Normalization of microarray data using a spatial mixed model analysis which includes splines

Transcriptomic Profiling of Virus-Host Cell Interactions following Chicken Anaemia Virus (CAV) Infection in an In Vivo Model

Chicken Anaemia Virus (CAV) is an economically important virus that targets lymphoid and erythroblastoid progenitor cells leading to immunosuppression. This study aimed to investigate the interplay between viral infection and the host's immune response to better understand the pathways that lead to CAV-induced immunosuppression. To mimic vertical transmission of CAV in the absence of maternally-derived antibody, day-old chicks were infected and their responses measured at various time-points post-infection by qRT-PCR and gene expression microarrays. The kinetics of mRNA expression levels of signature cytokines of innate and adaptive immune responses were determined by qRT-PCR. The global gene expression profiles of mock-infected (control) and CAV-infected chickens at 14 dpi were also compared using a chicken immune-related 5K microarray. Although in the thymus there was evidence of induction of an innate immune response following CAV infection, this was limited in magnitude. There was little evidence of a Th1 adaptive immune response in any lymphoid tissue, as would normally be expected in response to viral infection. Most cytokines associated with Th1, Th2 or Treg subsets were down-regulated, except IL-2, IL-13, IL-10 and IFNγ, which were all up-regulated in thymus and bone marrow. From the microarray studies, genes that exhibited significant (greater than 1.5-fold, false discovery rate <0.05) changes in expression in thymus and bone marrow on CAV infection were mainly associated with T-cell receptor signalling, immune response, transcriptional regulation, intracellular signalling and regulation of apoptosis. Expression levels of a number of adaptor proteins, such as src-like adaptor protein (SLA), a negative regulator of T-cell receptor signalling and the transcription factor Special AT-rich Binding Protein 1 (SATB1), were significantly down-regulated by CAV infection, suggesting potential roles for these genes as regulators of viral infection or cell defence. These results extend our understanding of CAV-induced immunosuppression and suggest a global immune dysregulation following CAV infection.

Correction of spatial bias in oligonucleotide array data

Background. Oligonucleotide microarrays allow for high-throughput gene expression profiling assays. The technology relies on the fundamental assumption that observed hybridization signal intensities (HSIs) for each intended target, on average, correlate with their target's true concentration in the sample. However, systematic, nonbiological variation from several sources undermines this hypothesis. Background hybridization signal has been previously identified as one such important source, one manifestation of which appears in the form of spatial autocorrelation. Results. We propose an algorithm, pyn, for the elimination of spatial autocorrelation in HSIs, exploiting the duality of desirable mutual information shared by probes in a common probe set and undesirable mutual information shared by spatially proximate probes. We show that this correction procedure reduces spatial autocorrelation in HSIs; increases HSI reproducibility across replicate arrays; increases differentially expressed gene detection power; and performs better than previously published methods. Conclusions. The proposed algorithm increases both precision and accuracy, while requiring virtually no changes to users' current analysis pipelines: the correction consists merely of a transformation of raw HSIs (e.g., CEL files for Affymetrix arrays). A free, open-source implementation is provided as an R package, compatible with standard Bioconductor tools. The approach may also be tailored to other platform types and other sources of bias.

Escherichia coli- and Staphylococcus aureus-induced mastitis differentially modulate transcriptional responses in neighbouring uninfected bovine mammary gland quarters

Background

The most important disease of dairy cattle is mastitis, caused by the infection of the mammary gland by various micro-organisms. Although the transcriptional response of bovine mammary gland cells to in vitro infection has been studied, the interplay and consequences of these responses in the in vivo environment of the mammary gland are less clear. Previously mammary gland quarters were considered to be unaffected by events occurring in neighbouring quarters. More recently infection of individual quarters with mastitis causing pathogens, especially Escherichia coli, has been shown to influence the physiology of neighbouring uninfected quarters. Therefore, the transcriptional responses of uninfected mammary gland quarters adjacent to quarters infected with two major mastitis causing pathogens, E. coli and Staphylococcus aureus, were compared.

