# A neomorphic mutation in the interferon activation domain of IRF4 causes a dominant primary immunodeficiency

Romane Thouenon, Loïc Chentout, Nidia Moreno-Corona, Lucie Poggi, Emilia Puig Lombardi, Benedicte Hoareau, Yohann Schmitt, Chantal Lagresle-Peyrou, Jacinta Cecilia Bustamante, Isabelle André, et al.

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## To cite this version:

Romane Thouenon, Loïc Chentout, Nidia Moreno-Corona, Lucie Poggi, Emilia Puig Lombardi, et al. A neomorphic mutation in the interferon activation domain of IRF4 causes a dominant primary immunodeficiency: A neomorphic variant in IRF4 alters transcriptional activity. Journal of Experimental Medicine, 2023, 220 (6), 10.1084/jem.20221292. hal-04029076

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Submitted on 14 Mar 2023

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A neomorphic mutation in the interferon activation domain of IRF4 causes a dominant primary immunodeficiency

Romane Thouenon<sup>1,2</sup>, Loïc Chentout<sup>1,2</sup>, Nidia Moreno-Corona<sup>1,2</sup>, Lucie Poggi<sup>1,2</sup>, Emilia Puig Lombardi<sup>3</sup>, Benedicte Hoareau<sup>4</sup>, Yohann Schmitt<sup>5</sup>, Chantal Lagresle-Peyrou<sup>1,2,6</sup>, Jacinta Cecilia Bustamante<sup>7,8,9</sup>, Isabelle Andre<sup>1,2</sup>, Marina Cavazzana<sup>1,6,10,11</sup>, Anne Durandy<sup>2</sup>, Jean-Laurent Casanova<sup>7,8,12,13</sup>, Lionel Galicier<sup>14,15</sup>, Jehane Fadlallah<sup>14,15</sup>, Alain Fischer<sup>11,12,16</sup>, Sven Kracker<sup>1,2</sup>

(1) Université Paris Cité, F-75015, Paris, France

(2) Laboratory of Human Lymphohematopoiesis, Imagine Institute, INSERM UMR 1163, F-75015, Paris, France

(3) Université de Paris, Bioinformatics Core Facility, Imagine Institute, INSERM UMR 1163, F-75015, Paris, France

(4) Sorbonne Université, UMS037, PASS, Plateforme de Cytométrie de la Pitié-Salpêtrière CyPS, Paris, France.

(5) Plateforme de génomique, Institut Imagine-Structure Fédérative de Recherche Necker, INSERM U1163 et INSERM US24/CNRS UMS3633, Université de Paris, France

(6) Biotherapy Clinical Investigation Center, Groupe Hospitalier Universitaire Ouest, Assistance Publique-Hôpitaux de Paris (APHP), INSERM, Paris, France

(7) St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY, USA.

(8) Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, Paris, France.

(9) Paris Hospital, Study Center for Primary Immunodeficiencies, APHP, Paris, France

(10) Departement de Biotherapie Hôpital Universitaire Necker-Enfants malades, groupe Hospitalier Paris centre APHP de Paris, Paris, France

(11) Imagine Institute, INSERM UMR 1163, Paris, FRANCE

(12) Necker Hospital, Pediatric Hematology-Immunology and Rheumatology Unit, APHP Paris, France

(13) Howard Hughes Medical Institute, New York, USA.

(14) Clinical Immunology Department, Hôpital Saint Louis, Université de Paris, Paris, France

(15) National Reference Center for Castleman disease, Hôpital Saint Louis, Université de Paris, Paris, France.

(16) Collège de France, Paris, France.

**Correspondence:**

Sven Kracker

INSERM U1163, Imagine Institute, 24 Boulevard du Montparnasse, F-75015 Paris, France

e-mail: sven.kracker@inserm.fr; tel.: +33-142-754-336

http://orcid.org/0000-0003-4543-8236

**Keywords:**

Interferon regulatory factor 4, hypogammaglobulinemia, Plasma cell differentiation, Terminal T cell differentiation, interferon-stimulated response elements
**Non-standard abbreviations:**

IRF4: Interferon regulatory factor 4

PIDs: Primary immunodeficiencies

PADs: Primary antibody deficiencies

DBD: DNA-binding domain

IAD: Interferon activation domain

ISRE: Interferon-stimulated response element

ETS: Erythroblast-transformation-specific

EICEs: ETS interferon composite elements

AICE: AP-1-IRF composite elements

Tw: Tropheryma whipplei

ExAC: Exome Aggregation Consortium

gnomAD: Genome Aggregation Database

ESP: Exome Sequencing Project

SIFT: Sorting Intolerant From Tolerant Prediction

EBV: Epstein-Barr virus

B-EBV: EBV-immortalized lymphoblastoid B cells

CyTOF: Time-of-flight mass cytometry

PCA: Principal component analysis

DE: differentially expressed

ChIP-Seq: Chromatin immunoprecipitation sequencing

RIME: Rapid immunoprecipitation mass spectrometry of endogenous proteins

PC4: Positive coactivator 4

**GRAPHICAL ABSTRACT**

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**ABSTRACT**

Here, we report on a heterozygous Interferon regulatory factor 4 (IRF4) missense variant identified in three patients from a multigeneration family with hypogammaglobulinemia. Patients' low blood plasmablast/plasma cell and naïve CD4 and CD8 T cell counts contrasted with high terminal effector CD4 and CD8 T cell counts. Expression of the mutant IRF4 protein in control lymphoblastoid B cell lines reduced the expression of BLIMP-1 and XBP1 (key transcription factors in plasma cell differentiation). In B cell lines, the mutant IRF4 protein as wildtype was found to bind to known IRF4 binding motifs. The mutant IRF4 failed to efficiently regulate the transcriptional activity on interferon-stimulated response elements (ISREs). Rapid immunoprecipitation mass spectrometry of endogenous proteins indicated that the mutant and wildtype IRF4 proteins differed with regard to their respective sets of binding partners. Our findings highlight a novel mechanism for an autosomal-dominant primary immunodeficiency through altered protein binding by mutant IRF4 at ISRE, leading to defective plasma cell differentiation.
INTRODUCTION

Characterization of the pathophysiological
mechanisms that underlie newly recognized
primary immunodeficiencies (PIDs) provides
researchers with a unique opportunity to study
the molecular details of the human immune
system. The most common PIDs in humans
are primary antibody deficiencies (PADs),
which can result from intrinsic or extrinsic
defects in B cell development, antibody
maturation, plasma cell differentiation, and/or
T cell development (Durandy et al., 2013).
Although the genetic diagnosis of PAD
patients is improving rapidly (due notably to
greater availability of next-generation
sequencing), most affected individuals still do
not obtain a firm molecular diagnosis
(Bousfiha et al., 2020; Fusaro et al., 2021;
Tangye et al., 2020).

Interferon regulatory factor 4 (IRF4, also known as NF-EM5, Pip, LSIRF, ICSAT, and MUM1) belongs to a family of nine human transcription factors (De Silva et al., 2012). Unlike the other eight family members, IRF4 is not regulated by interferons (Nam and Lim, 2016). IRF4 expression is activated by antigen receptor signaling, Toll-like receptors, and CD40 (De Silva et al., 2012; Negishi et al., 2005). Initial studies of murine models demonstrated the IRF4's essential role in IgM, IgG and IgA secretion, antibody responses, and the generation of germinal center B cells (Mittrücker et al., 1997). Later studies revealed that IRF4's function is required for Ig class-switch recombination and plasma cell differentiation (Klein et al., 2006; Sciammas et al., 2006). The differentiation of activated B lymphocytes into plasma cells depends on IRF4-induced expression of the *PRDM1* gene (coding for BLIMP-1) (Klein et al., 2006; Sciammas et al., 2006). In the “kinetic control” model, signaling-induced changes in the IRF4 protein level control the fate of activated B lymphocytes (Ochiai et al., 2013).

According to this model, the IRF4 gene locus
"senses" the strength of antigen receptor
signaling and the IRF4 protein "writes" the B
cells' trajectories. It has been shown that low
levels of IRF4 expression favor the
differentiation of BCL6-expressing germinal
center B cells, whereas high levels favor the
expression of BLIMP-1-expressing plasma
cells (Ochiai et al., 2013). Along with this
important role in B-cell-mediated immunity,
IRF4's function is also involved in the
differentiation and/or function of several T
cell populations, including CD4+ T helper 2
(Th2), Th9, Th17, and T follicular helper (Tfh)
cells, effector regulatory T cells, and CD8+
cytotoxic effector and memory T cells (Huber
and Lohoff, 2014). As with the model
suggested for B lymphocyte fate, it has been
suggested that IRF4 senses the T-cell receptor
(TCR) signaling strength (Krishnamoorthy et
al., 2017) and thus acts as a rheostat by
translating TCR avidity into the appropriate
transcriptional programs (Man et al., 2013).
IRF4's ability to control various
transcriptional programs arises from its
interactions with several transcriptional
partners (Remesh et al., 2015). The IRF4
protein (like all members of the IRF family)
contains two conserved, functional domains: a
N-terminal helix-turn-helix DNA-binding
domain (DBD) containing conserved
tryptophan residues, and a C-terminal
interferon activation domain (IAD, also
known as the IRF-association domain) known
to be critical in mediating protein-protein
interactions (Remesh et al., 2015; Sundararaj
et al., 2021). In contrast to the other IRF
family members, the IRF4 protein contains a
C-terminal flexible autoinhibitory region that
binds directly to the DBD and modulates the
interaction with target DNA (Remesh et al.,
2015). Various DNA binding motifs have
been described for IRF4, depending on the
interaction partner and the cellular context.
High IRF4 concentration within cells enables IRF4's binding to the canonical interferon-stimulated response element (ISRE) allowing to establish IRF4 homodimer DNA complexes (Sundararaj et al., 2021). These stable ternary complexes are known to have an important role in plasma cell differentiation and BLIMP-1 expression (Sciammas et al., 2006). As a heterodimer with the PU.1, SPIB or BATF transcription factors, IRF4 binds to erythroblast-transformation-specific (ETS) interferon composite elements (EICEs) or AP-1-IRF composite elements (AICE 1 or 2), respectively, and thus mediates transcriptional activity (Brass et al., 1996; Li et al., 2012; Ochiai et al., 2013). The interactions between ETS transcription factors and IRF4 have been described especially in B lymphocytes and dendritic cells, whereas the functions of the heterodimeric complexes between AP-1 and IRF4 have been described for T and B lymphocytes (Brass et al., 1996; Li et al., 2012; Ochiai et al., 2013). Recently, it was reported that a complex including IKAROS and IRF4 binds to zinc finger-IRF composite elements and represses a subset of genes during plasma cell differentiation (Ochiai et al., 2018).

It has also been reported that IRF4 deficiency (associated with a splice acceptor site mutation (c.1213-2A>G,pV405GfsTer127; NM_001195286) in the *IRF4* gene, in conjunction with uniparental isodisomy) causes a PID (Bravo García-Morato et al., 2018). This patient suffered from a combined immunodeficiency with agammaglobulinemia, eosinophilia, normal lymphocyte counts, low memory T- and B-cell counts, and elevated GM-CSF-induced macrophage polarization (Bravo García-Morato et al., 2018). Age-dependent, incomplete penetrance of Whipple's disease (a clinical manifestation due to *Tropheryma whipplei* (Tw) exposure) has been associated

with IRF4 haploinsufficiency caused by a heterozygous loss-of-function mutation affecting IRF4's DBD (Guérin et al., 2018). A genome-wide association study found that a single nucleotide polymorphism (SNP) (rs12203592, located in intron 4 of the IRF4 gene) was linked to hair and skin color – indicating that IRF4 also has a role outside the immune system (Han et al., 2008). Subsequent functional studies demonstrated that this SNP lies within an enhancer of IRF4 transcription in melanocytes and impairs binding of the TFAP2A transcription factor that (together with the melanocyte master regulator MITF) regulates the enhancer's activity (Praetorius et al., 2013). Another genome-wide association study linked this SNP to not only hair color but also hair greying (Adhikari et al., 2016).

