Cardiometabolic Disease: Big Data Tackles a Big Health Problem

Cardiometabolic risk lociMore and more studies are popping up that demonstrate the power of Big Data analyses to get at the underlying molecular pathology of some of our most common diseases. A great example, which may have flown a bit under the radar during the summer holidays, involves cardiometabolic disease. It’s an umbrella term for common vascular and metabolic conditions, including hypertension, impaired glucose and lipid metabolism, excess belly fat, and inflammation. All of these components of cardiometabolic disease can increase a person’s risk for a heart attack or stroke.

In the study, an international research team tapped into the power of genomic data to develop clearer pictures of the complex biocircuitry in seven types of vascular and metabolic tissue known to be affected by cardiometabolic disease: the liver, the heart’s aortic root, visceral abdominal fat, subcutaneous fat, internal mammary artery, skeletal muscle, and blood. The researchers found that while some circuits might regulate the level of gene expression in just one tissue, that’s often not the case. In fact, the researchers’ computational models show that such genetic circuitry can be organized into super networks that work together to influence how multiple tissues carry out fundamental life processes, such as metabolizing glucose or regulating lipid levels. When these networks are perturbed, perhaps by things like inherited variants that affect gene expression, or environmental influences such as a high-carb diet, sedentary lifestyle, the aging process, or infectious disease, the researchers’ modeling work suggests that multiple tissues can be affected, resulting in chronic, systemic disorders including cardiometabolic disease.

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Snapshots of Life: Imperfect but Beautiful Intruder

RSV Particle

Credit: Boon Chong Goh, Beckman Institute, University of Illinois at Urbana-Champaign

The striking image you see above is an example of what can happen when scientists combine something old with something new. In this case, a researcher took the Rous sarcoma virus (RSV)—a virus that’s been studied for more than century because of its ability to cause cancer in chickens and the insights it provided on human oncogenes [1, 2]—and used modern computational tools to generate a model of its atomic structure.

Here you see an immature RSV particle that’s just budded from an infected chicken cell and entered the avian bloodstream. A lattice of proteins (red) held together by short peptides (green) cover the outer shell of the immature virus, shielding other proteins (blue) that make up an inner shell.

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