Results

The bacteriologically sterile, within-animal control quarters exhibited a transcriptional response to the infection of neighbouring quarters. The greatest response was associated with E. coli infection, while a weaker, yet significant, response occurred during S. aureus infection. The transcriptional responses of these uninfected quarters included the enhanced expression of many genes previously associated with mammary gland infections. Comparison of the transcriptional response of uninfected quarters to S. aureus and E. coli infection identified 187 differentially expressed genes, which were particularly associated with cellular responses, e.g. response to stress. The most affected network identified by Ingenuity Pathway analysis has the immunosuppressor transforming growth factor beta 1 (TGFB1) at its hub and largely consists of genes more highly expressed in control quarters from S. aureus infected cows.

Conclusions

Uninfected mammary gland quarters reacted to the infection of neighbouring quarters and the responses were dependent on pathogen type. Therefore, bovine udder quarters exhibit interdependence and should not be considered as separate functional entities. This suggests that mastitis pathogens not only interact directly with host mammary cells, but also influence discrete sites some distance away, which will affect their response to the subsequent spread of the infection. Understanding the underlying mechanisms may provide further clues for ways to control mammary gland infections. These results also have implications for the design of experimental studies investigating immune regulatory mechanisms in the bovine mammary gland.

Complex traits analysis of chicken growth using targeted genetical genomics

Dissecting the genetic control of complex trait variation remains very challenging, despite many advances in technology. The aim of this study was to use a major growth quantitative trait locus (QTL) in chickens mapped to chromosome 4 as a model for a targeted approach to dissect the QTL. We applied a variant of the genetical genomics approach to investigate genome-wide gene expression differences between two contrasting genotypes of a marked QTL. This targeted approach allows the direct quantification of the link between the genotypes and the genetic responses, thus narrowing the QTL-phenotype gap using fewer samples (i.e. microarrays) compared with the genome-wide genetical genomics studies. Four differentially expressed genes were localized under the region of the QTL. One of these genes is a potential positional candidate gene (AADAT) that affects lysine and tryptophan metabolism and has alternative splicing variants between the two genotypes. In addition, the lysine and glycolysis metabolism pathways were significantly enriched for differentially expressed genes across the genome. The targeted approach provided a complementary route to fine mapping of QTL by characterizing the local and the global downstream effects of the QTL and thus generating further hypotheses about the action of that QTL.

Novel gene expression responses in the ovine abomasal mucosa to infection with the gastric nematode Teladorsagia circumcincta

Infection of sheep with the gastric nematode Teladorsagia circumcincta results in distinct Th2-type changes in the mucosa, including mucous neck cell and mast cell hyperplasia, eosinophilia, recruitment of IgA/IgE producing cells and neutrophils, altered T-cell subsets and mucosal hypertrophy. To address the protective mechanisms generated in animals on previous exposure to this parasite, gene expression profiling was carried out using samples of abomasal mucosa collected pre- and post- challenge from animals of differing immune status, using an experimental model of T. circumcincta infection. Recently developed ovine cDNA arrays were used to compare the abomasal responses of sheep immunised by trickle infection with worm-naïve sheep, following a single oral challenge of 50 000 T. circumcincta L3. Key changes were validated using qRT-PCR techniques. Immune animals demonstrated highly significant increases in levels of transcripts normally associated with cytotoxicity such as granulysin and granzymes A, B and H, as well as mucous-cell derived transcripts, predominantly calcium-activated chloride channel 1 (CLCA1). Challenge infection also induced up-regulation of transcripts potentially involved in initiating or modulating the immune response, such as heat shock proteins, complement factors and the chemokine CCL2. In contrast, there was marked infection-associated down-regulation of gene expression of members of the gastric lysozyme family. The changes in gene expression levels described here may reflect roles in direct anti-parasitic effects, immuno-modulation or tissue repair. (Funding; DEFRA/SHEFC (VT0102) and the BBSRC (BB/E01867X/1)).

The gastrointestinal nematode Trichostrongylus colubriformis down-regulates immune gene expression in migratory cells in afferent lymph

Background

Gastrointestinal nematode (GIN) infections are the predominant cause of economic losses in sheep. Infections are controlled almost exclusively by the use of anthelmintics which has lead to the selection of drug resistant nematode strains. An alternative control approach would be the induction of protective immunity to these parasites. This study exploits an ovine microarray biased towards immune genes, an artificially induced immunity model and the use of pseudo-afferent lymphatic cannulation to sample immune cells draining from the intestine, to investigate possible mechanisms involved in the development of immunity.

