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Genetic variation was first shown to be important in systemic lupus erythematosus (SLE or lupus) in the 1970s with associations in the human leukocyte antigen region. Almost four decades later, and with the help of increasingly powerful genetic approaches, more than 25 genes are now known to contribute to the mechanisms that predispose individuals to lupus. Over half of these loci have been discovered in the past 2 years, underscoring the extraordinary success of genome-wide association approaches in SLE. Well-established risk factors include alleles in the major histocompatibility complex region (multiple genes), IRF5 , ITGAM , STAT4 , BLK , BANK1 , PDCD1 , PTPN22 , TNFSF4 , TNFAIP3, SPP1 , some of the Fcγ receptors, and deficiencies in several complement components, including C1q, C4 and C2. As reviewed here, many susceptibility genes fall into key pathways that are consistent with previous studies implicating immune complexes, host immune signal transduction and interferon pathways in the pathogenesis of SLE. Other loci have no known function or apparent immunological role and have the potential to reveal novel disease mechanisms. Certainly, as our understanding of the genetic etiology of SLE continues to mature, important new opportunities will emerge for developing more effective diagnostic and clinical management tools for this complex autoimmune disease.

Sjögren's syndrome (SS) is a common chronic autoimmune disease characterized by lymphocytic infiltration of exocrine glands. The affected cases commonly present with oral and ocular dryness, which is thought to be the result of inflammatory cell-mediated gland dysfunction. To identify important molecular pathways involved in SS, we used high-density microarrays to define global gene expression profiles in the peripheral blood. We first analyzed 21 SS cases and 23 controls, and identified a prominent pattern of overexpressed genes that are inducible by interferons (IFNs). These results were confirmed by evaluation of a second independent data set of 17 SS cases and 22 controls. Additional inflammatory and immune-related pathways with altered expression patterns in SS cases included B- and T-cell receptor, insulin-like growth factor-1, granulocyte macrophage-colony stimulating factor, peroxisome proliferator-activated receptor-α/retinoid X receptor-α and PI3/AKT signaling. Exploration of these data for relationships to clinical features of disease showed that expression levels for most interferon-inducible genes were positively correlated with titers of anti-Ro/SSA ( P <0.001) and anti-La/SSB ( P <0.001) autoantibodies. Diagnostic and therapeutic approaches targeting interferon-signaling pathway may prove most effective in the subset of SS cases that produce anti-Ro/SSA and anti-La/SSB autoantibodies. Our results strongly support innate and adaptive immune processes in the pathogenesis of SS, and provide numerous candidate disease markers for further study.

TNFAIP3 encodes the ubiquitin-modifying enzyme, A20, a key regulator of inflammatory signaling pathways. We previously reported association between TNFAIP3 variants and systemic lupus erythematosus (SLE). To further localize the risk variant(s), we performed a meta-analysis using genetic data available from two Caucasian case–control datasets (1453 total cases, 3381 total control subjects) and 713 SLE trio families. The best result was found at rs5029939 ( P =1.67 × 10 −14 , odds ratio=2.09, 95% confidence interval 1.68–2.60). We then imputed single nucleotide polymorphisms (SNPs) from the CEU Phase II HapMap using genotypes from 431 SLE cases and 2155 control subjects. Imputation identified 11 SNPs in addition to three observed SNPs, which together, defined a 109 kb SLE risk segment surrounding TNFAIP3 . When evaluating whether the rs5029939 risk allele was associated with SLE clinical manifestations, we observed that heterozygous carriers of the TNFAIP3 risk allele at rs5029939 have a twofold increased risk of developing renal or hematologic manifestations compared to homozygous non-risk subjects. In summary, our study strengthens the genetic evidence that variants in the region of TNFAIP3 influence risk for SLE, particularly in patients with renal and hematologic manifestations, and narrows the risk effect to a 109 kb DNA segment that spans the TNFAIP3 gene.

