In a paper published online in advance in Science this week, the Broad Institute’s Chad Nusbaum and his colleagues present a comparative annotation of the Schizosaccharomyces pombe, S. octosporus, S. cryophilus, and S. japonicus fission yeast genomes, in which they “identified a near extinction of transposons and … transposon-free centromeres.” In the team’s expression analyses, it found that “trans-acting regulators control new genes within the context of expanded functional modules for meiosis and stress response,” and gene content and regulation to suggest why, “unlike the Saccharomycotina, fission yeasts cannot use ethanol as a primary carbon source,” Nusbaum et al. write.
Researchers at the Albert Einstein College of Medicine in New York City this week describe their “real-time observation of transcription initiation and elongation on an endogenous yeast gene” via fluctuation analysis of fluorescently labeled RNA in vivo. In observing the activity of nascent RNA, the Einstein Med team found “no transcriptional memory between initiation events,” and also that “elongation speed can vary by three-fold throughout the cell cycle,” it writes.
In another paper appearing in this week’s Science, investigators at the University of North Carolina, Chapel Hill, along with their colleagues at the University of British Columbia, show that “DNA synthesis generates terminal duplications that seal end-to-end chromosome fusions.” More specifically, in examining end-to-end fusions from Caenorhabditis elegans telomere replication mutants, the researchers observed genome-level rearrangements “similar to disease-associated duplications of interstitial segments of the human genome.” The team also discusses how the Fork Stalling and Template Switching model, wherein “promiscuous replication of large, non-contiguous segments of the genome occurs,” may serve to partially explain these duplications.
And over in Science Translational Medicine, a team led by researchers at Chicago’s Northwestern University present results of the Electronic Medical Records and Genomics Network – eMERGE – study, which aimed to assess “whether data captured during routine clinical care using EMRs [electronic medical records] can identify disease phenotypes with sufficient positive and negative predictive values for use in genome-wide association studies.” In poring over data derived from five distinct sets of EMRs, the team found that most records “captured key information used to define phenotypes in a structured format,” and, further, it found, “five disease phenotypes with positive predictive values of 73 percent to 98 percent and negative predictive values of 98 percent to 100 percent.”