A hallmark of human cancer is global DNA hypomethylation (GDHO), but the mechanisms accounting for this defect and its pathological consequences have not been defined in human epithelial ovarian cancer (EOC). In EOC, GDHO was associated with advanced disease and reduced overall and disease-free survival. GDHO(+) EOC was enriched for a proliferative gene expression signature, including CCNE1 and FOXM1 overexpression. DNA hypomethylation preferentially occurred within genomic blocks (hypomethylated blocks) overlapping late-replicating, lamina-associated domains, PRC2 binding, and H3K27me3. Increased proliferation coupled with hypomethylated block formation at late replicating regions suggested passive hypomethylation, which was further supported by the observation that cytosine DNA methyltransferases (DNMTs) and UHRF1 showed significantly reduced expression in GDHO(+) EOC, after normalization to proliferation markers. Importantly, GDHO(+) EOC showed elevated chromosomal instability (CIN), and copy number alterations (CNA) were enriched at hypomethylated blocks. Together, these findings implicate a passive demethylation mechanism for GDHO that promotes genomic instability and poor prognosis in EOC.

BackgroundThe rhesus macaque (Macaca mulatta) is a key species for advancing biomedical research. Like all draft mammalian genomes, the draft rhesus assembly (rheMac2) has gaps, sequencing errors and misassemblies that have prevented automated annotation pipelines from functioning correctly. Another rhesus macaque assembly, CR_1.0, is also available but is substantially more fragmented than rheMac2 with smaller contigs and scaffolds. Annotations for these two assemblies are limited in completeness and accuracy. High quality assembly and annotation files are required for a wide range of studies including expression, genetic and evolutionary analyses.ResultsWe report a new de novo assembly of the rhesus macaque genome (MacaM) that incorporates both the original Sanger sequences used to assemble rheMac2 and new Illumina sequences from the same animal. MacaM has a weighted average (N50) contig size of 64 kilobases, more than twice the size of the rheMac2 assembly and almost five times the size of the CR_1.0 assembly. The MacaM chromosome assembly incorporates information from previously unutilized mapping data and preliminary annotation of scaffolds. Independent assessment of the assemblies using Ion Torrent read alignments indicates that MacaM is more complete and accurate than rheMac2 and CR_1.0. We assembled messenger RNA sequences from several rhesus tissues into transcripts which allowed us to identify a total of 11,712 complete proteins representing 9,524 distinct genes. Using a combination of our assembled rhesus macaque transcripts and human transcripts, we annotated 18,757 transcripts and 16,050 genes with complete coding sequences in the MacaM assembly. Further, we demonstrate that the new annotations provide greatly improved accuracy as compared to the current annotations of rheMac2. Finally, we show that the MacaM genome provides an accurate resource for alignment of reads produced by RNA sequence expression studies.ConclusionsThe MacaM assembly and annotation files provide a substantially more complete and accurate representation of the rhesus macaque genome than rheMac2 or CR_1.0 and will serve as an important resource for investigators conducting next-generation sequencing studies with nonhuman primates.ReviewersThis article was reviewed by Dr. Lutz Walter, Dr. Soojin Yi and Dr. Kateryna Makova.

Background: Ada3 is a core component of HAT containing coactivator complexes. Results: Germline deletion of Ada3 is embryonic lethal, and cell deletion leads to abnormal cell cycle progression. Conclusion: Ada3 is a critical protein at organismic and cellular level. Significance: This study describes a novel role of Ada3, a component of HAT complexes, as a critical regulator of cell survival.

BackgroundUnderstanding protein subcellular localization is a necessary component toward understanding the overall function of a protein. Numerous computational methods have been published over the past decade, with varying degrees of success. Despite the large number of published methods in this area, only a small fraction of them are available for researchers to use in their own studies. Of those that are available, many are limited by predicting only a small number of organelles in the cell. Additionally, the majority of methods predict only a single location for a sequence, even though it is known that a large fraction of the proteins in eukaryotic species shuttle between locations to carry out their function.FindingsWe present a software package and a web server for predicting the subcellular localization of protein sequences based on the ngLOC method. ngLOC is an n-gram-based Bayesian classifier that predicts subcellular localization of proteins both in prokaryotes and eukaryotes. The overall prediction accuracy varies from 89.8% to 91.4% across species. This program can predict 11 distinct locations each in plant and animal species. ngLOC also predicts 4 and 5 distinct locations on gram-positive and gram-negative bacterial datasets, respectively.ConclusionsngLOC is a generic method that can be trained by data from a variety of species or classes for predicting protein subcellular localization. The standalone software is freely available for academic use under GNU GPL, and the ngLOC web server is also accessible at http://ngloc.unmc.edu.

LocSigDB (http://genome.unmc.edu/LocSigDB/) is a manually curated database of experimental protein localization signals for eight distinct subcellular locations; primarily in a eukaryotic cell with brief coverage of bacterial proteins. Proteins must be localized at their appropriate subcellular compartment to perform their desired function. Mislocalization of proteins to unintended locations is a causative factor for many human diseases; therefore, collection of known sorting signals will help support many important areas of biomedical research. By performing an extensive literature study, we compiled a collection of 533 experimentally determined localization signals, along with the proteins that harbor such signals. Each signal in the LocSigDB is annotated with its localization, source, PubMed references and is linked to the proteins in UniProt database along with the organism information that contain the same amino acid pattern as the given signal. From LocSigDB webserver, users can download the whole database or browse/search for data using an intuitive query interface. To date, LocSigDB is the most comprehensive compendium of protein localization signals for eight distinct subcellular locations.Database URL: http://genome.unmc.edu/LocSigDB/