Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

A variant in human AIOLOS impairs adaptive immunity by interfering with IKAROS


In the present study, we report a human-inherited, impaired, adaptive immunity disorder, which predominantly manifested as a B cell differentiation defect, caused by a heterozygous IKZF3 missense variant, resulting in a glycine-to-arginine replacement within the DNA-binding domain of the encoded AIOLOS protein. Using mice that bear the corresponding variant and recapitulate the B and T cell phenotypes, we show that the mutant AIOLOS homodimers and AIOLOS–IKAROS heterodimers did not bind the canonical AIOLOS–IKAROS DNA sequence. In addition, homodimers and heterodimers containing one mutant AIOLOS bound to genomic regions lacking both canonical motifs. However, the removal of the dimerization capacity from mutant AIOLOS restored B cell development. Hence, the adaptive immunity defect is caused by the AIOLOS variant hijacking IKAROS function. Heterodimeric interference is a new mechanism of autosomal dominance that causes inborn errors of immunity by impairing protein function via the mutation of its heterodimeric partner.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: A heterozygous IKZF3 variant associated with B cell deficiency.
Fig. 2: Impaired binding of AIOLOSG159R to the AIOLOS consensus sequence.
Fig. 3: Altered DNA-binding specificity of the AIOLOSG159R variant.
Fig. 4: Ikzf3G158R knock-in mice recapitulate impaired B cell differentiation in the patient.
Fig. 5: Genes involved in B cell development are dysregulated in Ikzf3+/G158R pre-B cells.
Fig. 6: AIOLOSG159R and AiolosG158R interfere with Ikaros function.
Fig. 7: Restored B cell development and T cell abnormalities by removal of the dimerization domain of the AiolosG158R mutant.

Data availability

Genome-wide reads were deposited at the Gene Expression Omnibus. They are accessible for analysis at accession no. GSE167487. Source data are provided with this paper.


  1. 1.

    Tangye, S. G. et al. Human inborn errors of immunity: 2019 update on the classification from the International Union of Immunological Societies Expert Committee. J. Clin. Immunol. 40, 24–64 (2020).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Angulo, I. et al. Phosphoinositide 3-kinase δ gene mutation predisposes to respiratory infection and airway damage. Science 342, 866–871 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. 3.

    Lucas, C. L. et al. Dominant-activating germline mutations in the gene encoding the PI3K catalytic subunit p110δ result in T cell senescence and human immunodeficiency. Nat. Immunol. 15, 88–97 (2014).

    CAS  PubMed  Google Scholar 

  4. 4.

    Liu, L. et al. Gain-of-function human STAT1 mutations impair IL-17 immunity and underlie chronic mucocutaneous candidiasis. J. Exp. Med. 208, 1635–1648 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5.

    van de Veerdonk, F. L. et al. STAT1 mutations in autosomal dominant chronic mucocutaneous candidiasis. N. Engl. J. Med. 365, 54–61 (2011).

    CAS  PubMed  Google Scholar 

  6. 6.

    Boutboul, D. et al. Dominant-negative IKZF1 mutations cause a T, B, and myeloid cell combined immunodeficiency. J. Clin. Invest. 128, 3071–3087 (2018).

    PubMed  PubMed Central  Google Scholar 

  7. 7.

    Minegishi, Y. et al. Dominant-negative mutations in the DNA-binding domain of STAT3 cause hyper-IgE syndrome. Nature 448, 1058–1062 (2007).

    CAS  PubMed  Google Scholar 

  8. 8.

    Punwani, D. et al. Multisystem anomalies in severe combined immunodeficiency with mutant BCL11B. N. Engl. J. Med. 375, 2165–2176 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Bogaert, D. J. et al. A novel IKAROS haploinsufficiency kindred with unexpectedly late and variable B-cell maturation defects. J. Allergy Clin. Immun. 141, 432–435.e7 (2018).

    CAS  PubMed  Google Scholar 

  10. 10.

    Georgopoulos, K. et al. The Ikaros gene is required for the development of all lymphoid lineages. Cell 79, 143–156 (1994).

    CAS  PubMed  Google Scholar 

  11. 11.