Background:AI-enabled algorithms can increase the speed and accuracy of identifying key histological features and enable researchers and clinicians to more readily and thoroughly understand the tissue collected from their patients. Sjogren’s Disease (SJD), in particular, is ripe with opportunity given the reliance on tissue reads for focus scores and overall histological examinations of patients.Objectives:To develop an AI-enabled algorithm that automatically identifies key histological features of the minor salivary gland of SJD patients, and then test this algorithm on samples from a diverse patient population.Methods:Minor salivary glands from control and SJD patients were collected via standard-of-care and formalin fixed and paraffin embedded. 5micron sections were collected from blocks containing 3-5 minor salivary glands from a single patient, stained with H&E, then imaged on a whole-slide scanner. Images were loaded into HALO-AI v4.0 (Indica Labs). 5 cases and 5 controls were fully annotated under the guidance of a trained pathologist for background (no tissue), adipose/connective tissue, stroma, glandular tissue, and immune infiltrates. The classes were trained via HALO-AI’s DenseNet v2 for >10,000 iterations with a final entropy of <1 (a measure of agreement between annotation input and AI-prediction). After successful training and implementation on the training set on 10 cases/controls, the algorithm was applied to a set of 40 cases to evaluate performance (n=50 total). Percent area of all classes were then calculated.Results:The algorithm accurately identifies all 5 classes across varying degrees of disease severity across SJD patients and controls, as well as differences in staining intensity. Though inaccuracies are observed, the overall results were better than previous machine-learning methodologies (data not shown/published) and were quickly applied to samples (approximately 30sec of analysis time per sample). Resulting data show a high degree in variability in percent immune infiltration and gland.Conclusion:Though pathologist reads are important for the understanding SjD and clinical workups, it is time consuming and costly to annotate entire images manually in order to measure all major histological features in the minor salivary gland of SJD patients. Though the AI-Tissue classifier produced here would not outperform a trained pathologist in accuracy, it is much faster and much more cost effective to run. Therefore, this algorithm in combination with current focus scores (and other clinical data) could provide novel insights and key findings for both clinicians and researchers concerning SJD progression, severity, or other meaningful metrics.REFERENCES:NIL.Figure 1.AI-Tissue Classifier Results of Example Trainer and Test Images.Figure 2.Percent Histological Features as Identified by the AI-Tissue Classifier and Stratified by Percent Immune Infiltration (High to low, top 50% of immune infiltrate)Acknowledgements:NIL.Disclosure of Interests:None declared.

Background:A loss of tolerance to self-antigens leads to increased levels of autoantibodies against nuclear components (ANAs) prior to clinical disease onset. However, only about 4-8% develop autoimmune disease. Patients with incomplete lupus erythematosus (ILE) exhibit some clinical symptoms with most never progressing to Systemic Lupus Erythematosus (SLE). Exact mechanisms involved in T cell dysregulation and progression of autoimmune disease remain unclear.Objectives:Investigate whether alterations in T cell populations and activation of cellular pathways are dysregulated during autoimmunity development.Methods:PBMCs from 64 subjects, divided evenly among ancestry (African, European American) and disease group: healthy (ANA-), healthy with autoantibodies (ANA+), ILE, SLE, were sorted with microfluidic flow cytometer to remove dead cells and used for multiomics single-cell analysis with 5’scRNA-seq/137-plex Total-seq, BCR/TCR repertoire to identify distinct disease-associated clusters, differential gene signatures and dysregulated pathways. Cell counts were confirmed via CyTOF. Serum soluble biomarkers levels were obtained via Olink Proximity Extension Assay (Explore HT).Results:We obtained profiles for ~650,000 cells across all PBMCs. Differences in T cell fractions were observed by disease group. Analysis of differentially expressed genes revealed the importance of metabolic processes, such as autophagy and oxidative