Oligoprotein type I interferon signatures, but not TREX1 variants, increase risk of systemic lupus erythematosus in UK Biobank

Domain: neuroscience
Published: 2026-01-27
Editorial outcome: major_revision

Abstract
The 3′−5′ DNA exonuclease, TREX1, is a negative regulator of the type I interferon response, while TREX1 variants are considered to confer risk for non-monogenic systemic lupus erythematosus (SLE). Here we analyse TREX1 sequences in 469,229 UK Biobank participants together with multi-omics data from the UK Biobank Pharma Proteomics Project to reappraise the contribution of reported TREX1 risk variants in SLE. We find that TREX1 variants are not associated with increased risk for SLE in UK Biobank, and most reported risk variants are functionally neutral in mutagenesis experiments. Deriving an oligoprotein interferon signature from broad capture proteomics, we find that this signature is associated with elevated SLE risk, but is not elevated in TREX1 variant carriers. Furthermore, TREX1 variants are not associated with other autoimmune diseases with a prominent oligoprotein interferon signature. Finally, meta-analysis of published studies confirms the lack of support for the association between SLE and TREX1 risk variants. In summary, we find that, while oligoprotein type I interferon signatures increase risk of SLE, TREX1 variants do not.

Introduction
TREX1 is the major 3′−5′ human exonuclease, which degrades ssDNA/dsDNA and, through removal of self-nucleic acids, acts as a negative regulator of the type I interferon response to prevent autoinflammation and autoimmunity1. TREX1 is a dimer, and reduced exonuclease activity in humans is most commonly due to biallelic loss of function variants2, leading to an autosomal recessive condition called Aicardi–Goutières syndrome (AGS), characterised by widespread activation of the type I interferon response3 and manifestations that can include features of systemic lupus erythematosus (SLE). Parents of children with AGS appear to produce increased levels of antinuclear antibodies4, and dominant variants in TREX1 can cause a monogenic cutaneous form of lupus known as familial chilblain lupus (FCL)5. Such a link between TREX1 variants and lupus-like conditions is mechanistically plausible given that SLE is an autoimmune disease associated with anti-nucleic acid antibodies and type I interferon activation6. Candidate gene studies have specifically tested this hypothesis of a link between TREX1 variants and SLE. The first study7 to do so reported an association of encoding TREX1 variants with SLE or Sjogren disease (SjD). These variants conferred a 25-fold increased risk of developing SLE. This association between TREX1 and manifestations of SLE was considered to be confirmed following a large candidate gene study8, contributing to the general acceptance that TREX1 variants predispose to non-monogenic SLE, which includes annotation of variants in clinical reference databases9–13. As such, the association between TREX1 variants and strong risk of SLE is widely considered as valid. However, with the development of more powerful tools to dissect genetic associations with immune diseases and associated endotypes, including the potential to measure markers of type I interferon activation at scale, it is important to re-evaluate what are considered to be canonical associations.

UK Biobank (UKB) is a large population-based study of 502k volunteers recruited through UK patient registries14. It offers a number of features which allow this genetic association to be tested in detail, including TREX1 exome sequencing in ~469k participants and reliable capture of SLE diagnoses through routinely collected healthcare data15,16. Moreover, UKB participants were aged 40–69 years at enrolment in 2006–2010, and the study captures both incident and prevalent diagnoses, meaning that most participants predisposed to SLE will by now have been diagnosed, as SLE incidence peaks in middle age17. Since elevated type I interferon is an important feature of SLE, blood proteomics generated in the UKB Pharma Proteomics Project (UKB-PPP) in ~54k participants also offers an opportunity to develop and detect oligoprotein interferon signatures, which form an important part of the immune endotypes associated with SLE. UK Biobank is therefore a powerful multi-omics resource to evaluate the TREX1-SLE association.

In this study, we re-examine the association between rare coding TREX1 variants and SLE. We begin by showing that previously reported TREX1 risk variants are largely neutral in functional studies and are not associated with SLE or SjD risk in UKB. We next develop and validate a type I interferon signature score using blood proteomics in UKB-PPP to define a cluster of type I interferon-related autoimmune conditions, and show that TREX1 variants are not associated with such type I interferon signature or interferonopathic autoimmunity. We finally conduct a systematic review and meta-analysis of published TREX1-lupus association studies, which does not support a contribution of TREX1 variants in SLE. Together, these findings suggest that rare coding TREX1 variants are not associated with type I interferon dysregulation and do not confer risk for SLE or other type I interferon-related autoimmunity.

Results
No association between TREX1 variants and SLE in UK Biobank
We first aimed to replicate results from a previous genetic association study7 linking monoallelic TREX1 variants to SLE. Using a set of 10 TREX1 risk variants found in SLE or SjD cases but not in controls from this study, we identified 866 carriers among 469,229 UKB participants (0.18%) with whole-exome sequencing after sample and variant quality control (Fig. 1A, B). Three variants had allele counts ≤2 (c.635del [P212Hfs], c.812_813insAA [D272Rfs] and c.917G>C [G306A]), whereas c.341G>A (R114H) accounted for 619 (71.5%) of TREX1 variant carriers. There was no homozygote for minor alleles and one variant (c.739G>C [A247P]) was always found in compound heterozygotes with c.679G>A (G227S). Carriers, as compared to non-carriers, had similar age and sex distributions but a higher proportion of non-European ancestry (27.0 vs. 16.1%; see Supplementary Table 1 for details).

Before testing the association of these TREX1 variants with SLE in UKB, we performed recombinant protein studies in Trex1−/− mouse embryonic fibroblasts (MEFs) to examine their impact on exonuclease function and localisation. As expected, the two frameshift variants (P212Hfs and D272Rfs) affected protein localisation while the remaining variants had no impact on TREX1 localisation. The nuclease activity assay showed that only the two AGS-causing variants (R114H and P212Hfs) had any effect on nuclease activity, while the remaining variants had no effect on nuclease function (Fig. 1C, D; see Supplementary Fig. 1 for details).

We next identified 1306 UKB participants with SLE, of which most (69.5%) had at least one diagnostic code from hospital inpatient admissions data (Fig. 2A; see Supplementary Table 2 for details). Participants with SLE were most frequently diagnosed between 40 and 60 years old (42.9%) and, compared to those without SLE, were more frequently females (82.8 vs. 54.1%) and of non-European ancestry (24.9 vs. 16.1%; see Supplementary Table 3 for details). The prevalence of TREX1 variants among people without SLE was higher in UKB (0.19%) than in previously reported cohorts7 (Fig. 2B). None of the carriers were diagnosed with SLE in UKB, yielding a neutral association between this set of TREX1 risk variants and SLE with confidence intervals (CIs) that exclude a clinically meaningful effect (odds ratio [OR] = 0.178; 95% CI: 0.001, 1.207; p = 0.090; Fig. 2C). Peripheral markers of type I interferon activation are also observed in SjD18, and prior candidate gene analyses have also included patients with either SLE or SjD7. Combining SLE and SjD to define cases as previously done because of a common link with type I interferon7 (see Supplementary Table 4 and Supplementary Fig. 2 for sources of diagnosis) yielded similar neutral findings, with a single case among carriers (0.1%; heterozygote for c.341G>A [R114H]) as compared to 2642 cases among non-carriers (0.6%). Comparable results were obtained after excluding self-reports and for SjD only (Fig. 2C). These neutral findings were also reproduced for European ancestry participants (n=393,433) using burden tests and optimal sequence kernel association tests (SKAT-O) implemented in SAIGE-GENE+19 using weights based on minor allele frequency (MAF) and the inverse of residual enzymatic function from our in vitro exonuclease assays (Supplementary Table 5).

An oligoprotein interferon signature identifies interferonopathic autoimmune diseases
SLE is considered the prototypical example of a sporadic autoimmune disease associated with prominent activation of the type I interferon response6. Early studies linking TREX1 with SLE also included conditions such as SjD which is similarly associated with elevated type I interferon. This is biologically plausible since TREX1 is a negative regulator of the type I interferon response. We therefore asked whether we could use UKB-PPP to identify interferonopathic autoimmune disorders and test their association with TREX1 mutations. To date, however, there has been no systematic identification of autoimmune disorders associated with activation of the type I interferon response. This is because quantification of type I interferon has been challenging due to the low abundance of these proteins. Measuring interferon-α concentrations requires high sample volumes, and for this reason is challenging to measure at scale in large cohort studies such as UKB. An alternative surrogate for interferon-α protein concentrations is RNA-based interferon signatures, which are often used as a biomarker in clinical studies of interferonopathic diseases20. However, transcriptomic data are not currently available for UKB.

To overcome these challenges, we sought to develop and validate an oligoprotein signature of type I interferon activation derived from broad-capture proteomics measured at recruitment in UKB. We included 22 correlates of type I interferon levels available in UKB-PPP (Olink Explore 3072) in a penalised logistic regression model trained to achieve the best cross-sectional prediction of recombinant type I interferon therapy (interferon-β in people with multiple sclerosis at the time of baseline blood sampling, Fig. 3A). The oligoprotein interferon signature, which we termed the Markers of type I Interferon Response in Olink (MIRO) score, was then derived as the sum of the three proteins retained in the model (SIGLEC1, RIG-I and IFIT3) weighted by their penalised coefficient followed by standardisation. We tested the score in three validation experiments. Firstly, the oligoprotein interferon signatures were strongly increased in individuals treated with recombinant interferon-α at the time of blood sampling (Fig. 3B). Secondly, MIRO scores had the strongest correlation with proteins involved in the type I interferon response across other analytes available in UKB-PPP (Fig. 3C). Thirdly, we externally validated the score in a cohort of individuals with SjD from the United Kingdom Primary Sjogren’s Syndrome Registry (UKPSSR), where paired interferon-α single molecule ELISA, Olink proteomics and expression data were measured in blood. We confirmed that components of the MIRO score are interferon-stimulated genes (ISGs) and found a significant correlation between the oligoprotein interferon signature and interferon-α concentrations, but not interferon-γ (Supplementary Fig. 3).

We next used this oligoprotein signature to survey type I interferon activation across 21 common autoimmune diseases (see Supplementary Tables 6 and 7 for definitions). Ten autoimmune conditions were associated with a high (β>0.6: SjD, systemic sclerosis, dermatomyositis, SLE) or moderate (β≥0.2 and ≤0.6: primary biliary cirrhosis, coeliac disease, vasculitis, vitiligo, rheumatoid arthritis and type 1 diabetes) type I interferon signature (Fig. 3B). Eleven other conditions including multiple sclerosis were only weakly or not associated (β<0.2) with such signature.

TREX1 variants are not associated with interferonopathic autoimmunity
We used this oligoprotein interferon signature approach to test a broader hypothesis that carriers of any disease-causing TREX1 variants have an increased risk of type I interferon-related disease. We first expanded the set of TREX1 variants reported to cause AGS, FCL or autoimmunity from a systematic search of ClinVar21, the Leiden Open source Variation Database (LOVD)22 and two reviews9,20, which yielded 84 variants involved in monogenic conditions (AGS and FCL) or autoimmunity (Fig. 4A; see Supplementary Table 8 for full description). Variants linked to autoimmunity all cited a previous genetic association study7. We found 35 of these 84 variants in UKB (41.7%; Fig. 4B), yielding 1,216 carriers (all heterozygotes with one compound heterozygote [c.553C>T+c.739G>C]). These carriers of reported disease-causing TREX1 variants did not have an increased risk of any type I interferon-related autoimmune disease (Fig. 4C). Given prior reported associations between neurological manifestations of lupus and TREX1 variants8, we also examined associations between TREX1 variants and six neurological comorbidities of SLE. No association was found (Fig. 4C; see Supplementary Fig. 4 for all individual traits tested), and imaging-derived brain volumes associated with SLE23,24 did not differ by carrier status (Fig. 4D).

Oligoprotein interferon signatures are associated with increased risk of SLE, but are not elevated in TREX1 mutation carriers
Serological evidence of SLE can precede diagnosis by many years25. Indeed, in UKB-PPP, we found that elevated type I interferon signatures at baseline in people without an SLE diagnosis were strongly associated with future risk of SLE diagnosis (Fig. 5A; see Supplementary Table 9 for all estimates). Oligoprotein interferon signatures could be detected up to 9 years prior to diagnosis (Supplementary Fig. 5). We therefore explored whether TREX1 variant carriers might have evidence of peripheral type I interferon activation. We found no difference in MIRO scores between carriers and non-carriers of TREX1 variants (Fig. 5B; see Supplementary Fig. 6 for individual component analytes). To examine whether TREX1 variants with a higher likelihood to result in biologically deleterious effects are associated with a proteomic interferon signature, we next classified reported disease-causing variants into high-confidence predicted loss-of-function (pLOF) vs. non-pLOF variants using the Loss-Of-Function Transcript Effect Estimator (LOFTEE)26. There were 30 carriers of any of 16 pLOF variants and 80 carriers of any of 19 non-pLOF variants with MIRO available (see Supplementary Table 10 for full description). Compared to non-carriers, MIRO scores were not increased in people carrying pLOF variants or non-pLOF variants (Fig. 5C). Similarly, MIRO scores were not associated with greater Combined Annotation-Dependent Depletion (CADD; v1.6)27 scaled scores without significant interaction with LOFTEE classification (Fig. 5D). All results were comparable in 399,587 genetically unrelated participants (Supplementary Fig. 7) and were replicated in 393,433 European ancestry participants using SAIGE-GENE+19 to account for sample relatedness (Supplementary Table 11). Likewise, no ancestry-specific trends were observed for SLE/SjD across individuals of South Asian (n=6429), West African (n=3854) or East Asian (n=1787) ancestries, whether cases were defined from any sources or hospital records only, although sample sizes were lower (Supplementary Table 12).

Systematic review and meta-analysis of TREX1-SLE genetic association studies
Narrative reviews of SLE genetics typically cite the TREX1-SLE association as a finding which has been replicated in further studies as well as case reports9–13. Given that biases can develop in narrative reviews, we performed a systematic review of genetic association studies on the risk of SLE in carriers of rare TREX1 variants to contrast our findings with current available evidence. We searched Ovid EMBASE and Ovid MEDLINE with concepts ‘genetic variants’, ‘TREX1’ and ‘SLE’ to identify studies examining the association of rare TREX1 variants with SLE in humans from inception to 15 August 2024 (see Supplementary Table 13 for full search strategies). References from a recent review on the genetics of SLE28 were also added to the screening phase to ensure studies most relevant to the field were captured. This search yielded 339 records after removing 69 duplicates, of which 23 were assessed through full-text reading to include three articles in the review (Fig. 6A). The first study (Lee-Kirsch et al.)7 focused on the association of nonsynonymous TREX1 variants (exon plus part of the 3’ UTR) with SLE/SjD in three European cohorts (UK, Germany, Finland). The second study (Namjou et al.)8 aimed to replicate findings from this first study and expand the association of TREX1 variants with SLE in multi-ancestral cohorts using array genotyping for previously reported rare variants (i.e. not capturing all rare variants) and more common single nucleotide polymorphisms. The third study (Jiang et al.)29 compared the frequency of rare coding variants in 76 SLE-associated genes (including TREX1) in small cohorts from Australia and the US. These three studies included a total of 7618 cases and 8870 controls, mostly from Namjou et al. (90.3% of cases and 75.4% of controls; see Supplementary Tables 14 and 15 for full study details and risk of bias). Lee-Kirsch et al. reported a significant association of any nonsynonymous TREX1 variants with SLE (with a gene-level test), whereas Namjou et al. reported no significant association of individual TREX1 variants with SLE (with variant-level tests). Jiang et al. reported the frequency of TREX1 carriers among cases and controls without formal statistical test. When compared to the first-in-topic publication (Lee-Kirsch et al.), all subsequent studies, including UKB, had a lower prevalence of carriers in cases (0.2–1.5% vs. 1.8%) and a higher prevalence of carriers in controls (0.3–1.0% vs. 0.1%).

We meta-analysed data from these three publications with those of UKB to provide an updated pooled risk estimate. Using a random effects model, pooled estimates showed no significant association of rare TREX1 variants with SLE (OR = 2.16; 95% CI: 0.63, 7.34; p = 0.219; Fig. 6B; see Supplementary Fig. 8 for funnel plot). The substantial heterogeneity across the four studies (Q test for heterogeneity: p = 0.005; proportion of total variance due to heterogeneity [I2] = 76.6%) was reduced by defining subgroups based on the potential for residual confounding due to population stratification (Q test for subgroup differences: p = 0.020; residual I2 = 42.9%; proportion of heterogeneity explained [R2] = 76.8%). None of the pooled estimates within subgroups were significant, even for studies that did not control for potential population stratification. In summary, this systematic review and meta-analysis does not support a strong role of TREX1 variants in SLE.

