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PLOS Papers on Plasmodium vivax Variation in Ethiopia, Childhood BMI Loci, More

For a paper in PLOS Neglected Tropical Diseases, researchers from the University of North Carolina-Charlotte present findings from a genome sequencing analysis of the malaria parasite Plasmodium vivax. The team analyzed SNP, copy number, and other features in sequence data for 44 P. vivax isolates from individuals being treated for malaria in Ethiopia, uncovering more than 123,700 SNPs. The variants were over-represented in certain parts of the P. vivax genome, the authors note, including structural changes affecting genes suspected of having roles in binding host red blood cells. Likewise, selection and copy number expansion analyses highlighted the importance of erythrocyte binding and other interactions between the parasite and infected hosts. "It was previously thought that most African populations were immune to P. vivax infections due to the absence of Duffy antigen chemokine receptor (DARC) gene expression required for erythrocyte invasion," they write, though "recent reports have indicated the emergence and potential spread of P. vivax across human populations across human populations in Africa."

In PLOS Genetics, an international team led by investigators in the Netherlands describes body mass index (BMI)-related loci identified in children of European ancestry that appear to overlap with parts of the genome implicated in cardiometabolic traits in adults. The researchers tracked down more than two dozen genome-wide significant loci through a genome-wide association meta-analysis involving more than 61,000 children ranging in age from two to 10 years old — a set that included two previously unappreciated BMI-related loci, along with broader genetic contributors linked to a range of adult traits such as BMI, type 2 diabetes, and blood pressure. "Our results suggest that the biological processes underlying childhood BMI largely overlap with those underlying adult BMI," they write, though they caution that this overlap seemed to be incomplete.

Researchers in Germany, Brazil, and Norway outline a strategy for systematically comparing multiple transcript sequence networks in PLOS One. The team's approach — known as the "Co-expression differential network analysis," or CoDiNA — relies on a statistical framework to track down overlapping or distinct links and nodes within transcriptomic networks. For their paper, the authors used CoDiNA to find new clues to neuronal differentiation from induced pluripotent stem cells, HIV-related expression signatures, and more with the help of transcriptome network data. "We expect that our method will be helpful for many diverse studies comparing network data generated from multiple conditions, such as different disease, tissues, species, or experimental treatments," they write, noting that "CoDiNA is not limited to the analysis of co-expression networks, but can be applied to comparing any type of network."

The Scan

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