Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

The main purpose of a gene interaction network is to map the relationships of the genes that are out of sight when a genomic study is tackled. DNA microarrays allow the measure of gene expression of thousands of genes at the same time. These data constitute the numeric seed for the induction of the gene networks. In this paper, we propose a new approach to build gene networks by means of Bayesian classifiers, variable selection and bootstrap resampling. The interactions induced by the Bayesian classifiers are based both on the expression levels and on the phenotype information of the supervised variable. Feature selection and bootstrap resampling add reliability and robustness to the overall process removing the false positive findings. The consensus among all the induced models produces a hierarchy of dependences and, thus, of variables. Biologists can define the depth level of the model hierarchy so the set of interactions and genes involved can vary from a sparse to a dense set. Experimental results show how these networks perform well on classification tasks. The biological validation matches previous biological findings and opens new hypothesis for future studies

Differential Micro RNA Expression in PBMC from Multiple Sclerosis Patients

Differences in gene expression patterns have been documented not only in Multiple Sclerosis patients versus healthy controls but also in the relapse of the disease. Recently a new gene expression modulator has been identified: the microRNA or miRNA. The aim of this work is to analyze the possible role of miRNAs in multiple sclerosis, focusing on the relapse stage. We have analyzed the expression patterns of 364 miRNAs in PBMC obtained from multiple sclerosis patients in relapse status, in remission status and healthy controls. The expression patterns of the miRNAs with significantly different expression were validated in an independent set of samples. In order to determine the effect of the miRNAs, the expression of some predicted target genes of these were studied by qPCR. Gene interaction networks were constructed in order to obtain a co-expression and multivariate view of the experimental data. The data analysis and later validation reveal that two miRNAs (hsa-miR-18b and hsa-miR-599) may be relevant at the time of relapse and that another miRNA (hsa-miR-96) may be involved in remission. The genes targeted by hsa-miR-96 are involved in immunological pathways as Interleukin signaling and in other pathways as wnt signaling. This work highlights the importance of miRNA expression in the molecular mechanisms implicated in the disease. Moreover, the proposed involvement of these small molecules in multiple sclerosis opens up a new therapeutic approach to explore and highlight some candidate biomarker targets in MS

MEDVIR: 3D visual interface applied to gene profile analisys.

The origins for this work arise in response to the increasing need for biologists and doctors to obtain tools for visual analysis of data. When dealing with multidimensional data, such as medical data, the traditional data mining techniques can be a tedious and complex task, even to some medical experts. Therefore, it is necessary to develop useful visualization techniques that can complement the expert’s criterion, and at the same time visually stimulate and make easier the process of obtaining knowledge from a dataset. Thus, the process of interpretation and understanding of the data can be greatly enriched. Multidimensionality is inherent to any medical data, requiring a time-consuming effort to get a clinical useful outcome. Unfortunately, both clinicians and biologists are not trained in managing more than four dimensions. Specifically, we were aimed to design a 3D visual interface for gene profile analysis easy in order to be used both by medical and biologist experts. In this way, a new analysis method is proposed: MedVir. This is a simple and intuitive analysis mechanism based on the visualization of any multidimensional medical data in a three dimensional space that allows interaction with experts in order to collaborate and enrich this representation. In other words, MedVir makes a powerful reduction in data dimensionality in order to represent the original information into a three dimensional environment. The experts can interact with the data and draw conclusions in a visual and quickly way.

Transcriptional changes and oxidative stress associated with the synergistic interaction between potato virus X and potato virus Y and their relationship with symptom expression.

Many virus diseases of economic importance to agriculture result from mixtures of different pathogens invading the host at a given time. This contrasts with the relatively scarce studies available on the molecular events associated with virus---host interactions in mixed infections. Compared with single infections, co-infection of Nicotiana benthamiana with Potato virus X (PVX) and Potato virus Y (PVY) resulted in increased systemic symptoms (synergism) that led to necrosis of the newly emerging leaves and death of the plant. A comparative transcriptional analysis was undertaken to identify quantitative and qualitative differences in gene expression during this synergistic infection and correlate these changes with the severe symptoms it caused. Global transcription profiles of doubly infected leaves were compared with those from singly infected leaves using gene ontology enrichment analysis and metabolic pathway annotator software. Functional gene categories altered by the double infection comprise suites of genes regulated coordinately, which are associated with chloroplast functions (downregulated), protein synthesis and degradation (upregulated), carbohydrate metabolism (upregulated), and response to biotic stimulus and stress (upregulated). The expressions of reactive oxygen species?generating enzymes as well as several mitogen-activated protein kinases were also significantly induced. Accordingly, synergistic infection induced a severe oxidative stress in N. benthamiana leaves, as judged by increases in lipid peroxidation and by the generation of superoxide radicals in chloroplasts, which correlated with the misregulation of antioxidative genes in microarray data. Interestingly, expression of genes encoding oxylipin biosynthesis was uniquely upregulated by the synergistic infection. Virus-induced gene silencing of ?-dioxygenase1 delayed cell death during PVX?PVY infection.