Haploinsufficiency of ARFGEF1 is associated with developmental delay, intellectual disability, and epilepsy with variable expressivity

ADP ribosylation factor guanine nucleotide exchange factors (ARFGEFs) are a family of proteins implicated in cellular trafficking between the Golgi apparatus and the plasma membrane through vesicle formation. Among them is ARFGEF1/BIG1, a protein involved in axon elongation, neurite development, and polarization processes. ARFGEF1 has been previously suggested as a candidate gene for different types of epilepsies, although its implication in human disease has not been well characterized. International data sharing, in silico predictions, and in vitro assays with minigene study, western blot analyses, and RNA sequencing. We identified 13 individuals with heterozygous likely pathogenic variants in ARFGEF1. These individuals displayed congruent clinical features of developmental delay, behavioral problems, abnormal findings on brain magnetic resonance image (MRI), and epilepsy for almost half of them. While nearly half of the cohort carried de novo variants, at least 40% of variants were inherited from mildly affected parents who were clinically re-evaluated by reverse phenotyping. Our in silico predictions and in vitro assays support the contention that ARFGEF1-related conditions are caused by haploinsufficiency, and are transmitted in an autosomal dominant fashion with variable expressivity. We provide evidence that loss-of-function variants in ARFGEF1 are implicated in sporadic and familial cases of developmental delay with or without epilepsy

Innovative bioinformatics approaches for the analysis of high-throughput sequencing data applied to the study of rare genetic pathologies with developmental abnormalities

