Autoantibodies targeting TLR and SMAD pathways define new … · 2018-11-21 · Fig. 1. Novel...

lable at ScienceDirect

Journal of Autoimmunity xxx (2018) 1e12

Contents lists avai

Journal of Autoimmunity

journal homepage: www.elsevier .com/locate/ jaut imm

Autoantibodies targeting TLR and SMAD pathways define newsubgroups in systemic lupus erythematosus

Myles J. Lewis a, **, Michael B. McAndrew b, Colin Wheeler b, Nicholas Workman b,Pooja Agashe b, Jens Koopmann c, Ezam Uddin b, David L. Morris d, Lu Zou a,Richard Stark b, John Anson b, Andrew P. Cope e, Timothy J. Vyse d, *

a Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, QueenMary University of London, London, EC1M 6BQ, UKb Oxford Gene Technology, Unit 15, Oxford Industrial Park, Yarnton, Oxfordshire, OX5 1QU, UKc MedImmune, Aaron Klug Building, Granta Park, Cambridge, CB21 6GH, UKd Department of Medical and Molecular Genetics, King's College London, London, SE1 9RT, UKe Academic Department of Rheumatology, Centre for Molecular and Cellular Biology of Inflammation, King's College London, London, SE1 9RT, UK

a r t i c l e i n f o

Article history:Received 8 January 2018Received in revised form20 February 2018Accepted 23 February 2018Available online xxx

Keywords:Systemic lupus erythematosusAutoantibodiesAutoimmunityPersonalised medicineDiagnostic assayProtein microarray

* Corresponding author.** Corresponding author.

E-mail addresses: [email protected] (M.J. Le(T.J. Vyse).

https://doi.org/10.1016/j.jaut.2018.02.0090896-8411/© 2018 Elsevier Ltd. All rights reserved.

Please cite this article in press as: M.J. Lewiserythematosus, Journal of Autoimmunity (2

a b s t r a c t

Objectives: The molecular targets of the vast majority of autoantibodies in systemic lupus erythematosus(SLE) are unknown. We set out to identify novel autoantibodies in SLE to improve diagnosis and identifysubgroups of SLE individuals.Methods: A baculovirus-insect cell expression systemwas used to create an advanced protein microarraywith 1543 full-length human proteins expressed with a biotin carboxyl carrier protein (BCCP) folding tag,to enrich for correctly folded proteins. Sera from a discovery cohort of UK and US SLE individuals(n¼ 186) and age/ethnicity matched controls (n¼ 188) were assayed using the microarray to identifynovel autoantibodies. Autoantibodies were validated in a second validation cohort (91 SLE, 92 controls)and a confounding rheumatic disease cohort (n¼ 92).Results: We confirmed 68 novel proteins as autoantigens in SLE and 11 previous autoantigens in bothcohorts (FDR<0.05). Using hierarchical clustering and principal component analysis, we observed foursubgroups of SLE individuals associated with four corresponding clusters of functionally linked auto-antigens. Two clusters of novel autoantigens revealed distinctive networks of interacting proteins:SMAD2, SMAD5 and proteins linked to TGF-b signalling; and MyD88 and proteins involved in TLR sig-nalling, apoptosis, NF-kB regulation and lymphocyte development. The autoantibody clusters wereassociated with different patterns of organ involvement (arthritis, pulmonary, renal and neurological). Apanel of 26 autoantibodies, which accounted for four SLE clusters, showed improved diagnostic accuracycompared to conventional antinuclear antibody and anti-dsDNA antibody assays.Conclusions: These data suggest that the novel SLE autoantibody clusters may be of prognostic utility forpredicting organ involvement in SLE patients and for stratifying SLE patients for specific therapies.

© 2018 Elsevier Ltd. All rights reserved.

1. Introduction

Although first described in 1957, anti-nuclear antibodies (ANA)and anti-double-stranded DNA (dsDNA) antibody assays remainthe primary diagnostic tests for systemic lupus erythematosus

wis), [email protected]

, et al., Autoantibodies targeti018), https://doi.org/10.1016/

(SLE) [1,2]. Following the development of assays for extractablenuclear antigens (ENA) Ro, La, Sm and U1-RNP, there have been nosignificant improvements in diagnostic assays for SLE for manyyears [3]. In contrast, the identification of citrullinated proteins asautoantigen epitopes in rheumatoid arthritis (RA) led to a markedimprovement in RA diagnostic tests with the development of anti-cyclic citrullinated peptide (CCP) assays. Although numerous SLE-associated autoantibodies have been described [4], they have notsignificantly improved upon the diagnostic and biomarker abilitiesof conventional ANA, dsDNA and ENA tests, and in many cases the

ng TLR and SMAD pathways define new subgroups in systemic lupusj.jaut.2018.02.009

mailto:[email protected]

mailto:[email protected]

www.sciencedirect.com/science/journal/08968411

http://www.elsevier.com/locate/jautimm

https://doi.org/10.1016/j.jaut.2018.02.009



M.J. Lewis et al. / Journal of Autoimmunity xxx (2018) 1e122

true molecular targets remain undefined. Initial protein micro-arrays used to detect autoantibodies in SLE sera were largely basedon existing autoantigens, but have identified several glomerularproteins and serum factors including B cell-activating factor (BAFF)as SLE autoantigens [5e10]. Microarrays utilising large scale de novosynthesis of thousands of proteins have detected autoantibodies incancer and other diseases [11,12], but only identified a single SLEautoantigen [13]. Older protein microarrays may have failed toidentify autoantibodies due to poor protein conformation causedby misfolding or lack of post-translational modification.

We used a novel protein microarray utilising 1543 distinctproteins chosen from multiple functional and disease pathways, toidentify novel autoantigens in SLE. Our aim was to identify previ-ously undiscovered autoantibodies that might act as SLE bio-markers to improve diagnostic (and potentially prognostic)performance over existing clinical assays and to determinewhethersubgroups of SLE patients with different autoantibody repertoiresexisted. Full-length human proteins bound to the microarray wereexpressed in a baculovirus-insect cell expression system with abiotin carboxyl carrier protein (BCCP) folding tag. The BCCP tagenriches for correctly folded proteins, conserving protein epitopesin their native conformation, which may be necessary for high af-finity antibody binding (Fig. 1A) [14]. In this study, we used thisnewer design of protein microarray to elucidate the underlyingnature of autoantigens in SLE.

