Application of multivariate curve resolution to the analysis of yeast genome-wide screens

In this work, the application of Multivariate Curve Resolution to the analysis of yeast genome-wide screens obtained by means of DNA microarray technology is shown. In order to perform the analysis of this type of data, two algorithms based on Alternating Least Squares (MCR-ALS) and on its maximum likelihood weighted projection (MCR-WALS) variant are compared. The utilization of the modified weighted alternating least (WALS) squares algorithm is motivated by the rather poor quality, uncertainties and experimental noise associated to DNA microarray data. Moreover, a large number of missing values are usually present in these data sets and the weighted WALS approach allowed circumventing this problem. Two different experimental datasets were used for this comparison. In the first dataset, gene expression values in budding yeast were monitored in-response to glucose limitation. In the second dataset, the changes in the gene expression caused by the daunorubicin drug were monitored as a function of time. Results obtained by application of Multivariate Curve Resolution in the two cases allowed a good recovery of the evolving gene expression profiles and the identification of metabolic pathways and individual genes involved in these gene expression changes.     

Gene networks reconstruction and time-series prediction from microarray data using recurrent neural fuzzy networks

Reverse engineering problems concerning the reconstruction and identification of gene regulatory networks through gene expression data are central issues in computational molecular biology and have become the focus of much research in the last few years. An approach has been proposed for inferring the complex causal relationships among genes from microarray experimental data, which is based on a novel neural fuzzy recurrent network. The method derives information on the gene interactions in a highly interpretable form (fuzzy rules) and takes into account the dynamical aspects of gene regulation through its recurrent structure. To determine the efficiency of the proposed approach, microarray data from two experiments relating to Saccharomyces cerevisiae and Escherichia coli have been used and experiments concerning gene expression time course prediction have been conducted. The interactions that have been retrieved among a set of genes known to be highly regulated during the yeast cell-cycle are validated by previous biological studies. The method surpasses other computational techniques, which have attempted genetic network reconstruction, by being able to recover significantly more biologically valid relationships among genes     

Gaussian graphical model for identifying significantly responsive regulatory networks from time course high‐throughput data

With rapid accumulation of functional relationships between biological molecules, knowledge‐based networks have been constructed and stocked in many databases. These networks provide curated and comprehensive information for functional linkages among genes and proteins, whereas their activities are highly related with specific phenotypes and conditions. To evaluate a knowledge‐based network in a specific condition, the consistency between its structure and conditionally specific gene expression profiling data are an important criterion. In this study, the authors propose a Gaussian graphical model to evaluate the documented regulatory networks by the consistency between network architectures and time course gene expression profiles. They derive a dynamic Bayesian network model to evaluate gene regulatory networks in both simulated and true time course microarray data. The regulatory networks are evaluated by matching network structure with gene expression to achieve consistency measurement. To demonstrate the effectiveness of the authors method, they identify significant regulatory networks in response to the time course of circadian rhythm. The knowledge‐based networks are screened and ranked by their structural consistencies with dynamic gene expression profiling.Inspec keywords: Bayes methods, biology computing, circadian rhythms, Gaussian processes, genetics, genomics, graphs, molecular biophysics, proteinsOther keywords: Gaussian graphical model, responsive regulatory networks, time course high‐throughput data, biological molecules, dynamic gene expression proflling, circadian rhythm, consistency measurement, matching network structure, simulated time course microarray data, true time course microarray data, dynamic Bayesian network model, time course gene expression proflles, network architectures, documented regulatory networks, speciflc gene expression proflling data, phenotypes, proteins, functional linkages, databases, knowledge‐based networks     

条斑紫菜cDNA微阵列制备及其在世代差异基因表达检测中的应用

为了高通量研究不同世代条斑紫菜的基因表达情况,选择了467个包含诸多功能基因的条斑紫菜基因克隆,经PCR扩增纯化后,点样于多聚赖氨酸包被的基片上制备了条斑紫菜功能基因cDNA微阵列.采用Cy3-dCTP和Cy5-dCTP 荧光分别标记条斑紫菜配子体和孢子体cRNA,与阵列进行杂交后,获得了信号清晰、重复性良好的扫描图像.经对标准化处理后的467个基因的数据进行分析发现:在配子体中表达量上调的基因有55个,其中有21个基因与已知功能基因或推测功能基因相匹配;在孢子体中表达量上调的基因有86个,其中与已知功能或假定功能基因相匹配的基因有24个.实验证明,优化的cDNA微阵列制备技术用于条斑紫菜基因表达分析有较高的效率和可靠的实用性能.     

