Neutralizing IFN-γ autoantibodies are rare and pathogenic in HLA-DRB1*15:02 or 16:02 individuals
Jessica N. Peel, … , Cheng-Lung Ku, Jean-Laurent Casanova
Jessica N. Peel, … , Cheng-Lung Ku, Jean-Laurent Casanova
Published March 12, 2024
Citation Information: J Clin Invest. 2024;134(8):e178263. https://doi.org/10.1172/JCI178263.
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Neutralizing IFN-γ autoantibodies are rare and pathogenic in HLA-DRB1*15:02 or 16:02 individuals

Abstract

BACKGROUND Weakly virulent environmental mycobacteria (EM) can cause severe disease in HLA-DRB1*15:02 or 16:02 adults harboring neutralizing anti-IFN-γ autoantibodies (nAIGAs). The overall prevalence of nAIGAs in the general population is unknown, as are the penetrance of nAIGAs in HLA-DRB1*15:02 or 16:02 individuals and the proportion of patients with unexplained, adult-onset EM infections carrying nAIGAs.METHODS This study analyzed the detection and neutralization of anti-IFN-γ autoantibodies (auto-Abs) from 8,430 healthy individuals of the general population, 257 HLA-DRB1*15:02 or 16:02 carriers, 1,063 patients with autoimmune disease, and 497 patients with unexplained severe disease due to EM.RESULTS We found that anti-IFN-γ auto-Abs detected in 4,148 of 8,430 healthy individuals (49.2%) from the general population of an unknown HLA-DRB1 genotype were not neutralizing. Moreover, we did not find nAIGAs in 257 individuals carrying HLA-DRB1* 15:02 or 16:02. Additionally, nAIGAs were absent in 1,063 patients with an autoimmune disease. Finally, 7 of 497 patients (1.4%) with unexplained severe disease due to EM harbored nAIGAs.CONCLUSION These findings suggest that nAIGAs are isolated and that their penetrance in HLA-DRB1*15:02 or 16:02 individuals is low, implying that they may be triggered by rare germline or somatic variants. In contrast, the risk of mycobacterial disease in patients with nAIGAs is high, confirming that these nAIGAs are the cause of EM disease.FUNDING The Laboratory of Human Genetics of Infectious Diseases is supported by the Howard Hughes Medical Institute, the Rockefeller University, the St. Giles Foundation, the National Institutes of Health (NIH) (R01AI095983 and U19AIN1625568), the National Center for Advancing Translational Sciences (NCATS), the NIH Clinical and Translational Science Award (CTSA) program (UL1 TR001866), the French National Research Agency (ANR) under the “Investments for the Future” program (ANR-10-IAHU-01), the Integrative Biology of Emerging Infectious Diseases Laboratory of Excellence (ANR-10-LABX-62-IBEID), ANR-GENMSMD (ANR-16-CE17-0005-01), ANR-MAFMACRO (ANR-22-CE92-0008), ANRSECTZ170784, the French Foundation for Medical Research (FRM) (EQU201903007798), the ANRS-COV05, ANR GENVIR (ANR-20-CE93-003), and ANR AI2D (ANR-22-CE15-0046) projects, the ANR-RHU program (ANR-21-RHUS-08-COVIFERON), the European Union’s Horizon 2020 research and innovation program under grant agreement no. 824110 (EASI-genomics), the Square Foundation, Grandir - Fonds de solidarité pour l’enfance, the Fondation du Souffle, the SCOR Corporate Foundation for Science, the Battersea & Bowery Advisory Group, William E. Ford, General Atlantic’s Chairman and Chief Executive Officer, Gabriel Caillaux, General Atlantic’s Co-President, Managing Director, and Head of business in EMEA, and the General Atlantic Foundation, Institut National de la Santé et de la Recherche Médicale (INSERM) and of Paris Cité University. JR was supported by the INSERM PhD program for doctors of pharmacy (poste d’accueil INSERM). JR and TLV were supported by the Bettencourt-Schueller Foundation and the MD-PhD program of the Imagine Institute. MO was supported by the David Rockefeller Graduate Program, the Funai Foundation for Information Technology (FFIT), the Honjo International Scholarship Foundation (HISF), and the New York Hideyo Noguchi Memorial Society (HNMS).

Authors

Jessica N. Peel, Rui Yang, Tom Le Voyer, Adrian Gervais, Jérémie Rosain, Paul Bastard, Anish Behere, Axel Cederholm, Aaron Bodansky, Yoann Seeleuthner, Clément Conil, Jing-Ya Ding, Wei-Te Lei, Lucy Bizien, Camille Soudee, Mélanie Migaud, Masato Ogishi, Ahmad Yatim, Danyel Lee, Jonathan Bohlen, Thomas Perpoint, Laura Perez, Fernando Messina, Roxana Genet, Ludovic Karkowski, Mathieu Blot, Emmanuel Lafont, Laurie Toullec, Claire Goulvestre, Souad Mehlal-Sedkaoui, Jérôme Sallette, Fernando Martin, Anne Puel, Emmanuelle Jouanguy, CONSTANCES cohort, 3C-Dijon Study, Etablissement du Sang study group, Mark S. Anderson, Nils Landegren, Pierre Tiberghien, Laurent Abel, Stéphanie Boisson-Dupuis, Jacinta Bustamante, Cheng-Lung Ku, Jean-Laurent Casanova

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Figure 2

The common IFN-γ auto-Abs of the general population differ from patients with nAIGA.

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The common IFN-γ auto-Abs of the general population differ from patients...
(A) Detection of anti-IFN-γ IgG subclasses from 20 individuals of the general population (white circles) and 7 patients with nAIGA (red triangles) is illustrated by ELISA, showcasing IgG1 (blue), IgG2 (red), IgG3 (purple), and IgG4 (yellow). (B) ELISA results display proportions of total IgG subclasses and anti-IFN-γ IgG from individuals of the general population and patients with nAIGA, highlighting the differences in subclass distribution. (C) Detection of anti-IFN-γ IgL from individuals of the general population and patients with nAIGA is shown by ELISA. (D) The correlation between detection of IFN-γ auto-Abs against glycosylated and nonglycosylated IFN-γ is depicted for individuals of the general population and patients with nAIGA. (E) Detection of linear peptides of IFN-γ from patients with nAIGA, individuals of the general population negative for detection against full-length IFN-γ auto-Abs, and individuals of the general population positive for detection against full-length IFN-γ auto-Abs is represented. Optical densities are plotted with respect to the amino acid position of IFN-γ. (F) Detection of high-affinity IFN-γ auto-Abs by ELISA with acid elution is shown, indicating citric acid concentration with respect to the percentage of bound IFN-γ auto-Ab remaining from patients with nAIGA and individuals of the general population positive for detection of IFN-γ auto-Abs. Data are representative of 2 independent experiments (A–C, E, and F), with each sample tested once for D. Statistical significance was calculated using an unpaired 2-tailed student’s t test, *P < 0.05, **P ≤ 0.01, ***P ≤ 0.001, ****P ≤ 0.0001.