De novo variants of CSNK2B cause a new intellectual disability-craniodigital syndrome by disrupting the canonical Wnt signaling pathway

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

GestaltMatcher Database - A global reference for facial phenotypic variability in rare human diseases

The most important factor that complicates the work of dysmorphologists is the significant phenotypic variability of the human face. Next-Generation Phenotyping (NGP) tools that assist clinicians with recognizing characteristic syndromic patterns are particularly challenged when confronted with patients from populations different from their training data. To that end, we systematically analyzed the impact of genetic ancestry on facial dysmorphism. For that purpose, we established the GestaltMatcher Database (GMDB) as a reference dataset for medical images of patients with rare genetic disorders from around the world. We collected 10,980 frontal facial images - more than a quarter previously unpublished - from 8,346 patients, representing 581 rare disorders. Although the predominant ancestry is still European (67%), data from underrepresented populations have been increased considerably via global collaborations (19% Asian and 7% African). This includes previously unpublished reports for more than 40% of the African patients. The NGP analysis on this diverse dataset revealed characteristic performance differences depending on the composition of training and test sets corresponding to genetic relatedness. For clinical use of NGP, incorporating non-European patients resulted in a profound enhancement of GestaltMatcher performance. The top-5 accuracy rate increased by +11.29%. Importantly, this improvement in delineating the correct disorder from a facial portrait was achieved without decreasing the performance on European patients. By design, GMDB complies with the FAIR principles by rendering the curated medical data findable, accessible, interoperable, and reusable. This means GMDB can also serve as data for training and benchmarking. In summary, our study on facial dysmorphism on a global sample revealed a considerable cross ancestral phenotypic variability confounding NGP that should be counteracted by international efforts for increasing data diversity. GMDB will serve as a vital reference database for clinicians and a transparent training set for advancing NGP technology.

Casein Kinase 2 Affects Epilepsy by Regulating Ion Channels: A Potential Mechanism

Epilepsy, characterized by recurrent seizures and abnormal brain discharges, is the third most common chronic disorder of the Central Nervous System (CNS). Although significant progress has been made in the research on antiepileptic drugs (AEDs), approximately one-third of patients with epilepsy are refractory to these drugs. Thus, research on the pathogenesis of epilepsy is ongoing to find more effective treatments. Many pathological mechanisms are involved in epilepsy, including neuronal apoptosis, mossy fiber sprouting, neuroinflammation, and dysfunction of neuronal ion channels, leading to abnormal neuronal excitatory networks in the brain. CK2 (Casein kinase 2), which plays a critical role in modulating neuronal excitability and synaptic transmission, has been shown to be associated with epilepsy. However, there is limited research on the mechanisms involved. Recent studies have suggested that CK2 is involved in regulating the function of neuronal ion channels by directly phosphorylating them or their binding partners. Therefore, in this review, we will summarize recent research advances regarding the potential role of CK2 regulating ion channels in epilepsy, aiming to provide more evidence for future studies.

Facilitating the Molecular Diagnosis of Rare Genetic Disorders Through Facial Phenotypic Scores

With recent advances in computer vision, many applications based on artificial intelligence have been developed to facilitate the diagnosis of rare genetic disorders through the analysis of patients' two-dimensional frontal images. Some of these have been implemented on online platforms with user-friendly interfaces and provide facial analysis services, such as Face2Gene. However, users cannot run the facial analysis processes in house because the training data and the trained models are unavailable. This article therefore provides an introduction, designed for users with programming backgrounds, to the use of the open-source GestaltMatcher approach to run facial analysis in their local environment. The Basic Protocol provides detailed instructions for applying for access to the trained models and then performing facial analysis to obtain a prediction score for each of the 595 genes in the GestaltMatcher Database. The prediction results can then be used to narrow down the search space of disease-causing mutations or further connect with a variant-prioritization pipeline. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol: Using the open-source GestaltMatcher approach to perform facial analysis.

