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Early neurophysiological biomarkers and spinal cord pathology in inherited prion disease

2019 Brain

Peter Rudge, Zane Jaunmuktane, Harpreet Hyare, Matthew Ellis, Martin Koltzenburg, John Collinge, Sebastian Brandner, Simon Mead. Early neurophysiological biomarkers and spinal cord pathology in inherited prion disease. Brain, awy358, https://doi.org/10.1093/brain/awy358. The authors would like to apologise for two of the authors' affiliations being listed incorrectly in the original manuscript, they are listed correctly below: John Collinge1,2 and Simon Mead1,2 1 National Prion Clinic, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust (UCLH), London, UK 2 MRC Prion Unit at UCL, Institute of Prion Diseases, 33 Cleveland St. London, W1W 7FF, UK The manuscript has been corrected online. © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.

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Plasma tau/amyloid-β1–42 ratio predicts brain tau deposition and neurodegeneration in Alzheimer’s disease

2019 Brain

doi: 10.1093/brain/awz033pmid: 30753351

Jong-Chan Park, Sun-Ho Han, Dahyun Yi, Min Soo Byun, Jun Ho Lee, Sukjin Jang, Kang Ko, So Yeon Jeon, Yun-Sang Lee, Yu Kyeong Kim, Dong Young Lee, Inhee Mook-Jung for the KBASE Research Group. Plasma tau/amyloid-β1–42 ratio predicts brain tau deposition and neurodegeneration in Alzheimer’s disease. Brain 2019; 142: 1–16; doi:10.1093/brain/awy347. The authors would like to apologize for an omission from the Acknowledgements section, which should read as follows: We sincerely thank the subjects for their participation in this study and technical supporting team members (EH Kim and DB Jung) for plasma processing in Prof. Mook-Jung’s lab. Also, we thank the staff of Department of Neuropsychiatry in Clinical Research Institute of Seoul National University Hospital for this study. The precursor of [18F]flortaucipir was provided by AVID Radiopharmaceuticals. © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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Key role of SMN/SYNCRIP and RNA-Motif 7 in spinal muscular atrophy: RNA-Seq and motif analysis of human motor neurons

2019 Brain

doi: 10.1093/brain/awz036pmid: 30778531

Federica Rizzo, Monica Nizzardo, Shikha Vashisht, Erika Molteni, Valentina Melzi, Michela Taiana, Sabrina Salani, Pamela Santonicola, Elia Di Schiavi, Monica Bucchia, Andreina Bordoni, Irene Faravelli, Nereo Bresolin, Giacomo Pietro Comi, Uberto Pozzoli, Stefania Corti. Key role of SMN/SYNCRIP and RNA-Motif 7 in spinal muscular atrophy: RNA-Seq and motif analysis of human motor neurons. Brain 2018; 142: 276–294, https://doi.org/10.1093/brain/awy330. The authors would like to apologise for a number of the authors’ affiliations being listed incorrectly in the original manuscript, they are listed correctly below: Nereo Bresolin,1,3 Giacomo Pietro Comi,1,3 Uberto Pozzoli2 and Stefania Corti1,3 1 Dino Ferrari Centre, Neuroscience Section, Department of Pathophysiology and Transplantation (DEPT), University of Milan, Milan, Italy 2 Scientific Institute IRCCS E. MEDEA, Computational Biology, Bosisio Parini, Lecco, Italy 3 Foundation IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy The manuscript has been corrected online. © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.

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Variant Creutzfeldt-Jakob disease strain is identical in individuals of two PRNP codon 129 genotypes

2019 Brain

doi: 10.1093/brain/awz129pmid: 31077583

Abigail B. Diack, Aileen Boyle, Christopher Plinston, Emma Hunt, Matthew T. Bishop, Robert G. Will and Jean C. Manson. Variant Creutzfeldt-Jakob disease strain is identical in individuals of two PRNP codon 129 genotypes. Brain 2019; 142: 1416–1428, doi:10.1093/brain/awz076. In the original version of this article, the abstract mistakenly read ‘129 methionine/valine individuals’, rather than the correct ‘129 methionine/methionine individuals’. The corrected line now reads: While some differences were observed at primary and first subpassage, following the second subpassage, strain characteristics in the methionine/valine individual were totally consistent with those of variant Creutzfeldt-Jakob disease transmitted to 129 methionine/methionine individuals thus demonstrated no alteration in strain properties were imposed by passage through the different host genotype. The article has been corrected both online and in print; the authors wish to apologize for this error. © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.

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Heterogeneous neuroimaging findings, damage propagation and connectivity: an integrative view

