https://www.selleckchem.com/products/am580.html DNA methyl transferase-1 or DNMT1 maintains DNA methylation in the genome and is important for regulating gene expression in cells. Aberrant changes in DNMT1 activity and DNA methylation are commonly observed in cancers and many other diseases. Recently, a number of long intergenic non-protein-coding RNAs or lincRNAs have been shown to play a role in regulating DNMT1 activity. CCDC26 is a nuclear lincRNA that is frequently mutated in cancers and is a hotbed for disease-associated single nucleotide changes. However, the functional mechanism of CCDC26 is not understood. Here, we show that this lincRNA is concentrated on the nuclear periphery. Strikingly, in the absence of CCDC26 lincRNA, DNMT1 is mis-located in the cytoplasm, and the genomic DNA is significantly hypomethylated. This is accompanied by double-stranded DNA breaks and increased cell death. These results point to a previously unrecognized mechanism of lincRNA-mediated subcellular localization of DNMT1 and regulation of DNA methylation.With the advent of deep generative models in computational chemistry, in-silico drug design is undergoing an unprecedented transformation. Although deep learning approaches have shown potential in generating compounds with desired chemical properties, they disregard the cellular environment of target diseases. Bridging systems biology and drug design, we present a reinforcement learning method for de novo molecular design from gene expression profiles. We construct a hybrid Variational Autoencoder that tailors molecules to target-specific transcriptomic profiles, using an anticancer drug sensitivity prediction model (PaccMann) as reward function. Without incorporating information about anticancer drugs, the molecule generation is biased toward compounds with high predicted efficacy against cell lines or cancer types. The generation can be further refined by subsidiary constraints such as toxicity. Our cancer-type-specific candid