    Wang, J.-H. et al. Selective defects in the development of the fetal and adult lymphoid system in mice with an Ikaros null mutation. Immunity 5, 537–549 (1996).

    CAS  PubMed  Google Scholar 

  12. 12.

    Schwickert, T. A. et al. Stage-specific control of early B cell development by the transcription factor Ikaros. Nat. Immunol. 15, 283–293 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Kuehn, H. S. et al. Loss of B cells in patients with heterozygous mutations in IKAROS. N. Engl. J. Med. 374, 1032–1043 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Hoshino, A. et al. Abnormal hematopoiesis and autoimmunity in human subjects with germline IKZF1 mutations. J. Allergy Clin. Immun. 140, 223–231 (2017).

    CAS  PubMed  Google Scholar 

  15. 15.

    Yoshida, N. et al. Germline IKAROS mutation associated with primary immunodeficiency that progressed to T-cell acute lymphoblastic leukemia. Leukemia 31, 1221–1223 (2017).

    CAS  PubMed  Google Scholar 

  16. 16.

    Nieuwenhove, E. V. et al. A kindred with mutant IKAROS and autoimmunity. J. Allergy Clin. Immun. 142, 699–702.e12 (2018).

    PubMed  Google Scholar 

  17. 17.

    Eskandarian, Z. et al. Assessing the functional relevance of variants in the IKAROS family zinc finger protein 1 (IKZF1) in a cohort of patients with primary immunodeficiency. Front Immunol. 10, 568 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Kuehn, H. S. et al. Germline IKAROS dimerization haploinsufficiency causes hematologic cytopenias and malignancies. Blood 137, 349–363 (2021).

    CAS  PubMed  Google Scholar 

  19. 19.

    Mullighan, C. G. et al. Genome-wide analysis of genetic alterations in acute lymphoblastic leukaemia. Nature 446, 758–764 (2007).

    CAS  Google Scholar 

  20. 20.

    Mullighan, C. G. et al. Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. N. Engl. J. Med. 360, 470–480 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21.

    Churchman, M. L. et al. Germline genetic IKZF1 variation and predisposition to childhood acute lymphoblastic leukemia. Cancer Cell 33, 937–948.e8 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Wang, J.-H. et al. Aiolos regulates B cell activation and maturation to effector state. Immunity 9, 543–553 (1998).

    CAS  PubMed  Google Scholar 

  23. 23.

    Morgan, B. et al. Aiolos, a lymphoid restricted transcription factor that interacts with Ikaros to regulate lymphocyte differentiation. EMBO J. 16, 2004–2013 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Patel, A., Zhang, X., Blumenthal, R. M. & Cheng, X. Structural basis of human PR/SET domain 9 (PRDM9) allele C-specific recognition of its cognate DNA sequence. J. Biol. Chem. 292, 15994–16002 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Cobb, B. S. et al. Targeting of Ikaros to pericentromeric heterochromatin by direct DNA binding. Gene Dev. 14, 2146–2160 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Ott de Bruin, L. M., Volpi, S. & Musunuru, K. Novel genome-editing tools to model and correct primary immunodeficiencies. Front. Immunol. 6, 250 (2015).

    PubMed  PubMed Central  Google Scholar 

  27. 27.

    Hu, Y. et al. Superenhancer reprogramming drives a B-cell–epithelial transition and high-risk leukemia. Gene Dev. 30, 1971–1990 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Prosser, J., Frommer, M., Paul, C. & Vincent, P. C. Sequence relationships of three human satellite DNAs. J. Mol. Biol. 187, 145–155 (1986).

    CAS  PubMed  Google Scholar 

  29. 29.

    Zhang, J. et al. Harnessing of the nucleosome-remodeling-deacetylase complex controls lymphocyte development and prevents leukemogenesis. Nat. Immunol. 13, 86–94 (2012).

    CAS  Google Scholar 

  30. 30.

    Papathanasiou, P. et al. Widespread failure of hematolymphoid differentiation caused by a recessive niche-filling allele of the Ikaros transcription factor. Immunity 19, 131–144 (2003).

    CAS  PubMed  Google Scholar 

  31. 31.

