Objective Sjögren’s disease (SjD) is a chronic exocrine disorder typified by inflammation and dryness, but also profound fatigue, suggesting a pathological basis in cellular bioenergetics. In healthy states, dysfunctional mitochondria are recycled by mitophagic processes; when impaired, poorly functioning mitochondria persist and produce inflammatory reactive oxygen species. Employing a case–control study, we tested our hypothesis that mitochondrial dysregulation in T cells is associated with fatigue in SjD.Methods We isolated pan T cells from peripheral blood mononuclear cells of 13 SjD and 4 non-Sjögren’s sicca (NSS) subjects, who completed several fatigue questionnaires, along with 8 healthy subjects. Using Seahorse, we analysed T cells for mitochondrial oxygen consumption rate (OCR) and extracellular acidification rate, which we assessed for correlation with fatigue measures. Using public microarray data available for 190 SjD and 32 healthy subjects, we identified a mitophagic transcriptional signature that stratified SjD patients into 5 discrete clusters. Comparisons between the SjD subjects in these clusters to healthy individuals identified differentially expressed transcripts, which we subjected to bioinformatic interrogation.Results Basal OCR, ATP-linked respiration, maximal respiration and reserve capacity were significantly lower in SjD and NSS subjects compared with healthy individuals, with no differences in non-mitochondrial respiration, basal glycolysis or glycolytic reserve. Scores related to a sleep questionnaire and Bowman’s Profile of Fatigue and Discomfort showed correlation with altered OCR in SjD. Subgroup differential expression analysis revealed dynamic transcriptional activity between mitophagy subgroups, expanding the number of differentially expressed transcripts tenfold.Conclusions Mitochondrial dysfunction and fatigue are significant problems in SjD warranting further investigation.

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, 2020 Acknowledgements: NIL. Disclosure of Interests: None declared.Figure 1A. 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)

Sjögren's Disease (SjD) is a systemic autoimmune disease without a clear etiology or effective therapy. Utilizing unbiased single-cell and spatial transcriptomics to analyze human minor salivary glands in health and disease we developed a comprehensive understanding of the cellular landscape of healthy salivary glands and how that landscape changes in SjD patients. We identified novel seromucous acinar cell types and identified a population of PRR4+CST3+WFDC2- seromucous acinar cells that are particularly targeted in SjD. Notably, GZMK+CD8 T cells, enriched in SjD, exhibited a cytotoxic phenotype and were physically associated with immune-engaged epithelial cells in disease. These findings shed light on the immune response's impact on transitioning acinar cells with high levels of secretion and explain the loss of this specific cell population in SjD. This study explores the complex interplay of varied cell types in the salivary glands and their role in the pathology of Sjögren's Disease.

Sjogren's disease (SjD) is a chronic autoimmune disorder characterized by inflammation of the exocrine glands, leading to dry mouth and dry eyes. This study investigates the role of interleukin-9 (IL-9) and T helper 9 (Th9) cells in the pathogenesis of SjD. We found that serum IL-9 levels were significantly elevated in SjD patients and correlated with clinical laboratory parameters, including autoantibody production. In a mouse model of SjD, IL-9 and Th9-associated cytokines were also elevated, and Th9 cells were enriched in the salivary glands. Our results suggest that IL-9 is produced by multiple cell types, including macrophages, CD4+ T cells, and NK cells, and that Th9 cells contribute to the development of SjD by promoting inflammation and autoantibody production. We also found that Th9 and Th17 polarization conditions increased Th2 and Th17 cells in SjD mice, indicating a shared epigenetic program that renders T cells permissive to multiple differentiation pathways. Anti-IL-9 treatment had a sex-dependent effect, reducing autoantibody production in male mice but worsening focal glandular infiltration in female mice. Our findings suggest that IL-9 plays a complex role in SjD pathobiology, contributing to both local immunoregulation and systemic autoantibody response. Overall, this study provides new insights into the role of IL-9 and Th9 cells in SjD and highlights the potential for therapeutic targeting of the IL-9/Th9 axis in the treatment of this disease. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This research was funded by the National Institutes of Health (NIH) and the National Institute of Dental and Craniofacial Research. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Institutional Review Board of each institution approved all procedures, and each participant provided written informed consent prior to entering the study. The study was conducted in accordance with current regulations protecting human subjects participating in research, HIPAA, and the Declaration of Helsinki. Studies were approved by the Institutional Review Board of the Oklahoma Medical Research Foundation I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present work are contained in the manuscript

OBJECTIVES:Diagnosis of Sjögren's disease (SjD) consists of clinical examinations that include invasive studies such as lower lip biopsies and blood collection to identify presence of serum autoantibodies. Salivary glands of patients with SjD are sites where antibody-secreting cells accumulate and secrete immunoglobulins. Many patients with dry manifestations who do not meet classification criteria are grouped as non-Sjögren's sicca (NSS) and are heavily understudied. We undertook this cross-sectional observational study to investigate the role of salivary autoantibodies as a diagnostic tool and determine presence of salivary autoantibodies in patients with NSS. METHODS:In this cross-sectional observational study, we screened whole unstimulated saliva from 446 subjects by direct enzyme-linked immunosorbent assays and capillary western blotting for anti-Ro60, anti-La, and rheumatoid factor (immunoglobulin (Ig)G and IgA) antibodies. All subjects were classified following the ACR/EULAR classification at the Oklahoma Medical Research Foundation Sjögren's Research Clinic. RESULTS:Patients with SjD were significantly more likely to have salivary antibodies compared with those with NSS and healthy control subjects. In this cohort, there were 88 subjects with NSS with seronegative profiles who had detectable salivary autoantibodies and objective measures of dryness. CONCLUSIONS:Of these 88 subjects with NSS, we identified 75 subjects that had positive objective examinations of dryness and could belong to an early-onset SjD group. Alternatively, these 75 subjects with NSS could belong to a distinct phenotype of SjD that will remain seronegative while being saliva positive for anti-Ro60. These data showed a relationship between ocular and oral dryness and the presence of salivary anti-Ro60 antibodies in subjects with NSS.

Fine mapping and bioinformatic analysis of the DDX6-CXCR5 genetic risk association in Sjögren's Disease (SjD) and Systemic Lupus Erythematosus (SLE) identified five common SNPs with functional evidence in immune cell types: rs4938573, rs57494551, rs4938572, rs4936443, rs7117261. Functional interrogation of nuclear protein binding affinity, enhancer/promoter regulatory activity, and chromatin-chromatin interactions in immune, salivary gland epithelial, and kidney epithelial cells revealed cell type-specific allelic effects for all five SNPs that expanded regulation beyond effects on DDX6 and CXCR5 expression. Mapping the local chromatin regulatory network revealed several additional genes of interest, including lnc-PHLDB1-1. Collectively, functional characterization implicated the risk alleles of these SNPs as modulators of promoter and/or enhancer activities that regulate cell type-specific expression of DDX6, CXCR5, and lnc-PHLDB1-1, among others. Further, these findings emphasize the importance of exploring the functional significance of SNPs in the context of complex chromatin architecture in disease-relevant cell types and tissues.

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: 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. Acknowledgements: NIL. Disclosure of Interests: None declared.Figure 1AI-Tissue Classifier Results of Example Trainer and Test Images. Figure 2Percent Histological Features as Identified by the AI-Tissue Classifier and Stratified by Percent Immune Infiltration (High to low, top 50% of immune infiltrate)