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血清 N-糖组学分析可能为 COVID-19 的监测提供潜在的特征。

Serum N-glycomic profiling may provide potential signatures for surveillance of COVID-19.

机构信息

National Institute for Bioprocessing Research and Training, Foster Avenue, Mount Merrion, Blackrock, Co., Dublin, A94 X099, Ireland.

出版信息

Glycobiology. 2022 Sep 19;32(10):871-885. doi: 10.1093/glycob/cwac051.

Abstract

Disease development and progression are often associated with aberrant glycosylation, indicating that changes in biological fluid glycome may potentially serve as disease signatures. The corona virus disease-2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) represents a significant threat to global human health. However, the effect of SARS-CoV-2 infection on the overall serum N-glycomic profile has been largely unexplored. Here, we extended our 96-well-plate-based high-throughput, high-sensitivity N-glycan profiling platform further with the aim of elucidating potential COVID-19-associated serum N-glycomic alterations. Use of this platform revealed both similarities and differences between the serum N-glycomic fingerprints of COVID-19 positive and control cohorts. Although there were no specific glycan peaks exclusively present or absent in COVID-19 positive cohort, this cohort showed significantly higher levels of glycans and variability. On the contrary, the overall N-glycomic profiles for healthy controls were well-contained within a narrow range. From the serum glycomic analysis, we were able to deduce changes in different glycan subclasses sharing certain structural features. Of significance was the hyperbranched and hypersialylated glycans and their derived glycan subclass traits. T-distributed stochastic neighbor embedding and hierarchical heatmap clustering analysis were performed to identify 13 serum glycomic variables that potentially distinguished the COVID-19 positive from healthy controls. Such serum N-glycomic changes described herein may indicate or correlate to the changes in serum glycoproteins upon COVID-19 infection. Furthermore, mapping the serum N-glycome following SARS-CoV-2 infection may help us better understand the disease and enable "Long-COVID" surveillance to capture the full spectrum of persistent symptoms.

摘要

疾病的发生和发展通常与异常的糖基化有关,这表明生物体液糖组的变化可能潜在地作为疾病标志物。由严重急性呼吸系统综合征冠状病毒 2 (SARS-CoV-2)引起的 2019 年冠状病毒病 (COVID-19)大流行对全球人类健康构成了重大威胁。然而,SARS-CoV-2 感染对整体血清 N-糖组谱的影响在很大程度上尚未被探索。在这里,我们进一步扩展了我们基于 96 孔板的高通量、高灵敏度 N-聚糖分析平台,旨在阐明潜在的 COVID-19 相关血清 N-糖组变化。使用该平台揭示了 COVID-19 阳性和对照组血清 N-糖组指纹图谱之间的相似性和差异。虽然 COVID-19 阳性组中没有特定的聚糖峰存在或缺失,但该组显示出明显更高水平的聚糖和可变性。相反,健康对照组的整体 N-糖组谱很好地包含在一个狭窄的范围内。从血清糖组分析中,我们能够推断出具有某些结构特征的不同聚糖亚类的变化。重要的是高度支化和高唾液酸化的聚糖及其衍生的聚糖亚类特征。进行 t 分布随机邻域嵌入和层次热图聚类分析,以确定 13 个潜在区分 COVID-19 阳性与健康对照组的血清糖组学变量。本文描述的这种血清 N-糖组变化可能表明或与 COVID-19 感染时血清糖蛋白的变化有关。此外,对 SARS-CoV-2 感染后血清 N-聚糖组的映射可能有助于我们更好地了解疾病,并能够进行“长 COVID”监测,以捕捉持续症状的全貌。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b33c/9487901/b1c6c65828db/cwac051f1.jpg

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