    Berman, H. M. et al. The Protein Data Bank. Nucleic Acids Res. 28, 235–242 (2000).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990).

    CAS  Google Scholar 

  33. 33.

    Šali, A. & Blundell, T. L. Comparative protein modelling by satisfaction of spatial restraints. J. Mol. Biol. 234, 779–815 (1993).

    PubMed  Google Scholar 

  34. 34.

    Shen, M. & Sali, A. Statistical potential for assessment and prediction of protein structures. Protein Sci. 15, 2507–2524 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Case, D. A. et al. AMBER 14 (University of California, San Francisco, 2014);

  36. 36.

    Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L. Comparison of simple potential functions for simulating liquid water. J. Chem. Phys. 79, 926–935 (1983).

    CAS  Google Scholar 

  37. 37.

    Maier, J. A. et al. ff14SB: improving the accuracy of protein side chain and backbone parameters from ff99SB. J. Chem. Theory Comput. 11, 3696–3713 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Peters, M. B. et al. Structural survey of zinc-containing proteins and development of the Zinc AMBER Force Field (ZAFF). J. Chem. Theory Comput. 6, 2935–2947 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Berendsen, H. J. C., Postma, J. P. M., van Gunsteren, W. F., DiNola, A. & Haak, J. R. Molecular dynamics with coupling to an external bath. J. Chem. Phys. 81, 3684–3690 (1984).

    CAS  Google Scholar 

  40. 40.

    Ryckaert, J.-P., Ciccotti, G. & Berendsen, H. J. C. Numerical integration of the cartesian equations of motion of a system with constraints: molecular dynamics of n-alkanes. J. Comput. Phys. 23, 327–341 (1977).

    CAS  Google Scholar 

  41. 41.

    Essmann, U. et al. A smooth particle mesh Ewald method. J. Chem. Phys. 103, 8577–8593 (1995).

    CAS  Google Scholar 

  42. 42.

    Humphrey, W., Dalke, A. & Schulten, K. VMD: visual molecular dynamics. J. Mol. Graph. 14, 33–38 (1996).

    CAS  PubMed  Google Scholar 

  43. 43.

    Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  PubMed  Google Scholar 

  44. 44.

    Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45.

    Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. Omics J. Integr. Biol. 16, 284–287 (2012).

    CAS  Google Scholar 

  46. 46.

    Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008).

    PubMed  PubMed Central  Google Scholar 

  48. 48.

    Stark, R. & Brown, G. DiffBind: Differential Binding Analysis of ChIP-Seq Peak Data v3.2.2 (Bioconductor, 2011);

  49. 49.

    Heinz, S. et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank members of I.T.’s laboratory for technical advice and discussions, Y. Iizuka for microinjection of RNA for genome editing in mice, H. Asahara and T. Chiba for the advice on genome editing in human cell lines, and P. Burrows for critical reading of the manuscript. We apologize to colleagues whose work we could not cite because of space restrictions. This work was supported by RIKEN IMS PID project (to I.T.), MEXT KAKENHI (no. JP18H02778 to T.M.) and Health Labour Sciences Research grant (no. 20FC1053 to T.M.), JSPS Grant-in-Aid for Young Scientists (no. JP20K16884 to M.Y.), JSPS Fund for the Promotion of Joint International Research (no. JP18KK0228 to S.O.), AMED The Practical Research Project for Rare/Intractable Diseases (to S.O.), JSPS KAKENHI (no. JP18H02395 to K.Y.J.Z.), JSPS research fellowship (to A.K.P.), and the National Institute of Allergy and Infectious Diseases (P01AI061093 to B.B. and J.-L.C.).

Author information




M.Y. and K.O. analyzed mice and performed experiments using cell lines. H.S.K. performed the reporter gene assay, EMSA and co-localization assays. S.O. performed EMSA. N.M. and O.O. performed genetic analysis. M. Takeuchi, N.S. and Y.I. provided clinical information and patient samples. A.K.P and K.Y.J.Z. performed the homology modeling and molecular dynamics simulations. K.I., M. Takagi, H.K. and S.D.R. provided intellectual guidance. B.B. and J.L.C. provided cohort information. T.M. and I.T. supervised the project. M.Y., I.T. and T.M. wrote the manuscript.