Cauda,, Franco;Mancuso,, Lorenzo;Nani,, Andrea;Costa,, Tommaso

2019 Brain

doi: 10.1093/brain/awz080pmid: 30907405

Sir, The paper by Darby and colleagues ‘Network localization of heterogeneous neuroimaging findings’ published recently in Brain (Darby et al., 2019) is of great importance, as it highlights the relevant problem of the limited reproducibility of neuroimaging findings: specifically, the inconsistency between the brain alterations variously associated with clinical manifestations in different studies. This is so because disparate lesions could produce a similar neuropsychological syndrome, or because diverse imaging modalities could lead to divergent results on the same disease, or just because symptoms could be related inconsistently to certain findings (Ismail et al., 2011; Schroeter and Neumann, 2011; Boes et al., 2015; Laganiere et al., 2016; Darby et al., 2017). The authors acutely remark that, though a significant part of this inconsistency is likely dependent on methodological factors (Button et al., 2013), another part could derive from the fact that altered areas should be always considered as elements of a network. In other words, if different alterations pertain to the network that is identified to be the substrate of the impaired function, then these alterations might be associated with the same diagnosis or symptomatology (Boes et al., 2015). Darby and colleagues show that the heterogeneity of the locations of alterations could be merged in disease-specific or symptom-specific networks, thus arguing that part of the lack of replicability of neuroimaging studies is due to an erroneous interpretation of what is deemed to be reproducible. As a matter of fact, research approaches that do not take into account the network-like pattern of brain alterations are likely destined to produce inconsistent findings. Darby and colleagues, however, do not discuss this idea in the light of the growing literature that is currently investigating the patterns of pathological connectivity. Indeed, not only could a specific disease present different morphological alterations in different studies, but also various disorders could present similar anatomo-pathological underpinnings (Goodkind et al., 2015; Cauda et al., 2018, 2019). In this case, meta-analytical techniques have shown that the pathological networks are remarkably similar across different diagnoses. In particular, the executive control network (ECN) and the salience network (SN) seem to be largely involved in many brain diseases, an interesting find that reflects the impairment of attention, working memory and control of behaviour that is commonly seen in a variety of diagnostic categories (McTeague et al., 2016). For example, although we are inclined to think about brain pathology based on traditional diagnostic classification, our group has demonstrated that differences between brain alterations, which involve the ECN and the SN and are associated with schizophrenia, autism, and obsessive-compulsive spectrum disorders, are not straightforward and clear-cut (Cauda et al., 2017). Therefore, as these three diseases have several points of similarity from the clinical point of view, it is possible that their categorization may have been artificially constructed over a latent multidimensional system of symptoms (Buckholtz and Meyer-Lindenberg, 2012). These observations, which appear to be in contrast with the network-specificity for each neurodegenerative disease reported by Darby and colleagues, raise at least a couple of questions that lead to two different views on brain disorders. Can each brain disease be associated with a specific pathological network as its anatomical correlate? Or is it possible that many pathological conditions might disrupt different overlapping networks? Taken together, these two perspectives describe a scenario in which the intra-diagnosis variability of alterations is higher than expected, while the inter-diagnosis is lower. Such a situation would imply the impossibility to recognize a clear correspondence between anatomical patterns of alterations and disease, thus denying the ability of neuroimaging research to shed light on brain pathology. However, the two perspectives may also have points of convergence. First, they both rely on a connectivity paradigm. Second, they both highlight the relationship between networks and symptoms. These points suggest the possibility of an integrative model capable of linking the anatomical heterogeneity with the transdiagnostic overlap of brain alterations through the conceptualisation of a ‘pathoconnectivity’ framework, as illustrated in Fig. 1. Figure 1 View largeDownload slide Overview of the pathoconnectivity model: symptoms arise from network dysfunction. Alterations to a set of areas can occur in a network-like fashion because of their pathoconnectivity patterns. This model proposes that large-scale networks convey a symptom-specific risk for brain diseases, and that nosological diagnoses can be seen as the results of aggregates of altered networks. Figure 1 View largeDownload slide Overview of the pathoconnectivity model: symptoms arise from network dysfunction. Alterations to a set of areas can occur in a network-like fashion because of their pathoconnectivity patterns. This model proposes that large-scale networks convey a symptom-specific risk for brain diseases, and that nosological diagnoses can be seen as the results of aggregates of altered networks. As put forward by Zhou et al. (2012), there are four not-mutually-exclusive mechanisms for the development of network-like patterns of co-alterations: (i) that all the nodes of a pathological network share a specific vulnerability to the disease, which can depend on a mutual biological factor such as the same gene expression; (ii) that the most connected nodes (hubs) of the brain are especially exposed to metabolic stress due to their intense work of integration of information; (iii) that the disruption of the release of inter-nodal trophic factor would produce anatomical alterations in the areas connected to an impaired region; and (iv) that misfolded proteins or other toxic molecules could spread across connected neurons in a prion-like fashion. The first mechanism suggests that genetic co-expression could explain at least part of the pathological connectivity, while the second one takes into account the strong involvement of hubs in brain diseases (Crossley et al., 2014). In turn, the last two mechanisms imply that brain pathology might propagate along structural pathways. So, it seems that genetic, structural and functional profiles of connectivity are all involved in the development of brain diseases (Cauda et al., 2018). It is therefore possible that a pathology originates in a set of areas sharing a common vulnerability, and then spreads to other regions following axonal pathways. Within this picture, hubs would be particularly affected as they are easily reachable by toxic agents and especially subjected to metabolic stress. In the first stages of a disease, one or more networks would be partially affected by pathological alterations, which then would gradually propagate within and between networks through intermodular hubs. This would impair the normal function of a network, thus leading to a specific symptom. Furthermore, if more networks are involved, because of a shared vulnerability or the pathological spread, a whole symptomatology will be associated to the pathology, possibly resulting in a typical syndrome or in