L’avènement du séquençage haut débit d’exome (SHD-E) en diagnostic et en recherche ces dernières années a conduit à l’identification des bases génétiques de nombreuses pathologies mendéliennes, permettant de résoudre de nombreuses situations d’errance diagnostique. Néanmoins, l’analyse des données de SHD-E permet uniquement d’identifier des variations pathogènes ou probablement pathogènes dans 30 à 45 % des situations sans diagnostic. En effet, certaines limites existent, tant au niveau clinique, moléculaire et bioinformatique. L’évolution constante des connaissances cliniques, du nombre de nouveaux gènes impliqués en pathologie humaine, et des corrélations clinico-biologique a un impact important sur l’analyse des données, entraînant une amélioration progressive de la recherche diagnostique. Des limites techniques inhérentes à la technologie, avec en particulier des régions non couvertes, existent, mais se sont également significativement réduites ces dernières années. Enfin, au-delà de l’analyse de SNV et de CNV, d’autres anomalies génétiques peuvent être responsables de maladies rares, nécessitant un développement bioinformatique pour optimiser les résultats. Bien que le séquençage à haut débit du génome permette de résoudre des observations, en particulier en cas de variations dans les régions non codantes ou les variants de structure, il existe encore de nombreuses informations à extraire et à exploiter à partir des données de SHD-E.L’objectif de cette thèse a donc été de participer à l’amélioration des approches bioinformatiques d’analyse de données de SHD-E pour l’identification de nouveaux gènes ou mécanismes moléculaires impliqués dans des maladies génétiques rares afin de réduire l’errance diagnostique des patients.Plusieurs stratégies ont ainsi été mises en place. La première stratégie a consisté en une réanalyse recherche de données de 80 patients ayant bénéficié d’un SHD-E au laboratoire CERBA (thèse CIFRE) dont la lecture diagnostique était négative. Elle a conduit à la mise en évidence deux nouveaux gènes candidats dans la déficience intellectuelle syndromique, dont le gène OTUD7A (article 1). La deuxième stratégie a consisté en la mise au point d’un pipeline bioinformatique pour extraire les données du génome mitochondrial à partir des données de SHD-E. L’ADN mitochondrial n’est pas ciblé par les kits de capture d’exome mais peut être extrait des données capturées indirectement rendant son analyse possible à partir de données de SHD-E préexistantes. A partir de la collection GAD d’exomes de patients sans diagnostic, deux variations causales ont été identifiées chez deux individus atteints de troubles neuro-développementaux sur 928 personnes étudiées, et ainsi résoudre une errance diagnostique dans 0,2 % des patients sans diagnostic (article 2). La troisième stratégie a consisté en la mise en place d’un pipeline bioinformatique d’identification des éléments mobiles au sein des données d’exome, étant attendu qu’environ 0,3 % des variations pathogènes du génome humain ont pour origine l’insertion de novo d’un élément mobile. A partir de la collection GAD d’exomes de 3322 patients sans diagnostic, cette étape a permis d’identifier deux cas en lien avec l’insertion d’un élément Alu au sein d’un exon du gène FERMT1 et du gène GRIN2B (article 3 en cours d’écriture).Cette thèse a permis de repousser certaines limites de la technologie d’exome. D’autres perspectives existent, et sont explorées par l’équipe, en lien avec le projet Européen Solve-RD.In the last years, the advent of exome sequencing (ES) in diagnosis and in research led to the identification of the genetic bases of many Mendelian disorders, allowing many diagnostic wavering cases to be solved. Nevertheless, ES data analysis only leads to the identification of pathogenic or likely pathogenic variants in 30 to 45 % of the undiagnosed cases. Indeed, some limits exist, both at clinical, molecular and bioinformatic levels. The constant evolution of the clinical knowledge, of the number of genes involved in human diseases, and of the clinical-biological correlations, has a significant impact on data analysis, leading to a progressive improvement in diagnostic research. Limits of the current technologies, especially not covered regions, exist, but have been significantly reduced in the recent years. Although genome sequencing will solve some undiagnosed cases, especially in case of non-coding or structural variants, there is still a lot of information to be extracted and analyzed from ES data. Finally, beyond SNV and CNV analyzes, other genetic events can be involved in rare disorders, requiring a bioinformatic development to optimize results.The aim of the project was therefore to improve bioinformatic approaches of ES data analysis in order to identify new molecular mechanisms involved in rare genetic disorders and reduce diagnostic wavering.Several strategies were established. The first one consisted in reanalysing ES data from 80 undiagnosed patients, who were sequenced by the Laboratoire CERBA (CIFRE thesis). It led to the identification of 2 new candidate genes involved in ID, especially OTUD7A gene (article 1). The second strategy was the development of a bioinformatic pipeline in order to extract mitochondrial DNA data from ES data. The mitochondrial genome is not targeted by exome capture kits but can be extracted from off-target data, giving the opportunity to analyze it from preexisting ES data. From the GAD exomes cohort of undiagnosed patients, 2 causal variations were identified in 2 individuals out of 928, affected with neuro-developmental disorder. It thus solved the diagnostic wavering in 0.2 % of patients without diagnosis (article 2). The third strategy consisted in the development of a bioinformatic pipeline to identify mobile elements insertion within ES data, with the expectation that about 0.03 % of the pathogenic variants originate from de novo mobile element insertion. From the GAD exomes cohort of 3322 undiagnosed patients, this step led to the identification of two Alu element insertions in FERMT1 and GRIN2B gene exons (article 3, in process).This PhD permitted to push out some ES limits. Other perspectives exist, and are explored by the GAD team, in connection with the European Solve-RD project

Approches bioinformatiques innovantes pour l’analyse de données de séquençage à haut-débit appliquées à l’étude de pathologies génétiques rares avec anomalies du développement

