# Type I interferon associated epistasis may contribute to early disease-onset and high disease activity in juvenile-onset lupus

Yves Renaudineau, Amandine Charras, Valentina Natoli, Mathieu Fusaro, Eve

M.D. Smith, Michael Beresford, Christian Hedrich

► To cite this version:

Yves Renaudineau, Amandine Charras, Valentina Natoli, Mathieu Fusaro, Eve M.D. Smith, et al. Type I interferon associated epistasis may contribute to early disease-onset and high disease activity in juvenile-onset lupus. Clinical Immunology, 2024, 262, pp.110194. 10.1016/j.clim.2024.110194. hal-04532026

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## Review Article

# Type I interferon associated epistasis may contribute to early disease-onset and high disease activity in juvenile-onset lupus

Yves Renaudineau<sup>a,b,*†</sup>, Amandine Charras<sup>c,†</sup>, Valentina Natoli<sup>c,d</sup>, Mathieu Fusaro<sup>a,b</sup>, Eve M.D. Smith<sup>c,e</sup>, Michael W. Beresford<sup>c,e</sup>, Christian M. Hedrich<sup>c,e,*</sup>, on behalf of the UK jSLE Cohort Study<sup>2</sup>

<figure><img src="image_3.png" /></figure>

## ARTICLE INFO

**Keywords:**

Juvenile-onset systemic lupus erythematosus
jSLE
Interferon
QTL
Epistasis
SLEDAI-2 K

## ABSTRACT

Pathologic type I interferon (T1IFN) expression is a key feature in systemic lupus erythematosus (SLE) that associates with disease activity. When compared to adult-onset disease, juvenile-onset (j)SLE is characterized by increased disease activity and damage, which likely relates to increased genetic burden. To identify T1IFN-associated gene polymorphisms (TLR7, IRAK1, miR-3142/miR-146a, IRF5, IRF7, IFIH1, IRF8, TYK2, STAT4), identify long-range linkage disequilibrium and gene:gene interrelations, 319 jSLE patients were genotyped using panel sequencing. Coupling phenotypic quantitative trait loci (QTL) analysis identified 10 jSLE QTL that associated with young age at onset (<12 years; IRAK1 [rs1059702], TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760, rs3747517], STAT4 [rs3021866], TYK2 [rs280501], IRF8 [rs1568391, rs6638]), global disease activity (SLEDAI-2 K >10; IFIH1 [rs1990760], STAT4 [rs3021866], IRF8 [rs903202, rs1568391, rs6638]), and mucocutaneous involvement (TLR7 [rs3853839], IFIH1 [rs11891191, rs1990760]). This study suggests T1IFN-associated polymorphisms and gene:gene interrelations in jSLE. Genotyping of jSLE patients may allow for individualized treatment and care.

## 1. Introduction

Systemic lupus erythematosus (SLE) is a rare systemic autoimmune/inflammatory disease that is characterized by profound immune dysfunction, tissue damage, and organ failure [1-3]. Approximately

15-20% of patients with SLE develop disease in childhood or adolescence and are referred to as having juvenile-onset (j)SLE. Notably, when compared to adult-onset disease, jSLE is more severe with higher disease activity and more damage at diagnosis, higher mortality, and an increased need for immunomodulating treatment [2,4-6]. Furthermore,

<footnote><sup>a</sup> Immunology Department Laboratory, Referral Medical Biology Laboratory, Institut Fédératif de Biologie, Toulouse University Hospital Center, France</footnote>

<footnote><sup>b</sup> INFINITY, Toulouse Institute for Infectious and Inflammatory Diseases, INSERM U1291, CNRS U5051, University Toulouse III, Toulouse, France</footnote>

<footnote><sup>c</sup> Department of Women's & Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool, UK</footnote>

<footnote><sup>d</sup> Università degli Studi di Genova, Dipartimento di Neuroscienze, riabilitazione, oftalmologia, genetica e scienze materno-infantili, DINOGMI, Genoa, Italy</footnote>

<footnote><sup>e</sup> Department of Rheumatology, Alder Hey Children's NHS Foundation Trust, Liverpool, UK</footnote>

<footnote>* Corresponding authors.</footnote>

<footnote>E-mail address: christian.hedrich@liverpool.ac.uk (C.M. Hedrich).</footnote>

<footnote n="1">¹ Equal contribution.</footnote>

<footnote n="2">² Carla Roberts, Department of Women's & Children's Health, Institute of Life Course and Medical Sciences, University of Liverpool.Eslam Al-Abadi, Department of Rheumatology, Birmingham Children's Hospital, Birmingham.Kate Armon, Department of Paediatric Rheumatology, Cambridge University Hospitals, Cambridge. Kathryn Bailey, Department of Paediatric Rheumatology, Oxford University Hospitals NHS Foundation Trust, Oxford.Coziana Ciurtin, Centre for Adolescent Rheumatology, University College London, London.Janet Gardner-Medwin, Department of Child Health, University of Glasgow, Glasgow.Kirsty Haslam, Department of Paediatrics, Bradford Royal Infirmary, Bradford.Daniel P Hawley, Department of Paediatric Rheumatology, Sheffield Children's Hospital, Sheffield.Alice Leahy, Department of Paediatric Rheumatology, Southampton General Hospital, Southampton.Valentina Leone, Department of Paediatric Rheumatology, Leeds Children Hospital, Leeds.Flora McErlane, Paediatric Rheumatology, Great North Children's Hospital, Royal Victoria Infirmary, Institute of Cellular Medicine, Newcastle University, Newcastle upon Tyne.Gita Modgil, Department of Paediatrics, Musgrove Park Hospital, Taunton.Clarissa Pilkington, Department of Paediatric Rheumatology, Great Ormond Street Hospital, London.Athimalaipet V Ramanan, University Hospitals Bristol NHS Foundation Trust & Bristol Medical School, University of Bristol, Bristol.Satyapal Rangaraj, Department of Paediatric Rheumatology, Nottingham University Hospitals, Nottingham.Phil Riley, Department of Paediatric Rheumatology, Royal Manchester Children's Hospital, Manchester.Arani Sridhar, Department of Paediatrics, Leicester Royal Infirmary, Leicester, UK.</footnote>
phenotypical and pathophysiological differences even exist within the jSLE cohort, with sometimes “atypical” presentations in young patients with disease-onset before puberty (fewer autoantibodies, less renal and more CNS involvement), and a more “classical” picture in adolescents (high-titre autoantibodies, antibody-mediated features including anemia, nephritis, etc.) [7,8].