a clinical diagnosis of co-morbidity of diseases. As several disorders have specific and different starting points, which are determined by genetic or environmental factors, it is reasonable to expect, as observed by Darby and colleagues, that each disorder may be characterized by a particular pathological network. Still, during its progression, the disease would spread to different networks through the brain hubs. And, similar to what happens for the hub nodes, the networks characterized by a high centrality within the connectome would be more vulnerable, which is why the ECN, the SN and the default mode network are frequently found to be involved in brain disorders (Sha et al., 2019). This pathoconnectivity model could explain both the transdiagnostic overlap between co-alteration networks of different diseases and the reason why certain symptoms are particularly recurrent across diseases. Furthermore, our pathoconnectivity model produces three important results. First, it clarifies that the heterogeneity observed by Darby and colleagues in neurodegenerative diseases has a different nature from that of focal lesions. Although both neurodegenerative progressions and focal lesions involve a connectivity paradigm, they also differ with regard to their manifestations. In fact, while networks are mostly anatomically intact in case of focal lesions, so that the heterogeneity really reflects different loci of alteration for each subject, in case of neurodegenerative diseases entire networks are likely to be affected as the pathological spread of alterations gradually advances. Therefore, the variability in the location of alterations of brain diseases likely depends on the starting point of the pathological propagation, so that such heterogeneity should be considered the result of methodological limitations rather than a true inconsistence. Second, the fact that networks of co-alterations are more tightly associated with symptoms than with current nosological categories suggests that pathological classifications should be revised and considered as multidimensional systemizations, which could take into account more accurately both biological and semeiotic complexities. At the same time, the converging evidence of the pathological involvement of different networks, including the ECN and the SN, could substantiate the hypothesis of the existence of a general psychopathological factor at the root of different brain diseases (Caspi et al., 2014; Elliott et al., 2018). Third, the pathoconnectivity model that we have described is consistent with a hierarchical structure of brain pathology, which is thought to match better the hierarchical structure of the human connectome. In conclusion, the paper by Darby et al. (2019) is deeply interesting, not only because it stirs up attention for the issue of the heterogeneity of neuroimaging findings, but also because it opens a fruitful debate on the best theoretical and methodological approach in order to deal with the complex picture that transdiagnostic studies have revealed about how brain disorders originate and develop. Data availability Data sharing is not applicable to this article as no new data were created or analysed. Funding This study was supported by the Fondazione Carlo Molo (F.C., PI), Turin. Competing interests The authors report no competing interests. References Boes AD , Prasad S , Liu H , Liu Q , Pascual-Leone A , Caviness VS et al. . Network localization of neurological symptoms from focal brain lesions . Brain 2015 ; 138 : 3061 – 75 . Google Scholar Crossref Search ADS PubMed Buckholtz JW , Meyer-Lindenberg A . Psychopathology and the human connectome: toward a transdiagnostic model of risk for mental illness . Neuron 2012 ; 74 : 990 – 1004 . Google Scholar Crossref Search ADS PubMed Button KS , Ioannidis JPA , Mokrysz C , Nosek BA , Flint J , Robinson ESJ et al. . Power failure: why small sample size undermines the reliability of neuroscience . Nat Rev Neurosci 2013 ; 14 : 365 – 76 . Google Scholar Crossref Search ADS PubMed Caspi A , Houts RM , Belsky DW , Goldman-Mellor SJ , Harrington H , Israel S et al. . The p factor: one general psychopathology factor in the structure of psychiatric disorders? Clin Psychol Sci 2014 ; 2 : 119 – 37 . Google Scholar Crossref Search ADS PubMed Cauda F , Costa T , Nani A , Fava L , Palermo S , Bianco F et al. . Are schizophrenia, autistic, and obsessive spectrum disorders dissociable on the basis of neuroimaging morphological findings?: A voxel-based meta-analysis . Autism Res 2017 ; 10 : 1079 – 95 . Google Scholar Crossref Search ADS PubMed Cauda F , Nani A , Manuello J , Liloia D , Tatu K , Vercelli U et al. . The alteration landscape of the cerebral cortex . Neuroimage 2019 ; 184 : 359 – 71 . Google Scholar Crossref Search ADS PubMed Cauda F , Nani A , Manuello J , Premi E , Palermo S , Tatu K et al. . Brain structural alterations are distributed following functional, anatomic and genetic connectivity . Brain 2018 ; 141 : 3211 – 32 . Google Scholar Crossref Search ADS PubMed Crossley NA , Mechelli A , Scott J , Carletti F , Fox PT , Mcguire P et al. . The hubs of the human connectome are generally implicated in the anatomy of brain disorders . Brain 2014 ; 137 : 2382 – 95 . Google Scholar Crossref Search ADS PubMed Darby RR , Joutsa J , Fox MD . Network localization of heterogeneous neuroimaging findings . Brain 2019 ; 142 : 70 – 9 . Google Scholar Crossref Search ADS PubMed Darby RR , Laganiere S , Pascual-Leone A , Prasad S , Fox MD . Finding the imposter: Brain connectivity of lesions causing delusional misidentifications . Brain 2017 ; 140 : 497 – 507 . Google Scholar Crossref Search ADS PubMed Elliott ML , Romer A , Knodt AR , Hariri AR . A Connectome-wide Functional Signature of Transdiagnostic Risk for Mental Illness . Biol Psychiatry 2018 ; 84 : 452 – 59 . Google Scholar Crossref Search ADS PubMed Goodkind M , Eickhoff SB , Oathes DJ , Jiang Y , Chang A , Jones-Hagata LB et al. . Identification of a common neurobiological substrate for mental Illness . JAMA Psychiatry 2015 ; 72 : 305 – 15 . Google Scholar Crossref Search ADS PubMed Ismail Z , Nguyen MQ , Fischer CE , Schweizer TA , Mulsant BH , Mamo D . Neurobiology of delusions in Alzheimer’s disease . Curr Psychiatry Rep 2011 ; 13 : 211 – 18 . Google Scholar Crossref Search ADS PubMed Laganiere S , Boes AD , Fox MD . Network localization of hemichorea-hemiballismus . Neurology 2016 ; 86 : 2187 – 95 . Google Scholar Crossref Search ADS PubMed McTeague LM , Goodkind MS , Etkin A . Transdiagnostic impairment of cognitive control in mental illness . J. Psychiatry Res . 2016 ; 83 : 37 – 46 . Google Scholar Crossref Search ADS Schroeter ML , Neumann J . Combined imaging markers dissociate Alzheimer’s disease and frontotemporal lobar degeneration - An ALE meta-analysis . Front Aging Neurosci 2011 ; 3 : 1 – 6 . Google Scholar Crossref Search ADS PubMed Sha Z , Wager TD , Mechelli A , He Y . Common dysfunction of large-scale neurocognitive networks across psychiatric disorders . Biol Psychiatry 2019 ; 85 : 379 – 88 . Google Scholar Crossref Search ADS PubMed Zhou J , Gennatas ED , Kramer JH , Miller BL , Seeley WW . Predicting regional neurodegeneration from the healthy brain functional connectome . Neuron 2012 ; 73 : 1216 – 27 . Google Scholar Crossref Search ADS PubMed © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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Reply: Novel GABRA2 variants in epileptic encephalopathy and intellectual disability with seizures