2. Materials and methods

2.1. Study population

Serum samples from SLE individuals were collected from mul-tiple UK institutions and USA (Seralabs). Serum samples from age/ethnicity matched controls for UK individuals were obtained fromthe TwinsUK resource (part of the National Institute for HealthResearch (NIHR) BioResource) and for USA individuals from Ser-alabs. SLE and control samples were randomly assigned 2:1 to theDiscovery cohort (186 SLE and 188 controls) and the Validationcohort (91 SLE and 92 controls). SLE patients were almost all femalereflecting the sexual dimorphism of SLE, while healthy controlswere exclusively female. All SLE patients fulfilled the 1997 revisedAmerican College of Rheumatology (ACR) criteria for classificationof SLE. The validation cohort was compared with a Confounding/interfering disease cohort included patients with the followingconditions: systemic sclerosis (n¼ 12), primary Sj€ogren's syndrome(n¼ 6), polymyositis (n¼ 3) and mixed connective tissue disease(n¼ 3) sourced from USA (Seralabs), and rheumatoid arthritis (RA)(n¼ 68) obtained from multiple UK institutions. RA patients ful-filled the 2010 ACR-EULAR (European League against Rheumatism)criteria for diagnosis of RA. Ethical approval was granted by theIndependent Investigational Review Board Inc. (4/16/2008) and theUK National Research Ethics Service London (reference numbersMREC98/2/06, 06/MRE02/9 and 07/H0718/49).

2.2. Protein microarray

Protein microarray assay using the Discovery Array v3.0 proteinmicroarray is described in the Supplementary Methods. Themicroarray data are available at ArrayExpress accession E-MTAB-5900.

2.3. Serum autoantibody measurement

Anti-dsDNA and ANA titres were measured in all serum samplesby ELISA (Inova Diagnostics, San Diego, USA), according to themanufacturer's instructions. ANA and dsDNA positive/negative

Please cite this article in press as: M.J. Lewis, et al., Autoantibodies targetierythematosus, Journal of Autoimmunity (2018), https://doi.org/10.1016/

results were defined using thresholds determined by the manu-facturer, and were not based on historical case record results.

2.4. Statistical analysis

Statistical analysis, protein-protein interaction analysis, geno-typing, HLA imputation and analysis, and predictive models aredescribed in detail in the Supplementary Methods. The STARDchecklist was completed and is available in the online supplement.

3. Results

3.1. Identification of novel SLE-associated autoantigens bymicroarray

Serum samples from a discovery cohort of 186 SLE patients and188 age/ethnicity matched healthy controls (Table S1) were ana-lysed for IgG autoantibody levels against 1543 correctly folded, full-length human proteins using a custom protein microarray (OxfordGene Technology, UK) (Fig. 1A). Samples were assayed by ELISA forANA and anti-dsDNA antibodies for comparison. Normalisedautoantibody levels were compared between SLE individuals andhealthy controls in the discovery cohort, using a linear regressionmodel adjusting for age, gender, ethnicity and country. A total of226 autoantibodies, which were increased in the SLE individualscompared to controls in the discovery cohort at FDR-correctedP< 0.05, were investigated in a validation cohort of 91 SLE in-dividuals and 92 age/ethnicity matched controls. Demographics forthe discovery and validation cohorts are shown in Table S1. Of 226autoantigens observed in the discovery cohort, a total of 79 auto-antibodies were also significantly increased in SLE individuals inthe validation cohort at FDR<0.05 (Fig. 1C, Table S2). The well-known SLE autoantigens TROVE2 (Ro60) and SSB (La) showed themost significant difference between SLE and control groups in bothcohorts. The array validated a further nine previously reported SLEautoantigens (Fig. 1D, Table S2). A total of 68 novel autoantigenswere validated by the microarray, with the most statistically sig-nificant four novel autoantibodies shown in Fig. 1E.

A post-validation meta-analysis was performed using a regres-sion model adjusting for age, gender, ethnicity and country. Sug-gestive evidence at FDRmeta<0.01 was found for a further 41autoantibodies (Table S3), of which 38 were novel. Nine of thevalidated autoantigens have been shown to be implicated in SLEpathogenesis through immunological studies, but were not previ-ously known to be autoantigens: CREB1, ZAP70, VAV1, PPP2CB,IRF4, IRF5, EGR2, PPP2R5A and LYN [15e19], while TEK (Tie2 re-ceptor) was identified in the meta-analysis [20]. Five novel auto-antigens are the products of SLE susceptibility genes: IRF5, LYN,PIK3C3, NFKBIA and DNAJA1 [21e25]. In summary, 26 of 120 auto-antigens (79 validated and 41 identified in the meta-analysis) havea previously identified link to SLE, either as known autoantibodytargets or directly implicated in SLE pathogenesis.

