especially important in preventing aberrant biological conclusions based on spurious gene expression correlation or masked phenotypes that arise due to aneuploidies. Previously suggested approaches for aneuploidy detection from microarray data mostly focus on array CGH, address only whole-chromosome or whole-arm copy number changes, and rely on thresholds or other heuristics, making them unsuitable for fully automated general application to gene expression datasets. There is a need for a general and robust method for identification of aneuploidies of any size from both array CGH and gene expression microarray data. RESULTS: We present ChARM (Chromosomal Aberration Region Miner), a robust and accurate expectation-maximization based method for identification of segmental aneuploidies (partial chromosome changes) from gene expression and array CGH microarray data. Systematic evaluation of the algorithm on synthetic and biological data shows that the method is robust to noise, aneuploidal segment size and P-value cutoff. Using our approach, we identify known chromosomal changes and predict novel potential segmental aneuploidies in commonly used yeast deletion strains and in breast cancer. ChARM can be routinely used to identify aneuploidies in array CGH datasets and to screen gene expression data for aneuploidies or array biases. Our methodology is sensitive enough to detect statistically significant and biologically relevant aneuploidies even when expression or DNA content changes are subtle as in mixed populations of cells. AVAILABILITY: Code available by request from the authors and on Web supplement at http://function.cs.princeton.edu/ChARM/

Algorithms↗