Here, based on our investigation of a multigeneration family, we describe a novel autosomal dominant PAD caused by a pathogenic *IRF4* variant affecting the IAD. All three patients in the family presented with low IgM, IgG and IgA serum levels (diagnosed during childhood), low plasma cell counts, abnormal T cell subsets and early hair greying.

## RESULTS

### Clinical history

We report herein on three patients from two generations of a family affected by a PAD (Figure 1A). Both of the male index patient's (P1) parents and the patient's two brothers and sister were reportedly healthy. Patient P1 started to suffer from diarrhea at 6 months of age. Due to recurrent nasopharyngitis and persistence of diarrhea at the age of 11 months, P1 was diagnosed with panhypogammaglobulinemia (reported as affecting IgM, IgG and IgA, although the levels were not available) and put on a gluten-free diet for 6 years. At the age of 15 months, he was hospitalized for severe diarrhea, and Ig
A neomorphic variant in IRF4 alters transcriptional activity

<figure><img src="image_4.png" /><figcaption>**Figure 1.** A novel hypogammaglobulinemia caused by a heterozygous IRF4 mutation.</figcaption></figure>

(A) The pedigree of the index family, with allele segregation. (B and C) Changes in serum IgM (B) and IgA (C) levels over time for the patients (P1: pink; P2: light red; P3: dark red). The lines indicate the upper and lower reference boundaries. (D) The hair of P1 and P3 (at the ages of 60 and 26, respectively). (E) Schematic representation of the IRF4 protein's functional domains, depicting the positions of loss of DNA binding mutations (R98A-C99A and R98W), a loss of interaction with PU-1 variant (L368P), and the novel F359L variant. (F) Alignment of human IRF4 with orthologs. Protein sequences were extracted from the Ensembl Genome Browser and aligned using Clustal Omega. The black arrow indicates the location of the patient's mutation (F359). (G) Alignment of IRF4's IAD (around the F359L (in red) and L368P (in violet) variants) with IRF family members IRF3-IRF9. (H) Sanger sequencing of RT-PCR products of T cells blast generated from two controls and all the patients. (I) qPCR analysis of IRF4 transcripts from T cells blast generated from controls and patients. Data represent mean of n=3 controls and n=3 patients ± SD. (J-M) IRF4 expression levels in T cell blasts (J and K) or B-EBV cells).
(L and M) were analyzed in Western blots of total cell lysates of from control (C) and patient (P GAPDH was used as loading control. (K) Data of quantification represent mean of n=3 controls and n=3 patients ± SD. (L) Representative images and (M) data represent mean ± SD of quantifications of n=3 repetition with n=2 patient and n=2 control derived B-EBV cells.

replacement therapy was initiated. He also suffered an episode of pneumonitis at the age of 28 months. His tonsils and adenoids were removed at 7.5 years of age. From the age of 8 until his last evaluation in adulthood (at the age of 60), P1 had normal IgA levels and low IgM levels. P1 experienced a meningococcal infection (at the age of 15y), a *Giardia lamblia* infection, a rectal cytomegalovirus infection, two separate disseminated varicella zoster virus infections, onychomycosis, and oral mycosis (all in adulthood). The persistence of diarrhea (it is noteworthy that the patient was negative for Tw infection in a PCR test), led to diagnose inflammatory bowel disease and to initiate an efficient treatment with an anti-tumor necrosis factor agent when the patient was 48 years of age. At the age of 56, P1 presented with hepatosplenomegaly. A liver biopsy indicated (predominant CD8) T cell infiltration. Nonhomogeneous hair pigmentation changes were reported from 12 years of age onwards and evolved into a grey color two years later; at 25 years of age, P1 had a completely grey head of hair. Skin lesions with depigmentation spots (diameter: 3 cm) had been also noted (Figure S1A).

P1's daughter (P2) and son (P3) presented with recurrent ENT infections from early childhood onwards. P2 and P3 were screened for PIDs at the age of 4 and 2 years, respectively; panhypogammaglobulinemia was observed (Figure 1B-C and Table 1) and Ig replacement therapy was initiated in both cases. The IgA levels (and IgM levels, for P3) normalized over time. P2's clinical manifestations also included conjunctivitis, severe varicella, recurrent herpes virus infections, and a systemic *Bartonella henselae*

infection at 9 years of age. P3 had his tonsils and adenoids removed at 4 years of age, and a molluscum contagiosum virus infection was reported during childhood. In adulthood, P3 experienced onychomycosis and cutaneous infection due to *Epidermophyton floccosum*, and diarrhea. P2 and P3 both presented with early hair greying as their father and P3 with the same skin depigmentation lesions (Figure 1D and Figure S1A).

*Identification of a mutation in the IRF4 gene*

Whole-exome sequencing of DNA samples from P1, his parents, and P2 identified a germline heterozygous missense *IRF4* gene variant (Chr6: 401753; hg19 build 137; NM_002460.3, exon 7, c.1075 T>C, p.F359L) located in the protein's IAD (Figure 1E and Figure S1B). The mutation was *de novo* for P1 and inherited for P2 and P3. IRF4's IAD binds cofactors, such as PU.1 (Brass et al., 1999; Remesh et al., 2015). The variant (confirmed by Sanger sequencing) had an combined annotation-dependent depletion score of 23; this value is well above the mutation significance cut-off reported for the *IRF4* gene (Guérin et al., 2018). An earlier analysis of the population genetics indicated that the *IRF4* gene has evolved with purifying selection (Guérin et al., 2018). The F359L missense variant's potential association with disease was supported by its absence in our in-house database and in several open-access human genetic variation databases, including the Exome Aggregation Consortium (ExAC), the Exome Sequencing Project (ESP), and the Genome Aggregation Database (gnomAD). However, the PolyPhen and Sorting Intolerant From Tolerant Prediction (SIFT) tools
predicted that the IRF4 F359L missense
variant was benign (with scores of 0.174 and
1, respectively) (Figure S1B). Protein
alignments of IRF4 orthologs indicated the
evolutionary conservation of the IRF4 IAD as
a whole (including the F359 position), with the
exception of the zebrafish *Danio rerio* protein
ortholog (Figure 1F). Alignment of IADs of
the human IRF family members IRF3-9
showed that F359 is conserved in all the
assessed IADs ((Meraro et al., 1999) and
Figure 1G). IRF4's documented role in
immune cells, the possible
structural/functional importance of amino acid
F359, the close segregation of the mutation
with the disease in the probands' family, and
the absence of other variants segregating with
the disease in genes reported to be associated
to PIDs (Bousfiha et al., 2020; Tangye et al.,
2020), indicated that the IRF4 F359L variant
likely contributed to PAD phenotype.

*F359L preferentially alters IRF4 activity at ISRE and AICE promoters*

Next, we investigated if the IRF4 F359L
variant affects total IRF4 mRNA and protein
level. Sanger sequencing of cDNAs from both
T cell blast and patient-derived Epstein-Barr
virus (EBV)-immortalized lymphoblastoid B
cells (B-EBV cells) suggested that the WT and
mutant alleles are likely expressed at similar
levels (Figure 1H and S1C). We found that
total IRF4 mRNA expression in T cell blasts
was comparable to controls (Figure 1I). A
Western blot analysis of total cell lysates
indicated that IRF4 protein was similarly
abundant in patient and control T cell blasts as
well as in patient-derived and healthy-donor-
derived B-EBV cells (Figure 1J-M). To assess
the F359L variant's effect on IRF4 protein
function, we transiently expressed either
wildtype (WT) IRF4 or IRF4 F359L in
HEK293T cells. The previously described
IRF4 mutants IRF4 R98A-C99A and R98W

(located in the DBD and associated with loss
of DNA binding (Brass et al., 1999; Guérin et
al., 2018)) and IRF4 L368P (an IRF4 mutant
located in the IAD and associated with the loss
of ability to interact with PU.1 (Meraro et al.,
1999)) were included as controls.
Immunoblots of total cell extracts with an anti-
IRF4 antibody showed that the IRF4 F359L
protein was as abundant as IRF4 WT and IRF4
L368P and less abundant than the DNA-
binding-deficient mutants IRF4 R98A-C99A
and R98W (Figure 2A and (Guérin et al.,
2018)). The IRF4 F359L and IRF4 WT
proteins were similar with regard to their
subcellular localization in the cytoplasm and
the nucleus (Figure 2B and Figure S2 A). The
transcriptional function of IRF4 is mediated
by ISREs (to which IRF4 binds as a
homodimer), EICEs (to which IRF4 binds
through an interaction with PU.1) and AICEs
(which require a cooperative interaction
between IRF4 and the BATF-JUNB
heterodimer). To assess IRF4 F359L's ability
to induce transcription by binding to ISRE,
EICE and AICE motifs, we performed the
corresponding luciferase reporter assays.
Unlike the IRF4 WT protein, all the analyzed
mutant IRF4 proteins (F359L, L368P, R98A-
C99A and R98W) failed to activate the
(ISRE)₃ promoter (Figure 2C). It is
noteworthy that the amount of luciferase
activity was lower for IRF4 proteins F359L
and L368P than for IRF4 R98A-C99A and
R98W and even the empty vector control. In
contrast, IRF4 WT and F359L had similar
levels of transcriptional activity via the EICE
promoter in the presence of PU.1, whereas
IRF4 L368P, R98A-C99A and R98W failed to
activate the EICE promoter (Figure 2D).
Robust transcriptional activity of IRF4 WT,
F359L and L368P was also observed with the
AICE promoter in the presence of AP1
(BATF-JUND heterodimer), with greater
activity for IRF4 F359L than for IRF4 WT
<figure><img src="image_5.png" /><figcaption>Figure 2. Functional consequences of the F359L mutation.</figcaption></figure>

(A) Total cell lysates of HEK293T cells transfected with the various IRF4 variants were analyzed in Western blots. GAPDH was used as an internal control. IRF4 protein expression was quantified. Quantification data represents mean ± SD of n=3 independent experiments. (B) Immunofluorescent staining of IRF4 (green) in HELA cells transfected with the various IRF4 mutants. The nucleus was stained with DAPI (blue) and actin filaments in the cytoplasm were stained with phalloidin (Red), quantification data represent mean ± SD of 18 cells per condition analyzed within n=2 independent experiments. A one-way analysis of variance was used to determine whether or not differences were statistically significant (****p<0.0001). (C-E) Luciferase activity of HEK293T cells co-transfected with an (ISRE)₃ (C), EICE (D) or AICE (E) reporter plasmid plus 75 ng of empty plasmid, or with plasmids encoding the IRF4 variants. (F and G) Luciferase activity of HEK293T cells co-transfected with an (ISRE)₃ (F) or AICE (G) reporter plasmid in the presence of 25 ng of IRF4 WT expressing plasmid plus the indicated amount of plasmid encoding the respective IRF4 variants. The quantity of plasmid was
normalized to 75 ng by the addition of empty plasmid. For AICE and EICE assays, plasmids encoding
BATF and JUN or PU-1 cofactors (25 ng) were added respectively under the indicated conditions.
Results are shown as the fold-induction in activity, relative to cells transfected with empty plasmid. (C-
G) The dotted line indicates the mean level of activity for transfected cells with the empty plasmid. Data
represent mean ± SD of n=3 independent experiments for all conditions depicted in (D, F and G) and
for IRF4 mutant L368P, R98A-C99A and R98W in (C), mean ± SD n=4 independent experiments for
IRF4 WT, IRF4 F359L end the empty vector control are shown in (C). (E) Data represent mean ± SD of
n=3 independent experiments for empty vector and all +AP-1 conditions and mean ± SD of n=2
independent experiments for conditions without AP-1.