Sensitive quantification of Escherichia coli O157:H7, Salmonella enterica , and Campylobacter jejuni by combining stopped polymerase chain reaction with chemiluminescence flow-through DNA microarray analysis

Rapid analysis of pathogenic bacteria is essential for food and water control to preserve the public health. Therefore, we report on a chemiluminescence (CL) flow-through DNA microarray assay for the rapid and sensitive quantification of the pathogenic bacteria Escherichia coli O157:H7, Salmonella enterica , and Campylobacter jejuni in water. Using the stopped polymerase chain reaction (PCR) strategy, the amount of amplified target DNA was strongly dependent on the applied cell concentration. The amplification was stopped at the logarithmic phase of the PCR to quantify the DNA products on the DNA microarray chip. The generation of single-stranded DNA sequences is essential for DNA hybridization assays on microarrays. Therefore, the DNA strands of the PCR products were separated by streptavidin-conjugated magnetic nanoparticles. This was achieved by introducing a reverse primer labeled with biotin together with a digoxigenin labeled forward primer for CL microarray imaging. A conjugate of an antidigoxigenin antibody and horseradish peroxidase recognized the digoxigenin-labeled antistrands bound to the probes on the microarray surface and catalyzed the reaction of luminol and hydrogen peroxide. The generated light emission was recorded by a sensitive charge-coupled device (CCD) camera. The quantification was conducted by a flow-through CL microarray readout system. The DNA microarrays were based on an NHS-activated poly(ethylene glycol)-modified glass substrate. The DNA probes which have the same DNA sequence as the reverse primer were immobilized on this surface. The full assay was characterized by spiking experiments with heat-inactivated bacteria in water. The total assay time was 3.5 h, and the detection limits determined on CL microarrays were for E. coli O157:H7, S. enterica , and C. jejuni 136, 500, and 1 cell/mL, respectively. The results of the DNA microarray assay were comparable to the SYBR green-based assays analyzed with a real-time PCR device. The advantage of the new microarray analysis method is seen in the ability of a high multiplex degree on DNA microarrays, the high specificity of DNA hybridization on DNA microarrays, and the possibility to get quantitative results on an automated CL flow-through microarray analysis system.     

Quantification of N-(deoxyguanosin-8-yl)-4-aminobiphenyl adducts in human lymphoblastoid TK6 cells dosed with N-hydroxy-4-acetylaminobiphenyl and their relationship to mutation, toxicity, and gene expression profiling

Gene expression profiles that are anchored to phenotypic endpoints may lead to the identification of signatures that predict mutagenicity or carcinogenicity. The study presented here describes the analysis of DNA adducts in the human TK6 lymphoblastoid cell line after exposure to N-hydroxy-4-aminobiphenyl, a mutagenic metabolite of 4-aminobiphenyl. A validated nano-LC microelectrospray mass spectrometry assay is reported for the detection and quantification of N-(deoxyguanosin-8-yl)-4-aminobiphenyl (dG-C8-ABP), the principal DNA adduct of 4-aminobiphenyl. Limits of quantification, based on a signal-to-noise ratio of 10:1, are determined to correspond to approximately 27 fg of dG-C8-ABP injected on-column. The assay has been used to measure the steady-state levels of the adduct in the human TK6 lymphoblastoid cell line as a function of dose (0.5, 1.0, and 10.0 microM) and time (2, 6, and 27 h) after exposure to N-hydroxy-4-aminobiphenyl. The levels of dG-C8-ABP adducts in the cells, ranging from 18 to 500 adducts in 10(9) nucleotides, were then correlated to cell toxicity, induced mutation at the TK (thymidine kinase) and HPRT loci, and gene expression profiling through microarray analysis. Cell cultures were evaluated for toxicity by growth curve extrapolation, mutation assays were performed on the HPRT and TK loci, and gene expression profiles were generated by analyses using microarray technology. In the mutation assay analysis, as the toxicant concentration increased, there was an increase in mutation fraction, indicating a direct correlation to metabolite dosing level and mutations occurring at these two loci. Statistical analysis of the gene expression data determined that a total of 2250 genes exhibited statistically significant changes in expression after treatment with N-OH-AABP (P < 0.05). Among the genes identified, 2245 were up-regulated, whereas 5 genes that had functions in cell survival and cell growth and, hence, could be indicators of toxicity, were down-regulated relative to controls. The results demonstrate the value of anchoring gene expression patterns to phenotypic markers, such as DNA adduct levels, toxicity, and mutagenicity.     