phosphorylation; downregulation of mitochondrial dysfunction in ANA+ and upregulation of MAPK and receptor kinase signaling in ILE and SLE. Pathway analysis indicates downregulation of TNFR Signaling in SLE compared to ILE and cytokine storm signaling in ANA+ compared to ANA-. These finding were confirmed by protein. Gene set enrichment analysis of serum soluble biomarkers indicated upregulation of T cell activation, proliferation, antigen presentation, MAPK cascade and receptor kinase signaling in ILE and SLE. We observed upregulation of MAP2K6 and MAP3K5 proteins in ILE compared to ANA+ (non-parametric test; pad <0.05). Furthermore, we identified a CD4+ T cell population (CTL) with elevated expression of cytotoxic markers PRF1, GZMB, NKG7, CCL5 and transcription factors: ZNF683, IKZF1, TBX21, ZEB2. Individuals with that population express higher level of IFN related genes. Pathway analysis of CTL indicates upregulation of antiviral response, cellular cytotoxicity and exhaustion in ILE and SLE witth IFNG, STAT3 and IL10 determined as activated upstream regulators. Olink assay confirmed these results and revealed IFNB1 upregulation and viral response. TCR analysis indicates both CTL and CD8+ cytotoxic T cells have largest fraction of expanded clonotypes, with increased levels of TRAV19, TRAV8, TRAV38. Clonotypes similar transcriptionally, restricted to those two populations, are associated with higher expression of TXNIP, TMSB4X, HLA, GZMB and shared among ANA+ and ILE individuals.Conclusion:Dysregulation of signaling in T cell activation appears to be manifesting in increased oxidative phosphorylation, dysregulation of MAPK kinases or alterations in apoptotic pathways and might be suggestive of a preclinical autoimmunity development trajectory and associated with clonal expansion. Alterations of these processes vary by ancestral background, reflecting the heterogeneity of SLE presentation.REFERENCES:[1] Dorner, T. and R. Furie, Novel paradigms in systemic lupus erythematosus. Lancet, 2019[2] Slight-Webb, S., et al., Autoantibody-positive healthy individuals with lower lupus risk display a unique immune endotype. J Allergy Clin Immunol, 2020Figure 1.A. UMAP projection of distinct T cell clusters. B. T cell density for total population, by ancestry and disease groups. C. Pathway analysis of CD8+, CD4+ T cells with most distinct differences between ANA+ compared to ILE. D. Expression of cytotoxic markers across T cell clusters with CTL population highlighted. E. Fractions of CTL by disease group. F. Pathway analysis of CTL. G. Clonal expansion by T cell population (blue – singleton, orange – clonotypes with 2 cells, green – >3 cells)Acknowledgements:NIL.Disclosure of Interests:None declared.

Background:10X Visium spatial transcriptomics evaluates mRNA-binding tiles (55μm diameter) of a sectioned tissue, yielding heterogeneous cell sampling. The SpatialPCA algorithm was developed to identify like tissue regions and determine the cellular context of spatial coordinates using homogenous tissue types with distinct boundaries [1] However, while proficient in analyzing homogenous tissue types, SpatialPCA is less effective at differentiating like tiles from heterogenous tissue types.Objectives:To develop a novel analysis pipeline, HistoSpatialPCA, that leverages spatially aware dimensional reduction to model spatially correlated structures across tiles from heterogeneous tissue types such as a target tissue of Sjögren’s Disease (SjD), the minor salivary gland (MSG). Then, to apply HistoSpatialPCA to MSGs biopsied from SjD patients and healthy controls (HC) to identify disease-specific differential gene expression (DE) and pathway dysregulation in the salivary gland.Methods:MSG sections were arranged on 10X Visium capture slide chambers. Nuclei segmentation and classification was performed, followed by images annotation by tissue type (fibrosis, glandular, inflammatory, fat) (HALO Image Analysis Platform). Imaging data were extracted from tiles and integrated with spatial coordinates using HistoPCA. After quality control to filter low-quality and non-tissue tiles, SpatialPCA was performed [1]. Subsequently, data integration (Harmony) and UMAP with KMeans clustering were performed. SjD case-control differential expression (DE) was analyzed using pseudo-bulk gene expression. Finally, DE transcripts were analyzed by Ingenuity Pathway Analysis.Results:HistoSpatialPCA, followed by UMAP with KMeans clustering, detected 