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ObjectivesFamilies that contain multiple siblings affected with childhood onset of systemic lupus erythematosus (SLE) likely have strong genetic predispositions. We performed whole exome sequencing (WES) to identify familial rare risk variants and to assess their effects in lupus.MethodsSanger sequencing validated the two ultra-rare, predicted pathogenic risk variants discovered by WES and identified additional variants in 562 additional patients with SLE. Effects of a splice site variant and a frameshift variant were assessed using a Minigene assay and CRISPR/Cas9-mediated knock-in (KI) mice, respectively.ResultsThe two familial ultra-rare, predicted loss-of-function (LOF)SAT1variants exhibited X-linked recessive Mendelian inheritance in two unrelated African–American families. Each LOF variant was transmitted from the heterozygous unaffected mother to her two sons with childhood-onset SLE. The p.Asp40Tyr variant affected a splice donor site causing deleterious transcripts. The young hemizygous male and homozygous femaleSat1p.Glu92Leufs*6KI mice spontaneously developed splenomegaly, enlarged glomeruli with leucocyte infiltration, proteinuria and elevated expression of type I interferon-inducible genes.SAT1is highly expressed in neutrophils and encodes spermidine/spermine-N1-acetyltransferase 1 (SSAT1), a rate-limiting enzyme in polyamine catabolism. Young male KI mice exhibited neutrophil defects and decreased proportions of Foxp3 +CD4+ T-cell subsets. Circulating neutrophil counts and proportions of Foxp3 +CD4+ T cells correlated with decreased plasma levels of spermine in treatment-naive, incipient SLE patients.ConclusionsWe identified two novelSAT1LOF variants, showed the ability of the frameshift variant to confer murine lupus, highlighted the pathogenic role of dysregulated polyamine catabolism and identifiedSAT1LOF variants as new monogenic causes for SLE.

OBJECTIVE Elevated interleukin 10 (IL10) in SLE patients has B-cell promoting effects, contributing to autoantibody production and tissue damage. We aimed to characterize upregulated IL10+ B-cell subsets and dysregulated IL10 expression in SLE B cells for new therapeutic options. METHODS Proportions of Th10 and IL10+ B-cell subsets in PBMCs were assessed using flow cytometry. The IL10-3'UTR dual-luciferase vector was constructed and co-transfected with siRNA, miRNA mimics or inhibitors into RAJI cell line. Transcript levels were quantified using Taqman assays. RESULTS Culture conditions that induced IL10+ Breg in healthy controls (HC) resulted in expansion of IL10+ DN2 (IgD- CD27- CD21- CD11c+ ) B-cells in SLE PBMCs. Proportions of IL10+ DN2, but not those of IL10- DN2, correlated with disease activity, levels of antibodies to dsDNA, and associated with high levels or seropositivity of anti-Smith and anti-cardiolipin IgG in SLE patients from two cohorts of mainly African Americans and Asians, respectively. Proportions of Th10 (CD45RA- CXCR5- CXCR3+ PD1hi CD4+ ) cells correlated with IL10+ DN2 frequencies, ANA titers and proteinuria levels in SLE patients. Screening predicted IL10 3'UTR-targeting miRNAs in SLE B cells identified miR-17-5p and miR-20a-5p with their levels inversely correlated with IL10 and transcription factor E2F2. In RAJI cells, knockdown of E2F2 expression resulted in increased levels of miR-17-5p and miR-20a-5p and decreased IL10 mRNA and protein levels, and overexpression and inhibition of miR-17-5p down-regulated and up-regulated IL10 mRNA levels respectively; suggesting regulation of IL10 expression by E2F2-miR-17-5p loop. CONCLUSION IL10 promotes extrafollicular autoimmune responses in active SLE patients, which might be dampened by targeting the E2F2-miR-17-5p circuitry.

In order to develop prediction models of one-year treatment response in lupus nephritis, an approach using machine learning to combine traditional clinical data and novel urine biomarkers was undertaken. Contemporary lupus nephritis biomarkers were identified through an unbiased PubMed search. Thirteen novel urine proteins contributed to the top 50% of ranked biomarkers and were selected for measurement at the time of lupus nephritis flare. These novel markers along with traditional clinical data were incorporated into a variety of machine learning algorithms to develop prediction models of one-year proteinuria and estimated glomerular filtration rate (eGFR). Models were trained on 246 individuals from four different sub-cohorts and validated on an independent cohort of 30 patients with lupus nephritis. Seven models were considered for each outcome. Three quarters of these models demonstrated good predictive value with areas under the receiver operating characteristic curve over 0.7. Overall, prediction performance was the best for models of eGFR response to treatment. Furthermore, the best performing models contained both traditional clinical data and novel urine biomarkers, including cytokines, chemokines, and markers of kidney damage. Thus, our study provides further evidence that a machine learning approach can predict lupus nephritis outcomes at one year using a set of traditional and novel biomarkers. However, further validation of the utility of machine learning as a clinical decision aid to improve outcomes will be necessary before it can be routinely used in clinical practice to guide therapy.