GestaltMatcher facilitates rare disease matching using facial phenotype descriptors

Democratising or disrupting diagnosis? Ethical issues raised by the use of AI tools for rare disease diagnosis

Computational phenotyping (CP) technology uses facial recognition algorithms to classify and potentially diagnose rare genetic disorders on the basis of digitised facial images. This AI technology has a number of research as well as clinical applications, such as supporting diagnostic decision-making. Using the example of CP, we examine stakeholders' views of the benefits and costs of using AI as a diagnostic tool within the clinic. Through a series of in-depth interviews (n ​= ​20) with: clinicians, clinical researchers, data scientists, industry and support group representatives, we report stakeholder views regarding the adoption of this technology in a clinical setting. While most interviewees were supportive of employing CP as a diagnostic tool in some capacity we observed ambivalence around the potential for artificial intelligence to overcome diagnostic uncertainty in a clinical context. Thus, while there was widespread agreement amongst interviewees concerning the public benefits of AI assisted diagnosis, namely, its potential to increase diagnostic yield and enable faster more objective and accurate diagnoses by up skilling non specialists and thereby enabling access to diagnosis that is potentially lacking, interviewees also raised concerns about ensuring algorithmic reliability, expunging algorithmic bias and that the use of AI could result in deskilling the specialist clinical workforce. We conclude that, prior to widespread clinical implementation, on-going reflection is needed regarding the trade-offs required to determine acceptable levels of bias and conclude that diagnostic AI tools should only be employed as an assistive technology within the dysmorphology clinic.

Krawitz PDMDPP. The future role of facial image analysis in ACMG classification guidelines

The use of next-generation sequencing (NGS) has dramatically improved the diagnosis of rare diseases. However, the analysis of genomic data has become complex with the increasing detection of variants by exome and genome sequencing. The American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) developed a 5-tier classification scheme in 2015 for variant interpretation, that has since been widely adopted. Despite efforts to minimise discrepancies in the application of these criteria, inconsistencies still occur. Further specifications for individual genes were developed by Variant Curation Expert Panels (VCEPs) of the Clinical Genome Resource (ClinGen) consortium, that also take into consideration gene or disease specific features. For instance, in disorders with a highly characerstic facial gestalt a "phenotypic match" (PP4) has higher pathogenic evidence than e.g. in a non-syndromic form of intellectual disability. With computational approaches for quantifying the similarity of dysmorphic features results of such analysis can now be used in a refined Bayesian framework for the ACMG/AMP criteria.

PSMC3 proteasome subunit variants are associated with neurodevelopmental delay and type I interferon production

A critical step in preserving protein homeostasis is the recognition, binding, unfolding, and translocation of protein substrates by six AAA-ATPase proteasome subunits (ATPase-associated with various cellular activities) termed PSMC1-6, which are required for degradation of proteins by 26S proteasomes. Here, we identified 15 de novo missense variants in the PSMC3 gene encoding the AAA-ATPase proteasome subunit PSMC3/Rpt5 in 23 unrelated heterozygous patients with an autosomal dominant form of neurodevelopmental delay and intellectual disability. Expression of PSMC3 variants in mouse neuronal cultures led to altered dendrite development, and deletion of the PSMC3 fly ortholog Rpt5 impaired reversal learning capabilities in fruit flies. Structural modeling as well as proteomic and transcriptomic analyses of T cells derived from patients with PSMC3 variants implicated the PSMC3 variants in proteasome dysfunction through disruption of substrate translocation, induction of proteotoxic stress, and alterations in proteins controlling developmental and innate immune programs. The proteostatic perturbations in T cells from patients with PSMC3 variants correlated with a dysregulation in type I interferon (IFN) signaling in these T cells, which could be blocked by inhibition of the intracellular stress sensor protein kinase R (PKR). These results suggest that proteotoxic stress activated PKR in patient-derived T cells, resulting in a type I IFN response. The potential relationship among proteosome dysfunction, type I IFN production, and neurodevelopment suggests new directions in our understanding of pathogenesis in some neurodevelopmental disorders.

