Introduction Inflammatory mediators are detected in the cerebrospinal fluid of systemic lupus erythematosus patients with central nervous system involvement (NPSLE), yet the underlying cellular and molecular mechanisms leading to neuropsychiatric disease remain elusive. Methods We performed a comprehensive phenotyping of NZB/W-F1 lupus-prone mice including tests for depression, anxiety and cognition. Immunofluorescence, flow cytometry, RNA-sequencing, qPCR, cytokine quantification and blood–brain barrier (BBB) permeability assays were applied in hippocampal tissue obtained in both prenephritic (3-month-old) and nephritic (6-month-old) lupus mice and matched control strains. Healthy adult hippocampal neural stem cells (hiNSCs) were exposed ex vivo to exogenous inflammatory cytokines to assess their effects on proliferation and apoptosis. Results At the prenephritic stage, BBB is intact yet mice exhibit hippocampus-related behavioural deficits recapitulating the human diffuse neuropsychiatric disease. This phenotype is accounted by disrupted hippocampal neurogenesis with hiNSCs exhibiting increased proliferation combined with decreased differentiation and increased apoptosis in combination with microglia activation and increased secretion of proinflammatory cytokines and chemokines. Among these cytokines, IL-6 and IL-18 directly induce apoptosis of adult hiNSCs ex vivo. During the nephritic stage, BBB becomes disrupted which facilitates immune components of peripheral blood, particularly B-cells, to penetrate into the hippocampus further augmenting inflammation with locally increased levels of IL-6, IL-12, IL-18 and IL-23. Of note, an interferon gene signature was observed only at nephritic-stage. Conclusion An intact BBB with microglial activation disrupting the formation of new neurons within the hippocampus represent early events in NPSLE. Disturbances of the BBB and interferon signature are evident later in the course of the disease.

Objectives Diagnostic reasoning in systemic lupus erythematosus (SLE) is a complex process reflecting the probability of disease at a given timepoint against competing diagnoses. We applied machine learning in well-characterised patient data sets to develop an algorithm that can aid SLE diagnosis. Methods From a discovery cohort of randomly selected 802 adults with SLE or control rheumatologic diseases, clinically selected panels of deconvoluted classification criteria and non-criteria features were analysed. Feature selection and model construction were done with Random Forests and Least Absolute Shrinkage and Selection Operator-logistic regression (LASSO-LR). The best model in 10-fold cross-validation was tested in a validation cohort (512 SLE, 143 disease controls). Results A novel LASSO-LR model had the best performance and included 14 variably weighed features with thrombocytopenia/haemolytic anaemia, malar/maculopapular rash, proteinuria, low C3 and C4, antinuclear antibodies (ANA) and immunologic disorder being the strongest SLE predictors. Our model produced SLE risk probabilities (depending on the combination of features) correlating positively with disease severity and organ damage, and allowing the unbiased classification of a validation cohort into diagnostic certainty levels (unlikely, possible, likely, definitive SLE) based on the likelihood of SLE against other diagnoses. Operating the model as binary (lupus/not-lupus), we noted excellent accuracy (94.8%) for identifying SLE, and high sensitivity for early disease (93.8%), nephritis (97.9%), neuropsychiatric (91.8%) and severe lupus requiring immunosuppressives/biologics (96.4%). This was converted into a scoring system, whereby a score >7 has 94.2% accuracy. Conclusions We have developed and validated an accurate, clinician-friendly algorithm based on classical disease features for early SLE diagnosis and treatment to improve patient outcomes.

Objective: This study aimed to analyse the phenotype of systemic lupus erythematosus (SLE) at first presentation and during follow-up in a newly established SLE cohort based at ‘Attikon’ University Hospital. The hospital combines primary, secondary and tertiary care for the region of Western Attica, Greece. Methods: This study comprised a mixed prevalent and incident cohort of 555 Caucasian patients diagnosed with SLE according to American College of Rheumatology 1997 criteria and/or the Systemic Lupus Erythematosus International Collaborating Clinics (SLICC) 2012 criteria. Demographic and clinical characteristics, patterns of severity, treatments and SLICC damage index were recorded for each patient at the time of diagnosis and at last evaluation. Results: The mean age at lupus diagnosis was 38.3 years (standard deviation = 15.6 years), with a median disease duration at last follow-up of two years (interquartile range 1-11). At initial presentation, the most common ‘classification’ manifestations were arthritis (73.3%), acute cutaneous lupus (65%) and unexplained fever (25%), while among symptoms not included in any criteria set, Raynaud’s phenomenon (33%) was the most common. Kidney and neuropsychiatric involvement as presenting manifestations were present in 10.3% and 11.5% cases, respectively. Irreversible damage accrual was present in 17.8% within six months of disease diagnosis, attributed mainly to thrombotic and neuropsychiatric disease. At last evaluation, 202 (36.4%) patients had developed severe disease, of whom more than half were treated with pulse cyclophosphamide. Conclusion: In this cohort of Caucasian patients, lupus nephritis is not as common as in older cohorts, while neuropsychiatric disease is emerging as a major frontier in lupus prevention and care. These data may help to document changes in the natural history and treatment of SLE over time and may have implications for its early recognition and management.

