Dynamic partitioning of branched-chain amino acids-derived nitrogen supports 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 integrate multimodal analyses of primary and metastatic clonally-related clear cell renal cancer cells (ccRCC) grown in physiological media to identify key stage-specific metabolic vulnerabilities. We show that a VHL loss-dependent reprogramming of branched-chain amino acid catabolism sustains the de novo biosynthesis of aspartate and arginine enabling tumor cells with the flexibility of partitioning the nitrogen of the amino acids depending on their needs. Importantly, we identify the epigenetic reactivation of argininosuccinate synthase (ASS1), a urea cycle enzyme suppressed in primary ccRCC, as a crucial event for metastatic renal cancer cells to acquire the capability to generate arginine, invade in vitro and metastasize in vivo. Overall, our study uncovers a mechanism of metabolic flexibility occurring during ccRCC progression, paving the way for the development of novel stage-specific therapies.

Expansin Engineering Database: A navigation and classification tool for expansins and homologues.

Expansins have the remarkable ability to loosen plant cell walls and cellulose material without showing catalytic activity and therefore have potential applications in biomass degradation. To support the study of sequence-structure-function relationships and the search for novel expansins, the Expansin Engineering Database (ExED, https://exed.biocatnet.de) collected sequence and structure data on expansins from Bacteria, Fungi, and Viridiplantae, and expansin-like homologues such as carbohydrate binding modules, glycoside hydrolases, loosenins, swollenins, cerato-platanins, and EXPNs. Based on global sequence alignment and protein sequence network analysis, the sequences are highly diverse. However, many similarities were found between the expansin domains. Newly created profile hidden Markov models of the two expansin domains enable standard numbering schemes, comprehensive conservation analyses, and genome annotation. Conserved key amino acids in the expansin domains were identified, a refined classification of expansins and carbohydrate binding modules was proposed, and new sequence motifs facilitate the search of novel candidate genes and the engineering of expansins.