(Figure 2E). These results indicate that IRF4 F359L selectively fails to activate the ISRE promoter. We therefore looked for haploinsufficiency or a dominant-negative effect of the IRF4 F359L protein on the IRF4 WT protein. We found that in the presence of increasing amounts of the IRF4 F359L protein, the IRF4 WT protein failed to efficiently induce transcription via the ISRE promoter (Figure 2F). In contrast, increasing amount of IRF4 R98W did not interfere with the IRF4 WT protein’s activity with the ISRE promoter (Figure 2F). Taken as a whole, these observations suggest that the IRF4 F359L protein had a dominant-negative effect on the ISRE-motif-containing promoter. It is also noteworthy that the presence of IRF4 F359L protein increased the activity of IRF4 WT protein on the AICE-motif containing promoter (and to a lesser magnitude) the EICE-motif containing promoter (Figure 2G, Figure S2B). These results highlighted a selective, trans-dominant negative effect of IRF4 F359L on the ISRE promoter associated with a gentle gain of function on the AICE promoter.

The immune phenotype of patients indicates impaired plasmablast differentiation and abnormal T cell phenotype

Although all three patients with the IRF4
F359L mutation developed
hypogammaglobulinemia early in life, they all
displayed normal B cell counts and a normal
proportion of switched memory B cells in

adulthood (Table 1). A high proportion of
CD21<sup>low</sup> B cells was observed in P2, P3 and at
the last evaluation of P1. The absolute T cell
counts were low for P1 and P2, whereas high
or normal for P3. Similarly, CD4 T cell counts
were low for both P1 and P2, whereas variable
for P3. In contrast, the CD8 T cell counts were
normal or slightly above-normal. All three
patients had markedly low proportions of
naïve CD4 and naïve CD8 T cells and elevated
proportions of CD4 and CD8 memory T cells
(and especially the memory effector subtype
(CD45RA-CCR7-) and/or terminal effector
subtype (CD45RA+CCR7-)). Natural killer
(NK) cell counts were reported for P1 or
normal for P2 and P3. To assess the
differential expression of *IRF4* transcripts, we
analyzed 3' single-cell RNA sequencing data
of the recently published multimodal atlas of
human of peripheral blood mononuclear cells
(PBMCs) (Hao et al., 2021). Expression of
*IRF4* mRNA was observed in all T cell
subsets, B cells, NK cells, monocytes and
dendritic cells, with high levels of expression
in plasmablasts and plasmacytoid dendritic
cells (pDCs) (Figure S2C). Subsequent time-
of-flight mass cytometry (CyTOF) analysis of
PBMCs from all three patients confirmed the
normal proportions of naïve and memory B
cell populations but revealed a low proportion
of plasmablasts in all three individuals –
suggesting a defect in plasmablast
differentiation (Figure 3A-B). To investigate
this, we isolated B cells from patients and
controls and assessed their ability to
<figure><img src="image_6.png" /><figcaption>Figure 3. In-depth immunophenotyping of the patients’ PBMCs and analysis of B cells differentiation</figcaption></figure>

(A-B) Shows results of an automated Maxpar® Pathsetter™ analysis of CyTOF acquisitions for n=5 control samples (blue) and n=3 patient samples (red). Indicated lymphocyte compartments after gating on intact cells (A) and B cell populations after gating on B cells (B) are represented as bar graphs. (A, B) One-way analyses of variance were used to determine whether or not differences were statistically significant (*** p<0.001 and * p<0.05). (C and D) Flow cytometry analysis of (from left to right) the plasmablast subset (CD20-CD38+) or plasmacell subset (CD138+CD38+) at day (D) 0, day 6 or day 14 after activation of enriched B cells from n=3 controls and n=3 patients. Representative plots for control and patient samples are shown (C) and bar graph representing the mean ± SD of the analyzed n=3 control and n=3 patient samples (D). (E and F) Analysis of secreted IgM (left) or IgG (right) in culture supernatant at D6 (E) or D14 (F), the mean ± SD of the analyzed n=3 control and n=3 patient samples is depicted. (D-F) One-way analyses of variance were used to determine whether or not differences were statistically significant (*** p<0.001 and ** p<0.01).

differentiate into plasmablasts *in vitro* after activation. Plasmablast generation (Figure 3C-D) and immunoglobulin secretion (Figure 3E-F) was lost or greatly reduced with the patients B cells compared to healthy controls, suggesting an intrinsic defect in differentiation
into plasmablasts and plasma cells. For all three patients, the CyTOF analysis also confirmed the very low naïve CD4 and CD8 T cell counts and the markedly elevated counts of CD4 and CD8 terminal effector T cells with

high expression of CD57 and CCR6 and low expression of CD127, CD28, CD27 and CCR4; these results further highlighted their terminal differentiation phenotype (Figure 4A-L). Inhibitory receptors (like TIGIT,

<figure><img src="image_7.png" /><figcaption>Figure 4. In-depth immunophenotyping of CD4 T cells, NK cells, monocytes, and dendritic cells.</figcaption></figure>

(A-C) Shows results of an automated Maxpar® Pathsetter™ analysis of CyTOF acquisitions for control samples (blue) and patient samples (red). Percentages of indicated CD4+ T cell subsets (A and C) and CD8+ T cell subsets (B) are represented after gating on intact cells (A and C) or additional gating on CD4+CD45RA- cells (C). (D) T<sub>FH</sub> cells identified by manual gating as a proportion of CD4+CD45RA-T cells for control samples (blue) and patient samples (red). (A-D) Data represent mean ± SD of n=5
control samples and n=3 patient samples. (E) A uniform manifold approximation and projection (UMAP) obtained by concatenation of n=3 control samples and n=3 patient samples, using the OMIQ Data Science Platform. The main lymphocyte populations (identified by manual gating) are shown. (F) Density plots of lymphocyte distributions in n=3 controls (left) and n=3 patients (right). (G and H) The CD4+ T cell subpopulation represented as a UMAP projection (G) and a density plot (H). (I and J) CD8+ cells subpopulation represented as a UMAP projection (I) and a density plot (J). (K and L) Heatmaps for the expression of surface markers of differentiation on CD4+ T cells (K) and CD8+ T cells (L). Red indicates high expression, yellow indicates intermediate expression, and blue indicates low expression. (M-O) Bar graphs of NK cells (M) monocytes (N) and dendritic cells (O) subpopulations defined by automated Maxpar® Pathsetter™ analysis. (M-O) Data represent mean ± SD of n=5 control samples (blue) and n=3 patient samples (red).

CD279 (PD-1) and CD366 (TIM-3)) were expressed to a similar extent in patient and control samples (Figure 4K-L). Similar proportions of CD4 TH17-like, Th1-like, T regulatory (Treg) and circulating T follicular helper (T~FH~) cells (CXCR5+ cells) were observed in patient and control samples (Figure 4C-D). However, a lower proportion of CD4 TH2-like cells was observed in the patient samples (Figure 4C). The patient's had normal NK, monocyte, dendritic cell and plasmacytoid dendritic cell proportions (Figure 4M-O).

### IRF4 F359L impacts the T cell phenotype

The PBMC immunophenotyping had consistently shown that the patients had low naïve CD4 and CD8 T cell counts and elevated memory CD4 and CD8 T cell counts. We therefore investigated phenotypic changes on naïve CD4 T cells when lentiviral induced expression of IRF4 F359L was triggered. After flow cytometry sorting, CD4 (CD4+CD45RA+CCR7+) T cells were activated with anti-CD3/-CD28-coupled beads. Three days later, the cells were infected with lentivirus constructs for green fluorescent protein (GFP) alone (the empty vector) or for GFP with IRF4 proteins (the F359L, L368P and R98W mutants and the WT). An analysis of GFP expression six days later indicated that all lentiviral constructs had produced a robust infection (Figure 5A). In contrast to cells expressing IRF4 L369P, R98W or only GFP,

cells expressing IRF4 WT and F359L had a lower proportion of CD45RA-negative cells. However, the proportion of CD45RA-negative cells was consistently higher in IRF4-F359L-expressing cells than in IRF4-WT-expressing cells (Figure 5A and B). CCR7 expression was slightly lower in IRF4-F359L-expressing cells than in all the other cells (Figure 5A and C). In contrast, IRF4-F359L-expressing CD4 T cells included a slightly higher proportion of CD25-positive cells, relative to all other conditions (Figure 5A and D). Similar results were obtained for CD8 T cells (Figure S3A-D). Taken as a whole, these results indicated that phenotypic changes were induced by the ectopic expression of IRF4 F359L protein in activated naïve CD4 and CD8 T cells.

To investigate the IRF4 F359L mutation's functional impact on the regulation of transcription in activated naïve CD4 T cells, we performed RNA sequencing (RNA-Seq) to analyze mRNA expression in cells infected with lentivirus constructs 6 days after infection. Elevated IRF4 transcript count per million mapped reads (cpm) were observed in cells infected with lentivirus constructs for IRF4 proteins (the F359L, L368P and R98W mutants and the WT) in comparison to the empty vector (Figure S3E). The RNA-Seq profiles were normalized to the empty vector condition to allow integration of the different experiments performed with different healthy donor samples. A principal component
analysis (PCA) of transcriptomic data for
genes differently expressed in CD4 T cell
subsets (top differentially expressed genes in
the CD4 T cell subset of the multimodal atlas
of human of PBMCs (Hao et al., 2021)

indicated clear differences between cells
expressing the IRF4 F359L mutant protein and
cells expressing either IRF4 L368P and R98W
mutants or the IRF4 WT (Figure 5E).
Increased expression of transcripts for marker

<figure><img src="image_8.png" /><figcaption>Figure 5. Phenotypic changes in naïve CD4 T cells in the presence of IRF4 F359L.</figcaption></figure>

(A) Flow cytometry analysis of (from left to right) the GFP signal, and gated on GFP positive cells, CCR7, CD25 or CD45RA expression CD4+ T cells 72 hours after transduction with lentiviral vectors expressing (from top to bottom) empty vector, IRF4-WT, IRF4-F359L, IRF4-L368P, or IRF4-R98W. Before activation and transduction, naïve CD4 T cells were sorted (based on CCR7+ and CD45RA+ expression) to obtain a homogenous cell population at the start of the culture. The mean fluorescence intensity (MFI) for CCR7 expression is shown. Representative data for n=2 independent experiments (IRF4-L368P and IRF4-R98W) or n=3 independent experiments (Empty, IRF4-WT, IRF4-F359L) are shown. (B-D) Bar graph representing the CD45RA-negative cells (B) the CCR7 MFI (C) and the percentage of CD25-negative (D) (normalized against the empty vector. One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001, *** p<0.001 and ** p<0.01). (E and F) RNA-Seq analysis of transduced CD4+ T sorted from n=3 independent healthy controls. Representation of the sample distribution in a principal component analysis (PCA) (E). (F) Heatmap showing z-score of expressed genes comparing transcripts from IRF4-WT, IRF4-F359L, IRF4-L368P, or IRF4-R98W expressing cells normalized on empty vector
expressing CD4+ T cells. Shown genes are top differentially expressed genes for naïve, T cell memory (TCM), T effector memory (TEM), T regulatory (Treg) and cytotoxic lymphocytes (CTL) CD4 subsets identified in (27). Transcript names are shown along the top axis, and associated CD4 subtypes are indicated. (G) Proportion of IRF4-binding sequence motifs from ChIP-Seq peaks extracted from activated CD4+ T cells analysis (GSM2810038) associated to differentially expressed genes. Binding sequences were determined in a HOMER known *de novo* motif analysis. Motifs are summarized in Supplemental Information Figure 3M.

