Toward understanding the role of altered glycosylation in cancer

项目摘要 糖基化改变与恶性转化是相伴的，但其功能意义涉及 癌症的进展还不是很清楚。这种知识差距在很大程度上可归因于这些困难 与研究细胞表面糖蛋白有关。因此，我们必须了解监管失衡的原因。 糖基化有助于免疫逃避，并开发出能够方便地探测多糖的方法。 癌症的变化。我们寻求通过结合糖蛋白组学的跨学科方法来实现这些目标， 生物化学、生物信息学和葡聚糖的空间可视化。 首先，糖基化参与了肿瘤微环境中重要的免疫信号事件。为 例如，T细胞免疫球蛋白和粘蛋白结构域包含3(TIM3)是一种新的检查点抑制物 目前正在接受癌症免疫疗法的研究。TIM3是一种糖基化的粘蛋白糖蛋白，至少具有 10个预测的糖点。然而，人们对TIM3糖基化及其对蛋白质的影响知之甚少 功能、配体结合和免疫信号。已知TIM3的一个配体Galectin-9(Gal-9)能与糖链结合 TIM3导致T细胞活化下调。靶向特定的糖链结构和/或糖位 Gal-9目前尚不清楚。因此，在项目1中，我们将通过以下方式探索Gal-9/TIM3轴的糖表位关键 将完整的糖蛋白组学和糖类工程与生化和免疫学分析相结合。 在项目2中，我们的目标是开发O-葡聚糖空间分布的可视化方法。目前， 空间检查N-糖链的主要方法是MALDI质谱学成像(MSI)，其中N-糖链 被酶法去除并成像；然而，O-葡聚糖没有已知的同源酶。我们会 通过使用选择性O-糖基化的蛋白酶组合来克服这一问题，称为O-糖基化 糖蛋白水解酶或粘胶酶。此外，我们将进行组织内微型糖蛋白组学，这将 使我们能够将O-葡聚糖微卫星信息与相关的糖蛋白偶联。总而言之，这些实验 将提供失调的O-多糖及其潜在蛋白质的空间分辨率。 最后，糖蛋白组学领域目前面临的最大挑战之一是缺乏准确的 和可靠的数据处理方法。在项目3中，我们试图通过首先比较 目前在速度、准确性、用户界面和评分方面的现有软件算法。我们的长期合作 然而，我的目标是创建一个新的界面，利用我的实验室的丰富经验 手动验证、从头开始排序和C#编程。我们的程序将包括INR de NOVO测序，同时容纳多种类型的葡聚糖、化学修饰和MALDI MSI数据。 最终，在实现这项提案的目标后，我们开发的方法和见解将被证明是无价的 涉及到质谱学、糖蛋白组学和MALDI MSI等领域。此外，生物信息 收集到的信息将阐明糖基化如何促进肿瘤进展。

Project Summary Altered glycosylation is concomitant with malignant transformation, but the functional significance with regard to cancer progression is not well understood. This gap in knowledge is largely attributable to the difficulties associated with studying cell-surface glycoproteins. Thus, it is imperative to understand how dysregulated glycosylation contributes to immune evasion, and to develop methods that allow for facile probing of glycan changes in cancer. We seek to attain these goals with an interdisciplinary approach combining glycoproteomics, biochemistry, bioinformatics, and spatial visualization of glycans. First, glycosylation contributes to important immune signaling events within the tumor microenvironment. For instance, T cell immunoglobulin and mucin-domain containing 3 (TIM3) is an emerging checkpoint inhibitor currently under investigation for cancer immunotherapy. TIM3 is a glycosylated mucin glycoprotein, with at least 10 predicted glycosites. However, very little is known regarding TIM3 glycosylation or its effects on protein function, ligand binding, and immune signaling. One ligand of TIM3, galectin-9 (Gal-9), is known to bind glycans on TIM3 resulting in downregulation of T cell activation. The particular glycan structure and/or glycosite targeted by Gal-9 is currently unknown. Thus, in Project 1, we will probe the glycoepitope key to the Gal-9/TIM3 axis by combining intact glycoproteomics and glycome engineering with biochemical and immunological assays. In Project 2, our goal is to develop methods for visualization of O-glycan spatial distribution. Currently, the premier method to spatially examine N-glycans is MALDI mass spectrometry imaging (MSI), wherein N-glycans are removed enzymatically and imaged; however, no homologous enzyme is known for O-glycans. We will overcome this issue by using a combination of proteases selective for O-glycosylation, referred to as O- glycoproteases or mucinases. Additionally, we will perform on-tissue microscale glycoproteomics, which will allow us to couple the O-glycan MSI information with the associated glycoproteins. Together, these experiments will provide spatial resolution of dysregulated O-glycans and their underlying proteins. Finally, one of the most significant challenges currently facing the glycoproteomic field is the dearth of accurate and reliable methods for data processing. In Project 3 we seek to address this disparity by first comparing currently existing software algorithms with regard to speed, accuracy, user interface, and scoring. Our long-term goal, though, is to create a novel interface that takes advantage of my laboratory’s extensive experience with manual validation, de novo sequencing, and C# programming. Our program will include methods used inr de novo sequencing while accommodating multiple types of glycans, chemical modifications, and MALDI MSI data. Ultimately, upon attaining the goals of this proposal, the methods and insights we develop will prove invaluable to the fields of mass spectrometry, glycoproteomics, and MALDI MSI. Additionally, the biological information gathered will shed light on how glycosylation contributes to tumor progression.