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In-silico gene essentiality analysis of polyamine biosynthesis reveals APRT as a potential target in cancer

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 ca...

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Detalles Bibliográficos
Autores principales: 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 J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5662602/
https://www.ncbi.nlm.nih.gov/pubmed/29084986
http://dx.doi.org/10.1038/s41598-017-14067-8
Descripción
Sumario: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 improve in-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 in in 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 for in-silico gene essentiality analyses in order to maximize the potential for drug target identification in cancer.