Based on these findings, we next explored whether confounding by population stratification might partly explain prior positive associations. Three of the ten previously reported risk variants were >40× more frequent in the African/African American as compared to the Non-Finnish European subpopulation of the Genome Aggregation Database (gnomAD) exome26 (v2.1; Fig. 6C). Similarly, in UKB, the likelihood of carrying any of these 10 TREX1 variants or diagnosed with SLE both differed by ancestry. Specifically, non-European ancestry was more frequent in both carriers (27.0 vs. 16.1% in non-carriers) and people with SLE (24.9 vs. 16.1% without). Finally, no variants were observed in the family-based Finnish cohort controlling for population stratification and included in a previous TREX1-SLE study7. These observations suggest population stratification is a plausible source of confounding, which might have contributed to the observed heterogeneity in prior TREX1-SLE studies.

Methods
This study complies with the Strengthening the Reporting of Genetic Association Studies (STREGA)42 and the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA)43 statements (see Supplementary Notes 1 and 2 for checklists).

UK Biobank
UKB is a large population-based cohort of 502k volunteers aged 40–69 years at recruitment (2006–2010) who were identified through UK patient registries (response rate: 5.5%)14. Self-reported medical conditions were ascertained through questionnaires followed by verbal interviews conducted by trained staff at baseline for all and during follow-up for a subset of participants. Longitudinal health-related outcomes are captured through linkage with hospital episodes (primary or secondary diagnoses; International Classification of Diseases [ICD] v9 and v10 codes), national death registries (primary and secondary causes of death; ICD v10 codes), and primary care records (Read v2 and v3 codes; available in ~46% of the cohort). Primary care data include both diagnoses from general practitioners and those from outpatient secondary care (e.g. rheumatology clinics) as these are generally reported back to the practice44.

We defined SLE and SjD from published ICD (v9 and v10) and Read (v2 and v3) codes, as well as UKB mappings available online (see https://biobank.ndph.ox.ac.uk/ukb/)15,45. We included diagnostic codes for both lupus erythematosus (ICD v10: L93.X) and SLE (ICD v10: M32.X) because of a shared pathophysiology, although the former accounted for a minority of cases (15.1% for hospital episodes used to define SLE/SjD). Expanded clinical and radiological phenotypes were defined from autoimmune diseases46, common neurological comorbidities of SLE (due to the previous association of TREX1 variants with neurological manifestations in SLE8), and radiological phenotypes observed in SLE (white matter hyperintensities23 and brain atrophy24). Brain magnetic resonance imaging (MRI) scans were obtained in a subset of the cohort on 3T Siemens Skyra scanners running VD13A SP4 with a standard Siemens 32-channel radio-frequency receiver head coil47. We used pre-processed imaging-derived phenotypes made available by UKB, obtained from validated segmentation algorithms (the Brain Intensity Abnormality Classification Algorithm tool [BIANCA]48 for white matter hyperintensity volume; FMRIB’s Automated Segmentation Tool [FAST]49 and SIENAX50 for total and region brain volumes). All imaging-derived phenotypes were normalised to head size using the UKB scaling factor (derived from the external surface of the skull) and z-scored for analyses. White matter hyperintensity volumes were log-transformed before standardising given their right-skewed (log-normal) distribution. These brain volumes were available for a range of 188-193 participants with SLE/SjD, 42,337-43,621 participants without SLE/SjD, 85-87 TREX1 variant carriers and 42,440-43,727 non-carriers.

Proteomic analysis of blood plasma samples from ~54k UKB participants was performed by the UKB-PPP using Olink Explore 3072, a high-throughput protein biomarker discovery platform using the antibody-based proximity extension assay technology with up to 2923 unique analytes per sample51. Olink protein levels are quantified as normalised protein expression (NPX), a relative measure on the log2 scale with a roughly normal distribution. Readings were split into batches, which underwent within- and between-batch intensity normalisation. We used data on randomly selected (~47k) and consortium-chosen (~6k) participants at baseline, excluding those who took part in the COVID-19 repeat-imaging study (~1k). We excluded analytes with >20% missing data (n=12) and NPX values were standardised for analysis (mean=0 and SD=1).

UKB has received ethical approval from the North West Multi-centre Research Ethics Committee, and all participants provided written informed consent at recruitment. This research was conducted under UKB Resource application number 93160.

Whole-exome sequencing and genotypes in UK Biobank
Raw sequencing data (final exome data release, July 2022) were generated with the Illumina NovaSeq 6000 platform (S2 and S4 flow cells) and a target-enrichment probe kit (IDT xGen® Exome Research Panel v1.0) to enable deep and uniform coverage of ~39 Mbp (19,396 genes). We used multi-sample project-level VCF (pVCF) files made available by UKB52, which were obtained after (i) mapping raw sequences with the Original Quality Functionally Equivalent (OQFE) standard protocol and (ii) producing variant calls with DeepVariant (v0.10.0; a tool that uses a deep convolutional neural network to determine the most likely genotype at each locus) and GLnexus (v1.2.6; to aggregate sample-level variant files into joint genotype pVCF files)53. We applied a set of quality control metrics as previously described for UKB exome54: (i) no withdrawal from the cohort, (ii) no mismatch between self-reported (UKB field 31) and genetically determined sex (UKB field 22001), (iii) no sex chromosome aneuploidy (UKB field 22019), (iv) individual and variant missingness <10%, (v) Hardy Weinberg equilibrium p-value >10−15, (vi) at least one sample per site with allele balance threshold >0.15 for single nucleotide variants (SNVs) and >0.20 for small indels, (vii) minimum read coverage depth of seven for SNVs and 10 for indels, and (viii) sequencing depth ≥10× in 90% of samples (to prevent spurious association that may result from batch effect)55.

TREX1 variants
Reported disease-causing TREX1 variants involved in AGS, FCL or autoimmunity were retrieved from genotype-phenotype databases and publications using a systematic approach. We searched ClinVar21 and LOVD (v3.0)22 and included variants exclusively reported as pathogenic or likely pathogenic by individual submitters according to the American College of Medical Genetics and Genomics (ACMG) standard terminology56. Variants with a conflicting interpretation of pathogenicity across submitters were included if none of the individual submitters had classified variants as benign or likely benign (i.e. the conflict was between pathogenic/likely pathogenic and uncertain significance). We also included variants reported in a comprehensive review of TREX1 genotype-phenotype associations (Rice et al.)9 and a case-control study of 17 individuals with AGS (Rice et al.)20. We excluded variants fulfilling both of the following criteria to account for potential misclassification of benign variants as pathogenic: (i) MAF ≥ 0.1% in UKB, gnomAD exome (v2.1)26, or 1000 Genomes Project57 (including any subpopulation of these two last sequencing databases) and (ii) CADD scaled score <10, indicating a lower probability of deleteriousness. The impact of variants on the canonical transcript was assessed on Ensembl with the Variant Effect Predictor58.

The 3D structure of TREX1 was exported from alpha-fold, which uses existing experimental structures in the Protein Data Bank and combines them with predicted structure for unresolved regions producing a per residue confidence score (AF-Q9NSU2-F1, 23/8/24). For illustrative purposes, we have shortened the long flexible linker region between the enzymatic domain and the endoplasmic reticulum transmembrane domain. SLE risk variants were mapped and highlighted in PyMoL (The PyMOL Molecular Graphics System, Version 3.0, Schrödinger, LLC). Dimer interface was approximated from previous structural studies2.

Functional analysis of TREX1 variants
Gateway cloning was used to construct mammalian expression vectors. Briefly, the coding sequence of human TREX1 (ENST00000625293.3) was amplified by polymerase chain reaction (PCR) to include attB sites and cloned into pDONR221 (Invitrogen) via BP reaction (BP Clonase II kit; Invitrogen) to generate an ENTRY vector (pENTRY-TREX1). pEGFP-TREX1 was constructed by recombining the TREX1 coding sequence from the ENTRY vector into a Gateway converted pEGFP-C2 destination vector (Clontech) via LR reaction (LR Clonase II kit; Invitrogen). Minipreps of plasmid DNA (Qiagen) were performed for verification by Sanger sequencing (Source Bioscience). Midiprep of plasmid DNA (Zymo Research) was performed for mammalian cell transfection. Mutations were introduced into the pEGFP-TREX1 mammalian expression construct by site-directed mutagenesis, as per the manufacturer’s instructions (Q5 Site-Directed Mutagenesis Kit, NEB). Mutations were confirmed by Sanger sequencing.

TREX1 nuclease activity was assayed by transfecting Trex1−/− MEFs with pEGFP-TREX1 (WT or variants generated via site-directed mutagenesis) using Lipofectamine 3000 (Invitrogen), as per the manufacturer’s recommendations. Both ssDNA nuclease activity and TREX1 localisation are similar across the mouse and human species. Whole cell protein lysate was extracted with RIPA lysis buffer with complete mini protease inhibitor (EDTA-free, Roche), by incubation for 30 min on ice with occasional agitation. Remaining unlysed cells and debris were removed by centrifugation at 10,000×g for 10 min at 4°C. Protein lysates were used immediately or stored at −80°C. Protein concentration of the whole cell lysate was determined by BCA assay (Pierce).

Whole cell lysates (final concentration 100 ng/µl) were incubated with nucleic acid substrate (21-mer single stranded oligo with 3′ fluorescein and an internal DABCYL quencher (3’ fl-int DABCYL-TREX1-21mer, sequence TAGACATTGCCCTCG5AGGTAC (Dabcyl dT position marked 5, 3′ fluorescein)); final concentration 200 nM) in reaction buffer (20 mM Tris-HCl, 5 mM MgCl2, 2 mM DTT, 100 µg/ml BSA) in an opaque 96-well plate. Fluorescent measurements were taken with a Clariostar plate reader (483-14/530-30; BMG labtech), with temperature control set to 37° over a 90-min time course with a reading every minute. The area under the curve was calculated for each variant for each experiment. This value was then normalised against wild type (WT) for each experiment to get enzymatic activity as a percent of WT. Localisation of EGFP-TREX1 was assayed by imaging 4% PFA fixed cells grown on coverslips, at 63× on a Zeiss Axioimager. Images were analysed and assembled in ImageJ.

United Kingdom primary Sjogren’s syndrome registry
The UKPSSR is a national observational cohort of SjD patients fulfilling the 2002 American European Consensus Group classification criteria59 and recruited across 30 UK centres with serum samples and whole blood (PAXgene tubes [Becton, Dickinson and Company]) stored at recruitment (Eppendorf tubes at −80°C)60. RNA was assayed with the Illumina Human HT-12 v4 Expression BeadChip (Illumina). Globin signal suppression was performed using the Affymetrix Globin reduction protocol (Affymetrix). A random sample of 39 patients from UKPSSR was selected for a serum proteomics substudy quantifying 454 unique analytes with five Olink Target 96 panels (inflammation, immune response, organ damage, cardiovascular III and metabolism) and multi-subtype interferon-α levels with a high-sensitivity Simoa assay on an HD-X Analyzer as per manufacturer’s instructions (Quanterix, USA). We log-transformed interferon-α levels to obtain a normal distribution for analyses and both transcriptomic and proteomic data were standardised for analysis (mean=0 and SD=1).

Participants of the UKPSSR gave written consent for the collection of clinical and peripheral blood samples, and the research ethics approval was granted by the UK National Research Ethics Committee North West — Haydock.

Derivation and validation of a proteomic type I interferon signature
We sought to develop an oligoprotein interferon score derived from Olink proteomics in the UKB-PPP, which we refer to as MIRO score. Because interferon-α and -β are not measured on Olink Explore 3072, we developed this score to capture their downstream proteomic response, analogous to the transcriptomic interferon score previously described for monogenic type I interferonopathies, which is defined as the median fold change in six canonical ISGs61,62. We first identified gene products quantified on Olink Explore 3072 that correlate with type I interferon levels from either (i) a total RNA expression fold change >20× after interferon-α or -β stimulation from a publicly available expression database on ISGs (INTERFEROME v2)63 using in vitro experiments on human peripheral blood cells under normal conditions, or (ii) an ISG (more broadly defined using Chaussabel modules 1.2, 3.4 and 5.1264) with a positive association with log-interferon-α at p value <0.1 in a sample of 39 individuals with SjD from UKPSSR.

We next included these ISGs in a penalised logistic regression model using L1 regularisation (Least Absolute Shrinkage and Selection Operator or LASSO regression), which shrinks coefficients to select features and account for collinearity, to achieve the best and most interpretable prediction of type I interferon stimulation. The model was trained on UKB participants with multiple sclerosis treated vs. not treated by exogenous interferon-β therapy (any formulation). This class of injectable disease-modifying therapies is used in relapsing-remitting multiple sclerosis and induces a strong transcriptomic response of classical type I ISGs that persists for several days even in long-term users65. The underlying disease, multiple sclerosis, is not associated with elevated type I interferon66, and so the interferon signatures in these patients are driven by the recombinant interferon protein. We considered multiple sclerosis diagnoses either prevalent at baseline or captured within 5 years of follow-up to balance statistical power and biological relevance, as multiple sclerosis pathogenesis precedes diagnosis by 5–10 years67 and because event dates reaching UKB through administrative healthcare databases may lag behind actual diagnostic dates68. Of 235 participants with multiple sclerosis who had readings on all candidate ISGs and did not have comorbid autoimmunity, chronic kidney disease or liver disease at baseline (to limit potential noise due to other sources of inflammation), we included 21 participants on interferon-β therapy who were randomly matched (1:4 ratio) on age (5-year strata) with 84 participants who did not report using interferon-β at baseline. This sample was used to train and select the most regularised model within one standard error of the regularisation parameter, minimising the mean ten-fold cross-validated binomial deviance (λ1se). The MIRO score was calculated as the sum of standardised protein levels for the three included ISGs, each weighted by their L1 penalised coefficient, followed by standardisation (mean=0, SD=1; see Supplementary Table 16 for weights). A score was available for 36,966 UKB participants (mean age at baseline = 56.8 years [SD=8.2]; 54.1% females).

We validated the MIRO score in three experiments. First, we explored the cross-sectional associations at baseline of MIRO with positive control exposures (exogenous interferon-α and -β) and diseases (chronic viral hepatitis69, organ/tissue transplant70, human immunodeficiency virus infection71, liver disease72 and chronic kidney disease73,74) and one negative control (haemorrhoids).

Second, we performed a gene set enrichment analysis75 using Pearson’s correlation coefficients of MIRO with 2906 Olink analytes (with unique UniProt entries) as ranking metric76. We interrogated the 50 hallmark (H) gene sets from the Human Molecular Signatures Database (MSigDB) plus a set of classical ISGs (Chaussabel module 3.164) without restricting gene set sizes (range of genes per set: 5-107). We report normalised enrichment scores (to account for gene set size in enrichment estimates) along with false discovery rate-adjusted p values (padj) from the Benjamini–Hochberg procedure.

Third, we used a sample of 39 SjD patients with proteomics from UKPSSR to calculate Pearson’s correlation coefficients for RIG-I (from Olink) and a partial MIRO score with log-interferon-α (from high-sensitivity Simoa assay) and interferon-γ (from Olink; excluding n=19 with levels below the limit of detection). Because SIGLEC1 and IFIT3 were not captured in the UKPSSR Olink Target 96 panels, the partial MIRO score was calculated using CD163 as a surrogate for SIGLEC1 (correlation between the two proteins in SjD: r=0.673, p<0.0001). Pearson’s correlation coefficients between log-interferon-α (from high-sensitivity Simoa assay) and expression of components of the MIRO score were also examined in 177 UKPSSR participants.

Having validated the score, we next surveyed 21 common autoimmune diseases46 to identify type I interferon-related autoimmune conditions using MIRO. In analyses of controls and autoimmune diseases, medical conditions were defined from diagnoses up to 10 years prior to baseline after excluding people with comorbid autoimmunity, prior organ/tissue transplant (except when tested as a trait of interest) or on interferon therapy. Associations were tested using linear regressions of MIRO (dependent variable) with each condition of interest adjusted for age (continuous) and sex. Models of medical conditions were also adjusted for interferon-γ levels (to account for its potential association with the type I interferon proteomic signature and traits) and, except for instances with low sample sizes (interferon therapy, dermatomyositis, Addison’s disease and myasthenia gravis), adjusted for ethnicity (white vs. non-white), body mass index (BMI; continuous), smoking (current, previous, never), alcohol use (four intake levels: none, low, moderate, high77) and Townsend deprivation index78 (continuous) at baseline.

We used Cox proportional hazards models to test the association of MIRO scores at baseline with incident diagnoses (SLE and multiple sclerosis) adjusted for potential predictors of autoimmunity (age, sex, ethnicity, smoking, Townsend deprivation index, presence of other autoimmune diseases). For each condition, participants without prevalent diagnosis were included at baseline and followed until the first of outcome occurrence, death, loss of follow-up or study end date (31/OCT/2022; last day of the month with mostly complete data for National Health Service [NHS] England inpatient records, the main source of diagnoses).

Systematic review and meta-analysis of TREX1-SLE genetic association studies
We considered any study reporting a measure of association (or sufficient details on genotypes in cases and controls to build a contingency table) between rare coding sequence variants (as per individual studies but using a MAF < 1% for general guidance) in TREX1 and diagnosis of SLE (defined as per individual studies) in humans. We did not restrict our search by genotyping/sequencing technique (e.g. Sanger or next-generation sequencing), sampling frame (e.g. specialised clinics or large biobanks) or language of publication. We did not consider studies reporting monogenic conditions which may display features of autoimmunity (e.g. AGS or FCL) as our question pertained to risk variants only. We excluded case reports and small (n<5) case series as well as studies without a comparator group or without original human data (i.e. reviews, comments, editorials) and abstracts without corresponding full-text publication. This review was not pre-registered.