genes for CD4 T central memory, effector memory, regulatory and CD4 + T cells with cytotoxic activity was observed in the F359L expressing cells compared to cells expressing IRF4 WT, or either IRF4 L368P or R98W mutants. In contrast, lower expression of transcripts for CD4 T naïve marker genes was observed in cells expressing the IRF4 F359L mutant protein compared to cells expressing IRF4 WT (Figure 5F). To identify the DNA binding motifs associated with the deregulation in gene expression we intersected publicly available anti-IRF4 chromatin immunoprecipitation sequencing (ChIP-Seq) data from human CD4 T cells (GSM2810038) and our RNA-Seq data. In a motif analysis using HOMER software (http://homer.ucsd.edu/homer/), we analyzed AICE, EICE and ISRE motifs (Figure S3M) in the ChIP-Seq peaks for genes showing increased and decreased expression in the presence of IRF4 F359L mutant protein compared to cells expressing IRF4 WT. Genes that were more strongly expressed in IRF4 F359L cells had a greater number of AICE-containing motifs (Figure 5G). In contrast, genes that were less expressed in the IRF4 F359L cells had a greater number of ISRE-containing motifs (Figure 5G). Taken as a whole, these results indicate that binding of IRF4 F359L to AICE sites in CD4 T cells was correlated with higher expression of a set of genes, some of which higher expressed in CD4 T central memory, effector memory, regulatory and CD4 + T cells with cytotoxic activity. Since the CyTOF analysis of patients PBMCs indicated a lower proportion of CD4

TH2-like cells we investigated polarization of activated naïve CD4 T cells toward CD4 TH2-like when lentiviral induced expression of IRF4 F359L was triggered. Evaluation of chemokine receptors and cytokine secretion of CD4 T cells expressing IRF4 F359L, IRF4 WT, IRF4 L368P or IRF4 R98W protein cultured under polarizing condition indicated no impact of IRF4 F359L on polarization to TH1-like cells, whereas IRF4 F359L impaired polarization to TH2-like and increased polarization to TH17-like cells when compared to IRF4 WT and either IRF4 L368P or IRF4 R98W mutant condition was observed (Figure S3F-L).

### IRF4 F359L impairs plasmablast/plasma cell generation

It has been reported that EBV infection immortalizes human B lymphocytes; the latter proliferate and have much the same transcriptomic profile as early plasma cells (Mrozek-Gorska et al., 2019). To investigate the IRF4 F359L mutation's functional impact on the regulation of transcription, we used RNA sequencing (RNA-Seq) to analyze mRNA expression in the patient- and donor-derived B-EBV cell lines. For P3, two B-EBV cell lines derived from two independent blood samples were analyzed. A PCA of transcriptomic data for genes marking various steps in the differentiation of mature B cells into early plasma cells (the “immune gene” transcriptome list for the top 138 differentially expressed genes in (Mrozek-Gorska et al., 2019)) highlighted clear differences between patient cells and control cells (Figure 6A).
Since the RNA-Seq profiles of both of P3's independent B-EBV cell lines clustered together within the PCA we used the mean of the two for all subsequent analysis.

<figure><img src="image_9.png" /><figcaption>Figure 6. IRF4 F359L-expressing B-EBV cell lines present a plasmablast/plasma cell differentiation defect.</figcaption></figure>

(A and B) RNA-Seq analysis of patient and control B-EBV cell lines; in total n=6 samples, n=3 controls and n=3 patient samples. Patient samples were obtained from different blood samples (n=1 for P1 and n=2 for P3). Representation of the sample distribution in a principal component analysis (PCA) of 138 genes (the top differentially expressed (DE) genes in (Mrozek-Gorska et al., 2019)) (A). Heatmap of 115 genes expressed differentially when comparing control (n=3) B-EBV cells with patient (n=2) B-EBV cells (B). Mean expression of (n=2) B-EBV cell lines is shown for P3. Transcript names are shown along the top axis, and key genes in plasma cell differentiation are highlighted. Clusters 1 and 2 respectively encompass genes that are downregulated or upregulated in patient cells. (C and D) ChIP-Seq analysis of patient (n=1) and control (n=1) B-EBV cell lines. Venn diagrams represent the distribution of ChIP-Seq peaks present in both the control and patient samples (C). Proportion of peaks in control and patient samples detected for the different IRF4 binding sequences determined in a HOMER known *de novo* motif analysis (D). (E) Correlation between RNA gene expression and IRF4 binding (the respective ChIP-Seq peaks of patient and control samples were compared). Two proportion z statistical analyses were performed (*** p<0.001). (F) Proportion of IRF4-binding sequence motifs (determined in a HOMER known *de novo* motif analysis) from ChIP-Seq peaks of differentially expressed genes. (G) qPCR analysis of selected indicated transcripts. Data represent mean ± SD of n=3
different control B-EBV cells transduced with lentiviral vectors expressing IRF4 WT or various IRF4 mutant proteins. One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001, *** p<0.001 and ** p<0.01).

Hierarchical clustering of the changes in expression in healthy donor B-EBV cells vs. patient cells identified three clusters: cluster I contained 426 genes expressed more strongly in control samples than in patient samples; cluster II contained 422 genes expressed more strongly in patient samples than in control samples (Figure 6B); and cluster III contained 14,920 genes expressed to a similar extent in control and patient samples. Interestingly, genes associated with the terminal differentiation of activated B cells into plasma cells (including *PRDM1*, *XBP1*, and *CD38*) were found in cluster I – suggesting that plasma blast/plasma cell gene expression program was altered in patients B-EBV lines (Figure 6B).

It has been reported that a shift in IRF4 binding from AICE sites to ISRE sites is associated with the IRF4 protein concentration and the pattern of B lymphocyte differentiation (Cocco et al., 2019; Ochiai et al., 2013). To analyze IRF4's binding to regulatory elements, we performed ChIP-Seq on both patient and healthy donor (control) B-EBV cell lines. Overall, the number of IRF4 bound regions was lower in the patient sample than in the control sample (Figure 6C). In a motif analysis using HOMER software, however, the IRF4-bound regions in patient and control cells had similar distributions of AICE, EICE and ISRE motifs (Figure 6D, Figure S3M). It is noteworthy that the number of IRF4-bound sites within proximal promoter was lower in the patient cells than in control cells (Figure S4A). Next, we intersected the anti-IRF4 ChIP-Seq data and the RNA-Seq data. Gene transcripts that were similarly expressed or were upregulated in the patient B-EBV cells (relative to control cells) displayed similar frequencies of ChIP-Seq

peaks (44% and 43%, respectively) (Figure 6E), whereas gene transcripts that were downregulated in patient cells (relative to controls) had a greater frequency of ChIP-Seq peaks (56%); these findings suggest that IRF4 F359L's binding to chromatin impaired gene expression (Figure 6E). To identify the DNA binding motifs associated with the impairment in gene expression, we analyzed the motifs in the ChIP-Seq peaks for genes in cluster I and cluster II. Genes that were more strongly expressed in control cells had a greater number of ISRE-containing motifs (Figure 6F). In contrast, genes that were less expressed in the patient cells had a greater number of ISRE-containing motifs (Figure 6F). Of note, genes that were more strongly expressed in patient cells had a greater number of AICE-containing motifs (Figure 6F). Taken as a whole, these results indicate that binding of IRF4 F359L to ISRE sites was correlated with low expression of a set of genes, some of which were involved in plasma cell differentiation. Furthermore, the results of the motif analysis were in line with the low observed level of transcription on the ISRE-driven promoter and the high observed level of transcription on the AICE-driven promoter induced by IRF4 F359L in HEK293T cells (Figure 2C and F).

We next looked at whether or not expression of IRF4 F359L protein interfered with the transcription of *XBP1* and *PRDM1*. Both genes are involved in plasma cell differentiation and were found in cluster I. Using a lentiviral vector, control B-EBV cells were transduced so that they expressed either GFP alone (the empty vector) or GFP with IRF4 proteins (WT, F359L, L368P or R98W). The transduced cells were sorted (based on GFP expression) and the transcripts of *IRF4*,
XBP1, PRDM1, PAX5, SPIB and PPIB were quantified. Relative to all other conditions, cells transduced with IRF4 F359L expressed lower levels of XBP1 and PRDM1 transcripts. In contrast, cells expressing IRF4 F359L and cells expressing GFP only had similar levels of SPIB, PAX5 and PPIB transcripts (Figure 6G). It is noteworthy that the transcription levels of SPIB and PAX5 were lower in cells expressing IRF4 WT ectopically than in cells expressing GFP only or IRF4 F359L. Taken as a whole, these results demonstrate that IRF4 F359L interferes with the expression of PRDM1 and XBP1 and further support the hypothesis whereby IRF4 F359L expression impairs the differentiation of activated B cells into plasma cells.

### F359L alters IRF4 binding to transcription cofactors and chromatin-associated proteins

To investigate the IRF4 F359L mutation's impact on binding to transcription cofactors and chromatin-associated proteins we performed rapid immunoprecipitation mass spectrometry of endogenous proteins (RIME) (Mohammed et al., 2013) experiments on both patient and healthy donor (control) B-EBV cell lines. The immunoprecipitated protein complexes showed a good level of coverage (55%-60%) of the IRF4 bait protein (Figure S4 C). The RIME analysis reproducibly detected 233 IRF4-associated proteins in the control sample and 161 IRF4-associated proteins in the patient sample. A total of 134 proteins were common to both datasets (Figure 7A, Table S1), including IRF4-interacting proteins annotated in the BioGRID interaction database (e.g. TOP1, FKBP4, and ARM1) or in the Reactome Functional Interaction network (SMARCC1, SMARCC2 and SMARCA4). The gene ontology terms for these components were related to RNA processing (including transport, localization, and splicing) and chromatin remodeling

(Figure 7B). Our analysis also showed that 99 proteins were reproducibly detected in the control sample but not in the patient (Figure 7A, Table S1); the gene ontology terms of these proteins were related to RNA splicing and nuclear pore organization (Figure 7C). Conversely, 27 components were detected in the patient sample but not in the control sample (Figure 7A, Table S1); the related gene ontology terms were positive regulation of type I interferon production, and ATP-dependent chromatin remodeling (Figure 7D). It is noteworthy that ETV6 (one of the proteins detected solely in the patient sample, also known as TEL) has been linked to transcriptional regulation (repression) at ISRE sites (Kuwata et al., 2002). No differences in mRNA expression were observed for 95 (96%) of the 99 IRF4-immunoprecipitated, chromatin-associated proteins detected solely in control samples and 25 (93%) of the 27 proteins detected solely in patient samples. This finding suggests strongly that the distinct detection patterns were related to bona fide modifications in interaction with the IRF4 F359L mutant, rather than differences in protein expression. Thus, the RIME analysis revealed that the chromatin-bound IRF4 F359L protein interacts with a partially different set of cell components.