Computational facial analysis for rare Mendelian disorders

With the advances in computer vision, computational facial analysis has become a powerful and effective tool for diagnosing rare disorders. This technology, also called next-generation phenotyping (NGP), has progressed significantly over the last decade. This review paper will introduce three key NGP approaches. In 2014, Ferry et al. first presented Clinical Face Phenotype Space (CFPS) trained on eight syndromes. After 5 years, Gurovich et al. proposed DeepGestalt, a deep convolutional neural network trained on more than 21,000 patient images with 216 disorders. It was considered a state-of-the-art disorder classification framework. In 2022, Hsieh et al. developed GestaltMatcher to support the ultra-rare and novel disorders not supported in DeepGestalt. It further enabled the analysis of facial similarity presented in a given cohort or multiple disorders. Moreover, this article will present the usage of NGP for variant prioritization and facial gestalt delineation. Although NGP approaches have proven their capability in assisting the diagnosis of many disorders, many limitations remain. This article will introduce two future directions to address two main limitations: enabling the global collaboration for a medical imaging database that fulfills the FAIR principles and synthesizing patient images to protect patient privacy. In the end, with more and more NGP approaches emerging, we envision that the NGP technology can assist clinicians and researchers in diagnosing patients and analyzing disorders in multiple directions in the near future.

Do PACS1 variants impeding adaptor protein binding predispose to syndromic intellectual disability?

To date, PACS1-neurodevelopmental disorder (PACS1-NDD) has been associated with recurrent variation of Arg203 and is considered diagnostic of PACS1-NDD, an autosomal dominant syndromic intellectual disability disorder. Although incompletely defined, the proposed disease mechanism for this variant is altered PACS1 affinity for its client proteins. Given this proposed mechanism, we hypothesized that PACS1 variants that interfere with binding of adaptor proteins might also give rise to syndromic intellectual disability. Herein, we report a proposita and her mother with phenotypic features overlapping PACS1-NDD and a novel PACS1 variant (NM_018026.3:c.[755C > T];[=], p.(Ser252Phe)) that impedes binding of the adaptor protein GGA3 (Golgi-associated, gamma-adaptin ear-containing, ARF-binding protein 3). We hypothesize that attenuating PACS1 binding of GGA3 also gives rise to a disorder with features overlapping those of PACS1-NDD. This observation better delineates the mechanism by which PACS1 variation predisposes to syndromic intellectual disability.

Predictive functional, statistical and structural analysis of CSNK2A1 and CSNK2B variants linked to neurodevelopmental diseases

Okur-Chung Neurodevelopmental Syndrome (OCNDS) and Poirier-Bienvenu Neurodevelopmental Syndrome (POBINDS) were recently identified as rare neurodevelopmental disorders. OCNDS and POBINDS are associated with heterozygous mutations in the CSNK2A1 and CSNK2B genes which encode CK2α, a serine/threonine protein kinase, and CK2β, a regulatory protein, respectively, which together can form a tetrameric enzyme called protein kinase CK2. A challenge in OCNDS and POBINDS is to understand the genetic basis of these diseases and the effect of the various CK2⍺ and CK2β mutations. In this study we have collected all variants available to date in CSNK2A1 and CSNK2B, and identified hotspots. We have investigated CK2⍺ and CK2β missense mutations through prediction programs which consider the evolutionary conservation, functionality and structure or these two proteins, compared these results with published experimental data on CK2α and CK2β mutants, and suggested prediction programs that could help predict changes in functionality of CK2α mutants. We also investigated the potential effect of CK2α and CK2β mutations on the 3D structure of the proteins and in their binding to each other. These results indicate that there are functional and structural consequences of mutation of CK2α and CK2β, and provide a rationale for further study of OCNDS and POBINDS-associated mutations. These data contribute to understanding the genetic and functional basis of these diseases, which is needed to identify their underlying mechanisms.