Field of Drug Discovery Research, Faculty of Advanced Life Science, Hokkaido University, N21, W11, Kita-ku, Sapporo, 001-0021, Japan.
Laboratory of Bioinformatics, Artificial Intelligence Center for Health and Biomedical Research, National Institutes of Biomedical Innovation, Health and Nutrition, 7-6-8 Saito-Asagi, Ibaraki, Osaka, 567-0085, Japan.
Sci Rep. 2022 Oct 24;12(1):17804. doi: 10.1038/s41598-022-21758-4.
This study presents "mouse tissue glycome atlas" representing the profiles of major N-glycans of mouse glycoproteins that may define their essential functions in the surface glycocalyx of mouse organs/tissues and serum-derived extracellular vesicles (exosomes). Cell surface glycocalyx composed of a variety of N-glycans attached covalently to the membrane proteins, notably characteristic "N-glycosylation patterns" of the glycocalyx, plays a critical role for the regulation of cell differentiation, cell adhesion, homeostatic immune response, and biodistribution of secreted exosomes. Given that the integrity of cell surface glycocalyx correlates significantly with maintenance of the cellular morphology and homeostatic immune functions, dynamic alterations of N-glycosylation patterns in the normal glycocalyx caused by cellular abnormalities may serve as highly sensitive and promising biomarkers. Although it is believed that inter-organs variations in N-glycosylation patterns exist, information of the glycan diversity in mouse organs/tissues remains to be elusive. Here we communicate for the first-time N-glycosylation patterns of 16 mouse organs/tissues, serum, and serum-derived exosomes of Slc:ddY mice using an established solid-phase glycoblotting platform for the rapid, easy, and high throughput MALDI-TOFMS-based quantitative glycomics. The present results elicited occurrence of the organ/tissue-characteristic N-glycosylation patterns that can be discriminated to each other. Basic machine learning analysis using this N-glycome dataset enabled classification between 16 mouse organs/tissues with the highest F1 score (69.7-100%) when neural network algorithm was used. A preliminary examination demonstrated that machine learning analysis of mouse lung N-glycome dataset by random forest algorithm allows for the discrimination of lungs among the different mouse strains such as the outbred mouse Slc:ddY, inbred mouse DBA/2Crslc, and systemic lupus erythematosus model mouse MRL-lpr/lpr with the highest F1 score (74.5-83.8%). Our results strongly implicate importance of "human organ/tissue glycome atlas" for understanding the crucial and diversified roles of glycocalyx determined by the organ/tissue-characteristic N-glycosylation patterns and the discovery research for N-glycome-based disease-specific biomarkers and therapeutic targets.
本研究呈现了“小鼠组织糖组图谱”,代表了小鼠糖蛋白中主要 N-聚糖的图谱,这些图谱可能定义了它们在器官/组织表面糖萼和血清衍生的细胞外囊泡(外泌体)中的基本功能。由共价连接到膜蛋白上的各种 N-聚糖组成的细胞表面糖萼,具有显著的“N-聚糖模式”,对于调节细胞分化、细胞黏附、稳态免疫反应以及分泌的外泌体的生物分布起着关键作用。鉴于细胞表面糖萼的完整性与细胞形态和稳态免疫功能的维持密切相关,细胞异常引起的正常糖萼中 N-聚糖模式的动态改变可能成为高度敏感和有前途的生物标志物。虽然人们相信器官之间存在 N-聚糖模式的差异,但有关小鼠器官/组织中聚糖多样性的信息仍然难以捉摸。在这里,我们首次使用建立的固相糖印迹平台,对 Slc:ddY 小鼠的 16 种器官/组织、血清和血清衍生的外泌体进行了快速、简便、高通量的 MALDI-TOFMS 基于定量糖组学的 N-聚糖模式分析。这些结果表明存在器官/组织特征性的 N-聚糖模式,这些模式可以相互区分。使用基本的机器学习分析,当使用神经网络算法时,该 N-糖组数据集可以对 16 种小鼠器官/组织进行分类,准确率高达 69.7-100%。初步研究表明,使用随机森林算法对小鼠肺 N-糖组数据集进行机器学习分析,可以区分不同品系的小鼠肺,例如近交系小鼠 DBA/2Crslc、系统性红斑狼疮模型小鼠 MRL-lpr/lpr 和远交系小鼠 Slc:ddY,准确率最高(74.5-83.8%)。我们的结果强烈表明,“人类器官/组织糖组图谱”对于理解糖萼决定的关键和多样化作用以及基于 N-糖组的疾病特异性生物标志物和治疗靶点的发现研究具有重要意义。