Results

During the development of immunity to, and a subsequent challenge infection with Trichostrongylus colubriformis, the transcript levels of 2603 genes of cells trafficking in afferent intestinal lymph were significantly modulated (P < 0.05). Of these, 188 genes were modulated more than 1.3-fold and involved in immune function. Overall, there was a clear trend for down-regulation of many genes involved in immune functions including antigen presentation, caveolar-mediated endocytosis and protein ubiquitination. The transcript levels of TNF receptor associated factor 5 (TRAF5), hemopexin (HPX), cysteine dioxygenase (CDO1), the major histocompatability complex Class II protein (HLA-DMA), interleukin-18 binding protein (IL-18BP), ephrin A1 (EFNA1) and selenoprotein S (SELS) were modulated to the greatest degree.

Conclusions

This report describes gene expression profiles of afferent lymph cells in sheep developing immunity to nematode infection. Results presented show a global down-regulation of the expression of immune genes which may be reflective of the natural temporal response to nematode infections in livestock.

Early transcriptomic changes induced by magnesium deficiency in Arabidopsis thaliana reveal the alteration of circadian clock gene expression in roots and the triggering of abscisic acid-responsive genes

*Plant growth and development ultimately depend on environmental variables such as the availability of essential minerals. Unravelling how nutrients affect gene expression will help to understand how they regulate plant growth. *This study reports the early transcriptomic response to magnesium (Mg) deprivation in Arabidopsis. Whole-genome transcriptome was studied in the roots and young mature leaves 4, 8 and 28 h after the removal of Mg from the nutrient solution. *The highest number of regulated genes was first observed in the roots. Contrary to other mineral deficiencies, Mg depletion did not induce a higher expression of annotated genes in Mg uptake. Remarkable responses include the perturbation of the central oscillator of the circadian clock in roots and the triggering of abscisic acid (ABA) signalling, with half of the up-regulated Mg genes in leaves being ABA-responsive. However, no change in ABA content was observed. *The specificity of the response of some Mg-regulated genes was challenged by studying their expression after other mineral deficiencies and environmental stresses. The possibility to develop markers for Mg incipient deficiency is discussed here.

Supervised normalization of microarrays

Motivation: A major challenge in utilizing microarray technologies to measure nucleic acid abundances is ‘normalization’, the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date.

Results: We show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods.

Availability: An R package called snm implementing the methodology will be made available from Bioconductor (http://bioconductor.org).

Contact:jstorey@princeton.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Transcriptome profiling of Streptococcus uberis-induced mastitis reveals fundamental differences between immune gene expression in the mammary gland and in a primary cell culture model

Streptococcus uberis is a prevalent causative organism of mastitis and resides naturally in the environment of the dairy cow making prevention of the disease difficult. A bovine cDNA microarray comprising approximately 22,000 expressed sequence tags was used to evaluate the transcriptional changes that occur in the mammary gland after the onset of clinical Strep. uberis mastitis. Five lactating Friesian heifers were intramammary infused in an uninfected quarter with approximately 1,000 to 1,500 cfu of a wild-type strain of Strep. uberis. Microarray results showed that Strep. uberis mastitis led to the differential expression of more than 2,200 genes by greater than 1.5-fold compared with noninfected control quarters. The most highly upregulated genes were associated with the immune response, programmed cell death, and oxidative stress. Quantitative real-time reverse transcription PCR analysis confirmed the increase in mRNA expression of immune-related genes complement component 3, clusterin, IL-8, calgranulin C, IFN-gamma , IL-10, IL-1beta, IL-6, toll-like receptor-2, tumor necrosis factor-alpha, serum amyloid A3, lactoferrin, LPS-bonding protein, and oxidative stress-related genes metallothionein 1A and superoxide dimutase 2. In contrast, a decrease of mRNA levels was observed for the major milk protein genes. Bovine mammary epithelial cells in culture challenged with the same Strep. uberis strain used to induce clinical mastitis in the in vivo animal experiment did not cause a change in the mRNA levels of the immune-related genes. This suggests that the expression of immune-related genes by mammary epithelial cells may be initiated by host factors and not Strep. uberis. However, challenging epithelial cells with different Strep. uberis strains and Staphylococcus aureus resulted in an increase in the mRNA expression of a subset of the immune-related genes measured. In comparison, an Escherichia coli challenge caused an increase in the majority of immune-related genes measured. Results demonstrate the complexity of the bovine mammary gland immune response to an infecting pathogen and indicate that a coordinated response exists between the resident, recruited, and inducible immune factors.

