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SLE is marked by immune dysregulation linked to varied clinical disease activity. Using a unique longitudinal cohort of SLE patients, this study seeks to identify optimal immune mediators informing an empirically refined Flare Risk Index (FRI) reflecting altered immunity prior to clinical disease flare.SLE-associated plasma mediators (n=37) were evaluated by microfluidic immunoassay in 46 Pre-Flare and 53 Pre-Nonflare samples selected from a unique longitudinal cohort of 106 patients with classified SLE (meeting ACR and SLICC criteria). Autoantibody specificities, hybrid SLEDAI (hSLEDAI) scores, clinical features, and medication usage were also compared at Pre-Flare (111±47 days prior to Flare) vs. Pre-Nonflare (99±21 days prior to Nonflare) time points. Variable importance was determined by random forest with logistic regression subsequently applied to determine the optimal number and type of analytes informing a refined FRI.Pre-Flare vs. Pre-Nonflare differences were not associated with demographics, autoantibody specificities, hSLEDAI scores, clinical features, nor medication usage. Forward selection and backward elimination of mediators ranked by variable importance drew up to 17 candidates differentiating Pre-Flare from Pre-Nonflare. A final combination of 11 mediators best informed a newly refined FRI, achieving a maximum of 97% sensitivity and 98% specificity after applying decision curve analysis to define low, medium, and high FRI scores.We verified altered immune mediators associated with imminent disease flare; a subset improved the FRI to identify SLE patients at risk of imminent flare. This molecularly-informed proactive management approach could be critical in prospective clinical trials and the clinical management of lupus.

ObjectivePatients with incomplete lupus erythematosus (ILE) have lupus features but insufficient criteria for SLE classification. Some patients with ILE transition to SLE, but most avoid major organ involvement. This study tested whether the milder disease course in ILE is influenced by reduced SLE risk allele genetic load.MethodsWe calculated the genetic load based on 99 SLE-associated risk alleles in European American patients with SLE (≥4 American College of Rheumatology (ACR) 1997 criteria, n=170), patients with ILE (3 ACR 1997 criteria, n=169), a subset of patients with ILE not meeting Systemic Lupus International Collaborating Clinics (SLICC) classification (ILESLICC, n=119) and healthy controls (n=133). Unweighted genetic loads were calculated as the total sum of risk alleles for each individual, while weighted genetic loads were defined as the sum of risk alleles multiplied by the natural logarithm of the previously published OR of each risk allele for SLE susceptibility.ResultsThe median unweighted and weighted SLE risk allele genetic load was significantly greater in patients with ILE (unweighted: 81, p value=0.01; weighted: 16.3, p value=0.001) and patients with SLE (80, p value=0.02; 16.29, p value=0.0006) compared with healthy controls (78, 15.76). Patients with ILESLICCtrended towards an increased genetic load, although not statistically significant (unweighted: 80, p value=0.14; weighted: 16.05, p value=0.07). However, the median genetic load did not significantly differ between ILE and SLE, and genetic load did not differentiate patients with ILE and SLE (area under the curve=0.51, p=0.78) by receiver operator characteristic analysis.ConclusionsPatients with ILE and SLE have comparable genetic loads of SLE risk loci, suggesting similar genetic predispositions between these conditions. Phenotypical differences between SLE and ILE may instead be influenced by ILE-specific variants and gene–environment interactions.

Clinical diagnoses rely on a wide variety of laboratory tests and imaging studies, interpreted alongside physical examination findings and the patient’s history and symptoms. Currently, the tools of diagnosis make limited use of the immune system’s internal record of specific disease exposures encoded by the antigen-specific receptors of memory B cells and T cells, and there has been little integration of the combined information from B cell and T cell receptor sequences. Here, we analyze extensive receptor sequence datasets with three different machine learning representations of immune receptor repertoires to develop an interpretive framework,MAchine Learning for Immunological Diagnosis (Mal-ID), that screens for multiple illnesses simultaneously. This approach is effective in identifying a variety of disease states, including acute and chronic infections and autoimmune disorders. It is able to do so even when there are other differences present in the immune repertoires, such as between pediatric or adult patient groups. Importantly, many features of the model of immune receptor sequences are human-interpretable. They independently recapitulate known biology of the responses to infection by SARS-CoV-2 and HIV, provide evidence of receptor antigen specificity, and reveal common features of autoreactive immune receptor repertoires, indicating that machine learning on immune repertoires can yield new immunological knowledge. This framework could be useful in identifying immune responses to new infectious diseases as they emerge.