Two reviewers (BR and KR) independently screened records by title and abstract for eligibility, followed by full-text assessment of all records deemed eligible by any reviewer for final inclusion, at which stage disagreements were settled through consensus with a third author (DH). Reference lists of included records were explored through the same process to identify potentially missed relevant publications. Relevant abstracts were searched in Ovid MEDLINE for related full-text publications, which were included in the full-text assessment if available.

Two reviewers (BR and KR) independently abstracted relevant information using a customised spreadsheet, which included record details (authors, journal, year of publication), study samples (size, source, demographics), genotyping/sequencing technique, SLE definition, frequency of variants in cases and controls, and measures of association. Odds ratios for gene-level associations with SLE obtained after controlling for population stratification were preferentially used. If not reported, crude ORs were considered (either abstracted or calculated from reported frequencies with continuity correction), and other measures of association were converted to ORs. Discordant data were jointly re-abstracted, and any conflicting interpretation was settled through consensus with a third author (DH). Missing data relevant to the review were queried by emailing contact authors. Risk of bias was assessed with a validated tool for genetic association studies (Q-Genie) to report 11 domain-specific ratings (averaged between the two reviewers) and a summary quality assessment (as per Q-Genie guidance)79.

Key features of the included studies were first synthesised qualitatively. Meta-analyses were then performed to pool TREX1-SLE associations across included studies in addition to UKB data to provide updated results. UKB estimate and variance were obtained from a logistic regression adjusted for age, sex and the first ten genetic principal components (PCs). Meta-analyses were first conducted using a random effects model with the DerSimonian and Laird method and were replicated with a mixed effects logistic regression model to account for potential bias due to sparse event data80. We report summary measures along with 95% CIs (to quantify the accuracy of the mean) and 95% prediction intervals for the overall estimate (to quantify the dispersion of true effect sizes about the mean). Heterogeneity was explored visually with forest plots and statistically with τ2, the I2 statistics and Q tests. We performed subgroup analyses by potential for residual confounding due to population stratification to explore sources of heterogeneity. The funnel plot is presented, but statistical tests for publication bias were not performed because there were too few studies to yield sufficient power.

Genetic analyses
The association between TREX1 genotype and binary outcomes was tested using logistic regressions adjusted for age, sex and the first ten genetic PCs (UKB field 22009) to account for population stratification81. Firth penalisation was used to reduce bias in maximum likelihood estimators due to data sparsity, and point estimates are presented along with profile penalised log likelihood confidence intervals82. Association tests for imaging-derived phenotypes were performed using linear regressions adjusted for the same covariates, in addition to scanning centre (to control for potential technical confounding)83. Proteomic changes were compared by SLE/SjD status (with vs. without) and TREX1 genotype (carriers vs. non-carriers) using linear regressions adjusted for age and sex (and the first ten genetic PCs for TREX1 genotype).

Analyses were replicated after restriction of the cohort to participants without close relatives up to the third degree of relatedness (from kinship coefficients made available by UKB81) using Greedy Related (v1.2.1; https://gitlab.com/choishingwan/GreedyRelated) to preferentially retain carriers (n=69,642 excluded). Ethnic background was defined from self-reports (UKB field 21000), and European ancestry was defined from PCs in participants with self-reported ‘White British’ background (UKB field 22006). Non-European ancestry groups were based on a published approach using Euclidean distances on the PC space84 and genome-wide genotyping data (UKB field 22009)81. The association of TREX1 variants with clinical outcomes and MIRO scores was also replicated for participants of European ancestry (n=393,433) with burden tests (for effect estimates) and optimal sequence kernel association tests (SKAT-O; for p values) implemented in SAIGE-GENE+ to account for sample relatedness (using logistic/linear mixed models from a sparse genetic relationship matrix adjusted for age, sex and the first ten PCs in addition to scanning centre for radiological traits) and to control for type I error inflation due to case-control imbalances and data sparsity from ultra-rare variants19. We defined nominal significance as p value <0.05, and used a more stringent statistical significance threshold to account for multiple testing across outcomes for the main genetic analysis (p value <0.0010, i.e. 0.05/49 tests; clinical: n=35, radiological: n=3, proteomics: n=11).

