Mast cells, cortistatin, and its receptor, MRGPRX2, are linked to the pathogenesis of chronic prurigo.

BACKGROUND: Chronic prurigo (CPG) is characterized by intensive itch and interactions among nerves, neuropeptides, and mast cells (MCs). The role of some neuropeptides such as cortistatin (CST) and its receptor, Mas-related G protein-coupled receptor X2 (MRGPRX2), in CPG remains poorly investigated. OBJECTIVES: We evaluated first whether CST activates human skin MCs, and second whether CST and MRGPRX2 are expressed in the skin of CPG patients, and by which cells. METHODS: Skin prick tests and microdialysis with CST were performed in 6 and 1 healthy volunteers, respectively. Degranulation of human skin MCs was assessed using ß-hexosaminidase and histamine release assays. Skin samples from 10 patients with CPG and 10 control subjects were stained for CST, MCs, and MRGPRX2 (protein and mRNA) using immunohistochemistry, immunofluorescence, and/or in situ hybridization. Flow cytometry was used to assess CST in human skin MCs. MRGPRX2 levels were measured in serum by ELISA. RESULTS: CST induced concentration-dependent degranulation of human skin MCs in vivo and ex vivo. Skin lesions of CPG patients exhibited markedly higher numbers of CST-expressing cells, CST-expressing MCs, MRGPRX2-expressing cells, and MRGPRX2 mRNA-expressing cells than nonlesional skin. MCs were the main MRGPRX2 mRNA-expressing cells in the lesions of most CPG patients (70%). Stimulation of human skin MCs with anti-IgE led to a release of CST. The number of MRGPRX2-expressing cells correlated with disease severity (r = 0.649, P = .04). MRGPRX2 serum levels in CPG patients correlated with disease severity (r = 0.704, P = .023) and quality-of-life impairment (r = 0.687, P = .028). CONCLUSIONS: CST and MRGPRX2 may contribute to the pathogenesis of CPG and should be evaluated in further studies as potential biomarkers and novel therapeutic targets.

GLI3 variants causing isolated polysyndactyly are not restricted to the protein's C-terminal third.

Loss of function variants of GLI3 are associated with a variety of forms of polysyndactyly: Pallister-Hall syndrome (PHS), Greig-Cephalopolysyndactyly syndrome (GCPS), and isolated polysyndactyly (IPD). Variants affecting the N-terminal and C-terminal thirds of the GLI3 protein have been associated with GCPS, those within the central third with PHS. Cases of IPD have been attributed to variants affecting the C-terminal third of the GLI3 protein. In this study, we further investigate these genotype-phenotype correlations. Sequencing of GLI3 was performed in patients with clinical findings suggestive of a GLI3-associated syndrome. Additionally, we searched the literature for reported cases of either manifestation with mutations in the GLI3 gene. Here, we report 48 novel cases from 16 families with polysyndactyly in whom we found causative variants in GLI3 and a review on 314 previously reported GLI3 variants. No differences in location of variants causing either GCPS or IPD were found. Review of published data confirmed the association of PHS and variants affecting the GLI3 protein's central third. We conclude that the observed manifestations of GLI3 variants as GCPS or IPD display different phenotypic severities of the same disorder and propose a binary division of GLI3-associated disorders in either PHS or GCPS/polysyndactyly.

Efficiency of Computer-Aided Facial Phenotyping (DeepGestalt) in Individuals With and Without a Genetic Syndrome: Diagnostic Accuracy Study.

BACKGROUND: Collectively, an estimated 5% of the population have a genetic disease. Many of them feature characteristics that can be detected by facial phenotyping. Face2Gene CLINIC is an online app for facial phenotyping of patients with genetic syndromes. DeepGestalt, the neural network driving Face2Gene, automatically prioritizes syndrome suggestions based on ordinary patient photographs, potentially improving the diagnostic process. Hitherto, studies on DeepGestalt's quality highlighted its sensitivity in syndromic patients. However, determining the accuracy of a diagnostic methodology also requires testing of negative controls. OBJECTIVE: The aim of this study was to evaluate DeepGestalt's accuracy with photos of individuals with and without a genetic syndrome. Moreover, we aimed to propose a machine learning-based framework for the automated differentiation of DeepGestalt's output on such images. METHODS: Frontal facial images of individuals with a diagnosis of a genetic syndrome (established clinically or molecularly) from a convenience sample were reanalyzed. Each photo was matched by age, sex, and ethnicity to a picture featuring an individual without a genetic syndrome. Absence of a facial gestalt suggestive of a genetic syndrome was determined by physicians working in medical genetics. Photos were selected from online reports or were taken by us for the purpose of this study. Facial phenotype was analyzed by DeepGestalt version 19.1.7, accessed via Face2Gene CLINIC. Furthermore, we designed linear support vector machines (SVMs) using Python 3.7 to automatically differentiate between the 2 classes of photographs based on DeepGestalt's result lists. RESULTS: We included photos of 323 patients diagnosed with 17 different genetic syndromes and matched those with an equal number of facial images without a genetic syndrome, analyzing a total of 646 pictures. We confirm DeepGestalt's high sensitivity (top 10 sensitivity: 295/323, 91%). DeepGestalt's syndrome suggestions in individuals without a craniofacially dysmorphic syndrome followed a nonrandom distribution. A total of 17 syndromes appeared in the top 30 suggestions of more than 50% of nondysmorphic images. DeepGestalt's top scores differed between the syndromic and control images (area under the receiver operating characteristic [AUROC] curve 0.72, 95% CI 0.68-0.76; P<.001). A linear SVM running on DeepGestalt's result vectors showed stronger differences (AUROC 0.89, 95% CI 0.87-0.92; P<.001). CONCLUSIONS: DeepGestalt fairly separates images of individuals with and without a genetic syndrome. This separation can be significantly improved by SVMs running on top of DeepGestalt, thus supporting the diagnostic process of patients with a genetic syndrome. Our findings facilitate the critical interpretation of DeepGestalt's results and may help enhance it and similar computer-aided facial phenotyping tools.