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The ӳý and its collaborating institutions have received two major grants from the National Human Genome Research Institute (NHGRI) that will support the use of genome sequencing and analysis to identify the genes and genetic variation that underlie both rare and common diseases. The funding will support two state-of-the-art centers — the Center for Mendelian Genomics (CMG) and the Center for Common Disease Genomics (CCDG)—involving scientists from the ӳý, in the greater Boston area, and across the world.

Conventional flow cytometry is a powerful technique for measuring cell phenotype and function, but it relies on fluorescent stains, or labels, to identify particular cell subpopulations. At times, those labels can be incompatible with live cells, or unavailable to researchers. Now, researchers from the ӳý’s Imaging Platform, Swansea University’s College of Engineering, and fellow international collaborators have found a new way to detect these cellular subpopulations, by applying machine learning to the hidden information in images of unlabeled cells generated from image flow cytometry. Their method is described in and a Swansea University , and their is available online.

Studies have shown that obese mice and humans have increased serum levels of the fatty acid binding protein aP2, and that elevated aP2 levels correlate with metabolic complications. Since genetic loss of aP2 in mouse models and in humans results in lowered risk of cardiometabolic disease, the molecule offers an exciting opportunity for new intervention strategies.

Now, in a proof-of-principle study led by ӳý associate member Gökhan S. Hotamisligil of the Harvard T.H. Chan School of Public Health's Sabri Ülker Center, researchers have shown that the protein may be a viable therapeutic target for type 2 diabetes. In the study, the authors identified a monoclonal antibody to aP2 that lowered fasting blood glucose, increased insulin sensitivity, and lowered both fat mass and incidence of fatty liver in obese mouse models. Their paper is published online in .

A team led by Pablo Tamayo and Jill Mesirov of the ӳý and University of California, San Diego, and ӳý bioinformatician Arthur Liberzon, has generated “hallmark” gene sets from the (MSigDB), one of the most comprehensive and widely used databases for gene set enrichment analysis. Through both automated and manual approaches, the team curated a refined collection of MSigDB gene sets that reduce redundancy and produce more robust analyses. Their paper is published in .