CARMA: A platform for analyzing microarray datasets that incorporate replicate measures

BACKGROUND: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust experimental design coupled with an analysis of variance (ANOVA) incorporating a model that accounts for known sources of experimental variability can significantly improve the determination of differences in gene expression and estimations of their significance. RESULTS: To realize the full benefits of performing analysis of variance on microarray data we have developed CARMA, a microarray analysis platform that reads data files generated by most microarray image processing software packages, performs ANOVA using a user-defined linear model, and produces easily interpretable graphical and numeric results. No pre-processing of the data is required and user-specified parameters control most aspects of the analysis including statistical significance criterion. The software also performs location and intensity dependent lowess normalization, automatic outlier detection and removal, and accommodates missing data. CONCLUSION: CARMA provides a clear quantitative and statistical characterization of each measured gene that can be used to assess marginally acceptable measures and improve confidence in the interpretation of microarray results. Overall, applying CARMA to microarray datasets incorporating repeated measures effectively reduces the number of gene incorrectly identified as differentially expressed and results in a more robust and reliable analysis

TLR7-mediated skin inflammation remotely triggers chemokine expression and leukocyte accumulation in the brain

Background: The relationship between the brain and the immune system has become increasingly topical as, although it is immune-specialised, the CNS is not free from the influences of the immune system. Recent data indicate that peripheral immune stimulation can significantly affect the CNS. But the mechanisms underpinning this relationship remain unclear. The standard approach to understanding this relationship has relied on systemic immune activation using bacterial components, finding that immune mediators, such as cytokines, can have a significant effect on brain function and behaviour. More rarely have studies used disease models that are representative of human disorders. Methods: Here we use a well-characterised animal model of psoriasis-like skin inflammation—imiquimod—to investigate the effects of tissue-specific peripheral inflammation on the brain. We used full genome array, flow cytometry analysis of immune cell infiltration, doublecortin staining for neural precursor cells and a behavioural read-out exploiting natural burrowing behaviour. Results: We found that a number of genes are upregulated in the brain following treatment, amongst which is a subset of inflammatory chemokines (CCL3, CCL5, CCL9, CXCL10, CXCL13, CXCL16 and CCR5). Strikingly, this model induced the infiltration of a number of immune cell subsets into the brain parenchyma, including T cells, NK cells and myeloid cells, along with a reduction in neurogenesis and a suppression of burrowing activity. Conclusions: These findings demonstrate that cutaneous, peripheral immune stimulation is associated with significant leukocyte infiltration into the brain and suggest that chemokines may be amongst the key mediators driving this response

Penalized Clustering of Large Scale Functional Data with Multiple Covariates

In this article, we propose a penalized clustering method for large scale data with multiple covariates through a functional data approach. In the proposed method, responses and covariates are linked together through nonparametric multivariate functions (fixed effects), which have great flexibility in modeling a variety of function features, such as jump points, branching, and periodicity. Functional ANOVA is employed to further decompose multivariate functions in a reproducing kernel Hilbert space and provide associated notions of main effect and interaction. Parsimonious random effects are used to capture various correlation structures. The mixed-effect models are nested under a general mixture model, in which the heterogeneity of functional data is characterized. We propose a penalized Henderson's likelihood approach for model-fitting and design a rejection-controlled EM algorithm for the estimation. Our method selects smoothing parameters through generalized cross-validation. Furthermore, the Bayesian confidence intervals are used to measure the clustering uncertainty. Simulation studies and real-data examples are presented to investigate the empirical performance of the proposed method. Open-source code is available in the R package MFDA

microRNA expression in peripheral blood cells following acute ischemic stroke and their predicted gene targets.

BackgroundmicroRNA (miRNA) are important regulators of gene expression. In patients with ischemic stroke we have previously shown that differences in immune cell gene expression are present. In this study we sought to determine the miRNA that are differentially expressed in peripheral blood cells of patients with acute ischemic stroke and thus may regulate immune cell gene expression.MethodsmiRNA from peripheral blood cells of forty-eight patients with ischemic stroke and vascular risk factor controls were compared. Differentially expressed miRNA in patients with ischemic stroke were determined by microarray with qRT-PCR confirmation. The gene targets and pathways associated with ischemic stroke that may be regulated by the identified miRNA were characterized.ResultsIn patients with acute ischemic stroke, miR-122, miR-148a, let-7i, miR-19a, miR-320d, miR-4429 were decreased and miR-363, miR-487b were increased compared to vascular risk factor controls. These miRNA are predicted to regulate several genes in pathways previously identified by gene expression analyses, including toll-like receptor signaling, NF-κβ signaling, leukocyte extravasation signaling, and the prothrombin activation pathway.ConclusionsSeveral miRNA are differentially expressed in blood cells of patients with acute ischemic stroke. These miRNA may regulate leukocyte gene expression in ischemic stroke including pathways involved in immune activation, leukocyte extravasation and thrombosis

Distal Tumors Elicit Distinctive Gene Expression Changes in Mouse Brain, Different from Those Induced by Arthritis