Analyses were performed on the UKB Research Analysis Platform using JupyterLab (v3.6.1) with Hail (v0.2.78), Python (v3.9.16) and R (v4.2.0). Logistic and Cox regressions were conducted on R using logistf (v1.26.0) and survival (v3.7.0), and meta-analyses were conducted using metafor (v4.6-0). Visualisation of genomic and phenotype data was conducted on R using trackViewer85, UpSetR (v1.4.0), forestploter (v1.1.1) and ggplot2 (v.3.4.4).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The study re-evaluated the association between TREX1 variants and lupus using exome sequences data from UK Biobank participants, alongside other methodologies. The researchers reported no association between reported TREX1 variants and non-monogenic lupus, type I interferon-associated autoimmune diseases, or interferon signatures in blood. These findings are intriguing, given that TREX1 variants are widely believed to increase the risk of lupus. The study also suggested that the previously reported associations between lupus and TREX1 risk variants might have been confounded by population stratification. I have the following comments:\n1.The researchers did not replicate the previously reported TREX1 gene burden association with lupus, type I interferon-associated autoimmune diseases, or interferon signatures in the UK Biobank. The replication study appears robust, as multiple methods yielded consistent results. However, it is still challenging to conclude definitively that there is no association between TREX1 variants and SLE, type I interferon signatures, or autoimmunity. It is possible that only very rare mutations are causal in this gene. If this hypothesis is true, there may be insufficient power to detect such effects due to the extreme rarity of these variants, or the combined gene burden might be primarily driven by non-causal variants with relatively higher minor allele frequencies (MAF). In fact, the authors observed that only the two frameshift variants (P212Hfs and D272Rfs) affected protein localization, and both variants had very low MAF. Consequently, no significant association between reported TREX1 variants and lupus was observed. The authors should discuss these points in greater detail, as some of the conclusions drawn from the data may be overstated and should be moderated.\n2.Evidence from murine models regarding some TREX1 mutations, such as D18N and P61Q, has shown an impact on type I interferon-driven signatures and autoimmunity, although the MAF of these mutations remains very low. This is also supported by a recent study involving 55 patients with SLE, which reported that one 27-year-old SLE patient with overlapping dermatomyositis carried a heterozygous p.Asp130Asn mutation in the TREX1 gene. This patient had significantly elevated serum interferon (IFN)-α levels compared with healthy controls and other patients with SLE. Therefore, I suggest that the following pertinent reports should be discussed to provide a more comprehensive view of the evidence:\nDOI: 10.1016/j.clim.2021.108732\nDOI: 10.1093/hmg/ddae089\nDOI: 10.4049/jimmunol.1900220\nDOI: 10.1016/j.jaut.2019.03.001\nDOI: 10.4049/jimmunol.1600722\nDOI: 10.1073/pnas.2411747121\n3. The age distribution at diagnosis of SLE patients in the UK Biobank should be detailed. For instance, early disease onset (e.g. 20-year-old) in lupus, which is associated with higher serum IFNα activity (DOI: 10.1002/art.23619). However, since UK Biobank participants were aged 40-69 years at enrollment during 2006-2010, it remains unclear whether those with early disease onset represent a small proportion of this study. This uncertainty may affect the gene burden estimation for TREX1.", "reviewer_2": "Re-evaluating the association between TREX1 variants, systemic lupus erythematosus and 1 oligoprotein interferon signatures in UK Biobank\nOverall, this is an interesting manuscript but will require major editing before it is suitable for publication. The genetic association of rare variants in the smaller study by lee et al did not replicate in the larger study is the strength of this paper. Most of the variants in Lee et al did not have any functional validation, except for the 2 fs mutations which were found to affect localization, replicated in this work. I think that in the current age of large genomic analysis this paper should conclude that functional validation of novel rare variant associations is necessary due to the issues of population of stratification. I think the meta-analysis is also strong.\nThe IFN scores and the MRI associations have many methodological drawbacks that either need to be very clearly stated and included in the limitations or removed.\nThe genetic ancestry of the UK Biobank is largely European and may add another degree of important bias that should be addressed in limitations.\nWe first show that TREX1 variants 21 do not confer risk for lupus in UK Biobank and that most reported risk variants are functionally neutral 22 in mutagenesis experiments.\nMoreover, UKB participants were aged 40-69 years at 62 enrolment in 2006-2010, meaning that the large majority of participants predisposed to SLE will by 63 now have been diagnosed as SLE incidence peaks in middle age.\nThis is true, but SLE can be diagnosed after enrolment, and this won’t be detected in the database. I think the cross-sectional nature of the study is a major limitation that must be mentioned.\nthe genetic association of rare variants in the smaller study by lee et al did not replicate in the larger study is the strength of this paper. Most of the variants in Lee et al did not have any functional validation, except for the 2 fs mutations which were found to affect localization, replicated in this work. I think that in the current age of large genomic analysis this paper should conclude that functional validation of novel rare variant associations are necessary due to the issues of population of stratification?\n~469k participants and reliable capture of SLE diagnoses 61 through routinely collected healthcare data .~54k UKB participants, also offers an opportunity to develop and detect 66 oligoprotein interferon signatures which form an important part of the immune endophenotypes 67 associated with SLE.\nWhat is the demographic constitution of the 54k which have proteomic data. Is there a difference in the demographics of this cohort compared to those on whom we have exome data? It is only 10% of the sample. There is a large potential for bias here.\nWe use this oligoprotein signature 25 to identify nine autoimmune diseases associated with elevated type I interferon: primary biliary 26 cirrhosis, dermatomyositis, lupus, Sjogren disease, systemic sclerosis, Addison’s disease, type 1 27 diabetes, coeliac disease and rheumatoid arthritis. Using this approach, we find no association between 28 TREX1 variants, type I interferon-associated autoimmune disease nor interferon signatures in blood.\nsystematic review and meta-analysis of published studies confirms the lack of support for the 30 association between lupus and TREX1 risk variants and suggests a potential for strong associations to 31 be confounded by population stratification\n10 coding TREX1 risk variants initially reported7 are not associated with SLE 71 or SjD in UKB.\nI am curious to why the authors only selected the variants in this study, which is an old association study without much functional validation.\nWe then develop a proteomic interferon signature to define a cluster of type I 72 interferon-related autoimmune conditions. We show that TREX1 variants causing monogenic disease 73 are not associated with this proteomic interferon signature and do not confer risk for such 74 interferonopathic autoimmunity, even after in silico functional annotations to enrich 75 deleteriousness.\nPage 5, line 92- Please do use the term white for race or European for ancestry and do not use Caucasian:\nPopejoy AB. Too many scientists still say Caucasian. Nature. 2021;596(7873):463-.\nWe therefore sought to develop and validate a proteomic signature 129 of type I interferon activation. We defined 12 Markers of Interferon-  _Response in Olink (MIROs) 130 from analytes on the Olink Explore 3072 platform and built a composite oligoprotein measurement (the 131 MIRO score) which averages their standardized levels (Figure 3A).\nDid the authors do a training and test set for the MIRO validation? Using a new, not validated measure of Type I IFNs without more detailed validation gives me pause. The proteins selected are IFNs but very few are part of the validated scores most commonly used for SLE. I do not see enough validation to include this in the paper.\nKim H, de Jesus AA, Brooks SR, Liu Y, Huang Y, VanTries R, Montealegre Sanchez GA, Rotman Y, Gadina M, Goldbach-Mansky R. Development of a validated interferon score using NanoString technology. Journal of Interferon & Cytokine Research. 2018 Apr 1;38(4):171-85.\nRice GI, Melki I, Frémond ML, Briggs TA, Rodero MP, Kitabayashi N, Oojageer A, Bader-Meunier B, Belot A, Bodemer C, Quartier P. Assessment of type I interferon signaling in pediatric inflammatory disease. Journal of clinical immunology. 2017 Feb;37:123-32.\nPage 18, lines 360-362:\nThese brain 360 volumes were available for a range of 188-193 participants with SLE/SjD, 42,337-43,621 participants 361 without SLE/SjD, 85-87 TREX1 variant carriers and 42,440-43,727 non-carriers.\nAgain, this is a small subset of the entire cohort. Was an analysis done to determine if they differ from the larger cohorts in important ways which may bias the results?\nPage 19 lines 388-392- the exclusion criteria seem to be too liberal. Why did they choose a CADD <10, when 12-20 is the current cutoff, and with a population based approach, searching for rare, deleterious variants seems most pragmatic?\nThe section of the paper examining TREX 1 variants with interferonopathic autoimmunity seems to be a different story. If the paper wants to look at all causes of Type I IFNopathies, they should assess it, but including these here seems to dilute the paper. Again, the choice of variants to assess may not be stringent enough and bias the results to the see no association.\nPage 22, line 458: We replicated these neutral associations with RF and CRP, two blood biomarkers associated 458 with lupus.\nThese are not common lupus biomarkers. ANA and ESR are much more commonly associated with lupus. Overall, I think adding someone with rheumatology expertise and specifically lupus expertise to the study team would greatly enhance the manuscript.\nThe GitHub link provided did not work for me to review the code.", "reviewer_3": "The authors have taken a revisionist approach using unbiased methods to the question of the associations of TREX gene variants with non-monogenic systemic lupus erythematosus (SLE). Using the exome sequencing data from the large UK Biobank linked to health system diagnostic codes, and further using a protein signature claimed to represent type I interferon (IFN) activation, they find no evidence for an association of TREX variants with SLE or IFN-associated proteins and conclude with a clinical recommendation against TREX sequencing in the management or diagnosis of SLE.\nOn face value these are negative studies of value to the research community. They underpin the disconnect between monogenic interferonopathies such as AGS where TEX mutations are clearly causal, and the syndrome diagnosed as SLE in adults, notwithstanding the overlap between these conditions and what the former has taught us about the latter.\nThe paper lacks a section on its limitations, which should be added, along with addressing other matters listed below.\n1. Medical record diagnosis is notoriously weak, in particular in relation to heterogeneous autoimmune diseases where both under- and over-diagnosis are common. 35% in total of cases had the diagnosis based on a primary care-assigned diagnosis, which in this reviewer’s experience is likely to represent a significant proportion of false positive SLE diagnoses, weakening any associations.\n2. Ancestry linkage studies are informative, suggesting a possible reason for previous reported associations between TREX variants and SLE. However, UKB is hugely dominated by white/European ancestry subjects, known to have the lowest prevalence and severity of SLE. Significant missing ancestries include East Asian ancestries, in which with a very high prevalence of young onset SLE may have a different result. African ancestries are also <>10% of the cohort.\n3. The cohort is also light-on for young onset disease, where genetic associations are believed to be stronger. <2% of cases were diagnosed at age <20.\n4. The so-called MIRO protein signature is entirely derived from public domain sources, namely mRNA expression data after IFN-alpha stimulation, and predicted secretion of the gene products. This is a far from ideal way to develop such a signature, which ideally would be based on detection of proteins from human cells exposed to IFNAR activation, which in turn would not be limited to IFN-alpha stimulation as multiple other type I IFNs are implicated in SLE. Earlier work such as that of Bauer et al (10.1371/journal.pmed.0030491.st002) combined measurement of proteins directly in patient samples with in vitro exposure of cells to IFN in order to increase confidence that the signature was valid; that has not been done here. Experimental replication of this signature is required, and its lack is a significant caveat.\n5. Similarly, the ‘shoulders’ of individual MIRO protein levels greater than those in the health population are modest (Suppl Figure 4), notwithstanding the small p values for the difference in means potentially driven by the large sample size, though somewhat more impressive for the composite MIRO score.\n6. The meaning of Figures 3 C&D is unclear\n7. Figure 4C appears to be monochromatic but the legend refers to red and black components.\n8. It is not immediately clear what data are represented in Figure 5B – I take it the dots, box, and whiskers are individual MIRO proteins? Each is counted when >2SD - above what?\n9. Noting that IFNs are generally low abundance in human serum samples, they are detectable in some individuals. Were any type I IFN proteins detected in any sample? Did they relate to TREX variants?\n10. CRP is an unusual biomarker to choose in the setting of SLE, as in most studies it has little or no correlation with disease activity and is commonly measured in the reference range in people with SLE, even when active. I do not recognise this as a valid reference biomarker for the research question being addressed here and recommend deletion of this section.\n11. In vitro variant testing was done in murine cells – does this risk missing functionally important effects restricted to human cells?\n12. The authors appear to be from a neurosciences centre, but apart from this why were CNS manifestations of SLE chosen to be called out?\n13. The discussion is too long. TREX sequencing is not part of the diagnosis or clinical care of SLE."}, "author_response": "Many thanks for the encouraging response to our original submission. We are pleased that the reviewers felt our manuscript was of interest. Please find below a point-by-point rebuttal to the constructive comments of the reviewers, and a fully revised manuscript.\nIn particular we have given careful consideration to the concerns regarding the proteomic interferon signature score. We have fully addressed these, further developed the rigour of the methodology and provided additional validation.\nWe hope the reviewers find that we have addressed their comments.\nColour code\nBlack = reviewer comments\nPurple = author response\nGreen = new text in revised manuscript\nReviewer 1\nComment 1: The researchers did not replicate the previously reported TREX1 gene burden association with lupus, type I interferon-associated autoimmune diseases, or interferon signatures in the UK Biobank. The replication study appears robust, as multiple methods yielded consistent results. However, it is still challenging to conclude definitively that there is no association between TREX1 variants and SLE, type I interferon signatures, or autoimmunity.It is possible that only very rare mutations are causal in this gene. If this hypothesis is true, there may be insufficient power to detect such effects due to the extreme rarity of these variants, or the combined gene burden might be primarily driven by non-causal variants with relatively higher minor allele frequencies (MAF). In fact, the authors observed that only the two frameshift variants (P212Hfs and D272Rfs) affected protein localization, and both variants had very low MAF. Consequently, no significant association between reported TREX1 variants and lupus was observed. The authors should discuss these points in greater detail, as some of the conclusions drawn from the data may be overstated and should be moderated.\nWe thank the reviewer for this comment. We would highlight first that the first study (Lee-Kirsch et al. Nature Genetics 2007) to demonstrate a TREX1-SLE association found a strongly increased relative risk of ~25. Our UK Biobank study is adequately powered to test this widely-cited strong risk conferred by rare variants, and our metaanalysis approach further strengthens this.\nWe agree with the reviewer that it may be very difficult to disprove the hypothesis that only ultra rare mutations predispose to the development of sporadic lupus. Our combined UKB analyses, taken together with our meta-analysis do examine rare variants and together offer considerable power, and would suggest this is not the case.\nWe would also highlight that it is clear that mutations in TREX1 can cause monogenic interferonopathic autoimmunity (e.g. biallelic loss of function mutations or dominant negative mutations cause AGS and monoallelic mutations can cause familial chilblain lupus). Some of these monogenic disorders can display elements of a lupus-like phenotype, although they are a distinct entity from SLE.\nWe agree that we need to make these points clearer, and have modified the discussion to add this important nuance and moderated some statements to avoid overinterpreting our results. In particular we have now added substantial section at the end of the discussion on the limitations of our study and these include the following modifications:\nDiscussion (last paragraph on limitations): “Our study has a number of limitations. First, we cannot exclude a predisposition to SLE by some very rare TREX1 variants due to insufficient statistical power, especially in non-European ancestry populations which are underrepresented in UKB. The meta-analysis, however, includes a large study with SLE patients of European American (3,936 cases), Asian (1,265 cases), African American (1,527 cases), Gullah (152 cases) and Hispanic (1,492 cases) ancestries reporting neutral associations within each group8, suggesting that any strong ancestry-specific TREX1-SLE association is unlikely.”\nDiscussion (1st paragraph): “In this UK Biobank study, rare TREX1 variants did not increase the risk of interferonopathic disease and were not associated with endophenotypes of type I interferon autoimmunity.”\nDiscussion (on overlap between monogenic interferonopathy and SLE – see response to comment 2 below)\nComment 2: Evidence from murine models regarding some TREX1 mutations, such as D18N and P61Q, has shown an impact on type I interferon-driven signatures and autoimmunity, although the MAF of these mutations remains very low. This is also supported by a recent study involving 55 patients with SLE, which reported that one 27-year-old SLE patient with overlapping dermatomyositis carried a heterozygous p.Asp130Asn mutation in the TREX1 gene. This patient had significantly elevated serum interferon (IFN)-α levels compared with healthy controls and other patients with SLE. Therefore, I suggest that the following pertinent reports should be discussed to provide a more comprehensive view of the evidence:\nDOI: 10.1016/j.clim.2021.108732\nDOI: 10.1093/hmg/ddae089\nDOI: 10.4049/jimmunol.1900220\nDOI: 10.1016/j.jaut.2019.03.001\nDOI: 10.4049/jimmunol.1600722\nDOI: 10.1073/pnas.2411747121\nWe thank the reviewer for these comments. We agree that there is a need to address case reports, and we have included those suggested. Some of these, such as D18N and D200N are established dominant negative mutations (hence cause monogenic disease causing a distinct autoinflammatory syndrome with lupus-like features, rather than “sporadic” SLE). However others may well simply represent false positive associations, which can falsely amplify the appearance of a gene association without adding any statistical weight. Single cases do not add weight of evidence if the underpinning genetic association is not sound.\nDiscussion: “It is well established that these monoallelic TREX1 mutations cause monogenic immune disease, with mutations such as c.52G>A (D18N) exerting a dominant negative effect due to its catastrophic consequences on enzymatic activity. Within these monogenic diseases, a link with lupus is well recognized: some children with AGS manifest an early-onset form of SLE while FCL is a distinct type of cutaneous lupus erythematosus. Biallelic TREX1 mutations (c.290G>A [R97H] and c.341G>A [R114H]) have also been reported in monogenic lupus without typical features of AGS, which may be due to phenotypic variability within the clinical spectrum of TREX1-related monogenic disorders. Conversely, a number of case studies of individuals with SLE have reported TREX1 mutations, often with limited functional evidence for deleteriousness. The clinical significance of such mutations is uncertain, but our results suggests that the SLE risk conferred by such mutations is not as strong as previously thought.”\nComment 3: The age distribution at diagnosis of SLE patients in the UK Biobank should be detailed. For instance, early disease onset (e.g. 20-year-old) in lupus, which is associated with higher serum IFNα activity (DOI: 10.1002/art.23619). However, since UK Biobank participants were aged 40-69 years at enrollment during 2006-2010, it remains unclear whether those with early disease onset represent a small proportion of this study. This uncertainty may affect the gene burden estimation for TREX1.\nWe thank the reviewer for this comment and we have made the age breakdown for lupus patients in UKB more prominent in the text (originally Supplementary Table 3). We have also added a limitation on the few early-onset cases in UKB.\nIntroduction: “Moreover, UKB participants were aged 40-69 years at enrolment in 2006-2010 and the study captures both incident and prevalent diagnoses, meaning that the large majority of participants predisposed to SLE will by now have been diagnosed, as SLE incidence peaks in middle age.”