Palo Alto Research Center, Palo Alto, California 94304, USA.
Mol Cell Proteomics. 2013 Apr;12(4):1026-35. doi: 10.1074/mcp.M112.026641. Epub 2013 Feb 11.
Lectin-glycan interactions have critical functions in multiple normal and pathological processes, but the binding partners and functions for many glycans and lectins are not known. An important step in better understanding glycan-lectin biology is enabling systematic quantification and analysis of the interactions. Glycan arrays can provide the experimental information for such analyses, and the thousands of glycan array datasets available through the Consortium for Functional Glycomics provide the opportunity to extend the analyses to a broad scale. We developed software, based on our previously described Motif Segregation algorithm, for the automated analysis of glycan array data, and we analyzed the entire storehouse of 2883 datasets from the Consortium for Functional Glycomics. We mined the resulting database to make comparisons of specificities across multiple lectins and comparisons between glycans in their lectin receptors. Of the lectins in the database, viral lectins were the most different from other organism types, with specificities nearly always restricted to sialic acids, and mammalian lectins had the most diverse range of specificities. Certain mammalian lectins were unique in their specificities for sulfated glycans. Simple modifications to a lactosamine core structure radically altered the types of lectins that were highly specific for the glycan. Unmodified lactosamine was specifically recognized by plant, fungal, viral, and mammalian lectins; sialylation shifted the binding mainly to viral lectins; and sulfation resulted in mainly mammalian lectins with the highest specificities. We anticipate that this analysis program and database will be valuable in fundamental glycobiology studies, detailed analyses of lectin specificities, and practical applications in translational research.
凝集素-糖相互作用在多种正常和病理过程中具有关键功能,但许多糖和凝集素的结合伙伴和功能尚不清楚。更好地了解糖-凝集素生物学的一个重要步骤是能够系统地定量和分析相互作用。糖芯片可以为这些分析提供实验信息,而通过功能糖组学联合会提供的数千个糖芯片数据集为扩展分析提供了机会。我们开发了一种软件,该软件基于我们之前描述的 Motif Segregation 算法,用于自动分析糖芯片数据,并分析了功能糖组学联合会的整个 2883 个数据集存储库。我们挖掘了由此产生的数据库,以比较多种凝集素之间的特异性,以及它们在凝集素受体中的糖之间的特异性。在数据库中的凝集素中,病毒凝集素与其他生物体类型的差异最大,特异性几乎总是局限于唾液酸,而哺乳动物凝集素的特异性范围最广。某些哺乳动物凝集素在其对硫酸化糖的特异性方面是独特的。乳糖胺核心结构的简单修饰极大地改变了高度特异性糖的凝集素类型。未修饰的乳糖胺被植物、真菌、病毒和哺乳动物凝集素特异性识别;唾液酸化将结合主要转移到病毒凝集素;而硫酸化则主要导致具有最高特异性的哺乳动物凝集素。我们预计,该分析程序和数据库将在基础糖生物学研究、凝集素特异性的详细分析以及转化研究中的实际应用中具有重要价值。