In a secondary analysis of the discovery cohort, autoantibodiesfrom the array were ranked by positivity in SLE patients, defined asautoantibody levels >2 SD of the control population, and tested forstatistical significance using Fisher's exact test, corrected for mul-tiple testing. Autoantibodies with FDR-corrected P< 0.05 wereanalysed for positivity in the validation cohort. A total of 60 auto-antibodies showed a significant increase in antibody positivity inboth discovery and validation cohorts (Fig. 2A). The most prevalentautoantibodies were the known SLE autoantigens Ro60 (overallprevalence 37.5%), SSB/La (35.4%), HNRNPA2B1 (29.6%) and PSME3/Ki (23.8%). The most prevalent novel autoantibodies were LIN28A(22.4%), IGF2BP3 (21.7%) and HNRNPUL1 (21.3%). SLE patients ten-ded to be simultaneously positive for multiple autoantibodies in


Fig. 1. Novel autoantigens identified by protein microarray in Systemic Lupus Erythematosus (SLE). (A) Novel protein microarray technology used BCCP folding tag to improveprotein folding conformation of array-bound proteins. (B) Volcano plot of autoantigens in the Discovery cohort displaying each microarray autoantigen as a single point with P valueon the y-axis versus log2 fold change in antibody levels between SLE and matched controls on the x-axis. P values were calculated using a linear regression model adjusting forcohort, sex, age and ethnicity. Blue points signify FDR-corrected Ptrain<0.01. (C) Volcano plot of autoantigens validated in the validation cohort. Red points show autoantigensvalidated in both cohorts (FDR-corrected Ptrain and Ptest<0.01), blue points show autoantigens found in Discovery cohort but not replicated in Validation cohort. Red points showautoantigens validated in both cohorts, blue points show autoantigens with FDRmeta<0.01. (D & E) Tukey boxplots of median normalised IgG binding data showing IgG autoantibodyreactivity against specific antigens on the protein microarray in the discovery cohort (Control1, n¼ 188; SLE1, n¼ 186) and the validation cohort (Control2, n¼ 92; SLE2, n¼ 91). (D)Top four previously identified autoantigens confirming validation of lesser known antigens PABPC1 and HMGB2. (E) Top four novel autoantigens identified by microarray. Box plotsshow median, upper and lower quartiles, with whiskers denoting maximal and minimal data within 1.5� interquartile range (IQR). Dark blue dots represent antibody positivitydefined as >2 SD of control population. Confounding group includes individuals with rheumatoid arthritis, Sjogren's syndrome and other connective tissue diseases.

M.J. Lewis et al. / Journal of Autoimmunity xxx (2018) 1e12 3

Please cite this article in press as: M.J. Lewis, et al., Autoantibodies targeting TLR and SMAD pathways define new subgroups in systemic lupuserythematosus, Journal of Autoimmunity (2018), https://doi.org/10.1016/j.jaut.2018.02.009

Fig. 2. Hierarchy of autoantibody positivity in SLE individuals. (A) Autoantigens ranked by positivity in SLE patients in both Discovery and Validation cohorts. P values werecalculated by Fisher test with FDR correction for multiple testing. FDR-corrected P< 0.05 in both discovery and validation cohorts was considered significant. (B) Distributionhistogram showing total number of positive autoantibodies for each individual showing that sera from SLE patients can recognise over 60 discrete autoantigens.


Fig. 3. Hierarchical clustering identifies four SLE autoantibody subgroups. (A) Heatmap of unsupervised hierarchical clustering of 79 validated autoantibody levels in SLE individualsfrom the discovery cohort (n¼ 186) and validation cohort (n¼ 91), using correlation as distance metric and Ward's clustering method. Rows were clustered based on the discoverycohort. Autoantibody levels were Z score normalised against control population mean and SD, with Z scores >2 corresponding to positive autoantibody levels. Autoantibodiescluster into four major clusters, with four matching clusters SLE1a, SLE1b, SLE2 and SLE3 identified in SLE individuals and the four patient clusters were observed in both thediscovery and validation cohorts. (B) Correlation plot of Pearson r values shows significant cross-correlation of autoantibodies within each cluster.




contrast to the control group (Kruskal-Wallis test with post-hocNemenyi test, P< 2� 10�16) and confounding disease group(P¼ 6.7� 10�9), with some individuals producing antibodiesagainst over 60 antigens (Fig. 2B).

3.2. SLE autoantibodies cluster into four distinct subgroups

Since we observed that groups of autoantibodies showed strongcross-correlation, we performed unsupervised hierarchical clus-tering of autoantibody levels in SLE individuals in the discoverycohort and compared with clustering of the validation cohort. Inboth the discovery and validation cohorts, SLE individuals clusteredinto four subgroups, designated: SLE 1a, 1b, 2 and 3 (Fig. 3A). Eachsubgroup was associated with four distinct clusters of autoanti-bodies (Clusters 1a, 1b, 2 and 3). We have applied this nomencla-ture based on functional characterization of the autoantigenclusters (see below). SLE subgroup 1a individuals were charac-terised by being strongly anti-Ro60 and anti-La positive. SLE sub-group 2 showed the broadest range of autoantibody positivity, SLEsubgroup 3 were mainly positive for cluster 3 antibodies, while SLEgroup 1b showed amixed pattern. The existence of these groupingsis borne out in a condensed subspace heatmap of the SLE subgroupswhich also shows the striking similarity in antibody patterns acrossthe four patient clusters between discovery and validation cohorts(Fig. S1).

Cross-correlation of the autoantigens (Fig. 3B) revealed stronginternal correlation within each antibody cluster, confirming exis-tence of four autoantibody clusters, with certain autoantigens (e.g.RQCD1, SUB1) showing a tendency to inverse correlation with au-toantibodies from other clusters. The existence of the autoantibodysubgroups of response was confirmed by clustering of all four SLEsubgroups when data were re-analysed with inclusion of controls(n¼ 280) using principal component analysis (PCA) (Fig. 4A, MovieS1 and Fig. S2). PCA showed delineation between SLE patientclusters 1a, 2 and 3 on PC2 and PC3, with PC1 aiding delineationbetween control and SLE individuals as well as SLE cluster 1b.Component loadings plots showed clear separation of the foursubgroups of autoantigens (Fig. 4B, Movie S2 and Fig. S2).

Supplementary video related to this article can be found athttps://doi.org/10.1016/j.jaut.2018.02.009.

