Quantitative Characterization of Glycopeptide Isomers

由于迫切需要全面了解糖基化的生物学属性， 许多关键的生物学功能，如免疫反应、细胞发育、细胞免疫应答等， 尽管糖蛋白质组学在分化/粘附和宿主-病原体相互作用方面具有重要的作用，但糖蛋白质组学仍然是一个高度动态的研究领域。 研究领域。此外，几十年来，异常糖基化已被认为是许多疾病的特征。 哺乳动物疾病，包括骨关节炎、囊性纤维化和癌症。聚糖的多种生物学作用 以及它们在疾病中的意义已经产生了对可靠的基于MS的糖蛋白组学方法的需求， 允许对生物系统中的糖蛋白进行灵敏的监测。我们在这里提出三个具体的建议， 目标：目标1。多孔石墨柱分离糖肽异构体及结构鉴定 与柱后酶反应器偶联的高温;目的2.双代谢稳定同位素标记 细胞培养物中的糖蛋白（DSILGC）;和Aim 3.开发自动识别软件工具， 糖肽的定量。这些目标的结果将是技术，促进明确的 与生物活性相关的糖蛋白异构微异质性的定量评估 样品这一建议的创新之处源于所提出的分析方法的独特性 和软件PGC高温分离糖肽异构体的新方法 在我们的实验室开发的，允许分离与蛋白质糖基化相关的所有聚糖异构体。 虽然酶反应器已被证明是由其他人，他们通常用于蛋白质消化或聚糖 释放据我们所知，这是第一次柱后反应器与MS接口，用于 提出了异构糖肽结构的表征（目的1）。同时双稳定同位素 我们在此提出的蛋白质和聚糖的标记将能够同时分析糖组学， 蛋白质组学和糖蛋白质组学，更准确和有效的定量（目标2）。从生物信息学 从这个角度来看，我们的项目有几个新的方面（目标3）。基于HCD/CID的聚糖测序算法 糖肽的光谱报告全部（或部分，当MS/MS中缺少某些碎片离子时 光谱）拓扑结构的聚糖（除了连接），而不是只有单糖组成， 上述方法可以解释。此外，我们的算法可以潜在地识别新的聚糖 这是因为它不依赖于先前已知的聚糖结构。更重要的是聚糖测序 算法为肽鉴定提供补充信息（例如，从去糖基化的 肽或ETD。本提案的可交付成果是：㈠现有的分析方法， 适应性强，负担得起的定量表征和分离糖肽异构体和（ii）开源 一种软件，允许自动解释、注释和定量来自 生物样本预计拟议的技术将使人们能够更好地了解生物 糖蛋白在食管癌、乳腺癌和肝癌的发生和发展中的属性。

Because of the pressing needs to comprehensively understand the biological attributes of glycosylation in many critical biological functions such as the immune response, cell development, cellular differentiation/adhesion and host-pathogen interactions, glycoproteomics continues to be a highly dynamic research area. Moreover, aberrant glycosylation for decades has been recognized as the attribute of many mammalian diseases, including osteoarthritis, cystic fibrosis, and cancer. The diverse biological roles of glycans and their implications in diseases have created a demand for reliable MS-based glycoproteomic approaches, permitting sensitive monitoring of glycoproteins in biological systems. We are proposing here three specific aims: Aim 1. Isomeric separation and structural identification of glycopeptides by porous graphitic columns at high temperatures coupled to post-column enzyme reactor; Aim 2. Double metabolic Stable Isotope Labeling of Glycoproteins in Cell cultures (DSILGC); and Aim 3. Develop software tools for automated identification and quantitation of glycopeptides. The outcome of these aims will be technologies that facilitate the unequivocal quantitative assessment of the isomeric microheterogenieties of glycoproteins associated with biological samples. The innovations of this proposal originate from the uniqueness of the proposed analytical methods and software. Isomeric separation of glycopeptides on PGC at high temperatures, a highly innovative method developed in our laboratory, permits the separation of all glycan isomers associated with protein glycosylation. Although enzyme reactors have been demonstrated by others, they usually used for protein digestion or glycan releasing. To our best knowledge, it is the first time a post-column reactor interfacing with MS for the characterization of isomeric glycopeptide structures is proposed (Aim 1). The simultaneous double stable isotope labeling of both proteins and glycans that we propose here will enable simultaneous analysis of glycomics, proteomics, and glycoproteomics with more accurate and effective quantitation (Aim 2). From the bioinformatics point of view, our project has several novel aspects (Aim 3). Our glycan sequencing algorithm based on HCD/CID spectra of glycopeptides reports the whole (or partial when some fragment ions are missing in the MS/MS spectra) topology of glycan (except linkages) instead of only the monosaccharide compositions that abovementioned methods can elucidate. Additionally, our algorithm can potentially identify new glycan structures, since it does not rely on previously known glycan structures. More importantly, the glycan sequencing algorithm provides complementary information to peptide identification (e.g., from CID spectra of de-glycosylated peptides or the ETD. The deliverables of this proposal are (i) analytical methods that are readily available, adaptable, and affordable to quantitively characterize and separate glycopeptide isomers and (ii) open-source software that allows automated interpretation, annotation, and quantitation of glycopeptide isomers derived from biological samples. The proposed technologies are expected to enable a better understanding of the biological attributes of glycoproteins in the development and progression of esophagus, breast and liver cancers.