To assess whether ETV6 was responsible for IRF4 F359L's inability to activate the (ISRE)₃ promoter, we performed luciferase reporter assays in short hairpin RNA (shRNA) mediated ETV6 knockdown HEK293T cells (Figure 7E-F). Efficient knockdown of ETV6 (by two different shRNA) was confirmed on RNA and protein level (Figure S4C-E). The IRF4 F359L protein was active on the (ISRE)₃ promoter in ETV6 knockdown cells albeit to a lower level compared to IRF4 WT protein (Figure 7F). It is noteworthy that ETV6 knockdown affected only the capability of IRF4 F359L to activate the (ISRE)₃ promoter
<figure><img src="image_10.png" /><figcaption>**Figure 7** Disturbance of the IRF4 protein's interactions with chromatin, functional impact of ETV6 and localization of trimethylated (K9) histone H3 in patient B-EBV cells. (A-D) The results of an IRF4 RIME analysis in patient B-EBV cells (P3, n=1) and control (C3, n=1) B-EBV cells. The Venn diagram shows the distribution of reproducibly identified proteins in n=2 independent experiments (A). (B-D) A gene ontology biological process pathway analysis for protein interactions with IRF4 on chromatin in both control and patient B-EBV cell samples (B), in the control sample only (C), or in the patient sample only (D). (E-G) Luciferase activity, data represent mean ± SD of control (shScramble n=3, empty vector n=3) (E) or ETV6 knockdown (shETV6-1 n=3, shETV6-2)</figcaption></figure>
n=3) (F) infected HEK293T cells co-transfected with an (ISRE)₃ reporter plasmid plus 75 ng of empty plasmid, or with plasmids encoding the IRF4 variants. (G) Changed induction of luciferase activity due to ETV6 knockdown for the different IRF4 variants. The results present the relative luciferase activity determined in ETV6 deficient cells (presented in (F)) – relative luciferase activity determined in ETV6 proficient cells (presented in (E)). One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001). (H) qPCR analysis of selected indicated transcripts from n=2 control and n=2 patient B-EBV cell lines transduced with lentiviral vectors expressing for control conditions (shScramble, empty vector) or ETV6 knockdown (shETV6-1, shETV6-2). Three independent experiments were performed. Data represent mean ± SD of n=12 samples for ETV6+ control cells, n=12 samples for ETV6+ patient cells, n=12 samples for ETV6- control cells and n=12 samples for ETV6- patient cells. One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001). (I) Representative images of immunofluorescent staining of H3K9me3 (red) in n=2 control and n=2 patient B-EBV cell lines. The nucleus was stained with DAPI (blue) and the inner nuclear membrane was stained for lamin B1 (green). (J) Shown is quantification of (I) (using Fiji software) in 40 cells per conditions from a total of n=2 independent experiments; line indicates median. One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001).

analyzed mutant proteins (WT, L368P, R98A-
C99A and R98W) (Figure 7G). To investigate
whether ETV6 is responsible for the
suppressed gene expression of *PRDM1* and
*XBP1* in patients B-EBV cell lines, we
knocked down ETV6 in these cells. In patients
ETV6 knockdown B-EBV cell lines we
observed an increased gene expression of
*PRDM1* and *XBP1* compared to patients B-
EBV cell lines without ETV6 knockdown
(Figure 7H). Taken as a whole, these results
demonstrate that the transcriptional repressor
ETV6 interferes with the activity of IRF4
F359L on ISRE sites.

IRF4 was found to bind to the positive
coactivator 4 (PC4; encoded by *SUB1*) in the
control sample but not in the patient sample. It
is noteworthy that a PC4 protein complex
containing IRF4 has been described in
activated murine B cells (Ochiai et al., 2020).
Given that PC4 has been linked functionally to
the positioning of heterochromatin (Ochiai et
al., 2020), we next analyzed the subcellular
localization of histone H3 K9 trimethylation
(H3K9me3, a heterochromatin marker) in
control and patient B-lymphoblastoid cell
lines. In control cells, the H3K9me3 signal

colocalized with lamin B1 at the boundary of
the nucleus (Figure 7H-I). In contrast, the
H3K9me3 signal in patient cells was spread
throughout the nucleus. Overall, these results
indicate that the interaction between
heterochromatin and the nuclear membrane is
impaired in the presence of IRF4 F359L.

DISCUSSION

Here, we reported on three patients from a
multigenerational family carrying a private
heterozygous missense variant of IRF4
(c.1075 T>C, p.F359L). The inheritance
pattern and the results of our functional
analysis suggest that the IRF4 F359L variant
causes a novel PAD that manifests itself as
hypogammaglobulinemia with T cell
abnormalities and hair and skin pigmentation
anomalies. The disease segregated in an
autosomal dominant manner and appeared to
have complete penetrance; it therefore differs
from a previously reported IRF4 deficiency,
i.e. a combined immunodeficiency
characterized by agammaglobulinemia,
eosinophilia, normal lymphocyte counts, low
memory T- and B-cell counts, and elevated
GM-CSF-induced macrophage polarization (Bravo García-Morato et al., 2018) and IRF4 haploinsufficiency with incomplete, age-dependent penetrance (caused by a heterozygous loss-of-function mutation affecting IRF4's DBD and the clinical manifestations of which are due to Tw exposure (Guérin et al., 2018)).

The IRF4 F359L mutation is located within the IAD – a well-defined, evolutionarily conserved structural domain involved in homo- and heterodimer protein-protein interactions (Remesh et al., 2015; Sundararaj et al., 2021). Functional IRF4 forms in the context of high protein abundance and ISRE sites homodimer and binds to EICE and AICE sites as a heterodimer (Cook et al., 2020). Our ChIP-Seq data indicated that the IRF4 WT protein and the mutant IRF4 F359L protein bound to ISRE, EICE and AICE motifs to similar extents. While the results of our experiments with various luciferase reporter constructs showed that IRF4 F359L did not negatively impact AICE- and EICE-dependent promoter activities, mutant IRF4 F359L was not able to activate ISRE-driven promoters and even exerted a dominant-negative effect on IRF4 WT. These findings indicate that structural changes in IRF4 F359L do not negatively interfere with binding to ISRE, EICE and AICE motifs nor with the interaction of PU1 and AP1 but do interfere with the IRF4-driven transcriptional activity through ISRE. We even observed a gentle gain of function of IRF4 F359L on AICE promoter in the presence of AP1 (BATF-JUND heterodimer).

Apart from hypogammaglobulinemia diagnosed early in life, a very low plasmablast/plasma cell count in the blood was the only consistent B lineage defect observed in the three patients. The observed normalization of IgA titers with age might be related to the accumulation of long-lived

plasma cells over time (Manz et al., 2005). Defective *in vitro* differentiation of purified B cells as well as low RNA expression of *PRDM1*, *XBP1*, and *CD38* detected by RNA-Seq in B-EBV cell lines indicated a likely intrinsic defect in plasmablast/plasma cell differentiation. This is consistent with the known association between IRF4-driven expression of *PRDM1* and a conserved, non-coding region containing ISRE binding sites within the gene (Sciammas et al., 2006). We also showed that the ectopic expression of IRF4 F359L in B-EBV cell lines derived from healthy individuals repressed *PRDM1* and *XBP1* gene expression. Furthermore, the combined analysis of RNA expression and IRF4 ChIP-Seq data indicated the presence of a greater number of ISRE sites in genes that were expressed less strongly in patient cells than in control cells. These data can be interpreted as an ISRE-promoted dominant-negative effect of IRF4 F359L's transcriptional activity.

Many of the IRF4 partner proteins identified in the RIME analysis are known to be components of (i) a complex with PC4 (encoded by *Sub1*) (Ochiai et al., 2020) and (ii) SWI/SNF remodeling complexes (Centore et al., 2020); this observation indicates that functional IRF4 not only acts as a transcriptional activator but also is an important factor in chromatin remodeling and chromosome organization. The IRF4 F359L protein might hinder the interaction with components involved in nuclear pore organization (these components were identified predominantly in the control RIME sample) and probably disturbs chromatin remodeling, chromosome organization, and chromatin accessibility. This hypothesis is supported by the altered localization of the H3K9me3 signal with the nuclear membrane in patient cells. The transcriptional regulators ETV6 and ARID1B were detected solely in
patient cells. ETV6 reportedly interacted with IRF8 (Humblin et al., 2017) at ISRE sites in particular (Kuwata et al., 2002). Our (ISRE)₃ promoter luciferase reporter assays in ETV6 knockdown cells demonstrated that IRF4 F359L's binding to ETV6 is at least partly responsible for the dominant-negative impact on ISRE-motif-mediated transcriptional regulation. This notion is further supported by the increased gene expression of *PRDM1* and *XBP1* in patients B-EBV cell lines due to ETV6 knockdown.

Studies of murine models have suggested that IRF4 senses TCR signaling strength (Krishnamoorthy et al., 2017) or acts as a “rheostat” by translating TCR affinity into the appropriate transcriptional programs (Man et al., 2013). These studies also found that IRF4 promotes CD8 T cell exhaustion (Man et al., 2017). Accordingly, our observation with human cells indicated that IRF4 F359L promotes terminal differentiation into CD4 and CD8 T cell subsets. Firstly, the IRF4 F359L patients had abnormal T cells, with low naïve CD4 and CD8 T cell counts and elevated proportions of terminal effector CD4 and CD8 T cells. Secondly, the ectopic expression of mutant IRF4 F359L in naïve CD4 and CD8 T cells was associated with transcriptomic changes and lower proportions of CD45RA-negative cells, relative to expression in non-transduced cells and control cells. The combined analysis of RNA expression and public available IRF4 ChIP-Seq data indicated the presence of a greater number of AICE sites in genes that were expressed more strongly in IRF4 F359L expressing cells than in control cells. These data can be interpreted as an AICE-promoted gain of function effect of IRF4 F359L's transcriptional activity reminding on the gentle gain of function of IRF4 F359L on AICE promoter in the presence of AP1 (BATF-JUND heterodimer). A gain of function of IRF4 F359L on AICE

sites could possibly be explained by an enhanced IRF4 F359L – JUND (or other JUN family member) protein interaction, since JUNB protein was one of the 27 components detected in the patient samples but not in the control samples in our RIME analysis. Taken together, our phenotyping and functional data suggest that the dysfunctional IRF4 F359L protein accelerates the differentiation of naïve T cells into terminal effector memory T cells and impairs the differentiation of B cells into plasma cells. However, the low overall naïve B and naïve T cell counts in patients might indicate that IRF4 F359L not only impacts the T and B cell differentiation processes but also interferes with early B and T cell development and/or the homeostasis of naïve B and naïve T cells. Splenomegaly and lymphadenopathy (due to the expansion of both T and B lymphocytes) have been reported in 10-15 week old *Irf4-deficient* mice, which suggests that IRF4 has as functional role in the homeostasis of both B and T lymphocytes (Mittrücker et al., 1997). It has also been suggested that IRF4 has a functional role in the homeostasis of mature B cells (their positioning in lymphoid microenvironments, to be precise) (Simonetti et al., 2013).

The results of genome-wide association studies have suggested a role for IRF4 in pigmentation (Praetorius et al., 2013) and in hair greying (Adhikari et al., 2016). The premature hair greying observed in our three patients (none of whom carried the rs12203592 SNP) strongly suggests that the process was mediated by IRF4 F359L dysfunction.

Several PIDs are known to be caused by heterozygous missense variants in transcription factors (Bousfiha et al., 2020; Tangye et al., 2020) via gain-of-function, negative dominance, haploinsufficiency, or heterodimeric interference (Yamashita et al., 2021). Our data fit with a novel disease-
causing role for IRF4 F359L. This mutant enhances AICE-motif-mediated transcription and interferes with ISRE-motif-mediated transcriptional regulation in a dominant negative manner. Our evidence of an interaction between IRF4 F359L and ETV6 in the context of ISRE sites suggest that this neomorphic mutation causes an autosomal dominant disease with a novel disease mechanism: protein function is impaired by a change in binding partners and/or functional interference between protein complexes, rather than by the loss or gain of function of individual proteins.

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# MATERIAL AND METHODS

Blood sample collection from patients and healthy donors, and study approval

Peripheral blood samples were collected from the patients after the provision of written, informed consent. Genetic studies and data collection procedures were approved by the local institutional review board (Comité de Protection des Personnes Ile de France II, Paris, France; reference: 2015-01-05; 2015-01-05 MS2) and the French Advisory Committee on Data Processing in Medical Research (Comité Consultatif sur le Traitement de l'Information en matière de Recherche dans le domaine de la Santé, Paris, France; reference: 15.297bis).