34,948 tiles from n=41 subjects, resulting in 8 distinct clusters in the MSG (Figure 1A,B). Comparison of dysregulated genes and pathways revealed cluster-specific differences between Ro+ and Ro- SjD cases verses HCs (Figure 1C). Ro+ SjD cases exhibited dysregulation across all clusters, whereas Ro- cases showed no significant dysregulated pathways in clusters 0, 2, and 7 and fewer altered pathways in clusters 1 and 6. Rank order of the dysregulated pathways also differed between Ro+ and Ro- SjD cases. Interferon gamma was the top pathway in all SjD cases and Ro+ across all clusters, but was only dysregulated in Ro- cluster 5 and modestly in clusters 1 and 3. Cluster 5 was the most similar between Ro+ and Ro- and showed the highest percentage of inflammation (upregulation of many proinflammatory pathways; downregulation of CTLA4, IL-10, and PD-1 signaling).Conclusion:HistoSpatialPCA successfully grouped like tiles from spatial transcriptomic analysis of heterogeneous MSG. Cluster annotation, followed by DE and pathway analyses revealed dysregulation of tiles across all clusters in Ro+ SjD cases, while Ro- cases exhibited the most pronounced dysregulation in cluster 5, 4, and 3. Notably, cluster 5 demonstrated the highest inflammation, sharing many dysregulated pathways between Ro+ and Ro- SjD cases. This spatially aware technology will provide new insights into the role of different cell/tissue types in SjD pathobiology of the salivary gland.REFERENCES:[1] Shang L, et al. Nat Commun. 2022; 13:7203.Acknowledgements:National Institutes of Health (NIH): R01ARO7385503 (CJL); R21 DE029302 (ADF).Disclosure of Interests:Songyuan Yao: None declared, Rick Wilbrink: None declared, Paulina Czarnota: None declared, Matthew Caleb Marlin: None declared, Bhuwan Khatri: None declared, Anna M Stolarczyk: None declared, Cherilyn Pritchett Frazee: None declared, Chuang Li: None declared, Kyle Wright: None declared, Kandice L Tessneer: None declared, Judith A. James: None declared, R Hal Scofield Received consulting fees from Johnson and Johnson Innovative Medicine (formerly Janssen) and Merk Pharmaceuticals., Indra Adrianto: None declared, Astrid Rasmussen: None declared, Joel M Guthridge: None declared, A Darise Farris Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/23)., Christopher J Lessard Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/23).

Background:SjD is a chronic, heterogeneous autoimmune disease characterized by signs of oral and ocular dryness in patients who are either positive for anti-Ro/SSA and/or focus score positive on biopsies from labial/parotid salivary glands. Although involvement of salivary glands is a distinguishing disease feature, little is known about the transcriptomics of discrete cell populations in the glands.Objectives:To determine the optimal dissociation approach and single-cell (sc) transcriptomics platform for the use of matched viably frozen and fixed minor salivary glands (MSG) biopsied from SjD patients and healthy controls (HCs); then, to expand the dataset using the optimal approach.Methods:Viably frozen MSG (n=7; 2 SjD and 5 HCs) were thawed, dissociated, and counted, showing >90% viable cells. Samples were split and captured using 3’ and 5’ scRNA-seq, targeting to capture 8000-10000 cells. In addition, 25μm sections from formalin-fixed paraffin-embedded (FFPE) tissues containing 4-5 labial salivary glands (LSG) each were dissociated, counted, labeled following the 10X Flex/scFFPE protocol, and captured, yielding 8000-20000 cells per subject. Raw sequencing data were analyzed using 3’, 5’, or scFFPE analysis pipelines in CellRanger. Ambient RNAs were corrected (SoupX) and doublets detected (scDblfinder). Cells with feature counts <200 & >5000, mitochondrial percent >5%, and doublets were removed. Samples were merged, integrated, and batch corrected using Harmony (Seurat). Data were normalized, scaled, and dimensional reduction performed using UMAP (Seurat). Cell clusters were annotated using CellTypist and MSG reference data1. Cells were separated into immune and non-immune clusters and annotated using reference data from CellTypist or MSG reference data1. Differentially expressed (DE) genes in each cell type in SjD compared to HC were identified using pseudobulk approach (DESeq2). DE genes (p<0.05) were subjected to Ingenuity Pathway Analysis (IPA).Results:Overall quality of the 3’ and 5’ approaches were similar. In comparison, scFFPE yielded similar