Establishing substantial equivalence: transcriptomics

Regulatory authorities in Western Europe require transgenic crops to be substantially equivalent to conventionally bred forms if they are to be approved for commercial production. One way to establish substantial equivalence is to compare the transcript profiles of developing grain and other tissues of transgenic and conventionally bred lines, in order to identify any unintended effects of the transformation process. We present detailed protocols for transcriptomic comparisons of developing wheat grain and leaf material, and illustrate their use by reference to our own studies of lines transformed to express additional gluten protein genes controlled by their own endosperm-specific promoters. The results show that the transgenes present in these lines (which included those encoding marker genes) did not have any significant unpredicted effects on the expression of endogenous genes and that the transgenic plants were therefore substantially equivalent to the corresponding parental lines.

Combining partial correlation and an information theory approach to the reversed engineering of gene co-expression networks

MOTIVATION

We present PCIT, an algorithm for the reconstruction of gene co-expression networks (GCN) that combines the concept partial correlation coefficient with information theory to identify significant gene to gene associations defining edges in the reconstruction of GCN. The properties of PCIT are examined in the context of the topology of the reconstructed network including connectivity structure, clustering coefficient and sensitivity.

RESULTS

We apply PCIT to a series of simulated datasets with varying levels of complexity in terms of number of genes and experimental conditions, as well as to three real datasets. Results show that, as opposed to the constant cutoff approach commonly used in the literature, the PCIT algorithm can identify and allow for more moderate, yet not less significant, estimates of correlation (r) to still establish a connection in the GCN. We show that PCIT is more sensitive than established methods and capable of detecting functionally validated gene-gene interactions coming from absolute r values as low as 0.3. These bona fide associations, which often relate to genes with low variation in expression patterns, are beyond the detection limits of conventional fixed-threshold methods, and would be overlooked by studies relying on those methods.

AVAILABILITY

FORTRAN 90 source code to perform the PCIT algorithm is available as Supplementary File 1.

GEDI: a user-friendly toolbox for analysis of large-scale gene expression data

BACKGROUND

Several mathematical and statistical methods have been proposed in the last few years to analyze microarray data. Most of those methods involve complicated formulas, and software implementations that require advanced computer programming skills. Researchers from other areas may experience difficulties when they attempting to use those methods in their research. Here we present an user-friendly toolbox which allows large-scale gene expression analysis to be carried out by biomedical researchers with limited programming skills.

RESULTS

Here, we introduce an user-friendly toolbox called GEDI (Gene Expression Data Interpreter), an extensible, open-source, and freely-available tool that we believe will be useful to a wide range of laboratories, and to researchers with no background in Mathematics and Computer Science, allowing them to analyze their own data by applying both classical and advanced approaches developed and recently published by Fujita et al.

CONCLUSION

GEDI is an integrated user-friendly viewer that combines the state of the art SVR, DVAR and SVAR algorithms, previously developed by us. It facilitates the application of SVR, DVAR and SVAR, further than the mathematical formulas present in the corresponding publications, and allows one to better understand the results by means of available visualizations. Both running the statistical methods and visualizing the results are carried out within the graphical user interface, rendering these algorithms accessible to the broad community of researchers in Molecular Biology.