\nDiscussion (last paragraph on limitations): “Second, our analysis focused on SLE in adults and may not be generalizable to early-onset SLE which is enriched for monogenic causes, although evidence for TREX1 risk variants in children is limited. For example, in one study of 117 children with SLE, a single monoallelic frameshift TREX1 variant was identified (c.236_243dup [S82Lfs]), which our analysis suggests is not associated with lupus.”\nReviewer 2\nComment 1: Overall, this is an interesting manuscript but will require major editing before it is suitable for publication. The genetic association of rare variants in the smaller study by lee et al did not replicate in the larger study is the strength of this paper. Most of the variants in Lee et al did not have any functional validation, except for the 2 fs mutations which were found to affect localization, replicated in this work. I think that in the current age of large genomic analysis this paper should conclude that functional validation of novel rare variant associations is necessary due to the issues of population of stratification. I think the meta-analysis is also strong.\nWe thank the reviewer for these encouraging comments. We have added the comment on functional validation to the discussion: “Similarly, our functional characterization of these variants suggests few (2/10) of them impair the exonuclease function of TREX1, highlighting the important role of functional validation for candidate lupus risk variants.”\nComment 2: The IFN scores and the MRI associations have many methodological drawbacks that either need to be very clearly stated and included in the limitations or removed.\nWe thank the reviewer for these comments. We have comprehensively addressed the concerns about IFN scores and describe this in detail in our respose to Comment 9 below (and also reviewer 3 comment 5). We have added details about the brain MRI features in the following sections:\nResults: “No association was found (Figure 4C; see Supplementary Figure 4 for all individual traits tested), and imaging-derived brain volumes associated with SLE did not differ by carrier status (Figure 4D).”\nMethods: “Expanded clinical and radiological phenotypes were defined from autoimmune diseases, common neurological comorbidities of lupus (due to the previous association of TREX1 variants with neurological manifestations in lupus), and radiological phenotypes observed in lupus (white matter hyperintensities and brain atrophy).”\nDiscussion (limitations section): “Finally, neurological manifestations of SLE are heterogeneous and not limited to the six clinical phenotypes examined here. Brain imaging changes are also heterogeneous in SLE and in our neuroradiological analyses we have analysed both brain volumes as well as white matter hyperintensity volume. The latter is the major structural imaging abnormality in people with SLE, and likely reflects accelerated microvascular disease, but is not seen in all patients.”\nWe have finally added a description of the demographics in people with versus without SLE and broke down this description by proteomics and MRI availability in an expanded Supplementary Table 3.\nComment 3: The genetic ancestry of the UK Biobank is largely European and may add another degree of important bias that should be addressed in limitations.\nWe thank the reviewer for this comment and agree that the European ancestry of UK Biobank is a potential limitation. We have added this potential source of bias to the limitations. We have also broken down the analysis for lupus and Sjogren as well as MIRO score by ancestry group (European, South Asian, West African, East Asian) and present this in a new Supplementary Table 12. Although the numbers are low in the three last groups, we observed a higher proportion of lupus/Sjogren cases and TREX1 carriers in these other ancestries as compared to Europeans, but no TREX1 carriers were diagnosed with lupus or Sjogren.\nWe would highlight that in our meta-analysis we have included a large multiancestral cohort, and these findings are consistent.\nResults: “Likewise, no ancestry-specific trends were observed for SLE/SjD across individuals of South Asian (n=6,429), West African (n=3,854) or East Asian (n=1,787) ancestries, whether cases were defined from any sources or hospital records only, although samples sizes were lower (Supplementary Table 12)”.\nDiscussion (last paragraph on limitations): “First, we cannot exclude a predisposition to lupus by some ultra rare TREX1 variants due to insufficient statistical power, especially in non-European ancestry populations which are underrepresented in UKB. The meta-analysis, however, includes a large study with SLE patients of European American (3,936 cases), Asian (1,265 cases), African American (1,527 cases), Gullah (152 cases) and Hispanic (1,492 cases) ancestries reporting neutral associations within each group, suggesting that any strong ancestry-specific TREX1-lupus association is unlikely.”\nComment 4: “We first show that TREX1 variants 21 do not confer risk for lupus in UK Biobank and that most reported risk variants are functionally neutral 22 in mutagenesis experiments.”\nMoreover, UKB participants were aged 40-69 years at 62 enrolment in 2006-2010, meaning that the large majority of participants predisposed to SLE will by 63 now have been diagnosed as SLE incidence peaks in middle age.” This is true, but SLE can be diagnosed after enrolment, and this won’t be detected in the database. I think the cross-sectional nature of the study is a major limitation that must be mentioned.\nWe thank the reviewer for this comment and we have made it clearer in the text that, while the cross-sectional nature of the study is a limitation, diagnoses which were both prevalent and incident are captured.\nIntroduction: “Moreover, UKB participants were aged 40-69 years at enrolment in 2006-2010 and the study captures both incident and prevalent diagnoses, meaning that the large majority of participants predisposed to SLE will by now have been diagnosed, as SLE incidence peaks in middle age.”\nComment 5: the genetic association of rare variants in the smaller study by lee et al did not replicate in the larger study is the strength of this paper. Most of the variants in Lee et al did not have any functional validation, except for the 2 fs mutations which were found to affect localization, replicated in this work. I think that in the current age of large genomic analysis this paper should conclude that functional validation of novel rare variant associations are necessary due to the issues of population of stratification?\nWe have added this comment to the discussion: “Similarly, our functional characterization of these variants suggests few of them (2/10) impair the exonuclease function of TREX1, highlighting the important role of functional validation for candidate lupus risk variants.”\nComment 6: “~469k participants and reliable capture of SLE diagnoses 61 through routinely collected healthcare data .~54k UKB participants, also offers an opportunity to develop and detect 66 oligoprotein interferon signatures which form an important part of the immune endophenotypes 67 associated with SLE.” What is the demographic constitution of the 54k which have proteomic data. Is there a difference in the demographics of this cohort compared to those on whom we have exome data? It is only 10% of the sample. There is a large potential for bias here.\nWe thank the reviewer for highlighting the potential for selection bias in substudies. We have expanded Supplementary Table 3 to include demographics of participants with vs without proteomic score.\nMost participants (87%) in the proteomics project of UKB were selected randomly from baseline participants and were representative of the UKB project1. A small fraction of participants with proteomics (11%) was chosen to enrich certain conditions such as SLE by the consortium of pharmaceutical companies. At most, this selection has increased the statistical power (optimizing the proportion of participants with lupus who also have proteomics) and is unlikely to have been conditioned on both SLE status and TREX1 variants (necessary to induce selection bias, whereby people with SLE and TREX1 variants are enriched and contribute to drive the signal). The absence of signal for SLE risk in TREX1 variant carriers also argues against this scenario.\nComment 7: “10 coding TREX1 risk variants initially reported7 are not associated with SLE 71 or SjD in UKB.” I am curious to why the authors only selected the variants in this study, which is an old association study without much functional validation.\nWe thank the reviewer for this question. We chose these variants because this paper is the most highly cited example of a link between TREX1 mutations and SLE (Lee Kirsch et al. ~800 citations), and has been influential in the perception of a link between TREX1 mutations and strong increased risk for SLE (relative risk ~25). For example “SLE risk variants” in ClinVar cite this reference. This paper is often cited as the primary source of evidence of a link between TREX1 mutations and SLE (e.g. Belot et al: PMID: 38263665). Later in the paper we test a braoder set of variants.\nComment 8: Page 5, line 92- Please do use the term white for race or European for ancestry and do not use Caucasian: Popejoy AB. Too many scientists still say Caucasian. Nature. 2021;596(7873):463-.\nThank you for highlighting this, together with this article. We have corrected the terminology throughout and used ‘European’ ancestry when referring to genetic background.\nComment 9: “We therefore sought to develop and validate a proteomic signature 129 of type I interferon activation. We defined 12 Markers of Interferon-a _Response in Olink (MIROs) 130 from analytes on the Olink Explore 3072 platform and built a composite oligoprotein measurement (the 131 MIRO score) which averages their standardized levels (Figure 3A).” Did the authors do a training and test set for the MIRO validation? Using a new, not validated measure of Type I IFNs without more detailed validation gives me pause. The proteins selected are IFNs but very few are part of the validated scoresmost commonly used for SLE. I do not see enough validation to include this in the paper.\nKim H, de Jesus AA, Brooks SR, Liu Y, Huang Y, VanTries R, Montealegre Sanchez GA, Rotman Y, Gadina M, Goldbach-Mansky R. Development of a validated interferon score using NanoString technology. Journal of Interferon & Cytokine Research. 2018 Apr 1;38(4):171-85.\nRice GI, Melki I, Frémond ML, Briggs TA, Rodero MP, Kitabayashi N, Oojageer A, Bader-Meunier B, Belot A, Bodemer C, Quartier P. Assessment of type I interferon signaling in pediatric inflammatory disease. Journal of clinical immunology. 2017 Feb;37:123-32.\nWe thank the reviewer for these comments regarding the interferon scores derived from Olink proteomic datasets. We are also grateful for similar comments from Reviewer 3 (comment 5).\nWe agree that it is important to develop and validate this oligoprotein interferon score further, in accordance with the reviewers’ suggestions. We have done this in the revised manuscript, including further experimental validation.\n The revised score is developed using proteomic datasets in UK Biobank participants who are being treated with recombinant type I interferon therapies (IFN- and IFN-) at the time of blood sampling. This leverages an important natural experiment within UK Biobank.\n Specifically, we have generated the score from proteomic datasets from people being treated with recombinant IFN- and internally validated the score against individuals receiving recombinant IFN- (Note: transcriptomic responses induced by IFN- and IFN- are mediated through IFNAR and are are very similar PMCID: PMC5159705).\n We have used penalized logistic regression model trained to achieve the best cross-sectional prediction of recombinant type I interferon therapy.\n In addition to internal validation of the score against individuals treated with recombinant IFN- in UK Biobank, we have performed multiple additional validations, including external validation described below.\nOur revised oligoprotein interferon signature comprises three proteins encoded by well-recognised ISGs: SIGLEC1, RIG-I and IFIT3. For example SIGLEC1 is a core component of interferon transcriptomic signatures/scores used in both the papers cited by the reviewer above. RIG-I and IFIT3 which are also widely recognized ISGs, and the latter is widely used in transcriptomic signatures.\nIn addition to this more robust methodology trained on individuals exposed to recombinant type I IFN, we show multiple independent approaches for validation:\n1) The score is highly elevated in individuals in UKB who are receiving both recombinant IFN- and IFN- therapies (revised Figure 3B, below)\n2) MIRO scores have the strongest correlation with proteins involved in the type I interferon response across other analytes available in UKB-PPP (revised Figure 3C, below)\n3) We have externally validated the new oligoprotein interferon signature against gold-standard single molecule ELISA for IFN-in an interferonopathic disorder (Sjogren disease). This analysis is based on paired samples where we have generated both Olink proteomic data and Single molecule ELISA data. (New supplementary Figure 3, below)\n4) The new oligoprotein interferon signature shows specificity for type I IFN (e.g. IFN-and does not correlate with type II IFN (IFN). (New supplementary Figure 3, below)\nWe present the new results in a revised Figure 3.\nResults:\n“An oligoprotein interferon signature identifies interferonopathic autoimmune diseases\nLupus is considered the prototypical example of a sporadic autoimmune disease associated with prominent activation of the type I interferon response2. Early studies linking TREX1 with SLE also included conditions such as SjD which is similarly associated with elevated type I interferon. This is biologically plausible since TREX1 is a negative regulator of the type I interferon response. We therefore asked whether we could use UKB-PPP to identify “interferonopathic” autoimmune disorders and test their association with TREX1 mutations. To date, however, there has been no systematic identification of autoimmune disorders associated with activation of the type I interferon response. This is because quantification of type I interferon in serum has been challenging due to the low abundance of these proteins. Measuring interferonis nontrivial and requires high sample volumes, and for this reason is challenging to measure at scale in large cohort studies such as UKB. An alternative surrogate for interferon protein concentrations are RNA-based interferon signatures, which are often used as a biomarker in clinical studies of interferonopathic diseases3. However, transcriptomic data are not currently available for UKB.\nTo overcome these challenges, we therefore sought to develop and validate an oligoprotein interferon signature of type I interferon activation derived from broad-capture proteomic datasets. We included 22 correlates of type I interferon levels available in UKB-PPP (Olink Explore 3072 proteomics platform) in a penalized logistic regression model trained to achieve the best cross-sectional prediction of recombinant type I interferon therapy (interferon- in people with multiple sclerosis at the time of baseline blood sampling, Figure 3A). The oligoprotein interferon signature, which we termed the Markers of type I interferon Response in Olink (MIRO) score, was then derived as the sum of the three proteins retained in the model (SIGLEC1, RIG-I and IFIT3) weighted by their penalized coefficient followed by standardization. We tested the score in three validation experiments. Firstly, the oligoprotein interferon signatures are strongly increased in individuals treated with recombinant interferon- at the time of blood sampling (Figure 3B). Secondly, MIRO scores have the strongest correlation with proteins involved in the type I interferon response across other analytes available in UKB-PPP (Figure 3C). Thirdly, we externally validated the score in a cohort of individuals with SjD from the United Kingdom Primary Sjogren's Syndrome Registry (UKPSSR) where paired interferon- single molecule ELISA and Olink proteomics were measured. We found significant correlation between the oligoprotein interferon signature and interferon- concentrations, but not interferon- (Supplementary Figure 3).”\nFigure 3. Derivation and validation of an oligoprotein type I interferon signature from Olink proteomics (Markers of type I Interferon Response in Olink or MIRO score) and identification of interferonopathic autoimmune diseases in UK Biobank.\nPanel A: Derivation of the MIRO score using Olink Explore 3072 analytes in UK Biobank. Circle packing of included interferon-stimulated genes (SIGLEC1, RIG-I, IFIT3) show area proportional to their absolute coefficient.\nPanel B: Association of MIRO with controls and 21 common autoimmune diseases. Cross-sectional design of prevalent conditions (up to 10 years before baseline) in UK Biobank. Positive controls: interferon therapy, chronic viral hepatitis, organ/tissue transplant, HIV infection, liver and kidney disease. Negative control: haemorrhoids. Ten autoimmune conditions had a high (red) or moderate (blue) type I interferon signature (as per point estimates). Statistical significance defined as p <0.05 (adjusted for the number of tests within controls and autoimmunity separately).\nPanel C: Gene set enrichment analysis of MIRO across biological processes from MSigDB (hallmark) and classical ISGs. Rankings defined using Pearson’s correlation coefficients of MIRO with 2,906 Olink analytes and classical ISGs defined using Chaussabel module 3.1.\nPartly created with BioRender.com. Abbreviations: CI, confidence interval; HIV, human immunodeficiency virus; ISG, interferon-stimulated gene; LASSO, Least Absolute Shrinkage and Selection Operator; MIRO, Markers of type I Interferon Response in Olink; MSigDB, Human Molecular Signatures Database; p , false discovery rate adjusted p-value (Benjamini-Hochberg procedure); SD, standard deviation.\nSupplementary Figure 3. Derivation and validation of the Markers of type I Interferon Response in Olink (MIRO) score.\nPanel A: Candidate correlates of type I interferons included in the penalized logistic regression.\nPanel B: External validation of MIRO in 39 Sjogren disease patients from the UKPSSR. The partial MIRO score was calculated using RIG-I and CD163 (surrogate for SIGLEC1 with correlation between the two proteins in Sjogren disease = 0.673, p<0.0001). SIGLEC1 and IFIT3 were not captured in UKPSSR.\nAbbreviations: IFN, interferon; MIRO, Markers of type I Interferon Response in Olink; NPX, normalized protein expression; UKPSSR, United Kingdom Primary Sjogren's Syndrome Registry.\nWe describe the revised methodology in detail below:\nMethods\n“United Kingdom Primary Sjogren's Syndrome Registry\nThe UKPSSR is a national observational cohort of SjD patients fulfilling the 2002 American European Consensus Group classification criteria4 and recruited across 30 UK centres with serum samples stored at recruitment (Eppendorf tubes at -80°C)5. A random sample of 39 patients from UKPSSR was selected for a serum proteomic substudy quantifying 454 unique analytes with five Olink Target 96 panels (inflammation, immune response, organ damage, cardiovascular III and metabolism) and multi-subtype interferon- levels with a high-sensitivity Simoa assay on an HD-X Analyzer as per manufacturer’s instructions (Quanterix, USA). We log-transformed interferon- levels to obtain a normal distribution for analyses. Participants of the UKPSSR gave written consent for collection of clinical and peripheral blood samples and the research ethics approval was granted by the UK National Research Ethics Committee North West – Haydock.\nDerivation and validation of a proteomic type I interferon signature\nWe sought to develop an oligoprotein interferon score derived from Olink proteomics in the UKB-PPP, which we refer to as Markers of type I interferon Response in Olink (MIRO) score. Because interferon- and - are not measured on Olink Explore 3072, we developed this score to capture their downstream proteomic response, analogous to the transcriptomic ‘interferon score’ previously described for monogenic type I interferonopathies which is defined as the median fold change in six canonical ISGs6, 7. We first identified gene products quantified on Olink Explore 3072 that correlate with type I interferon levels from either i) a total RNA expression fold change >20x after interferon- or - stimulation from a publicly available expression database on ISGs (INTERFEROME v2)8 using in vitro experiments on human peripheral blood cells under normal conditions, or ii) an ISG (more broadly defined using Chaussabel modules 1.2, 3.4 and 5.129) with a positive association with log-interferon- at p-value <0.1 in a sample of 39 individuals with SjD from UKPSSR.\nWe next included these ISGs in a penalized logistic regression model using L1 regularization (Least Absolute Shrinkage and Selection Operator or LASSO regression), which shrinks coefficients to select features and account for collinearity, to achieve the best and most interpretable prediction of type I interferon stimulation. The model was trained on UKB participants with multiple sclerosis treated vs not treated by exogenous interferon- therapy (any formulation). This class of injectable disease-modifying therapies is used in relapsing-remitting multiple sclerosis and induces a strong transcriptomic response of classical type I ISGs that persists for several days even in long-term users10. The underlying disease, multiple sclerosis, is not associated with elevated type I interferon, and so the interferon signatures in these patients are driven by the recombinant interferon protein. We considered multiple sclerosis diagnoses either prevalent at baseline or captured within five years of follow-up to balance statistical power and biological relevance as multiple sclerosis pathogenesis precedes diagnosis by 5-10 years11 and because event dates reaching UKB through administrative healthcare databases may lag behind actual diagnostic dates12. Of 235 participants with multiple sclerosis who had readings on all candidate ISGs and did not have comorbid autoimmunity, chronic kidney disease or liver disease at baseline (to limit potential noise due to other sources of inflammation), we included 21 participants on interferon- therapy who were randomly matched (1:4 ratio) on age (5-year strata) with 84 participants who did not report using interferon- at baseline. This sample was used to train and select the most regularized model within one standard error of the regularization parameter minimizing the mean 10-fold cross-validated binomial deviance ( ).\n1se\nThe MIRO score was calculated as the sum of standardized protein levels for the three included ISGs, each weighted by their L1 penalized coefficient, followed by standardization (mean=0, SD=1; see Supplementary Table 16 for weights). A score was available for 36,966 UKB participants (mean age at baseline=56.8 years [SD=8.2]; 54.1% females).