In the last years, the advent of exome sequencing (ES) in diagnosis and in research led to the identification of the genetic bases of many Mendelian disorders, allowing many diagnostic wavering cases to be solved. Nevertheless, ES data analysis only leads to the identification of pathogenic or likely pathogenic variants in 30 to 45 % of the undiagnosed cases. Indeed, some limits exist, both at clinical, molecular and bioinformatic levels. The constant evolution of the clinical knowledge, of the number of genes involved in human diseases, and of the clinical-biological correlations, has a significant impact on data analysis, leading to a progressive improvement in diagnostic research. Limits of the current technologies, especially not covered regions, exist, but have been significantly reduced in the recent years. Although genome sequencing will solve some undiagnosed cases, especially in case of non-coding or structural variants, there is still a lot of information to be extracted and analyzed from ES data. Finally, beyond SNV and CNV analyzes, other genetic events can be involved in rare disorders, requiring a bioinformatic development to optimize results.The aim of the project was therefore to improve bioinformatic approaches of ES data analysis in order to identify new molecular mechanisms involved in rare genetic disorders and reduce diagnostic wavering.Several strategies were established. The first one consisted in reanalysing ES data from 80 undiagnosed patients, who were sequenced by the Laboratoire CERBA (CIFRE thesis). It led to the identification of 2 new candidate genes involved in ID, especially OTUD7A gene (article 1). The second strategy was the development of a bioinformatic pipeline in order to extract mitochondrial DNA data from ES data. The mitochondrial genome is not targeted by exome capture kits but can be extracted from off-target data, giving the opportunity to analyze it from preexisting ES data. From the GAD exomes cohort of undiagnosed patients, 2 causal variations were identified in 2 individuals out of 928, affected with neuro-developmental disorder. It thus solved the diagnostic wavering in 0.2 % of patients without diagnosis (article 2). The third strategy consisted in the development of a bioinformatic pipeline to identify mobile elements insertion within ES data, with the expectation that about 0.03 % of the pathogenic variants originate from de novo mobile element insertion. From the GAD exomes cohort of 3322 undiagnosed patients, this step led to the identification of two Alu element insertions in FERMT1 and GRIN2B gene exons (article 3, in process).This PhD permitted to push out some ES limits. Other perspectives exist, and are explored by the GAD team, in connection with the European Solve-RD project.L’avènement du séquençage haut débit d’exome (SHD-E) en diagnostic et en recherche ces dernières années a conduit à l’identification des bases génétiques de nombreuses pathologies mendéliennes, permettant de résoudre de nombreuses situations d’errance diagnostique. Néanmoins, l’analyse des données de SHD-E permet uniquement d’identifier des variations pathogènes ou probablement pathogènes dans 30 à 45 % des situations sans diagnostic. En effet, certaines limites existent, tant au niveau clinique, moléculaire et bioinformatique. L’évolution constante des connaissances cliniques, du nombre de nouveaux gènes impliqués en pathologie humaine, et des corrélations clinico-biologique a un impact important sur l’analyse des données, entraînant une amélioration progressive de la recherche diagnostique. Des limites techniques inhérentes à la technologie, avec en particulier des régions non couvertes, existent, mais se sont également significativement réduites ces dernières années. Enfin, au-delà de l’analyse de SNV et de CNV, d’autres anomalies génétiques peuvent être responsables de maladies rares, nécessitant un développement bioinformatique pour optimiser les résultats. Bien que le séquençage à haut débit du génome permette de résoudre des observations, en particulier en cas de variations dans les régions non codantes ou les variants de structure, il existe encore de nombreuses informations à extraire et à exploiter à partir des données de SHD-E.L’objectif de cette thèse a donc été de participer à l’amélioration des approches bioinformatiques d’analyse de données de SHD-E pour l’identification de nouveaux gènes ou mécanismes moléculaires impliqués dans des maladies génétiques rares afin de réduire l’errance diagnostique des patients.Plusieurs stratégies ont ainsi été mises en place. La première stratégie a consisté en une réanalyse recherche de données de 80 patients ayant bénéficié d’un SHD-E au laboratoire CERBA (thèse CIFRE) dont la lecture diagnostique était négative. Elle a conduit à la mise en évidence deux nouveaux gènes candidats dans la déficience intellectuelle syndromique, dont le gène OTUD7A (article 1). La deuxième stratégie a consisté en la mise au point d’un pipeline bioinformatique pour extraire les données du génome mitochondrial à partir des données de SHD-E. L’ADN mitochondrial n’est pas ciblé par les kits de capture d’exome mais peut être extrait des données capturées indirectement rendant son analyse possible à partir de données de SHD-E préexistantes. A partir de la collection GAD d’exomes de patients sans diagnostic, deux variations causales ont été identifiées chez deux individus atteints de troubles neuro-développementaux sur 928 personnes étudiées, et ainsi résoudre une errance diagnostique dans 0,2 % des patients sans diagnostic (article 2). La troisième stratégie a consisté en la mise en place d’un pipeline bioinformatique d’identification des éléments mobiles au sein des données d’exome, étant attendu qu’environ 0,3 % des variations pathogènes du génome humain ont pour origine l’insertion de novo d’un élément mobile. A partir de la collection GAD d’exomes de 3322 patients sans diagnostic, cette étape a permis d’identifier deux cas en lien avec l’insertion d’un élément Alu au sein d’un exon du gène FERMT1 et du gène GRIN2B (article 3 en cours d’écriture).Cette thèse a permis de repousser certaines limites de la technologie d’exome. D’autres perspectives existent, et sont explorées par l’équipe, en lien avec le projet Européen Solve-RD

