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CBCB faculty Eytan Ruppin’s group has published a paper titled “Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia” on March 3, 2015 in the journal Molecular Systems Biology.

High-throughputomics have proven invaluable in studying human disease, and yet day-to-day clinical practice still relies on physiological, non-omic markers. The metabolic syndrome, for example, is diagnosed and monitored by blood and urine indices such as blood cholesterol levels. Nevertheless, the association between the molecular and the physiological manifestations of the disease, especially in response to treatment, has not been investigated in a systematic manner. To this end, these researchers studied a mouse model of dietinduced dyslipidemia and atherosclerosis that was subject to various drug treatments relevant to the disease in question. Both physiological data and gene expression data (from the liver and white adipose) were analyzed and compared. They found that treatments that restore gene expression patterns to their norm are associated with the successful restoration of physiological markers to their baselines. This holds in a tissue-specific manner—treatments that reverse the transcriptomic signatures of the disease in a particular tissue are associated with positive physiological effects in that tissue. Further, treatments that introduce large non-restorative gene expression alterations are associated with unfavorable physiological outcomes. These results provide a sound basis to in silico methods that rely on omic metrics for drug repurposing and drug discovery by searching for compounds that reverse a disease’s omic signatures. Moreover, they highlight the need to develop drugs that restore the global cellular state to its healthy norm rather than rectify particular disease phenotypes.

“Drugs that reverse disease transcriptomic signatures are more effective in a mouse model of dyslipidemia“ article: http://msb.embopress.org/content/11/3/791

CBCB faculty Zia Khan and collaborators have published a paper titled “Impact of regulatory variation from RNA to protein” on December 18, 2014 in the journal Science.

In this study, Dr. Khan and collaborators mapped genetic variants that are associated with changes in gene expression, ribosome occupancy, and protein abundance in human cell lines. This is the first study to map the impact of genetic variation on three distinct regulatory layers, transcriptional, translational, and post-translational, from RNA to protein.

The study provides two main findings: (1) genetic variation that impacts gene expression tends to have attenuated or buffered affects on protein abundance (2) a new class of functional genetic variation that acts post-translationally to alter protein abundance with little affect on gene expression or protein translation. The study is the first of its kind and provides an important resource for the broader scientific community.

“Impact of regulatory variation from RNA to protein” article: http://www.sciencemag.org/content/early/2014/12/17/science.1260793.abstract

CBCB researchers Keith Hughitt, Lee Mendelowitz, and Joseph Paulson volunteered for a visualization hackathon for a D.C. non-profit called Bread for the World. They cleaned up some data and prepared a visualization of the relationship/correlation between malnutrition and women's empowerment throughout the world. Data shows that countries that empower women tend to have lower rates of stunting, which is a measure of chronic undernutrition. Their work was published in Bread for the World’s annual hunger report.

For their interactive tool that compares country data on women’s empowerment and stunting, please see: http://hungerreport.org/2015/empowerment-to-improve-nutrition/

Bread for the World’s annual hunger report: http://hungerreport.org/2015/

CBCB faculty Héctor Corrada Bravo and CBCB doctoral student Florin Chelaru, along with undergraduate research assistants, have developed a new, web-based tool that enables researchers to quickly and easily visualize and compare large amounts of genomic information resulting from high-throughput sequencing experiments. They have described the free tool, called Epiviz, in their published paper titled “Epiviz: interactive visual analytics for functional genomics data” on Aug. 3, 2014 in the journal Nature Methods.

Next-generation sequencing has revolutionized functional genomics. These techniques are key to understanding the molecular mechanisms underlying cell function in healthy and diseased individuals and the development of diseases like cancer. Data from multiple experiments need to be integrated, but the growing number of data sets causes a thorough comparison and analysis of results to be challenging. In Corrada Bravo and Chelaru’s paper, Epiviz is shown to be capable of visualizing and analyzing DNA methylation and gene expression data in colon cancer.

“Epiviz: interactive visual analytics for functional genomics data” article: http://www.nature.com/nmeth/journal/v11/n9/full/nmeth.3038.html

For Florin Chelaru’s discussion on Epiviz and his research in bioinformatics and genomic research, please see: http://www.cs.umd.edu/article/2014/08/florin-chelaru-epiviz-research-and...

For more information, please see: http://cmns.umd.edu/news-events/features/2376