Corresponding authors

Correspondence to Ichiro Taniuchi or Tomohiro Morio.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Immunology thanks the anonymous reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. L. A. Dempsey was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Lymphocyte and dendritic cell subset analyses of the P1.

a, Flow cytometric analysis of peripheral blood lymphocytes and monocytes in the P1 and a control subject. Gating strategies are shown above each plot. Numbers within the plots represent percentage of the defined populations. Briefly, the ratio of CD4/CD8 cells was inverted and T cells had an activated phenotype (HLA−DR+CD38+). CD4+ T cells were skewed to memory phenotype (CD45RACD45RO+) and TH1* cells (CD4+CD45RO+CD161+CXCR3+CCR6+) were increased. iNKT cells were decreased. NK cells, mDCs, and eosinophils were also decreased. b, CD3 and TCR expression levels in T cells of the P1 (blue) and control (red). Numbers represent MFI of P1’s T cell subsets relative to a control subject. Surface expression levels of TCRαβ, CD3, and CD8 were decreased in T cells of the P1, despite comparable expression of CD4. iNKT: invariant NK T cell, mDC: myeloid dendritic cell, pDC: plasmacytoid dendritic cell, Eo: Eosinophil, Baso: Basophil, MFI: Mean fluorescence intensity.

Extended Data Fig. 2 Whole exome analysis of the patients.

a, Patients and healthy family members who were analyzed by whole-exome sequencing are indicated with an asterisk. b, Filtering strategy for whole-exome analysis. Patient-specific variants were selected by familial segregation. Variants resulting from sequencing errors were filtered out by ignoring the variants with high in-house frequency. Variants shared by the P1 and the P2 were selected and further narrowed down to rare variants by using cut-off as dbSNP minor allele frequency (MAF) of 0.0001. Candidate variants other than IKZF3 were BCL9 (NM_004326.2:c.3934delG, NP_004317.2:p.Gly1312Alafs, and NM_004326.2:c.3936_3937insTTT, NP_004317.2:p.Gly1311_His1312insGly), BMS1 (NM_014753.3:c.3557C>T, NP_055568.3:p.Ala1186Val), CCDC102A (NM_033212.3:c.1135C>T, NP_149989.2:p.Arg379Trp), DENND4B (NM_014856.2:c.307G>A, NP_055671.2:p.Val103Ile), KIAA1462 (NM_020848.2:c.2215G>A, NP_065899.1:p.Gly739Ser), KRTAP2-2 (NM_033032.2:c.333_334delGA, NP_149021.2:p.Thr112Profs), NCSTN (NM_015331.2:c.464A>G, NP_056146.1:p.Glu155Gly), TCHH (NM_007113.2:c.1072_1074delGAG, NP_009044.2:p.Glu358del), and TMEM129 (NM_138385.3:c.40G>C, NP_612394.1:p.Val14Leu). c, Alignment of amino acid sequences of the second zinc finger domain of AIOLOS orthologues from several species. Gray-shaded letters indicate identical amino acid in relation to human AIOLOS. Glycine residue at 159 position in human AILOS is well conserved beyond species. d, Expression pattern of IKZF family genes during human B cell development. CLP/Pre-pro-B cell (CD34+CD10+CD19), pro-B cells (CD34+CD10+CD19+), large pre-B cells (CD34CD10+CD19+CD79B+IgM) and small pre-B cells (CD34CD10+CD19+CD79BIgM) were isolated by FACS sorting from the bone marrow aspirate of a healthy donor. RNA was extracted and subjected to RNA-seq analysis. FPKM of IKZF genes in the indicated populations are shown.