feature/gene counts per cell (median = 5186 (3’), 7452 (5’), 4571 (scFFPE)), but was superior in the reduction of ambient RNA (or soup) and in identifying cell populations that were very low in 3’ and 5’ assays: seromucous acini, basal cells, macrophages, myoepithelial cells, among others. The scFFPE dataset was expanded to include 8 Ro+ SjD and 9 HCs, yielding 477,071 cells before QC, 371,454 after QC, among 23 cell states (Figure 1A). It revealed significantly DE genes in Ro+ SjD compared to HCs that mapped to common and different pathways between immune (Figure 1B and C) and glandular cells (Figure 1B and D). For example, many cell types showed dysregulation of Type I interferon, including downregulation (plasma cells, plasmacytoid DCs, naive B cells), upregulation (migratory DCs, DC1, serous and mucus acini, macrophages, mast cells), or no dysregulation (DC2, memory B, cytotoxic T, helper T, and natural killer cells).Conclusion:scFFPE yielded superior single-cell data compared to 3’ and 5’ using viable cells. Further, the data analysis method permitted discrimination of targeted/affected cell types from effector cell types. Expansion of the scFFPE pilot study showed many DE genes and dysregulated pathways. Ongoing work with AMP-AIM STAMP projects will further expand this dataset.REFERENCES:[1] Huang N, et al. SARS-CoV-2 infection of the oral cavity and saliva. Nat Med. 2021 May; 27(5):892-903.Acknowledgements:National Institutes of Health: UM2 AR067678, UC2 AR081023, UC2 AR081032, UC2 AR081032-S1, UC2 AR081032-02S1, UC2 DE032254, UC2 AR081033, P30 AR073750, U54 GM104938, NIDCR 15-D-0051; Presbyterian Health Foundation; OMRF Institutional Funds; NIH, NCI Intramural Program; Jerome L. Greene Foundation.Disclosure of Interests:Bhuwan Khatri: None declared, Anna M Stolarczyk: None declared, Matthew Caleb Marlin: None declared, Miles Smith: None declared, Cherilyn Pritchett Frazee: None declared, Margaret Beach: None declared, Eileen Pelayo: None declared, Zohreh Khavandgar: None declared, Paola Pérez: None declared, David E Kleiner: None declared, Stephen E Hewitt: None declared, Kevin Wei Received a sponsored research agreement from Gilead Sciences and 10X Genomics., Erin M Theisen: None declared, Kandice L Tessneer: None declared, Soumya Raychaudhuri: None declared, Michael B Brenner: None declared, Johann E. Gudjonsson: None declared, Nir Hacohen: None declared, Judith A. James: None declared, R Hal Scofield Received consulting fees from Johnson and Johnson Innovative Medicine (formerly Janssen) and Merk Pharmaceuticals., Stephen Shiboski: None declared, Astrid Rasmussen: None declared, Alan Baer Received consulting fees from Bristol Myers Squibb (BMS) and iCell Gene Therapeutics., A Darise Farris Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023)., Caroline Shiboski: None declared, Blake M Warner Receives funding to support research from Pfizer, Inc., and Mitobridge, Inc., a subsidiary of Astellas Bio., Joel M Guthridge: None declared, Christopher J Lessard Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023).

Background:In our previous Sjögren’s Disease (SjD) Genome-Wide Association Study (GWAS) in European populations, significant single nucleotide polymorphism (SNP) peaks were identified between PRDM1 and ATG5 [1]. ATG5 is an autophagy-related protein that plays a crucial role in neutrophil extracellular trap (NET) formation, degranulation, and limiting autoantigens in blood. Dysregulated autophagy has been implicated in SjD and systemic lupus erythematosus (SLE) pathology and poor disease outcomes [2, 3]. The transcriptional repressor PRDM1 plays a role in regulating lymphocyte differentiation [4].Objectives:Identify and functionally evaluate SjD and SLE risk variants in the PRDM1-ATG5 risk locus.Methods:By conducting a meta-analysis of SjD and SLE GWAS datasets, we defined a credible SNP set in the PRDM1-ATG5 locus. A GWAS involving 15,691 SjD and SLE cases and 52,521 population controls of European ancestry was performed, and SNP-trait associations were tested using logistic regression models in PLINK. Bioinformatic analyses (RegulomeDB, HaploReg v4.2, promoter capture Hi-C, eQTLs, etc.) further prioritized SNPs. A CRISPR inhibition (CRISPRi) assay was used to assess the effects of these SNPs on ATG5 expression. Luciferase assays in A235 salivary gland epithelial cell line, PLB985 malignant myelomonoblasts, and GM12878 EBV-transformed B cells tested the activation and/or