Esteban Vegas

Search for specific biomarkers of IFNβ bioactivity in patients with multiple sclerosis.

Myxovirus A (MxA), a protein encoded by the MX1 gene with antiviral activity, has proven to be a sensitive measure of IFNβ bioactivity in multiple sclerosis (MS). However, the use of MxA as a biomarker of IFNβ bioactivity has been criticized for the lack of evidence of its role on disease pathogenesis and the clinical response to IFNβ. Here, we aimed to identify specific biomarkers of IFNβ bioactivity in order to compare their gene expression induction by type I IFNs with the MxA, and to investigate their potential role in MS pathogenesis. Gene expression microarrays were performed in PBMC from MS patients who developed neutralizing antibodies (NAB) to IFNβ at 12 and/or 24 months of treatment and patients who remained NAB negative. Nine genes followed patterns in gene expression over time similar to the MX1, which was considered the gold standard gene, and were selected for further experiments: IFI6, IFI27, IFI44L, IFIT1, HERC5, LY6E, RSAD2, SIGLEC1, and USP18. In vitro experiments in PBMC from healthy controls revealed specific induction of selected biomarkers by IFNβ but not IFNγ, and several markers, in particular USP18 and HERC5, were shown to be significantly induced at lower IFNβ concentrations and more selective than the MX1 as biomarkers of IFNβ bioactivity. In addition, USP18 expression was deficient in MS patients compared with healthy controls (p = 0.0004). We propose specific biomarkers that may be considered in addition to the MxA to evaluate IFNβ bioactivity, and to further explore their implication in MS pathogenesis.

Interleukin-19 Impairment in Active Crohn’s Disease Patients

The exact function of interleukin-19 (IL-19) on immune response is poorly understood. In mice, IL-19 up-regulates TNFα and IL-6 expression and its deficiency increases susceptibility to DSS-induced colitis. In humans, IL-19 favors a Th2 response and is elevated in several diseases. We here investigate the expression and effects of IL-19 on cells from active Crohn’s disease (CD) patient. Twenty-three active CD patients and 20 healthy controls (HC) were included. mRNA and protein IL-19 levels were analyzed in monocytes. IL-19 effects were determined in vitro on the T cell phenotype and in the production of cytokines by immune cells. We observed that unstimulated and TLR-activated monocytes expressed significantly lower IL-19 mRNA in active CD patients than in HC (logFC = −1.97 unstimulated; −1.88 with Pam3CSK4; and −1.91 with FSL-1; p<0.001). These results were confirmed at protein level. Exogenous IL-19 had an anti-inflammatory effect on HC but not on CD patients. IL-19 decreased TNFα production in PBMC (850.7±75.29 pg/ml vs 2626.0±350 pg/ml; p<0.01) and increased CTLA4 expression (22.04±1.55% vs 13.98±2.05%; p<0.05) and IL-4 production (32.5±8.9 pg/ml vs 13.5±2.9 pg/ml; p<0.05) in T cells from HC. IL-10 regulated IL-19 production in both active CD patients and HC. We observed that three of the miRNAs that can modulate IL-19 mRNA expression, were up-regulated in monocytes from active CD patients. These results suggested that IL-19 had an anti-inflammatory role in this study. Defects in IL-19 expression and the lack of response to this cytokine could contribute to inflammatory mechanisms in active CD patients.

Memory Improvement in the AβPP/PS1 Mouse Model of Familial Alzheimer's Disease Induced by Carbamylated-Erythropoietin is Accompanied by Modulation of Synaptic Genes

Abstract Neuroprotection of erythropoietin (EPO) following long-term administration is hampered by the associated undesirable effects on hematopoiesis and body weight. For this reason, we tested carbamylated-EPO (CEPO), which has no effect on erythropoiesis, and compared it with EPO in the AβPP/PS1 mouse model of familial Alzheimers disease. Groups of 5-month-old wild type (WT) and transgenic mice received chronic treatment consisting of CEPO (2,500 or 5,000 UI/kg) or EPO (2,500 UI/kg) 3 days/week for 4 weeks. Memory at the end of treatment was assessed with the object recognition test. Microarray analysis and quantitative-PCR were used for gene expression studies. No alterations in erythropoiesis were observed in CEPO-treated WT and AβPP/PS1 transgenic mice. EPO and CEPO improved memory in AβPP/PS1 animals. However, only EPO decreased amyloid-β (Aβ) plaque burden and soluble Aβ40. Microarray analysis of gene expression revealed a limited number of common genes modulated by EPO and CEPO. CEPO but not EPO significantly increased gene expression of dopamine receptors 1 and 2, and adenosine receptor 2a, and significantly down-regulated adrenergic receptor α1D and gastrin releasing peptide. CEPO treatment resulted in higher protein levels of dopamine receptors 1 and 2 in WT and AβPP/PS1 animals, whereas the adenosine receptor 2a was reduced in WT animals. The present results suggest that the improved behavior observed in AβPP/PS1 transgenic mice after CEPO treatment may be mediated, at least in part, by the observed modulation of the expression of molecules involved in neurotransmission.