Source data

Extended Data Fig. 3 Wild-type AIOLOS and AIOLOSG159R ChIP-seq in NALM-6 human pre-B cell line.

a, Genomic sequence of the IKZF3 knock-out (KO) NALM-6 cell line. Exon 2 of IKZF3 was targeted by CRISPR-Cas9. Each allele of IKZF3 was cloned and sequenced. The knock-out clone had an indel in exon 2, resulting in a frameshift and premature termination of the protein. Grey shading indicates inserted nucleotides. Amino acid in red were changed by the frameshift. b, Western blotting of AIOLOS in wild-type (WT) and IKZF3-KO NALM-6 cell lines. Representative of three independent experiments. c, Triplicates of ChIP-seq tracks showing five representative loci with unique and common binding by AIOLOSWT and AIOLOSG159R in the IKZF3-KO NALM-6 cell line reconstituted with FLAG-tagged AIOLOSWT or AIOLOSG159R. Numbers represent the signal values of binding enrichment of the detected peaks. Structure of the genes are shown at the bottom. Locations of binding motifs (GGGAA and GGAGC) within the ChIP-seq track regions are indicated at the bottom. d, The top significant DNA binding motifs with p-values for AIOLOSWT and AIOLOSG159R abstracted from the peaks with all statistically different bindings and non-differential bindings between quadruplicate ChIP-seq samples. The AIOLOS consensus binding sequence (GGGAA) is delineated by the red square and TGGAA motif is delineated by the black square, whereas binding motifs specific to the AIOLOSG159R peaks (GGAGC, GGAGG, and GCAGG) are delineated by the blue square. GGGAA and TGGAA motifs were consistently associated with AIOLOSWT, whereas GGAGC, GGAGG, GCAGG, and CCCAGA motifs were repeatedly shown association with AIOLOSG159R. Peaks with non-differential binding between AIOLOSWT and AIOLOSG159R were enriched with relatively low accumulation of AIOLOS canonical binding motifs. e, EMSA showing binding of AIOLOSWT and AIOLOSG159R binding to AIOLOS consensus sequence (indicated in red font, IK-BS4 probe) or AIOLOSG159R specific motif (GGAGC, indicated in blue font) containing probe designed from genome regions with high AIOLOSG159R peaks. Direct binding of AIOLOSG159R to GGAGC motif was observed. Representative of three independent experiments.

Source data

Extended Data Fig. 4 Expression of Aiolos in thymocyte of Ikzf3+/+, Ikzf3+/G158R and Ikzf3 G158R/G158R mice, and supplementary flowcytometric analysis.

a, Total cell lysates from the thymus of Ikzf3+/+ and Ikzf3−/− mice were subjected for immunoblot using anti-Aiolos antibody. Representative of three independent experiments. b, Expression levels of IKZF family genes determined by RNA-seq in pre-B cells of mice with the indicated genotype (n = 3 for each genotypes). Bar graphs show mean with SD. * p <0.0094, ** p <0.0054, determined by one-way ANOVA. c,d, Total cell lysates from the thymus of Ikzf3+/+, Ikzf3+/G158R and Ikzf3G158R/G158R mice were subjected for immunoblot using anti-Aiolos antibody. Expression levels of Aiolos were normalized by Gapdh protein. Numbers indicate relative intensity of Aiolos of indicated genotypes to Ikzf3+/+ sample(c). Graphs showing summary of relative quantity (RQ) of three independent experiments d. The expression levels of Aiolos were comparable between the genotypes (n = 3 for each genotypes). Bar graphs show mean with SD. e, Relative expression of wild-type and mutant Ikzf3 alleles in pre-B cells calculated from RNA-seq data (n = 3 for each genotypes). Bar graphs show mean with SD. f, Flowcytometric analysis of CD19 and B220 staining in bone marrow and splenic cells in Ikzf3+/+, Ikzf3+/G158R and Ikzf3G158R/G158R mice. g, Flowcytometric analysis of follicular B cell and marginal zone B cells in indicated cell subsets. IgM and IgD expression in B220+CD19+ cells were also shown.