Population-specific facial traits and diagnosis accuracy of genetic and rare diseases in an admixed Colombian population

Up to 40% of rare disorders (RD) present facial dysmorphologies, and visual assessment is commonly used for clinical diagnosis. Quantitative approaches are more objective, but mostly rely on European descent populations, disregarding diverse population ancestry. Here, we assessed the facial phenotypes of Down (DS), Morquio (MS), Noonan (NS) and Neurofibromatosis type 1 (NF1) syndromes in a Latino-American population, recording the coordinates of 18 landmarks in 2D images from 79 controls and 51 patients. We quantified facial differences using Euclidean Distance Matrix Analysis, and assessed the diagnostic accuracy of Face2Gene, an automatic deep-learning algorithm. Individuals diagnosed with DS and MS presented severe phenotypes, with 58.2% and 65.4% of significantly different facial traits. The phenotype was milder in NS (47.7%) and non-significant in NF1 (11.4%). Each syndrome presented a characteristic dysmorphology pattern, supporting the diagnostic potential of facial biomarkers. However, population-specific traits were detected in the Colombian population. Diagnostic accuracy was 100% in DS, moderate in NS (66.7%) but lower in comparison to a European population (100%), and below 10% in MS and NF1. Moreover, admixed individuals showed lower facial gestalt similarities. Our results underscore that incorporating populations with Amerindian, African and European ancestry is crucial to improve diagnostic methods of rare disorders.

The neurodevelopmental and facial phenotype in individuals with a TRIP12 variant

Haploinsufficiency of TRIP12 causes a neurodevelopmental disorder characterized by intellectual disability associated with epilepsy, autism spectrum disorder and dysmorphic features, also named Clark-Baraitser syndrome. Only a limited number of cases have been reported to date. We aimed to further delineate the TRIP12-associated phenotype and objectify characteristic facial traits through GestaltMatcher image analysis based on deep-learning algorithms in order to establish a TRIP12 gestalt. 38 individuals between 3 and 66 years (F = 20, M = 18) - 1 previously published and 37 novel individuals - were recruited through an ERN ITHACA call for collaboration. 35 TRIP12 variants were identified, including frameshift (n = 15) and nonsense (n = 6) variants, as well as missense (n = 5) and splice (n = 3) variants, intragenic deletions (n = 4) and two multigene deletions disrupting TRIP12. Though variable in severity, global developmental delay was noted in all individuals, with language deficit most pronounced. About half showed autistic features and susceptibility to obesity seemed inherent to this disorder. A more severe expression was noted in individuals with a missense variant. Facial analysis showed a clear gestalt including deep-set eyes with narrow palpebral fissures and fullness of the upper eyelids, downturned corners of the mouth and large, often low-set ears with prominent earlobes. We report the largest cohort to date of individuals with TRIP12 variants, further delineating the associated phenotype and introducing a facial gestalt. These findings will improve future counseling and patient guidance.

A diagnostic support system based on pain drawings: binary and k-disease classification of EDS, GBS, FSHD, PROMM, and a control group with Pain2D

BACKGROUND AND OBJECTIVE

The diagnosis of rare diseases (RDs) is often challenging due to their rarity, variability and the high number of individual RDs, resulting in a delay in diagnosis with adverse effects for patients and healthcare systems. The development of computer assisted diagnostic decision support systems could help to improve these problems by supporting differential diagnosis and by prompting physicians to initiate the right diagnostic tests. Towards this end, we developed, trained and tested a machine learning model implemented as part of the software called Pain2D to classify four rare diseases (EDS, GBS, FSHD and PROMM), as well as a control group of unspecific chronic pain, from pen-and-paper pain drawings filled in by patients.

METHODS

Pain drawings (PDs) were collected from patients suffering from one of the four RDs, or from unspecific chronic pain. The latter PDs were used as an outgroup in order to test how Pain2D handles more common pain causes. A total of 262 (59 EDS, 29 GBS, 35 FSHD, 89 PROMM, 50 unspecific chronic pain) PDs were collected and used to generate disease specific pain profiles. PDs were then classified by Pain2D in a leave-one-out-cross-validation approach.