Transcriptional profiling of Ovis aries identifies Ovar-DQA1 allele frequency differences between nematode-resistant and susceptible selection lines

Gastrointestinal nematodes are a major cause of disease in grazing livestock; however, individual animals differ in their response to infection. To identify genes whose expression correlates with resistance status, transcriptional profiling of resistant and susceptible sheep was undertaken. Transcription profiles were taken at three time points during the growth of lambs. The number of genes differentially expressed increased as animals were exposed to longer nematode challenge. Almost 300 genes, with a variety of functions, were differentially expressed overall, although genes more highly expressed in resistant animals typically had major histocompatibility complex (MHC) II, free radical scavenging or smooth muscle-specific functions. The Ovar-DQA1 gene was 8.4-fold more highly expressed in resistant animals. This was due in part to a higher frequency of DQA1 null alleles in susceptible animals. The null allele of DQA1 was also associated with susceptibility in a separate selection flock, presenting the hypothesis that failure to present parasite antigens to immune cells led to nematode susceptibility. To test this hypothesis, commercial rams from three breeds were genotyped for the null allele of DQA1. The homozygous null allele was associated with susceptibility in only one of the three breeds tested indicating that the null allele does not cause susceptibility to intestinal parasites per se but is probably in linkage disequilibrium with additional polymorphisms in the MHC region. A combination of these polymorphisms may contribute to susceptibility in some populations. The extent of linkage disequilibrium between polymorphisms may vary from breed to breed or population to population.

Analysis of the immune-inducible genes of Plutella xylostella using expressed sequence tags and cDNA microarray

In the present study, the complex gene expression responses of Plutella xylostella to microbial challenges and injury were surveyed using a newly constructed expressed sequence tag (EST) clone collection and cDNA microarray analysis. A total of 1132 P. xylostella ESTs were cloned, annotated and categorized by their putative functions; these included proteases, protease inhibitors, recognition molecules and anti-microbial peptides. GeneOntology revealed that 4% of the P. xylostella ESTs corresponded to immunity-related genes potentially involved in innate immunity. We then used microarray analysis to identify 44 genes that were differentially expressed with at least a two-fold expression difference in P. xylostella before and after pathogen challenge. Together, our EST categorization and microarray profiling analyses allowed us to identify 70 genes that should be considered candidate immune response genes, providing important new insights into the molecular events that occur during the innate immune response in P. xylostella.

Microarray RNA transcriptional profiling: part II. Analytical considerations and annotation

This review summarizes the various data filtration, transformation and normalization processes for different array platforms (cDNA, oligos, one- and two-color), data analysis methods and their validation, and databases and annotation for RNA transcriptional profiling microarrays. This review is intended to introduce the beginner to the analyses and interpretation of gene expression studies using a nonmathematical approach for easier comprehension. Microarray analysis is not a trivial undertaking as there is no single method that works well for all, and results obtained from these analyses should be considered as a complement to other approaches.

Evaluating different methods of microarray data normalization

Background

With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration.

Results

Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets.

Conclusion

In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve.

Development and validation of a bovine macrophage specific cDNA microarray

Background

The response of macrophages to danger signals is an important early stage in the immune response. Our understanding of this complex event has been furthered by microarray analysis, which allows the simultaneous investigation of the expression of large numbers of genes. However, the microarray resources available to study these events in livestock animals are limited.

Results

Here we report the development of a bovine macrophage specific (BoMP) cDNA microarray. The BoMP microarray contains 5026 sequence elements (printed in duplicate) and numerous controls. The majority of the clones incorporated on the microarray were derived from the BoMP cDNA library generated from bovine myeloid cells subjected to various stimuli, including over 900 sequences unique to the library. Additional clones representing immunologically important genes have been included on the BoMP microarray. The microarray was validated by investigating the response of bovine monocytes to stimulation with interferon-γ and lipopolysaccharide using amplified RNA. At 2 and 16 hours post stimulation 695 genes exhibited statistically significant differential expression, including; 26 sequences unique to the BoMP library, interleukin 6, prion protein and toll-like receptor 4.

Conclusion

A 5 K cDNA microarray has been successfully developed to investigate gene expression in bovine myeloid cells. The BoMP microarray is available from the ARK-Genomics Centre for Functional Genomics in Farm Animals, UK.