Jenkins,, Andrew;Escayg,, Andrew

2019 Brain

doi: 10.1093/brain/awz086pmid: 31032848

Sir, We read the letter from Maljevic et al. (2019) on GABRA2 variants with great interest as there are remarkable similarities between their work and our study as described in Butler et al. (2018). While it is clear that mutations in GABAA receptors are responsible for various types of epilepsy, only two GABRA2 mutations were reported prior to the current study (Butler et al., 2018; Orenstein et al., 2018). The authors now describe the identification and functional characterization of four additional GABRA2 variants. Consistent with Orenstein et al. (2018) and our data, three of the four mutations reported by Maljevic et al. arose de novo in the affected individuals. Interestingly, they also identified a relatively less severe variant that was inherited from a mosaic parent, a phenomenon that has been observed in other genetic forms of epilepsy. Functional characterization of the variants described by Maljevic et al. and in our study, revealed properties that are consistent with both gain and loss of ion channel function, yet, all are associated with epilepsy. How do we rationalize these apparently opposing molecular effects that result in the same disease? Loss of function variants are relatively straightforward to explain. A loss of inhibition will result in an excitation/inhibition (E/I) imbalance, with excess excitation driving seizures. However, four of the epilepsy variants reported between the studies of Maljevic et al. (2019) and Butler et al. (2018) are predicted to result in a gain of inhibitory function. A priori, this seems counter-intuitive: how does more inhibition lead to seizures? This is something we addressed in our previous study (Butler et al., 2018). GABRA2 expression turns on early in the developing nervous system, before the mature chloride gradient is established. Therefore, early in life, GABAA receptor α2 subunits form excitatory ion channels. The constant depolarization of neurons expressing these variants will likely lead to profound changes during development, resulting not only in seizures, but perhaps developmental delay and cognitive impairment as well. Not surprisingly, all of the mutations that were identified by Maljevic et al. are located in critical positions in the receptor. The Met263Thr variant is located in a region that is coupled to the GABA binding site. It is part of the recently reported low affinity benzodiazepine binding site (Laverty et al., 2019; Masiulis et al., 2019). It is quite possible that alterations at this site mimic the changes induced by benzodiazepine binding, a potent positive allosteric modulator of GABAARs, resulting in increased activation. The M2 variants at the 2′ and 9′ positions, Val284Ala and Leu291Val, form the narrowest part of the ion channel pore and possibly the gate itself. The Leu291Val variant, which is adjacent to the Thr292Lys variant that we reported, is especially interesting, given that it reduces current amplitude. Leu291 is reported to close the ion channel in the presence of picrotoxin and likely serves as part of the channel gate. Since valine is smaller than leucine, it is possible that the Leu291Val variant results in a channel that cannot close as easily, or may even remain tonically open. Therefore, Maljevic et al. may have discovered another variant that behaves much like our own GABRA2 Thr292Lys variant. Further experiments with picrotoxin will be needed to confirm this hypothesis. Finally, the Phe325Leu variant located at the top of the M3 segment, is located close to the binding pocket for a group of positive allosteric modulators. The residue is also close to another important segment, the M2-M3 link, known to be associated with channel activation and epilepsy (Baulac et al., 2001; Kash et al., 2003) However, unlike the other variants, no specific role has been ascribed to this residue. The more modest role Phe325 might play in channel activation is consistent with the less severe phenotype associated with the Phe325Leu variant. Both of our studies underscore the importance of functional characterization of variants. If we are to understand how molecular differences lead to disease, it is critical that we map the effect of all possible variants across all of the functionally important parts of each receptor and ion channel linked to epilepsy. This is now a practical problem that we hope to solve using high-throughput patch clamp systems (Forkuo et al., 2018). Furthermore, it will be important to also examine the functional effects of these variants in more physiologically relevant cell types, since current data were derived from transfected HEK293T cells (our study) and Xenopus laevis (Maljevic et al.). Studying how variants alter E/I balance in human neurons is now possible using iPSCs to generate cultured neurons or organoids (Birey et al., 2017; Pasca et al., 2015). Repeating our electrophysiological studies in human cells obtained from epilepsy patients might yield more accurate insight into disease etiology, thereby providing opportunities for personalized treatments. Data availability Data sharing is not applicable to this article as no new data were created or analysed in this work. Funding No funding was received towards this work. Competing interests The authors report no competing interests. References Baulac S , Huberfeld G , Gourfinkel-An I , Mitropoulou G , Beranger A , Prud'homme JF , et al. First genetic evidence of GABA(A) receptor dysfunction in epilepsy: a mutation in the gamma2-subunit gene . Nat Genet 2001 ; 28 : 46 – 8 . Google Scholar PubMed Birey F , Andersen J , Makinson CD , Islam S , Wei W , Huber N , et al. Assembly of functionally integrated human forebrain spheroids . Nature 2017 ; 545 : 54 – 9 . Google Scholar Crossref Search ADS PubMed Butler KM , Moody OA , Schuler E , Coryell J , Alexander JJ , Jenkins A , et al. De novo variants in GABRA2 and GABRA5 alter receptor function and contribute to early-onset epilepsy . Brain 2018 ; 141 : 2392 – 405 . Google Scholar Crossref Search ADS PubMed Forkuo GS , Nieman AN , Kodali R , Zahn NM , Li G , Rashid Roni MS , et al. A novel orally available asthma drug candidate that reduces smooth muscle constriction and inflammation by targeting GABAA receptors in the lung . Mol Pharm 2018 ; 15 : 1766 – 77 . Google Scholar Crossref Search ADS PubMed Kash TL , Jenkins A , Kelley JC , Trudell JR , Harrison NL . Coupling of agonist binding to channel gating in the GABA(A) receptor . Nature 2003 ; 421 : 272 –7 5 . Google Scholar Crossref Search ADS PubMed Laverty D , Desai R , Uchański T , Masiulis S , Stec WJ , Malinauskas TJ , et al. Cryo-EM structure of the human alpha1beta3gamma2 GABAA receptor in a lipid bilayer . Nature 2019 ; 565 : 516 – 20 . Google Scholar Crossref Search ADS PubMed Maljevic S , Kerem B , Aung YH , Forster IC , Mignot C , Buratti J , et al. Novel GABRA2 variants in epileptic encephalopathy and intellectual disability with seizures . Brain 2019 ; 142 : e15 . Masiulis S , Desai R , Uchański T , Serna Martin I , Laverty D , Karia DT , et al. GABAA receptor signaling mechanisms revealed by structural pharmacology . Nature 2019 ; 565 : 454 – 9 . Google Scholar Crossref Search ADS PubMed Orenstein N , Goldberg-Stern H , Straussberg R , Bazak L , Weisz Hubshman M , Kropach N , et al. A de novo GABRA2 missense mutation in severe early-onset epileptic encephalopathy with a choreiform movement disorder . Eur J Paediatr Neurol 2018 ; 22 : 516 – 24 . Google Scholar Crossref Search ADS PubMed Paşca AM , Sloan SA , Clarke LE , Tian Y , Makinson CD , Huber N , et al. Functional cortical neurons and astrocytes from human pluripotent stem cells in 3D culture . Nat Methods 2015 ; 12 : 671 – 8 . Google Scholar Crossref Search ADS PubMed © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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Reply: Heterogeneous neuroimaging findings, damage propagation and connectivity: an integrative view

