PURA syndrome: clinical delineation and genotype-phenotype study in 32 individuals with review of published literature

Background De novo mutations in PURA have recently been described to cause PURA syndrome, a neurodevelopmental disorder characterized by severe intellectual disability (ID), epilepsy, feeding difficulties and neonatal hypotonia. Objectives To delineate the clinical spectrum of PURA syndrome and study genotype-phenotype correlations. Methods Diagnostic or research-based Exome or Sanger sequencing was performed in individuals with ID. We systematically collected clinical and mutation data on newly ascertained PURA syndrome individuals, evaluated data of previously reported individuals, and performed a computational analysis of photographs. We classified mutations based on predicted effect using 3D in silico models of crystal structures of Drosophila-derived Pur-alpha homologues. Finally, we explored genotype-phenotype correlations by analysis of both recurrent mutations as well as mutation classes. Results We report mutations in PURA in 32 individuals, the largest cohort described so far. Evaluation of clinical data, including 22 previously published cases, revealed that all have moderate to severe ID and neonatal-onset symptoms, including hypotonia (96%), respiratory problems (57%), feeding difficulties (77%), exaggerated startle response (44%), hypersomnolence (66%) and hypothermia (35%). Epilepsy (54%), gastro-intestinal- (69%), ophthalmological- (51%), and endocrine problems (42%) were observed frequently. Computational analysis of facial photographs showed subtle facial dysmorphism. No strong genotype-phenotype correlation was identified by subgrouping mutations into functional classes. Conclusion We delineate the clinical spectrum of PURA syndrome with the identification of 32 additional individuals. The identification of one individual through targeted Sanger sequencing points towards the clinical recognizability of the syndrome. Genotype-phenotype analysis showed no significant correlation between mutation classes and disease severity

De novo and inherited loss-of-function variants in TLK2: clinical and genotype-phenotype evaluation of a distinct neurodevelopmental disorder

Next-generation sequencing is a powerful tool for the discovery of genes related to neurodevelopmental disorders (NDDs). Here, we report the identification of a distinct syndrome due to de novo or inherited heterozygous mutations in Tousled-like kinase 2 (TLK2) in 38 unrelated individuals and two affected mothers, using whole-exome and whole-genome sequencing technologies, matchmaker databases, and international collaborations. Affected individuals had a consistent phenotype, characterized by mild-borderline neurodevelopmental delay (86%), behavioral disorders (68%), severe gastro-intestinal problems (63%), and facial dysmorphism including blepharophimosis (82%), telecanthus (74%), prominent nasal bridge (68%), broad nasal tip (66%), thin vermilion of the upper lip (62%), and upslanting palpebral fissures (55%). Analysis of cell lines from three affected individuals showed that mutations act through a loss-of-function mechanism in at least two case subjects. Genotype-phenotype analysis and comparison of computationally modeled faces showed that phenotypes of these and other individuals with loss-of-function variants significantly overlapped with phenotypes of individuals with other variant types (missense and C-terminal truncating). This suggests that haploinsufficiency of TLK2 is the most likely underlying disease mechanism, leading to a consistent neurodevelopmental phenotype. This work illustrates the power of international data sharing, by the identification of 40 individuals from 26 different centers in 7 different countries, allowing the identification, clinical delineation, and genotype-phenotype evaluation of a distinct NDD caused by mutations in TLK2

RAC1 missense mutations in developmental disorders with diverse phenotypes

RAC1 is a widely studied Rho GTPase, a class of molecules that modulate numerous cellular functions essential for normal development. RAC1 is highly conserved across species and is under strict mutational constraint. We report seven individuals with distinct de novo missense RAC1 mutations and varying degrees of developmental delay, brain malformations, and additional phenotypes. Four individuals, each harboring one of c.53G>A (p.Cys18Tyr), c.116A>G (p.Asn39Ser), c.218C>T (p.Pro73Leu), and c.470G>A (p.Cys157Tyr) variants, were microcephalic, with head circumferences between -2.5 to -5 SD. In contrast, two individuals with c.151G>A (p.Val51Met) and c.151G>C (p.Val51Leu) alleles were macrocephalic with head circumferences of +4.16 and +4.5 SD. One individual harboring a c.190T>G (p.Tyr64Asp) allele had head circumference in the normal range. Collectively, we observed an extraordinary spread of ∼10 SD of head circumferences orchestrated by distinct mutations in the same gene. In silico modeling, mouse fibroblasts spreading assays, and in vivo overexpression assays using zebrafish as a surrogate model demonstrated that the p.Cys18Tyr and p.Asn39Ser RAC1 variants function as dominant-negative alleles and result in microcephaly, reduced neuronal proliferation, and cerebellar abnormalities in vivo. Conversely, the p.Tyr64Asp substitution is constitutively active. The remaining mutations are probably weakly dominant negative or their effects are context dependent. These findings highlight the importance of RAC1 in neuronal development. Along with TRIO and HACE1, a sub-category of rare developmental disorders is emerging with RAC1 as the central player. We show that ultra-rare disorders caused by private, non-recurrent missense mutations that result in varying phenotypes are challenging to dissect, but can be delineated through focused international collaboration

De novo missense variants in RRAGC lead to a fatal mTORopathy of early childhood

Introduction Mechanistic target of rapamycin complex 1 (mTORC1) regulates cell growth in response to nutritional status. Central to mTORC1 function is the Rag-GTPase heterodimer. One component of the Rag heterodimer is RagC (Ras-related GTP-binding protein C), which is encoded by the RRAGC gene. Material and Methods Genetic testing via trio exome sequencing was applied to identify the underlying disease cause in three infants who suffered from dilated cardiomyopathy, hepatopathy and brain abnormalities including pachygyria, polymicrogyria, and septo-optic dysplasia. Studies in patient-derived skin fibroblasts and in a HEK293 cell model were performed to investigate the cellular consequences. Results We identified three de novo missense variants in RRAGC (NM_022157.4: c.269C>A, p.(Thr90Asn), c.353C>T, p.(Pro118Leu), and c.343T>C, p.(Trp115Arg)), which were previously reported as occurring somatically in follicular lymphoma. Studies of patient-derived fibroblasts carrying the p.(Thr90Asn) variant revealed increased cell size as well as dysregulation of mTOR-related p70S6K (ribosomal protein S6 kinase 1) and TFEB (transcription factor EB) signaling. Moreover, subcellular localization of mTOR was decoupled from metabolic state. We confirmed the key-findings for all RRAGC variants described in this study in a HEK293 cell model. Discussion The above results are in line with a constitutive over-activation of the mTORC1 pathway. Our study establishes de novo missense variants in RRAGC as cause of an early-onset mTORopathy with unfavorable prognosis

Enabling global clinical collaborations on identifiable patient data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health

Enabling global clinical collaborations on identifiable patient data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health