RESULTS

Pain2D was able to classify the four rare diseases with an accuracy of 61-77% with its binary classifier. EDS, GBS and FSHD were classified correctly by the Pain2D k-disease classifier with sensitivities between 63 and 86% and specificities between 81 and 89%. For PROMM, the k-disease classifier achieved a sensitivity of 51% and specificity of 90%.

CONCLUSIONS

Pain2D is a scalable, open-source tool that could potentially be trained for all diseases presenting with pain.

Perspectives on the future of dysmorphology

The field of clinical genetics and genomics continues to evolve. In the past few decades, milestones like the initial sequencing of the human genome, dramatic changes in sequencing technologies, and the introduction of artificial intelligence, have upended the field and offered fascinating new insights. Though difficult to predict the precise paths the field will follow, rapid change may continue to be inevitable. Within genetics, the practice of dysmorphology, as defined by pioneering geneticist David W. Smith in the 1960s as "the study of, or general subject of abnormal development of tissue form" has also been affected by technological advances as well as more general trends in biomedicine. To address possibilities, potential, and perils regarding the future of dysmorphology, a group of clinical geneticists, representing different career stages, areas of focus, and geographic regions, have contributed to this piece by providing insights about how the practice of dysmorphology will develop over the next several decades.

Facial clues to the photosensitive trichothiodystrophy phenotype in childhood

Among genodermatoses, trichothiodystrophies (TTDs) are a rare genetically heterogeneous group of syndromic conditions, presenting with skin, hair, and nail abnormalities. An extra-cutaneous involvement (craniofacial district and neurodevelopment) can be also a part of the clinical picture. The presence of photosensitivity describes three forms of TTDs: MIM#601675 (TTD1), MIM#616390 (TTD2) and MIM#616395 (TTD3), that are caused by variants afflicting some components of the DNA Nucleotide Excision Repair (NER) complex and with more marked clinical consequences. In the present research, 24 frontal images of paediatric patients with photosensitive TTDs suitable for facial analysis through the next-generation phenotyping (NGP) technology were obtained from the medical literature. The pictures were compared to age and sex-matched to unaffected controls using 2 distinct deep-learning algorithms: DeepGestalt and GestaltMatcher (Face2Gene, FDNA Inc., USA). To give further support to the observed results, a careful clinical revision was undertaken for each facial feature in paediatric patients with TTD1 or TTD2 or TTD3. Interestingly, a distinctive facial phenotype emerged by the NGP analysis delineating a specific craniofacial dysmorphic spectrum. In addition, we tabulated every single detail within the observed cohort. The novelty of the present research includes the facial characterization in children with the photosensitive types of TTDs through the 2 different algorithms. This result can become additional criteria for early diagnosis, and for subsequent targeted molecular investigations as well as a possible tailored multidisciplinary personalized management.

Rare diseases of epigenetic origin: Challenges and opportunities

Rare diseases (RDs), more than 80% of which have a genetic origin, collectively affect approximately 350 million people worldwide. Progress in next-generation sequencing technology has both greatly accelerated the pace of discovery of novel RDs and provided more accurate means for their diagnosis. RDs that are driven by altered epigenetic regulation with an underlying genetic basis are referred to as rare diseases of epigenetic origin (RDEOs). These diseases pose unique challenges in research, as they often show complex genetic and clinical heterogeneity arising from unknown gene-disease mechanisms. Furthermore, multiple other factors, including cell type and developmental time point, can confound attempts to deconvolute the pathophysiology of these disorders. These challenges are further exacerbated by factors that contribute to epigenetic variability and the difficulty of collecting sufficient participant numbers in human studies. However, new molecular and bioinformatics techniques will provide insight into how these disorders manifest over time. This review highlights recent studies addressing these challenges with innovative solutions. Further research will elucidate the mechanisms of action underlying unique RDEOs and facilitate the discovery of treatments and diagnostic biomarkers for screening, thereby improving health trajectories and clinical outcomes of affected patients.