Across age groups, activation of type I interferon (T1IFN) pathways is a key event SLE [9] that associates with disease activity [10]. The central pathomechanistic role of T1IFN in SLE is supported by the study of “monogenic SLE” that affects a relatively small subset of patients (<5%). Notably, “monogenic SLE” patients exhibit either gain of function (GOF) mutations in activators or signalling molecules of the T1FN pathway, or loss of function (LOF) mutations in enzymes clearing nucleic acids from the cytoplasm of blood cells. Either act as strong T1FN inducers [3,8,11–15]. Besides (ultra-)rare high impact gene variants, in the remaining cohort of SLE patients, a long and constantly growing list of “common” low effect size gene variants (affecting the same genes altered in “monogenic” SLE) has been identified [1,3,16,17]. Notably, the frequency of individual “common” variants, their composition, and their additive effects vary between ethnicities/ancestries and paediatric-versus adult-onset disease cohorts [2,17,18]. Currently, however, the exact contribution of “common” variants to disease expression, clinical phenotypes and outcomes remains largely unclear (early disease-onset, disease activity, organ involvement).

This study aimed to identify T1IFN-associated gene variants and extended haplotypes in jSLE patients from the UK jSLE Cohort Study (http://www.liv.ac.uk/ukjsle) to associate genotypes with disease presentations and outcomes, and predict gene:gene interrelations (“epistasis”). We focused on paediatric patients, because of a previously proposed higher “genetic impact” in jSLE when compared to aSLE [2,17,18].

## 2. Material and methods

### 2.1. Study cohort

A total of 319 jSLE patients enrolled in the UK jSLE Cohort Study (http://www.liv.ac.uk/ukjsle) were included in this study. All patients were < 18 years of age at diagnosis and fulfilled at least four ACR-1997 classification criteria for SLE [19]. This cohort enrolled the jSLE patients who were included in a panel sequencing project that resulted in the publication of patients with “monogenic jSLE” [8]. Notably, data on common variants have not been published yet, and patients previously classified as having “monogenic SLE” (n = 12) were excluded from this analysis [8,20]. The following datasets were collected: (i) demographic information (age at disease onset, sex, ethnic background); (ii) global disease activity scores, including the paediatric British Isles Lupus Assessment Grade 2004 (pBILAG-2004) and the SLE Disease Activity Index 2000 (SLEDAI-2 K) scores [21,22]; (iii) positivity (pBILAG-2004 score A, B or C) and severity (using the numeric version of the pBILAG-2004 score: A = 12, B = 8, C = 1, and D/E = 0) of disease at the organ/system level [21]; and (iv) SLE-associated laboratory markers, including anti-double-stranded (ds)DNA antibodies, complement C3 and C4 fractions, and white blood cell (WBC) count as previously described [23,24]. Conducted in accordance with the declaration of Helsinki, the study received ethical approval from the National Research Ethics Service Northwest (REC 06/Q1502/77), and all participants and/or their legal guardians gave written informed patient assent/consent.

### 2.2. Sequencing and bioinformatic analysis

Before sequencing (MiSeq2500 technology, Illumina, San Diego, CA, USA), sequence capture enrichment (NimbleGen/Roche, Switzerland) was conducted as described previously [8]. Next, FastQ reads (Sickle v1.2) were obtained after demultiplexing, adaptor removal, and quality trimming (Cutadapt v1.2.1) [25,26]. Then, sequencing data were

<table><caption>**Table 1**
Demographic and clinical information at diagnosis.</caption><thead><tr><th></th><th>Juvenile-onset systemic lupus erythematosus patients (n = 319)</th></tr></thead><tbody><tr><td>Age at diagnosis (mean ± SEM, years)</td><td>12.1 ± 0.2</td></tr><tr><td>Ethnicity</td><td></td></tr><tr><td>African</td><td>54 (16.9%)</td></tr><tr><td>Asian</td><td>110 (34.5%)</td></tr><tr><td>European</td><td>146 (45.7%)</td></tr><tr><td>Unknown</td><td>9 (8.2%)</td></tr><tr><td>pBILAG-2004 score (at diagnosis)</td><td>10.0 ± 0.5</td></tr><tr><td>SLEDAI-2 K score (at diagnosis)</td><td>10.4 ± 0.5</td></tr><tr><td>Constitutional (pBILAG-2004 [% positivity])</td><td>2.9 ± 0.3 [40.6%]</td></tr><tr><td>Mucocutaneous (pBILAG-2004 [% positivity])</td><td>3.4 ± 0.2 [58.8%]</td></tr><tr><td>Musculoskeletal (pBILAG-2004 [% positivity])</td><td>2.6 ± 0.2 [55.3%]</td></tr><tr><td>Haematological (pBILAG-2004 [% positivity])</td><td>3.0 ± 0.2 [62.9%]</td></tr><tr><td>Renal (pBILAG-2004 [% positivity])</td><td>3.0 ± 0.2 [39.0%]</td></tr><tr><td>Neurological (pBILAG-2004 [% positivity])</td><td>0.90 ± 0.16 [9.7%]</td></tr><tr><td>Cardiorespiratory (pBILAG-2004 [% positivity])</td><td>1.2 ± 0.2 [12.6%]</td></tr><tr><td>Gastrointestinal (pBILAG-2004 [% positivity])</td><td>0.6 ± 0.1 [7.2%]</td></tr><tr><td>Ophthalmic (pBILAG-2004 [% positivity])</td><td>0.1 ± 0.0 [1.6%]</td></tr><tr><td>Anti-dsDNA IgG level (IU/mL [% positivity])</td><td>192 ± 39 [60.8%]</td></tr><tr><td>C3 level (g/L), [% low level]</td><td>0.94 ± 0.03 [40.3%]</td></tr><tr><td>C4 level (g/L), [% low level]</td><td>0.16 ± 0.01 [55.9%]</td></tr><tr><td>White blood cell count (10<sup>9</sup>/L)</td><td>6.5 ± 0.2</td></tr></tbody></table>

