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This Week in PNAS: Feb 5, 2019

Researchers from China and the US explore sepsis-related copy number changes involving the DEFA1/ DEFA3 genes, which code for human neutrophil peptide alpha-defensins. Following an earlier genetic association study that revealed copy number upticks in sepsis-prone patients from a Han Chinese population, the team turned to transgenic mouse models to compare sepsis susceptibility in mice with higher or lower DEFA1/ DEFA3 copy numbers in their neutrophil white blood cells. Results from the experiments shore up ties increased copy numbers for the genes and sepsis complications such as organ damage, the authors report — an effect that could be allayed with a monoclonal antibody designed to block DEFA1/ DEFA3-encoded protein interactions. 

A University of California, San Diego-led team maps RNA-DNA interactions detected in non-cancerous cells in relation to cancer-associated fusions. Using a sequencing-based chromatin-associated RNA pipeline called iMARGI, the researchers profiled two non-cancerous cell lines, uncovering enrichment for RNA-DNA interaction at several sites in the genome where fusions were previously reported in 33 cancer types. When they subsequently sequenced RNA transcripts in 96 lung cancer samples, the authors identified 42 fusions, including 37 sites overlapping with RNA-DNA interaction sites in the cell lines. From these and other results, they point to a possible RNA poise model, in which "spatial proximity of RNA and DNA could poise for the creation of fusion transcripts." 

Researchers in Taiwan and the US track three-dimensional leaf transcriptomes over time in developing maize plants grown in darkness or in light-dark cycles, comparing the expression regulators found in the resulting "time-ordered gene co-expression networks" to those mediating photosynthesis-related enzymes in rice. "Through application of our approach to the temporal expression data under [light-dark] and [total darkness] in maize leaves and to the leaf developmental transcriptomes from maize and rice, TO-GCNs were inferred, providing a wealth of regulatory interaction predictions," the authors write, noting that their general approach "provides a means for mining … expression data to obtain biological insights."