Whole-exome sequencing

Whole-exome sequencing and analysis was performed as described previously (Bouafia et al., 2019). Exome capture was performed using the SureSelect Human All Exon Kit (Agilent Technologies). Agilent SureSelect Human All Exon (58 Mb, V6) libraries were prepared from 3 µg of genomic DNA sheared with an ultrasonicator (Covaris), as recommended by the manufacturer. Barcoded exome libraries were pooled and sequenced using a HiSeq 2500 system (Illumina), to generate paired-end reads. After demultiplexing, sequences were mapped against the human genome reference (NCBI build37/hg19 version) with the Burrows-Wheeler Aligner (version 0.7.12) (Li and Durbin, 2009). The mean depth of coverage obtained for the two exome libraries exceeded 150X, and more than 97% and more than 96% of the targeted exonic bases were covered by at least 15 and 30 independent sequencing reads, respectively (≥97% at ×15 and ≥96% at ×30). Variants were called with the Genome Analysis Toolkit (GATK), SAMtools, and Picard Tools. SNPs were called with the GATK Unified Genotyper, whereas indels

were called with the GATK IndelGenotyper, version 2. All variants with a read coverage of ×2 or less and a Phred-scaled quality of 20 or less were filtered out. All the variants were annotated and filtered using PolyWeb (our in-house annotation software).

Cell culture

Using SepMate™ PBMC Isolation Tubes (STEMCELL Technologies, #85450) and Ficoll, we isolated PBMCs from cytopheresis rings (from healthy volunteers) or whole-blood samples (from patients). PBMCs and EBV-B cells were cultured in RPMI medium supplemented with 10% fetal bovine serum, whereas HEK293T cells were cultured in DMEM medium supplemented with 10% fetal bovine serum. T cell blast were generated by stimulation of PBMCs with PMA/ ionomycin (Invivogen, #tlrl-pma and Sigma-Aldrich®, #I0634) and cultured in Xvivo 15 supplemented with 10% SAB (Sigma-Aldrich®) and interleukin-2 (300 IU/mL).

Plasmablast differentiation assay

PBMCs isolated from Healthy donors or patients' blood were thawed and B cell enrichment was performed using both Pan B cell isolation and dead cell removal kits (Miltenyi, #130-101-638, #130-090-101). B cells were seeded at 50 000 cells per well in 96 well plate in RPMI medium supplemented with 10% fetal bovine serum containing IL2 (100 UI/mL), IL4 (100 ng/mL), IL21 (100 ng/mL), IL10 (25 ng/mL), CD40L (200 ng/m) and anti IgM antibody (5 µg/mL). Medium was renewed every 3 days. Cell differentiation was analyzed by flow cytometry and Ig secretion by ELISA (Thermo Fisher Scientific, #BMS2098, #BMS2091) at day 6 and day 14.

Plasmids

Full-length constructs carrying mutant alleles
were generated from pcDNA 3.1D/V5-His-
TOPO IRF4 WT (Guérin et al., 2018) using
the GeneArt™ Site-Directed Mutagenesis
System (Thermo Fisher Scientific, #A13282),
according to the manufacturer's instructions.
We used the lentiviral pWPI backbone
provided by Addgene. All the constructs (WT,
F359L, L368P, and R98W IRF4) were
generated by GenScript. The SFR BioSciences
Gerland-Lyon Sud (Lyon, France) vector
facility produced the lentiviral supernatant,
and a multiplicity of infection of 30 was used
for both CD4, CD8 and B-EBV cells.

For ETV6 knockdown experiments shRNA
expressing lentiviral constructs (Sigma-
Aldrich®, TRCN0000003853 (shETV6-1) and
TRCN0000003856 (shETV6-2)) and
corresponding controls (Sigma-Aldrich®,
SHC016-1EA (shScramble) and Addgene,
10878 (Empty vector) were used.

Transfection

HEK293T and HELA cells were transfected transiently with the various constructs by using Lipofectamine 2000 (Thermo Fisher Scientific) in accordance with the manufacturer's instructions.

Cell lysis and Western blotting

Total protein extracts were prepared by
incubating cells on ice for 45 min with lysis
buffer (50 mM Tris-HCl pH 7.4, 150 mM
NaCl, 0.5% Triton X-100, and 2 mM EDTA)
supplemented with protease and phosphatase
inhibitors (Thermo Fisher Scientific, #87786).
In a two-step procedure, the cytoplasmic and
nuclear contents were separated using Thermo
Scientific™ NE-PER™ Nuclear and
Cytoplasmic Extraction Reagents (Thermo
Fisher Scientific) according to the
manufacturer's instructions. Equal amounts of
protein (according to a Bradford protein assay
(Thermo Fisher Scientific, Micro BCA™
Protein Assay Kit)) were resolved by SDS-

PAGE in a NuPAGE™ 10%, Bis-Tris gel (Invitrogen) and transferred to a low-fluorescence PVDF membrane. Membranes were probed with nonconjugated antibody: an anti-IRF4 (Cell Signaling, #4948) antibody was used at a dilution of 1:1000, ETV6 (Thermo Fisher Scientific, #PA5-81865) and antibodies against GAPDH (Santa Cruz, sc-32233), tubulin (Sigma, T5168), and histone H3 (Cell Signaling, #9715) were used at the same dilution as loading controls. Antibodies bound to the membrane were detected by incubation with the appropriate infrared-dye-conjugated secondary antibody (Li-Cor, 926-68071, 926-32211 and 926-32210) in a Licor Odyssey CLx system (Li-Cor). Images were analyzed and quantified with Image Studio Lite software.

Immunofluorescent staining in B-EBV cells

B-EBV cells were harvested and fixed by incubation with 4% paraformaldehyde for 15 min and permeabilized in a 10 min incubation with 1X PBS 5% BSA/0.1% TritonTM X-100. Cells were washed with filtered PBS, blocked by a 40 min incubation with PBS 5% BSA, stained for IRF4 (Cell Signaling, #4948), H3K9me3 (Thermo Fisher Scientific, #710816) and Lamin B1 (Santa Cruz, #sc-377000) for 1 h, washed, and incubated with a secondary antibody (anti-rabbit FITC Jackson, #50784, donkey anti-rabbit Invitrogen, #A21206, and goat anti-mouse Invitrogen, #A11003) DAPI (Chemometech, #910-3018) and phalloidin (Cell Signaling, #8940) were added for 40 min. After several washes, the cells were placed on precoated coverslips with poly-D-lysine (Gibco, #A3890401). Fluorescence was detected with a confocal Leica SP8 microscope. Images were analyzed with Fiji software.

Luciferase reporter assays

The (ISRE)3 reporter plasmid (containing
three repeats of the *ISRE* sequence separated by spacers), the AICE reporter plasmid and the EICE reporter plasmid have been described elsewhere (Doody et al., 2007; Guérin et al., 2018). HEK293T cells were transiently transfected with the (*ISRE*)3 reporter plasmid (100 ng/well in a 96-well plate), the pRL-SV40 vector (Promega # E2231, 40 ng/well), and an *IRF4* WT or mutant pcDNA 3.1D/V5-His-TOPO plasmid (Invitrogen #K4900-01, 75 ng or the amount indicated, together with an empty plasmid to give 75 ng) and Lipofectamine 2000 (Thermo Fisher Scientific, #11668019), according to the manufacturer's instructions. The same protocol was used for the *AICE* and *EICE* assays, except that cofactor expression plasmids (respectively *BATF* and *JUN* or *PU-1*) were added (25 ng/well). Cells were analyzed 24 hr after transfection, using the Dual-Luciferase 1000 assay system kit (Promega, #E1980) and according to the manufacturer's instructions. Signal intensity was determined with an EnVision multimode plate reader (Perkin Elmer). Experiments were performed in triplicate, and reporter activity was expressed as the fold-induction relative to cells transfected with the empty vector. Negative dominance or haploinsufficiency was assessed in cells transfected with a constant amount of WT plasmid (25 ng/well), various amounts of mutant plasmid (from 12.5 ng to 50 ng/well, together with empty plasmid to give a total of 75 ng), (*ISRE*)3 reporter plasmid (100 ng/well for a 96-well plate), and pRL-SV40 vector (40 ng/well). The same protocol was used for the AICE and EICE luciferase reporter assays, except that cofactor expression plasmids (25 ng/well each) were added.

### RNA-Seq and qPCR assays

Total RNA was prepared from the EBV-B cells of individuals with a heterozygous *IRF4* mutation (two patients) from healthy, homozygous WT individuals (*n* = 4). RNA was prepared (including a DNase treatment step) from 500,000 cells by using the RNeasy Plus Kit (Qiagen), according to the manufacturer's instructions.

For RNA-seq, RNA quality was assessed by capillary electrophoresis with high-sensitivity RNA reagents and a Fragment Analyzer (Agilent Technologies). The RNA concentration was measured spectrophometrically with an Xpose system (Trinean) and by using capillary electrophoresis (Fragment Analyzer). RNA-Seq libraries were prepared from an initial total of 200 ng RNA using the Universal Plus mRNA-Seq kit (Nugen), as recommended by the manufacturer (libraries can be prepared from 10 ng to 1 µg of starting total RNA). The oriented cDNAs produced from the poly-A+ fraction were sequenced on a NovaSeq6000 system (Illumina: paired-end reads 100 bases + 100 bases). Around 50 million filtered paired-end reads were produced for each library. Reads were aligned with the hg38 human reference genome using HISAT2 (Kim et al., 2019), and those that mapped uniquely to GENCODE-annotated genes were summarized using featureCounts (Liao et al., 2014). The raw gene count matrix was imported into the R environment (https://www.R-project.org/) for further processing and analysis. Genes with low read counts (less than ~10 reads in more than 3 samples) were filtered out, leaving a set of approximately 18,000 genes to be tested for differential expression in healthy homozygous WT individuals (control) vs. individuals with heterozygous IRF4 mutations (patients). Read counts were normalized and differential expression was analyzed by applying three independent, complementary statistical methods: DESeq2 (Love et al., 2014), edgeR (Robinson et al., 2010) and Limma-voom.
(Law et al., 2014). We applied the optimal procedure, which consisted in simultaneously filtering the analysis results with a statistical significance threshold (Benjamini-Hochberg adjusted *p*-values <5%) and a fold change threshold (1-fold change >2). Z-score calculated as following ((gene expression value in sample of interest) - (mean expression across all samples)) / (Standard Deviation) was used as a scaling method for visualization in heatmaps. For quantitative real-time PCR, a High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) was used to generate cDNA. Quantitative real-time PCR was performed with TaqMan Universal PCR Master Mix (Thermo Fisher Scientific), specific primers (all from Thermo Fisher Scientific) for *IRF4* (Hs00180031_m1), *XBP1* (Hs00231936_m1), *PRDM1* (Hs00153357_m1), *SPIB* (Hs00162150_m1), *PPIB* (Hs00168719_m1), *PAX5* (Hs00277134_m1), *ETV6* (Hs00231101_m1) and (as a control) endogenous human glyceraldehyde-3-phosphate dehydrogenase (*GAPDH*; Hs02786624_g1) or *HBS1L* (Hs04188641_g1). The data were analyzed using the ΔΔCt method, with normalization against *GAPDH*.

### CyTOF phenotyping of PBMCs

A combination of the Maxpar® Direct™ Immune Profiling Assay™ (Fluidigm, #201325) and the Maxpar® Direct™ T cell Expansion Panel 2 (Fluidigm, #201406) was used for the high-dimensional immune profiling of PBMCs. The antibody markers analyzed are summarized in Supplemental Information Table 2. For each sample, cells were thawed, washed once, and checked for viability (>80%). After a 10 min incubation with FC Blocker (Biolegend, TruStain FCX) in MaxPar staining buffer, cells were directly transferred into the antibody-containing tube. Cells were then incubated for 10 min in 1.6%

formaldehyde solution, washed once, transferred into Intercalator ID solution, and incubated overnight at 4°C. Immediately before acquisition, the cells were washed, resuspended in Maxpar Cell Acquisition Solution (1 million cells per mL) and mixed with 10% v/v EQ Beads. An average of 500,000 events were acquired per sample on a Helios mass cytometer. The acquisition data were analyzed with CyTOF software (Fluidigm, version 6.7.1014), enabling Maxpar® Pathsetter™ automated single-cell analysis at the Cytometry Facility in Pitié-Salpetriere Hospital (Paris, France) or by manual gating with the OMIQ analysis platform. Data were analyzed with either standardized Maxpar® Pathsetter™ automated single-cell analysis and populations were defined following markers' expression given in (Bagwell et al., 2020) or by unsupervised clustering with manual identification, in this case, population were defined following markers' expression given in (Geanon et al., 2020).