3.3. Autoantibody cluster-defined SLE subgroups show differentdisease characteristics

To probe whether the autoantibody clusters were linked todifferential SLE phenotype, we compared ANA and dsDNA antibodylevels in the four SLE subgroups. SLE group 1a, whose individualsare strongly Ro60 and La positive, showed very high levels of ANApositivity (P< 0.01, Kruskal-Wallis test), while SLE group 2 showedthe highest levels of anti-dsDNA antibodies (P< 0.01) (Fig. 4C).Groups 1b and 3 showed significantly lower levels of both ANA anddsDNA antibodies, consistent with these groups being distinct en-tities. Furthermore, analysis of ANA negative individuals (at thetime of the assay) showed that these were almost exclusively insubgroups 1b and 3, which constituted 90% of ANA negative in-dividuals (P¼ 1.3� 10�9, c2 test) (Fig. 4D). Similarly, subgroups 1b

Fig. 4. Autoantibody cluster-defined SLE subgroups show different disease characteristicscontrols. Principal component (PC) scores for PC1-3 showing clusters of SLE individuals identscores for PC2, PC3 and PC4 showing clustering of autoantigens identified by hierarchical clSLE cluster groups SLE1a, SLE1b, SLE2 and SLE3. ANA and dsDNA sera levels were measuredWallis test. (D) Comparison of SLE subgroups among ANA and dsDNA negative individuals,SLE3. (E) Subphenotype comparison of autoantibody clusters. Heatmap represents subphencalculated for interaction between autoantibody cluster and subphenotype by two-way ANPositivity of individual autoantibodies across subphenotypes identified in E. (G) Differential


and 3 made up 69% of dsDNA negative SLE individuals(P¼ 8.4� 10�5). ANA and dsDNA antibody levels weremeasured byELISA and were not based on patient clinical records. This suggeststhat the novel autoantibodies from cluster 1b and 3 are particularlyimportant for diagnosing SLE patients with negative ANA/dsDNAantibodies for whom existing diagnostic tests are unreliable.

SLE subphenotype clinical data available on 184 UK SLE in-dividuals was analysed for trends in autoantibody positivity acrossautoantibody clusters. While some subphenotype characteristicssuch as skin rash were similar across all four clusters, autoantibodyclusters showed specificity for presence or absence of arthritis(P¼ 0.00063 for interaction between cluster and subphenotype bytwo-way ANOVA), pulmonary (P¼ 0.0059) and neurologicalinvolvement (P¼ 0.038) (Fig. 4E). Group 2 autoantibody positivitywas higher in the presence of arthritis, while group 1A was lower.Pulmonary involvement was associatedwith higher TROVE2 (Ro60)positivity (Fig. 4F and Fig. S3). Renal involvement was associatedwith IGF2BP3 positivity and higher cluster 1B positivity. Neuro-logical involvement was associated with lower RQCD1/cluster 1Apositivity. Subphenotype results were based on ACR classificationcriteria data and therefore need clarification with additionaldetailed information on specific SLE manifestations. Neurologicalmanifestations of SLE, for example, vary massively in terms oflesion type, process and severity.

Medication usage data was available on 234 SLE individualsfrom both UK and US cohorts. No difference was seen for the ma-jority of immunosuppressive medications between SLE clusters,however, SLE3 individuals showed lower prednisolone usage(P¼ 0.0044, Fisher's exact test) and higher warfarin usage(P¼ 0.0078). This raises the possibility that the cluster 3 autoanti-bodies might be associated with anti-phospholipid syndrome.

3.4. SLE autoantigen clusters associate with functional protein-protein interaction networks

To examine whether the clusters of autoantigens identifiedassociated with different SLE subgroups showed themes of mo-lecular or functional categorisation, each cluster of autoantigenswas investigated using STRING (Search Tool for the Retrieval ofInteracting Genes/Proteins) database, and cross-referenced againstIngenuity Pathway Analysis and the PANTHER (protein annotationthrough evolutionary relationship) classification system. The 79validated autoantigens were insufficient for meaningful pathwayanalysis, so were supplemented with 41 putative autoantigensidentified by post-validation meta-analysis. Protein interactionnetworks identified using STRING were discernible in cluster 2 andcluster 3 autoantigens. In cluster 2, the largest network of inter-acting proteins was centred around SMAD2, SMAD5 and includedproteins associated with TGF-b, Wnt and bone morphogenic pro-tein (BMP) signalling such as PPP2CB, ID2, TWIST2, CSNK2A1 andCSNK2A2 (Fig. 5A). In cluster 3, a protein network of genes involvedin toll-like receptor (TLR) signalling and NF-kB activation includingMYD88, BIRC3 (cIAP-2), NFKBIA (IkBa), MAP3K7 (TAK1) andMAP3K14 (NIK) was observed, interlinked with genes involved inapoptosis regulation such as BIRC3 (cIAP-2), ANXA1 (Annexin A1),CASP9 (caspase-9), ZMYND11 and BCL2A1 (Fig. 5B). The cluster 3

. (A) Principal component analysis of 79 autoantibody levels in SLE individuals andified by hierarchical clustering. Ellipsoids show 95% confidence intervals. (B) PC loadingustering. (C) Anti-nuclear and anti-double-stranded DNA antibody results according toby ELISA, and are not based on patient clinical records. Statistical analysis by Kruskal-showing that ANA negative individuals are predominantly from subgroups SLE1b andotype fold change for mean autoantibody levels for each autoantibody cluster. P valuesOVA. *P < 0.05 for pairwise comparisons for presence/absence of subphenotype. (F)usage of medications in SLE clusters. Statistical analysis in D, F, G by Fisher's exact test.



Fig. 5. SLE autoantigen clusters demonstrate functional protein-protein interaction networks. (A & B) Protein-protein interaction networks were derived from STRING (Search Toolfor the Retrieval of Interacting Genes/Proteins) database and plotted using Cytoscape for (A) cluster 2 and (B) cluster 3 autoantigens. Line thickness represents strength ofinteraction confidence. Predicted nodes are shown in orange. (C) Phylogenetic tree of autoantigens summarising key protein functions for autoantigens in each cluster.


network also incorporated key proteins involved in lymphocytedifferentiation such as VAV1, EGR2, ZAP70 and SH2B1. STRINGidentified TGFBR1 (TGF-b receptor 1) and RELA (NF-kB p65) aspredicted functional nodes for clusters 2 and 3 respectively (pre-diction score 0.999). The functional themes of the autoantigenclusters are summarised in Fig. 5C.


