Methods for determination of glycoprotein glycosylation similarities among disease states

摘要 本申请涉及NIGMS PAR-17 - 045“重点技术研究和开发（R01）”。 该倡议支持那些只注重开发有潜力使 生物医学研究 细胞微环境的失调发生在癌症、神经发育和神经精神疾病中。 疾病被称为基质体的细胞外基质和细胞表面分子的集合控制着细胞的生长。 生长因子对细胞受体的可用性和细胞的机械-物理性质 微环境目前，对基质体糖基化调控的有限理解阻碍了 了解糖基化依赖性基质体网络在必要的基本机制中的作用 对多种疾病进行针对性干预。 基质体功能依赖于糖基化蛋白和聚糖结合之间的相互作用网络 凝集素。使用目前的蛋白质组学和糖蛋白质组学方法不可能使用严格的方法进行比较。 统计学相似性的糖蛋白，不同的疾病相关的变化，位点特异性糖基化。我们 开发技术来满足这一需求。目前的蛋白质组学方法使用 每个基因产物的代表性肽很少;大多数蛋白质的序列覆盖率低。如此低 序列覆盖度不足以重建糖基化蛋白质的主要活性形式， 生物背景。我们建议开发技术来比较生物之间的糖蛋白相似性， 样本集。为此，我们将开发MS采集和生物信息学方法，用于快速、灵敏和 糖蛋白糖基化的可重复作图， 相似之处通过提供这些技术，我们将使人们对这些角色的理解达到一个新的水平。 在人类疾病中的重要作用。

Abstract This application addresses NIGMS PAR-17-045 “Focused Technology Research and Development (R01)”. This initiative supports projects that focus solely on development of technologies with the potential to enable biomedical research. Dysregulation of the cellular microenvironment occurs in cancers, neurodevelopmental and neuropsychiatric diseases. Known as the matrisome, the set of extracellular matrix and cell surface molecules control the availability of growth factors to cellular receptors and the mechanical-physical properties of the cell microenvironment. Currently, the limited understanding of regulation of matrisome glycosylation hinders understanding of the roles of glycosylation-dependent matrisome networks in the basic mechanisms necessary for targeted intervention of many diseases. Matrisome function depends on networks of interaction among glycosylated proteins and glycan-binding lectins. It is not possible using present proteomics and glycoproteomics methods to compare using rigorous statistics similarities of glycoproteins that differ by disease-related changes in site-specific glycosylation. We propose to develop technologies to meet this need. Present proteomics methods quantify proteins using a few representative peptides per gene product; sequence coverage for most proteins is low. Such low sequence coverage does not suffice to reconstruct the predominant glycosylated proteoforms active in a biological context. We propose to develop technologies to compare glycoprotein similarities among biological sample sets. To do this, we will develop MS acquisition and bioinformatics methods for rapid, sensitive and reproducible mapping of glycoprotein glycosylation to enable statistically rigorous comparison of glycoprotein similarities. By making these technologies available, we will enable a new level of understanding of the roles of matrisome networks in human diseases.