Targeted Sequencing Approach and Its Clinical Applications for the Molecular Diagnosis of Human Diseases

The outbreak of COVID-19 has positively impacted the NGS market recently. Targeted sequencing (TS) has become an important routine technique in both clinical and research settings, with advantages including high confidence and accuracy, a reasonable turnaround time, relatively low cost, and fewer data burdens with the level of bioinformatics or computational demand. Since there are no clear consensus guidelines on the wide range of next-generation sequencing (NGS) platforms and techniques, there is a vital need for researchers and clinicians to develop efficient approaches, especially for the molecular diagnosis of diseases in the emergency of the disease and the global pandemic outbreak of COVID-19. In this review, we aim to summarize different methods of TS, demonstrate parameters for TS assay designs, illustrate different TS panels, discuss their limitations, and present the challenges of TS concerning their clinical application for the molecular diagnosis of human diseases.

The Genetics of Intellectual Disability

Intellectual disability (ID) has a prevalence of ~2-3% in the general population, having a large societal impact. The underlying cause of ID is largely of genetic origin; however, identifying this genetic cause has in the past often led to long diagnostic Odysseys. Over the past decades, improvements in genetic diagnostic technologies and strategies have led to these causes being more and more detectable: from cytogenetic analysis in 1959, we moved in the first decade of the 21st century from genomic microarrays with a diagnostic yield of ~20% to next-generation sequencing platforms with a yield of up to 60%. In this review, we discuss these various developments, as well as their associated challenges and implications for the field of ID, which highlight the revolutionizing shift in clinical practice from a phenotype-first into genotype-first approach.

PHIP-associated Chung-Jansen syndrome: Report of 23 new individuals

In 2016 and 2018, Chung, Jansen and others described a new syndrome caused by haploinsufficiency of PHIP (pleckstrin homology domain interacting protein, OMIM *612,870) and mainly characterized by developmental delay (DD), learning difficulties/intellectual disability (ID), behavioral abnormalities, facial dysmorphism and obesity (CHUJANS, OMIM #617991). So far, PHIP alterations appear to be a rare cause of DD/ID. "Omics" technologies such as exome sequencing or array analyses have led to the identification of distinct types of alterations of PHIP, including, truncating variants, missense substitutions, splice variants and large deletions encompassing portions of the gene or the entire gene as well as adjacent genomic regions. We collected clinical and genetic data of 23 individuals with PHIP-associated Chung-Jansen syndrome (CHUJANS) from all over Europe. Follow-up investigations (e.g. Sanger sequencing, qPCR or Fluorescence-in-situ-Hybridization) and segregation analysis showed either de novo occurrence or inheritance from an also (mildly) affected parent. In accordance with previously described patients, almost all individuals reported here show developmental delay (22/23), learning disability or ID (22/23), behavioral abnormalities (20/23), weight problems (13/23) and characteristic craniofacial features (i.e. large ears/earlobes, prominent eyebrows, anteverted nares and long philtrum (23/23)). To further investigate the facial gestalt of individuals with CHUJANS, we performed facial analysis using the GestaltMatcher approach. By this, we could establish that PHIP patients are indistinguishable based on the type of PHIP alteration (e.g. missense, loss-of-function, splice site) but show a significant difference to the average face of healthy individuals as well as to individuals with Prader-Willi syndrome (PWS, OMIM #176270) or with a CUL4B-alteration (Intellectual developmental disorder, X-linked, syndromic, Cabezas type, OMIM #300354). Our findings expand the mutational and clinical spectrum of CHUJANS. We discuss the molecular and clinical features in comparison to the published individuals. The fact that some variants were inherited from a mildly affected parent further illustrates the variability of the associated phenotype and outlines the importance of a thorough clinical evaluation combined with genetic analyses for accurate diagnosis and counselling.

