This Week in Nature

In Nature this week, investigators at the Washington University School of Medicine in St. Louis and their colleagues report their sequencing of the metagenomes of virus-like particles — or viromes — "isolated from fecal samples collected from healthy adult female monozygotic twins and their mothers at three time points over a one-year period." Viromes, the team found, "are unique to individuals regardless of their degree of genetic relatedness." Intrapersonal virome diversity is also "very low," the authors write, adding that greater than 95 percent of "virotypes" were retained over the survey period.

In a Nature paper published online in advance this week, researchers at the Whitehead Institute and WashU describe the "convergent evolution of chicken Z and human X chromosomes by expansion and gene acquisition." The team writes that the chicken Z chromosome, which they've sequenced "essentially to completion," contains a "massive tandem array containing hundreds of duplicated genes expressed in testes." The team found that the human X chromosome and chicken Z chromosomes "each evolved independently from different portions of the ancestral genome," and suggest that the avian and mammalian chromosomes "followed convergent evolutionary trajectories, despite their evolving with opposite — female versus male — systems of heterogamety."

Also in Nature this week, an international research team reports that "mutations in the homologous histone 3 lysine 27 (H3K27) monomethyltransferases, Arabidopsis trithorax-related protein5 (ATXR5), and ATXR6, lead to re-replication of specific genomic locations," most of which "correspond to transposons and other repetitive and silent elements of the Arabidopsis genome." The authors write that mutations of ATXR5 and ATXR6 have pleiotropic effects on plant development and cause the up-regulation of transposon expression.

In Nature Communications this week, investigators at the National Research Council Canada and their colleagues report their novel algorithm that "identifies prognostic markers using tumor gene microarrays focusing on metastasis-driving gene expression signals." The team applied their algorithm to both ER-positive and ER-negative breast cancer subtype samples, and was able to stratify patients into low-, intermediate-, and high-risk groups. "Integrative network analysis identified modules in which each module contained the genes of a signature and their direct interacting partners that are cancer driver-mutating genes," the authors write.