We investigated the conformational change in the human prion protein owing to an ¹¹⁷ →Val mutation by using molecular dynamics simulations. This mutation is related to Gerstmann-Sträussler-Sheinker disease, one of the familial prion diseases. Five prion protein structures were simulated in the periodic or non-periodic system. The results of molecular dynamics calculations indicated that the globular domains of wild-type structures (109-228 and 90-228) were stable. In contrast, the globular domains of mutant structures (109-228 and 90-228) were sensitive to the N-terminal region possessing the Ala¹¹⁷ →Val mutation, and the ß-sheet regions were increased.

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We analysed the gene expression pattern of Saccharomyces cerevisiae in response to lindane, a strong toxicant widely used as a pesticide in agriculture and by public health services. cDNA preparations from untreated cells and from cells treated with lindane were used to screen the DNA microarrays of about 6, 000 genes. A total of 288 genes showed >2-fold induction in transcript levels, out of which 112 have not yet been characterized. The functional analysis of most known genes indicates that genes involved with mitochondrial dysfunction, oxidative stress, ionic homeostasis, mitochondrial organization, or biogenesis responded to lindane-mediated stress. In addition, several induced genes were shown to contribute to ER-mediated degradation and quality control. However, no significant changes in the transcript levels of ORFs related to DNA damage and repair were observed. Furthermore, the mRNA levels of some uncharacterized genes are significantly high, and the unveiling of these genes, along with that of known genes, might provide the opportunity to illustrate how yeast responds to environmental perturbation. This analysis will also facilitate the identification of some specific genes that could be used as biomarkers for a toxicity assay of lindane or other similar environmental pollutants.

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Among samples analyzed for gene expression, samples incorrectly labeled or identified as likely contaminated are those whose expression patterns are markedly different. Such samples should be designated outliers, since they can exert a negative effect on the selection of informative genes for sample classification. We developed a method based on Akaike's Information Criterion (AIC) to detect such outliers. Our method is advantageous because it is free from a significance level and it facilitates objective decision-making. We applied our method to analyze the public microarray data of Alon et al. (1999) and found that some of the detected outlying samples coincided with samples considered as likely contaminated. Application of our method produced a higher discrimination level for informative genes in tumor- and normal tissues and, upon exclusion of the outliers, yielded higher classification accuracy. The detection of outlying samples prior to sample classification is essential, and the method described here serves as a valuable check.

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