Objective Delayed detection of LN associates with worse outcomes. There are conflicting recommendations regarding a threshold level of proteinuria at which biopsy will likely yield actionable management. This study addressed the association of urine protein:creatinine ratios (UPCR) with clinical characteristics and investigated the incidence of proliferative and membranous histology in patients with a UPCR between 0.5 and 1. Methods A total of 275 SLE patients (113 first biopsy, 162 repeat) were enrolled in the multicentre multi-ethnic/racial Accelerating Medicines Partnership across 15 US sites at the time of a clinically indicated renal biopsy. Patients were followed for 1 year. Results At biopsy, 54 patients had UPCR Conclusion In this prospective study, three-quarters of patients with UPCR

BACKGROUND: For relapsing-remitting multiple sclerosis (RRMS), there is a need for biomarker development beyond clinical manifestations and MRI. Soluble neurofilament light chain (sNfL) has emerged as a biomarker for inflammatory activity in RRMS. However, there are limitations to the accuracy of sNfL in identifying relapses. Here, we sought to identify a panel of biomarkers that would increase the precision of distinguishing patients in relapse compared to sNfL alone. METHODS: We used a multiplex approach to measure levels of 724 blood proteins in two distinct RRMS cohorts. Multiple t-tests with covariate correction determined biomarkers that were differentially regulated in relapse and remission. Logistic regression models determined the accuracy of biomarkers to distinguish relapses from remission. RESULTS: The discovery cohort identified 37 proteins differentially abundant in active RRMS relapse compared to remission. The verification cohort confirmed four proteins, including sNfL, were altered in active RRMS relapse compared to remission. Logistic regression showed that the 4-protein panel identified active relapse with higher accuracy (AUC = 0.87) than sNfL alone (AUC = 0.69). CONCLUSION: Our studies confirmed that sNfL is elevated during relapses in RRMS patients. Furthermore, we identified three other blood proteins, uPA, hK8 and DSG3 that were altered during relapse. Together, these four biomarkers could be used to monitor disease activity in RRMS patients.

SLE is a clinically heterogeneous disease characterized by an unpredictable relapsing-remitting disease course. Although the etiology and mechanisms of SLE flares remain elusive, Epstein-Barr virus (EBV) reactivation is implicated in SLE pathogenesis. This study examined the relationships between serological measures of EBV reactivation, disease activity, and interferon (IFN)-associated immune pathways in SLE patients. Sera from adult SLE patients (n = 175) and matched unaffected controls (n = 47) were collected and tested for antibodies against EBV-viral capsid antigen (EBV-VCA; IgG and IgA), EBV-early antigen (EBV-EA; IgG), cytomegalovirus (CMV; IgG), and herpes simplex virus (HSV-1; IgG). Serological evidence of EBV reactivation was more common in SLE patients compared to controls as demonstrated by seropositivity to EBV-EA IgG (39% vs 13%; p = 0.0011) and EBV-VCA IgA (37% vs 17%; p = 0.018). EBV-VCA, CMV1, and HSV-1 IgG seropositivity rates did not differ between SLE patients and controls. Furthermore, concentrations of EBV-VCA (IgG and IgA) and EBV-EA (IgG) were higher in SLE patients. SLE patients with high disease activity had increased concentrations of EBV-VCA IgA (mean ISR 1.34 vs. 0.97; p = 0.041) and EBV-EA IgG levels (mean ISR 1.38 vs. 0.90; p = 0.007) compared with those with lower disease activity. EBV reactivation was associated with enhanced levels of the IFN-associated molecule IP-10 (p, Highlights • Serologic markers of EBV reactivation are more common in SLE patients. • Elevated EBV reactivation is associated with higher SLE disease activity. • EBV serologic reactivation correlates with elevated IP-10, IL-10, and BLyS levels. • EBV reactivation occurs in SLE clusters with robust inflammatory and IFN responses.