Advancement in Human Face Prediction Using DNA

The rapid improvements in identifying the genetic factors contributing to facial morphology have enabled the early identification of craniofacial syndromes. Similarly, this technology can be vital in forensic cases involving human identification from biological traces or human remains, especially when reference samples are not available in the deoxyribose nucleic acid (DNA) database. This review summarizes the currently used methods for predicting human phenotypes such as age, ancestry, pigmentation, and facial features based on genetic variations. To identify the facial features affected by DNA, various two-dimensional (2D)- and three-dimensional (3D)-scanning techniques and analysis tools are reviewed. A comparison between the scanning technologies is also presented in this review. Face-landmarking techniques and face-phenotyping algorithms are discussed in chronological order. Then, the latest approaches in genetic to 3D face shape analysis are emphasized. A systematic review of the current markers that passed the threshold of a genome-wide association (GWAS) of single nucleotide polymorphism (SNP)-face traits from the GWAS Catalog is also provided using the preferred reporting items for systematic reviews and meta-analyses (PRISMA), approach. Finally, the current challenges in forensic DNA phenotyping are analyzed and discussed.

Does Father Christmas Have a Distinctive Facial Phenotype?

We investigated whether Father Christmas has a distinguishable facial phenotype by performing a cross-sectional cohort study examining the facial feature vectors of all publicly available photographs obtained from a google image search of individuals meeting our eligibility criteria presenting as Father Christmas compared with other adult and elderly bearded men. Facial feature vectors were determined using the open-source OpenFace facial recognition system and assessed by support vector machines (SVM). SVM classifiers were trained to distinguish between the facial feature vectors from our groups. Accuracy, precision, and recall results were calculated and the area under the curve (AUC) of the receiver operating characteristic (ROC) were reported for each classifier. SVM classifiers were able to distinguish the face of Father Christmas from other adult men with a high degree of accuracy and could discriminate Father Christmas from elderly bearded men but with lower accuracy. Father Christmas appears to have a distinct facial phenotype when compared to adult men and elderly bearded men. This will be reassuring to children who may be keen to recognise him but raises some interesting questions about the careful use of two-dimensional facial analysis, particularly when employed to explore the relationships between genotype and facial phenotype in a clinical dysmorphology setting.

“I don’t think people are ready to trust these algorithms at face value”: trust and the use of machine learning algorithms in the diagnosis of rare disease

Background

As the use of AI becomes more pervasive, and computerised systems are used in clinical decision-making, the role of trust in, and the trustworthiness of, AI tools will need to be addressed. Using the case of computational phenotyping to support the diagnosis of rare disease in dysmorphology, this paper explores under what conditions we could place trust in medical AI tools, which employ machine learning.

Methods

Semi-structured qualitative interviews (n = 20) with stakeholders (clinical geneticists, data scientists, bioinformaticians, industry and patient support group spokespersons) who design and/or work with computational phenotyping (CP) systems. The method of constant comparison was used to analyse the interview data.

Results

Interviewees emphasized the importance of establishing trust in the use of CP technology in identifying rare diseases. Trust was formulated in two interrelated ways in these data. First, interviewees talked about the importance of using CP tools within the context of a trust relationship; arguing that patients will need to trust clinicians who use AI tools and that clinicians will need to trust AI developers, if they are to adopt this technology. Second, they described a need to establish trust in the technology itself, or in the knowledge it provides—epistemic trust. Interviewees suggested CP tools used for the diagnosis of rare diseases might be perceived as more trustworthy if the user is able to vouchsafe for the technology’s reliability and accuracy and the person using/developing them is trusted.

Conclusion

This study suggests we need to take deliberate and meticulous steps to design reliable or confidence-worthy AI systems for use in healthcare. In addition, we need to devise reliable or confidence-worthy processes that would give rise to reliable systems; these could take the form of RCTs and/or systems of accountability transparency and responsibility that would signify the epistemic trustworthiness of these tools. words 294.

