In a Nature paper published online in advance this week, investigators at the University of Tokyo show that the fission yeast kinetochore proteins Pcs1 and Mde4 "act as a condensin recruiter at kinetochores," and that "overall condensin association with chromatin is governed by the chromosomal passenger kinase Aurora B." The Tokyo team says that, overall, its study reports the "molecular basis for the spatiotemporal regulation of mitotic chromosome architecture."
A trio of investigators in Switzerland this week shows that cryptic variation — that which "is caused by the robustness of phenotypes to mutations" — promotes "rapid evolutionary adaptation" in RNA enzymes. The researchers show that "populations of RNA enzymes with accumulated cryptic variation adapt more rapidly to a new substrate than a population without cryptic variation," and, further, that cryptic variation allows a population of RNA enzymes "to explore new genotypes that become adaptive only in a new environment," they add.
Over in Nature Genetics, a team led by investigators at The Jackson Laboratory in Bar Harbor, Maine, presents a high-resolution, genome-wide map of "the phylogenetic origin of the genome of most extant laboratory mouse inbred strains." The team shows that the genomes of classic laboratory mouse strains are "overwhelmingly Mus musculus domesticus in origin," while wild-derived laboratory mouse strains show a "broad sampling of diversity within M. musculus," the authors write. The researchers add that the haplotype diversity and identity by descent maps they've generated can be visualized using the Mouse Phylogeny Viewer genome browser out of the University of North Carolina at Chapel Hill.
An international research team reports "an integrated approach to characterize genetic interaction networks in yeast metabolism" online in advance in Nature Genetics this week. First, the team quantitatively measured genetic interactions between [approximately] 185,000 metabolic gene pairs in Saccharomyces cerevisiae, before superimposing the data "on a detailed systems biology model of metabolism" and introducing machine-learning methods to interpret the data with model predictions. As a result, the team deduced a "mechanistic explanation for the link between the degree of genetic interaction, pleiotropy, and gene dispensability," which it also reports in its paper.