\nWe validated the MIRO score in three experiments:\nFirst, we explored the cross-sectional associations at baseline of MIRO with positive control exposures (exogenous interferon- and -) and diseases (chronic viral hepatitis13, organ/tissue transplant14, human immunodeficiency virus [HIV] infection15, liver disease16 and chronic kidney disease17, 18) and one negative control (haemorrhoids).\nSecond, we performed a gene set enrichment analysis (GSEA)19 using Pearson’s correlation coefficients of MIRO with 2,906 Olink analytes (with unique UniProt entries) as ranking metric20. We interrogated the 50 hallmark (H) gene sets from the Human Molecular Signatures Database (MSigDB) plus a set of classical ISGs (Chaussabel module 3.19) without restricting gene set sizes (range of genes per set: 5-107). We report normalized enrichment scores (to account for gene set size in enrichment estimates) along with false discovery rate (FDR)-adjusted p-values (p ) from the Benjamini-Hochberg procedure.\nThird, we used a sample of 39 SjD patients with proteomics from UKPSSR to calculate Pearson’s correlation coefficients for RIG-I (from Olink) and a partial MIRO score with log-interferon- (from high-sensitivity Simoa assay) and interferon- (from Olink; excluding n=19 with levels below the limit of detection). Because SIGLEC1 and IFIT3 were not captured in the UKPSSR Olink Target 96 panels, the partial MIRO score was calculated using CD163 as a surrogate for SIGLEC1 (correlation between the two proteins in SjD: r=0.673, p<0.0001).\nHaving validated the score, we next surveyed 21 common autoimmune diseases21 to identify type I interferon-related autoimmune conditions using MIRO. In these two last cross-sectional experiments, medical conditions were defined from diagnoses up to 10 years prior to baseline after excluding people with comorbid autoimmunity, prior organ/tissue transplant (except when tested as trait of interest) or on interferon therapy. Associations were tested using linear regressions of MIRO (dependent variable) with each condition of interest adjusted for age (continuous) and sex. Models of medical conditions were also adjusted for interferon- levels (to account for its potential association with the type I interferon proteomic signature and traits) and, except for instances with low sample sizes (interferon therapy, dermatomyositis, Addison’s disease and myasthenia gravis), adjusted for ethnicity (white vs non-white), body mass index (BMI; continuous), smoking (current, previous, never), alcohol use (four intake levels: none, low, moderate, high22) and Townsend deprivation index23 (continuous) at baseline.”\nComment 10: Page 18, lines 360-362: “These brain 360 volumes were available for a range of 188-193 participants with SLE/SjD, 42,337-43,621 participants 361 without SLE/SjD, 85-87 TREX1 variant carriers and 42,440-43,727 non-carriers.” Again, this is a small subset of the entire cohort. Was an analysis done to determine if they differ from the larger cohorts in important ways which may bias the results?\nWe provide this analysis in Supplementary Table 3 which shows that the population with vs without data are broadly comparable.\nComment 11: Page 19 lines 388-392- the exclusion criteria seem to be too liberal. Why did they choose a CADD <10, when 12-20 is the current cutoff, and with a population based approach, searching for rare, deleterious variants seems most pragmatic?\nThank you for this comment. We used the CADD score to filter out some potentially mislabeled “pathogenic/likely pathogenic” variants from data sources (ClinVar, LOVD and reviews). This cut-off was chosen to avoid removing known causal variants such as c.150_151del (p.Gln51Glyfs) (CADD=17.6). To avoid excluding deleterious variants, we only removed those fulfilling both a CADD <10 and a MAF ≥0.1% in UKB, gnomAD or 100GP, which resulted in the exclusion of 2 out of 86 variants (c.679G>A and c.720G>C), both of which were already included and assessed in the first analysis of “previously reported TREX1 lupus risk variants” with neutral results (no lupus cases for both variants) (see Figure 1).\nMost included variants had CADD >20: of the 84 TREX1 variants included from our systematic search, only 3 (3.6%) had a CADD <10, and 11 (13.1%) had a CADD <20. When we tested the impact of predicted deleteriousness (by LOFTEE and CADD), we did not observe an effect of these annotations on the association with interferon sigantures (Figure 5). This suggests that the CADD threshold had no meaningful impact on our results.\nWe additionally provide a more detailed Supplementary Table 9, showing the CADD score and the number of participants with lupus and MIRO score (along with sample sizes) for each variant.\nComment 12: The section of the paper examining TREX 1 variants with interferonopathic autoimmunity seems to be a different story. If the paper wants to look at all causes of Type I IFNopathies, they should assess it, but including these here seems to dilute the paper. Again, the choice of variants to assess may not be stringent enough and bias the results to the see no association.\nWe thank the reviewer for this comment regarding the narrative flow of the paper, in particular our lack of clarity in explaining how the proteomic aspect of the UKBiobank study offers additional strengths in unpicking the relationship between TREX1, type I interferon and SLE-related phenotypes. This is particularly important because the role of TREX1 as a negative regulator of type I interferon underlies its original choice as an SLE candidate gene.\nIn the revised manuscript we have explained more clearly the rationale for our approach. There are two reasons we integrated oligoprotein interferon signature-associated phenotypes into this study.\nFirstly, we wished to expand our UKB genetic association study beyond SLE to other autoimmune disease with prominent interferon activation, because this is the approach used in the influential Lee-Kirsch Nature Genetics 2007 paper. Specifically, a Sjogren syndrome cohort was included in that paper because of its phenotypic overlap with SLE, including elevated type I interferon. A TREX1 mutation was reported in that Sjogren disease cohort which strengthened the disease association. Our approach also includes SjD, but also includes a wider analysis to include other related interferon-associated autoimmune phenotypes, which we systematically identify using interferon signatures. This allows us to address the issue of phenotypic overlap (raised in the original Lee-Kirsch paper) in a more systemic and comprehensive way.\nSecondly, the immune dysregulation that characterises SLE is detectable in serum years prior to diagnosis (Arbuckle NEJM 2003), and can identify individuals at high risk of SLE. As such interferon signatures are likely to represent an important intermediate phenotype (and in some individuals represent a “forme fruste” of SLE). In the revised manuscript we have demonstrated this and provide clear evidence that the presence of oligoprotein signatures in UKB identify individuals at future risk of a clinical diagnosis of SLE. We have done this by performing a time to event analysis of interferon signatures measured at baseline, showing increased risk of incident diagnosis of SLE. As such, our analysis that not only is risk of SLE diagnosis not elevated in TREX1 mutation carriers, but interferon signatures (which may herald SLE diagnosis in the decade ahead) are not present either.\nWe have expanded the results section to better justify our exploration of related clinical and immune phenotypes:\nResults 1: “Early studies linking TREX1 with SLE also included conditions such as SjD which exhibits phenotypic overlap with SLE and is similarly associated with elevated type I interferon. This is biologically plausible since TREX1 is a negative regulator of the type I interferon response. We therefore asked whether we could use UKB-PPP to identify “interferonopathic” autoimmune disorders and test their association with TREX1 mutations.”\nResults 2: “Serological evidence of SLE can precede diagnosis by many years. Indeed, in UKB-PPP, we found that elevated type I interferon signatures at baseline in people without an SLE diagnosis were strongly associated with future risk of SLE diagnosis (Figure 5A; see Supplementary Table 9 for all estimates). Interferon signatures could be reliably detected up to 9 years prior to diagnosis (SupplementaryFigure 5). We therefore explored whether TREX1 variant carriers might have evidence of peripheral type I interferon activation. We found no difference in MIRO scores between carriers and non-carriers of TREX1 variants (Figure 5B; see Supplementary Figure 6 for individual component analytes).”\nFigure 5. Oligoprotein interferon signatures are associated with high risk of SLE in UKB-PPP, but are not elevated in TREX1 mutation carriers\nPanel A: A peripheral proteomic signature for type I interferon activation (MIRO score) predicts incident SLE (left) but not multiple sclerosis (right). Adjusted time-to-event curves by quartile of MIRO score from Cox regression models (HRs with 95% CIs).\nPanel B: Density plot of MIRO score by TREX1 genotype.\nPanel C: Density plot of MIRO score by LOFTEE classification.\nPanel D: Scatter plot of MIRO score by CADD and LOFTEE classification.\nAbbreviations: CADD, Combined Annotation-Dependent Depletion score (PHRED-like scaled); CI, confidence interval; HR, hazard ratio; pLOF, high-confidence predicted loss-of-function from LOFTEE; MIRO, Markers of type I Interferon Response in Olink.\nSupplementary Figure 5. Detection of oligoprotein interferon signatures before diagnosis of lupus in UK Biobank.\nPanel A: Distribution of MIRO scores relative to recorded SLE diagnosis in UK Biobank. The blue dotted line at y=0 indicates the mean MIRO score in UKB. Smooth curve produced by locally estimated scatterplot smoothing (LOESS) along with standard errors (grey).\nPanel B: Analysis of MIRO scores by 3-year epoch prior to SLE diagnosis in UK Biobank. Participants with SLE were matched 1:10 to controls selected at baseline on age and sex. Plot shows mean MIRO scores with 95% confidence intervals by group and p-values obtained through linear regressions. Intervals are closed to the left and open to the right, i.e. -15: [-15, -12), -12: [-12, -9), -9: [-9, -6), -6: [-6, -3), -3: [-3, 0).\nAbbreviations: MIRO, Marker of type I Interferon Response in Olink; SLE, systemic lupus erythematosus.\nUpdated methods: “We used Cox proportional hazards models to test the association of MIRO scores at baseline with incident diagnoses (SLE and multiple sclerosis) adjusted for potential predictors of autoimmunity (age, sex, ethnicity, smoking, Townsend deprivation index, presence of other autoimmune diseases). For each condition, participants without prevalent diagnosis were included at baseline and followed until the first of outcome occurrence, death, loss of follow-up or study end date (31/OCT/2022; last day of the month with mostly complete data for National Health Service [NHS] England inpatient records, the main source of diagnoses).”.\nComment 13:Page 22, line 458: We replicated these neutral associations with RF and CRP, two blood biomarkers associated 458 with lupus. These are not common lupus biomarkers. ANA and ESR are much more commonly associated with lupus. Overall, I think adding someone with rheumatology expertise and specifically lupus expertise to the study team would greatly enhance the manuscript.\nWe thank the reviewer for this relevant comment as well as review 3 (comment 11) for raising a similar question.\nWe agree that CRP and RF are not lupus biomarkers commonly used in clinic and that ANA and ESR would have been preferrable. The latter, however, were not available in UKB. In light of these comments and those of reviewer 3, we agree that this section could be misinterpreted, and we have removed it.\nWe would reassure that reviewer that the lead author, although a neurologist, has substantial lupus expertise, leading joint clinics and research in the area for over a decade. We would highlight the inclusion of two collaborators to the revised manuscript, including a rheumatologist with expertise in autoimmunity (Professor Fai Ng, University of Cork) and a lupus nephrologist Professor Neeraj Dhuan (University of Edinburgh). These authors have been involved in the validation of the MIRO score, but have also contributed to redrafting of the manuscript.\nComment 14: The GitHub link provided did not work for me to review the code.\nPlease find the link here: https://github.com/barioux/UKB_TREX1/tree/main\nThis link was also added in the “Code availability” section.\nReviewer 3\nComment 1:The paper lacks a section on its limitations, which should be added, along with addressing other matters listed below.\nWe thank the reviewer for this comment. We have added this as a dedicated section, and cover amongst other issues: very rare variants, other ancestries, age at SLE diagnosis and diagnostic accuracy.\nComment 2: Medical record diagnosis is notoriously weak, in particular in relation to heterogeneous autoimmune diseases where both under- and over-diagnosis are common. 35% in total of cases had the diagnosis based on a primary care-assigned diagnosis, which in this reviewer’s experience is likely to represent a significant proportion of false positive SLE diagnoses, weakening any associations.\nWe thank the reviewer for this comment and address this in the revised manuscript.\nAmong UKB participants with lupus, 241 (18.5%) were diagnosed from primary care records without diagnostic codes from hospital records. We should note that these diagnoses may be either from a GP or from outpatient secondary care as diagnoses following referrals are generally reported back and recorded by community practices. In UKB, most lupus cases (69.5%) originated from inpatient hospital records with high positive predictive value (>95%). In addition, the meta-analysis included a well phenotyped cohort of >6k cases fulfilling the American College of Rheumatology criteria.\nWe have expanded the description of the data sources for primary care records in the Methods:\n“Primary care data include both diagnoses from general practitioners and those from outpatient secondary care (e.g., rheumatology clinics) as these are generally reported back to the practice.”.\nWe have also added the potential for misclassification as a limitation in the discussion (limitations section): “Third, lupus diagnoses from administrative health records are prone to misclassification. The majority of UKB cases, however, were found through inpatient hospital records which have a high positive predictive value (>95%), and the neutral meta-analysis included a well phenotyped cohort fulfilling the American College of Rheumatology criteria.”\nWe finally re-analyzed lupus and Sjogren disease by defining cases only from hospital records, which yielded similar results (presented in the new Supplementary Table 12).\nComment 3: Ancestry linkage studies are informative, suggesting a possible reason for previous reported associations between TREX variants and SLE. However, UKB is hugely dominated by white/European ancestry subjects, known to have the lowest prevalence and severity of SLE. Significant missing ancestries include East Asian ancestries, in which with a very high prevalence of young onset SLE may have a different result. African ancestries are also <>10% of the cohort.\nThank you for this comment. We have answered this point in reviewer 2 comment 3 above. Briefly, we have highlighted this in the limitations section and described how prior studies with multi-ancestral structures included in the meta-analysis do not support an association.\nComment 4: The cohort is also light-on for young onset disease, where genetic associations are believed to be stronger. <2% of cases were diagnosed at age <20.\nThis is an important point which is addressed in the response to reviewer 1 comment 3 above. This is now addressed in the limitations section.\nComment 5: The so-called MIRO protein signature is entirely derived from public domain sources, namely mRNA expression data after IFN-alpha stimulation, and predicted secretion of the gene products. This is a far from ideal way to develop such a signature, which ideally would be based on detection of proteins from human cells exposed to IFNAR activation, which in turn would not be limited to IFN-alpha stimulation as multiple other type I IFNs are implicated in SLE. Earlier work such as that of Bauer et al (10.1371/journal.pmed.0030491.st002) combined measurement of proteins directly in patient samples with in vitro exposure of cells to IFN in order to increase confidence that the signature was valid; that has not been done here. Experimental replication of this signature is required, and its lack is a significant caveat.\nWe thank the reviewer for this important comment. As outlined in our response to reviewer 2 comment 9, we have developed our methology to address these concerns.\nWe also agree with the reviewer that our approach has limitations, and we address this explicitly in the discussion section of the revised manuscript.\nDiscussion: “The UKB-PPP offers an important opportunity to study the link between type I interferon, SLE and TREX1 mutations. Interferon-a is typically not captured in proteomic datasets, and we show here that an oligoprotein signature can be derived from broad capture proteomics which can act as a reasonable proxy measure of interferon-a. This allows us to leverage the additional strength of the UKB-PPP study to evaluate whether TREX1 mutations confer risk for other “interferonopathic” autoimmune diseases, as well as look for the presence of interferon signatures in mutation carriers. We show that interferon signatures can precede SLE diagnosis for many years, and are observed in a number of other autoimmune conditions, but such type I interferon endophenotypes are not associated with TREX1 mutations. Given the utility of transcriptomic interferon signatures in the study of lupus, we note the potential for future refinement of this oligoprotein approach for the study of interferonopathic disorders in large deeply phenotyped cohorts such as UKB.”\nComment 6: Similarly, the ‘shoulders’ of individual MIRO protein levels greater than those in the health population are modest (Suppl Figure 4), notwithstanding the small p values for the difference in means potentially driven by the large sample size, though somewhat more impressive for the composite MIRO score.\nWe thank the reviewer for this comment and highlight that the development of composite scores for detection of interferon signatures has historically provided a more robust outcome measure than single ISGs – and we replicate this with the proteomic signature. Our revised methology also further strengthens the “shoulders” (see Figure 5 and Supplementary Figure 6). We would also highlight that the plots showing NPX values (the units used by Olink proteomics) are on the log2 scale which may attenuate differences (as compared to a non-transformed scale).\nComment 7:The meaning of Figures 3 C&D is unclear.\nPanels in Figure 3C showed how autoimmune diseases can be divided into “interferonopathic” and “noninterferonopathic” disorders. This is displayed as packed circles, area proportional to the number of individuals in UK Biobank. We have revised this approach to visual display of information since we agree many readers may not be familiar with this approach.\nIn the revised Figure 3, we have dropped this circle packing and have kept an improved forest plot showing diseases with a high, moderate and lower type I interferon signature (Figure 3B).\nComment 8: Figure 4C appears to be monochromatic but the legend refers to red and black components.\nIn the legend we are referring back to Figure 3B but in the revised version we have made this clearer and apologise for the lack of clarity.\nFigure 4C legend: “Autoimmune diseases grouped by type I interferon signature from MIRO scores (Figure 3B)”.\nComment 9: It is not immediately clear what data are represented in Figure 5B – I take it the dots, box, and whiskers are individual MIRO proteins? Each is counted when >2SD - above what?\nWe thank the reviewer for this comment. These plots were showing the proportion of participants with protein values > 2SD of the mean for each Olink analyte included in the MIRO score, showing that participants with lupus had more often extreme values across these analytes than people without lupus but that this was not the case for TREX1 carriers. We have refined the MIRO score and now with only 3 components these plots are not as informative and we decided to remove them as they duplicate information from the revised Supplementary Figure 5.\nComment 10: Noting that IFNs are generally low abundance in human serum samples, they are detectable in some individuals. Were any type I IFN proteins detected in any sample? Did they relate to TREX variants?\nThank you for this comment, which gets to the heart of the challenges of measuring IFNa in large scale human studies. The low abundance of IFNa in human blood has always meant accurate measurement can only be performed using single molecule ELISA. Furthermore IFNa is not captured using Olink proteomics. UKB has strict release critieria for stored serum and, given that our Simoa assay uses ~200uL of volume per datapoint, it is unlikely that we are able to access this depletable resource because of the large volumes currently required. Hence we have validated the MIRO score against Simoa to demonstrate that this is a reasonable proxy measure.\nWe have added an explanation in the Results: “Measuring interferon-a is nontrivial and requires high sample volumes, and for this reason is challenging to measure at scale in large cohort studies such as UKB. An alternative surrogate for interferon-a protein concentrations are RNA-based interferon signatures, which are often used as a biomarker in clinical studies of interferonopathic diseases. However, transcriptomic data are not currently available for UKB. To overcome these challenges, we therefore sought to develop and validate a proteomic signature of type I interferon activation.”\nComment 11:CRP is an unusual biomarker to choose in the setting of SLE, as in most studies it has little or no correlation with disease activity and is commonly measured in the reference range in people with SLE, even when active. I do not recognise this as a valid reference biomarker for the research question being addressed here and recommend deletion of this section.\nPlease refer to comment 13 of reviewer 2 on this point.\nComment 12: In vitro variant testing was done in murine cells – does this risk missing functionally important effects restricted to human cells?