Nam Hojung, Lee Jinwon, Lee Doheon
Department of Bio and Brain Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, Republic of Korea.
Biotechnol Bioeng. 2009 Jul 1;103(4):835-43. doi: 10.1002/bit.22320.
Understanding altered metabolism is an important issue because altered metabolism is often revealed as a cause or an effect in pathogenesis. It has also been shown to be an important factor in the manipulation of an organism's metabolism in metabolic engineering. Unfortunately, it is not yet possible to measure the concentration levels of all metabolites in the genome-wide scale of a metabolic network; consequently, a method that infers the alteration of metabolism is beneficial. The present study proposes a computational method that identifies genome-wide altered metabolism by analyzing functional units of KEGG pathways. As control of a metabolic pathway is accomplished by altering the activity of at least one rate-determining step enzyme, not all gene expressions of enzymes in the pathway demonstrate significant changes even if the pathway is altered. Therefore, we measure the alteration levels of a metabolic pathway by selectively observing expression levels of significantly changed genes in a pathway. The proposed method was applied to two strains of Saccharomyces cerevisiae gene expression profiles measured in very high-gravity (VHG) fermentation. The method identified altered metabolic pathways whose properties are related to ethanol and osmotic stress responses which had been known to be observed in VHG fermentation because of the high sugar concentration in growth media and high ethanol concentration in fermentation products. With the identified altered pathways, the proposed method achieved best accuracy and sensitivity rates for the Red Star (RS) strain compared to other three related studies (gene-set enrichment analysis (GSEA), significance analysis of microarray to gene set (SAM-GS), reporter metabolite), and for the CEN.PK 113-7D (CEN) strain, the proposed method and the GSEA method showed comparably similar performances.
了解代谢变化是一个重要问题，因为代谢变化在发病机制中常表现为病因或结果。它也被证明是代谢工程中操纵生物体代谢的一个重要因素。不幸的是，目前还无法在代谢网络的全基因组范围内测量所有代谢物的浓度水平；因此，一种推断代谢变化的方法是有益的。本研究提出了一种计算方法，通过分析KEGG通路的功能单元来识别全基因组范围内的代谢变化。由于代谢途径的控制是通过改变至少一个限速步骤酶的活性来实现的，即使途径发生改变，该途径中并非所有酶的基因表达都会有显著变化。因此，我们通过选择性观察途径中显著变化基因的表达水平来测量代谢途径的变化水平。所提出的方法应用于在超高重力（VHG）发酵中测量的两株酿酒酵母基因表达谱。该方法识别出了与乙醇和渗透胁迫反应相关的代谢途径变化，由于生长培养基中高糖浓度和发酵产物中高乙醇浓度，这些变化在VHG发酵中是已知会出现的。利用所识别出的变化途径，与其他三项相关研究（基因集富集分析（GSEA）、微阵列对基因集的显著性分析（SAM-GS）、报告代谢物）相比，所提出的方法在红星（RS）菌株上实现了最佳的准确率和灵敏度；对于CEN.PK 113-7D（CEN）菌株，所提出的方法和GSEA方法表现出相当相似的性能。
