Defective glycosylation and multisystem abnormalities characterize the primary immunodeficiency XMEN disease
Juan C. Ravell, … , Matthias Mann, Michael J. Lenardo
Juan C. Ravell, … , Matthias Mann, Michael J. Lenardo
Published November 5, 2019
Citation Information: J Clin Invest. 2020;130(1):507-522. https://doi.org/10.1172/JCI131116.
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Research Article Immunology

Defective glycosylation and multisystem abnormalities characterize the primary immunodeficiency XMEN disease

Abstract

X-linked immunodeficiency with magnesium defect, EBV infection, and neoplasia (XMEN) disease are caused by deficiency of the magnesium transporter 1 (MAGT1) gene. We studied 23 patients with XMEN, 8 of whom were EBV naive. We observed lymphadenopathy (LAD), cytopenias, liver disease, cavum septum pellucidum (CSP), and increased CD4–CD8–B220–TCRαβ+ T cells (αβDNTs), in addition to the previously described features of an inverted CD4/CD8 ratio, CD4+ T lymphocytopenia, increased B cells, dysgammaglobulinemia, and decreased expression of the natural killer group 2, member D (NKG2D) receptor. EBV-associated B cell malignancies occurred frequently in EBV-infected patients. We studied patients with XMEN and patients with autoimmune lymphoproliferative syndrome (ALPS) by deep immunophenotyping (32 immune markers) using time-of-flight mass cytometry (CyTOF). Our analysis revealed that the abundance of 2 populations of naive B cells (CD20+CD27–CD22+IgM+HLA-DR+CXCR5+CXCR4++CD10+CD38+ and CD20+CD27–CD22+IgM+HLA-DR+CXCR5+CXCR4+CD10–CD38–) could differentially classify XMEN, ALPS, and healthy individuals. We also performed glycoproteomics analysis on T lymphocytes and show that XMEN disease is a congenital disorder of glycosylation that affects a restricted subset of glycoproteins. Transfection of MAGT1 mRNA enabled us to rescue proteins with defective glycosylation. Together, these data provide new clinical and pathophysiological foundations with important ramifications for the diagnosis and treatment of XMEN disease.

Authors

Juan C. Ravell, Mami Matsuda-Lennikov, Samuel D. Chauvin, Juan Zou, Matthew Biancalana, Sally J. Deeb, Susan Price, Helen C. Su, Giulia Notarangelo, Ping Jiang, Aaron Morawski, Chrysi Kanellopoulou, Kyle Binder, Ratnadeep Mukherjee, James T. Anibal, Brian Sellers, Lixin Zheng, Tingyan He, Alex B. George, Stefania Pittaluga, Astin Powers, David E. Kleiner, Devika Kapuria, Marc Ghany, Sally Hunsberger, Jeffrey I. Cohen, Gulbu Uzel, Jenna Bergerson, Lynne Wolfe, Camilo Toro, William Gahl, Les R. Folio, Helen Matthews, Pam Angelus, Ivan K. Chinn, Jordan S. Orange, Claudia M. Trujillo-Vargas, Jose Luis Franco, Julio Orrego-Arango, Sebastian Gutiérrez-Hincapié, Niraj Chandrakant Patel, Kimiyo Raymond, Turkan Patiroglu, Ekrem Unal, Musa Karakukcu, Alexandre G.R. Day, Pankaj Mehta, Evan Masutani, Suk S. De Ravin, Harry L. Malech, Grégoire Altan-Bonnet, V. Koneti Rao, Matthias Mann, Michael J. Lenardo

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Figure 4

Deep immunophenotyping of PBMCs shows distinctive immune subsets for XMEN compared with HCs and ALPS.

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Deep immunophenotyping of PBMCs shows distinctive immune subsets for XME...
HAL-x on CyTOF data acquired from PBMCs from patients with XMEN (n = 18), patients with ALPS (n = 11), and HCs (n = 24) identified 69 CoD. (A) 2D projection of the identified CoD as visualized by t-distributed stochastic neighbor embedding (t-SNE). (B) Dendrogram showing these CoD based on their abundance of surface epitopes. (C) Dendrogram of the frequencies of these CoD based on unsupervised grouping showing the clustering of patients with XMEN (blue), patients with ALPS (magenta), and HCs (green). (D) ROCs for the random forest classification of XMEN, ALPS, and HCs based on the frequency of their CoD. The AUC value is the mean AUC taken after a 4-fold cross-validation. Each fold of the cross-validation represents a different separation (of the patients) into training and testing sets. 95% CIs are shown (gray).