Source data

Extended Data Fig. 5 T cell phenotypes in Ikzf3+/G158R and Ikzf3G158R/G158R mice.

a, Flow cytometric analysis of thymocyte and lymph node T cells in Ikzf3+/+, Ikzf3+/G158R, and Ikzf3G158R/G158R mice. Expression of indicated surface markers in total thymocytes, lymphocyte gated lymph node cells and CD3ε+ lymph node cells are shown. Numbers indicate the percentage of cells in each gate or each quadrant. Mature T cells in lymph node of Ikzf3G158R/G158R mice showed decrease of CD8+ T cells and increase of CD4CD8 T cells. Similar but milder phenotypes were observed in Ikzf3+/G158R mice. CD4+ T cells in lymph node of Ikzf3G158R/G158R mice showed skewing to CD44+ memory phenotype, which also recapitulated the patient’s phenotype. b, TCRβ, CD3ε, CD4, and CD8α expression levels in thymocyte and lymph node T cell subsets of Ikzf3+/+, Ikzf3+/G158R, and Ikzf3G158R/G158R mice. Numbers represent relative MFI against Ikzf3+/+ mice. Similar to the human patients, Ikzf3+/G158R and Ikzf3G158R/G158R mice showed decreased expression of TCRβ and CD3ε expressions in thymocytes and lymph node T cells, respectively. c, Emergence of CD4loCD8+ cells in thymus of Ikzf3G158R/G158R mice. CD4 expression in CD8α+ thymocytes (delineated by red line) is shown in the histogram. Numbers represent relative MFI against Ikzf3+/+ mice.

Extended Data Fig. 6 Accumulation of Wild-type AIOLOS in pericentromeres.

FLAG-ChIP-seq was performed in IKZF3-KO NALM-6 cell line reconstituted with FLAG-tagged AIOLOSWT or AIOLOSG159R. Quadruplicates of ChIP-seq tracks showing the pericentromeric regions with TGGAA repeats, and a non-pericentromeric region containing TGGAA repeats. The locations of TGGAA motif are indicated at the bottom. As indicated by the binding motif analyses, AIOLOSWT predominantly bound to TGGAA-rich regions.

Extended Data Fig. 7 C-terminal sequence of Ikzf3G158R:Δc-ZF (Ikzf3G158R:D461fs) allele and T cell phenotypes of Ikzf3+/G158R:Δc-ZF mouse.

a, Direct sequencing of Ikzf3 mRNA by Ikzf3G158R allele-specific PCR amplification of cDNA amplified using an Ikzf3G158R mutant allele-specific 5′ primer and universal C-terminal 3′ primer. Complementary DNA was synthesized from total RNA extracted from peripheral blood of F0 founder mice generated by CRISPR-Cas9 genome editing. The C-terminal sequence of the Ikzf3G158R mutant allele confirmed the single nucleotide insertion (indicated as a purple letter) which results in the frame-shift and disruption of ZF5-6 structure. b, Expressions of CD4 and CD8 in lymph node T cells of Ikzf3+/G158R, Ikzf3+/+, and Ikzf3+/G158R:Δc-ZF mice. Numbers represent relative MFI against Ikzf3+/+ mice.

Supplementary information

Supplementary Information

Supplementary Clinical Information and Tables 1–5.

Reporting Summary

Peer Review Information

Supplementary Data 1

Flow cytometry gating strategy.

Source data

Source Data Fig. 2

Unprocessed immunoblot images for Fig. 2d,e.

Source Data Fig. 3

Unprocessed immunoblot images for Fig. 3b.

Source Data Fig. 4

Statistical source data for Fig. 4c–f.

Source Data Fig. 5

Graph source data for Fig. 5d.

Source Data Fig. 6

Unprocessed immunoblot images for Fig. 6a.

Source Data Fig. 6

Statistical source data for Fig. 6g.

Source Data Fig. 7

Unprocessed immunoblot images for Fig. 7b.

Source Data Fig. 7

Statistical source data for Fig. 7d,e.

Source Data Extended Data Fig. 2

Graph source data for Extended Data Fig 2d.

Source Data Extended Data Fig. 3

Unprocessed immunoblot images for Extended Data Fig 3b,e.

Source Data Extended Data Fig. 4

Unprocessed immunoblot images for Extended Data Fig 4a,c.

Source Data Extended Data Fig. 4

Statistical source data for Extended Data Fig. 4b,d. Graph source data for Extended Data Fig. 4e.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Yamashita, M., Kuehn, H.S., Okuyama, K. et al. A variant in human AIOLOS impairs adaptive immunity by interfering with IKAROS. Nat Immunol 22, 893–903 (2021).

Download citation


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing