Mutations in isocitrate dehydrogenase 1 and 2 occur frequently in intrahepatic cholangiocarcinomas and share hypermethylation targets with glioblastomas.

Mutations in the genes encoding isocitrate dehydrogenase, IDH1 and IDH2, have been reported in gliomas, myeloid leukemias, chondrosarcomas and thyroid cancer. We discovered IDH1 and IDH2 mutations in 34 of 326 (10%) intrahepatic cholangiocarcinomas. Tumor with mutations in IDH1 or IDH2 had lower 5-hydroxymethylcytosine and higher 5-methylcytosine levels, as well as increased dimethylation of histone H3 lysine 79 (H3K79). Mutations in IDH1 or IDH2 were associated with longer overall survival (P=0.028) and were independently associated with a longer time to tumor recurrence after intrahepatic cholangiocarcinoma resection in multivariate analysis (P=0.021). IDH1 and IDH2 mutations were significantly associated with increased levels of p53 in intrahepatic cholangiocarcinomas, but no mutations in the p53 gene were found, suggesting that mutations in IDH1 and IDH2 may cause a stress that leads to p53 activation. We identified 2309 genes that were significantly hypermethylated in 19 cholangiocarcinomas with mutations in IDH1 or IDH2, compared with cholangiocarcinomas without these mutations. Hypermethylated CpG sites were significantly enriched in CpG shores and upstream of transcription start sites, suggesting a global regulation of transcriptional potential. Half of the hypermethylated genes overlapped with DNA hypermethylation in IDH1-mutant gliobastomas, suggesting the existence of a common set of genes whose expression may be affected by mutations in IDH1 or IDH2 in different types of tumors.

Microstructure and crystallization kinetics analysis of the (In15Sb85)(100-x)Zn(x) phase change recording thin films.

The (In15Sb85)(100-x)Zn(x) films (x = 0 - 17.4) were deposited on nature oxidized Si wafer and glass substrate at room temperature by magnetron co-sputtering of Sb target and InZn composite target. The thermal property of the films was examined by a homemade reflectivity thermal analyzer. Microstructures of the films were analyzed by transmission electron microscope (TEM). We examined the effects of Zn addition on the thermal property, crystallization kinetics, and crystallization mechanism of the In15Sb85 recording film. As x = 0 - 17.4, thermal analysis shows that the (In15Sb85)(100-x)Zn(x) films have two phase transition temperature ranges which are 189 degrees C-215 degrees C and 300 degrees C-350 degrees C. It is found that the activation energy is increased with Zn content. This indicates that the thermal stability of amorphous state is improved by doping Zn. The optical contrasts of the films are all larger than 15%, as x = 0 - 6.2, indicating that the films have the potential in blue laser optical recording media application.

Crystallization mechanisms of phase change (GeSbSn)(100-x)Co(x) optical recording films.

In this study, the (GeSbSn)(100-x0Co(x) films (x = 0-13.3) were deposited on natural oxidized silicon wafer and glass substrate by dc magnetron co-sputtering of GeSbSn and Co targets. The ZnS-SiO2 films were used as protective layers. The thicknesses of the (GeSbSn)(100-x)Co(x) films and protective layer were 100 nm and 30 nm, respectively. We investigated the effects of Co addition on the thermal property, crystallization kinetics, and crystallization mechanism of the GeSbSn recording film. The crystallization temperatures of (GeSbSn)(100-x)Co(x) films were decreased with Co content. It was found that the activation energy of the (GeSbSn)(100-x)Co(x) films will decrease from 1.53 eV to 0.55 eV as Co content increased from 0 at.% to 13.3 at.%.

Phylogenetic motif detection by expectation-maximization on evolutionary mixtures.

The preferential conservation of transcription factor binding sites implies that non-coding sequence data from related species will prove a powerful asset to motif discovery. We present a unified probabilistic framework for motif discovery that incorporates evolutionary information. We treat aligned DNA sequence as a mixture of evolutionary models, for motif and background, and, following the example of the MEME program, provide an algorithm to estimate the parameters by Expectation-Maximization. We examine a variety of evolutionary models and show that our approach can take advantage of phylogenic information to avoid false positives and discover motifs upstream of groups of characterized target genes. We compare our method to traditional motif finding on only conserved regions. An implementation will be made available at http://rana.lbl.gov.

Visualizing associations between genome sequences and gene expression data using genome-mean expression profiles.

The combination of genome-wide expression patterns and full genome sequences offers a great opportunity to further our understanding of the mechanisms and logic of transcriptional regulation. Many methods have been described that identify sequence motifs enriched in transcription control regions of genes that share similar gene expression patterns. Here we present an alternative approach that evaluates the transcriptional information contained by specific sequence motifs by computing for each motif the mean expression profile of all genes that contain the motif in their transcription control regions. These genome-mean expression profiles (GMEP's) are valuable for visualizing the relationship between genome sequences and gene expression data, and for characterizing the transcriptional importance of specific sequence motifs. Analysis of GMEP's calculated from a dataset of 519 whole-genome microarray experiments in Saccharomyces cerevisiae show a significant correlation between GMEP's of motifs that are reverse complements, a result that supports the relationship between GMEP's and transcriptional regulation. Hierarchical clustering of GMEP's identifies clusters of motifs that correspond to binding sites of well-characterized transcription factors. The GMEP's of these clustered motifs have patterns of variation across conditions that reflect the known activities of these transcription factors. Software that computed GMEP's from sequence and gene expression data is available under the terms of the Gnu Public License from http://rana.lbl.gov/.