Darby, R, Ryan;Fox, Michael, D

2019 Brain

doi: 10.1093/brain/awz081pmid: 30907402

Sir, We thank Cauda et al. (2019) for their letter discussing the implications of our recent paper in which we showed that seemingly heterogeneous neuroimaging findings are reproducible to common brain networks (Darby et al., 2018a). The authors propose a generalized ‘pathoconnectivity model’ for understanding brain diseases. They suggest three implications of their model, which we respond to below. We agree with Cauda et al. (2019) that network localization matches symptoms, not underlying pathology. We investigated neuroimaging studies of patients with the same clinical dementia syndromes, but not necessarily the same underlying neuropathological diagnosis. For instance, patients with behavioral variant frontotemporal dementia can have either tau or TDP-43 pathology, and patients with corticobasal syndrome can have either tau or Alzheimer’s pathology, yet these different pathologies result in similar clinical symptoms and localize to the same brain network. Second, we found common network localization for delusions in patients with brain lesions and Alzheimer’s disease (Darby et al., 2018a), and for disordered free will perception in patients with brain lesions and psychiatric diseases like catatonia, non-epileptic seizures, and conversion disorder (Darby et al., 2018b). These results and others support the idea that neuropsychiatric symptoms localize to common brain networks regardless of the underlying pathology or diagnosis. We also agree that there is a hierarchical structure to brain-behavior relationships matching the structure of the human connectome. Prior studies have suggested that simple neurological symptoms localize to specific brain regions, while more complex symptoms localize to brain networks (Siegel et al., 2016). Similarly, we found previously that lesions causing delusions localized to one brain network, but lesions causing a specific and complex type of delusion (delusional misidentifications) were also part of a second brain network related to the content of the delusion, suggesting an internetwork localization (Darby et al., 2017). Finally, prior studies have related consciousness to global changes across the entire brain/connectome (Chennu et al., 2017). This prior work supports the hypothesis that different symptoms may localize to different levels of brain organization (i.e. regional, subnetwork, network, internetwork, and global levels). However, we disagree with Cauda et al. (2019) that the reasons for network localization in focal brain lesions and neurodegenerative disorders are different. It is true that some mechanisms proposed to account for network localization in neurodegenerative disease may not apply to network localization of focal brain lesions, such as prion-like spread of misfolded proteins (Seeley et al., 2009; Zhou et al., 2012). However, most mechanisms proposed to account for network localization of focal brain lesions could apply to network localization of neurodegenerative disease, such as diaschisis (Carrera and Tononi, 2014; Fox, 2018). Heterogeneity of lesion locations across different patients with the same symptom may be analogous to the heterogeneity of neurodegenerative ‘epicentres’ across different patients with similar symptoms, which may be analogous to heterogeneous neuroimaging findings across different studies of that symptom. This possibility is supported by our finding that lesions and dementia studies investigating the same clinical symptom (delusions) had the same network localization (Darby et al., 2018a). As such, while we agree that different mechanisms ‘may’ underlie network localization of lesions and neurodegenerative disease, a common mechanism is also possible, and this convergence could have important implications for understanding brain disease in general. In summary, we thank the authors for discussing how our paper on network localization of heterogeneous neuroimaging findings contributes to a broader theory on understanding brain behavior relationships. We agree with the authors that (i) network localization is not disease specific, and can be used to localize common symptoms across different neurological and psychiatric diseases; and (ii) specific symptoms may localize to different levels of the connectome, based on the complexity of the cognitive processes involved. Whether the mechanisms underlying network localization of focal brain lesions, neurodegenerative epicenters, and heterogenous neuroimaging findings are the same or different requires further work. Data availability Data sharing is not applicable to this article as no new data were created or analysed in this work. Funding Investigators were supported by funding from the Alzheimer’s Association (R.D.), the BrightFocus Foundation (R.D.), The Vanderbilt Institution for clinical and translational research (R.D.), the NIH (R01MH113929, K23NS083741 to M.F.), the Nancy Lurie Marks Foundation (M.F.), and the Dystonia Medical Research Foundation (M.F.). Competing interests The authors report no competing interests. References Cauda F , Mancuso L , Nani A , Costa T . Heterogeneous neuroimaging findings, damage propagation and connectivity: an integrative view . Brain 2019 ; 142 : e17 . Carrera E , Tononi G . Diaschisis: past, present, future . Brain 2014 ; 137 : 2408 – 22 . Google Scholar Crossref Search ADS PubMed Chennu S , Annen J , Wannez S , Thibaut A , Chatelle C , Cassol H et al. . Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness . Brain 2017 ; 140 : 2120 – 32 . Google Scholar Crossref Search ADS PubMed Darby RR , Joutsa J , Fox MD . Network Localization of Neuroimaging Findings . Brain 2018a ; 142; 70 – 9 . Darby RR , Joutsa J , Fox MD . Lesion network localization of free will . Proc Natl Acad Sci USA 2018b ; 115 : 10792 – 97 . Google Scholar Crossref Search ADS Darby RR , Laganiere S , Pascual-Leone A , Prasad S , Fox MD . Finding the imposter: brain connectivity of lesions causing delusional misidentifications . Brain 2017 ; 140 : 497 – 507 . Google Scholar Crossref Search ADS PubMed Fox MD . Localizing symptoms to brain networks using the human connectome . N Engl J Med 2018 : 2237 – 45 . Seeley WW , Crawford RK , Zhou J , Miller BL , Greicius MD . Neurodegenerative diseases target large-scale human brain networks . Neuron 2009 ; 62 : 42 – 52 . Google Scholar Crossref Search ADS PubMed Siegel JS , Ramsey LE , Snyder AZ , Metcalf NV , Chacko RV , Weinberger K et al. . Disruptions of network connectivity predict impairment in multiple behavioral domains after stroke . Proc Natl Acad Sci 2016 ; 113 : E4367 – 376 . Google Scholar Crossref Search ADS PubMed Zhou J , Gennatas ED , Kramer JH , Miller BL , Seeley WW . Predicting regional neurodegeneration from the healthy brain functional connectome . Neuron 2012 ; 73 : 1216 – 27 . Google Scholar Crossref Search ADS PubMed © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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Novel GABRA2 variants in epileptic encephalopathy and intellectual disability with seizures

Maljevic,, Snezana;Keren,, Boris;Aung, Ye, Htet;Forster, Ian, C;Mignot,, Cyril;Buratti,, Julien;Lafitte,, Aurélie;Freihuber,, Cécile;Rodan, Lance, H;Bergin,, Ann;Hubert,, Laurence;Poirier,, Karine;Munnich,, Arnold;Besmond,, Claude;Hauser,, Natalie;