repressive activity of candidate SNPs.Results:Our investigation revealed several candidate functional SNPs within the PRDM1-ATG5 region. Among them, rs533733 (p=1.15E-18), rs34582442 (p=2.79E-08), rs34599047 (p=2.82E-08), rs77846660 (p=8.39E-04), and rs56886418 (p=4.23E-03) emerged as noteworthy, suggesting their involvement in the regulatory landscape of this locus. Expanding our focus, the inclusion of rs1152966 (p=3.39E-15), rs11152964 (p=7.18E-11), rs573775 (p=6.13E-06), and rs12175062 (p=2.67E-03) in the analysis revealed a broader understanding of eQTLs and chromatin accessibility and modification patterns associated with these SNPs. The extensive influence of these SNPs was observed across various cell types, including minor salivary glands and blood cells, emphasizing their relevance in the pathogenesis of SjD and SLE. Functional assays further elucidated the allele-specific effects of selected SNPs. Notably, rs56885418 and rs3804333 demonstrated a significant decrease in enhancer activity, while rs533733 and rs62422881 exhibited an increase in enhancer activity, particularly in the A253 cell line. These findings underscore the dynamic regulatory impact of these SNPs on gene expression, providing valuable insights into the molecular mechanisms at play in the PRDM1-ATG5 locus.Conclusion:Functional characterization of SNPs in the PRDM1-ATG5 locus provides new insights into the regulatory mechanisms governing gene expression in SjD and SLE. Ongoing studies will focus on in vitro validation of predicted functional SNPs in A235 and GM12878 cells.REFERENCES:[1] Khatri B, et al. Nat Commun. 2022 Jul;13(1):4287.[2] Byun YS, et al. Sci Rep. 2017 Dec;7(1):17280.[3] Wible DJ, et al. Cell Discov. 2019; 5:42.[4] Kallies A, Nutt SL. Curr Opin Immunol. 2007 Apr;19(2):156-62.Acknowledgements:National Institutes of Health (NIH): R01AR071410, R01AR073855, R01AR065953, P50AR060804, U01DE028891; National Research Foundation of Korea (NRF-2021R1A6A1A03038899); Sjögren’s Foundation; Presbyterian Health Foundation; Jerome L. Greene Foundation.Disclosure of Interests:Marcin Radziszewski: None declared, Mandi M Wiley: None declared, Bhuwan Khatri: None declared, Astrid Rasmussen: None declared, Kandice L Tessneer: None declared, Kwangwoo Kim: None declared, Edward M. Vital: None declared, Nick Dand: None declared, Chen Gong: None declared, David Morris: None declared, Phil Tombleson: None declared, Elena Pontarini: None declared, Michele Bombardieri: None declared, Maureen Rischmueller: None declared, Marie Wahren-Herlenius: None declared, Marika Kvarnström: None declared, Torsten Witte: None declared, Hendrika Bootsma: None declared, Gwenny M. Verstappen: None declared, Frans G.M. Kroese: None declared, Arjan Vissink: None declared, Sarah Pringle: None declared, Athanasios Tzioufas: None declared, Clio Mavragani: None declared, Alan Baer Received consulting fees from Bristol Myers Squibb (BMS) and iCell Gene Therapeutics., Marta Alarcon-Riquelme: None declared, Javier Martin: None declared, Xavier Mariette: None declared, Gaetane Nocturne: None declared, Jacques-Olivier Pers: None declared, Jacques-Eric Gottenberg: None declared, Wan-Fai Ng I have consulted for Novartis, BMS, Janssen, Sanofi, Abbvie, IQVIA, Argenx, Resolve Therapeutics., Caroline Shiboski: None declared, Kimberly E Taylor: None declared, Lindsey Criswell: None declared, Blake M Warner: None declared, A Darise Farris Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023)., Patrick M Gaffney: None declared, Judith A. James: None declared, R Hal Scofield Received consulting fees from Johnson and Johnson Innovative Medicine (formerly Janssen) and Merk Pharmaceuticals., Joel M Guthridge: None declared, Daniel J Wallace: None declared, Swamy Venuturupali: None declared, Michael T Brennan: None declared, Juliana Imgenberg-Kreuz: None declared, Lars Ronnblom: None declared, Eva Baecklund: None declared, Maija-leena Eloranta: None declared, Lara A Aqrawi: None declared, Øyvind Palm: None declared, Johan G Brun: None declared, Daniel Hammenfors: None declared, Malin V Jonsson: None declared, Silke Appel: None declared, Sara Magnusson Bucher: None declared, Helena Forsblad-d’Elia: None declared, Thomas Mandl Employee of UCB., Per Eriksson: None declared, Sang-Cheol Bae: None declared, Timothy J Vyse: None declared, Betty Tsao: None declared, Gunnel Nordmark: None declared, Christopher J Lessard Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023).

