Biallelic human SHARPIN loss of function induces autoinflammation and immunodeficiency.

The linear ubiquitin assembly complex (LUBAC) consists of HOIP, HOIL-1 and SHARPIN and is essential for proper immune responses. Individuals with HOIP and HOIL-1 deficiencies present with severe immunodeficiency, autoinflammation and glycogen storage disease. In mice, the loss of Sharpin leads to severe dermatitis due to excessive keratinocyte cell death. Here, we report two individuals with SHARPIN deficiency who manifest autoinflammatory symptoms but unexpectedly no dermatological problems. Fibroblasts and B cells from these individuals showed attenuated canonical NF-κB responses and a propensity for cell death mediated by TNF superfamily members. Both SHARPIN-deficient and HOIP-deficient individuals showed a substantial reduction of secondary lymphoid germinal center B cell development. Treatment of one SHARPIN-deficient individual with anti-TNF therapies led to complete clinical and transcriptomic resolution of autoinflammation. These findings underscore the critical function of the LUBAC as a gatekeeper for cell death-mediated immune dysregulation in humans.

Variant STAT4 and Response to Ruxolitinib in an Autoinflammatory Syndrome.

BACKGROUND: Disabling pansclerotic morphea (DPM) is a rare systemic inflammatory disorder, characterized by poor wound healing, fibrosis, cytopenias, hypogammaglobulinemia, and squamous-cell carcinoma. The cause is unknown, and mortality is high. METHODS: We evaluated four patients from three unrelated families with an autosomal dominant pattern of inheritance of DPM. Genomic sequencing independently identified three heterozygous variants in a specific region of the gene that encodes signal transducer and activator of transcription 4 (STAT4). Primary skin fibroblast and cell-line assays were used to define the functional nature of the genetic defect. We also assayed gene expression using single-cell RNA sequencing of peripheral-blood mononuclear cells to identify inflammatory pathways that may be affected in DPM and that may respond to therapy. RESULTS: Genome sequencing revealed three novel heterozygous missense gain-of-function variants in STAT4. In vitro, primary skin fibroblasts showed enhanced interleukin-6 secretion, with impaired wound healing, contraction of the collagen matrix, and matrix secretion. Inhibition of Janus kinase (JAK)-STAT signaling with ruxolitinib led to improvement in the hyperinflammatory fibroblast phenotype in vitro and resolution of inflammatory markers and clinical symptoms in treated patients, without adverse effects. Single-cell RNA sequencing revealed expression patterns consistent with an immunodysregulatory phenotype that were appropriately modified through JAK inhibition. CONCLUSIONS: Gain-of-function variants in STAT4 caused DPM in the families that we studied. The JAK inhibitor ruxolitinib attenuated the dermatologic and inflammatory phenotype in vitro and in the affected family members. (Funded by the American Academy of Allergy, Asthma, and Immunology Foundation and others.).

Predicting the impact of rare variants on RNA splicing in CAGI6.

Variants which disrupt splicing are a frequent cause of rare disease that have been under-ascertained clinically. Accurate and efficient methods to predict a variant's impact on splicing are needed to interpret the growing number of variants of unknown significance (VUS) identified by exome and genome sequencing. Here, we present the results of the CAGI6 Splicing VUS challenge, which invited predictions of the splicing impact of 56 variants ascertained clinically and functionally validated to determine splicing impact. The performance of 12 prediction methods, along with SpliceAI and CADD, was compared on the 56 functionally validated variants. The maximum accuracy achieved was 82% from two different approaches, one weighting SpliceAI scores by minor allele frequency, and one applying the recently published Splicing Prediction Pipeline (SPiP). SPiP performed optimally in terms of sensitivity, while an ensemble method combining multiple prediction tools and information from databases exceeded all others for specificity. Several challenge methods equalled or exceeded the performance of SpliceAI, with ultimate choice of prediction method likely to depend on experimental or clinical aims. One quarter of the variants were incorrectly predicted by at least 50% of the methods, highlighting the need for further improvements to splicing prediction methods for successful clinical application.