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Structural and functional characteristics of two novel members of pathogensis-related multigene family of class 10 from yellow lupine+

100%
Handschuh L., Femiak I., Kasperska A., Figlerowicz M., Sikorski M. M.
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2007
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vol. 54
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issue 4
783-796
EN
PR-10 proteins (pathogensis-related), ubiquitous within the plant kingdom, are usually encoded by multigene families. To date we have identified 10 homologous pr-10 genes in a yellow lupine cDNA library. Here, the structure and expression of two newly identified yellow lupine pr-10 genes (LlYpr10-2b and LlYpr10-2f) are presented. Many potential regulatory sites were found in both gene promoters including common ones as well as those unique for each gene. However, promoter deletion analysis in transgenic tobacco plants revealed similar patterns of reporter gene (gus) expression. Shortened fragments of both gene promoters studied caused high GUS activity in leaves (along vascular bundles), stamen stigma, anthers and pollen grains. When conjugated with longer LlYpr-10.2 promoter fragments, GUS was additionally present in petal edges. Only a long fragment of the LlYpr10-2b gene promoter caused GUS expression in the stem. In yellow lupine the pr-10.2 genes are present in all studied organs, but their level of expression depends on the stage of development and is affected by wounding, oxidative stress and salicylic acid treatment. Silencing of the Llpr-10.2b gene in 4-week-old yellow lupine plants did not lead to any visible symptoms, which suggests that the function of the silenced gene is supplemented by its close homologues, still present in the studied plants.
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Impact of DNA microarray data transformation on gene expression analysis - comparison of two normalization methods

86%
Schmidt M. T., Handschuh L., Zyprych J., Szabelska A., Olejnik-Schmidt A. K., Siatkowski I., Figlerowicz M.
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2011
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vol. 58
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issue 4
573-580
EN
Two-color DNA microarrays are commonly used for the analysis of global gene expression. They provide information on relative abundance of thousands of mRNAs. However, the generated data need to be normalized to minimize systematic variations so that biologically significant differences can be more easily identified. A large number of normalization procedures have been proposed and many softwares for microarray data analysis are available. Here, we have applied two normalization methods (median and loess) from two packages of microarray data analysis softwares. They were examined using a sample data set. We found that the number of genes identified as differentially expressed varied significantly depending on the method applied. The obtained results, i.e. lists of differentially expressed genes, were consistent only when we used median normalization methods. Loess normalization implemented in the two software packages provided less coherent and for some probes even contradictory results. In general, our results provide an additional piece of evidence that the normalization method can profoundly influence final results of DNA microarray-based analysis. The impact of the normalization method depends greatly on the algorithm employed. Consequently, the normalization procedure must be carefully considered and optimized for each individual data set.
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