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

Asif, Maria; Kaygusuz, Emrah; Shinawi, Marwan; Nickelsen, Anna; Hsieh, Tzung-Chien; Wagle, Prerana; Budde, Birgit; Hochscherf, Jennifer; Abdullah, Uzma; Höning, Stefan; Nienberg, Christian; Lindenblatt, D.; Noegel, Angelika A.; Altmüller, Janine; Thiele, Holger; Motameny, Susanne; Fleischer, Nicole; Segal, Idan; Pais, Lynn; Tinschert, Sigrid; Samra, Nadra G.; Savatt, Juliann M.; Rudy, Natasha; Luca, Chiara De; Fortugno, Paola; White, Susan; Krawitz, Peter; Hurst, Anna; Niefind, Karsten; Jose, Joachim; Brancati, Francesco; Nürnberg, Peter; Hussain, Muhammad Sajid

doi:10.1016/j.xhgg.2022.100111

Cited by 13 publications

(19 citation statements)

References 73 publications

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“…The first conserved surface cluster is composed of Asp32, Phe34, Asn35 and Arg86 and is directed away from the rest of the protein ( Chantalat et al, 1999 ) ( Figures 4C , 7B ). Interestingly, the missense mutations of each of the residues (Asp32Asn/Ala/His, Phe34Ser/Cys, Asn35Lys and Arg86Cys) have been linked to neurodevelopmental disability and/or early onset epilepsy in POBINDS and in a new CK2β-linked intellectual disability-craniodigital syndrome ( Asif et al, 2022 ). As discussed above, this could be a novel functional domain of CK2β.…”

Section: Resultsmentioning

confidence: 99%

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

Unni

Friend

Weinberg

et al. 2022

Front. Mol. Biosci.

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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.

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Section: Resultsmentioning

confidence: 99%

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

Unni

Friend

Weinberg

et al. 2022

Front. Mol. Biosci.

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“…With the recent advances in computer vision, many AI‐based applications have begun to be used to facilitate diagnosis through facial image analysis (Dingemans et al., 2022; Dudding‐Byth et al., 2017; Gurovich et al., 2019; Hsieh et al., 2022; Hustinx et al., 2023; Porras et al., 2021; Sümer et al., 2023) and to further delineate the phenotypes of novel diseases (Aerden et al., 2023; Asif et al., 2022; Averdunk et al., 2023; Ebstein et al., 2023; Guo et al., 2022; Kampmeier et al., 2022; Knaus et al., 2018; Lyon et al., 2023; Marbach et al., 2019; Oppermann et al., 2023; Pantel et al., 2018).…”

Section: Commentarymentioning

confidence: 99%

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

Hsieh,

Lesmann,

Krawitz

2023

Current Protocols

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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

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“…Afterward, GestaltMatcher was utilized to measure the facial similarity of the cohort with the same disorder. Asif et al were the first ones to utilize GestaltMatcher to describe the facial similarities of the patients with a new intellectual disability‐craniodigital syndrome (IDCS) associated with CSNK2B , which is distinct from POBINDS (OMIM #618732) (Asif et al, 2022). The pairwise rank matrix in Figure 5 shows the similarities among the seven CSNK2B patients compared to the control cohort (3533 images with 816 disorders in Face2Gene internal database).…”

Section: Facial Phenotype Descriptors Delineate Facial Gestalt Of Dis...mentioning

confidence: 99%

“…The clustering dendrogram further shows that the IDCS patients clustered together and differed from the patients of POBINDS. This figure was published in the original paper (Asif et al, 2022) as supplementary fig. 2.…”

Section: Facial Phenotype Descriptors Delineate Facial Gestalt Of Dis...mentioning

confidence: 99%

Computational facial analysis for rare Mendelian disorders

Hsieh

Krawitz

2023

American J of Med Genetics Pt C

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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.

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

Cited by 13 publications

References 73 publications

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

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

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

Computational facial analysis for rare Mendelian disorders

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