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Cladobranchia (Gastropoda: Nudibranchia) is a diverse (approx. 1000 species) but understudied group of sea slug molluscs. In order to fully comprehend the diversity of nudibranchs and the evolution of character traits within Cladobranchia, a solid understanding of evolutionary relationships is necessary.

CBCB faculty Michael Cummings, CBCB postdoctoral associate Adam Bazinet, and lead author and CBCB doctoral student Jessica Goodheart published a cover paper titled “Relationships within Cladobranchia (Gastropoda: Nudibranchia) based on RNA-Seq data: an initial investigation” on Sept. 23, 2015 in the journal Royal Society Open Science.

In this paper, they address some of the long-standing issues regarding the evolutionary history of Cladobranchia using RNA-Seq data (transcriptomes). Their data provides a well-supported and almost fully resolved phylogenetic hypothesis for Cladobranchia. Their results support the monophyly of Cladobranchia and the sub-clade Aeolidida, but reject the monophyly of Dendronotida.

“Relationships within Cladobranchia (Gastropoda: Nudibranchia) based on RNA-Seq data: an initial investigation” article: http://rsos.royalsocietypublishing.org/content/2/9/150196

For the news release regarding this paper, please see: https://cmns.umd.edu/news-events/features/3282

“Bayesian integration of genetics and epigenetics detects putatively causal SNPs underlying expression variability” article: http://www.nature.com/ncomms/2015/151005/ncomms9555/full/ncomms9555.html

"UMD researchers develop method to identify genetic factors of heart diseases" article: http://www.diamondbackonline.com/news/umd-researchers-develop-method-to-...

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, these researchers discuss the challenges that genome‐scale modeling of cancer metabolism has been facing. They survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network‐level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, they outline a few new steps that may further advance this field.

“Modeling cancer metabolism on a genome scale” article: http://msb.embopress.org/content/11/6/817

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