\nTREX1 nuclease function and localisation is highly conserved from human to mouse. While it is possible there are downstream effects our testing is restricted to enzymatic location and ssDNA nuclease activity which are conserved across mouse and human. The TREX1-null mouse cell assay is well accepted for these purposes. We have also tested a small subset of variants in human cells and the effects on nuclease function and localisation are the same.\nWe have expanded on this in the Methods: “Both ssDNA nuclease activity and TREX1 localisation are similar across mouse and human species”.\nComment 13: The authors appear to be from a neurosciences centre, but apart from this why were CNS manifestations of SLE chosen to be called out?\nThe author is correct that the research group has an interest in neuroimmunological conditions, including neurolupus. While the main focus is on SLE, we did focus CNS manifestiations for two reasons: (i) biallelic loss of function in TREX1 leads to AGS, an interferonopathy with mainly CNS manifestations, and (ii) the findings of Namjou et al showed a potential association between a TREX1 haplotype and CNS manifestations of lupus. In the experience of our centre, some clinicians perform TREX1 sequencing in lupus patients with atypical CNS manifestations (see comment 14 below). We have stated this rationale more clearly in the revised manuscript.\nResults: “We used this proteomic signature approach to test a broader hypothesis that carriers of any disease-causing TREX1 variants have an increased risk of type I interferon-related disease or neurological comorbidities of SLE (as suggest by a previous study).”.\nMethods: “Expanded clinical and radiological phenotypes were defined from autoimmune diseases, common neurological comorbidities of lupus (due to the previous association of TREX1 variants with neurological manifestations in lupus), and radiological phenotypes observed in lupus (white matter hyperintensities and brain atrophy).”\nComment 14: The discussion is too long. TREX sequencing is not part of the diagnosis or clinical care of SLE.\nThank you for this comment.\nWe agree with this comment (and also note that we have increased the “limitations” section of the discussion in view of reviewer comments). We have therefore removed sections of the discussion which were less directly relevant to the main findings of the paper, and hope this leads to a more focused and readable discussion. Deleted text is marked in blue on the revised manuscript.\nWe address TREX1 sequencing in the comment above. We have removed from the abstract “At a practical level, we suggest there is no merit in sequencing TREX1 in patients with lupus, where monogenic disease is not suspected.”\nReferences\n1. Sun BB, Chiou J, Traylor M, et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 2023;622:329-338.\n2. Banchereau J, Pascual V. Type I Interferon in Systemic Lupus Erythematosus and Other Autoimmune Diseases. Immunity 2006;25:383-392.\n3. Rice GI, Forte GM, Szynkiewicz M, et al. Assessment of interferon-related biomarkers in Aicardi-Goutieres syndrome associated with mutations in TREX1, RNASEH2A, RNASEH2B, RNASEH2C, SAMHD1, and ADAR: a case-control study. Lancet Neurol 2013;12:1159-1169.\n4. Vitali C. Classification criteria for Sjogren's syndrome: a revised version of the European criteria proposed by the American-European Consensus Group. Annals of the Rheumatic Diseases 2002;61:554-558.\n5. Ng W-F, Bowman SJ, Griffiths B, on behalf of the Usg. United Kingdom Primary Sjögren's Syndrome Registry—a united effort to tackle an orphan rheumatic disease. Rheumatology 2011;50:32-39.\n6. Rodero MP, Tesser A, Bartok E, et al. Type I interferon-mediated autoinflammation due to DNase II deficiency. Nature Communications 2017;8:2176.\n7. Burska A, Rodríguez-Carrio J, Biesen R, et al. Type I interferon pathway assays in studies of rheumatic and musculoskeletal diseases: a systematic literature review informing EULAR points to consider. RMD Open 2023;9:e002876.\n8. Rusinova I, Forster S, Yu S, et al. INTERFEROME v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Research 2013;41:D1040-D1046.\n9. Chaussabel D, Quinn C, Shen J, et al. A Modular Analysis Framework for Blood Genomics Studies: Application to Systemic Lupus Erythematosus. Immunity 2008;29:150-164.\n10. Feng X, Bao R, Li L, Deisenhammer F, Arnason BGW, Reder AT. Interferon-β corrects massive gene dysregulation in multiple sclerosis: Short-term and long-term effects on immune regulation and neuroprotection. eBioMedicine 2019;49:269-283.\n11. Tremlett H, Marrie RA. The multiple sclerosis prodrome: Emerging evidence, challenges, and opportunities. Multiple Sclerosis Journal 2021;27:6-12.\n12. Clifton L, Liu X, Collister JA, Littlejohns TJ, Allen N, Hunter DJ. Assessing the importance of primary care diagnoses in the UK Biobank. Eur J Epidemiol 2024;39:219-229.\n13. Bolen CR, Robek MD, Brodsky L, et al. The blood transcriptional signature of chronic hepatitis C virus is consistent with an ongoing interferon-mediated antiviral response. J Interferon Cytokine Res 2013;33:15-23.\n14. Rascio F, Pontrelli P, Accetturo M, et al. A type I interferon signature characterizes chronic antibody-mediated rejection in kidney transplantation. The Journal of Pathology 2015;237:72-84.\n15. Mackelprang RD, Filali-Mouhim A, Richardson B, et al. Upregulation of IFN-stimulated genes persists beyond the transitory broad immunologic changes of acute HIV-1 infection. iScience 2023;26:106454.\n16. Hackstein C-P, Spitzer J, Symeonidis K, et al. Interferon-induced IL-10 drives systemic T-cell dysfunction during chronic liver injury. Journal of Hepatology 2023;79:150-166.\n17. Zoccali C, Vanholder R, Massy ZA, et al. The systemic nature of CKD. Nature Reviews Nephrology 2017;13:344-358.\n18. Anders H-J, Lichtnekert J, Allam R. Interferon-alpha and -beta in kidney inflammation. Kidney International 2010;77:848-854.\n19. Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545-15550.\n20. Zyla J, Marczyk M, Weiner J, Polanska J. Ranking metrics in gene set enrichment analysis: do they matter? BMC Bioinformatics 2017;18:256.\n21. Conrad N, Verbeke G, Molenberghs G, et al. Autoimmune diseases and cardiovascular risk: a population-based study on 19 autoimmune diseases and 12 cardiovascular diseases in 22 million individuals in the UK. Lancet 2022;400:733-743.\n22. Smyth A, Teo KK, Rangarajan S, et al. Alcohol consumption and cardiovascular disease, cancer, injury, admission to hospital, and mortality: a prospective cohort study. The Lancet 2015;386:1945-1954.\n23. Nagar SD, Jordan IK, Mariño-Ramírez L. The landscape of health disparities in the UK Biobank. Database 2023;2023."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors' response has reduced some of my concerns, but several points warrant further discussion. The UKB and meta-analyses remain based on gene-level burden analyses, where simple gene burden tests may dilute true signals with numerous neutral variants, despite inclusion of diverse ethnicities (predominantly European). Moreover, genuine pathogenic variants such as p.Asp130Asn may not have extremely high ORs but could still contribute to sporadic SLE with elevated serum IFNα (DOI: 10.1016/j.clim.2021.108732). These are missense mutations rather than established dominant-negative mutations like D18N and D200N. The discussion of these critical points seems to be insufficient, with the authors instead vaguely claiming that the meta-analysis has adequate power without addressing the fundamental methodological limitations of burden testing in detecting variant-specific effects.", "reviewer_2": "The authors have revised the manuscript, which I still believe has merit and has findings that contribute to the body of literature on the role of TREX1 variants in SLE.\nHowever, I find that there continue to be methodological issues which have not been fully addressed.\nI think the authors need to clarify that heterozygous TREX1 variants are not equal, and while some contribute to SLE others do not and finding a rare variant in TREX1 requires functional evidence before considering causality. This should be reflected in the title.\nI do not think the authors have sufficiently addressed the weakness of SLE diagnosis from medical record as a limit\nThe meta-analysis is based on 2 very old studies with 2 very different approaches to sequencing (one is an Illumina chip assay, which is used for the large, diverse cohort, and may miss many rare novel variants in TREX1. This assay would be the weakest to detect variants in non-europeans populations) This needs to be stated as a limitation.\nThe Miro validation is not sufficient. The use of a cohort of 39 patients as a validation cohort is not sufficient. Also, to use a partial MIRO score which substitutes CD163 for SIGLEC1 is not robust methodology. The authors claim that many of the markers used are similar to validated scores does not substantiate the use of an unvalidated score. Also, the p value and r score for RIG-I are marginal in this validation.They either need to do a large validation of the method or drop this section of the manuscript.\nAdditionally, adding in MRI features often found in SLE does not address the limitations. There are no official MRI biomarkers for CNS SLE, so the lack of findings is not conclusive.\nFinally the point that some of this cohort may be diagnosed with SLE after the bio sample was entered was not mentioned in the study.", "reviewer_3": "The authors have responded appropriately to the comments raised in my review and the revisions are acceptable from the point of view specifically of my questions."}, "author_response": "Oligoprotein type I interferon signatures, but not TREX1 variants,\nincrease risk of systemic lupus erythematosus in UK Biobank\nDear Ching-yu and editorial team,\nMany thanks for the positive response to our revised manuscript. We are pleased that the reviewers felt our\nmanuscript was improved. Please find below a point-by-point rebuttal to the constructive comments of reviewer\n#2. We hope this addresses the concerns.\nColour code\nBlack = reviewer comments\nPurple = author response\nGreen = new text in revised manuscript\nReviewer 1\nComment 1: The authors' response has reduced some of my concerns, but several points warrant further\ndiscussion. The UKB and meta-analyses remain based on gene-level burden analyses, where simple gene burden\ntests may dilute true signals with numerous neutral variants, despite inclusion of diverse ethnicities\n(predominantly European).\nMoreover, genuine pathogenic variants such as p.Asp130Asn may not have extremely high ORs but could still\ncontribute to sporadic SLE with elevated serum IFNα (DOI: 10.1016/j.clim.2021.108732).\nThese are missense mutations rather than established dominant-negative mutations like D18N and D200N. The\ndiscussion of these critical points seems to be insufficient, with the authors instead vaguely claiming that the\nmeta-analysis has adequate power without addressing the fundamental methodological limitations of burden\ntesting in detecting variant-specific effects.\nWe thank the reviewer for these comments and are happy to discuss these points further. As requested in the\nnew revision we discuss further the methodological limitations of burden testing in detecting variant-specific\neffects (see below).\nOn the point of the mutation raised by the reviewer, we agree that very rare deleterious TREX1 mutations are\noccasionally reported in people with SLE, although the disease-relevance of these mutations is not clear. As\nhighlighted by the reviewer, the p.Asp130Asn is a good example. This is a pathogenic/deleterious mutation which\ncan causes Aicardi-Goutieres syndrome when inherited with another loss of function TREX1 mutation (Yanick\nCrow, personal communication). UK Biobank is underpowered to examine the SLE risk of individuals with this\nmutation (there are only 6 individuals with this mutation in UKB, none of whom have a diagnosis of SLE). However\nin our wider analysis we have tested the hypothesis that AGS TREX1 mutation carriers are predisposed to SLE\n(Figure 4), and we found no association. We highlight the need for caution before attributing disease-relevance to\nsuch mutations.\nDiscussion:\n“First, we cannot exclude a predisposition to SLE by some TREX1 variants due to insufficient statistical power\n(e.g., very rare variants or rare variants associated with disease with small effect sizes). Our UKB study,\nsystematic review and meta-analysis use gene-level analyses and demonstrate a lack of association between\nTREX1 variants and SLE. There is the potential for simple gene burden tests to dilute true signals with numerous\nneutral variants, which can occur despite the inclusion of diverse ancestries in the meta-analysis. It is\nestablished that very rare and highly functional deleterious loss of function mutations in TREX1 can cause\nmonogenic interferonopathic disease through a dominant negative mechanism (e.g., c.52G>A [D18N] and\nc.598G>A [D200N]), and there are other variants implicated in atypical interferonopathic syndromes with overlap\nwith lupus, such as c.388G>A (D130N) where disease-relevance remains unclear and cannot be addressed in this\nstudy because of the rarity (there are only 6 carriers of c.388G>A in UKB, none with a diagnosis of SLE).\nFurthermore, non-European ancestry populations are underrepresented in UKB.The meta-analysis, however,\nincludes a large study with SLE patients of European American (3,936 cases), Asian (1,265 cases), African\nAmerican (1,527 cases), Gullah (152 cases) and Hispanic (1,492 cases) ancestries reporting neutral associations\nfor previously reported risk variants within each group1, suggesting that any strong ancestry-specific TREX1-SLE\nassociation is unlikely. These analyses highlight the importance of stringent functional assessments to examine\ncausality in future studies of very rare TREX1 variants that our study has limited power to dissect.”\nReviewer 2\nComment 1: The authors have revised the manuscript, which I still believe has merit and has findings that\ncontribute to the body of literature on the role of TREX1 variants in SLE. However, I find that there continue to be\nmethodological issues which have not been fully addressed. I think the authors need to clarify that heterozygous\nTREX1 variants are not equal, and while some contribute to SLE others do not and finding a rare variant in TREX1\nrequires functional evidence before considering causality. This should be reflected in the title.\nWe are happy to provide this clarification below. We have also commented on this issue in reviewer 1, comment\n1, and have modified the discussion to further highlight the role of functional assessments. Our title is already\nquite long (and it is quite difficult to capture this nuance in a few words), but we would be happy to amend upon\neditorial request.\nDiscussion:\n“These results have implications for attributing disease relevance of heterozygous TREX1 mutations found in\npeople with SLE, because our results show that this association is weaker than previously thought. Firstly, TREX1\nvariants with no functional consequence are unlikely to be disease relevant. Secondly, monoallelic variants\nwhich do not lead to profound functional perturbation (such as seen in dominant negative nuclease-dead\nmutations like c.52G>A [D18N]) are unlikely to confer increased risk of SLE. This observation may also be relevant\nfor counselling of parents of children with AGS, who have monoallelic loss of function TREX1 mutations and no\ndemonstrable increased SLE risk. Finally, there remains some very rare deleterious mutations where we do not\nhave enough information to attribute nor exclude a causal role in SLE.”\nComment 2: I do not think the authors have sufficiently addressed the weakness of SLE diagnosis from medical\nrecord as a limit.\nWe thank the reviewer for this comment on diagnostic accuracies in large biobanks.\nWe acknowledge that medical diagnoses from administrative healthcare records are subject to various degrees\nof misclassification. Diagnostic accuracy studies have previously reported high positive predictive value (>95%)2\nfor SLE using International Classification of Diseases (ICD) codes from inpatient hospital episodes such as those\nused to define most cases in UKB. To explore whether misclassification might have led to bias towards the null,\nwe have performed sensitivity analyses by source of diagnosis in Supplementary Table 12, showing no\nenrichment in lupus diagnosis among TREX1 variant carriers for inpatient hospital diagnoses (less prone to\nmisclassification). In addition, type I interferon signatures, which are not subject to diagnostic misclassification,\nwere not associated with TREX1 variants.\nWe have added a further clarification on the impact of this misclassification in the limitations: “Third, diagnosis of\nSLE in UKB is based on administrative healthcare records, which can be subject to various degrees of\nmisclassification. This contrasts with more formal genetic studies where more stringent diagnostic criteria are\nused. SLE diagnoses from administrative health records are prone to misclassification leading to bias towards\nthe null. The majority of UKB cases, however, were found through inpatient hospital records which have a high\npositive predictive value (>95%), and the neutral meta-analysis included a well phenotyped cohort fulfilling the\nAmerican College of Rheumatology criteria. Furthermore, type I interferon signatures, which are not subject to\ndiagnostic misclassification, were not associated with TREX1 variants.\nComment 3: The meta-analysis is based on 2 very old studies with 2 very different approaches to sequencing\n(one is an Illumina chip assay, which is used for the large, diverse cohort, and may miss many rare novel variants\nin TREX1. This assay would be the weakest to detect variants in non-europeans populations) This needs to be\nstated as a limitation.\nWe thank the reviewer for this comment and we now state this as a limitation. We acknowledge that the study by\nNamjou et al (2011) included in our systematic review does not capture all rare TREX1 variants and used\ngenotyping array chips to generate data.\nThe authors examined a focused set of previously reported rare “risk variants”, which we believe are most\nrelevant because of their reported association with lupus. In addition, although performed on genotyping array\nchips, the authors used stringent variant- and sample-level quality control filters to ensure calls were accurate.\nThis study therefore takes a focused approach to replicate positive associations from Lee-Kirsch et al (2007) and\nreports neutral results.\nWe have clarified the scope of variants tested in this study in the systematic review: “The second study (Namjou\net al, 2011)1 aimed to replicate findings from this first study and expand the association of TREX1 variants with\nSLE in multi-ancestral cohorts using array genotyping for previously reported rare variants (i.e., not capturing all\nrare variants) and more common tag single nucleotide polymorphisms (SNPs).”\nWe have also added a clarification in the limitations to help interpret findings: “The meta-analysis, however,\nincludes a large study with SLE patients of European American (3,936 cases), Asian (1,265 cases), African\nAmerican (1,527 cases), Gullah (152 cases) and Hispanic (1,492 cases) ancestries reporting neutral associations\nfor previously reported risk variants within each group1, suggesting that any strong ancestry-specific TREX1-SLE\nassociation is unlikely.”\nComment 4: The Miro validation is not sufficient. The use of a cohort of 39 patients as a validation cohort is not\nsufficient. Also, to use a partial MIRO score which substitutes CD163 for SIGLEC1 is not robust methodology. The\nauthors claim that many of the markers used are similar to validated scores does not substantiate the use of an\nunvalidated score. Also, the p value and r score for RIG-I are marginal in this validation.They either need to do a\nlarge validation of the method or drop this section of the manuscript.\nWe thank the reviewer for these comments. In our original manuscript we provided extensive internal validation of\nthe MIRO score (see below) and in our last revision we provided new external validation, which demonstrated that\nthe MIRO score is a reasonable, albeit imperfect, proxy for IFNa.\nWe agree that if the score were to be widely used as a clinical tool, then more extensive external validation would\nbe needed, and we have clarified this in the revision. Transcriptomic interferon scores typically undergo extensive\nvalidation in multiple large cohorts if they are to be used in a clinical setting for diagnostic purposes e.g. Rice et\nal. 2013 Dec;12(12):1159-69. Indeed we will be seeking to develop this methodology in this way, but this is\nbeyond the scope of the current paper, and is not feasible in the timescale for revision (4 weeks).\nIt is important to emphasise we are not claiming that the score has clinical utility at this stage of its development,\nrather that the MIRO score is a reasonable proxy for IFN- and can act as an intermediate (and strongly\nbiologically relevant) phenotype for the purpose of this study. For the stated limited experimental purposes of our\npaper we feel that the validation of our oligoprotein score is sufficiently robust. We have validated the score in\nmultiple different and complementary ways, both internally and externally.\nOur other validation approaches include the following:\n1. The score is highly elevated in individuals in UKB who are receiving both recombinant\nIFN- and IFN- therapies (Figure 3B)\n2. MIRO scores have the strongest correlation with proteins involved in the type I interferon\nresponse across other analytes available in UKB-PPP (Figure 3C)\n3. The new oligoprotein interferon signature shows specificity for type I IFN (e.g. IFN- and\ndoes not correlate with type II IFN (IFN-) (Supplementary Figure 3)\n4. The score correctly classifies healthy controls, multiple sclerosis as “non-\ninterferonopathic” and SLE and Sjogren disease as “interferonopathic”. These\nclassifications have been confirmed by prior single molecule ELISA studies in cohorts of\nthese patients (summarized in doi: 10.1016/j.immuni.2024.05.017).\nAs such the MIRO score is sensitive enough to capture elevations in peripheral type I interferons in carriers\nof TREX1 variants to test our null hypothesis (H : TREX1 variants are not associated with an elevation in type I\n0\ninterferons. The MIRO score was externally validated in a sample sufficiently large to capture moderately strong\ncorrelation coefficients such as seen for MIRO and IFN-. This is reflected in confidence intervals that are narrow\nenough to practically exclude small and modest correlations (r=0.57; 95% CI: 0.31, 0.75, p=0.0001). Therefore,\nalthough we agree that increasing the sample size would reduce statistical uncertainty, we do not believe this\nwould change our interpretation of the data or conclusions.