Toward understanding the role of altered glycosylation in cancer

项目摘要 糖基化改变与恶性转化相伴，但其功能意义在于， 癌症的进展还不太清楚。这种知识上的差距主要是由于 与研究细胞表面糖蛋白有关。因此，必须了解监管失调是如何发生的 糖基化有助于免疫逃避，并开发允许轻松探测聚糖的方法 癌症的变化。我们试图通过结合糖蛋白组学， 生物化学、生物信息学和聚糖的空间可视化。 首先，糖基化有助于肿瘤微环境中的重要免疫信号传导事件。为 例如，含有T细胞免疫球蛋白和粘蛋白结构域3（TIM 3）是一种新兴检查点抑制剂 目前正在研究癌症免疫疗法。TIM 3是糖基化粘蛋白糖蛋白，具有至少 10个预测的糖位点。然而，关于TIM 3糖基化或其对蛋白质的影响知之甚少。 功能、配体结合和免疫信号传导。已知TIM 3的一种配体半乳糖凝集素-9（Gal-9）结合聚糖 导致T细胞活化的下调。靶向的特定聚糖结构和/或糖位点 Gal-9目前未知。因此，在项目1中，我们将通过以下方式探测Gal-9/TIM 3轴的糖表位关键： 将完整糖蛋白组学和糖组工程与生物化学和免疫学测定相结合。 在项目2中，我们的目标是开发O-聚糖空间分布的可视化方法。目前 空间检查N-聚糖的首要方法是MALDI质谱成像（MSI），其中N-聚糖 被酶促去除并成像;然而，对于O-聚糖没有已知的同源酶。我们将 通过使用对O-糖基化具有选择性的蛋白酶（称为O- 糖蛋白酶或粘蛋白酶。此外，我们将进行组织上的微观糖蛋白质组学，这将 允许我们将O-聚糖MSI信息与相关的糖蛋白偶联。总之，这些实验 将提供失调的O-聚糖及其基础蛋白的空间分辨率。 最后，糖蛋白组学领域目前面临的最重大挑战之一是缺乏准确的 可靠的数据处理方法。在项目3中，我们试图通过首先比较来解决这种差异 目前现有的软件算法在速度、准确性、用户界面和评分方面。我们的长期 我的目标是创造一个新颖的界面，利用我实验室在以下方面的丰富经验： 手动验证、从头测序和C#编程。我们的计划将包括用于inr de方法 新测序，同时容纳多种类型的聚糖，化学修饰和MALDI MSI数据。 最终，在实现本提案的目标后，我们开发的方法和见解将证明是无价的 质谱、糖蛋白组学和MALDI MSI领域。此外，生物信息 收集将阐明糖基化如何有助于肿瘤进展。

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.