Long-acting Erwinia chrysanthemi, Pegcrisantaspase, induces alternate amino acid biosynthetic pathways in a preclinical model of pancreatic ductal adenocarcinoma.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease without meaningful therapeutic options beyond the first salvage therapy. Targeting PDAC metabolism through amino acid restriction has emerged as a promising new strategy, with asparaginases, enzymes that deplete plasma glutamine and asparagine, reaching clinical trials. In this study, we investigated the anti-PDAC activity of the asparaginase formulation Pegcrisantaspase (PegC) alone and in combination with standard-of-care chemotherapeutics. METHODS: Using mouse and human PDAC cell lines, we assessed the impact of PegC on cell proliferation, cell death, and cell cycle progression. We further characterized the in vitro effect of PegC on protein synthesis as well as the generation of reactive oxygen species and levels of glutathione, a major cellular antioxidant. Additional cell line studies examined the effect of the combination of PegC with standard-of-care chemotherapeutics. In vivo, the tolerability and efficacy of PegC, as well as the impact on plasma amino acid levels, was assessed using the C57BL/6-derived KPC syngeneic mouse model. RESULTS: Here we report that PegC demonstrated potent anti-proliferative activity in a panel of human and murine PDAC cell lines. This decrease in proliferation was accompanied by inhibited protein synthesis and decreased levels of glutathione. In vivo, PegC was tolerable and effectively reduced plasma levels of glutamine and asparagine, leading to a statistically significant inhibition of tumor growth in a syngeneic mouse model of PDAC. There was no observable in vitro or in vivo benefit to combining PegC with standard-of-care chemotherapeutics, including oxaliplatin, irinotecan, 5-fluorouracil, paclitaxel, and gemcitabine. Notably, PegC treatment increased tumor expression of asparagine and serine biosynthetic enzymes. CONCLUSIONS: Taken together, our results demonstrate the potential therapeutic use of PegC in PDAC and highlight the importance of identifying candidates for combination regimens that could improve cytotoxicity and/or reduce the induction of resistance pathways.

mergem: merging, comparing, and translating genome-scale metabolic models using universal identifiers.

Numerous methods exist to produce and refine genome-scale metabolic models. However, due to the use of incompatible identifier systems for metabolites and reactions, computing and visualizing the metabolic differences and similarities of such models is a current challenge. Furthermore, there is a lack of automated tools that can combine the strengths of multiple reconstruction pipelines into a curated single comprehensive model by merging different drafts, which possibly use incompatible namespaces. Here we present mergem, a novel method to compare, merge, and translate two or more metabolic models. Using a universal metabolic identifier mapping system constructed from multiple metabolic databases, mergem robustly can compare models from different pipelines, merge their common elements, and translate their identifiers to other database systems. mergem is implemented as a command line tool, a Python package, and on the web-application Fluxer, which allows simulating and visually comparing multiple models with different interactive flux graphs. The ability to merge, compare, and translate diverse genome scale metabolic models can facilitate the curation of comprehensive reconstructions and the discovery of unique and common metabolic features among different organisms.