Characterization of water and wildlife strains as a subgroup of Campylobacter jejuni using DNA microarrays.

Campylobacter jejuni is the leading cause of human bacterial gastroenteritis worldwide, but source attribution of the organism is difficult. Previously, DNA microarrays were used to investigate isolate source, which suggested a non-livestock source of infection. In this study we analysed the genome content of 162 clinical, livestock and water and wildlife (WW) associated isolates combined with the previous study. Isolates were grouped by genotypes into nine clusters (C1 to C9). Multilocus sequence typing (MLST) data demonstrated that livestock associated clonal complexes dominated clusters C1-C6. The majority of WW isolates were present in the C9 cluster. Analysis of previously reported genomic variable regions demonstrated that these regions were linked to specific clusters. Two novel variable regions were identified. A six gene multiplex PCR (mPCR) assay, designed to effectively differentiated strains into clusters, was validated with 30 isolates. A further five WW isolates were tested by mPCR and were assigned to the C7-C9 group of clusters. The predictive mPCR test could be used to indicate if a clinical case has come from domesticated or WW sources. Our findings provide further evidence that WW C. jejuni subtypes show niche adaptation and may be important in causing human infection

Post-Normalization Quality Assessment Visualization of Microarray Data

Post-normalization checking of microarrays rarely occurs, despite the problems that using unreliable data for inference can cause. This paper considers a number of different ways to check microarrays after normalization for a variety of potential problems. Four types of problem with microarray data that these checks can identify are: clerical mistakes, array-wide hybridization problems, problems with normalization and mishandling problems. Any of these can seriously affect the results of any analysis. The three main techniques used to identify these problems are dimension reduction techniques, false array plots and correlograms. None of the techniques are computationally very intensive and all can be carried out in the R statistical package. Once discovered, problems can either be rectified or excluded from the data

Reassessing Design and Analysis of two-Colour Microarray Experiments Using Mixed Effects Models

Gene expression microarray studies have led to interesting experimental design and statistical analysis challenges. The comparison of expression profiles across populations is one of the most common objectives of microarray experiments. In this manuscript we review some issues regarding design and statistical analysis for two-colour microarray platforms using mixed linear models, with special attention directed towards the different hierarchical levels of replication and the consequent effect on the use of appropriate error terms for comparing experimental groups. We examine the traditional analysis of variance (ANOVA) models proposed for microarray data and their extensions to hierarchically replicated experiments. In addition, we discuss a mixed model methodology for power and efficiency calculations of different microarray experimental designs

Comparing transformation methods for DNA microarray data

BACKGROUND: When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer. RESULTS: We used the ratio between biological variance and measurement variance (which is an F-like statistic) as a quality measure for transformation methods, and we demonstrate a method for maximizing that variance ratio on real data. We explore a number of transformations issues, including Box-Cox transformation, baseline shift, partial subtraction of the log-reference signal and smoothing. It appears that the optimal choice of parameters for the transformation methods depends on the data. Further, the behavior of the variance ratio, under the null hypothesis of zero biological variance, appears to depend on the choice of parameters. CONCLUSIONS: The use of replicates in microarray experiments is important. Adjustment for the null-hypothesis behavior of the variance ratio is critical to the selection of transformation method

MetabR: an R script for linear model analysis of quantitative metabolomic data

Background Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data. Findings Here we present a simple menu-driven program, “MetabR”, designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program. Conclusions We developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org

MetabR: an R script for linear model analysis of quantitative metabolomic data

Background Metabolomics is an emerging high-throughput approach to systems biology, but data analysis tools are lacking compared to other systems level disciplines such as transcriptomics and proteomics. Metabolomic data analysis requires a normalization step to remove systematic effects of confounding variables on metabolite measurements. Current tools may not correctly normalize every metabolite when the relationships between each metabolite quantity and fixed-effect confounding variables are different, or for the effects of random-effect confounding variables. Linear mixed models, an established methodology in the microarray literature, offer a standardized and flexible approach for removing the effects of fixed- and random-effect confounding variables from metabolomic data. Findings Here we present a simple menu-driven program, “MetabR”, designed to aid researchers with no programming background in statistical analysis of metabolomic data. Written in the open-source statistical programming language R, MetabR implements linear mixed models to normalize metabolomic data and analysis of variance (ANOVA) to test treatment differences. MetabR exports normalized data, checks statistical model assumptions, identifies differentially abundant metabolites, and produces output files to help with data interpretation. Example data are provided to illustrate normalization for common confounding variables and to demonstrate the utility of the MetabR program. Conclusions We developed MetabR as a simple and user-friendly tool for implementing linear mixed model-based normalization and statistical analysis of targeted metabolomic data, which helps to fill a lack of available data analysis tools in this field. The program, user guide, example data, and any future news or updates related to the program may be found at http://metabr.r-forge.r-project.org/ webcite

Global transcriptional response of pig brain and lung to natural infection by Pseudorabies virus