3.5. Improved diagnostic accuracy of expanded autoantibodypanels

Elastic net regularized logistic regression was employed as avariable selection method to identify an optimal autoantibodypanel for SLE diagnosis. 10-fold cross-validation (using the discov-ery cohort) was used to select L1-L2 parameter a and shrinkage



parameter l (Fig. S4). The optimal penalised binomial logisticregression model (a¼ 0.7, l1se¼ 0.00764), employing 17 autoanti-bodies (Table S4), was tested on the Validation cohort usingReceiver Operating Characteristic (ROC) curve analysis (Fig. 6A).The performance of autoantibody models at discriminating SLEindividuals from a non-SLE group including both healthy controlsand confounding group individuals (mostly RA), to mimic the real-world situation of a typical rheumatology clinic. Low level ANApositivity is commonly observed in other autoimmune diseases,healthy elderly or pregnant individuals. Thus in clinical practiceANA performs more poorly, since it is significantly less specific thandsDNA antibodies at lower titres. The elastic net binomial regres-sion model showed improved sensitivity (59.3%) at high specificity(90%) (Fig. 6A and B), compared to standard ANA (37.0%) and dsDNAantibody (38.6%) assays and combined ANAþdsDNA regressionmodel (47.8%). However, this model did not reflect the differentpatient clusters as well as other autoantibodies (Fig. S5). We hy-pothesized that a biomarker model, which exploited the distinctclustering of autoantibodies in SLE individuals, could be superior tobinomial regression models. First, we used elastic net regularizedmultinomial logistic regression for variable selection to narrow theautoantibodies to a set of 26 autoantibodies which optimallyidentified the four SLE clusters in the discovery cohort (Fig. 6C). This

Fig. 6. Improved diagnostic performance of 26-autoantibody biomarker panel. (A-B) Diagnbinomial model (control, SLE) or multinomial model (control, 4 SLE clusters) for variable selastic net logistic regression and trained using penalised mixture discriminant analysis (exclusively on the Discovery cohort and tested on the Validation test cohort. (A) Receiver opnet derived biomarker panels compared with ANA, dsDNA and combined ANA þ dsDNA mo(MDA, binomial elastic net regression) compared to ANA, dsDNA and ANA þ dsDNA modeautoantibody biomarker panel. Statistical analysis by one-way ANOVA with FDR correction


reduced set of 26 autoantibodies was trained using penalisedmixture discriminant analysis (MDA) [26,27] to enable separationof clustered data, specifying one control cluster and four SLE clus-ters. The MDA model showed superior diagnostic classificationcompared to the binomial elastic net regression model, with asensitivity of 67.0% at specificity 90%. Addition of ANA and dsDNAantibodies to the MDA model did not improve prediction (Fig. S6).The improvement in the MDA model compared to the elastic netbinomial regression model is likely to be due to the non-lineardecision boundary (Fig. S7), which delineates four separate SLEclusters from healthy controls, in both discovery and validationcohorts. The panel of 26 autoantibodies was able to delineatedifferent patterns of subphenotype and organ involvement(Fig. S3).

4. Discussion

Using a novel protein microarray design optimised to enhancepresentation of correctly folded proteins, we have identified 68proteins as novel autoantigens in SLE, and confirmed 11 known SLEautoantibody targets. Post-validation meta-analysis found sugges-tive evidence for a further 41 autoantigens. The design of micro-array used in our study found a large number of novel autoantigens

ostic biomarker panels were derived by elastic net penalised logistic regression usingelection. The optimal model was the 26-autoantibody panel selected by multinomialMDA) employing one control cluster and four SLE clusters. All models were trainederating characteristic (ROC) curves for Validation cohort are shown for MDA and elasticdel. (B) Table of area under curve (AUC), sensitivity and specificity of biomarker panelsl. (C) Heatmap of Z scores of mean autoantibody levels in each SLE subgroup for 26-.



in stark contrast to previous proteomic approaches to autoantigendiscoverywhich have only identified a handful of newautoantigens[13,28]. A striking feature of the novel SLE autoantigens found inour study is that many are clearly identifiable as important immunesystem regulators, in multiple cases already implicated in SLEpathogenesis. Thirteen of the 106 novel autoantigens have beendirectly implicated in SLE pathogenesis or genetic susceptibility.This helps to confirm the validity of this new protein microarrayapproach for identification of novel autoantibodies.

Unsupervised hierarchical clustering of the novel autoantigensrevealed four SLE subgroups present in both the discovery andvalidation cohorts, associated with four clusters of autoantigens(Fig. 3A). The clustering designation of both the SLE subgroups andautoantigen clusters was strongly supported by principal compo-nent analysis (Fig. 4A and B). The most well-known autoantigensform cluster 1a, which includes TROVE2 (Ro60), SSB (La), the pro-teasome subunit PSME3 (Ki, PA28 gamma) and SMN1, whichcomplexes with Sm and U1-RNP autoantigens as part of the spli-ceosome. Cluster 1b includes known autoantigens HNRNPA2B1,PABPC1 and HMGB2. Autoantigens in clusters 1a and 1b aredistinguished by a functional theme of involvement in RNA pro-cessing including mRNA decay (RQCD1), mRNA splicing (SMN1),mRNA editing (APOBEC3G, PABPC1, IGF2BP3), nucleocytoplasmicRNA transport (Ro60, SSB/La and HNRNPA2B1) and microRNAbinding (LIN28A). Other 1b antigens are involved in chromatinremodelling and DNA binding. Comparison with ANA and dsDNAantibody levels showed that group 1a were strongly ANA positiveand group 2 were strongly dsDNA positive. Group 1b and 3 showedlower levels of ANA and dsDNA antibodies and 90% of the ANAnegative individuals were from SLE1b and SLE3. Thus, cluster 1band 3 autoantigens are of major clinical importance for detectingANA negative and/or anti-dsDNA antibody negative SLE individuals.