Cellular response to gene expression profiles of different hepatitis C virus core proteins in the Huh-7 cell line with microarray analysis

In the current investigation, we constructed recombinants of expression of HCV genotype 1b, 2a, and 4d whole core proteins and established a human hepatoma (Huh-7) cell line which expressed different core proteins constitutively. In the Affymetrix human gene chip, the HG-U133 A and B were employed for identification of variant core protein gene expression in the Huh-7 cell line. In data analysis, we applied a threshold that eliminated all genes that were not increased or decreased by at least a 3-fold change in a comparison between transfected cells and control cells. All of these genes were annotated by using NetAffx analysis through the Affymetrix website and categorized on the basis of their biological processes. The microarray analysis result suggested that the gene expression profiles caused by three kinds of core proteins were mainly shown in metabolism, signal transduction, protease activity, immune responses, etc. and that some pathogenesis/oncogenesis, apoptosis, or anti-apoptosis gene expression were up/down-regulated simultaneously in the Huh-7 cell line. In conclusion, the gene expression profiles of variant core proteins were implicated in HCV replication, pathogenesis, or oncogenesis in the Huh-7 cell line, which is useful for our understanding of HCV variant core protein biological function and its pathogenic mechanism.     

Latent factor analysis facilitates modelling of oncogenic genes for colon adenocarcinoma

Identification of oncogenic genes from a large sample number of genomic data is a challenge. In this study, a well‐established latent factor model, Bayesian factor and regression model, are applied to predict unknown colon cancer related genes from colon adenocarcinoma genomic data. Four important latent factors were addressed by the latent factor model, focusing on characterisation of heterogeneity of expression patterns of specific oncogenic genes by using microarray data of 174 colon cancer patients. Based on the fact that variables included in the same latent factor have some common characteristics and known cancer related genes in Online Mendelian Inheritance in Man, the authors found that the four latent factors can be employed to predict unknown colon cancer related genes that were never reported in the literature. The authors validated 15 identified genes by checking their somatic mutations of the same patients from DNA sequencing data.Inspec keywords: Bayes methods, biological organs, cancer, DNA, genetics, genomics, lab‐on‐a‐chip, medical diagnostic computing, molecular biophysics, physiological models, regression analysisOther keywords: latent factor analysis, oncogenic genes, colon adenocarcinoma, genomic data, Bayesian factor, colon cancer related genes, heterogeneity, expression patterns, DNA microarray data, Online Mendelian Inheritance in Man, somatic mutations, DNA sequencing data     

Study on the dynamic behavior of a DNA microarray

A theoretical dynamic kinetic model was derived and a series of experiments were carried out using low-density microarrays in various concentrations of spotting probe ([P]) and labeling target ([T]). It has been shown that target and probe determined the signal intensity together. At a certain range of DNA concentration, the signal intensity was in proportion to spotting [P]. At the higher DNA concentrations, there was a decrease in hybridization signal intensity, especially in cDNA microarrays. Since the DNA microarray was constructed on a solid surface, steric hindrance, which is induced by the solid surface and the high [P], decreased the probe immobilization efficiency, leading to a decrease of the immobilized probe density. The decreased hybridization efficiency also caused the compression in signal intensity when the target increased. Nevertheless, the intensity ratio of Cy5 to Cy3 was not compressed within a microarray in the two-color system. The ratio of Cy5/Cy3 is only determined by the ratio of two targets and independent of the density and the types of probe. Therefore, the two-color fluorescent strategy is more reasonable and reliable in detection of differential gene expression. All these results indicate that the DNA microarray can be used to detect differently expressed genes, though it cannot be used to detect the absolute mRNA abundance.     

