Disease-associated AIOLOS variants lead to immune deficiency/dysregulation by haploinsufficiency and redefine AIOLOS functional domains
Hye Sun Kuehn, … , Svetlana O. Sharapova, Sergio D. Rosenzweig
Hye Sun Kuehn, … , Svetlana O. Sharapova, Sergio D. Rosenzweig
Published November 28, 2023
Citation Information: J Clin Invest. 2024;134(3):e172573. https://doi.org/10.1172/JCI172573.
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Research Article Immunology

Disease-associated AIOLOS variants lead to immune deficiency/dysregulation by haploinsufficiency and redefine AIOLOS functional domains

Abstract

AIOLOS, also known as IKZF3, is a transcription factor that is highly expressed in the lymphoid lineage and is critical for lymphocyte differentiation and development. Here, we report on 9 individuals from 3 unrelated families carrying AIOLOS variants Q402* or E82K, which led to AIOLOS haploinsufficiency through different mechanisms of action. Nonsense mutant Q402* displayed abnormal DNA binding, pericentromeric targeting, posttranscriptional modification, and transcriptome regulation. Structurally, the mutant lacked the AIOLOS zinc finger (ZF) 5–6 dimerization domain, but was still able to homodimerize with WT AIOLOS and negatively regulate DNA binding through ZF1, a previously unrecognized function for this domain. Missense mutant E82K showed overall normal AIOLOS functions; however, by affecting a redefined AIOLOS protein stability domain, it also led to haploinsufficiency. Patients with AIOLOS haploinsufficiency showed hypogammaglobulinemia, recurrent infections, autoimmunity, and allergy, but with incomplete clinical penetrance. Altogether, these data redefine the AIOLOS structure–function relationship and expand the spectrum of AIOLOS-associated diseases.

Authors

Hye Sun Kuehn, Inga S. Sakovich, Julie E. Niemela, Agustin A. Gil Silva, Jennifer L. Stoddard, Ekaterina A. Polyakova, Ana Esteve Sole, Svetlana N. Aleshkevich, Tatjana A. Uglova, Mikhail V. Belevtsev, Vladislav R. Vertelko, Tatsiana V. Shman, Aleksandra N. Kupchinskaya, Jolan E. Walter, Thomas A. Fleisher, Luigi D. Notarangelo, Xiao P. Peng, Ottavia M. Delmonte, Svetlana O. Sharapova, Sergio D. Rosenzweig

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Figure 7

RNA-Seq and ATAC-Seq analyses.

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RNA-Seq and ATAC-Seq analyses. (A) RNA-Seq was performed on T cell blast...
(A) RNA-Seq was performed on T cell blasts (Q402*) or EBV-transformed B cell lines (E82K). Kendall correlation analyses of the count matrices are shown. The scale bar represents the range of the correlation coefficients (r). (B) Heatmap of variance stabilized counts for genes that were determined to be differentially expressed by both the DeSeq2 and CogentDS pipelines. Blue indicates genes with lower expression values and red represents highly expressed genes. (C) GO dotplots for selected biological process (BP) that are immune related and shared between T cell blasts (Q402*) and EBV-transformed B-cells (E82K) based on DEGs. DEGs have an adjusted P value under 0.01 and absolute log2 fold change of greater than 2. The color of the dots indicates the P values for each of the terms and the size of the dots depicts the gene ratio. The gene ratio equals the number of differentially expressed genes against the number of genes associated with a GO term in the genome. The P value scales indicated in the figure correspond to transformed P values across the selected categories. (D) IPA Diseases & Functions analysis from the patients with the indicated AIOLOS mutations. (E and F) Analysis of chromatin accessibility by ATAC-Seq in T cell blasts from healthy controls and the patients with AIOLOS mutations. Principal Component Analysis plot was generated using DeSeq2 (E). Correlation heatmap of all chromatin peaks detected by ATAC-Seq was generated by DiffBind (F).