Background:Sjögren’s disease (SjD) is a complex systemic autoimmune disease with substantial morbidity and 21 known genetic associations. The International Sjögren’s Genetics Network (SGENE) is a growing international collaboration focused on understanding how genetic variants influence SjD pathology. As sample sizes increase, we are focusing our efforts on the analyses of clinical subsets, which few studies have done.Objectives:Our genome-wide association study (GWAS) aimed to identify additional risk loci of genome-wide significance (GWS, p<5E-08; suggestive, p<5x10E-5) in European-derived subsets of SjD.Methods:This study was conducted with IRB/EC approvals. All SjD patients met the 2002 AECG criteria for SjD. A total of 5058 cases and 25943 controls were genotyped on GWAS arrays. After QC, 4855 cases and 25408 controls were included in the analyses. Logistic regression was calculated, adjusting for ancestry using the first 4 principal components to identify SjD-associated SNPs. Cases were split into Ro/SSA+ (n=2898) and Ro/SSA- (n=1313), and analyzed vs. each other, controls, and all-SjD.Results:We observed many differences in the genomic architecture of Ro/SSA- compared to Ro/SSA+ and all SjD (Figure 1a,b), most notably, a complete loss of the significance of the association with MHC on chromosome 6 in the Ro/SSA- cases(Figure 1b). The Ro/SSA+ subjects had a much stronger HLA association with OR ≈ 4 (Figure 1a), while the overall SjD showed OR ≈ 3. While none of the associations observed in the Ro/SSA- population reached GWS, 8 regions (near PLXNA2, PCDH7, IRF5-TNPO3, DLD, LOC100134229, JAK3, LOC643529, and TMTC1) show suggestive associations (Figure. 1a). Of these, only two, IRF5-TNPO3 and LOC643529, are also suggestive in the Ro/SSA+ subset. However, while the Ro/SSA+ have both the IRF5 promoter effect and the extended haplotype through TNPO3, the Ro/SSA- lack the IRF5 promoter effect. Interestingly, previous studies have shown that lupus and systemic sclerosis have both haplotypes while primary biliary cholangitis only has the haplotype extending into TNPO3, similar to Ro/SSA- SjD [1]. When comparing Ro/SSA- to the all-SjD dataset, PLXNA2 and LOC100134229 showed no association; PCDH7, DLD, and TMTC1 showed some association but did not reach suggestive levels; and LOC643529, IRF5-TNPO3, and JAK3 surpassed the suggestive threshold, the latter two nearing or surpassing GWS. Two of the novel suggestive associations in Ro/SSA- are particularly intriguing. PLXNA2 is a member of a semaphorin co-receptor family that mediates repulsive effects on axon pathfinding during nervous system development; interestingly, Ro/SSA- SjD has a higher frequency of neurological involvement. JAK3 is a member of the Janus kinase (JAK) family of tyrosine kinases involved in cytokine receptor-mediated intracellular signal transduction; it is predominantly expressed in immune cells. Mutations in this gene are associated with autosomal severe combined immunodeficiency disease. Novel drugs target the JAK-STAT pathways, making this finding markedly relevant.Conclusion:Our findings highlight the relevance of expanding genetic studies to specific subphenotypes of the disease. While we continue to increase our GWAS sample size and explore other subphenotypes, more work is needed to increase the power of these studies to determine if the suggestive regions will surpass the GWS threshold.REFERENCES:[1] Kottyan LC, et al. Hum Mol Genet. 2015 Jan 15;24(2):582-96.Acknowledgements:NIH/NIAMS R01 AR073855, P50 AR060804; NIH/NIDCR U01DE028891; Sjögren’s Foundation; Jerome L. Greene Foundation.Disclosure of Interests:Astrid Rasmussen: None declared, Marcin Radziszewski: None declared, Bhuwan Khatri: None declared, Kandice L Tessneer: None declared, Elena Pontarini: None declared, Michele Bombardieri: None declared, Maureen Rischmueller: None declared, Marie Wahren-Herlenius: None declared, Marika Kvarnström: None declared, Torsten Witte: None declared, Hendrika Bootsma: None declared, Gwenny M. Verstappen: None declared, Frans G.M. Kroese: None declared, Arjan Vissink: None declared, Sarah Pringle: None declared, Athanasios Tzioufas: None declared, Clio Mavragani: None declared, Alan Baer Received consulting fees from Bristol Myers Squibb (BMS) and iCell Gene Therapeutics., Marta Alarcon-Riquelme: None declared, Javier