A machine learning approach to brain epigenetic analysis reveals kinases associated with Alzheimer's disease.

Alzheimer's disease (AD) is influenced by both genetic and environmental factors; thus, brain epigenomic alterations may provide insights into AD pathogenesis. Multiple array-based Epigenome-Wide Association Studies (EWASs) have identified robust brain methylation changes in AD; however, array-based assays only test about 2% of all CpG sites in the genome. Here, we develop EWASplus, a computational method that uses a supervised machine learning strategy to extend EWAS coverage to the entire genome. Application to six AD-related traits predicts hundreds of new significant brain CpGs associated with AD, some of which are further validated experimentally. EWASplus also performs well on data collected from independent cohorts and different brain regions. Genes found near top EWASplus loci are enriched for kinases and for genes with evidence for physical interactions with known AD genes. In this work, we show that EWASplus implicates additional epigenetic loci for AD that are not found using array-based AD EWASs.

Cellular molecular and proteomic profiling deciphers the SIRT1 controlled cell death pathways in esophageal adenocarcinoma cells.

Worldwide prevalence of esophageal adenocarcinomas with high rates of mortality coupled with increased mutations in esophageal cells warrants investigation to understand deregulation of cell signaling pathways leading to cancer. To this end, the current study was undertaken to unravel the cell death signatures using the model human esophageal adenocarcinoma cell line-OE33. The strategy involved targeting the key epigenetic modulator SIRT1, a histone deacetylase by a small molecule inhibitor - sirtinol. Sirtinol induced a dose-dependent inhibition of cell viability under both normoxic and hypoxic conditions with long term impact on proliferation as shown by clonogenic assays. Signature apoptotic signaling pathways including caspase activation and decreased Bcl-2 were observed. Proteomic analysis highlighted an array of entities affected including molecules involved in replication, transcription, protein synthesis, cell division control, stress-related proteins, spliceosome components, protein processing and cell detoxification/degradation systems. Importantly, the stoichiometry of the fold changes of the affected proteins per se could govern the cell death phenotype by sirtinol. Sirtinol could also potentially curb resistant and recurrent tumors that reside in hypoxic environments. Overall, in addition to unraveling the cellular, molecular and proteomics basis of SIRT1 inhibition, the findings open up avenues for designing novel strategies against esophageal adenocarcinoma.