### Analysis of phenotypic changes in naïve T cells expressing IRF4 F359L

CD4+ and CD8+ naïve T cells were isolated from healthy donor PBMCs and then labelled with anti-CD4 (BD, #345768), anti-CD8 (BD, #345773), anti-CD45RA (Biolegend, #304138), and anti-CCR7 (Miltenyi, #130-099-363) antibodies. Naive T cells (defined as CD45RA+ CCR7+ CD4+ cells) were isolated (>98% purity) with a FACS AriaII cell sorter (BD Biosciences). Purified naïve CD4+ or CD8+ T cells were activated with a Dynabeads™ T cell activation kit (anti-CD3/CD28; Gibco, #11131D) and cultured in medium containing interleukin-2 (300 IU/mL). After 24 hr, the cells were transduced using adjuvant (Lentib boost, Sirion Biotech and PGE2 Cayman, #14750) and lentiviral vectors
(pWPI backbone) expressing WT or mutant IRF4. For T cell polarization one day after transduction cytokines were added to the culture as indicated: TH1 polarization IL12 (50 ng/mL), TH2 polarization IL4 (100 U/mL), TH17 polarization TGFβ (2,5 ng/mL), IL1β (20 ng/mL), IL6 (50 ng/mL), IL21 (50 ng/mL), IL23 (100 ng/mL) and PGE2 (50 ng/mL). Using flow cytometry, we evaluated the transduction efficiency, viability and differentiation 6 days after transduction, on the basis of GFP expression, a fixable viability dye (Thermo Fisher Scientific), and surface staining with anti-CD25 (BD, #563352), anti-CCR7 (BD, #557648) and anti-CD45RA (Biolegend, #304112), or anti-CCR6 (BD, #565925), CCR7 (BD, #557648), CXCR3 (Biolegend, #353706), CCR4 (BD, #557863) antibodies respectively. Cytokine secretion was evaluated using Human Th1/Th2/Th17 CBA Kit (BD, #560484) following the protocol given by the provider.

### ChIP-Seq

B-EBV cells (10x10⁶) from P3 and a healthy control C1 were fixed with freshly prepared 1% formaldehyde (#F-8775, Sigma) for 15 min, quenched with 0.125 M glycine (Sigma #G-7403) for 5 min and washed twice with 1x PBS containing 0.5% Igepal CA-630 (Sigma #I-8896) and (in the second wash only) 1 mM phenylmethanesulfonyl fluoride. Cell pellets were snap frozen and subjected to ChIP-Seq. Chromatin extraction, immunoprecipitation with an anti-IRF4 antibody (Cell Signaling, #4948), library preparation, next-generation sequencing, and a model-based analysis of the ChIP-Seq data (Zhang et al., 2008) were performed by Active Motif. Motifs were discovered using HOMER software (http://homer.ucsd.edu/homer/) with its default parameters.

### Rapid immunoprecipitation mass spectrometry of endogenous proteins

B-EBV cells (50x10⁶) from P3 and from a healthy control C1 were fixed with 1% formaldehyde for 15 min, quenched with 0.125 M glycine, and snap-frozen. RIME experiments (Mohammed et al., 2016) in B-EBV cells were performed by Active Motif, using an anti-IRF4 antibody (Cell Signaling, #4948) or an isotype-matched IgG. The RIME analysis was performed in replicates. Proteins present in both experimental replicates with a spectral count of ≥5 (upon filtering the experimental reaction data against the negative control IgG reaction data) were included into the final protein list.

### Induction of IRF4 F359L expression in control B-EBV cells

Healthy-donor-derived B-EBV cells were transduced using an adjuvant (Lentib boost, Sirion Biotech) and lentiviral vectors (pWPI backbone) expressing WT or mutant IRF4 proteins. Six days later, transduced cells were isolated (>98% purity) by cell sorting with an AriaII cell sorter (BD Biosciences) on the basis of their GFP expression. Total RNA was extracted.

### ETV6 knockdown

HEK 293T expressing WT or mutant IRF4 proteins or Healthy-donor/ patient derived B-EBV cells were transduced using an adjuvant (Sirion Biotech, Lentib boost) and lentiviral vectors (pLKO.1 backbone). Three days later, transduced cells were selected by addition of puromycin. Four days later knock down cells were selected and used for further analysis.

### Data availability

The RNA-seq data of lentiviral transduced CD4 T cells can be accessed in the Gene Expression Omnibus (GEO) repository under the accession number GSE214888. The ChIP-
seq and RNA-seq data of EBV-B cells can be
accessed in the GEO repository under the
accession numbers GSE199684 and
GSE199685 (GSE199686 SuperSeries).

Statistics

Differences between populations were probed
using a one-way analysis of variance. All
analyses were performed with GraphPad
Prism software (version 9.2.0,
https://www.graphpad.com). The threshold
for statistical significance was set to p<0.05.

Online supplemental material

The supplementary information shows clinical
features and genetic analysis (Figure S1),
further molecular characterization of IRF4
F359L's function (Figure S2), additional
exploration of phenotypic changes in T cells
in the presence of IRF4 F359L (Figure S3),
and further information concerning ChIP-Seq
and RIME analyses (Figure S4). Table S1 lists
the proteins found in RIME experiments and
Table S2 lists the antibodies used in CyTOF
experiments.

Author contributions

RT and SK designed and analyzed
experiments. RT, LC, NM, LP performed and
analyzed experiments. All authors contributed
to data analysis. SK supervised research.
MELP performed computational analysis. RT
LC and YS performed WES and RNA-Seq
experiments. RT and BH performed CyTOF
experiments. LG, JF and AF provided patient
care and clinical information. CLP, JLC and

JB provided reagents. AF, AD, LG and JF
provided samples from patients and controls.
SK and RT wrote the manuscript, and all the
co-authors revised, edited and approved the
manuscript.

Acknowledgements

We thank the patients and their families for
contributing to the study. We thank Dr.
Marion Espeli, Dr. Vivien Béziat and Dr.
Pierre Milpied for helpful discussions. We
thank the Necker Imagine Centre de
Resources Biologiques for generating EBV-
transformed lymphoblastoid cell lines; the
clinical research team at the Imagine Institute
for their support; the Etablissement Français
du Sang for blood supply from healthy donors;
SIRION biotech GmbH for providing
LentiBOOST™; Gisèle Froment, Didier
Nègre and Caroline Costa from the lentivector
production facility at SFR BioSciences
Gerland - Lyon Sud
(UMS3444/CNRS/US8/INSERM, ENS Lyon,
UCBL) for viral vector production; and Dr.
Gina Doody (University of Leeds, Leeds, UK)
for providing us with the EICE reporter
plasmid. The work was funded by the French
state (via the Agence Nationale de la
Recherche’s “Investissments d’avenir”
program (ANR-10-IAHU-01), and grants
ANR-19-CE17-0012-01, ANR-19-CE17-
0012-02, ANR-19-CE17-0012-03, and ANR-
19-CE17-0012-04 (ANR-AID)), the Ligue
Contre le Cancer–Comité de Paris, and
INSERM. SK is a Centre National de la
Recherche Scientifique staff researcher.

Funding:

The work was funded by the French state (via the Agence Nationale de la Recherche’s “Investissments d’avenir” program (ANR-10-IAHU-01), and grants ANR-19-CE17-0012-01, ANR-19-CE17-0012-02, ANR-19-CE17-0012-03, and ANR-19-CE17-0012-04 (ANR-AID)), the Ligue Contre le Cancer–Comité de Paris, and INSERM. SK is a Centre National de la Recherche Scientifique staff researcher.
A neomorphic variant in IRF4 alters transcriptional activity