Mining gene expression profiles: an integrated implementation of kernel principal component analysis and singular value decomposition.

The detection of genes that show similar profiles under different experimental conditions is often an initial step in inferring the biological significance of such genes. Visualization tools are used to identify genes with similar profiles in microarray studies. Given the large number of genes recorded in microarray experiments, gene expression data are generally displayed on a low dimensional plot, based on linear methods. However, microarray data show nonlinearity, due to high-order terms of interaction between genes, so alternative approaches, such as kernel methods, may be more appropriate. We introduce a technique that combines kernel principal component analysis (KPCA) and Biplot to visualize gene expression profiles. Our approach relies on the singular value decomposition of the input matrix and incorporates an additional step that involves KPCA. The main properties of our method are the extraction of nonlinear features and the preservation of the input variables (genes) in the output display. We apply this algorithm to colon tumor, leukemia and lymphoma datasets. Our approach reveals the underlying structure of the gene expression profiles and provides a more intuitive understanding of the gene and sample association.

Kernel-PCA data integration with enhanced interpretability

BackgroundNowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task.ResultsWe analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed.ConclusionsThe integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Kernel Methods for Dimensionality Reduction Applied to the «Omics» Data

Microarray technology has been advanced to the point at which the simultaneous monitoring of gene expression on a genome scale is now possible. Microarray experiments often aim to identify individual genes that are differentially expressed under distinct conditions, such as between two or more phenotypes, cell lines, under different treatment types or diseased and healthy subjects. Such experiments may be the first step towards inferring gene function and constructing gene networks in systems biology.

Variance reduction for Bernoulli response variables in simulation

Some statistical properties of the estimator associated with a variance reduction technique are presented. This technique is especially suitable in Monte-Carlo simulation studies in Statistics, e.g. in power estimation. The new estimator is unbiased. Its maximum likelihood variance estimator is negatively biased. Two new variance estimators, unbiased and nearly unbiased, are obtained. Two simulation studies are presented in order to illustrate the validity and usage of this variance reduction technique.

Efficiency and exponential models in a variance-reduction technique for dichotomous response variables

The statistical properties of a variance-reduction technique, applicable to simulations with dichotomous response variables, are examined from the standpoint of exponential models, that is, distribution families whose log-likelihood is a linear function of a sufficient statistic of fixed dimension. It is established that this variance-reduction technique induces some distortion that is explainable in terms of the statistical curvature of the resulting exponential model. The curvature concept used here is a multiparametric generalization of Efrons definition. It is calculated explicitly and its relation to the amount of variance reduction and to the asymptotic distribution of the relevant statistics is discussed. It is concluded that Efronss criteria for low curvature (associated with nice statistical properties) are valid in this context and generally met for the usual sample sizes in simulation (some thousands of replicates).

Inferring differentially expressed pathways using kernel maximum mean discrepancy-based test

BackgroundPathway expression is multivariate in nature. Thus, from a statistical perspective, to detect differentially expressed pathways between two conditions, methods for inferring differences between mean vectors need to be applied. Maximum mean discrepancy (MMD) is a statistical test to determine whether two samples are from the same distribution, its implementation being greatly simplified using the kernel method.ResultsAn MMD-based test successfully detected the differential expression between two conditions, specifically the expression of a set of genes involved in certain fatty acid metabolic pathways. Furthermore, we exploited the ability of the kernel method to integrate data and successfully added hepatic fatty acid levels to the test procedure.ConclusionMMD is a non-parametric test that acquires several advantages when combined with the kernelization of data: 1) the number of variables can be greater than the sample size; 2) omics data can be integrated; 3) it can be applied not only to vectors, but to strings, sequences and other common structured data types arising in molecular biology.

A comparison of spectrum kernel machines for protein subnuclear localization

In this article, we compare the performance of a new kernel machine with respect to support vector machines (SVM) for prediction of the subnuclear localization of a protein from the primary sequence information. Both machines use the same type of kernel but differ in the criteria to build the classifier. To measure the similarity between protein sequences we employ a k-spectrum kernel to exploit the contextual information around an amino acid and the conserved motif information. We choose Nuc-PLoc benchmark datasets to evaluate both methods. In most subnuclear locations our classifier has better overall accuracy than SVM. Moreover, our method shows less computational cost than SVM.