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CBCB faculty Eytan Ruppin’s group co-authors a paper in Nature on the diversion of aspartate in ASS1-deficient tumours, a study led by Ayelet Erez from the Weizmann Institute
Keren Yizhak from Eytan Ruppin’s group co-authored a paper titled “Diversion of aspartate in ASS1-deficient tumours fosters de novo pyrimidine synthesis”, published in Nature on November 11, 2015.
Cancer cells hijack and remodel existing metabolic pathways for their benefit. Argininosuccinate synthase (ASS1) is a urea cycle enzyme that is essential in the conversion of nitrogen from ammonia and aspartate to urea. A decrease in nitrogen flux through ASS1 in the liver causes the urea cycle disorder, citrullinaemia. In contrast to the well-studied consequences of loss of ASS1 activity on ureagenesis, the purpose of its somatic silencing in multiple cancers is largely unknown.
In this study, the authors show that decreased activity of ASS1 in cancers supports proliferation by facilitating pyrimidine synthesis via CAD (carbamoyl-phosphate synthase 2, aspartate transcarbamylase, and dihydroorotase complex) activation. Decreasing CAD activity by blocking citrin, the mTOR signalling, or pyrimidine synthesis decreases proliferation and thus may serve as a therapeutic strategy in multiple cancers where ASS1is downregulated. Their results demonstrate that ASS1downregulation is a novel mechanism supporting cancerous proliferation, and that ASS1 downregulation provides a metabolic link between the urea cycle enzymes and pyrimidine synthesis.
“Diversion of aspartate in ASS1-deficient tumours fosters de novo pyrimidine synthesis” article: http://www.nature.com/nature/journal/vaop/ncurrent/pdf/nature15529.pdf
CBCB scientists Eytan Ruppin and Rotem Katzir publish a paper in the Proceedings of the National Academy of Sciences (PNAS) on the quantitative identification of cancer SDLs in a model-based mechanistic manner
CBCB faculty Eytan Ruppin and CBCB doctoral student Rotem Katzir, along with collaborators, published a paper titled “Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival” on September 29, 2015 in the journal Proceedings of the National Academy of Sciences (PNAS).
Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly.
In this paper, a network-level computational modeling framework is introduced that quantitatively predicts human SDLs in metabolism. The approach presented can be used to identify SDLs in species and cell types in which “omics” data necessary for data-driven identification are missing. As expected, the predicted SDLs are less frequently active in tumors to avoid lethality. Cancer tumors with more and stronger SDLs have smaller tumor size and lead to increased patient survival. Beyond facilitating the development of novel anticancer therapies, model-based identification of metabolic SDLs can be used to model pathogenic bacteria and provide leads to new antibiotic targets.
“Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival” article: http://www.pnas.org/content/112/39/12217.full
CBCB scientists Matthew Oberhardt and Eytan Ruppin publish a paper in Nature Communications on predicting new organism-media pairings
Lead author and CBCB postdoctoral associate Matthew Oberhardt and CBCB faculty Eytan Ruppin, along with collaborators, have published a paper titled “Harnessing the landscape of microbial culture media to predict new organism-media pairings” on October 13, 2015 in the journal Nature Communications.
Culturing microorganisms is a critical step in understanding and utilizing microbial life. In this paper, Dr. Oberhardt, Dr. Ruppin, and collaborators map the landscape of existing culture media by extracting natural-language media recipes into a Known Media Database (KOMODO).
They leverage KOMODO to predict new organism–media pairings using a transitivity property (74% growth in new in vitro experiments) and a phylogeny-based collaborative filtering tool (83% growth in new in vitro experiments and stronger growth on predicted well-scored versus poorly scored media). These resources are integrated into a web-based platform that predicts media given an organism’s 16S rDNA sequence, facilitating future cultivation efforts.
“Harnessing the landscape of microbial culture media to predict new organism–media pairings” article: http://www.nature.com/ncomms/2015/151013/ncomms9493/full/ncomms9493.html
CBCB scientists Michael Cummings, Adam Bazinet, and Jessica Goodheart publish a paper in Royal Society Open Science that provides a well-supported phylogenetic hypothesis for Cladobranchia
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
A video on the mission and scope of CBCB is now posted to the UMIACS YouTube page
CBCB scientists Sridhar Hannenhalli and Avinash Das Sahu publish a paper in Nature Communications on a new method that identifies genetic factors that could contribute to the risk for heart disease
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-...
CBCB faculty work with freshmen on the terrapin genome project as part of the FIRE program
CBCB faculty Eytan Ruppin’s group publish a review paper in Molecular Systems Biology on genome-scale modeling of cancer metabolism describing current accomplishments and future challenges
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
CBCB faculty Zia Khan is part of multi-institutional team awarded $1M Human Frontier Science Grant
CBCB faculty Eytan Ruppin’s group publish a paper in Molecular Systems Biology on effectiveness of drugs that reverse disease transcriptomic signatures in a mouse model of dyslipidemia
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
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