KBG syndrome: videoconferencing and use of artificial intelligence driven facial phenotyping in 25 new patients

Genetic variants in Ankyrin Repeat Domain 11 (ANKRD11) and deletions in 16q24.3 are known to cause KBG syndrome, a rare syndrome associated with craniofacial, intellectual, and neurobehavioral anomalies. We report 25 unpublished individuals from 22 families with molecularly confirmed diagnoses. Twelve individuals have de novo variants, three have inherited variants, and one is inherited from a parent with low-level mosaicism. The mode of inheritance was unknown for nine individuals. Twenty are truncating variants, and the remaining five are missense (three of which are found in one family). We present a protocol emphasizing the use of videoconference and artificial intelligence (AI) in collecting and analyzing data for this rare syndrome. A single clinician interviewed 25 individuals throughout eight countries. Participants' medical records were reviewed, and data was uploaded to the Human Disease Gene website using Human Phenotype Ontology (HPO) terms. Photos of the participants were analyzed by the GestaltMatcher and DeepGestalt, Face2Gene platform (FDNA Inc, USA) algorithms. Within our cohort, common traits included short stature, macrodontia, anteverted nares, wide nasal bridge, wide nasal base, thick eyebrows, synophrys and hypertelorism. Behavioral issues and global developmental delays were widely present. Neurologic abnormalities including seizures and/or EEG abnormalities were common (44%), suggesting that early detection and seizure prophylaxis could be an important point of intervention. Almost a quarter (24%) were diagnosed with attention deficit hyperactivity disorder and 28% were diagnosed with autism spectrum disorder. Based on the data, we provide a set of recommendations regarding diagnostic and treatment approaches for KBG syndrome.

Facial features of lysosomal storage disorders

INTRODUCTION

The use of facial recognition technology has diversified the diagnostic toolbelt for clinicians and researchers for the accurate diagnoses of patients with rare and challenging disorders. Specific identifiers in patient images can be grouped using artificial intelligence to allow the recognition of diseases and syndromes with similar features. Lysosomal storage disorders are rare, and some have prominent and unique features that may be used to train the accuracy of facial recognition software algorithms. Noteworthy features of lysosomal storage disorders (LSDs) include facial features such as prominent brows, wide noses, thickened lips, mouth, and chin, resulting in coarse and rounded facial features.

AREAS COVERED

We evaluated and report the prevalence of facial phenotypes in patients with different LSDs, noting two current examples when artificial intelligence strategies have been utilized to identify distinctive facies.

EXPERT OPINION

Specific LSDs, including Gaucher disease, Mucolipidosis IV and Fabry disease have recently been distinguished using facial recognition software. Additional lysosomal disorders LSDs lysosomal storage disorders with unique and distinguishable facial features also merit evaluation using this technology. These tools may ultimately aid in the identification of specific LSDs and shorten the diagnostic odyssey for patients with these rare and under-recognized disorders.

Comprehensive genetic testing approaches as the basis for personalized management of growth disturbances: current status and perspectives

The implementation of high-throughput and deep sequencing methods in routine genetic diagnostics has significantly improved the diagnostic yield in patient cohorts with growth disturbances and becomes increasingly important as the prerequisite of personalized medicine. They provide considerable chances to identify even rare and unexpected situations; nevertheless, we must be aware of their limitations. A simple genetic test in the beginning of a testing cascade might also help to identify the genetic cause of specific growth disorders. However, the clinical picture of genetically caused growth disturbance phenotypes can vary widely, and there is a broad clinical overlap between different growth disturbance disorders. As a consequence, the clinical diagnosis and therewith connected the decision on the appropriate genetic test is often a challenge. In fact, the clinician asking for genetic testing has to weigh different aspects in this decision process, including appropriateness (single gene test, stepwise procedure, comprehensive testing), turnaround time as the basis for rapid intervention, and economic considerations. Therefore, a frequent question in that context is 'what to test when'. In this review, we aim to review genetic testing strategies and their strengths and limitations and to raise awareness for the future implementation of interdisciplinary genome medicine in diagnoses, treatment, and counselling of growth disturbances.

Künstliche Intelligenz bei der Diagnose Seltener Erkrankungen: die Entwicklung der Phänotyp-Analyse

Durch die Analyse des Erscheinungsbildes (Phänotyp) von Patient:innen kann die Diagnostik von Seltenen Erkrankungen unterstützt werden, da bei vielen genetischen Erkrankungen charakteristische Abweichungen (Dysmorphologien) auftreten. Diese betreffen z. B. die Merkmale des Gesichts – die „faziale Gestalt“.