Genome-wide screening for cis-regulatory variation using a classical diallel crossing scheme

Large-scale screening studies carried out to date for genetic variants that affect gene regulation are generally limited to descriptions of differences in allele-specific expression (ASE) detected in vivo. Allele-specific differences in gene expression provide evidence for a model whereby cis-acting genetic variation results in differential expression between alleles. Such gene surveys for regulatory variation are a first step in identifying the specific nucleotide changes that govern gene expression differences, but they leave the underlying mechanisms unexplored. Here, we propose a quantitative genetics approach to perform a genome-wide analysis of ASEdifferences (GASED). The GASED approach is based on a diallel design that is often used in plant breeding programs to estimate general combining abilities (GCA) of specific inbred lines and to identify high-yielding hybrid combinations of parents based on their specific combining abilities (SCAs). In a context of gene expression, the values of GCA and SCA parameters allow cis- and trans-regulatory changes to be distinguished and imbalances in gene expression to be ascribed to cis-regulatory variation. With this approach, a total of 715 genes could be identified that are likely to carry allelic polymorphisms responsible for at least a 1.5-fold allelic expression difference in a total of 10 diploid Arabidopsis thaliana hybrids. The major strength of the GASED approach, compared to other ASE detection methods, is that it is not restricted to genes with allelic transcript variants. Although a false-positive rate of 9/41 was observed, the GASED approach is a valuable pre-screening method that can accelerate systematic surveys of naturally occurring cis-regulatory variation among inbred lines for laboratory species, such as Arabidopsis, mouse, rat and fruitfly, and economically important crop species, such as corn.

Variance stabilization and normalization for one-color microarray data using a data-driven multiscale approach

MOTIVATION

Many standard statistical techniques are effective on data that are normally distributed with constant variance. Microarray data typically violate these assumptions since they come from non-Gaussian distributions with a non-trivial mean-variance relationship. Several methods have been proposed that transform microarray data to stabilize variance and draw its distribution towards the Gaussian. Some methods, such as log or generalized log, rely on an underlying model for the data. Others, such as the spread-versus-level plot, do not. We propose an alternative data-driven multiscale approach, called the Data-Driven Haar-Fisz for microarrays (DDHFm) with replicates. DDHFm has the advantage of being 'distribution-free' in the sense that no parametric model for the underlying microarray data is required to be specified or estimated; hence, DDHFm can be applied very generally, not just to microarray data.

RESULTS

DDHFm achieves very good variance stabilization of microarray data with replicates and produces transformed intensities that are approximately normally distributed. Simulation studies show that it performs better than other existing methods. Application of DDHFm to real one-color cDNA data validates these results.

AVAILABILITY

The R package of the Data-Driven Haar-Fisz transform (DDHFm) for microarrays is available in Bioconductor and CRAN.

Gene expression profiling of individual bovine nuclear transfer blastocysts

During somatic cell nuclear transfer the gene expression profile of the donor cell has to be changed or reprogrammed extensively to reflect that of a normal embryo. In this study we focused on the switching on of embryonic genes by screening with a microarray consisting of 5000 independent cDNA isolates derived from a bovine blastocyst library which we constructed for this purpose. Expression profiling was performed using linearly amplified RNA from individual day 7 nuclear transfer (NT) and genetically half-identicalin vitroproduced (IVP) blastocysts. We identified 92 genes expressed at lower levels in NT embryos whereas transcripts of 43 genes were more abundant in NT embryos (P≤ 0.05, ≥ 1.5-fold change). A range of functional categories was represented among the identified genes, with a preponderance of constitutively expressed genes required for the maintenance of basal cellular function. Using a stringent quantitative SYBR-green real time RT-PCR based approach we found, when comparing the means of the expression levels of a larger set of individual embryos, that differences were small (< 2-fold) and only significant for two of the seven analysed genes (KRT18,SLC16A1). Notably, examination of transcript levels of a single gene in individual embryos could not distinguish an NT from a control embryo. This unpredictability of individual gene expression on a global background of multiple gene expression changes argues for a predominantly stochastic nature of reprogramming errors.

Spatial normalization of array-CGH data

Background

Array-based comparative genomic hybridization (array-CGH) is a recently developed technique for analyzing changes in DNA copy number. As in all microarray analyses, normalization is required to correct for experimental artifacts while preserving the true biological signal. We investigated various sources of systematic variation in array-CGH data and identified two distinct types of spatial effect of no biological relevance as the predominant experimental artifacts: continuous spatial gradients and local spatial bias. Local spatial bias affects a large proportion of arrays, and has not previously been considered in array-CGH experiments.