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Editorial

Kullmann, Dimitri, M

2019 Brain

doi: 10.1093/brain/awz110pmid: 31032847

The cover of this issue relates to an article by Jae-Sik Nam and co-workers, which argues that incidental unruptured intracranial aneurysms are not linked to an increased risk of subarachnoid haemorrhage in patients undergoing cardiovascular surgery. Elsewhere in this issue Elaine Kingwell and colleagues report that beta-interferon treatment was associated with a 32% decrease in mortality in patients with multiple sclerosis in two cohorts from Canada and France. This article has already attracted considerable interest since it appeared online, and the accompanying Scientific Commentary by Gavin Giovannoni rehearses the argument for early and aggressive treatment of multiple sclerosis. Also arguing against therapeutic nihilism, albeit in a different field, Gabriel Brooks and co-workers report that successful recanalization in patients with extensive middle cerebral artery territory ischaemia, as defined by a low initial Alberta Stroke Program Early Computer Tomography Score, resulted in a reduction of oedema formation, fewer malignant infarctions and better clinical outcome. A further article by Daniel Rubin et al. describes the range of neurological complications observed in patients undergoing chimeric antigen receptor (CAR) T cell therapy. A common theme of these papers is that they were based on observational studies. A randomized double-blinded clinical trial is, however, the subject of another paper by Judith Pijpers, Dennis Kies and colleagues, who show that botulinum toxin A offered no additional benefit to patients with chronic migraine and medication overuse undergoing acute withdrawal. This issue of Brain also features a review of the hypotheses and available evidence relating to the pathogenic role of cytoplasmic aggregation of transactive response DNA-binding protein 43 (TDP-43) in ALS, by Rudolf Hergesheimer, Anna Chami and colleagues. Readers of Brain who enjoy the special luxury of holding the issue in their hands will easily navigate to the Dorsal Column, which this month consists of a review by Andrew Scull of Genetics in the Madhouse: The Unknown History of Human Heredity by Theodore Porter. Porter stresses the role of Karl Pearson FRS, who held the first Galton chair in eugenics as University College London and was arguably the founder of mathematical statistics, in providing the appearance of scientific rigour to the study of insanity and mental feebleness. Although Pearson never published in Brain, his mentor Sir Francis Galton FRS contributed an article entitled Psychometric Experiments to the second volume, which appeared in July 1879. The experiments described included such activities as walking along Pall Mall while counting the number of ideas that came into his head and noting how many mental associations he could make in an interval determined by starting and stopping a chronograph. ‘On throwing these results into a common statistical hotchpot’, Galton concludes that he was able to form 550 associations in 660 s, of which 289 were original. The current Instructions to Authors of Brain insist on greater transparency in the application of statistical methods, which may include such tools as principal component analysis, correlation coefficients and chi-squared tests, all of which were introduced by Pearson. © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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Survival: the ultimate long-term outcome in multiple sclerosis

