Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, United States of America.
PLoS Comput Biol. 2013;9(1):e1002813. doi: 10.1371/journal.pcbi.1002813. Epub 2013 Jan 10.
Abnormalities in glycan biosynthesis have been conclusively linked to many diseases but the complexity of glycosylation has hindered the analysis of glycan data in order to identify glycoforms contributing to disease. To overcome this limitation, we developed a quantitative N-glycosylation model that interprets and integrates mass spectral and transcriptomic data by incorporating key glycosylation enzyme activities. Using the cancer progression model of androgen-dependent to androgen-independent Lymph Node Carcinoma of the Prostate (LNCaP) cells, the N-glycosylation model identified and quantified glycan structural details not typically derived from single-stage mass spectral or gene expression data. Differences between the cell types uncovered include increases in H(II) and Le(y) epitopes, corresponding to greater activity of α2-Fuc-transferase (FUT1) in the androgen-independent cells. The model further elucidated limitations in the two analytical platforms including a defect in the microarray for detecting the GnTV (MGAT5) enzyme. Our results demonstrate the potential of systems glycobiology tools for elucidating key glycan biomarkers and potential therapeutic targets. The integration of multiple data sets represents an important application of systems biology for understanding complex cellular processes.
糖基化生物合成的异常与许多疾病密切相关，但糖基化的复杂性阻碍了糖谱数据的分析，从而难以确定导致疾病的糖型。为了克服这一限制，我们开发了一种定量 N-糖基化模型，通过整合关键糖基化酶活性来解释和整合质谱和转录组数据。利用雄激素依赖性至雄激素非依赖性前列腺淋巴结癌（LNCaP）细胞的癌症进展模型，N-糖基化模型鉴定并量化了通常无法从单阶段质谱或基因表达数据中得出的聚糖结构细节。揭示出细胞类型之间的差异包括 H(II)和 Le(y) 表位增加，这对应于雄激素非依赖性细胞中 α2-Fuc-转移酶（FUT1）活性增加。该模型进一步阐明了两种分析平台的局限性，包括微阵列在检测 GnTV（MGAT5）酶方面存在缺陷。我们的研究结果表明系统糖生物学工具在阐明关键聚糖生物标志物和潜在治疗靶点方面具有潜力。多数据集的整合代表了系统生物学在理解复杂细胞过程中的一个重要应用。