Results

We show that existing normalization techniques do not correct these spatial effects properly. We therefore developed an automatic method for the spatial normalization of array-CGH data. This method makes it possible to delineate and to eliminate and/or correct areas affected by spatial bias. It is based on the combination of a spatial segmentation algorithm called NEM (Neighborhood Expectation Maximization) and spatial trend estimation. We defined quality criteria for array-CGH data, demonstrating significant improvements in data quality with our method for three data sets coming from two different platforms (198, 175 and 26 BAC-arrays).

Conclusion

We have designed an automatic algorithm for the spatial normalization of BAC CGH-array data, preventing the misinterpretation of experimental artifacts as biologically relevant outliers in the genomic profile. This algorithm is implemented in the R package MANOR (Micro-Array NORmalization), which is described at and available from the Bioconductor site . It can also be tested on the CAPweb bioinformatics platform at .

Gene expression profiling of naïve sheep genetically resistant and susceptible to gastrointestinal nematodes

BACKGROUND

Gastrointestinal nematodes constitute a major cause of morbidity and mortality in grazing ruminants. Individual animals or breeds, however, are known to differ in their resistance to infection. Gene expression profiling allows us to examine large numbers of transcripts simultaneously in order to identify those transcripts that contribute to an animal's susceptibility or resistance.

RESULTS

With the goal of identifying genes with a differential pattern of expression between sheep genetically resistant and susceptible to gastrointestinal nematodes, a 20,000 spot ovine cDNA microarray was constructed. This array was used to interrogate the expression of 9,238 known genes in duodenum tissue of four resistant and four susceptible female lambs. Naïve animals were used in order to look at genes that were differentially expressed in the absence of infection with gastrointestinal nematodes. Forty one unique known genes were identified that were differentially expressed between the resistant and susceptible animals. Northern blotting of a selection of the genes confirmed differential expression. The differentially expressed genes had a variety of functions, although many genes relating to the stress response and response to stimulus were more highly expressed in the susceptible animals.

CONCLUSION

We have constructed the first reported ovine microarray and used this array to examine gene expression in lambs genetically resistant and susceptible to gastrointestinal nematode infection. This study indicates that susceptible animals appear to be generating a hyper-sensitive immune response to non-nematode challenges. The gastrointestinal tract of susceptible animals is therefore under stress and compromised even in the absence of gastrointestinal nematodes. These factors may contribute to the genetic susceptibility of these animals.

Differences of gene expression in bovine alveolar macrophages infected with virulent and attenuated isogenic strains of Mycobacterium bovis

Infection with Mycobacterium bovis is a significant human and animal health problem in many parts of the world. The first stage of pulmonary tuberculosis occurs after inhalation of the bacilli into an alveolus where they are ingested by resident macrophages. DNA microarray analysis was used to detect genes expressed in bovine lung alveolar macrophages infected with two isogenic strains of M. bovis, a virulent strain, ATCC35723 and an attenuated strain, WAg520 derived from ATCC35723. Chemokines, interleukin-8 and monocyte chemotactic protein 1, were more strongly expressed in ATCC35723-infected macrophages compared to WAg520-infected macrophages. Conversely, a group of genes, including fibrinogen-like protein 2 and legumain, were expressed at a higher level in macrophages infected with WAg520 compared to ATCC35723. Quantitative real-time PCR of a selected group of these differentially expressed genes confirmed enhanced levels of IL-8 mRNA in ATCC35723-infected macrophages compared to WAg520-infected macrophages. Microarray analysis of gene expression in macrophages infected with attenuated isogenic strains of M. bovis may identify key genes involved in early and protective immune responses to tuberculosis.

Validation of alternative methods of data normalization in gene co-expression studies

MOTIVATION

Clusters of genes encoding proteins with related functions, or in the same regulatory network, often exhibit expression patterns that are correlated over a large number of conditions. Protein associations and gene regulatory networks can be modelled from expression data. We address the question of which of several normalization methods is optimal prior to computing the correlation of the expression profiles between every pair of genes.

RESULTS

We use gene expression data from five experiments with a total of 78 hybridizations and 23 diverse conditions. Nine methods of data normalization are explored based on all possible combinations of normalization techniques according to between and within gene and experiment variation. We compare the resulting empirical distribution of gene x gene correlations with the expectations and apply cross-validation to test the performance of each method in predicting accurate functional annotation. We conclude that normalization methods based on mixed-model equations are optimal.