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This Week in PNAS: Sep 11, 2018

A University of Utah team takes a look at the impact that DNA architecture has on CRISPR-Cas9-based genome editing in vivo in the budding yeast model organism Saccharomyces cerevisiae. Using two S. cerevisiae sequences with distinct promoter and nucleosome occupancy patterns, the team tracked the cleavage wherewithal of a commonly used Cas9 nuclease originating in the bacterial species Streptococcus pyogenes. Results of the analysis showed that Cas9 cleavage activity was dialed down in nucleosomes, the authors report, adding that such findings "can inform target selection, particularly in cases where cells are quiescent or nucleosome mobility is limited."

Researchers at the British Columbia Centre for Excellence in HIV/AIDS, Simon Fraser University, and elsewhere explore within-host HIV evolution using a phylogenetic approach that takes into account RNA sequences from sequential HIV isolates collected prior to suppressive antiretroviral treatment in and up to a decade after in the same individual. The team applied its phylogenetic framework to simulated data, published longitudinal datasets for eight individuals with HIV, and new data for two participants tested over time. "Reconstruction of the ages of putative latent sequences sampled from HIV-infected individuals … revealed a genetically heterogeneous reservoir that recapitulated HIV's within-host evolutionary history," the authors write, noting that "[r]eservoir sequences were interspersed throughout multiple within-host lineages."

Northwestern University investigators describe a gene expression-based algorithm for determining circadian clock state. The approach, called TimeSignature, relies on machine-learning to interpret physiological time from blood-based gene expression profiles in human blood, the team says. For their proof-of-principle analyses, the researchers applied TimeSignature to three microarray datasets as well as to new RNA sequence data for blood samples collected periodically over 28 hours in 11 participants. In addition to showing that the approach compares favorably with other circadian time estimating methods, they found that TimeSignature can be "applied to samples from disparate studies and yield highly accurate results despite systematic differences between the studies."