Department of Internal Medicine, Division of Hematology and Oncology, Winthrop P, Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
BMC Bioinformatics. 2011 Oct 18;12 Suppl 10(Suppl 10):S8. doi: 10.1186/1471-2105-12-S10-S8.
Recent advances in genomics and proteomics have allowed us to study the nuances of the Warburg effect--a long-standing puzzle in cancer energy metabolism--at an unprecedented level of detail. While modern next-generation sequencing technologies are extremely powerful, the lack of appropriate data analysis tools makes this study difficult. To meet this challenge, we developed a novel application for comparative analysis of gene expression and visualization of RNA-Seq data.
We analyzed two biological samples (normal human brain tissue and human cancer cell lines) with high-energy, metabolic requirements. We calculated digital topology and the copy number of every expressed transcript. We observed subtle but remarkable qualitative and quantitative differences between the citric acid (TCA) cycle and glycolysis pathways. We found that in the first three steps of the TCA cycle, digital expression of aconitase 2 (ACO2) in the brain exceeded both citrate synthase (CS) and isocitrate dehydrogenase 2 (IDH2), while in cancer cells this trend was quite the opposite. In the glycolysis pathway, all genes showed higher expression levels in cancer cell lines; and most notably, digital gene expression of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and enolase (ENO) were considerably increased when compared to the brain sample.
The variations we observed should affect the rates and quantities of ATP production. We expect that the developed tool will provide insights into the subtleties related to the causality between the Warburg effect and neoplastic transformation. Even though we focused on well-known and extensively studied metabolic pathways, the data analysis and visualization pipeline that we developed is particularly valuable as it is global and pathway-independent.
基因组学和蛋白质组学的最新进展使我们能够以前所未有的细节水平研究沃伯格效应——癌症能量代谢中长期存在的谜题——的细微差别。虽然现代下一代测序技术非常强大，但缺乏适当的数据分析工具使得这项研究变得困难。为了应对这一挑战，我们开发了一种用于比较基因表达分析和 RNA-Seq 数据可视化的新应用程序。
我们分析了两个具有高能量、代谢需求的生物样本（正常人类脑组织和人类癌细胞系）。我们计算了每个表达转录物的数字拓扑和拷贝数。我们观察到柠檬酸（TCA）循环和糖酵解途径之间存在微妙但显著的定性和定量差异。我们发现，在 TCA 循环的前三个步骤中，大脑中 aconitase 2 (ACO2) 的数字表达超过了柠檬酸合成酶 (CS) 和异柠檬酸脱氢酶 2 (IDH2)，而在癌细胞中则恰恰相反。在糖酵解途径中，所有基因在癌细胞系中的表达水平都较高；更值得注意的是，与脑组织样本相比，甘油醛-3-磷酸脱氢酶 (GAPDH) 和烯醇酶 (ENO) 的数字基因表达都有明显增加。
我们观察到的变化应该会影响 ATP 产生的速率和数量。我们预计，开发的工具将为理解沃伯格效应与肿瘤转化之间的因果关系提供深入的见解。尽管我们专注于众所周知且广泛研究的代谢途径，但我们开发的数据分析和可视化管道特别有价值，因为它是全球性的，与途径无关。
