Mutations in PIGU Impair the Function of the GPI Transamidase Complex, Causing Severe Intellectual Disability, Epilepsy, and Brain Anomalies.

The glycosylphosphatidylinositol (GPI) anchor links over 150 proteins to the cell surface and is present on every cell type. Many of these proteins play crucial roles in neuronal development and function. Mutations in 18 of the 29 genes implicated in the biosynthesis of the GPI anchor have been identified as the cause of GPI biosynthesis deficiencies (GPIBDs) in humans. GPIBDs are associated with intellectual disability and seizures as their cardinal features. An essential component of the GPI transamidase complex is PIGU, along with PIGK, PIGS, PIGT, and GPAA1, all of which link GPI-anchored proteins (GPI-APs) onto the GPI anchor in the endoplasmic reticulum (ER). Here, we report two homozygous missense mutations (c.209T>A [p.Ile70Lys] and c.1149C>A [p.Asn383Lys]) in five individuals from three unrelated families. All individuals presented with global developmental delay, severe-to-profound intellectual disability, muscular hypotonia, seizures, brain anomalies, scoliosis, and mild facial dysmorphism. Using multicolor flow cytometry, we determined a characteristic profile for GPI transamidase deficiency. On granulocytes this profile consisted of reduced cell-surface expression of fluorescein-labeled proaerolysin (FLAER), CD16, and CD24, but not of CD55 and CD59; additionally, B cells showed an increased expression of free GPI anchors determined by T5 antibody. Moreover, computer-assisted facial analysis of different GPIBDs revealed a characteristic facial gestalt shared among individuals with mutations in PIGU and GPAA1. Our findings improve our understanding of the role of the GPI transamidase complex in the development of nervous and skeletal systems and expand the clinical spectrum of disorders belonging to the group of inherited GPI-anchor deficiencies.

The Discovery of a LEMD2-Associated Nuclear Envelopathy with Early Progeroid Appearance Suggests Advanced Applications for AI-Driven Facial Phenotyping.

Over a relatively short period of time, the clinical geneticist's "toolbox" has been expanded by machine-learning algorithms for image analysis, which can be applied to the task of syndrome identification on the basis of facial photographs, but these technologies harbor potential beyond the recognition of established phenotypes. Here, we comprehensively characterized two individuals with a hitherto unknown genetic disorder caused by the same de novo mutation in LEMD2 (c.1436C>T;p.Ser479Phe), the gene which encodes the nuclear envelope protein LEM domain-containing protein 2 (LEMD2). Despite different ages and ethnic backgrounds, both individuals share a progeria-like facial phenotype and a distinct combination of physical and neurologic anomalies, such as growth retardation; hypoplastic jaws crowded with multiple supernumerary, yet unerupted, teeth; and cerebellar intention tremor. Immunofluorescence analyses of patient fibroblasts revealed mutation-induced disturbance of nuclear architecture, recapitulating previously published data in LEMD2-deficient cell lines, and additional experiments suggested mislocalization of mutant LEMD2 protein within the nuclear lamina. Computational analysis of facial features with two different deep neural networks showed phenotypic proximity to other nuclear envelopathies. One of the algorithms, when trained to recognize syndromic similarity (rather than specific syndromes) in an unsupervised approach, clustered both individuals closely together, providing hypothesis-free hints for a common genetic etiology. We show that a recurrent de novo mutation in LEMD2 causes a nuclear envelopathy whose prognosis in adolescence is relatively good in comparison to that of classical Hutchinson-Gilford progeria syndrome, and we suggest that the application of artificial intelligence to the analysis of patient images can facilitate the discovery of new genetic disorders.

PIGT-CDG, a disorder of the glycosylphosphatidylinositol anchor: description of 13 novel patients and expansion of the clinical characteristics.

PURPOSE: To provide a detailed electroclinical description and expand the phenotype of PIGT-CDG, to perform genotype-phenotype correlation, and to investigate the onset and severity of the epilepsy associated with the different genetic subtypes of this rare disorder. Furthermore, to use computer-assisted facial gestalt analysis in PIGT-CDG and to the compare findings with other glycosylphosphatidylinositol (GPI) anchor deficiencies. METHODS: We evaluated 13 children from eight unrelated families with homozygous or compound heterozygous pathogenic variants in PIGT. RESULTS: All patients had hypotonia, severe developmental delay, and epilepsy. Epilepsy onset ranged from first day of life to two years of age. Severity of the seizure disorder varied from treatable seizures to severe neonatal onset epileptic encephalopathies. The facial gestalt of patients resembled that of previously published PIGT patients as they were closest to the center of the PIGT cluster in the clinical face phenotype space and were distinguishable from other gene-specific phenotypes. CONCLUSION: We expand our knowledge of PIGT. Our cases reaffirm that the use of genetic testing is essential for diagnosis in this group of disorders. Finally, we show that computer-assisted facial gestalt analysis accurately assigned PIGT cases to the multiple congenital anomalies-hypotonia-seizures syndrome phenotypic series advocating the additional use of next-generation phenotyping technology.

PEDIA: prioritization of exome data by image analysis.

PURPOSE: Phenotype information is crucial for the interpretation of genomic variants. So far it has only been accessible for bioinformatics workflows after encoding into clinical terms by expert dysmorphologists. METHODS: Here, we introduce an approach driven by artificial intelligence that uses portrait photographs for the interpretation of clinical exome data. We measured the value added by computer-assisted image analysis to the diagnostic yield on a cohort consisting of 679 individuals with 105 different monogenic disorders. For each case in the cohort we compiled frontal photos, clinical features, and the disease-causing variants, and simulated multiple exomes of different ethnic backgrounds. RESULTS: The additional use of similarity scores from computer-assisted analysis of frontal photos improved the top 1 accuracy rate by more than 20-89% and the top 10 accuracy rate by more than 5-99% for the disease-causing gene. CONCLUSION: Image analysis by deep-learning algorithms can be used to quantify the phenotypic similarity (PP4 criterion of the American College of Medical Genetics and Genomics guidelines) and to advance the performance of bioinformatics pipelines for exome analysis.