The organ/system numerical score of pBILAG-2004 considers: A = 12, B = 8, C = 1, and D/E = 0. Positivity was considered when the pBILAG-2004 score was equal to A, B, or C. SEM: standard error of mean, pBILAG-2004: paediatric British Isles Lupus Assessment Grade 2004, SLEDAI-2 K: SLE Disease Activity Index 2000, anti-dsDNA: anti-double-stranded-DNA, IgG: Immunoglobulin G, IU: International Unit.

aligned using BWA (Burrows-Wheeler Alignment) [27], and variants were subsequently detected, filtered and annotated [28,29].

The linkage disequilibrium (LD) parameter D' was investigated using SNPstats (http://bioinfo iconcologia.net/SNPstats) [30]. Ethnicity specific European, South Asian, and African/Caribbean minor allele frequencies (MAF) were extracted from the large-scale international genome Aggregation Database (gnomAD) v2.1.1 (https://gnomad.broadinstitute.org/) and the calculated (c)MAF was obtained from jSLE racial distribution (European 47.1%, Asian 35.5% (the majority of which South Asian: 69.1%), and African/Caribbean 17.4%). Moreover, to determine jSLE MAF at chromosome X (TLR7, IRAK1), we adopted an allele coding strategy by considering hemizygous males (0 null, 2 hemizygous for the minor allele) equivalent to homozygous females (0 null, 1 heterozygous, 2 homozygous), as proposed previously [31]. Regarding jSLE quantitative trait loci (QTL) characterization (November 2023), the publicly available Ensembl Variant Effect Predictor tool (https://www.ensembl.org/) using in silico Sorting Intolerant From Tolerant (SIFT) and Combined Annotation Dependent Depletion (CADD) scores were used to predict functional consequences of variants, as described previously [32–34]. Notably, QTL are genetic regions that influence phenotypic variation of a complex trait, often through genetic interactions. Clinical associations and cis/trans expression control were investigated using Pubmed (https://pubmed.ncbi.nlm.nih.gov/), Clin-Var (https://www.ncbi.nlm.nih.gov/clinvar), GWAS catalogue (https://www.ebi.ac.uk/gwas/) and GTEx (https://gtexportal.org/) [35].
A

Polymorphisms (MAF>0.05%, n=66/731)

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

B

<figure><img src="image_6.png" /></figure>

C

<figure><img src="image_7.png" /></figure>

**Fig. 1.** Short- and long-range interplay between single nucleotide polymorphisms (SNPs) from type I interferon pathway in 319 patients with juvenile-onset systemic lupus erythematosus (jSLE). **A:** Allele frequencies at endoplasmic (TLR7, IRAK1, miR-3142 that encodes miR-146a, IRF5, IRF7), cytosolic (IFIH1, IRF8), and signalling (TYK2 and STAT4) type I interferon loci from 319 jSLE patients, 66 SNPs with a MAF ≥ 0.05 were selected (dotted line). **B:** Locations of the non-synonymous 57 SNPs within the promoter/enhancer, exons (missense), miRNAs, introns, splice donors, and 3' untranslated region (UTR). **C:** Linkage disequilibrium (LD) between the 57 non-synonymous SNPs to explore at short-range unrecombined loci block (D' ≥ 0.95), and at long-range inter-loci high LD (D' ≥ 0.95), low LD (0.7 ≤ D' < 0.95), very low LD (0.2 ≤ D' < 0.7) and no LD (D' < 0.2).

## 2.3. Statistical analysis

Quantitative data are presented as mean ± standard error of mean (SEM). For quantitative trait analysis (QTL), a two-step strategy was used with first a Spearman's correlation test and, when positive (*p* < 0.01), an analysis of variance (ANOVA) test with Dunn's post-hoc correction was performed. This approach was chosen to meet two objectives: i) to minimize a Spearman's outlier effect due to non-equal distribution between groups (0, 1, 2 minor alleles) and ii) to discriminate between recessive effects (0&1 allele >2 alleles), dominant effects (1&2 alleles > or < 0 allele), and additive effects (2 alleles >1 allele >0 allele). For categorical data, differences among groups were analysed using Fisher's exact test. Analyses were completed using PRISM 9.5 (GraphPad Software, La Jolla, CA, USA).

# 3. Results

## 3.1. Demographic and clinical information

A total of 319 polygenic jSLE patients were enrolled from the UK jSLE Cohort Study, including 51 (16%) males and 268 (84%) females (Table 1). The mean age at diagnosis was 12.1 ± 0.2 years, and most patients were of European (45.8%), Asian (34.5%) and black African/ Caribbean (16.9%) ancestry. At first visit/inclusion, patients mainly exhibited haematological (62.9%), mucocutaneous (58.8%), musculoskeletal (55.3%), constitutional (40.6%) and renal (39.0%)

involvement.

## 3.2. Type 1 IFN pathway polymorphisms in jSLE

Nine genes involved in endoplasmic (TLR7, IRAK1, miR-3142 that encodes miR-146a, IRF5, and IRF7) and cytosolic (IFIH1, IRF8) sensing of nucleic acids, and transcriptional control of T1IFNs (TYK2, STAT4) were genotyped from in 319 jSLE patients (Supplementary Fig. 1). Using a MAF ≥ 0.05 threshold, 66 SNPs of 731 polymorphisms obtained during panel sequencing were identified (Fig. 1A). After excluding synonymous SNPs (n = 9), 57 SNPs affecting promoters/enhancers (29.8%), 3' Untranslated Regions (UTRs) (24.6%), exonic regions (21%), splice donors (12.3%), intronic regions (10.5%) and miRNA (1.75%) remained (Fig. 1B).