Giovannoni,, Gavin

2019 Brain

doi: 10.1093/brain/awz101pmid: 31032846

This scientific commentary refers to ‘Multiple sclerosis: effect of beta interferon treatment on survival’, by Kingwell et al. (doi:10.1093/brain/awz055). When the initial positive results of the pivotal interferon beta-1b trial in relapsing-remitting multiple sclerosis were published (Paty and Li, 1993; IFNB Multiple Sclerosis Study Group, 2001), they were met in some quarters with scepticism. Many commentators wondered if the efficacy was clinically meaningful and questioned whether interferon beta should be prescribed at all. The subsequent uptake of interferon beta as an ‘effective treatment’ for multiple sclerosis was slow in many areas and led to wide variation in prescribing and access to treatments, which persists to this day both regionally and globally. In the UK, NICE—formerly called the National Institute for Clinical Excellence—ruled that interferon beta treatments were not cost-effective and hence could not be prescribed under the National Health Service. This led the Department of Health to introduce a risk-sharing scheme (RSS) to allow people with multiple sclerosis to access interferon beta as part of a national study with the aim of ascertaining whether or not the three interferon beta formulations and glatiramer acetate were cost-effective in clinical practice. The RSS cohort, when compared with a modelled untreated control population from British Columbia, showed that declines in Expanded Disability Status Scale (EDSS) scores and in utility, or quality of life, were significantly reduced for relapsing-remitting patients, with an approximately 4-year delay to needing a walking stick (EDSS 6.0) (Palace et al., 2019). As a reasonable proxy for secondary progressive multiple sclerosis, this result indicates that interferon beta delays the onset of the more advanced stages of multiple sclerosis. Subgroup analyses of the data show that outcomes are better for patients treated earlier and with lower EDSS scores (Palace et al., 2019). In this issue of Brain, Kingwell and co-workers add to these data by revealing the long-term impact of interferon beta on survival in a ‘real-world’ setting (Kingwell et al., 2019). In a population-based observational study of patients with relapsing multiple sclerosis naïve to disease-modifying therapies (DMTs) and other immunosuppressant treatments, they show an association between all-cause mortality and interferon beta exposure. They used a nested case-control design with up to 20 controls, matched to cases or deaths by country, sex, age, year and disability level at study entry. The odds of interferon beta exposure were 32% lower among cases than controls (odds ratio 0.68; 95% confidence interval 0.53–0.89) and survival increased with more than 3 years of interferon beta exposure, although not with between 6 months and 3 years of exposure. The investigators assessed the cause of death from the death certificates and found the majority of deaths to be multiple sclerosis-related. However, they were unable to assess whether the reduction in mortality could have been due to the pleiotropic effects of interferon beta on, for example, infections and malignancies. The latter, however, seems unlikely in view of the causes of death. These results now confirm the, often criticized, 21-year observational follow-up study on all-cause mortality in the cohort of 372 subjects who participated in the pivotal clinical trial of interferon beta-1b (Goodin et al., 2012). Remarkably, 98.4% (366 of 372) of the original patients were identified, and, of these, 81 deaths were recorded. Subjects originally randomized to interferon beta-1b (250 μg) had a significant reduction in all-cause mortality over the 21-year period compared with placebo (P = 0.02, hazard ratio 0.53; 95% confidence interval 0.31–0.90). The majority of deaths were also considered to be multiple sclerosis-related, making off-target effects of interferon beta unlikely (Goodin et al., 2012). These Canadian and French results should surely establish beyond reasonable doubt the real-life efficacy of DMTs for the treatment of multiple sclerosis, which in turn should support the early effective treatment philosophy to maximize long-term outcomes (Giovannoni et al., 2016). Can we extrapolate these findings further? Numerous phase three programmes have shown that several of the newer and more effective DMTs result in better disease outcome, in terms of disability progression, when compared to interferon beta. In addition, these controlled data are supported by several real-life datasets showing that on average patients with multiple sclerosis do better when started on, or switched to, higher efficacy therapies compared to the injectable first-generation DMTs, interferon beta and glatiramer acetate. These results are likely to impact on time to higher EDSS scores, including time to secondary progressive multiple sclerosis, and time to EDSS 10 or death. It is reassuring to note that in a recently reported international study, with prospective data from 68 neurology centres in 21 countries, commencing therapy between 1988 and 2012 on the higher efficacy DMTs natalizumab, fingolimod or alemtuzumab was associated with a lower risk of conversion to secondary progressive multiple sclerosis compared to initial treatment with glatiramer acetate or interferon beta (Brown et al., 2019). In an epoch analysis using a time frame of over 30 years, Capra and colleagues (2017) in Brescia, Italy, demonstrate that patients diagnosed in more recent epochs need a walking aid (EDSS = 6) at an older age, which is associated with changes in the treatment patterns experienced across the period. It is clear that in the modern era, DMTs have not only changed the short-term outcomes of people with multiple sclerosis, but have had a long-term impact on time to secondary progressive multiple sclerosis and hard disability outcomes, in particular time to needing a walking stick, and death. It is also becoming clear that the earlier DMTs are started, the better the outcome, and that high efficacy therapies are superior to interferon beta and glatiramer acetate (He et al., 2015; Spelman et al., 2015; Kalincik et al., 2017; Harding et al., 2019). Based on these observations, treating to a target of ‘no evident disease activity’ and flipping the pyramid, i.e. starting with the most effective therapies first-line or early, is likely to improve disability outcomes further (Giovannoni, 2018). Whether these gains will be viewed as marginal and come with unacceptable risks and costs remains to be determined. Kingwell and colleagues, however, must be congratulated on filling in these knowledge gaps, which cannot be addressed by the pivotal phase 3 trials. The question now is not whether or not to treat multiple sclerosis, but whether or not we are undertreating the disease. Is the new therapeutic nihilism ‘undertreated’ or ‘smouldering’ multiple sclerosis? Competing interests In the last 5 years G.G. has received compensation for participating on Advisory Boards in relation to clinical trial design, trial steering committees and data and safety monitoring committees from: Abbvie, Actelion, Atara Bio, Biogen, Celgene, Genzyme-Sanofi, Genentech, GSK, Merck-Serono, Novartis, Roche, Synthon BV and Teva. References Brown JWL , Coles A , Horakova D , Havrdova E , Izquierdo G , Prat A et al. . Association of initial disease-modifying therapy with later conversion to secondary progressive multiple sclerosis . JAMA 2019 ; 321 : 175 – 87 . Google Scholar Crossref Search ADS PubMed Capra R , Cordioli C , Rasia S , Gallo F , Signori A , Sormani MP . Assessing long-term prognosis improvement as a consequence of treatment pattern changes in MS . Mult Scler 2017 ; 23 : 1757 – 61 . Google Scholar Crossref Search ADS PubMed Giovannoni G , Butzkueven H , Dhib-Jalbut S , Hobart J , Kobelt G , Pepper G et al. . Brain health: time matters in multiple sclerosis . Mult Scler Relat Disord 2016 ; 9 (Suppl 1): S5 – 48 . Google Scholar Crossref Search ADS PubMed Giovannoni G . Disease-modifying treatments for early and advanced multiple sclerosis: a new treatment paradigm . Curr Opin Neurol 2018 ; 31 : 233 – 43 . Google Scholar PubMed Goodin DS , Reder AT , Ebers GC , Cutter G , Kremenchutzky M , Oger J et al. . Survival in MS: a randomized cohort study 21 years after the start of the pivotal IFNβ-1b trial . Neurology 2012 ; 78 : 1315 – 22 . Google Scholar Crossref Search ADS PubMed Harding K , Williams O , Willis M , Hrastelj J , Rimmer A , Joseph F et al. . Clinical outcomes of escalation vs early intensive disease-modifying therapy in patients with multiple sclerosis . JAMA Neurol 2019 . doi: 10.1001/jamaneurol.2018.4905 . He A , Spelman T , Jokubaitis V , Havrdova E , Horakova D , Trojano M et al. . Comparison of switch to fingolimod or interferon beta/glatiramer acetate in active multiple sclerosis . JAMA Neurol 2015 ; 72 : 405 – 13 . Google Scholar Crossref Search ADS PubMed IFNB Multiple Sclerosis Study Group. Interferon beta-lb is effective in relapsing-remitting multiple sclerosis. I. Clinical results of a multicenter, randomized, double-blind, placebo-controlled trial . Neurology 1993 ; 43 : 655 – 61 . Crossref Search ADS PubMed Kalincik T , Brown JWL , Robertson N , Willis M , Scolding N , Rice CM et al. . Treatment effectiveness of alemtuzumab compared with natalizumab, fingolimod, and interferon beta in relapsing-remitting multiple sclerosis: a cohort study . Lancet Neurol 2017 ; 16 : 271 – 81 . Google Scholar Crossref Search ADS PubMed Kingwell E , Leray E , Zhu F , Petkau J , Edan G , Oger J , Tremlett H . Multiple sclerosis: effect of beta interferon treatment on survival . Brain 2019 ; 142 : 1324 – 33 . Palace J , Duddy M , Lawton M , Bregenzer T , Zhu F , Boggild M et al. . Assessing the long-term effectiveness of interferon beta and glatiramer acetate in multiple sclerosis: final 10-year results from the UK multiple sclerosis risk-sharing scheme . J Neurol Neurosurg Psychiatry 2019 ; 90 : 251 – 60 . Google Scholar Crossref Search ADS PubMed Paty DW , Li DK . Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. II. MRI analysis results of a multicenter, randomized, double-blind, placebo-controlled trial. UBC MS/MRI Study Group and the IFNB Multiple Sclerosis Study Group . Neurology 1993 ; 43 : 662 – 7 . Google Scholar Crossref Search ADS PubMed Spelman T , Kalincik T , Zhang A , Pellegrini F , Wiendl H , Kappos L et al. . Comparative efficacy of switching to natalizumab in active multiple sclerosis . Ann Clin Transl Neurol 2015 ; 2 : 373 – 87 . Google Scholar Crossref Search ADS PubMed © The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: [email protected] This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

