Striking phenotypic overlap between Nicolaides-Baraitser and Coffin-Siris syndromes in monozygotic twins with ARID1B intragenic deletion

Prenatal genetic testing in 19 fetuses with corpus callosum abnormality

Background

Corpus callosum abnormality (CCA) can lead to epilepsy, moderate severe neurologic or mental retardation. The prognosis of CCA is closely related to genetic etiology. However, copy number variations (CNVs) associated with fetal CCA are still limited and need to be further identified. Only a few scattered cases have been reported to diagnose CCA by whole exome sequencing (WES).

Methods

Karyotyping analysis, copy number variation sequencing (CNV‐seq), chromosomal microarray analysis (CMA) and WES were parallelly performed for prenatal diagnosis of 19 CCA cases.

Results

The total detection rate of karyotyping analysis, CMA (or CNV‐seq) and WES were 15.79% (3/19), 21.05% (4/19) and 40.00% (2/5), respectively. Two cases (case 11 and case 15) were diagnosed as aneuploidy (47, XY, + 13 and 47, XX, + 21) by karyotyping analysis and CNV‐seq. Karyotyping analysis revealed an unknown origin fragment (46,XY,add(13)(p11.2)) in case 3, which was further confirmed to originate from p13.3p11.2 of chromosome 17 by CNV‐seq. CMA revealed arr1q43q44 (238923617–246964774) × 1(8.04 Mb) in case 8 with a negative result of chromosome karyotype. WES revealed that 2 of 5 cases with negative results of karyotyping and CNV‐seq or CMA carried pathogenic genes ALDH7A1 and ARID1B.

Conclusion

Parallel genetic tests showed that CNV‐seq and CMA are able to identify additional, clinically significant cytogenetic information of CCA compared to karyotyping; WES significantly improves the detection rate of genetic etiology of CCA. For the patients with a negative results of CNV‐seq or CMA, further WES test is recommended.

Metacarpophalangeal profile pattern analysis in a further patient with a novel ARID1B variant

Acral clinical and radiographic characteristics of a further patient with Coffin-Siris syndrome (CSS), which is caused by mutations in the ARID1B gene, encoding a subunit of the BAF-complex, are here described. Metacarpophalangeal profile pattern analysis (MCPPPA) of the present proband and other two known ARID1B mutated individuals has been performed for the first time, demonstrating hands brachydactyly. In this novel study, the utility of an accurate appendicular radiographic examination and MCPPPA in this congenital condition is highlighted. The MCPPPA could be considered in the clinical practice, to better study the hand skeletal morphology in patients with a syndrome characterized by limb defects, including CSS.

A Case Series of Familial ARID1B Variants Illustrating Variable Expression and Suggestions to Update the ACMG Criteria

ARID1B is one of the most frequently mutated genes in intellectual disability (~1%). Most variants are readily classified, since they are de novo and are predicted to lead to loss of function, and therefore classified as pathogenic according to the American College of Medical Genetics and Genomics (ACMG) guidelines for the interpretation of sequence variants. However, familial loss-of-function variants can also occur and can be challenging to interpret. Such variants may be pathogenic with variable expression, causing only a mild phenotype in a parent. Alternatively, since some regions of the ARID1B gene seem to be lacking pathogenic variants, loss-of-function variants in those regions may not lead to ARID1B haploinsufficiency and may therefore be benign. We describe 12 families with potential loss-of-function variants, which were either familial or with unknown inheritance and were in regions where pathogenic variants have not been described or are otherwise challenging to interpret. We performed detailed clinical and DNA methylation studies, which allowed us to confidently classify most variants. In five families we observed transmission of pathogenic variants, confirming their highly variable expression. Our findings provide further evidence for an alternative translational start site and we suggest updates for the ACMG guidelines for the interpretation of sequence variants to incorporate DNA methylation studies and facial analyses.

The Evolutionary Conserved SWI/SNF Subunits ARID1A and ARID1B Are Key Modulators of Pluripotency and Cell-Fate Determination

Organismal development is a process that requires a fine-tuned control of cell fate and identity, through timely regulation of lineage-specific genes. These processes are mediated by the concerted action of transcription factors and protein complexes that orchestrate the interaction between cis-regulatory elements (enhancers, promoters) and RNA Polymerase II to elicit transcription. A proper understanding of these dynamics is essential to elucidate the mechanisms underlying developmental diseases. Many developmental disorders, such as Coffin-Siris Syndrome, characterized by growth impairment and intellectual disability are associated with mutations in subunits of the SWI/SNF chromatin remodeler complex, which is an essential regulator of transcription. ARID1B and its paralog ARID1A encode for the two largest, mutually exclusive, subunits of the complex. Mutations in ARID1A and, especially, ARID1B are recurrently associated with a very wide array of developmental disorders, suggesting that these two SWI/SNF subunits play an important role in cell fate decision. In this mini-review we therefore discuss the available scientific literature linking ARID1A and ARID1B to cell fate determination, pluripotency maintenance, and organismal development.

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.

BRM: the core ATPase subunit of SWI/SNF chromatin-remodelling complex-a tumour suppressor or tumour-promoting factor?

BRM (BRAHMA) is a core, SWI2/SNF2-type ATPase subunit of SWI/SNF chromatin-remodelling complex (CRC) involved in various important regulatory processes including development. Mutations in SMARCA2, a BRM-encoding gene as well as overexpression or epigenetic silencing were found in various human diseases including cancer. Missense mutations in SMARCA2 gene were recently connected with occurrence of Nicolaides-Baraitser genetics syndrome. By contrast, SMARCA2 duplication rather than mutations is characteristic for Coffin-Siris syndrome. It is believed that BRM usually acts as a tumour suppressor or a tumour susceptibility gene. However, other studies provided evidence that BRM function may differ depending on the cancer type and the disease stage, where BRM may play a role in the disease progression. The existence of alternative splicing forms of SMARCA2 gene, leading to appearance of truncated functional, loss of function or gain-of-function forms of BRM protein suggest a far more complicated mode of BRM-containing SWI/SNF CRCs actions. Therefore, the summary of recent knowledge regarding BRM alteration in various types of cancer and highlighting of differences and commonalities between BRM and BRG1, another SWI2/SNF2 type ATPase, will lead to better understanding of SWI/SNF CRCs function in cancer development/progression. BRM has been recently proposed as an attractive target for various anticancer therapies including the use of small molecule inhibitors, synthetic lethality induction or proteolysis-targeting chimera (PROTAC). However, such attempts have some limitations and may lead to severe side effects given the homology of BRM ATPase domain to other ATPases, as well as due to the tissue-specific appearance of BRM- and BRG1-containing SWI/SNF CRC classes. Thus, a better insight into BRM-containing SWI/SNF CRCs function in human tissues and cancers is clearly required to provide a solid basis for establishment of new safe anticancer therapies.