Then, non-random associations between the 57 SNPs were tested by Linkage Disequilibrium (LD) to explore short-range haplotype blocks, at locus level, and long-range gene-gene interactions (also known as epistatic selections), at inter- or intra-chromosomal level [36] (Fig. 1C). To this end, the disequilibrium D' value was interpreted as follow: "high" LD (D' ≥ 0.95), "low" LD (0.7 ≤ D' < 0.95), "very low" LD (0.2 ≤ D' < 0.7) and "no LD" (D' < 0.2). As for adult-onset, SLE haplotype blocks in "high" LD were identified for the IRAK1, IFIH1, IRF5, IRF7, IRF8, TYK2 and STAT4 loci from jSLE patients [37,38]. Moreover, SNP inter-locus LD was high in 18/1368 (1.3%) cases, low in 36/1368 (2.6%) cases, and very low in 406/1368 (29.7%) cases. In this cohort of patients, we observed interrelationships between genes affecting the T1IFN pathway,
<figure><img src="image_8.png" /><figcaption>**Fig. 2.** Type I interferon pathway jSLE quantitative trait loci (QTL). **A:** Manhattan plot of the 57 single nucleotide polymorphisms (SNPs) located in here studied type I interferon loci (TLR7, IRAK1, miR-3142 that encodes miR-146a, IRF5, IRF7, IFIH1, IRF8, TYK2, and STAT4) with a MAF ≥ 0.05 and their association with jSLE age at diagnosis (years), disease activity (pBILAG-2004 and SLEDAI-2 K scores), organ activity (pBILAG-2004 organ score) and bio-clinic features. On the y-axis, the -log10(p-value) is presented, which assesses the concordance between the genotype (0, 1, and 2 minor alleles) and the quantitative trait using Spearman's correlation test. The cut-off p-value was fixed at 0.01, corresponding to an empirical 2% percentile from all p-values. **B:** Genotype-phenotype associations are tested by ANOVA. Recessive (blue, 0&1 allele >2 alleles), dominant (red, 1&2 alleles >0 allele or 0 allele >1&2 alleles) and additive (green, 2 alleles >1 allele >0 allele) effects are shown. Mean ± SEM and p-values <0.05 are indicated. **C:** Linkage disequilibrium (LD) between the 6 jSLE QTL and the 57 SNPs with a MAF ≥ 0.05 located in the here studied type I interferon loci, which supports long-range and minor gene:gene interactions with the 6 jSLE QTL. LD was ranked as high LD (D' ≥ 0.95), low LD (0.7 ≤ D' < 0.95), very low LD (0.2 ≤ D' < 0.7) and no LD (D' < 0.2). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

which may result in long-range gene:gene interactions between one or
more variants.

3.3. *Type 1 IFN pathway quantitative trait loci (QTL) in jSLE*

Next, to investigate associations between genotypes and distinct jSLE
phenotypes and/or outcomes, QTL analysis was performed using a linear
model (Fig. 2A). To this end, Spearman's correlation test was used to
explore genotype:phenotype associations (0, 1, or 2 representing the
number of minor alleles) [39]. Using a Spearman's rho p value of 0.01 as
threshold, associations were identified with pre-pubertal/"early" dis-
ease onset (<12 years of age; IRAK1 rs1059702, TYK2 rs280501), "high"
disease activity (SLEDAI-2 K > 10; IFIH1 rs1990760, IFIH1 rs11891191,
and STAT4 rs3024866), and mucocutaneous activity (IFIH1 rs1990760,
IFIH1 rs11891191, and TLR7 rs3853839) (Fig. 2A). Analysis of the
profiles coupled with a positive ANOVA confirmatory test further sup-
ported a recessive effect for IRAK1 rs1059702, and TYK2 rs280501; a
dominant effect for IFIH1 rs1990760, IFIH1 rs11891191, and STAT4
rs3024866; and an additive effect for TLR7 rs3853839 (Fig. 2B).

Focusing on these 6 jSLE QTL/SNPs, we observed that inter-loci LD
was high between IFIH1 (rs11891191) and 5 SNPs in TYK2, and low

between IFIH1 (rs11891191) and IRAK1 (rs1059702), IRF5 (rs2070197), 3 SNPs in IRF8, and 2 additional SNPs in TYK2 (Fig. 2C). On the other hand, low LD was identified between IRAK1 (rs1059702) and STAT4 (rs13389408). Overall, these observations may support the hypothesis that long-range gene:gene interactions between jSLE QTL and T1IFN-associated gene variants exist and affect disease onset and outcomes.

3.4. Gene:gene interaction to control early jSLE onset

To test whether age at jSLE onset may be influenced by gene:gene
interaction (epistasis), a two-locus linear model with 9 possible geno-
types was tested as previously reported: (SNP1: 0, 1, 2 minor alleles) x
(SNP2: 0, 1, 2 minor alleles) [40]. Spearman's correlation test was used
to detect whether age at diagnosis associated with SNP2 genotype (0, 1,
and 2) when the SNP1 genotype was fixed (0, 1, or 2). In this analysis,
SNP2 was one of the 57 SNPs with a MAF ≥ 0.05 and SNP1 was either
the jSLE QTL IRAK1 rs1059702 or TYK2 rs280501, the two SNPs iden-
tified as associated with early disease onset in the here presented cohort
(Fig. 3A).