<p>Abstract</p> <p>Background</p> <p>Pseudorabies virus (PRV) is an alphaherpesviruses whose native host is pig. PRV infection mainly causes signs of central nervous system disorder in young pigs, and respiratory system diseases in the adult.</p> <p>Results</p> <p>In this report, we have analyzed native host (piglets) gene expression changes in response to acute pseudorabies virus infection of the brain and lung using a printed human oligonucleotide gene set from Illumina. A total of 210 and 1130 out of 23,000 transcript probes displayed differential expression respectively in the brain and lung in piglets after PRV infection (p-value < 0.01), with most genes displaying up-regulation. Biological process and pathways analysis showed that most of the up-regulated genes are involved in cell differentiation, neurodegenerative disorders, the nervous system and immune responses in the infected brain whereas apoptosis, cell cycle control, and the mTOR signaling pathway genes were prevalent in the infected lung. Additionally, a number of differentially expressed genes were found to map in or close to quantitative trait loci for resistance/susceptibility to pseudorabies virus in piglets.</p> <p>Conclusion</p> <p>This is the first comprehensive analysis of the global transcriptional response of the native host to acute alphaherpesvirus infection. The differentially regulated genes reported here are likely to be of interest for the further study and understanding of host viral gene interactions.</p

Analysis of oligonucleotide array experiments with repeated measures using mixed models

BACKGROUND: Two or more factor mixed factorial experiments are becoming increasingly common in microarray data analysis. In this case study, the two factors are presence (Patients with Alzheimer's disease) or absence (Control) of the disease, and brain regions including olfactory bulb (OB) or cerebellum (CER). In the design considered in this manuscript, OB and CER are repeated measurements from the same subject and, hence, are correlated. It is critical to identify sources of variability in the analysis of oligonucleotide array experiments with repeated measures and correlations among data points have to be considered. In addition, multiple testing problems are more complicated in experiments with multi-level treatments or treatment combinations. RESULTS: In this study we adopted a linear mixed model to analyze oligonucleotide array experiments with repeated measures. We first construct a generalized F test to select differentially expressed genes. The Benjamini and Hochberg (BH) procedure of controlling false discovery rate (FDR) at 5% was applied to the P values of the generalized F test. For those genes with significant generalized F test, we then categorize them based on whether the interaction terms were significant or not at the α-level (α(new )= 0.0033) determined by the FDR procedure. Since simple effects may be examined for the genes with significant interaction effect, we adopt the protected Fisher's least significant difference test (LSD) procedure at the level of α(new )to control the family-wise error rate (FWER) for each gene examined. CONCLUSIONS: A linear mixed model is appropriate for analysis of oligonucleotide array experiments with repeated measures. We constructed a generalized F test to select differentially expressed genes, and then applied a specific sequence of tests to identify factorial effects. This sequence of tests applied was designed to control for gene based FWER

Multi-targeted priming for genome-wide gene expression assays

<p>Abstract</p> <p>Background</p> <p>Complementary approaches to assaying global gene expression are needed to assess gene expression in regions that are poorly assayed by current methodologies. A key component of nearly all gene expression assays is the reverse transcription of transcribed sequences that has traditionally been performed by priming the poly-A tails on many of the transcribed genes in eukaryotes with oligo-dT, or by priming RNA indiscriminately with random hexamers. We designed an algorithm to find common sequence motifs that were present within most protein-coding genes of <it>Saccharomyces cerevisiae </it>and of <it>Neurospora crassa</it>, but that were not present within their ribosomal RNA or transfer RNA genes. We then experimentally tested whether degenerately priming these motifs with multi-targeted primers improved the accuracy and completeness of transcriptomic assays.</p> <p>Results</p> <p>We discovered two multi-targeted primers that would prime a preponderance of genes in the genomes of <it>Saccharomyces cerevisiae </it>and <it>Neurospora crassa </it>while avoiding priming ribosomal RNA or transfer RNA. Examining the response of <it>Saccharomyces cerevisiae </it>to nitrogen deficiency and profiling <it>Neurospora crassa </it>early sexual development, we demonstrated that using multi-targeted primers in reverse transcription led to superior performance of microarray profiling and next-generation RNA tag sequencing. Priming with multi-targeted primers in addition to oligo-dT resulted in higher sensitivity, a larger number of well-measured genes and greater power to detect differences in gene expression.</p> <p>Conclusions</p> <p>Our results provide the most complete and detailed expression profiles of the yeast nitrogen starvation response and <it>N. crassa </it>early sexual development to date. Furthermore, our multi-targeting priming methodology for genome-wide gene expression assays provides selective targeting of multiple sequences and counter-selection against undesirable sequences, facilitating a more complete and precise assay of the transcribed sequences within the genome.</p

A highly conserved transcriptional repressor controls a large regulon involved in lipid degradation in Mycobacterium smegmatis and Mycobacterium tuberculosis

The Mycobacterium tuberculosis TetR-type regulator Rv3574 has been implicated in pathogenesis as it is induced in vivo, and genome-wide essentiality studies show it is required for infection. As the gene is highly conserved in the mycobacteria, we deleted the Rv3574 orthologue in Mycobacterium smegmatis (MSMEG_6042) and used real-time quantitative polymerase chain reaction and microarray analyses to show that it represses the transcription both of itself and of a large number of genes involved in lipid metabolism. We identified a conserved motif within its own promoter (TnnAACnnGTTnnA) and showed that it binds as a dimer to 29 bp probes containing the motif. We found 16 and 31 other instances of the motif in intergenic regions of M. tuberculosis and M. smegmatis respectively. Combining the results of the microarray studies with the motif analyses, we predict that Rv3574 directly controls the expression of 83 genes in M. smegmatis, and 74 in M. tuberculosis. Many of these genes are known to be induced by growth on cholesterol in rhodococci, and palmitate in M. tuberculosis. We conclude that this regulator, designated elsewhere as kstR, controls the expression of genes used for utilizing diverse lipids as energy sources, possibly imported through the mce4 system