Journal Article

Upregulation of interferon-γ activation in patients with anti-interferon-γ autoantibodies immunodeficiency syndrome: insights from single-cell analysis

Luo, Zeng-tao, He, Zhi-yi, Wang, Meng-chan, Wu, Si-yao, Hong, Li-mei, Huang, Xue-mei, Liang, Xiao-na, Liang, Si-qiao, Chen, Ni, Ning, Yan

2026

Overview

Anti-interferon-γ autoantibodies (AIGAs) immunodeficiency syndrome is an emerging adult-onset immunodeficiency causing opportunistic infections. However, its comprehensive immune landscape remains elusive. This study presents the first single-cell RNA sequencing (scRNA-seq) analysis of AIGAs immunodeficiency syndrome, aiming to delineate its pathogenic mechanisms. We performed scRNA-seq on peripheral blood mononuclear cells (PBMCs) from 8 AIGAs immunodeficiency syndrome patients (4 infective, 4 stable phase) and 3 healthy controls. Findings were validated by flow cytometry in an expanded cohort (15 patients vs. 10 controls). Single-cell RNA sequencing of PBMCs from patients with AIGAs immunodeficiency syndrome identified a comprehensive immune subset profile, including effector memory CD4 T cells, naive CD4 T cells, regulatory T cells, GNLY CD8 Tem, GZMK CD8 Tem, naive CD8 T cells, naive B cells, memory B cells, plasma cells, ISG atypical B cells, monocytes, and NKT cells. ScRNA-seq analysis revealed a significantly higher proportion of Th1 cells (16.62% vs. 6.94% in controls) and ISG B cells (2.95% vs. 0.53%), alongside a lower proportion of plasma cells (9.30% vs. 17.79%) and memory B cells (9.54% vs. 27.35%). Flow cytometry consistently confirmed the increase in Th1 cells (21.84% [14.87-27.57] vs. 11.96% [7.19-15.74]) and decreases in marginal zone B cells (2.87% [1.71-4.45] vs. 8.60% [6.77-15.65]), memory B cells (13.85% [5.72-20.23] vs. 22.96% [16.39-33.83]), and class-switched B cells (6.11% [2.39-9.10] vs. 10.18% [5.35-15.77]). Transcriptome analysis demonstrated upregulated expression of interferon-response and HLA genes (e.g., HLA-DQB1, HLA-DQA1, HLA-DRB1), whereas IRF1 was downregulated across all subsets; functional enrichment analyses further highlighted significant activation in IFN signaling and B cell activation pathways. CellChat and pseudotime analyses indicated that CD4 Tem and CD14 monocytes drive sustained Th1 inflammation and monocyte hyperactivation through enhanced pro-inflammatory and antigen-presenting interactions, with T-cell differentiation skewed toward terminal effectors and B-cell development disrupted by ISG B cell emergence, premature plasma cell formation, and IGLC3-biased class switching, collectively delineating the interferon-mediated immunopathology of AIGAs immunodeficiency syndrome. In summary, this first single-cell atlas maps AIGAs immunodeficiency syndrome as a Th1-skewed, IFN-γ-driven disorder sustained by CD4 Tem-CD14 monocyte crosstalk. It combines T-cell activation, expanded Th1 and ISG B cells, and loss of memory/plasma B cells to drive autoantibody generation. Skewed T- and B-cell trajectories and polygenic up-regulation of interferon/HLA genes provide a clear mechanistic rationale for targeted therapy.

• Publisher: Frontiers Media SA,Frontiers Media S.A
• Subject: Adult / anti-interferon-γ autoantibodies / Autoantibodies / Autoantibodies - immunology / B cell / CD14 antigen / CD4 antigen / CD8 antigen / Cell activation / Cell differentiation / Class switching / Cytokines / DQA1 protein / Drb1 protein / Effector cells / Enzymes / Female / Flow cytometry / Gene regulation / Genes / Genomics / Histocompatibility antigen HLA / Humans / immune characteristics / Immune response / Immune system / Immunodeficiency / Immunologic Deficiency Syndromes - genetics / Immunologic Deficiency Syndromes - immunology / Immunological memory / Immunoregulation / Independent sample / Infections / Interferon / Interferon regulatory factor 1 / Interferon-gamma - immunology / Kinases / Leukocytes (mononuclear) / Lymphocyte Activation / Lymphocytes / Lymphocytes B / Lymphocytes T / Male / Middle Aged / Monocytes / Natural killer cells / Original Research / Pathogenesis / Pathogens / Plasma cells / Single-Cell Analysis / single-cell RNA sequencing / Software / Statistical analysis / T cell / Transcriptomes / Up-Regulation / Young Adult