On the basis of a positive Spearman's correlation test and a positive
<figure><img src="image_9.png" /><figcaption>**Fig. 3.** Type I interferon gene-gene interaction and association with an early jSLE disease onset. **A:** Heat map showing the SNP2 genotype effect on jSLE age at diagnosis after decomposition of the SNP1 genotype according to the number of minor alleles (0, 1 and 2; jSLE QTL *IRAK1* rs1059702 and *TYK2* rs280501). The Spearman's rho cut-off *p* value was set at 0.01 (red), and *p* values ranging from 0.01 to 0.05 are indicated (blue). **B/D:** ANOVA step confirmation of the minor SNP2 genotype-phenotype effect according to the SNP1 genotype: homozygous *IRAK1* major allele (*IRAK1* = 0; **B**), homozygous *IRAK1* minor allele (*IRAK1* = 2; **C**), and heterozygous/homozygous *TYK2* minor allele (*TYK2* = 1&2, **D**). A minor recessive effect predominates (0&1 allele > or < 2 alleles), although a minor dominant effect may be suspected between *IFIH1* rs1990760 genotype and age at diagnosis when considering the homozygous *IRAK1* major allele. Mean ± SEM and significant *p* values are indicated. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

ANOVA test, three main gene-gene profiles of co-dependence remained (Fig. 3B, C, D). Thus, in the absence of the minor allele in *IRAK1* rs1059702 (*IRAK1* = 0), 4 associations with recessive or dominant jSLE QTL were retrieved at 3 loci corresponding to *TLR7* (rs3853839), *IFIH1* (rs1990760 and rs3747517) and *STAT4* (rs3024866) (Fig. 3B). Secondly, when the minor allele of *IRAK1* rs1059702 was homozygote (*IRAK1* = 2), *IRF8* rs1568391 and rs6638 can act as a recessive jSLE QTL (Fig. 3C). Finally, a recessive effect was identified between the minor alleles of *TYK2* rs280501 (*TYK2* = 1 and 2) and *IRF8* rs6638 (Fig. 3D), which was not retrieved with *IRF8* rs305082, rs305077 and rs1568391 (non-significant ANOVA test, data not shown). These observations may underscore co-dependency of gene variants and epistasis in jSLE patients affecting several genes involved in the T1IFN signalling pathway.

### 3.5. Gene:gene interactions associate with jSLE global disease and mucocutaneous activity

Following the observation that *IFIH1*, *STAT4*, and *TLR7* jSLE QTLs

are linked to “high” SLE disease activity (SLEDAI-2 K >10) and/or to mucocutaneous activity, we sought to test gene:gene interactions between these loci (Fig. 4A). Inter-gene associations were retrieved between the jSLE QTL STAT4 rs3024866 and three *IRF8* SNPs: rs903202, rs1568391 and rs6638 (Fig. 4B). In contrast, associations between the jSLE QTL *TLR7* rs3853839 and *IFIH1* rs1990760 or rs111891191 were not confirmed by ANOVA (data not shown).

### 3.6. Quantitative trait loci associate with demographic and clinical features

The 10 genomic locations with an associated QTL identified in the here presented cohort of jSLE patients, included 3 SNPs causing missense variants (*IRAK1* rs1059702, *IFIH1* rs1990760 and *rs3747517*), 2 promoter SNPs (*TYK2* rs280501), one enhancer SNP (*STAT4* rs3024866), 1 splice donor variant (*IRF8* rs903202) and 4 SNPs in 3'-UTR regions (*TLR7* rs3853839, *IFIH1* rs11891191, *IRF8* rs1568391 and rs6638) (Table 2). All jSLE QTL MAFs were significantly increased in the
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /><figcaption>**Fig. 4.** Type I/II interferon gene-gene interaction and association with disease activity (SLEDAI-2 K) and mucocutaneous involvement (pBILAG-2004 score). A: Heat map showing the SNP2 genotype effect on disease activity (SLEDAI-2 K, upper panel) and mucocutaneous involvement (bottom panel) after decomposition of the SNP1 genotype according to the number of minor alleles (0, 1 and 2; jSLE QTL IFIH1 rs1990702, IFIH1 rs11891191, STAT4 rs3024866, and TLR7 rs3853839). The Spearman's rho cut-off *p* value was set at 0.01 (red), and *p* values ranging from 0.01 to 0.05 are indicated (blue). B: ANOVA step confirmation of the IRF8-SNP2 genotype-phenotype dominant effect (0 allele >1&2 alleles) according to the homozygous STAT4 minor allele (STAT4 = 2) for rs903202, rs1568391 and rs6638. Other Spearman's associations (e.g., IFIH1 = 2 rs1990702 and STAT4 rs3024866 or TLR7 = 0 rs3853839 and IFIH1 rs1990702) were not shown as not confirmed by ANOVA. Mean ± SEM and significant *p* values are indicated. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

jSLE population as compared to the control populations, considering ethnic background. Among the here identified QTLs, 6/10 have previously been reported as jSLE risk-factors, 6/10 affect gene expression through *cis* regulation, and T1IFN expression through *trans* regulation was proposed for 2 cases. Finally, sex and ethnicity influence jSLE QTL effects on disease onset, SLEDAI-2 K, and mucocutaneous involvement were recorded (Table 3).

## 4. Discussion

The key implication of T1IFN in SLE susceptibility is supported by several lines of evidence, including their involvement in “monogenic SLE” pathogenesis, the identification of common disease-associated variants in genome-wide association studies (GWAS) studies in adult-onset SLE patients, epigenetic studies and reports on SLE-onset in response to T1IFN treatment [14,15,41–43]. While a substantial and constantly growing body of literature supports a key role of (ultra-)rare T1IFN signalling associated variants in paediatric “monogenic SLE”, these cases make up only for a relatively small proportion of SLE patients

(<5%) [3,8,11–15]. While children with “multifactorial classical” jSLE carry an increased genetic burden when compared to adults [2,17,18], common variants affecting T1IFN signalling have not been systematically investigated in large jSLE patient cohorts.

In the here presented study, associations between jSLE, demographic and clinical features, and polymorphisms in genes involved in endoplasmic (TLR7, IRAK1, miR-3142 that encodes miR-146a, IRF5, and IRF7) and cytosolic (IFIH1, IRF8) sensing of nucleic acids, and transcriptional control of T1IFNs (TYK2, STAT4) were studied. The chosen approach identified associations with disease-onset before the 12th birthday (IRAK1, TLR7, IFIH1, STAT4, TYK2, IRF8), “high” disease activity (SLEDAI-2 K >10; IFIH1, STAT4, IRF8), and mucocutaneous involvement (TLR7, IFIH1). Moreover, genetic impact on disease expression and phenotype may be reinforced by gene:gene interrelations (Fig. 5).