Specific autoantibody clusters were associated with significantdifferences in subphenotype. The presence of arthritis was associ-ated with lower cluster 1A autoantibody positivity and higherlevels of cluster 2 autoantibodies such as PRKRA, consistent withthe importance of Wnt signalling in synovial biology. Pulmonaryinvolvement was most strongly associated with TROVE2 (Ro60)positivity. In comparison, Ro52 has been associated with interstitiallung disease in CTD [29]. Renal involvement was associated withhigher cluster 1B autoantibodies, specifically IGF2BP3. Another 1Bautoantibody HNRNPA2B1 has been previously associated withlupus nephritis [30], but showed less strong association thanIGF2BP3 in our cohort. Neurological involvement was associatedwith lower cluster 1A autoantibody levels, such as RQCD1. Thus, thenovel autoantibodies have potential prognostic utility for predict-ing specific organ involvement in SLE.

We used the STRING database to analyse the autoantigen clus-ters for protein-protein interactions (Fig. 5). Two key themesemerged: cluster 2 autoantigens centred around SMAD2 andSMAD5 were linked to TGF-b/Wnt/BMP signalling; cluster 3 auto-antigens were implicated in TLR/NF-kB signalling, apoptosis regu-lation, and B and T lymphocyte development. The SLE2 subgroupassociated with highest positivity for cluster 2 autoantigensshowed the highest levels of arthritis and Raynaud's, consistentwith TGF-b pathway involvement. Excess TLR7 activity is linked todevelopment of SLE [31], and we identified a distinct subgroup ofSLE patients (SLE3) associated with autoantibodies against the TLRadaptor MYD88, NF-kB signalling proteins TAK1 and MAP3K14(NIK), and apoptosis regulators BIRC3 (cIAP-2) and ANXA1 (annexinA1) [32]. The demonstration that anti-Ro and anti-La antibodies inSLE sera bound apoptotic cell blebs [33] led to the ‘waste disposalhypothesis’, which proposed that defective clearance of dying cellsis the source of autoantigen exposure [34]. Annexin A1 is releasedby apoptotic neutrophils and promotes phagocytosis of apoptotic


neutrophils by macrophages [35]. TLR7 is upregulated in SLE neu-trophils and primes neutrophils for production of neutrophilextracellular traps (NETs), which have been proposed as a source ofantigen for anti-dsDNA antibody formation [36]. It is conceivablethat some cluster 3 antigens may originate from neutrophils un-dergoing NETosis or apoptosis.

SLE3 cluster autoantigens also included transcription factorsand adaptors important for regulating lymphocyte developmentincluding ZAP70, EGR2, CREB1 and VAV1. Egr-2 controls T cell self-tolerance and Egr2 deficient mice develop lupus-like autoimmunedisease [18]. ZAP70 and VAV1, which strongly clustered together,are both recruited to the immunological synapse following T cellreceptor stimulation, and may reside in membrane microdomainsleading to inclusion in secreted exosomes [37]. Excess type Iinterferon activity plays an important role in SLE pathogenesis, andseveral notable interferon pathway genes (IRF4, IRF5) were iden-tified as autoantigens.

The clustering of antigens into functional groups hints atdifferent underlying pathogenic mechanisms defining SLE sub-groups. If the new classes of autoantibodies represent differentunderlying pathogenic mechanisms, this would have importantclinical ramifications, with the prediction that the SLE subgroupsdefined by this study might require different treatment strategies.For example, patients with NF-kB and B cell differentiation genes asantigens may be a subgroup which are more likely to respond to Bcell therapies (e.g. Rituximab, Belimumab), while patients withTGF-b/Wnt signalling pathways may be at risk of fibrotic manifes-tations overlapping with systemic sclerosis, and might respond tonon-B cell specific therapies (e.g. cyclophosphamide).

This study has a number of limitations including: the singletimepoint for sample collection; lack of clinical information ondisease activity at the time of sample collection; incomplete in-formation on anti-phospholipid syndrome clinical status andserology; lac of detailed information on specific patterns of organinvolvement (notably pulmonary and neurological); and absence ofHEp-2 ANA assay as a comparator. Pulmonary and neurologicalinvolvement display substantial clinical heterogeneity in SLE, sothese associations should be interpreted with caution unlessconfirmed in future studies with greater granularity on specificclinical features and patterns of organ involvement. ANA ELISAwasemployed in this study since it is less prone to operator-dependentsubjectivity than the standard HEp-2 ANA assay, however HEp-2ANA is more sensitive than ELISA. Thus, future studies to furtherinvestigate which of these novel autoantibodies are useful forprognosis, therapeutic stratification or monitoring disease activityalongside anti-dsDNA antibodies, will necessitate longitudinal,prospective studies to collect serial samples alongside moredetailed clinical information, particularly including specificneurological features. HEp-2 ANA assay should also be included inthe comparison. Since some autoantibodies, such as cardiolipinantibodies [38], can be triggered by acute infections, sera from aninfectious diseases cohort should be compared with the SLE cohort.Following the identification of TGF-b pathway autoantigens, theconfounding disease cohort should include a larger cohort of otherconnective tissue diseases including a large systemic sclerosiscohort with detailed clinical information on systemic sclerosis type(limited or diffuse) and patterns of organ involvement (interstitiallung disease, Raynaud's manifestations etc).

We identified a 17-autoantibody autoantibody biomarker panelwhich showed improved sensitivity and specificity for diagnosis ofSLE in comparison to standard ANA and anti-dsDNA assays (Fig. 6Aand B). However, this binomial model, whichwas trained for simplediscrimination of SLE patients from controls, was outperformed bya multinomial regression 26-autoantibody model trained todiscriminate four clusters of SLE individuals by penalised mixture



discriminant analysis (MDA). The MDA model, by accounting forclustering of SLE individuals, showed enhanced diagnostic perfor-mance in the validation cohort compared to conventional ANA anddsDNA assays. The use of a repertoire of autoantibodies for SLEdiagnosis has parallels with the peptide libraries employed by anti-CCP diagnostic assays for RA, and the 26-autoantibody biomarkerpanel demonstrates comparable sensitivity/specificity for SLEdiagnosis to anti-CCP assays in RA [39].