B cells orchestrate autoimmune responses in patients with systemic lupus erythematosus (SLE), but broad-based B cell–directed therapies show only modest efficacy while blunting humoral immune responses to vaccines and inducing immunosuppression. Development of more effective therapies targeting pathogenic clones is a currently unmet need. Here, we demonstrate enhanced activation of the ATR/Chk1 pathway of the DNA damage response (DDR) in B cells of patients with active SLE disease. Treatment of B cells with type I IFN, a key driver of immunity in SLE, induced expression of ATR via binding of interferon regulatory factor 1 to its gene promoter. Pharmacologic targeting of ATR in B cells, via a specific inhibitor (VE-822), attenuated their immunogenic profile, including proinflammatory cytokine secretion, plasmablast formation, and antibody production. Together, these findings identify the ATR-mediated DDR axis as the orchestrator of the type I IFN–mediated B cell responses in SLE and as a potential novel therapeutic target.

Objectives Patients with lupus nephritis (LN) are in urgent need for early diagnosis and therapeutic interventions targeting aberrant molecular pathways enriched in affected kidneys. Methods We used mRNA-sequencing in effector (spleen) and target (kidneys, brain) tissues from lupus and control mice at sequential time points, and in the blood from 367 individuals (261 systemic lupus erythematosus (SLE) patients and 106 healthy individuals). Comparative cross-tissue and cross-species analyses were performed. The human dataset was split into training and validation sets and machine learning was applied to build LN predictive models. Results In murine SLE, we defined a kidney-specific molecular signature, as well as a molecular signature that underlies transition from preclinical to overt disease and encompasses pathways linked to metabolism, innate immune system and neutrophil degranulation. The murine kidney transcriptome partially mirrors the blood transcriptome of patients with LN with 11 key transcription factors regulating the cross-species active LN molecular signature. Integrated protein-to-protein interaction and drug prediction analyses identified the kinases TRRAP, AKT2, CDK16 and SCYL1 as putative targets of these factors and capable of reversing the LN signature. Using murine kidney-specific genes as disease predictors and machine-learning training of the human RNA-sequencing dataset, we developed and validated a peripheral blood-based algorithm that discriminates LN patients from normal individuals (based on 18 genes) and non-LN SLE patients (based on 20 genes) with excellent sensitivity and specificity (area under the curve range from 0.80 to 0.99). Conclusions Machine-learning analysis of a large whole blood RNA-sequencing dataset of SLE patients using human orthologs of mouse kidney-specific genes can be used for early, non-invasive diagnosis and therapeutic targeting of LN. The kidney-specific gene predictors may facilitate prevention and early intervention trials.

Introduction Systemic lupus erythematosus with antiphospholipid syndrome (SLE-APS) represents a challenging SLE endotype whose molecular basis remains unknown. Methods We analysed whole-blood RNA-sequencing data from 299 patients with SLE (108 SLE-antiphospholipid antibodies (aPL)-positive, including 67 SLE-APS; 191 SLE-aPL-negative) and 72 matched healthy controls (HC). Pathway enrichment analysis, unsupervised weighted gene coexpression network analysis and machine learning were applied to distinguish disease endotypes. Results Patients with SLE-APS demonstrated upregulated type I and II interferon (IFN) pathways compared with HC. Using a 100-gene random forests model, we achieved a cross-validated accuracy of 75.6% in distinguishing these two states. Additionally, the comparison between SLE-APS and SLE-aPL-negative revealed 227 differentially expressed genes, indicating downregulation of IFN-α and IFN-γ signatures, coupled with dysregulation of the complement cascade, B-cell activation and neutrophil degranulation. Unsupervised analysis of SLE transcriptome identified 21 gene modules, with SLE-APS strongly linked to upregulation of the ‘neutrophilic/myeloid’ module. Within SLE-APS, venous thromboses positively correlated with ‘neutrophilic/myeloid’ and ‘B cell’ modules, while arterial thromboses were associated with dysregulation of ‘DNA damage response (DDR)’ and ‘metabolism’ modules. Anticardiolipin and anti-β2GPI positivity—irrespective of APS status—were associated with the ‘neutrophilic/myeloid’ and ‘protein-binding’ module, respectively. Conclusions There is a hierarchical upregulation and—likely—dependence on IFN in SLE with the highest IFN signature observed in SLE-aPL-negative patients. Venous thrombotic events are associated with neutrophils and B cells while arterial events with DDR and impaired metabolism. This may account for their differential requirements for anticoagulation and provide rationale for the potential use of mTOR inhibitors such as sirolimus and the direct fIIa inhibitor dabigatran in SLE-APS.