We further identified 10 SLE-QTLs located in gene loci centrally relevant to T1IFN expression that confer an increased risk of developing SLE when comparing allele frequencies between jSLE patients and healthy controls after ethnic adjustment. This observation agrees with
<caption>**Table 2**</caption>

Quantitative trait loci associated with age at diagnosis (onset), disease activity using the SLEDAI-2 K score and mucocutaneous disease activity.

<table><thead><tr><th></th><th>Position (hg38)</th><th>Effect (gene location)</th><th>cMAF in controls (Eur, Asi, Afr)</th><th>MAF in j-SLE (p value vs cMAF)</th><th>Association with SLE (risk allele)</th><th>Cis-eQTL and IFNα-eQTL</th><th>j-SLE QTL</th></tr></thead><tbody><tr><td>TLR7 rs3853839</td><td>X: 12,887,911</td><td>*881C > G (3'UTR)</td><td>G: 0.30 (0.17/0.52/0.18)</td><td>G: 0.55 (p &lt; 10-15)</td><td>Yes (G) [71-77]</td><td>(G > C) TLR7, IFNα [78-81]</td><td>Onset, mucocutaneous</td></tr><tr><td>IRAK1 rs1059702</td><td>X: 154,018,741</td><td>A > G: Phe196Ser (tolerated &amp; benign missense)</td><td>G: 0.73 (0.86/0.45/0.96)</td><td>G: 0.80 (p = 0.006)</td><td>Yes (A) [37,71,82-86]</td><td>(G > A) MECP2 [37]</td><td>Onset</td></tr><tr><td>IFIH1 rs11891191</td><td>2: 162,267,194</td><td>*9C > T (3'UTR)</td><td>T: 0.06 (0.00/0.00/0.33)</td><td>T: 0.10 (p = 0.006)</td><td>No</td><td>No</td><td>Onset, mucocutaneous</td></tr><tr><td>IFIH1 rs1990760</td><td>2: 162,267,541</td><td>C > T: Ala946Thr (tolerated &amp; benign missense)</td><td>T: 0.51 (0.61/0.53/0.18)</td><td>T: 0.71 (p = 2 × 10-12)</td><td>Yes (T) [87-94]</td><td>(T > C) IFNα [91]</td><td>Onset, activity, mucocutaneous</td></tr><tr><td>IFIH1 rs3747517</td><td>2: 162,272,314</td><td>C > T: His843Arg (tolerated &amp; benign missense)</td><td>C: 0.66 (0.73/0.59/0.62)</td><td>C: 0.89 (p &lt; 10-15)</td><td>Yes (C) [87]</td><td>unknown</td><td>Onset</td></tr><tr><td>TYK2 rs280501</td><td>19: 10,380,646</td><td>-452C > T302 (Promoter)</td><td>T: 0.14 (0.15/0.04/0.32)</td><td>T: 0.39 (p &lt; 10-15)</td><td>Yes (T) [95]</td><td>(T > C) TYK2</td><td>Onset</td></tr><tr><td>STAT4 rs3024866</td><td>2: 191,058,115</td><td>1113-4G > A (enhancer)</td><td>A: 0.62 (0.74/0.66/0.21)</td><td>A: 0.81 (p = 9 × 10-13)</td><td>Yes (A) [96,97]</td><td>STAT4</td><td>Onset, activity</td></tr><tr><td>IRF8 rs903202</td><td>16: 85,914,173</td><td>-60 + 2 T > C (splice donor)</td><td>T: 0.48 (0.47/0.47/0.51)</td><td>T: 0.70 (p = 3 × 10-15)</td><td>No</td><td>(T > C) IRF8</td><td>Activity</td></tr><tr><td>IRF8 rs1568391</td><td>16: 85,921,698</td><td>*416G > T (3'UTR)</td><td>T: 0.67 (0.51/0.83/0.75)</td><td>T: 0.79 (p = 2 × 10-6)</td><td>No</td><td>(T > C) IRF8</td><td>Onset, activity</td></tr><tr><td>IRF8 rs6638</td><td>16: 85,922,438</td><td>*1156 A > T (3'UTR)</td><td>T: 0.66 (0.51/0.84/0.70)</td><td>T: 0.78 (p = 9 × 10-6)</td><td>No (tuberculosis) [98]</td><td>No</td><td>Onset, activity</td></tr></tbody></table>

hg38: human genome build 38 (release in December 2013), cMAF: calculated Minor Allele Frequency, Eur: European, Asi: Asian, Afr: African/Caribbean, eQTL: Expression Quantitative Trait Loci.

See Material and methods section for online databases used.

<caption>**Table 3**</caption>

Sex and ethnical influence on jSLE quantitative trait loci (QTL).