In summary, using improved protein microarray technologywith attention to optimal protein folding and synthesis, we haveidentified a large number of novel SLE autoantigens. Our studysuggests that SLE can be subgrouped by molecular signaturethrough four distinct autoantibody patterns. We propose that eachSLE subgroup may have diverse pathogenic and/or genetic mech-anisms underlying the differential autoantigen response.

Declaration of interest

MBM, CW, NW, PA, JK, EU, RS, JA are or were full-time employeesof Oxford Gene Technology.

Funding

T.J.V. was awarded funding to from the George Koukis Founda-tion and an Arthritis Research UK Special Strategic Award. Thestudy received support from the National Institute for HealthResearch (NIHR)-funded BioResource, Clinical Research Facility andthe Biomedical Research Centre based at Guy's & St. Thomas' Na-tional Health Service (NHS) Foundation Trust, in partnership withKing's College London. The TwinsUK study was funded by theWellcome Trust; European Community's Seventh Framework Pro-gramme (FP7/2007e2013).

Acknowledgements

The authors thank the volunteers who participated in this study.We thank Su Wang for statistical advice (predictive models).

Appendix A. Supplementary data

Supplementary data related to this article can be found athttps://doi.org/10.1016/j.jaut.2018.02.009.

References

[1] G.J. Friou, Clinical application of a test for lupus globulin-nucleohistoneinteraction using fluorescent antibody, Yale J. Biol. Med. 31 (1958) 40e47.

[2] P. Miescher, R. Strassle, New serological methods for the detection of the L.E.factor, Vox Sang. 2 (1957) 283e287.

[3] D.A. Isenberg, J.J. Manson, M.R. Ehrenstein, A. Rahman, Fifty years of anti-dsDNA antibodies: are we approaching journey's end? Rheumatology (Oxford)46 (2007) 1052e1056.

[4] G. Yaniv, G. Twig, D.B. Shor, A. Furer, Y. Sherer, O. Mozes, et al., A volcanicexplosion of autoantibodies in systemic lupus erythematosus: a diversity of180 different antibodies found in SLE patients, Autoimmun. Rev. 14 (2015)75e79.

[5] W.H. Robinson, C. DiGennaro, W. Hueber, B.B. Haab, M. Kamachi, E.J. Dean, etal., Autoantigen microarrays for multiplex characterization of autoantibodyresponses, Nat. Med. 8 (2002) 295e301.

[6] Q.Z. Li, C. Xie, T. Wu, M. Mackay, C. Aranow, C. Putterman, et al., Identificationof autoantibody clusters that best predict lupus disease activity usingglomerular proteome arrays, J. Clin. Invest. 115 (2005) 3428e3439.

[7] Q.Z. Li, J. Zhou, A.E. Wandstrat, F. Carr-Johnson, V. Branch, D.R. Karp, et al.,Protein array autoantibody profiles for insights into systemic lupus erythe-matosus and incomplete lupus syndromes, Clin. Exp. Immunol. 147 (2007)60e70.

[8] B.F. Chong, L.C. Tseng, T. Lee, R. Vasquez, Q.Z. Li, S. Zhang, et al., IgG and IgMautoantibody differences in discoid and systemic lupus patients, J. Invest.Dermatol. 132 (2012) 2770e2779.

[9] K. Papp, P. Vegh, R. Hobor, Z. Szittner, Z. Voko, J. Podani, et al., Immunecomplex signatures of patients with active and inactive SLE revealed by


multiplex protein binding analysis on antigen microarrays, PLoS One 7 (2012),e44824.

[10] J.V. Price, D.J. Haddon, D. Kemmer, G. Delepine, G. Mandelbaum, J.A. Jarrell, etal., Protein microarray analysis reveals BAFF-binding autoantibodies in sys-temic lupus erythematosus, J. Clin. Invest. 123 (2013) 5135e5145.

[11] C. Desmetz, A. Mange, T. Maudelonde, J. Solassol, Autoantibody signatures:progress and perspectives for early cancer detection, J. Cell Mol. Med. 15(2011) 2013e2024.

[12] L. Abel, S. Kutschki, M. Turewicz, M. Eisenacher, J. Stoutjesdijk, H.E. Meyer, etal., Autoimmune profiling with protein microarrays in clinical applications,Biochim. Biophys. Acta 1844 (2014) 977e987.

[13] W. Huang, C. Hu, H. Zeng, P. Li, L. Guo, X. Zeng, et al., Novel systemic lupuserythematosus autoantigens identified by human protein microarray tech-nology, Biochem. Biophys. Res. Commun. 418 (2012) 241e246.

[14] J.M. Boutell, D.J. Hart, B.L. Godber, R.Z. Kozlowski, J.M. Blackburn, Functionalprotein microarrays for parallel characterisation of p53 mutants, Proteomics 4(2004) 1950e1958.

[15] C.G. Katsiari, V.C. Kyttaris, Y.T. Juang, G.C. Tsokos, Protein phosphatase 2A is anegative regulator of IL-2 production in patients with systemic lupus ery-thematosus, J. Clin. Invest. 115 (2005) 3193e3204.

[16] S. Krishnan, Y.T. Juang, B. Chowdhury, A. Magilavy, C.U. Fisher, H. Nguyen, etal., Differential expression and molecular associations of Syk in systemic lupuserythematosus T cells, J. Immunol. 181 (2008) 8145e8152.

[17] P.S. Biswas, S. Gupta, R.A. Stirzaker, V. Kumar, R. Jessberger, T.T. Lu, et al., Dualregulation of IRF4 function in T and B cells is required for the coordination ofT-B cell interactions and the prevention of autoimmunity, J. Exp. Med. 209(2012) 581e596.