Martin: None declared, Xavier Mariette: None declared, Gaetane Nocturne: None declared, Jacques-Olivier Pers: None declared, Jacques-Eric Gottenberg: None declared, Wan-Fai Ng I have consulted for Novartis, BMS, Janssen, Sanofi, Abbvie, IQVIA, Argenx, Resolve Therapeutics., Caroline Shiboski: None declared, Kimberly E Taylor: None declared, Lindsey Criswell: None declared, Blake M Warner: None declared, A Darise Farris Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023)., Judith A. James: None declared, R Hal Scofield Received consulting fees from Johnson and Johnson Innovative Medicine (formerly Janssen) and Merk Pharmaceuticals., Joel M Guthridge: None declared, Daniel J Wallace: None declared, Swamy Venuturupali: None declared, Michael T Brennan: None declared, Juliana Imgenberg-Kreuz: None declared, Lars Ronnblom: None declared, Eva Baecklund: None declared, Maija-leena Eloranta: None declared, Lara A Aqrawi: None declared, Øyvind Palm: None declared, Johan G Brun: None declared, Daniel Hammenfors: None declared, Malin V Jonsson: None declared, Silke Appel: None declared, Sara Magnusson Bucher: None declared, Helena Forsblad-d’Elia: None declared, Thomas Mandl Employee of UCB., Per Eriksson: None declared, Gunnel Nordmark: None declared, Christopher J Lessard Grant/research support from Johnson and Johnson Innovative Medicine (formerly Janssen; ended 12/31/2023).

BackgroundA subset of B cells expressing the inhibitory Fc receptor-like protein 4 (FcRL4) is found in salivary gland lesions of patients with primary Sjögren’s syndrome (pSS). FcRL4 +B cells are associated with ductal epithelial cells forming lymphoepithelial lesions (LEL), in particular within parotid glands1. Furthermore, FcRL4 is expressed by mucosa-associated lymphoid tissue (MALT) lymphoma B cells.ObjectivesWe aimed to investigate, by single cell and bulk RNA sequencing, how the gene expression profile of FcRL4 +B cells differs from FcRL4-negative naive and memory B cells in salivary gland tissue from pSS patients. We hypothesised that FcRL4 +B cells contribute to LEL formation and are prone to lymphomagenesis.MethodsParotid gland biopsies of 5 pSS patients without MALT lymphoma were obtained. Single cell suspensions were prepared by mechanical disruption and enzymatic digestion. The cells were incubated with anti-CD19, anti-CD27 and anti-FcRL4 antibodies, and sorted as single cells or 5 cells per well based on the following definitions: CD19 +CD27-FcRL4- (‘naive’), CD19 +CD27+FcRL4- (memory) and CD19 +FcRL4+ (FcRL4+). Library preparation was done using an in-house SMARTseq2 protocol and sequencing was done on an Illumina HiSeq2500.ResultsSamples from 4 pSS patients passed quality control and were included. A total of 160 single cells and 360 cells in bulk were included in the analysis. Genes identified by differential expression were subjected to gene pathway analysis. Both in single cell and bulk samples, multiple genes coding for integrins, such as ITGAX (CD11c), were significantly upregulated in FcRL4 +B cells. Gene Ontology pathways that showed the highest upregulation in FcRL4 +B cells (both single cell and bulk) were receptor binding, GTPase and protein kinase pathways. Analysis of bulk samples further revealed that expression levels of genes encoding for Src tyrosine kinases, genes involved in the NF-κB pathway, CXCR3, and TNFRSF13B (TACI), among others, were significantly upregulated in FcRL4 +B cells, compared with either naive or memory B cells. Expression levels of CD40 were significantly decreased in FcRL4 +B cells.ConclusionsFcRL4 +B cells in salivary glands of pSS patients show upregulation of genes involved in homing and cell adhesion, consistent with their tissue location close to the epithelium. FcRL4 +B cells also show increased levels of transcripts that induce inflammation and B cell survival. These cells exhibit all characteristics of chronically stimulated CD11c+memory B cells, and we speculate that FcRL4 +B cells contribute significantly to the epithelial damage seen in the glandular tissue of pSS patients.Reference[1] Haacke, et al. J Autoimmun. 2017;81:90–8.AcknowledgementsWe thank Kim de Lange and Gerben van der Vries from the Genome Analysis Facility of the University Medical Centre Groningen for excellent technical and bioinformatics assistance.Disclosure of InterestNone declared