<table><thead><tr><th>Gene variant</th><th>Association with</th><th>All (Spearman's p value)</th><th>Female (n = 268)</th><th>Male (n = 50)</th><th>European (n = 146)</th><th>Non-European (n = 163)</th></tr></thead><tbody><tr><td>IRAK1 rs1059702</td><td>Age at diagnosis</td><td>0.002*</td><td>0.012*</td><td>0.056</td><td>0.306</td><td>0.0002*</td></tr><tr><td>IRAK1 rs1059702</td><td>SLEDAI-2 K</td><td>0.124</td><td>0.043*</td><td>0.868</td><td>0.988</td><td>0.063</td></tr><tr><td>IRAK1 rs1059702</td><td>Mucocutaneaus</td><td>0.895</td><td>0.121</td><td>0.502</td><td>0.341</td><td>0.186</td></tr><tr><td>TYK2 rs280501</td><td>Age at diagnosis</td><td>0.008*</td><td>0.047</td><td>0.039*</td><td>0.266</td><td>0.013*</td></tr><tr><td>TYK2 rs280501</td><td>SLEDAI-2 K</td><td>0.088</td><td>0.039*</td><td>0.831</td><td>0.154</td><td>0.813</td></tr><tr><td>TYK2 rs280501</td><td>Mucocutaneaus</td><td>0.756</td><td>0.379</td><td>0.259</td><td>0.564</td><td>0.799</td></tr><tr><td>IFIH1 rs1990760</td><td>Age at diagnosis</td><td>0.153</td><td>0.262</td><td>0.083</td><td>0.330</td><td>0.196</td></tr><tr><td>IFIH1 rs1990760</td><td>SLEDAI-2 K</td><td>0.006*</td><td>0.099</td><td>0.001*</td><td>0.132</td><td>0.089</td></tr><tr><td>IFIH1 rs1990760</td><td>Mucocutaneaus</td><td>0.008*</td><td>0.006*</td><td>0.664</td><td>0.059</td><td>0.604</td></tr><tr><td>IFIH1 rs11891191</td><td>Age at diagnosis</td><td>0.663</td><td>0.415</td><td>0.563</td><td>0.207</td><td>0.391</td></tr><tr><td>IFIH1 rs11891191</td><td>SLEDAI-2 K</td><td>0.005*</td><td>0.01*</td><td>0.065</td><td>0.148</td><td>0.012*</td></tr><tr><td>IFIH1 rs11891191</td><td>Mucocutaneaus</td><td>0.003*</td><td>0.008*</td><td>0.131</td><td>0.107</td><td>0.01*</td></tr><tr><td>IFIH1 rs3747517</td><td>Age at diagnosis</td><td>0.929</td><td>0.971</td><td>0.711</td><td>0.442</td><td>0.323</td></tr><tr><td>STAT4 rs3024866</td><td>SLEDAI-2 K</td><td>0.006*</td><td>0.015*</td><td>0.338</td><td>0.020*</td><td>0.201</td></tr><tr><td>TLR7 rs3853839</td><td>Age at diagnosis</td><td>0.170</td><td>0.411</td><td>0.278</td><td>0.663</td><td>0.001*</td></tr><tr><td>TLR7 rs3853839</td><td>SLEDAI-2 K</td><td>0.764</td><td>0.620</td><td>0.713</td><td>0.441</td><td>0.125</td></tr><tr><td>TLR7 rs3853839</td><td>Mucocutaneaus</td><td>0.004*</td><td>0.014*</td><td>0.179</td><td>0.081</td><td>0.033*</td></tr><tr><td>IRF8 rs1568391</td><td>Age at diagnosis</td><td>0.03*</td><td>0.290</td><td>0.007*</td><td>0.316</td><td>0.148</td></tr><tr><td>IRF8 rs1568391</td><td>SLEDAI-2 K</td><td>0.01*</td><td>0.004*</td><td>0.567</td><td>0.005*</td><td>0.776</td></tr><tr><td>IRF8 rs1568391</td><td>Mucocutaneaus</td><td>0.816</td><td>0.613</td><td>0.630</td><td>0.557</td><td>0.842</td></tr><tr><td>IRF8 rs6638</td><td>Age at diagnosis</td><td>0.03*</td><td>0.205</td><td>0.02*</td><td>0.322</td><td>0.121</td></tr><tr><td>IRF8 rs6638</td><td>SLEDAI-2 K</td><td>0.01*</td><td>0.003*</td><td>0.464</td><td>0.004*</td><td>0.664</td></tr><tr><td>IRF8 rs6638</td><td>Mucocutaneaus</td><td>0.984</td><td>0.676</td><td>0.475</td><td>0.479</td><td>0.558</td></tr><tr><td>IRF8 rs903202</td><td>Age at diagnosis</td><td>0.142</td><td>0.662</td><td>0.01*</td><td>0.438</td><td>0.198</td></tr><tr><td>IRF8 rs903202</td><td>SLEDAI-2 K</td><td>0.206</td><td>0.041*</td><td>0.084</td><td>0.054</td><td>0.886</td></tr><tr><td>IRF8 rs903202</td><td>mucocutaneaus</td><td>0.529</td><td>0.690</td><td>0.646</td><td>0.729</td><td>0.499</td></tr></tbody></table>

previous reports on *TLR7* rs3853839, *IFIH1* rs1990760 and *rs3747517*, *TYK2* rs280501 and *STAT4* rs3024866 in adult-onset SLE [37,38]. Previously reported variants in distal enhancers regulating the *IRF8* gene that control T1IFN expression were, however, not covered by the chosen panel sequencing approach [44–46]. Thus, the here presented study suggests a possible role of 3 *IRF8* QTLs (rs903202, rs1568391 and rs6638), identified in silico, in the pathogenesis of jSLE. Concerning *IRAK1* rs1059702 (A > G, Phe196Ser), present in the highly conserved

*TMEM187-IRAK1-MECP2* haplotype [37], dual effects may be suspected. Previous studies have shown that, at the protein level, the SLE risk allele (A) controls Nuclear Factor kappa-light-chain-enhancer of activated B cells (NF-kB) activation, while at the transcriptional level, the protective allele (G) controls *in cis* Methyl CpG binding Protein 2 (MECP2), MECP2 being involved in epigenetic control and T cell self-reactivity [37,47–50]. Observations from the here presented study highlight the possible interrelationship or epistasis between the risk

<footnote>* Significant p value (Spearman's rho).</footnote>
<figure><img src="image_12.png" /><figcaption>**Fig. 5.** Gene-gene interaction analysis highlights the central role for endoplasmic (TLR7, IRAK1), cytoplasmic (IFIH1, IRF8) and signalling (TYK2, STAT4) type I interferon pathway in juvenile-onset systemic lupus erythematosus (jSLE). Differences are reported based on the phenotype, which supports selected pathways to control disease onset and disease activity.</figcaption></figure>

allele (A) at *IRAK1* rs1059702 (IRAK1 = 0) with *TLR7*, *IFIH1* and *STAT4*, and between the protective allele (G) at *IRAK1* rs1059702 (IRAK1 = 2) with *IRF8*. However, complementary in vitro approaches are required to further study the impact of these combinations of variants on NF-kB and T1IFN signalling.