Exome sequencing allows detection of relevant pharmacogenetic variants in epileptic patients

International audienceBeyond the identification of causal genetic variants in the diagnosis of Mendelian disorders, exome sequencing can detect numerous variants with potential relevance for clinical care. Clinical interventions can thus be conducted to improve future health outcomes for patients and their at-risk relatives, such as predicting late-onset genetic disorders accessible to prevention, treatment or identifying differential drug efficacy and safety. To evaluate the interest of such pharmacogenetic information, we designed an "in house" pipeline to determine the status of 122 PharmGKB (Pharmacogenomics Knowledgebase) variant-drug combinations in 31 genes. This pipeline was applied to a cohort of 90 epileptic patients who had previously an exome sequencing (ES) analysis, to determine the frequency of pharmacogenetic variants. We performed a retrospective analysis of drug plasma concentrations and treatment efficacy in patients bearing at least one relevant PharmGKB variant. For PharmGKB level 1A variants, CYP2C9 status for phenytoin prescription was the only relevant information. Nineteen patients were treated with phenytoin, among phenytointreated patients, none were poor metabolizers and four were intermediate metabolizers. While being treated with a standard protocol (10-23 mg/kg/30 min loading dose followed by 5 mg/kg/8 h maintenance dose), all identified intermediate metabolizers had toxic plasma concentrations (20 mg/L). In epileptic patients, pangenomic sequencing can provide information about common pharmacogenetic variants likely to be useful to guide therapeutic drug monitoring, and in the case of phenytoin, to prevent clinical toxicity caused by high plasma levels

High efficiency and clinical relevance of exome sequencing in the daily practice of neurogenetics

International audienceObjective To assess the efficiency and relevance of clinical exome sequencing (cES) as a first-tier or second-tier test for the diagnosis of progressive neurological disorders in the daily practice of Neurology and Genetic Departments. Methods Sixty-seven probands with various progressive neurological disorders (cerebellar ataxias, neuromuscular disorders, spastic paraplegias, movement disorders and individuals with complex phenotypes labelled ‘other’) were recruited over a 4-year period regardless of their age, gender, familial history and clinical framework. Individuals could have had prior genetic tests as long as it was not cES. cES was performed in a proband-only (60/67) or trio (7/67) strategy depending on available samples and was analysed with an in-house pipeline including software for CNV and mitochondrial-DNA variant detection. Results In 29/67 individuals, cES identified clearly pathogenic variants leading to a 43% positive yield. When performed as a first-tier test, cES identified pathogenic variants for 53% of individuals (10/19). Difficult cases were solved including double diagnoses within a kindred or identification of a neurodegeneration with brain iron accumulation in a patient with encephalopathy of suspected mitochondrial origin. Conclusion This study shows that cES is a powerful tool for the daily practice of neurogenetics offering an efficient (43%) and appropriate approach for clinically and genetically complex and heterogeneous disorders

Author Correction: Postzygotic inactivating mutations of RHOA cause a mosaic neuroectodermal syndrome

