背景:Warburg现象对化疗反应的影响尚不清楚。酿酒酵母模仿Warburg效应,在充足的葡萄糖存在下抑制呼吸。进行了酵母表型实验,以评估Warburg代谢对细胞对阿霉素反应的潜在基因-药物相互作用的潜在影响。分析来自酵母表型和癌症药物基因组学数据的同源基因,以推断基因-药物相互作用的进化保守性并预测治疗相关性。
方法:通过定量高通量细胞阵列表型(Q-HTCP)测量酵母基因敲除/敲低文库的细胞增殖表型(CPPs),在呼吸或糖酵解代谢条件下,用阿霉素浓度升高进行治疗。通过将多柔比星处理的突变菌株中观察到的CPP与基于相互作用模型预期的CPP偏离来定量多柔比星-基因相互作用。对相互作用的递归期望最大化聚类(REMc)和基于基因本体论(GO)的分析确定了功能性生物模块,这些模块在Warburg代谢方面差异缓冲或促进阿霉素的细胞毒性。整合了酵母表型和癌症药物基因组学数据,以预测影响阿霉素抗肿瘤功效的差异基因表达。
结果:酵母在染色质组织中起作用的基因受损,和其他几种细胞过程在糖酵解条件下对阿霉素更有抗性。因此,Warburg过渡似乎减轻了在呼吸道环境中缓冲阿霉素细胞毒性的细胞功能的要求。我们分析了在癌症药物基因组学数据中表现出基因-阿霉素相互作用的酵母基因的人类同源物,以预测与癌细胞中阿霉素细胞毒性相关的差异基因表达的因果关系。该分析表明,由于同源重组的影响,细胞对多柔比星的保守反应,鞘脂稳态,端粒束缚在核外围,肌动蛋白皮质斑块定位,和其他基因功能。
结论:Warburg状态改变了酵母缓冲阿霉素毒性所需的遗传网络。酵母表型和癌症药物基因组学数据的整合表明基因-药物相互作用网络的进化保守性,并提供了一种新的实验方法来模拟它们对化疗反应的影响。因此,酵母表型模型可以帮助精确的肿瘤学算法的发展,以预测有效的细胞毒性药物的癌症,基于个体肿瘤的遗传和代谢谱。
BACKGROUND: The influence of the Warburg phenomenon on chemotherapy response is unknown. Saccharomyces cerevisiae mimics the Warburg effect, repressing respiration in the presence of adequate glucose. Yeast phenomic experiments were conducted to assess potential influences of Warburg metabolism on gene-drug interaction underlying the cellular response to doxorubicin. Homologous genes from yeast phenomic and cancer pharmacogenomics data were analyzed to infer evolutionary conservation of gene-drug interaction and predict therapeutic relevance.
METHODS: Cell proliferation phenotypes (CPPs) of the yeast gene knockout/knockdown library were measured by quantitative high-throughput cell array phenotyping (Q-HTCP), treating with escalating doxorubicin concentrations under conditions of respiratory or glycolytic metabolism. Doxorubicin-gene interaction was quantified by departure of CPPs observed for the doxorubicin-treated mutant strain from that expected based on an interaction model. Recursive expectation-maximization clustering (REMc) and Gene Ontology (GO)-based analyses of interactions identified functional biological modules that differentially buffer or promote doxorubicin cytotoxicity with respect to Warburg metabolism. Yeast phenomic and cancer pharmacogenomics data were integrated to predict differential gene expression causally influencing doxorubicin anti-tumor efficacy.
RESULTS: Yeast compromised for genes functioning in chromatin organization, and several other cellular processes are more resistant to doxorubicin under glycolytic conditions. Thus, the Warburg transition appears to alleviate requirements for cellular functions that buffer doxorubicin cytotoxicity in a respiratory context. We analyzed human homologs of yeast genes exhibiting gene-doxorubicin interaction in cancer pharmacogenomics data to predict causality for differential gene expression associated with doxorubicin cytotoxicity in cancer cells. This analysis suggested conserved cellular responses to doxorubicin due to influences of homologous recombination, sphingolipid homeostasis, telomere tethering at nuclear periphery, actin cortical patch localization, and other gene functions.
CONCLUSIONS: Warburg status alters the genetic network required for yeast to buffer doxorubicin toxicity. Integration of yeast phenomic and cancer pharmacogenomics data suggests evolutionary conservation of gene-drug interaction networks and provides a new experimental approach to model their influence on chemotherapy response. Thus, yeast phenomic models could aid the development of precision oncology algorithms to predict efficacious cytotoxic drugs for cancer, based on genetic and metabolic profiles of individual tumors.