Autonomous system for Web-based microarray image analysis

Software-based feature extraction from DNA microarray images still requires human intervention on various levels. Manual adjustment of grid and metagrid parameters, precise alignment of superimposed grid templates and gene spots, or simply identification of large-scale artifacts have to be performed beforehand to reliably analyze DNA signals and correctly quantify their expression values. Ideally, a Web-based system with input solely confined to a single microarray image and a data table as output containing measurements for all gene spots would directly transform raw image data into abstracted gene expression tables. Sophisticated algorithms with advanced procedures for iterative correction function can overcome imminent challenges in image processing. Herein is introduced an integrated software system with a Java-based interface on the client side that allows for decentralized access and furthermore enables the scientist to instantly employ the most updated software version at any given time. This software tool is extended from PixClust as used in Extractiff incorporated with Java Web Start deployment technology. Ultimately, this setup is destined for high-throughput pipelines in genome-wide medical diagnostics labs or microarray core facilities aimed at providing fully automated service to its users.     

Relevant and significant supervised gene clusters for microarray cancer classification

An important application of microarray data in functional genomics is to classify samples according to their gene expression profiles such as to classify cancer versus normal samples or to classify different types or subtypes of cancer. One of the major tasks with gene expression data is to find co-regulated gene groups whose collective expression is strongly associated with sample categories. In this regard, a gene clustering algorithm is proposed to group genes from microarray data. It directly incorporates the information of sample categories in the grouping process for finding groups of co-regulated genes with strong association to the sample categories, yielding a supervised gene clustering algorithm. The average expression of the genes from each cluster acts as its representative. Some significant representatives are taken to form the reduced feature set to build the classifiers for cancer classification. The mutual information is used to compute both gene-gene redundancy and gene-class relevance. The performance of the proposed method, along with a comparison with existing methods, is studied on six cancer microarray data sets using the predictive accuracy of naive Bayes classifier, K-nearest neighbor rule, and support vector machine. An important finding is that the proposed algorithm is shown to be effective for identifying biologically significant gene clusters with excellent predictive capability.     

Robust ANOVA for microarray data

Analysis of Variance (ANOVA) separates the effects of different factors in a dataset. Typical examples for gene microarray data are the factors time and treatment. This separation can improve the interpretability of the results. However, the main effects and interactions, calculated in ANOVA, can be heavily influenced by outliers, large numbers of non-expressed genes with noise, and the heavy-tailedness of the distribution of expression values. Robust methods are less affected by these and will improve the analysis.In this paper, several methods to perform robust nonparametric ANOVA are applied to a large multi-treatment time series dataset. The results are compared with the results obtained with parametric ANOVA using Procrustes analysis. A further comparison is made by Gene Ontology (GO) enrichment analysis of groups of genes identified as significant by inspection of the interaction terms in ANOVA. It is shown that there are significant differences in the estimates of main effects and gene–treatment interactions. ROC curves show an improved representation of current biological knowledge for one particular robust form of ANOVA, using a combination of rank transformed data, with the median as location parameter.     

Retrieving relevant time‐course experiments: a study on Arabidopsis microarrays

Understanding time‐course regulation of genes in response to a stimulus is a major concern in current systems biology. The problem is usually approached by computational methods to model the gene behaviour or its networked interactions with the others by a set of latent parameters. The model parameters can be estimated through a meta‐analysis of available data obtained from other relevant experiments. The key question here is how to find the relevant experiments which are potentially useful in analysing current data. In this study, the authors address this problem in the context of time‐course gene expression experiments from an information retrieval perspective. To this end, they introduce a computational framework that takes a time‐course experiment as a query and reports a list of relevant experiments retrieved from a given repository. These retrieved experiments can then be used to associate the environmental factors of query experiment with the findings previously reported. The model is tested using a set of time‐course Arabidopsis microarrays. The experimental results show that relevant experiments can be successfully retrieved based on content similarity.Inspec keywords: botany, lab‐on‐a‐chip, genetics, bioinformatics, information retrieval, data mining, data analysis, associative processingOther keywords: relevant time‐course experiment retrieval, time‐course Arabidopsis microarray, time‐course gene regulation, stimulus response, systems biology, computational method, gene behaviour model, gene networked interaction, latent parameter, model parameter estimation, meta‐analysis, data analysis, time‐course gene expression experiment, information retrieval, computational framework, time‐course experiment query, relevant experiment list, repository, environmental factor, query experiment, experimental content similarity     

