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Masid, M.

Publications and source records attributed to Masid, M..

2 recordsLinked to original sources

Nitrogen partitioning between branched-chain amino acids and urea cycle enzymes sustains renal cancer progression

Metabolic reprogramming is critical for tumor initiation and progression. However, the exact impact of specific metabolic changes on cancer progression is poorly understood. Here, we combined multi-omics datasets of primary and metastatic clonally related clear cell renal cancer cells (ccRCC) and generated a computational tool to explore the metabolic landscape during cancer progression. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism is required to maintain the aspartate pool in cancer cells across all tumor stages. We also provide evidence that metastatic renal cancer cells reactivate argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, to enable invasion in vitro and metastasis in vivo. Overall, our study provides the first comprehensive elucidation of the molecular mechanisms responsible for metabolic flexibility in ccRCC, paving the way to the development of therapeutic strategies based on the specific metabolism that characterizes each tumor stage. HighlightsO_LIBranched-chain amino acids catabolism is reprogrammed in ccRCC tumors C_LIO_LIBCAT-dependent transamination supplies nitrogen for de novo biosynthesis of amino acids including aspartate and asparagine in ccRCC C_LIO_LIAspartate produced downstream of BCAT is used specifically by metastatic cells through argininosuccinate synthase (ASS1) and argininosuccinate lyase (ASL) to generate arginine, providing a survival advantage in the presence of microenvironments with rate limiting levels of arginine C_LIO_LIASS1 is re-expressed in metastatic 786-M1A through epigenetic remodeling and it is sensitive to arginine levels C_LIO_LISilencing of ASS1 impairs the metastatic potential in vitro and in vivo of ccRCC cells C_LI

cancer biology

A genome-scale metabolic model of Saccharomyces cerevisiae that integrates expression constraints and reaction thermodynamics

Eukaryotic organisms play an important role in industrial biotechnology, from the production of fuels and commodity chemicals to therapeutic proteins. To optimize these industrial systems, a mathematical approach can be used to integrate the description of multiple biological networks into a single model for cell analysis and engineering. One of the current most accurate models of biological systems include metabolism and expression (ME-models), and Expression and Thermodynamics FLux (ETFL) is one such formulation that efficiently integrates RNA and protein synthesis with traditional genome-scale metabolic models. However, ETFL is so far only applicable for E. coli. To therefore adapt this ME-model for Saccharomyces cerevisiae, we herein developed yETFL. To do this, we augmented the original formulation with additional considerations for biomass composition, the compartmentalized cellular expression system, and the energetic costs of biological processes. We demonstrated the predictive ability of yETFL to capture maximum growth rate, essential genes, and the phenotype of overflow metabolism. We envision that the extended ETFL formulation can be applied to ME-model development for a wide range of eukaryotic organisms. The utility of these ME-models can be extended into academic and industrial research.

systems biology