[18] B. Zhu, A.L. Symonds, J.E. Martin, D. Kioussis, D.C. Wraith, S. Li, et al., Earlygrowth response gene 2 (Egr-2) controls the self-tolerance of T cells andprevents the development of lupuslike autoimmune disease, J. Exp. Med. 205(2008) 2295e2307.

[19] K.L. Silver, T.L. Crockford, T. Bouriez-Jones, S. Milling, T. Lambe, R.J. Cornall,MyD88-dependent autoimmune disease in Lyn-deficient mice, Eur. J. Immu-nol. 37 (2007) 2734e2743.

[20] P. Kumpers, S. David, M. Haubitz, J. Hellpap, R. Horn, V. Brocker, et al., The Tie2receptor antagonist angiopoietin 2 facilitates vascular inflammation in sys-temic lupus erythematosus, Ann. Rheum. Dis. 68 (2009) 1638e1643.

[21] R.R. Graham, C. Kyogoku, S. Sigurdsson, I.A. Vlasova, L.R. Davies, E.C. Baechler,et al., Three functional variants of IFN regulatory factor 5 (IRF5) define risk andprotective haplotypes for human lupus, Proc. Natl. Acad. Sci. U. S. A. 104(2007) 6758e6763.

[22] S.N. Kariuki, B.S. Franek, R.A. Mikolaitis, T.O. Utset, M. Jolly, A.D. Skol, et al.,Promoter variant of PIK3C3 is associated with autoimmunity against Ro andSm epitopes in African-American lupus patients, J. Biomed. Biotechnol. 2010(2010), 826434.

[23] R. Lu, G.S. Vidal, J.A. Kelly, A.M. Delgado-Vega, X.K. Howard, S.R. Macwana, etal., Genetic associations of LYN with systemic lupus erythematosus, GeneImmun. 10 (2009) 397e403.

[24] Y. Li, H. Cheng, X.B. Zuo, Y.J. Sheng, F.S. Zhou, X.F. Tang, et al., Associationanalyses identifying two common susceptibility loci shared by psoriasis andsystemic lupus erythematosus in the Chinese Han population, J. Med. Genet.50 (2013) 812e818.

[25] P.S. Ramos, A.H. Williams, J.T. Ziegler, M.E. Comeau, R.T. Guy, C.J. Lessard, et al.,Genetic analyses of interferon pathway-related genes reveal multiple new lociassociated with systemic lupus erythematosus, Arthritis Rheum. 63 (2011)2049e2057.

[26] T. Hastie, A. Buja, R. Tibshirani, Penalized discriminant analysis, Ann. Stat. 23(1995) 73e102.

[27] T. Hastie, R. Tibshirani, Discriminant analysis by Gaussian mixtures, J R StatistSoc B 58 (1996) 155e176.

[28] Y. Katsumata, Y. Kawaguchi, S. Baba, S. Hattori, K. Tahara, K. Ito, et al., Iden-tification of three new autoantibodies associated with systemic lupus ery-thematosus using two proteomic approaches, Mol. Cell. Proteomics MCP(2011) 10. M110 005330.

[29] M. Wodkowski, M. Hudson, S. Proudman, J. Walker, W. Stevens, M. Nikpour, etal., Monospecific anti-Ro52/TRIM21 antibodies in a tri-nation cohort of 1574systemic sclerosis subjects: evidence of an association with interstitial lungdisease and worse survival, Clin. Exp. Rheumatol. 33 (2015) S131eS135.

[30] G. Schett, H. Dumortier, E. Hoefler, S. Muller, G. Steiner, B cell epitopes of theheterogeneous nuclear ribonucleoprotein A2: identification of a new specificantibody marker for active lupus disease, Ann. Rheum. Dis. 68 (2009)729e735.

[31] P. Pisitkun, J.A. Deane, M.J. Difilippantonio, T. Tarasenko, A.B. Satterthwaite,S. Bolland, Autoreactive B cell responses to RNA-related antigens due to TLR7gene duplication, Science 312 (2006) 1669e1672.

[32] J. Silke, R. Brink, Regulation of TNFRSF and innate immune signalling com-plexes by TRAFs and cIAPs, Cell Death Differ. 17 (2010) 35e45.

[33] L.A. Casciola-Rosen, G. Anhalt, A. Rosen, Autoantigens targeted in systemiclupus erythematosus are clustered in two populations of surface structures onapoptotic keratinocytes, J. Exp. Med. 179 (1994) 1317e1330.

[34] M.J. Walport, Complement. Second of two parts, N. Engl. J. Med. 344 (2001)1140e1144.

[35] M. Perretti, F. D'Acquisto, Annexin A1 and glucocorticoids as effectors of theresolution of inflammation, Nat. Rev. Immunol. 9 (2009) 62e70.

[36] G.S. Garcia-Romo, S. Caielli, B. Vega, J. Connolly, F. Allantaz, Z. Xu, et al.,



http://refhub.elsevier.com/S0896-8411(18)30023-4/sref1
















































































































































Netting neutrophils are major inducers of type I IFN production in pediatricsystemic lupus erythematosus, Sci. Transl. Med. 3 (2011), 73ra20.

[37] G.J. Wang, Y. Liu, A. Qin, S.V. Shah, Z.B. Deng, X. Xiang, et al., Thymusexosomes-like particles induce regulatory T cells, J. Immunol. 181 (2008)5242e5248.

[38] A.O. Adebajo, P. Charles, R.N. Maini, B.L. Hazleman, Autoantibodies in malaria,


tuberculosis and hepatitis B in a west African population, Clin. Exp. Immunol.92 (1993) 73e76.

[39] P. Taylor, J. Gartemann, J. Hsieh, J. Creeden, A systematic review of serumbiomarkers anti-cyclic citrullinated Peptide and rheumatoid factor as tests forrheumatoid arthritis, Autoimmune Dis. 2011 (2011), 815038.















Autoantibodies targeting TLR and SMAD pathways define new … · 2018-11-21 · Fig. 1. Novel...

Documents

Transcript of Autoantibodies targeting TLR and SMAD pathways define new … · 2018-11-21 · Fig. 1. Novel...