\nRevision 1\nWe have added a substantial new section highlighting the strengths, limitations and future opportunities for\noligoprotein interferon signatures:\n“The UKB-PPP offers an important opportunity to study the link between type I interferon, SLE\nand TREX1 mutations. Interferon-a is typically not captured in proteomic datasets, and we show here that an\noligoprotein signature can be derived from broad capture proteomics which can act as a reasonable proxy\nmeasure of interferon-. This allows us to leverage the additional strength of the UKB-PPP study to evaluate\nwhether TREX1 mutations confer risk for other “interferonopathic” autoimmune diseases, as well as look for the\npresence of interferon signatures in mutation carriers. We have developed these oligoprotein interferon\nsignatures from multiple sclerosis patients in UKB receiving recombinant type I interferon therapies, which are\nknown to strongly upregulate interferon response genes40. This identifies proteins (SIGLEC1, RIG-I, and IFIT3)\nwhich are encoded by established ISGs.We have validated the MIRO score both internally and externally against\ninterferon-, in both patients receiving recombinant interferon-a therapy (in UKB) and in patients with high\ninterferon- plasma concentrations (in UKPSSR). This shows that the MIRO score is a reasonable yet imperfect\nproxy for interferon- concentrations in UKB. We show that oligoprotein interferon signatures can precede SLE\ndiagnosis for many years, and are observed in a number of other autoimmune conditions, but such type I\ninterferon endophenotypes are not associated with TREX1 mutations. Given the utility of transcriptomic interferon\nsignatures in the study of lupus, we note the potential for future refinement of this oligoprotein\napproach for the study of interferonopathic disorders in large deeply phenotyped cohorts such as UKB. Further\ndevelopment and refinement of this oligoprotein signature approach, in particular cross-validation with\ntranscriptomic interferon signatures across multiple disease and control cohorts will be an important area for\nfuture study, especially if clinical applications of the signature are considered20.”\nRevision 2\nIn our revision we provide new data demonstrating that all three proteins are encoded by strongly upregulated\nISGs, which further strengthens the biological underpinning of the score.We provide data from 177 Sjogren\nDisease patients (where both Simoa and transcriptomic data were available) which shows that all three genes\nencoding MIRO components are strongly upregulated in UKPSSR, confirming that SIGLEC1, RIG-I and IFIT3 are all\nISGs.\nThese new data are presented in the new Supplementary Figure 3:\nA: Candidate correlates of type I interferons included in the penalized logistic regression.\nB: Genes encoding the MIRO score are interferon-stimulated genes. Components of the MIRO score correlate\nsignificantly with interferon- levels in UKPSSR.\nC: External validation of MIRO in 39 Sjogren disease patients from UKPSSR. The partial MIRO score was\ncalculated using RIG-I and CD163 (surrogate for SIGLEC1 with correlation between the two proteins in Sjogren\ndisease = 0.673, p<0.0001). SIGLEC1 and IFIT3 were not captured in UKPSSR.\nAbbreviations: CI, confidence interval; IFN, interferon; MIRO, Markers of type I Interferon Response in Olink; NPX,\nnormalized protein expression; UKPSSR, United Kingdom Primary Sjogren's Syndrome Registry.\nWe have also added a sentence to the results : “We confirmed that components of the MIRO score are ISGs and\nfound significant correlation between the oligoprotein interferon signature and interferon-a concentrations, but\nnot interferon- (Supplementary Figure 3).”\nMethods for UKPSSR were also modified to include RNA data: “The UKPSSR is a national observational cohort of\nSjD patients fulfilling the 2002 American European Consensus Group classification criteria3 and recruited across\nrecruitment (Eppendorf tubes at -80°C)4. RNA was assayed with the Illumina Human HT-12 v4 Expression\nBeadChip (Illumina). Globin signal suppression was performed using the Affymetrix Globin reduction protocol\n(Affymetrix).”\nComment 5: Additionally, adding in MRI features often found in SLE does not address the limitations. There are\nno official MRI biomarkers for CNS SLE, so the lack of findings is not conclusive.\nWe acknowledge that neutral associations for MRI features is not definite proof of neutrality for the association\nbetween TREX1 variants and lupus. These, however, were examined because of the proposed association\nbetween TREX1 genotypes and neurological manifestations of lupus (Namjou et al, 2011).\nWe have provided a clarification of this in the limitations: “Finally, neurological manifestations of SLE are\ncomplex and heterogeneous and not limited to the six clinical phenotypes examined here. These manifestations\nwill not be fully captured by the neurological diagnoses we have used, which is an important limitation from the\nperspective of studying neurolupus. Brain imaging changes are also heterogeneous in SLE and in our\nneuroradiological analyses we have analysed both brain volumes as well as white matter hyperintensity volume.\nThe latter is a the major structural imaging abnormality seen in people with SLE and likely that\nreflects accelerated microvascular disease23, although it is not seen in all patients and is not a\nspecific imaging marker for SLE.”\nComment 6: Finally the point that some of this cohort may be diagnosed with SLE after the bio sample was\nentered was not mentioned in the study.\nWe thank the reviewer for this clarification.\nThe ambispective nature of the cohort (assessing cases both retrospectively and prospectively) is described in\nthe introduction: “Moreover, UKB participants were aged 40-69 years at enrolment in 2006-2010 and the study\ncaptures both incident and prevalent diagnoses, meaning that the large majority of participants predisposed to\nSLE will by now have been diagnosed, as SLE incidence peaks in middle age5.”\nWe acknowledge that the timing of blood sampling can be more explicitly stated and we have clarified this in the\nrevised version.\nWe have clarified the timing of blood sampling in the first results section on proteomics: “To overcome these\nchallenges, we therefore sought to develop and validate an oligoprotein interferon signature of type I interferon\nactivation derived from broad-capture proteomic datasets measured at recruitment in UKB.”\nWe have also modified the Methods to clarify the timing of blood sampling for proteomics: “We used data on\nrandomly selected (~47k) and consortium-chosen (~6k) participants at baseline, excluding those who took part in\nthe COVID-19 repeat-imaging study (~1k).”\nReviewer 3\nComment 1: The authors have responded appropriately to the comments raised in my review\nand the revisions are acceptable from the point of view specifically of my questions.\nWe thank the reviewer for this positive comment.\nReferences\n1. Namjou B, Kothari PH, Kelly JA, et al. Evaluation of the TREX1 gene in a large multi-ancestral lupus cohort.\nGenes Immun 2011;12:270-279.\n2. Hanly JG, Thompson K, Skedgel C. Identification of patients with systemic lupus erythematosus in administrative\nhealthcare databases. Lupus 2014;23:1377-1382.\n3. Vitali C. Classification criteria for Sjogren's syndrome: a revised version of the European criteria proposed by the\nAmerican-European Consensus Group. Annals of the Rheumatic Diseases 2002;61:554-558.\n4. Ng W-F, Bowman SJ, Griffiths B, on behalf of the Usg. United Kingdom Primary Sjögren's Syndrome Registry—a\nunited effort to tackle an orphan rheumatic disease. Rheumatology 2011;50:32-39.\n5. Rees F, Doherty M, Grainge M, Davenport G, Lanyon P, Zhang W. The incidence and prevalence of systemic\nlupus erythematosus in the UK, 1999-2012. Ann Rheum Dis 2016;75:136-141."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The revisions address my concerns and the manuscript is now acceptable.", "reviewer_2": "The authors have revised the manuscript, which I still believe has merit and has findings that contribute to the body of literature on the role of TREX1 variants in SLE. However, I find that there continue to be issues with clear limitations to the manuscript which have not been fully addressed.\nThe language used, both in the title and throughout the paper, overstates the results with the limited data and cross-sectional design of the study. The SLE diagnosis from large data sets is not simply subject to misclassification bias. The cross-sectional design is the largest limitation here. We have no way of knowing that the subject without an ICD code for SLE at the time of the data pull did not go on to develop SLE 6 months or a year after. To state that most patients are in their 40-60s and most lupus patients present before that not sufficient. Patients present at a range of ages and we cannot make assumptions based on missing data. I would remove that statement that most patients will have been diagnosed, and simply state that you have cross sectional samples and the lack of prospective data for risk prediction is a limitation.\nPage 9 line 174: oligoprotein is misspelled.\nI agree with reviewer 1 on the dilution of variants in burden testing.\nI thank the reviewers for the detailed review of the MIRO testing. I think that validating a set of protein biomarkers for the first time in a manuscript requires a lot of effort with a high burden of proof, as they want to be using a validated test to make a conclusion. The authors have done a lot to provide evidence that MIRO is measuring Type I IFN.\nI think the neurologic association study also remains a weak portion of the paper.\nI recommend the authors remove this section, as it there are no validated MRI biomarkers in SLE, and more advanced MRI technologies are correlated with SLE CNS findings.\nAn ICD code for 6 common associations in SLE can miss many other features of neuroSLE. The authors should state in the limitations that there are no official MRI biomarkers for CNS SLE, so the lack of findings is not conclusive. Conventional MRI is not very sensitive for SLE brain changes. Conventional structural MRI abnormalities, such as white matter (WM) hyperintensities, gross brain tissue atrophy and ventricular enlargement in response to this atrophy are not always observed in patients with NPSLE. In contrast, more advanced postprocessing techniques enable the quantification of structural brain metrics beyond total tissue volumes from standard T1-weighted MRI, such as regional volumes, surface area and cortical grey matter (GM) thickness. Furthermore, other modalities such as diffusion MRI, have become more useful current NPSLE clinical research. Altered tissue microstructure has been reported in several brain regions of patients with NPSLE diagnosis when compared with healthy controls and they have correlated with higher cognitive dysfunction.\nIf these limitations can be clearly stated in the manuscript, I believe it is suitable for publication."}, "author_response": "Oligoprotein type I interferon signatures, but not TREX1 variants,\nincrease risk of systemic lupus erythematosus in UK Biobank\nDear Ching-yu and editorial team,\nMany thanks for the positive response to our revised manuscript. We are pleased that the reviewers felt our\nmanuscript was improved. Please find below a point-by-point rebuttal to the constructive comments of reviewer\n#2. We hope this addresses the concerns.\nColour code\nBlack = reviewer comments\nPurple = author response\nGreen = new text in revised manuscript\nReviewer 1\nComment 1: The revisions address my concerns and the manuscript is now acceptable.\nWe thank the reviewer for the positive feedback.\nReviewer 2\nComment 1: The authors have revised the manuscript, which I still believe has merit and has findings that\ncontribute to the body of literature on the role of TREX1 variants in SLE. However, I find that there continue to be\nissues with clear limitations to the manuscript which have not been fully addressed. The language used, both in\nthe title and throughout the paper, overstates the results with the limited data and cross-sectional design of the\nstudy. The SLE diagnosis from large data sets is not simply subject to misclassification bias. The cross-sectional\ndesign is the largest limitation here. We have no way of knowing that the subject without an ICD code for SLE at\nthe time of the data pull did not go on to develop SLE 6 months or a year after. To state that most patients are in\ntheir 40-60s and most lupus patients present before that not sufficient. Patients present at a range of ages and we\ncannot make assumptions based on missing data. I would remove that statement that most patients will have\nbeen diagnosed, and simply state that you have cross sectional samples and the lack of prospective data for risk\nprediction is a limitation.\nCross-sectional design and case ascertainment:\nWe acknowledge that some participants may be diagnosed with SLE after the latest data linkage of UK Biobank to\nadministrative healthcare datasets. We cannot exclude that a small portion of participants classified as ‘non-\nSLE’ might have gone to develop SLE, but we believe this is unlikely to alter our conclusions for three reasons.\nFirst, UK Biobank is an ambispective cohort study with both cross-sectional questionnaires at recruitment (for\nself-reported prevalent conditions) and data linkage to administrative healthcare datasets to capture both\nprevalent (prior to recruitment) and incident (after recruitment) conditions. As such, UK Biobank is not limited to\ncross-sectional assessments at the time of recruitment. Incident SLE diagnoses in the ~15 years following the\nrecruitment visit are also captured in the cohort.\nSecond, the median age at enrolment in UK Biobank was 58 years in 2006-2010, meaning that UK Biobank\nparticipants now have a median age of >70 years. Because SLE incidence peaks in middle age, we believe that\nmost participants with a genetic predisposition to lupus should by now have been diagnosed in the dataset we\nused (released in AUG/2023).\nThird, we used proteomic type I interferon signatures (MIRO score) to explore the association of TREX1 variants\nwith immune endotypes of SLE. Although we observed strong positive associations between higher MIRO scores\nand the risk of incident SLE, we did not observe an association between MIRO scores and TREX1. This suggests\nthat TREX1 variants are not associated with the type I interferon-related proteomic signature observed in people\npredisposed to SLE.\nHowever, we acknowledge this limitation and have made the following modification to the Introduction:\n“Moreover, UKB participants were aged 40-69 years at enrolment in 2006-2010 and the study captures both\nincident and prevalent diagnoses, meaning that the large majority of most participants predisposed to SLE will by\nnow have been diagnosed, as SLE incidence peaks in middle age1.”\nOverinterpretation of results:\nWe agree with the reviewer that the results should be interpreted with caution to avoid overstating the findings\nand to ensure that the conclusions are scientifically rigorous and well supported.\nRegarding the title, we believe it accurately and descriptively reflects the main study findings. Specifically, it\nconveys that we developed oligoprotein type I interferon signatures associated with incident SLE, while TREX1\nvariants were not associated with this risk in the UK Biobank cohort. We do not feel that this interpretation is\ncontroversial or overly bold given the results obtained. We acknowledge, however, that it is difficult to capture all\nnuances in a title and would be happy to alter upon editorial request.\nWe have tempered some of the statements in the manuscript:\nIntroduction: “This association between TREX1 and manifestations of SLE was considered to be confirmed\nfollowing a large candidate gene study2, contributing to the widespread generalacceptance that TREX1 variants\npredispose to non-monogenic SLE, which includes annotation of variants in clinical reference databases3-7.”\nIntroduction: “We first aimed to replicate results from a canonical previous genetic association study8 linking\nmonoallelic TREX1 variants to SLE.”\nResults: “In summary, this systematic review and meta-analysis does not support a clinically relevant strong role\nof TREX1 variants in SLE and suggests earlier studies might have been confounded by population stratification”\nResults: “Based on these findings, we next explored whether confounding by population stratification might partly\nexplain prior positive associations previously reported strong positive associations8.”\nResults: “Three of the 10 previously reported risk variants found in cases from that study were >40x more frequent\nin the African/African American as compared to the Non-Finnish European subpopulation of the Genome\nAggregation Database (gnomAD) exome.”\nResults: “Finally, no variants were observed in the family-based Finnish cohort included in this first publication\nwhich used a within-family design that inherently controlled for population stratification controlling for\npopulation stratification and included in a previous TREX1-SLE study8.”\nDiscussion: “These neutral results are consistent with prior genetic association studies retrieved through a\nsystematic review of the literature, which together with UKB data do not support the reputed strong risk of SLE\nconferred by rare TREX1 variants.”\nComment 2: Page 9 line 174: oligoprotein is misspelled.\nThank you. It has been corrected.\nComment 3: I agree with reviewer 1 on the dilution of variants in burden testing.\nThank you for this comment, which we addressed in the previous revision to reviewer 1’s satisfaction.\nComment 4: I thank the reviewers for the detailed review of the MIRO testing. I think that validating a set of\nprotein biomarkers for the first time in a manuscript requires a lot of effort with a high burden of proof, as they\nwant to be using a validated test to make a conclusion. The authors have done a lot to provide evidence that MIRO\nis measuring Type I IFN.\nWe thank the reviewer for this positive coment.\nComment 5: I think the neurologic association study also remains a weak portion of the paper. I recommend the\nauthors remove this section, as it there are no validated MRI biomarkers in SLE, and more advanced MRI\ntechnologies are correlated with SLE CNS findings. An ICD code for 6 common associations in SLE can miss\nmany other features of neuroSLE. The authors should state in the limitations that there are no official MRI\nbiomarkers for CNS SLE, so the lack of findings is not conclusive. Conventional MRI is not very sensitive for SLE\nbrain changes. Conventional structural MRI abnormalities, such as white matter (WM) hyperintensities, gross\nbrain tissue atrophy and ventricular enlargement in response to this atrophy are not always observed in patients\nwith NPSLE. In contrast, more advanced postprocessing techniques enable the quantification of structural brain\nmetrics beyond total tissue volumes from standard T1-weighted MRI, such as regional volumes, surface area and\ncortical grey matter (GM) thickness. Furthermore, other modalities such as diffusion MRI, have become more\nuseful current NPSLE clinical research. Altered tissue microstructure has been reported in several brain regions\nof patients with NPSLE diagnosis when compared with healthy controls and they have correlated with higher\ncognitive dysfunction. If these limitations can be clearly stated in the manuscript, I believe it is suitable for\npublication.\nThank you for these comments.\nBrain imaging:\nWe acknowledge the limitations of brain imaging data available in UK Biobank, and that the MRI-derived\nphenotypes used in the cohort (white matter hyperintensity volume9, total brain volume, and regional brain\nvolumes) are not the most sensitive biomarkers for diagnosing neuropsychiatric SLE (NPSLE).\nNevertheless, individuals with SLE are known to experience accelerated microvascular brain disease - typically\nmanifested as white matter hyperintensities - and brain atrophy10, both of which are captured by these MRI\nmeasures. Our objective was not to classify participants as having or not having NPSLE within UK Biobank, but\nrather to assess whether TREX1 variants are associated with MRI-derived markers relevant to SLE-related brain\ninvolvement.\nIn our limitations we have added substantial caveats that our approach to quantifiying brain disease in lupus has\nsignificant limitations. We have added the following comments in light of the reviewer suggestions:\nHowever as yet there is no reliable neuroimaging biomarker for neurolupus, and therefore the absence of an\nassociation is not conclusive.\nWe have also modified the limitations section to better reflect the value of brain imaging in the\nstudy: “Brain imaging changes are also heterogeneous in SLE and in our neuroradiological\nanalyses we have analysed both brain volumes as well as white matter hyperintensity volume.\nThe latter is a the major structural imaging abnormality seen in people with SLE and likely that\nreflects accelerated microvascular disease9, although it is not seen in all patients and is not a\nspecific imaging marker for SLE.”\nReferences\n1. Rees F, Doherty M, Grainge M, Davenport G, Lanyon P, Zhang W. The incidence and prevalence of systemic\nlupus erythematosus in the UK, 1999-2012. Ann Rheum Dis 2016;75:136-141.\n2. Namjou B, Kothari PH, Kelly JA, et al. Evaluation of the TREX1 gene in a large multi-ancestral lupus cohort.\nGenes Immun 2011;12:270-279.\n3. Rice GI, Rodero MP, Crow YJ. Human disease phenotypes associated with mutations in TREX1. J Clin Immunol\n2015;35:235-243.\n4. Mohan C, Putterman C. Genetics and pathogenesis of systemic lupus erythematosus and lupus nephritis. Nature\nReviews Nephrology 2015;11:329-341.\n5. Teruel M, Alarcon-Riquelme ME. The genetic basis of systemic lupus erythematosus: what are the risk factors\nand what have we learned. J Autoimmun 2016;74.\n6. Kaul A, Gordon C, Crow MK, et al. Systemic lupus erythematosus. Nature Reviews Disease Primers\n2016;2:16039.\n7. Caielli S, Wan Z, Pascual V. Systemic lupus erythematosus pathogenesis: interferon and beyond. Annu Rev\nImmunol 2023;41.\n8. Lee-Kirsch MA, Gong M, Chowdhury D, et al. Mutations in the gene encoding the 3′-5′ DNA exonuclease TREX1\nare associated with systemic lupus erythematosus. Nature Genetics 2007;39:1065-1067.\n9. Wiseman SJ, Bastin ME, Jardine CL, et al. Cerebral Small Vessel Disease Burden Is Increased in Systemic\nLupus Erythematosus. Stroke 2016;47:2722-2728.\n10. Liu S, Cheng Y, Zhao Y, et al. Clinical Factors Associated with Brain Volume Reduction in Systemic Lupus\nErythematosus Patients without Major Neuropsychiatric Manifestations. Frontiers in Psychiatry 2018;Volume 9 - 2018."}]

Decision letter