In diesem Beitrag wird ein Bereich der künstlichen Intelligenz (KI) beleuchtet, in dem es in den letzten Jahren große Fortschritte gegeben hat: die Erkennung charakteristischer Muster in medizinischen Bilddaten mittels vielschichtiger, gefalteter künstlicher neuronaler Netzwerke (Next-Generation Phenotyping – NGP). Die technischen Grundlagen der Methode werden kurz beschrieben und es wird auf die hohe Relevanz von frei zugänglichen Daten für die Wissenschaftsgemeinschaft zur Entwicklung von KI eingegangen. Des Weiteren wird erläutert, warum Entscheidungen von KI immer nachvollziehbar bleiben sollten und wie es gelingen kann, die Herausforderungen in Hinblick auf Datenschutz und Transparenz zu meistern.

Zukünftig können Software-Anwendungen mit KI ärztliche Fachkräfte bei der Diagnostik von Seltenen Erkrankungen unterstützen. Das Vertrauen in den Einsatz von KI wird steigen, wenn Patient:innen ihre Datenhoheit behalten und nachvollziehen können, auf welchem Weg die Diagnose entstanden ist.

Case Report: The success of face analysis technology in extremely rare genetic diseases in Korea: Tatton–Brown–Rahman syndrome and Say–Barber –Biesecker–Young–Simpson variant of ohdo syndrome

Tatton–Brown–Rahman syndrome (TBRS) and Say–Barber–Biesecker– Young–Simpson variant of Ohdo syndrome (SBBYSS) are extremely rare genetic disorders with less than 100 reported cases. Patients with these disorders exhibit a characteristic facial dysmorphism: TBRS is characterized by a round face, a straight and thick eyebrow, and prominent maxillary incisors, whereas SBBYSS is characterized by mask-like facies, blepharophimosis, and ptosis. The usefulness of Face2Gene as a tool for the identification of dysmorphology syndromes is discussed, because, in these patients, it suggested TBRS and SBBYSS within the top five candidate disorders. Face2Gene is useful for the diagnosis of extremely rare diseases in Korean patients, suggesting the possibility of expanding its clinical applications.

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

CSNK2B encodes for casein kinase II subunit beta (CK2β), the regulatory subunit of casein kinase II (CK2), which is known to mediate diverse cellular pathways. Variants in this gene have been recently identified as a cause of Poirier-Bienvenu neurodevelopmental syndrome (POBINDS), but functional evidence is sparse. Here, we report five unrelated individuals: two of them manifesting POBINDS, while three are identified to segregate a new intellectual disability-craniodigital syndrome (IDCS), distinct from POBINDS. The three IDCS individuals carried two different de novo missense variants affecting the same codon of CSNK2B. Both variants, NP_001311.3; p.Asp32His and NP_001311.3; p.Asp32Asn, lead to an upregulation of CSNK2B expression at transcript and protein level, along with global dysregulation of canonical Wnt signaling. We found impaired interaction of the two key players DVL3 and β-catenin with mutated CK2β. The variants compromise the kinase activity of CK2 as evident by a marked reduction of phosphorylated β-catenin and consequent absence of active β-catenin inside nuclei of the patient-derived lymphoblastoid cell lines (LCLs). In line with these findings, whole-transcriptome profiling of patient-derived LCLs harboring the NP_001311.3; p.Asp32His variant confirmed a marked difference in expression of genes involved in the Wnt signaling pathway. In addition, whole-phosphoproteome analysis of the LCLs of the same subject showed absence of phosphorylation for 313 putative CK2 substrates, enriched in the regulation of nuclear β-catenin and transcription of the target genes. Our findings suggest that discrete variants in CSNK2B cause dominant-negative perturbation of the canonical Wnt signaling pathway, leading to a new craniodigital syndrome distinguishable from POBINDS.