<table><thead><tr><th></th><th>P1</th><th>P2</th><th>P3</th><th>Standards</th></tr></thead><tbody><tr><td>Sex</td><td>Male</td><td>Female</td><td>Male</td><td></td></tr><tr><td>Age (at initial evaluation) (month)</td><td>11</td><td>48</td><td>24</td><td></td></tr><tr><td>Serum IgG levels (g/L)</td><td>NA</td><td><strong>2.27</strong></td><td><strong>2.50</strong></td><td>7-14</td></tr><tr><td>Serum IgA levels (g/L)</td><td>NA</td><td><strong>0.26</strong></td><td><strong>0.22</strong></td><td>0.90-4</td></tr><tr><td>Serum IgM levels (g/L)</td><td>NA</td><td><strong>0.43</strong></td><td><strong>0.18</strong></td><td>0.50-2.50</td></tr><tr><td>Age (at evaluation) (years)</td><td>35</td><td>31</td><td>28</td><td></td></tr><tr><td>Serum IgE levels (kUI/L)</td><td>&lt;2</td><td>&lt;5</td><td>&lt;5</td><td>&lt;100</td></tr><tr><td></td><td>P1</td><td>P2</td><td>P3</td><td>Standards</td></tr><tr><td>Age (at evaluation) (yrs)</td><td>53</td><td>57</td><td>60</td><td></td></tr><tr><td>T lymphocytes</td><td></td><td></td><td></td><td></td></tr><tr><td>CD3+ (/µL)</td><td><strong>590</strong></td><td><strong>344</strong></td><td><strong>819</strong></td><td><strong>903</strong></td><td><strong>4421</strong></td><td><strong>1157</strong></td><td><strong>1928</strong></td><td>1008-1647</td></tr><tr><td>CD3+ CD4+ (/µL)</td><td><strong>181</strong></td><td><strong>116</strong></td><td><strong>284</strong></td><td><strong>457</strong></td><td><strong>1350</strong></td><td><strong>450</strong></td><td><strong>597</strong></td><td>480-1320</td></tr><tr><td>Activated CD4+ (HLA-DR+/CD3+CD4+) (%)</td><td><strong>18.3</strong></td><td>NA</td><td>NA</td><td>NA</td><td>12</td><td>11.1</td><td>NA</td><td>5-12</td></tr><tr><td>Naïve CD4+ (CD45RA+CCR7+/CD3+CD4+) (%)</td><td><strong>7.5</strong></td><td><strong>8.8</strong></td><td><strong>4.6</strong></td><td><strong>11.9</strong></td><td><strong>10.7</strong></td><td><strong>15.0</strong></td><td><strong>10.8</strong></td><td>26-54</td></tr><tr><td>Memory CD4+ (CD45ROb+/CD3+CD4+) (%)</td><td><strong>84.7</strong></td><td>NA</td><td>NA</td><td>NA</td><td>54.5</td><td><strong>72.7</strong></td><td>NA</td><td>40-61</td></tr><tr><td>Central memory CD4+ (CD45RA-CCR7+/CD3+CD4+) (%)</td><td><strong>43.8</strong></td><td><strong>59.2</strong></td><td><strong>64.2</strong></td><td><strong>57.4</strong></td><td><strong>20</strong></td><td>35.5</td><td><strong>24.3</strong></td><td>28-51</td></tr><tr><td>Mem effector CD4+ (CD45RA-CCR7-/CD3+CD4+) (%)</td><td><strong>38.5</strong></td><td><strong>25</strong></td><td><strong>26.3</strong></td><td>18.2</td><td><strong>32.5</strong></td><td><strong>39.5</strong></td><td><strong>42.8</strong></td><td>7.8-23.2</td></tr><tr><td>Terminal effector CD4+ (CD45RA+CCR7-/CD3+CD4+) (%)</td><td><strong>10.3</strong></td><td><strong>6.9</strong></td><td><strong>4.9</strong></td><td><strong>12.6</strong></td><td><strong>36.8</strong></td><td><strong>10.1</strong></td><td><strong>22.2</strong></td><td>0-2.9</td></tr><tr><td>CD3+ CD8+ (/µL)</td><td>404</td><td>225</td><td>539</td><td>446</td><td><strong>3074</strong></td><td>691</td><td><strong>1290</strong></td><td>192-720</td></tr><tr><td>Activated CD8+ (HLA-DR+/CD3+CD8+) (%)</td><td>32.8</td><td>NA</td><td>NA</td><td>NA</td><td>19.1</td><td>19.6</td><td>NA</td><td>9-33</td></tr><tr><td>Naïve CD8+ (CD45RA+CCR7+/CD3+CD8+) (%)</td><td><strong>1.2</strong></td><td><strong>1.7</strong></td><td><strong>1.9</strong></td><td><strong>5.7</strong></td><td><strong>3.0</strong></td><td><strong>6.7</strong></td><td><strong>3.3</strong></td><td>24.2-53.6</td></tr><tr><td>Memory CD8+ (CD45ROb+/CD3+CD8+) (%)</td><td><strong>54.6</strong></td><td>NA</td><td>NA</td><td>NA</td><td><strong>63.4</strong></td><td><strong>73.9</strong></td><td>NA</td><td>21-48</td></tr><tr><td>Central memory CD8+ (CD45RA-CCR7+/CD3+CD8+) (%)</td><td><strong>4.3</strong></td><td><strong>4.9</strong></td><td>7.7</td><td>6.5</td><td><strong>1.2</strong></td><td><strong>3.8</strong></td><td><strong>2.6</strong></td><td>5.1-19.6</td></tr><tr><td>Mem effector CD8+ (CD45RA-CCR7-/CD3+CD8+) (%)</td><td><strong>37.7</strong></td><td><strong>34.1</strong></td><td><strong>39.1</strong></td><td>30.1</td><td><strong>58.6</strong></td><td><strong>73.0</strong></td><td><strong>73.5</strong></td><td>16.4-32.6</td></tr><tr><td>Terminal effector CD8+ (CD45RA+CCR7-/CD3+CD8+) (%)</td><td><strong>56.8</strong></td><td><strong>59.4</strong></td><td><strong>51.3</strong></td><td><strong>57.8</strong></td><td>37.3</td><td>16.5</td><td>20.5</td><td>5.2-37.4</td></tr><tr><td>NK cells</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CD3-CD16+CD56+ (/µL)</td><td><strong>19</strong></td><td><strong>18</strong></td><td><strong>44</strong></td><td>94</td><td>NA</td><td>82</td><td>69</td><td>56-400</td></tr><tr><td>B lymphocytes</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>CD19+ (/µL)</td><td>260</td><td><strong>30</strong></td><td>83</td><td>84</td><td>239</td><td>123</td><td>127</td><td>67-270</td></tr><tr><td>Naïve B cells (CD27-IgD+/CD19+) (%)</td><td><strong>88</strong></td><td><strong>85.2</strong></td><td>70.7</td><td>64.2</td><td><strong>41.4</strong></td><td><strong>42.6</strong></td><td>50.8</td><td>51-77</td></tr></tbody></table>
<table><tr><td>Memory B cells (CD27+/CD19+) (%)</td><td><b>12</b></td><td><b>10.9</b></td><td><b>16.7</b></td><td>32.3</td><td><b>52</b></td><td>49.3</td><td>41.1</td><td>23-49</td></tr><tr><td>Un-switched mem (CD27+IgD+/CD19+) (%)</td><td>6.4</td><td>5.1</td><td>9.5</td><td>15.4</td><td><b>45.5</b></td><td>32.5</td><td>31.3</td><td>3-40</td></tr><tr><td>Switched mem (CD27+IgD-/CD19+) (%)</td><td>5</td><td>6.1</td><td>6.8</td><td>16.5</td><td>7.7</td><td>16.6</td><td>11.1</td><td>4.4-20.5</td></tr><tr><td>CD21<sup>low</sup> B cells (CD19+CD21<sup>low</sup>) (%)</td><td>2.5</td><td>4.5</td><td><b>11.1</b></td><td><b>32.9</b></td><td><b>24.4</b></td><td><b>38.7</b></td><td><b>50.4</b></td><td>&lt;5</td></tr></table>

NA: not available, values above or below reference ranges are marked in bold.

<caption>**Table 1:** Immunophenotyping results from the hospital laboratory</caption>
A

<figure><img src="image_11.png" /></figure>

B

<table><thead><tr><th rowspan="2"></th><th colspan="2">Number of annotated variants</th></tr><tr><th>P1</th><th>P2</th></tr></thead><tbody><tr><td>Total</td><td>156 349</td><td>147 568</td></tr><tr><td>Heterozygous</td><td>95 718</td><td>88 910</td></tr><tr><td>Heterozygous MAF &lt;0.0001</td><td>3 840</td><td>3 851</td></tr><tr><td>Heterozygous<br>Removal of pipeline artefacts (in-house<br>MAF data; variants detected only in this<br>project)</td><td>385</td><td>361</td></tr><tr><td>Heterozygous<br>Non-synonymous, essential splicing or<br>indel</td><td>83</td><td>87</td></tr><tr><td>Heterozygous<br>Shared by both patients and absent in<br>parents</td><td>2</td><td>2</td></tr><tr><td>Absent in other healthy family member<br>(Sanger sequencing)</td><td>1</td><td>1</td></tr></tbody></table>

<table><thead><tr><th>Chr</th><th>pos</th><th>Allele</th><th>Gene</th><th>Consequence</th><th>exon</th><th>Polyphen</th><th>Sift</th><th>CADD</th></tr></thead><tbody><tr><td>6</td><td>401753<br>(GRCh38)</td><td>T/C</td><td>IRF4</td><td>Missense<br>(F359L)</td><td>7</td><td>0.174</td><td>1</td><td>23</td></tr></tbody></table>

C

<figure><img src="image_12.png" /></figure>

Supplementary Figure 1. Clinical features and genetic analysis

(A) Skin manifestations observed for P3 at 26 years of age. (B) A table summarizing the different filters applied to whole-exome sequencing data from patients, in order to identify possible disease-causing variants. The analysis was performed for P1, P3, and P1's parents and sister. A strict de novo model involved P1 and his parents only. The two variants identified in the strict de novo model are annotated.

(C) Sanger sequencing of RT-PCR products of B-EBV cells derived from n=1 control and n=2 patients (P1 and P3).
<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

<figure><img src="image_16.png" /><figcaption>**Supplementary Figure 2. Molecular characterization of IRF4 F359L's function.**</figcaption></figure>

(A) Western blot analysis of IRF4's abundance in nuclear and cytoplasmic cell compartments. HEK293T cells were transfected with an empty plasmid or with plasmids expressing IRF4 WT or IRF4 F359L. Nuclear and cytoplasmic protein extracts were analyzed. Tubulin and histone H3 were respectively used as control cytoplasmic and nuclear proteins. Quantification realized with Image Studio software represents the mean ± SD of n=2 independent experiments. The Mann-Whitney-Wilcoxon test was used to determine whether or not differences were statistically significant (p<0.05 were considered as statistically significant). (B) Luciferase activity of HEK293T cells co-transfected with an EICE reporter plasmid plus the indicated amount of plasmids encoding the various IRF4 variants. The quantity of plasmid was normalized to 75 ng by addition of empty vector. A plasmid encoding the PU-1 cofactor was added (25 ng) in the indicated conditions. The dotted line indicates the mean level of activity for transfected cells with the empty plasmid. Data represent mean ± SD of n=3 independent experiments for all conditions depicted. (C) IRF4 expression in the different annotated PBMCs' population analyzed by single cell RNA sequencing. Data extracted from (Li et. al., 2012).
<figure><img src="image_17.png" /><figcaption>Supplementary Figure 3. Phenotypic changes in T cells in the presence of IRF4 F359L.</figcaption></figure>

(A) Flow cytometry analysis of (from left to right) the GFP signal, and gated on GFP positive cells, CCR7, CD25 and CD45RA expression in CD8+ T cells 72 hours after transduction with lentiviral vectors expressing (from top to bottom) empty vector, IRF4-WT, IRF4-F359L, IRF4- L368P, or IRF4- R98W. Before activation and transduction, naïve CD8 T cells were sorted (based on CCR7+ and CD45RA+ expression) to obtain a homogenous cell population at the start of the culture. The mean
fluorescence intensity (MFI) for CCR7 expression is shown. Data are representative of n=2 independent experiments (empty, IRF4-WT, and IRF4-F359L) or n=1 experiment (IRF4-L368P and IRF4-R98W). **(B-D)** A bar graph showing (from left to right) the frequency of CD45RA-negative cells (**B**), CCR7 MFI (**C**) and the frequency of CD25-negative (**D**), **(B and D)** are normalized against the empty vector. **(B-D)** Data represent mean ± SD of n=2 independent experiments for empty vector, IRF4-WT, and IRF4-F359L conditions and n=1 experiment for IRF4-L368P and IRF4-R98W conditions. **(E)** IRF4 transcript count per million mapped reads (cpm) in CD4+ T cells infected with lentivirus constructs for IRF4 proteins (the F359L, L368P and R98W mutants and the WT) in comparison to the empty vector. Data represent mean ± SD of n=3 independent experiments. **(F-L)** Naive CD4+ T cells polarization towards Th1, Th2 or Th17 subtypes after transduction with lentivirus constructs for IRF4 proteins (the F359L, L368P and R98W mutants and the WT) or the empty vector. The polarization was performed on three independent healthy donors' cells. Bar graph representing the percentage of Th1 (**F**), Th2 (**G**) or Th17 (**H**) cells identified in the GFP+ population normalized on the proportion found in the GFP-population. Data represent mean ± SD of n=3 independent experiments for all conditions depicted. **(F-H)** One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001, *** p<0.001, ** p<0.01 and * p<0.05). **(I and J)** Analysis of secreted IFNγ (**I**) or TNFa (**J**) in supernatant of the Th1 culture. **(K)** Analysis of secreted IL-4 in supernatant of the Th2 culture. **(L)** Analysis of secreted IL-17a in supernatant of the Th17 culture. **(I-L)** Data represent mean ± SD of n=3 independent experiments for all conditions depicted. One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001, *** p<0.001, ** p<0.01 and * p<0.05). **(M)** Motifs taken into account for the annotation of ISRE, EICE and AICE sites in the HOMER analysis.
<figure><img src="image_18.png" /><figcaption>Supplementary Figure 4 ChIP-Seq and RIME analyses.</figcaption></figure>

(A) The genome-wide distribution of IRF4 ChIP-Seq peaks in B-EBV cells. The locations of ChIP-Seq peaks associated with differentially expressed genes in control and patient samples (based on the RNA-seq analysis) were annotated. (B) Peptide coverage of the IRF4 protein in RIME. Peptides recognized are highlighted in grey. High proportions of identified peptides are indicated by blue lines. (C) Evaluation of shRNA mediated knockdown of ETV6 in HEK293T cells by quantitative RT-PCR. Data represent mean ± SD of n=3 independent experiments. One-way analyses of variance were used to determine whether or not differences were statistically significant (**** p<0.0001, *** p<0.001, ** p<0.01 and * p<0.05). (D) Western blotting. (E) Quantification of (D) realized with Image Studio software. (D, E) Data represent mean ± SD of n=3 independent experiments. One-way analyses of variance were used to determine whether or not differences were statistically significant (*** p<0.001 and * p<0.05).