Bioinformatics Group, CEIT and TECNUN, University of Navarra, San Sebastian, 20018, Spain.
Mathematics for Life (M4L), San Sebastian, 20018, Spain.
Sci Rep. 2017 Oct 30;7(1):14358. doi: 10.1038/s41598-017-14067-8.
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.
近年来,基于约束的基因组规模代谢网络建模方法作为一种阐明癌症药物靶点的有前途的方法出现了。除了产生生物量的典型生物合成途径外,通过其他生物学手段调节维持增殖能力的代谢途径对于提高基于计算机的基因必需性分析至关重要。多胺是带正电荷的多聚物,在癌细胞增殖中具有核心作用,通过调节转录和翻译等,但是在基于计算机的癌症代谢模型中通常被忽视。在这项研究中,我们分析了多胺生物合成的必需基因。我们的分析证实了以前已知的途径调节剂的重要性,例如腺苷甲硫氨酸脱羧酶 1(AMD1),并揭示了预测对多胺动态平衡相关的新酶。我们专注于腺嘌呤磷酸核糖基转移酶(APRT),并证明了 APRT 基因沉默对不同白血病细胞系的有害后果。我们的结果强调了重新审视用于计算机基因必需性分析的代谢模型的重要性,以最大限度地提高癌症药物靶点识别的潜力。