Background Systemic lupus erythematosus (SLE) is a clinically heterogeneous disease oftentimes characterised by a waxing and waning disease course. However, mechanisms of disease flare remain elusive. Objectives This study examined relationships between SLE disease activity, immune pathways, and serologic evidence of viral exposures and reactivation within molecular subsets of SLE patients. Methods Serial or single samples of plasma, serum and RNA (n=290) were collected from 184 adult SLE patients who met ACR classification and cohort-matched controls (n=49). Disease activity was assessed by modified SELENA-SLEDAI. Immune pathways were evaluated by modular transcriptional analysis of Illumina Beadchip Microarray gene expression data, as well as by plasma soluble mediators (n=32) by multiplex, bead-based assay and ELISAs. This information was used to subset patients into seven molecular-defined categories by random forest modelling. Viral seropositivity and antibody concentrations were detected by ELISA for antibodies against EBV-Viral Capsid Antigen (VCA) (IgG and IgA), EBV-Early Antigen (EA) (IgG), Cytomegalovirus (CMV) (IgG), and Herpes Simplex Virus (HSV-1) (IgG). Results Serologic evidence of EBV reactivation was more common in SLE patients compared to controls, as measured by antibodies against EBV-EA [IgG] (40% vs 13%; OR=4.57, p=0.0006) or EBV-VCA [IgA] (36% vs 17%; OR=2.70, p=0.019). No differences were noted in CMV or HSV1 seropositivity rates between patients and controls. IgG responses against EBV-VCA were nearly universal among these adult patients and controls; however, concentrations of EBV-VCA IgG were higher in SLE patients compared to controls (ISR=4.44 vs 3.52; p=0.0021), as were EBV-VCA IgA and EBV-EA IgG antibody responses. In cross sectional analysis, SLE patients with higher disease activity (SLEDAI ≥6; n=126) had higher concentrations of EBV-EA IgG than patients with lower (n=166) disease activity (ISR=0.822 vs 0.540; p=0.033). SLE patients with serologic evidence of EBV reactivation by EA IgG responses had higher levels of interferon associated molecules, IP10 (p=3.4×10–14), BLyS (5.5×10–5), and IL-10 (p=0.00013). HSV1 IgG positive SLE patients also showed higher levels of IP10 (2.2×10–7). Antibody responses toward EBV-EA were enriched in molecularly defined patient clusters with higher expression levels of interferon and inflammatory modules, as well as with interferon and inflammatory soluble mediators. Patients within these clusters were also more likely to have major organ involvement, such as renal or neurologic disease. Conclusions Serologic evidence of EBV reactivation is more common in SLE patients compared to healthy controls. EBV-EA IgG responses are elevated in SLE patients with active disease and correspond with increases in interferon-associated mediators. This study provides serologic evidence suggesting a possible role for viral reactivation in SLE disease activity. Acknowledgements This work was supported in part by grants from the National Institutes of Health: U19AI082714, U01AI101934, U54GM104938, P30AR053483, R01AR072401. Disclosure of Interest None declared

Background Systemic lupus erythematosus (SLE) is characterised by remarkable clinical and pathophysiological diversity, hindering diagnosis, treatment and treatment development. Subsetting of patients based upon clinical presentations alone has not identified homogeneous groups of patients best treated with a directed therapeutic. Objectives To cluster SLE patients into more homogeneous subsets with common molecular pathway signatures, and to assess the clinical, therapeutic, and demographic features enriched in each cluster. Methods Serial or single plasma, serum and RNA samples (n=290) were collected from 198 SLE patients who met ACR classification. Disease activity was assessed by modified SELENA-SLEDAI at an average of 17 visits per patient. Transcriptional co-expression signature module scores were calculated from Illumina Beadchip Microarray gene expression data for 29 immune pathway related modules. Plasma soluble mediators (n=23) and 12 antinuclear autoantibodies (anti-dsDNA, chromatin, ribosomal P, Sm, Sm, SmRNP, RNP, Ro/SSA, La/SSB, centromere B, Scl-70 and Jo-1) were assessed by multiplex bead-based assay and ELISA. Spearman correlations were used for univariate and multivariate analysis using R. Patients were clustered on module signature scores and soluble mediators using random forest and tSNE. Results SLEDAI scores strongly correlated with interferon modules and were modestly correlated with plasmablast and select cell cycle signatures in this adult lupus collection. SLEDAI scores also correlated with soluble levels of IFNa, IL21, IL1a, IL17A, IP10 and MIG. Random forest defined seven clusters of SLE patients with unique molecular phenotypes based upon gene co-expression module signatures and soluble mediators. Inflammation and interferon (IFN) signatures were elevated in Clusters 1 (moderately) and 4, with decreased T cell signatures in Cluster 4. The other clusters had lower IFN and inflammation signatures, but differed in their monocyte, plasmablast and T cell signatures. Clusters 1 and 4 had the highest SLEDAI scores, with high rates of anti-dsDNA, low complement, proteinuria and hematuria; these features were also prominent in Cluster 3, which lacked the IFN and inflammation signatures. Cluster 6 had the highest plasmablast module score, highest IL1a levels, and SLEDAI scored rashes, but only moderate IFN and inflammation module scores. Cluster 2 had higher rates of SLEDAI scored alopecia, a slightly elevated inflammation signature, but lower interferon signature and lower soluble mediator levels. Conclusions SLE subsets can be distinguished by a range of molecular profiles encompassing IFN, T cell, neutrophil, plasmablast, and inflammation co-expression signatures, as well as soluble mediators that vary with disease activity. Prospective longitudinal studies of these molecular profiles may inform clinical trial design and personalised disease management. Acknowledgements This work was supported in part by grants from the National Institutes of Health: U19AI082714, U01AI101934, U54GM104938, and P30AR053483. Disclosure of Interest None declared