Separate enrichment analysis of pathways for up- and downregulated genes

Two strategies are often adopted for enrichment analysis of pathways: the analysis of all differentially expressed (DE) genes together or the analysis of up- and downregulated genes separately. However, few studies have examined the rationales of these enrichment analysis strategies. Using both microarray and RNA-seq data, we show that gene pairs with functional links in pathways tended to have positively correlated expression levels, which could result in an imbalance between the up- and downregulated genes in particular pathways. We then show that the imbalance could greatly reduce the statistical power for finding disease-associated pathways through the analysis of all-DE genes. Further, using gene expression profiles from five types of tumours, we illustrate that the separate analysis of up- and downregulated genes could identify more pathways that are really pertinent to phenotypic difference. In conclusion, analysing up- and downregulated genes separately is more powerful than analysing all of the DE genes together.     

Fold change based approach for identification of significant network markers in breast,lung and prostate cancer

Cancer belongs to a class of highly aggressive diseases and a leading cause of death in the world. With more than 100 types of cancers, breast, lung and prostate cancer remain to be the most common types. To identify essential network markers (NMs) and therapeutic targets in these cancers, the authors present a novel approach which uses gene expression data from microarray and RNA‐seq platforms and utilises the results from this data to evaluate protein–protein interaction (PPI) network. Differentially expressed genes (DEGs) are extracted from microarray data using three different statistical methods in R, to produce a consistent set of genes. Also, DEGs are extracted from RNA‐seq data for the same three cancer types. DEG sets found to be common in both platforms are obtained at three fold change (FC) cut‐off levels to accurately identify the level of change in expression of these genes in all three cancers. A cancer network is built using PPI data characterising gene sets at log‐FC (LFC)>1, LFC>1.5 and LFC>2, and interconnection between principal hub nodes of these networks is observed. Resulting network of hubs at three FC levels highlights prime NMs with high confidence in multiple cancers as validated by Gene Ontology functional enrichment and maximal complete subgraphs from CFinder.Inspec keywords: cancer, proteins, RNA, bioinformatics, statistical analysis, genetics, molecular biophysics, ontologies (artificial intelligence), lungOther keywords: cancer network, PPI data, gene sets, multiple cancers, Gene Ontology functional enrichment, prostate cancer, gene expression data, RNA‐seq platforms, protein–protein interaction network, DEG, microarray data, RNA‐seq data, cancer types, lung cancer, diseases, breast cancer, network markers, differentially expressed genes, fold change based approach, CFinder, statistical methods     

Changes in expression of genes involved in apoptosis in activated human T-cells in response to modeled microgravity

Space flights result in remarkable effects on various physiological systems, including a decline in cellular immune functions. Previous studies have shown that exposure to microgravity, both true and modeled, can cause significant changes in numerous lymphocyte functions. The purpose of this study was to search for microgravity-sensitive genes, and specifically for apoptotic genes influenced by the microgravity environment and other genes related to immune response. The experiments were performed on anti-CD3 and IL-2 activated human T cells. To model microgravity conditions we have utilized the NASA rotating wall vessel bioreactor. Control lymphocytes were cultured in static 1g conditions. To assess gene expression we used DNA microarray chip technology. We had shown that multiple genes (approximately 3–8% of tested genes) respond to microgravity conditions by 1.5 and more fold change in expression. There is a significant variability in the response. However, a certain reproducible pattern in gene response could be identified. Among the genes showing reproducible changes in expression in modeled microgravity, several genes involved in apoptosis as well as in immune response were identified. These are IL-7 receptor, Granzyme B, Beta-3-endonexin, Apo2 ligand and STAT1. Possible functional consequences of these changes are discussed.     

Improved processing of microarray data using image reconstruction techniques

Spotted cDNA microarray data analysis suffers from various problems such as noise from a variety of sources, missing data, inconsistency, and, of course, the presence of outliers. This paper introduces a new method that dramatically reduces the noise when processing the original image data. The proposed approach recreates the microarray slide image, as it would have been with all the genes removed. By subtracting this background recreation from the original, the gene ratios can be calculated with more precision and less influence from outliers and other artifacts that would normally make the analysis of this data more difficult. The new technique is also beneficial, as it does not rely on the accurate fitting of a region to each gene, with its only requirement being an approximate coordinate. In experiments conducted, the new method was tested against one of the mainstream methods of processing spotted microarray images. Our method is shown to produce much less variation in gene measurements. This evidence is supported by clustering results that show a marked improvement in accuracy.