Down-Regulation-Resistant STAT4 Risk Haplotype Contributes to Lupus Nephritis Through CD4+ T Cell Interferon-γ Production.

OBJECTIVE: Variants in STAT4 are associated with systemic lupus erythematosus (SLE) and other autoimmune diseases. We undertook this study to investigate how disease-associated variants affect STAT4 expression, in particular in CD4+ T cells where STAT4 plays an essential role. METHODS: We compared Th1 differentiation between naive CD4+ T cells from healthy donors homozygous for the risk (R/R) or nonrisk (NR/NR) alleles. We analyzed epigenetic marks in STAT4 and evaluated the relevance of its third intron, assessed the consequences of Stat4 overexpression in vivo in mice, and analyzed the effects of the STAT4 genotype in patients with lupus nephritis. RESULTS: Naive CD4+ T cells from NR/NR healthy donors down-regulated STAT4 in response to interleukin-12 (IL-12). In contrast, cells from R/R healthy donors maintained high levels. R/R cells exhibited a higher abundance of transcriptionally active STAT4 and increased interferon-γ production. Accordingly, R/R healthy donors exhibited a stronger induction of local active enhancer marks. Genetic editing confirmed the presence of a negative regulatory region in the STAT4 third intron, where most of the SLE-associated STAT4 single-nucleotide polymorphisms (SNPs) are located. In vivo forced expression demonstrated that increases in Stat4 levels in T cells enhanced glomerulonephritis in mice. Accordingly, the R/R genotype was associated with suboptimal response to treatment and with worse clinical outcomes in patients with proliferative lupus nephritis. CONCLUSION: The SLE-associated STAT4 haplotype correlates with an abnormal IL-12-mediated STAT4 transcriptional regulation. Carriers of the risk variant exhibit exaggerated CD4+ proinflammatory capacities that, in the context of SLE, contribute to more severe disease. R/R patients may benefit from blockade of the IL-12/STAT4 pathway.

Differential Treatments Based on Drug-induced Gene Expression Signatures and Longitudinal Systemic Lupus Erythematosus Stratification.

Systemic lupus erythematosus (SLE) is a heterogeneous disease with unpredictable patterns of activity. Patients with similar activity levels may have different prognosis and molecular abnormalities. In this study, we aimed to measure the main differences in drug-induced gene expression signatures across SLE patients and to evaluate the potential for clinical data to build a machine learning classifier able to predict the SLE subset for individual patients. SLE transcriptomic data from two cohorts were compared with drug-induced gene signatures from the CLUE database to compute a connectivity score that reflects the capability of a drug to revert the patient signatures. Patient stratification based on drug connectivity scores revealed robust clusters of SLE patients identical to the clusters previously obtained through longitudinal gene expression data, implying that differential treatment depends on the cluster to which patients belongs. The best drug candidates found, mTOR inhibitors or those reducing oxidative stress, showed stronger cluster specificity. We report that drug patterns for reverting disease gene expression follow the cell-specificity of the disease clusters. We used 2 cohorts to train and test a logistic regression model that we employed to classify patients from 3 independent cohorts into the SLE subsets and provide a clinically useful model to predict subset assignment and drug efficacy.