Objectives: CSF levels of A␤ 1-42 , t-tau, and p-tau 181p are potential early diagnostic markers for probable Alzheimer disease (AD). The influence of genetic variation on these markers has been investigated for candidate genes but not on a genome-wide basis. We report a genome-wide association study (GWAS) of CSF biomarkers (A␤ 1-42 , t-tau, p-tau 181p , p-tau 181p /A␤ 1-42 , and t-tau/A␤ GLOSSARY A␤ 1-42 ϭ amyloid-␤ 1-42 peptide; AD ϭ Alzheimer disease; ADNI ϭ Alzheimer's Disease Neuroimaging Initiative; GWAS ϭ genome-wide association study; LD ϭ linkage disequilibrium; LOAD ϭ late-onset Alzheimer disease; MAF ϭ minor allele frequency; MCI ϭ mild cognitive impairment; p-tau 181p ϭ tau phosphorylated at the threonine 181; QC ϭ quality control; SNP ϭ single nucleotide polymorphism; t-tau ϭ total tau.Alzheimer disease (AD) is the most common form of dementia, affecting an estimated 5.3 million Americans. Amyloid-␤ 1-42 peptide (A␤ 1-42 ), total tau (t-tau), and tau phosphorylated at the threonine 181 (p-tau 181p ), measured in CSF samples, are potential diagnostic biomarkers for AD. 1-3 A␤ 1-42 is decreased and t-tau and p-tau 181p are increased in the CSF of patients with AD. 4 Baseline A␤ 1-42 has been shown to be a good predictor of the 12-month change in e-Pub ahead of print on December 1, 2010, at www.neurology.org.*These authors contributed equally to this work.

Caveolin-1 (Cav1) is an integral membrane, scaffolding protein found in plasma membrane invaginations (caveolae). Cav1 regulates multiple cancer-associated processes. In breast cancer, a tumor suppressive role for Cav1 has been suggested; however, Cav1 is frequently overexpressed in aggressive breast cancer subtypes, suggesting an oncogenic function in advanced-stage disease. To further delineate Cav1 function in breast cancer progression, we evaluated its expression levels among a panel of cell lines representing a spectrum of breast cancer phenotypes. In basal-like (the most aggressive BC subtype) breast cancer cells, Cav1 was consistently upregulated, and positively correlated with increased cell proliferation, anchorage-independent growth, and migration and invasion. To identify mechanisms of Cav1 gene regulation, we compared DNA methylation levels within promoter ‘CpG islands' (CGIs) with ‘CGI shores', recently described regions that flank CGIs with less CG-density. Integration of genome-wide DNA methylation profiles (‘methylomes') with Cav1 expression in 30 breast cancer cell lines showed that differential methylation of CGI shores, but not CGIs, significantly regulated Cav1 expression. In breast cancer cell lines having low Cav1 expression (despite promoter CGI hypomethylation), we found that treatment with a DNA methyltransferase inhibitor induced Cav1 expression via CGI shore demethylation. In addition, further methylome assessments revealed that breast cancer aggressiveness associated with Cav1 CGI shore methylation levels, with shore hypermethylation in minimally aggressive, luminal breast cancer cells and shore hypomethylation in highly aggressive, basal-like cells. Cav1 CGI shore methylation was also observed in human breast tumors, and overall survival rates of breast cancer patients lacking estrogen receptor α (ERα) negatively correlated with Cav1 expression. Based on this first study of Cav1 (a potential oncogene) CGI shore methylation, we suggest this phenomenon may represent a new prognostic marker for ERα-negative, basal-like breast cancer.

Functionally related genes co-evolve, probably due to the strong selection pressure in evolution. Thus we expect that they are present in multiple genomes. Physical proximity among genes, known as gene team, is a very useful concept to discover functionally related genes in multiple genomes.there are also many gene sets that do not preserve physical proximity.In this we generalized the gene team model, that looks f o r gene clusters in a physically clustered form, to multiple genome cases with relaxed constraint. We propose a novel hybrid pattern model that combines the set and the sequential pattern models. Our model searches for gene clusters with without physical proximity constraint. This model is implemented and tested with 97 genomes (120 replicons). The result was analyzed to show the usefulness of our model. Especially, analysis of gene clusters that belong to B. subtilis and E. coli demonstrated that our model predicted many experimentally operons andfunctionally related clusters. Our program is fast enough to provide a sevice on the web at http://platcom. informatics. Users can select any combination of 97 genomes to predict gene teams.