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Systemic lupus erythematosus (SLE) affects 1 in 537 of African American (AA) women, which is >2-fold more than European American (EA) women. AA patients also develop the disease at a younger age, have more severe symptoms, and a greater chance of early mortality. We used a multi-omics approach to uncover ancestry-specific immune alterations in SLE patients and healthy controls that may contribute to disease disparities. Cell composition, signaling, and epigenetics were evaluated by mass cytometry; droplet-based single cell transcriptomics and paired proteogenomics (scRNA-Seq/scCITE-Seq). Soluble mediator levels were measured in plasma and stimulated whole blood. Toll-like receptor (TLRs) pathways are activated by vaccination and microbial infection, and are also key drivers of autoimmune disease. We observed enhanced TLR3/4/7/8/9-related gene expression in immune cells from AA versus EA SLE patients. TLR7/8/9 and IFNα phospho-signaling responses were heightened even in immune cells from healthy AA versus EA controls. TLR stimulation of healthy AA and EA immune cells recapitulated the distinct ancestry-associated SLE immunophenotypes. Thus, healthy individuals show ancestry-based differences in innate immune pathways that could influence the course and severity of lupus and other diseases.

BackgroundSystemic Lupus Erythematosus (SLE) is an interferon-related autoimmune disease characterized by immune cell dysfunction and is common among women and minorities. 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 an autoimmune disease. Patients with incomplete lupus erythematosus (ILE) exhibit some SLE clinical symptoms with most never progressing to SLE. Exact mechanisms involved in myeloid cell dysregulation and the progression of autoimmune disease remains unclear.ObjectivesDetermine whether alterations in myeloid cell population frequencies or activation of particular cellular processes is dysregulated in subjects with pre-clinical disease.MethodsPBMCs from 32 subjects (ANA-, ANA+, ILE and SLE) were sorted with Nanocellect Wolf microfluidic flow cytometer to remove dead and dying cells. Viable isolated single cells were used to do a multiomics single-cell analysis using 10x Genomics 5’ scRNA-Seq/137-plex Total-Seq multiomics kit, that also enable BCR and TCR repertoire analysis. Single-cell transcript and proteogenomics library preparation was done on a 10x Chromium X targeting 20,000 cells per channel, each sample encapsulated in a single channel. Normalized, pooled UDI labeled libraries were sequenced on Illumina Nova-Seq S4 flowcell (PE100, depth of 50,000 reads/cell). Data were analyzed for cluster identification, differential gene signatures in Python. Pathway analysis was performed in Ingenuity Pathway Analysis (IPA).ResultsAcross all subjects, 9 distinct myeloid clusters (Classical, Non-classical, Intermediate, CCR4+ monocytes) were identified with a community detection algorithm and visualized with Uniform Manifold Approximation Projection (UMAP). The proportion of cells in those clusters varied by disease group. Fractions of non-classical monocytes appear to be higher in ILE, SLE patients than in ANA- and ANA+ subjects. Classical monocytes have increasing cell fractions with disease progression, except ILE subjects that appear to have lower fractions than the other groups. Intermediate monocytes fractions are higher in ANA- controls and lower in the other groups. Pathway analysis revealed further differences within ethnicities (African, European Americans). Upregulation of autophagy related pathways was observed in AA ANA+ compared to ANA- and ILE, however it is downregulated in EA. Oxidative phosphorylation is upregulated in AA SLE compared to ILE and upregulated in AA ANA+ compared to ANA-. On the contrary, that pathway is upregulated in EA ANA+ compared to ANA- and ILE, as well as SLE compared to ILE. Further differences between subpopulations of major monocyte clusters were also observed.ConclusionDysregulation of signaling in monocyte activation appears to be manifesting in either increased oxidative phosphorylation or alteration in cellular apoptotic or autophagy pathway regulation. Alterations in these processes may vary by ancestral background reflected in the heterogeneity one sees in the presentation of lupus or trajectory of disease.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[3]Slight-Webb, S., et al.,Autoantibody-Positive Healthy Individuals Display Unique Immune Profiles That May Regulate Autoimmunity.Arthritis Rheumatol, 2016Figure 1.A. UMAP projection of 9 distinct myeloid clusters. B. Myeloid cell density for total population, African Americans and European Americans across 4 disease groups. C. Cell fractions for total population and each distinct cluster by disease group. D. Expression of genes involved in autophagy, death receptor signaling and oxidative phosphorylation pathways by race and disease group.Acknowledgements:NIL.Disclosure of InterestsNone Declared.

BackgroundAnti-nuclear antibody (ANA) positivity is a principal feature of individuals with an autoimmune disease, yet up to one in five healthy individuals are ANA-positive (ANA+) and will never develop overt disease. Understanding differences in immune cell physiology between ANA+healthy individuals and individuals with clinical SLE remains a critical goal in the understanding of SLE pathogenesis across ethnicities.MethodsBlood specimens and information on disease activity were collected from European (EA) and African American (AA) individuals classified and matched in groups as ANA- healthy controls (n=24), ANA +healthy (n=24) or SLE patients (n=24). Single-cell analysis of cell surface markers was completed by mass cytometry on PBMCs and cellular heterogeneity was visualized using tSNE (figure 1A–B) and manual gating. Further, phospho-specific flow cytometry was used to measure basal levels of pERK, pPLCg2 and p38 and expression following CD3/CD28 (TCR) and anti-IgG and IgM (BCR) stimulation. Whole genome RNA-sequencing was performed on flow cytometry sorted T cells, B cells and monocytes from 35 matched ANA-, ANA +and SLE patients followed by weighted correlation network analysis (WGCNA) and pathway enrichment analyses.ResultsBoth European and African American SLE patients were distinguished from healthy individuals by T cell proliferation (p=0.002) (figure 1C), plasmacytoid dendritic cell activation (p=0.021) and elevated stem cell factor (p=0.0003). EA ANA+healthy individuals exhibited greater immune regulation with reduced T cell numbers (p=0.002) (figure 1C), decreased activation of dendritic cells (p=0.039) and transitional B cells (0.033), and elevated expression of the inhibitory receptor CD85j (p=0.042) on specific immune cell subsets compared to ANA- healthy subjects. Further, a module associated with hematopoiesis, T cell activation and intrinsic apoptosis signaling pathways is expressed at a higher level in T cells of EA ANA+individuals. In contrast, AA ANA+healthy individuals had elevated plasma levels of IL-6 (p=0.018) and reduced inhibitory receptor expression (p=0.0089) compared to ANA- healthy controls. Gene expression modules associated with viral responses and type I IFN pathway activation were identified in AA SLE patient B cells, while similar expression modules were only found in the monocytes of European American SLE patients.Abstract 233 Figure 1Calculated cell numbers indicate elevated T cells in SLE patients and suppressed T cells in EA ANA+ healthy individuals. 20 cell surface marker expression is shown using dimensionality reduced t-SNE plots from PBMC data (110,00 cells) derived from 72 samples. (A) A density map is shown depicting the density of cells and are numbered according to phenotypic subset. (B) Density maps depicting European and African American ANA-, ANA+ and SLE patient PBMC t-SNE plots created using all 33 surface markers are plotted. All plots were derived from cumulative data from 12 individuals per group. (C) Cells numbers were calculated from cell subsets using fr...