Published Erratum: Correction to: Nature Genetics 51: 1438–1441 https://doi.org/10.1038/s41588-019-0498-4, published online 30 September 2019.In the version of this article initially published, authors Bénédicte Demeer and Bernard Devauchelle were missing the affiliation EA CHIMERE–7516, Université Picardie Jules Verne, Amiens, France. The error has been corrected in the HTML and PDF versions of the article.An amendment to this paper has been published and can be accessed via a link at the top of the paper

Author Correction: Postzygotic inactivating mutations of RHOA cause a mosaic neuroectodermal syndrome [Correction to: Nature Genetics https://doi.org/10.1038/s41588-019-0498-4, published online 30 September 2019]

Indexation en cours. In the version of this article initially published, support from the Wellcome Trust and NIHR to author Veronica A. Kinsler was not included in the Acknowledgements. The error has been corrected in the HTML and PDF versions of the article.International audienceAn amendment to this paper has been published and can be accessed via a link at the top of the paper

Postzygotic inactivating mutations of <em>RHOA</em> cause a mosaic neuroectodermal syndrome

International audienceHypopigmentation along Blaschko's lines is a hallmark of a poorly defined group of mosaic syndromes whose genetic causes are unknown. Here we show that postzygotic inactivating mutations of RHOA cause a neuroectodermal syndrome combining linear hypopigmentation, alopecia, apparently asymptomatic leukoencephalopathy, and facial, ocular, dental and acral anomalies. Our findings pave the way toward elucidating the etiology of pigmentary mosaicism and highlight the role of RHOA in human development and disease

Multiple molecular diagnoses in the field of intellectual disability and congenital anomalies: 3.5% of all positive cases

International audiencePurpose Wide access to clinical exome/genome sequencing (ES/GS) enables the identification of multiple molecular diagnoses (MMDs), being a long-standing but underestimated concept, defined by two or more causal loci implicated in the phenotype of an individual with a rare disease. Only few series report MMDs rates (1.8% to 7.1%). This study highlights the increasing role of MMDs in a large cohort of individuals addressed for congenital anomalies/intellectual disability (CA/ID). Methods From 2014 to 2021, our diagnostic laboratory rendered 880/2658 positive ES diagnoses for CA/ID aetiology. Exhaustive search on MMDs from ES data was performed prospectively (January 2019 to December 2021) and retrospectively (March 2014 to December 2018). Results MMDs were identified in 31/880 individuals (3.5%), responsible for distinct (9/31) or overlapping (22/31) phenotypes, and potential MMDs in 39/880 additional individuals (4.4%). Conclusion MMDs are frequent in CA/ID and remain a strong challenge. Reanalysis of positive ES data appears essential when phenotypes are partially explained by the initial diagnosis or atypically enriched overtime. Up-to-date clinical data, clinical expertise from the referring physician, strong interactions between clinicians and biologists, and increasing gene discoveries and improved ES bioinformatics tools appear all the more fundamental to enhance chances of identifying MMDs. It is essential to provide appropriate patient care and genetic counselling

2.5 years’ experience of GeneMatcher data-sharing: a powerful tool for identifying new genes responsible for rare diseases

International audienceExome sequencing (ES) powerfully identifies the molecular bases of heterogeneous conditions such as intellectual disability and/or multiple congenital anomalies (ID/MCA). Current ES analysis, combining diagnosis analysis restricted to disease-causing genes reported in OMIM database and subsequent research investigation extended to other genes, indicated causal and candidate genes around 40% and 10%. Nonconclusive results are frequent in such ultrarare conditions that recurrence and genotype-phenotype correlations are limited. International data-sharing permits the gathering of additional patients carrying variants in the same gene to draw definitive conclusions on their implication as disease causing. Several web-based tools have been developed and grouped in Matchmaker Exchange. In this study, we report our current experience as a regional center that has implemented ES as a first-line diagnostic test since 2013, working with a research laboratory devoted to disease gene identification