Skip to main content
Premium Trial:

Request an Annual Quote

This Week in PNAS: Jan 21, 2014

Editor's Note: Some of the articles described below are not yet available at the PNAS site, but they are scheduled to be posted some time this week.

In the early, online edition of the Proceedings of the National Academy of Sciences, a pair of researchers from Korea and the US described algorithms for incorporating common variant profiles to predict individuals' propensity for developing several cancer types. Using four prediction methods, the team assessed nearly 600 individuals who represented nine different traits — eight cancer types and a healthy, non-cancerous state. The approaches "made correct predictions substantially better than random predictions for most cancer classes, but not for some others," the duo writes, suggesting "the framework of this approach or its improvement can predict cancer susceptibility with probability estimates useful for making health decisions for individuals or for a population."

Using high-throughput deep sequencing and bioinformatics analyses, a French team tracked the appearance of mutations across the genomes of Saccharomyces cerevisiae yeast from a wild type strain and from mutator strains missing genes important for replication, recombination, stress response, cell cycle regulation, and the like over a few dozen to 100 growth passages in non-selective growth media. Depending on the specific mutator strain involved, the researchers saw a range of mutational events spanning size ranges from localized mutations to wide-reaching chromosomal rearrangements. "This comprehensive analytical approach of mutator defects provides a model to understand how genome variations might accumulate during clonal evolution of somatic cell populations, including tumor cells," the study's authors say.

Researchers from the University of Michigan and the Woods Hole Marine Biological Laboratory present a gene-centric strategy for bringing together biogeochemical models with genomic profiles that point to the functional wherewithal of microbial communities in a given environment. The group used the method to consider nitrogen cycling by members of microbial communities from an oxygen minimum zone from the Arabian Sea, for instance. Based on the findings from this and other analyses, the study's authors argue that a method incorporating both genomic and geochemical information "is critical for informing our understanding of the key role microbes play in modulating Earth's biogeochemistry."