Validation of diagnostic and predictive biomarkers for hereditary angioedema via plasma N-glycomics

Zejian Zhang; Xue Wang; Jianqing Gu; Jianqiang Wu; Yang Cao; Yingyang Xu; Lisha Li; Kai Guan; Peng Liu; Jia Yin; Yuxiang Zhi; Shuyang Zhang

doi:10.1002/clt2.12090

Validation of diagnostic and predictive biomarkers for hereditary angioedema via plasma N-glycomics

Clin Transl Allergy. 2021 Dec;11(10):e12090. doi: 10.1002/clt2.12090.

Authors

Zejian Zhang¹, Xue Wang², Jianqing Gu², Jianqiang Wu¹, Yang Cao², Yingyang Xu², Lisha Li², Kai Guan², Peng Liu¹, Jia Yin², Yuxiang Zhi², Shuyang Zhang³

Affiliations

¹ Department of Medical Research Center, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
² Department of Allergy & Clinical Immunology, National Clinical Research Center for Immunologic Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
³ Department of Cardiology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Abstract

Background: Hereditary angioedema (HAE) is a rare disease with heterogeneous clinical symptoms. It is vitally important to predict whether an HAE patient will develop severe symptoms in clinical practice, but there are currently no predictive biomarkers for HAE stratification. Plasma N-glycomes are disease-specific and have great potential for the discovery of non-invasive biomarkers. In this study, we profiled the plasma N-glycome of HAE patients from two independent cohorts to identify candidate biomarkers.

Methods: Linkage-specific sialylation derivatization combined with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry detection and automated data processing was employed to analyze the plasma N-glycome of two independent type-1 HAE cohorts.

Results: HAE patients had abnormal glycan complexity, galactosylation, and α2,3- and α2,6-linked sialylation compared to healthy controls (HC). The classification models based on dysregulated glycan traits could successfully discriminate between HAE and HC with area under the curves (AUCs) being greater than 0.9. Some of the aberrant glycans showed response to therapy. Moreover, we identified a series of glycan traits with strong associations with the occurrence of laryngeal or gastrointestinal angioedema or disease severity score. Predictive models based on these traits could be used to predict disease severity (AUC > 0.9). These results were replicated in an independent cohort.

Conclusions: We reported the full plasma N-glycomic signature of HAE for the first time, and identified potential biomarkers. These findings may play a critical role in predicting disease severity and guide the treatment of HAE in clinical practice. Further protein-specific and prospective studies are needed to validate our findings.

Keywords: biomarker; disease severity; galactosylation; galaktosylierung; plasma N-glycome; plasma-N-glykom; schwere der erkrankung; sialylation; sialylierung.

Abstract

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