Background: Tumor progression is characterized by high mutation rates, each mutation potentially generating an “alarm” signal. The brain is the main integrator of signals arising in the periphery from changes in homeostasis. We hypothesized that tumors growing at a distant site might be a stimulus strong enough to be molecularly sensed and integrated by the brain. Results: Transcriptome analysis of the mouse hypothalamus, midbrain, and pre-fontal cortex at different time points following administration at a distant site of mammary, lung and colon cancer cells evidenced cancer-type and brain-region specific changes in gene expression. On the contrary, no significant gene expression changes were detected in the liver. The hypothalamus was the region with the largest number of differentially expressed genes. On the array and off the array analysis of hypothalamic samples using real time PCR confirmed changes in genes associated with synaptic activity and sickness response, respectively. Gene clustering allowed the discrimination between each cancer model and between the cancer models and arthritis. Conclusions: The present data provides evidence of changes in gene expression in the brain during progression of distal tumors and arthritis highlighting a potential link between distal pathological processes and the brain.Fil: Alvarez, Mariano J.. Gentron Research Unit; ArgentinaFil: Salibe, Mariano C.. Gentron Research Unit; ArgentinaFil: Stolovitzky, Gustavo. Ibm Research. Thomas J. Watson Research Center; Estados UnidosFil: Rubinstein, Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones en Ingeniería Genética y Biología Molecular "Dr. Héctor N. Torres"; ArgentinaFil: Pitossi, Fernando Juan. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Podhajcer, Osvaldo Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; Argentin

Host-Virus Interactions of Infectious Laryngotracheitis Virus Infection in Cultured Cells

Infectious laryngotracheitis virus (ILTV; Gallid herpesvirus 1) causes upper respiratory diseases in mainly chickens and exhibits 90-100% of high morbidity and up to 70% of mortality, resulting in huge economic losses in the poultry industry worldwide. To study host-ILTV interactions, the changes in genome-wide gene expressions in response to wild-type and vaccine ILTV infections in primary chicken embryo lung cells were investigated using microarray analysis. Results provide crucial insights into host cell pathogenic and immunogenic responses against wild-type and vaccine ILTV infections. Using microarray method and Ingenuity Pathway Analysis (IPA) bioinformatics tool, 273 and 306 differentially expressed genes were identified responding to wild-type and vaccine ILTV infections, respectively. Further integrated analysis to compare differentially expressed genes revealed that eight host genes including coagulation factor II (thrombin) receptor-like 1 (F2RL1), bone morphogenetic protein 2 (BMP2), inhibitor of NF-kB (IkB) kinase subunit beta (IKBKB) interacting protein (IKBIP), thymidylate synthetase (TYMS), chromosome 8 open reading frame 79 (C8orf79), coagulation factor X (F10), prostaglandin-endoperoxide synthase 2 (PTGS2) and neuropeptide Y (NPY) were regulated differently between wild-type and vaccine ILTV infections in an opposite direction, suggesting that these host factors may play important roles in host immune responses against ILTV infection. In addition, the transcriptome changes of ILTV encoding genes were studied during infection time courses using quantitative PCR. In this study, infected-cells polypeptide (ICP) 4 showed the highest expression level and UL21 and UL42 showed unique expression patterns, unlike most of the other ILTV gene which exhibited continuous elevation of expression during lytic infection. Kinetic analysis of ILTV gene expression in host cells may provide new knowledge to understand ILTV pathogenesis

Peripheral inflammation is associated with remote global gene expression changes in the brain

Background: Although the central nervous system (CNS) was once considered an immunologically privileged site, in recent years it has become increasingly evident that cross talk between the immune system and the CNS does occur. As a result, patients with chronic inflammatory diseases, such as rheumatoid arthritis, inflammatory bowel disease or psoriasis, are often further burdened with neuropsychiatric symptoms, such as depression, anxiety and fatigue. Despite the recent advances in our understanding of neuroimmune communication pathways, the precise effect of peripheral immune activation on neural circuitry remains unclear. Utilizing transcriptomics in a well-characterized murine model of systemic inflammation, we have started to investigate the molecular mechanisms by which inflammation originating in the periphery can induce transcriptional modulation in the brain.<p></p> Methods: Several different systemic and tissue-specific models of peripheral toll-like-receptor-(TLR)-driven (lipopolysaccharide (LPS), lipoteichoic acid and Imiquimod) and sterile (tumour necrosis factor (TNF) and 12-O-tetradecanoylphorbol-13-acetate (TPA)) inflammation were induced in C57BL/6 mice. Whole brain transcriptional profiles were assessed and compared 48 hours after intraperitoneal injection of lipopolysaccharide or vehicle, using Affymetrix GeneChip microarrays. Target gene induction, identified by microarray analysis, was validated independently using qPCR. Expression of the same panel of target genes was then investigated in a number of sterile and other TLR-dependent models of peripheral inflammation.<p></p> Results: Microarray analysis of whole brains collected 48 hr after LPS challenge revealed increased transcription of a range of interferon-stimulated genes (ISGs) in the brain. In addition to acute LPS challenge, ISGs were induced in the brain following both chronic LPS-induced systemic inflammation and Imiquimod-induced skin inflammation. Unique to the brain, this transcriptional response is indicative of peripherally triggered, interferon-mediated CNS inflammation. Similar models of sterile inflammation and lipoteichoic-acid-induced systemic inflammation did not share the capacity to trigger ISG induction in the brain.<p></p> Conclusions: These data highlight ISG induction in the brain as being a consequence of a TLR-induced type I interferon response. As considerable evidence links type I interferons to psychiatric disorders, we hypothesize that interferon production in the brain could represent an important mechanism, linking peripheral TLR-induced inflammation with behavioural changes.<p></p&gt

Gene Expression Analysis Methods on Microarray Data a A Review

In recent years a new type of experiments are changing the way that biologists and other specialists analyze many problems. These are called high throughput experiments and the main difference with those that were performed some years ago is mainly in the quantity of the data obtained from them. Thanks to the technology known generically as microarrays, it is possible to study nowadays in a single experiment the behavior of all the genes of an organism under different conditions. The data generated by these experiments may consist from thousands to millions of variables and they pose many challenges to the scientists who have to analyze them. Many of these are of statistical nature and will be the center of this review. There are many types of microarrays which have been developed to answer different biological questions and some of them will be explained later. For the sake of simplicity we start with the most well known ones: expression microarrays