Epistasis summarizes interdependent effects of two or more genes on a clinical disease phenotype. This can result from multiple processes, including direct gene:gene interactions, functional redundancy, e.g., within gene clusters, and/or pathway activation or suppression (reviewed in [51]). Consistent with the genomic locations of here identified jSLE QTLs (4 × 3'UTR, 3× gene regulation/splicing elements) and predicted positive transcriptional effects based on published and freely accessible expression (e)QTL data (Table 2), we propose that jSLE QTLs act by positively controlling T1IFN expression. This may have implications on jSLE-associated sex bias (2 jSLE QTLs are located on the chromosome X), ethnicity distribution (important MAF variations between jSLE QTLs located at the same locus), organ involvement (tissue-specific gene expression), and age at disease-onset (additive effects) in jSLE and adult-onset SLE. A remaining key question is related to the role of gene:gene interactions on "other" SLE-associated pathways, such as pro-inflammatory cytokine expression [52,53], the NF-kB pathway [54], T cell activation [55], and B cell signalling [56]. To answer this, more data are required to better characterize major and minor (revealed by gene:gene interaction) SLE QTLs and their contribution to patients' age at diagnosis, ethnicity and organ involvement. Furthermore, coupling of transcriptomic (e.g., eQTL), proteomic (e.g., T1IFN), and phenotypic (p) QTL analysis are necessary to also propose new tools for individualized treatment and care.

Despite the established association between pathological T1IFN expression and SLE development and activity, the exact stimuli resulting in altered gene expression are less clear. The extracellular (endoplasmic) T1IFN pathway is mainly present in phagocytic cells and induced by uptake and recognition of nucleic acid-containing macromolecules (here investigated: *TLR7*, *IRAK1*, *miR-3142* that encodes miR-146a, *IRF5*, and *IRF7*); the ubiquitous intracellular (cytoplasmic) pathway senses cytoplasmic nuclei acid, thereby representing an innate antimicrobial mechanism (here investigated: *IFIH1*, *IRF8*) (**Supplementary Fig. 1**) [16,57]. Lastly, T1IFN expression can be induced by transcription factor signalling that is under the control of protein kinases (here investigated: *TYK2*, *STAT4*) [16,57]. Data from this study for the first time confirm a key role of the endoplasmic (*TLR7*, *IRAK1*) and cytoplasmic (*IFIH1*) pathways in "multifactorial classic" jSLE. The key importance and impact of these genes are confirmed by their involvement in rare "monogenic SLE", namely GOF in *TLR7* [15] and *IFIH1* [58,59] and lupus-prone mice with additional copies of *Tlr7* (Yaa strain) [60,61] or

*Ifih1* [59,62].

Whether T1IFN dysregulation in "multifactorial classical" SLE represents the starting point of disease pathophysiology or amplifies pathology induced by tissue damage remains under debate [3,11]. In SLE, enhanced NETosis and tissue inflammation result in cell death and the release of nuclear components that may increase T1IFN production through their recognition by the endoplasmic pathway [63]. Exposure to ultraviolet (UV) light may be the common denominator between endo- and cytoplasmic T1IFN induction in SLE. Acute UVB exposure, that has been linked with disease flares in SLE (and other autoimmune diseases, such as dermatomyositis and Sjögren syndrome) [64–67] controls T1IFN expression through direct (cytosolic: DNA damage, nucleic acid exposure) and/or indirect (endoplasmic: keratinocyte apoptosis and necrosis, immune complex formation) effects [68–70]. This is in line with the observation that additional copies of *Ifih1* are not sufficient to cause SLE in mice, but concomitant deletion of *FcγR2B* worsens the inflammatory state, resulting in disease expression. Thus, we hypothesise that the here observed interrelation between *IFH1* (rs1990760, rs11891191) and *TLR7* (rs3853839) may impact disease outcomes and mucocutaneous involvement (Fig. 2B) [62].

In conclusion, the here presented study involving 319 jSLE patients observed an interrelation and interdependence between genes associated with T1IFN pathways that associate with phenotypic features and disease outcomes. These included associations with early disease onset (IRAK1 rs1059702, *TYK2* rs280501), high disease activity (SLEDAI-2 K >10: *IFIH1* rs1990760, rs11891191, *STAT4* rs3024866), and mucocutaneous involvement (*IFIH1* rs1990760, rs11891191, *TLR7* rs3853839) (Fig. 5). These observations promise genotyping as a future tool for patient stratification and individualized treatment and care. Studies in larger, unrelated, and multi-ethnic cohorts are necessary to confirm these findings.

## Funding

This work has been supported by LUPUS UK (CMH, MWB), Versus Arthritis UK for the EATC4Children (CMH, MWB), and the NIHR (GOSH BRC PEI, CMH).

## Data availability

Data will be made available on request.

## Acknowledgements

This work is dedicated to Professor George C. Tsokos, a great mentor, role model and friend, who committed his career to understanding disease mechanisms in SLE and other lymphocyte-mediated autoimmune/inflammatory diseases. Over more than four decades, Professor Tsokos' work has been at the cutting edge of a highly complex and competitive field: T cell biology and SLE research. His work has centrally contributed to our current understanding of disease pathophysiology, especially the involvement of transcription factor dysregulation and epigenetic events that may be mediated by environmental exposures.

George's work influenced treatment and monitoring of patients with SLE and touched the lives of uncountable clinical and academic trainees as well as their families. The international collaborative group producing this manuscript is the result of George's generous and supportive approach to research and mentorship. As a postdoctoral fellow, CMH had the opportunity to represent the "Tsokos lab" at an international meeting in Changsha, China, where CMH and YR, a longstanding friend and collaborator of George's, met and laid the foundation to a strong and productive friendship.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.
org/10.1016/j.clim.2024.110194.

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