Adenosine-to-inosine RNA editing contributes to type I interferon responses in systemic sclerosis

Objective: Adenosine deaminase acting on RNA-1 (ADAR1) enzyme is a type I interferon (IFN)-stimulated gene (ISG) catalyzing the deamination of adenosine-to-inosine, a process called A-to-I RNA editing. A-to-I RNA editing takes place mainly in Alu elements comprising a primate-specific level of post-transcriptional gene regulation. Whether RNA editing is involved in type I IFN responses in systemic sclerosis (SSc) patients remains unknown. Methods: ISG expression was quantified in skin biopsies and peripheral blood mononuclear cells derived from SSc patients and healthy subjects. A-to-I RNA editing was examined in the ADAR1-target cathepsin S (CTSS) by an RNA editing assay. The effect of ADAR1 on interferon-α/β-induced CTSS expression was assessed in human endothelial cells in vitro. Results: Increased expression levels of the RNA editor ADAR1, and specifically the long ADAR1p150 isoform, and its target CTSS are strongly associated with type I IFN signature in skin biopsies and peripheral blood derived from SSc patients. Notably, IFN-α/β-treated human endothelial cells show 8-10-fold increased ADAR1p150 and 23-35-fold increased CTSS expression, while silencing of ADAR1 reduces CTSS expression by 60-70%. In SSc patients, increased RNA editing rate of individual adenosines located in CTSS 3′ UTR Alu elements is associated with higher CTSS expression (r = 0.36–0.6, P < 0.05 for all). Similar findings were obtained in subjects with activated type I IFN responses including SLE patients or healthy subjects after influenza vaccination. Conclusion: ADAR1p150-mediated A-to-I RNA editing is critically involved in type I IFN responses highlighting the importance of post-transcriptional regulation of proinflammatory gene expression in systemic autoimmunity, including SSc. © 2021 The Author

GestaltMatcher facilitates rare disease matching using facial phenotype descriptors.

Many monogenic disorders cause a characteristic facial morphology. Artificial intelligence can support physicians in recognizing these patterns by associating facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this ‘supervised’ approach means that diagnoses are only possible if the disorder was part of the training set. To improve recognition of ultra-rare disorders, we developed GestaltMatcher, an encoder for portraits that is based on a deep convolutional neural network. Photographs of 17,560 patients with 1,115 rare disorders were used to define a Clinical Face Phenotype Space, in which distances between cases define syndromic similarity. Here we show that patients can be matched to others with the same molecular diagnosis even when the disorder was not included in the training set. Together with mutation data, GestaltMatcher could not only accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism but also enable the delineation of new phenotypes