Methods for determination of glycoprotein glycosylation similarities among disease states

摘要 本申请针对NIGMS PAR-17-045“聚焦技术研究与开发(R01)”。 这一倡议支持那些只专注于开发有潜力的技术的项目 生物医学研究。 细胞微环境失调发生在癌症、神经发育和神经精神疾病中 疾病。被称为母体，细胞外基质和细胞表面分子控制着 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，对基质糖基化调控的有限认识阻碍了这一过程。 了解糖基化依赖的母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的干预。 配子体的功能依赖于糖基化蛋白和糖结合蛋白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组学方法来比较使用STRICAL 根据疾病相关部位糖基化变化而不同的糖蛋白的统计相似性。我们 建议开发满足这一需求的技术。目前的蛋白质组学方法使用一种 每个基因产物很少有代表性的多肽；大多数蛋白质的序列覆盖率很低。如此之低 序列覆盖率不足以重建主要的糖基化蛋白形式 生物学背景。我们建议开发技术来比较生物之间糖蛋白的相似性。 样本集。为此，我们将开发MS获取和生物信息学方法，用于快速、灵敏和 糖蛋白糖基化的可重复性图谱，以实现对糖蛋白的统计严格比较 相似之处。通过提供这些技术，我们将使对角色的理解达到新的水平 人类疾病中的母体网络。

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.