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This Week in Genome Research: Mar 1, 2017

A team from Italy and the US describes an algorithm called StrainPhIAn for identifying microbial strains from a metagenomic sequence mixture. The researchers applied the computational approach, which uses species-specific marker gene information in combination with dominant variant consensus sequence reconstruction in each sample, to tease apart strain phylogeny and population structure in gut metagenome sequence data for 1,590 adult individuals from sites around the world, characterizing strains from more than 125 species and uncovering geography-related microbial population patterns.

Researchers from Cincinnati Children's Hospital, the University of Cincinnati, and elsewhere present findings from a cell-cell interactome analysis of fetal and maternal cells cooperating at the placenta, based on RNA sequence data for dozens of individual placental cells, bulk placental tissue, and human endometrial stromal fibroblast cell cultures. In the process, the team saw cell type-specific receptor profiles, along with other clues to communication between fetal and maternal cells. "Among the signals transmitted, growth factors and immune signals dominate," the authors note, "and suggest a delicate balance of enhancing and suppressive signals."

An international team led by investigators in the UK introduces a chemical mutagenesis and sequencing-based approach aimed at tallying drug resistance mechanisms in cancer cells. Using the mutagen N-ethyl-N-nitrosourea, or ENU — in combination with high-throughput sequencing and informatics — the researchers screened dozens of colorectal cell lines, searching for mutations that altered response to the drug cetuximab, which inhibits the epidermal growth factor receptor. "We suggest that this approach is a powerful and facile means to draw the landscape of point mutations that confer resistance to targeted therapies," they write. "Such knowledge could be used to discover therapeutic strategies to re-sensitize resistant tumors as well as identify which genes should be prioritized for non-invasive monitoring during treatment using plasma DNA sequencing."