Two way clustering of Microarray Data using a Hybrid Approach

The Microarray technique is rather powerful, as it allows to test up thousands of genes at a time, but this produces an overwhelming set of data files containing huge amounts of data, which is quite difficult to pre-process, separate, classify and correlate for interesting conclusions to be extracted. Modern machine learning, data mining and clustering techniques based on information theory, are needed to read and interpret the information contents buried in those large data sets. Independent Component Analysis method can be used to correct the data affected by corruption processes or to filter the uncorrectable one and then clustering methods can group similar genes or classify samples. In this paper a hybrid approach is used to obtain a two way unsupervised clustering for a corrected microarray data.

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.

Microarray analysis of autoimmune diseases by machine learning procedures

—Microarray-based global gene expression profiling, with the use of sophisticated statistical algorithms is providing new insights into the pathogenesis of autoimmune diseases. We have applied a novel statistical technique for gene selection based on machine learning approaches to analyze microarray expression data gathered from patients with systemic lupus erythematosus (SLE) and primary antiphospholipid syndrome (PAPS), two autoimmune diseases of unknown genetic origin that share many common features. The methodology included a combination of three data discretization policies, a consensus gene selection method, and a multivariate correlation measurement. A set of 150 genes was found to discriminate SLE and PAPS patients from healthy individuals. Statistical validations demonstrate the relevance of this gene set from an univariate and multivariate perspective. Moreover, functional characterization of these genes identified an interferon-regulated gene signature, consistent with previous reports. It also revealed the existence of other regulatory pathways, including those regulated by PTEN, TNF, and BCL-2, which are altered in SLE and PAPS. Remarkably, a significant number of these genes carry E2F binding motifs in their promoters, projecting a role for E2F in the regulation of autoimmunity.

The involvement of thaumatin-like proteins in plant food cross-reactivity: a multicenter study using a specific protein microarray.

Cross-reactivity of plant foods is an important phenomenon in allergy, with geographical variations with respect to the number and prevalence of the allergens involved in this process, whose complexity requires detailed studies. We have addressed the role of thaumatin-like proteins (TLPs) in cross-reactivity between fruit and pollen allergies. A representative panel of 16 purified TLPs was printed onto an allergen microarray. The proteins selected belonged to the sources most frequently associated with peach allergy in representative regions of Spain. Sera from two groups of well characterized patients, one with allergy to Rosaceae fruit (FAG) and another against pollens but tolerant to food-plant allergens (PAG), were obtained from seven geographical areas with different environmental pollen profiles. Cross-reactivity between members of this family was demonstrated by inhibition assays. Only 6 out of 16 purified TLPs showed noticeable allergenic activity in the studied populations. Pru p 2.0201, the peach TLP (41%), chestnut TLP (24%) and plane pollen TLP (22%) proved to be allergens of probable relevance to fruit allergy, being mainly associated with pollen sensitization, and strongly linked to specific geographical areas such as Barcelona, Bilbao, the Canary Islands and Madrid. The patients exhibited mayor que50% positive response to Pru p 2.0201 and to chestnut TLP in these specific areas. Therefore, their recognition patterns were associated with the geographical area, suggesting a role for pollen in the sensitization of these allergens. Finally, the co-sensitizations of patients considering pairs of TLP allergens were analyzed by using the co-sensitization graph associated with an allergen microarray immunoassay. Our data indicate that TLPs are significant allergens in plant food allergy and should be considered when diagnosing and treating pollen-food allergy.