基于约束的建模预测低和高级浆液卵巢癌的代谢特征
Constraint-based modelling predicts metabolic signatures of low and high-grade serous ovarian cancer
影响因子:3.50000
分区:生物学2区 / 数学与计算生物学2区
发表日期:2024 Aug 24
作者:
Kate E Meeson, Jean-Marc Schwartz
摘要
卵巢癌是一种侵略性，异质性疾病，负担重新诊断和对化学疗法的抵抗。卵巢癌的临床特征可以通过研究其新陈代谢，以及特定途径的调节如何与单个表型联系起来。由于其异质性，卵巢癌对代谢研究特别感兴趣，并且已经鉴定出五个不同的亚型，每种亚型都可能显示出独特的代谢特征。为了阐明代谢差异，基于约束的建模（CBM）代表了一项强大的技术，邀请了“ OMICS”数据的集成，例如转录组学。但是，许多CBM方法尚未优先考虑准确的生长速率预测，并且卵巢癌基因组规模的研究很少。在这里，采用了基因组尺度模型Human1和通量平衡分析开发了一种新型CBM方法，从而使体外生长速率，转录组学数据和媒体条件的整合以预测细胞的代谢行为。使用低级和高级卵巢癌，已经预测了亚型特异性代谢差异，这些差异得到了癌细胞系百科全书的公开可用的CRISPR-CAS9数据和广泛的文献综述。已经提出了侵略性，侵入性表型的代谢驱动因素，以及导致低级细胞系化学耐药性增加的途径。实验基因依赖性数据已被用来验证五磷酸五磷酸途径的区域，这对于低级细胞生长至关重要，强调了这种卵巢癌亚型的潜在脆弱性。
Abstract
Ovarian cancer is an aggressive, heterogeneous disease, burdened with late diagnosis and resistance to chemotherapy. Clinical features of ovarian cancer could be explained by investigating its metabolism, and how the regulation of specific pathways links to individual phenotypes. Ovarian cancer is of particular interest for metabolic research due to its heterogeneous nature, with five distinct subtypes having been identified, each of which may display a unique metabolic signature. To elucidate metabolic differences, constraint-based modelling (CBM) represents a powerful technology, inviting the integration of 'omics' data, such as transcriptomics. However, many CBM methods have not prioritised accurate growth rate predictions, and there are very few ovarian cancer genome-scale studies. Here, a novel method for CBM has been developed, employing the genome-scale model Human1 and flux balance analysis, enabling the integration of in vitro growth rates, transcriptomics data and media conditions to predict the metabolic behaviour of cells. Using low- and high-grade ovarian cancer, subtype-specific metabolic differences have been predicted, which have been supported by publicly available CRISPR-Cas9 data from the Cancer Cell Line Encyclopaedia and an extensive literature review. Metabolic drivers of aggressive, invasive phenotypes, as well as pathways responsible for increased chemoresistance in low-grade cell lines have been suggested. Experimental gene dependency data has been used to validate areas of the pentose phosphate pathway as essential for low-grade cellular growth, highlighting potential vulnerabilities for this ovarian cancer subtype.