In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer. Issue 1 (December 2017)

• Record Type: Journal Article
• Authors: Pey, Jon; San José-Eneriz, Edurne; Ochoa, María Carmen; Apaolaza, Iñigo; de Atauri, Pedro; Rubio, Angel; Cendoya, Xabier; Miranda, Estíbaliz; Garate, Leire; Cascante, Marta; Carracedo, Arkaitz; Agirre, Xabier; Prosper, Felipe; Planes, Francisco
• Is Part Of: Scientific reports. Volume 7:Issue 1(2017)
• Page Start: 1
• Page End: 10
• Publication Date: 2017-12
• Subjects: Natural history -- Research -- Periodicals; Biology -- Research -- Periodicals; Physical sciences -- Research -- Periodicals; Earth sciences -- Research -- Periodicals; Environmental sciences -- Research -- Periodicals; 502.85
• DOI: 10.1038/s41598-017-14067-8
• Languages: English
• ISSNs: 2045-2322

Abstract

Abstract Constraint-based modeling for genome-scale metabolic networks has emerged in the last years as a promising approach to elucidate drug targets in cancer. Beyond the canonical biosynthetic routes to produce biomass, it is of key importance to focus on metabolic routes that sustain the proliferative capacity through the regulation of other biological means in order to improvein-silico gene essentiality analyses. Polyamines are polycations with central roles in cancer cell proliferation, through the regulation of transcription and translation among other things, but are typically neglected inin silico cancer metabolic models. In this study, we analysed essential genes for the biosynthesis of polyamines. Our analysis corroborates the importance of previously known regulators of the pathway, such as Adenosylmethionine Decarboxylase 1 (AMD1) and uncovers novel enzymes predicted to be relevant for polyamine homeostasis. We focused on Adenine Phosphoribosyltransferase (APRT) and demonstrated the detrimental consequence of APRT gene silencing on different leukaemia cell lines. Our results highlight the importance of revisiting the metabolic models used forin-silico gene essentiality analyses in order to maximize the potential for drug target identification in cancer.