Department of Chemical Engineering, Imperial College London, London SW7 2AZ, UK.
Int J Mol Sci. 2022 May 6;23(9):5180. doi: 10.3390/ijms23095180.
The effective treatment of autoimmune disorders can greatly benefit from disease-specific biomarkers that are functionally involved in immune system regulation and can be collected through minimally invasive procedures. In this regard, human serum IgG N-glycans are promising for uncovering disease predisposition and monitoring progression, and for the identification of specific molecular targets for advanced therapies. In particular, the IgG N-glycome in diseased tissues is considered to be disease-dependent; thus, specific glycan structures may be involved in the pathophysiology of autoimmune diseases. This study provides a critical overview of the literature on human IgG N-glycomics, with a focus on the identification of disease-specific glycan alterations. In order to expedite the establishment of clinically-relevant N-glycan biomarkers, the employment of advanced computational tools for the interpretation of clinical data and their relationship with the underlying molecular mechanisms may be critical. Glycoinformatics tools, including artificial intelligence and systems glycobiology approaches, are reviewed for their potential to provide insight into patient stratification and disease etiology. Challenges in the integration of such glycoinformatics approaches in N-glycan biomarker research are critically discussed.
自身免疫性疾病的有效治疗可以极大地受益于在功能上参与免疫系统调节的疾病特异性生物标志物,这些标志物可以通过微创程序进行采集。在这方面,人血清 IgG N-糖链在揭示疾病易感性和监测进展以及识别先进治疗的特定分子靶标方面具有很大的潜力。特别是,病变组织中的 IgG N-聚糖组被认为是疾病依赖性的;因此,特定的聚糖结构可能参与自身免疫性疾病的病理生理学。本研究对人 IgG N-糖组学的文献进行了批判性综述,重点介绍了疾病特异性聚糖改变的鉴定。为了加速建立临床相关的 N-糖生物标志物,使用先进的计算工具来解释临床数据及其与潜在分子机制的关系可能至关重要。综述了糖组信息学工具,包括人工智能和系统糖生物学方法,以探讨它们在患者分层和疾病病因中的潜在作用。批判性地讨论了将此类糖组信息学方法整合到 N-糖生物标志物研究中的挑战。