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Sir, We read with great interest the article by Butler et al. (2018) reporting patients with de novo variants in genes encoding GABAA receptors. In particular, they described a patient with severe early onset epileptic encephalopathy (EOEE) who had multiple and polymorphic seizures starting from the age of 6 weeks. The course of the disorder was marked by severe developmental delay, hypotonia, microcephaly, cortical visual impairment and profound intellectual disability. This patient had a de novo NM_000807.2: c.875C>A p.(Thr292Lys) missense variant in GABRA2, encoding the GABAA receptor (GABAA-R) alpha-2 subunit. Functional study showed a decrease of the expression of the mutant receptor at the cell surface and an alteration of the gating of the channel that was trapped in an open state (Butler et al., 2018). Previously, a single other patient with de novo c.1003A>C p.(Asn335His) GABRA2 variant had been reported, without functional characterization (Orenstein et al., 2018). This patient also had severe EOEE associated with poor cognitive development. Because only two patients have been reported so far, the clinical spectrum and the functional impact of GABRA2 variants remain to be fully defined. GABA is the major inhibitory neurotransmitter in the vertebrate brain and mediates neuronal inhibition by binding to the GABA receptors. GABRA2 is one of 19 genes encoding GABAA-R subunits. GABAA-Rs belong to the Cys loop ligand-gated ion channel superfamily and are generally composed of five different subunits combined from the selection of six α subunits, three β subunits, three γ subunits, three ρ subunits, and one ɛ, δ, θ and π subunit. GABAA-R including α2 subunits (principally α2βy2, or α2βy1) represent ∼25% of the GABAA-Rs in the rat brain (McKernan and Whiting, 1996; Whiting et al., 1999; Olsen and Sieghart, 2009). Variants responsible for epileptic encephalopathies have previously been identified in eight of the 19 genes encoding GABAA-R: GABRA1, GABRA3, GABRB1, GABRB2, GABRB3, GABRG2, and more recently, GABRA2 and GABRA5 (Baulac et al., 2001; Wallace et al., 2001; Epi4K Consortium et al., 2013; Carvill et al., 2014; Srivastava et al., 2014; Epi4K Consortium, 2016; Lien et al., 2016; Ishii et al., 2017; Møller et al., 2017; Niturad et al., 2017; Butler et al., 2018; Orenstein et al., 2018). In this letter, we describe the genetic and clinical data of five patients, including three sporadic cases and two affected siblings, with four novel de novo GABRA2 missense variants, and the functional characterization of these variants. Whole-exome sequencing was performed for all four families in diagnostic or research laboratories. GABRA2 variants were classified as pathogenic or likely pathogenic according to ACMG criteria (Richards et al., 2015). Data collection of several individuals with GABRA2 variants was enabled using GeneMatcher (Sobreira et al., 2015). The study was approved by the ethics committees of the respective institutions and biological samples were obtained following written informed consent from the legal representatives. Patients 1–3 presented with similarly severe EOEE phenotypes, with seizure onset ranging from 1 day to 1 month of life (Table 1). Seizure types included clonic seizures (n = 3), ocular revulsions (n = 2), absences (n = 1) and spasms (n = 1). All patients had multiple seizures (10 to 20) per day that were resistant to anti-epileptic treatments. The three patients had status epilepticus. All three patients showed hypotonia, poor eye contact and severe developmental delay, illustrated by the inability to walk or to speak at 2.5 years (Patient 2) and 5 years (Patient 3). Patient 1 was still very young (5 months) at the time of the study but she presented with neonatal hypotonia, failure to thrive and pyramidal syndrome. Exome sequencing respectively identified three novel de novo variants NM_000807.2 c.851T>C p.(Val284Ala), c.871C>G p.(Leu291Val), c.788T>C p.(Met263Thr) in GABRA2. Table 1 Clinical and genetic summary of the patients with GABRA2 variants Patient 1 Patient 2 Patient 3 Patient 4 Patient 5a Previously reported Orenstein et al. Butler et al. Age / sex 5.5 m / F 32 m / F 5 y / F 17 y / M 13 y / F 10 m / M 11 y / F Genomic change (Chr4, GRCh37) g.46305482A>G g.46264131G>C g.46305545A>G g.46264027G>T g.46264027G>T g.46263999T>G g.46264127G>T Base change, amino acid change (NM_000807.2) c.851T>C c.871C>G c.788T>C c.975C>A c.975C>A c.1003A>C c.875C>A p.(Val284Ala) p.(Leu291Val) p.(Met263Thr) p.(Phe325Leu) p.(Phe325Leu) p.(Asn335His) p.(Thr292Lys) Inheritance De novo De novo De novo Father (mosaic) Father (mosaic) De novo De novo CADD score 24.2 23.9 26.2 26.3 26.3 24.5 31 Pregnancy / neonatal mensurations N / N excess fetal hiccuping / N N / N N / N N/N N / N NA Neonatal findings Hypotonia, failure to thrive, respiratory distress Severe hypotonia Hypotonia, respiratory distress Jaundice NA Severe hypotonia, neonatal hypothermia NA Age of walking Too young Not able Not able 14 m Around 12 m Too young Not able Current language ability Too young Not able Very low Decreased ability Slightly decreased Too young Not able Cognitive development Severe Profound Severe Moderate Mild/moderate Severe Profound ASD Too young NA Yes Yes No Too young NA Age at seizure onset 1 m 1 d 2 d 17 y 2 y 10 w 6 w Seizure type at onset Ocular revulsion Ocular version, focal clonic seizures Focal clonic Generalized tonic-clonic Complex partial Mastication and blinking Clustered focal seizures, spasms Seizure types during disease course Absences, clonic Behavioral arrests, clonic, tonic Spasm, clonic Generalized tonic-clonic Complex partial Hypertonus, grima cing and facial flushing, partial tonic with clonic eyelid movements Tonic, tonic-clonic and myoclonic Status epilepticus No Yes Yes No Yes NA NA Frequency of seizures, max 20/d 15/d 10/d Only had 3 NA At least once daily NA Current AETs VPA, PHT PB, CLB, CLN, VPA,TPM Sabril, hydrocortisone LTG OXC TPM, GVG VPA, PB, CLB Pharmoresistance Yes Yes Yes No with treatment No with treatment Partial response Yes Head circumference, SD −2 −3.75 0 0 0 0 Microcephaly Neurological examination Hypotonia, pyramidal syndrome Severe hypotonia, reduced reflexes, cortical visual impairment Hypotonia Features of autism N Severe hypotonia, choreiform movements Severe hypotonia, spasticity, cortical visual impairment RMI: age / main abnormalities 3 m / N 23 m / generalized brain atrophy, abnormal myelination; CC thin and short 2 y / N 17 y / N 13 y / N 10 w / N 9 y / hypomyelination EEG: age / main abnormalities 2 m / isolated spike, rapid rythm 28 m / multifocal spikes right and left hemisphere; generalized spikes and polyspikes 8 d / discontinous, diffuse spike 17 y / normal, bilateral frontal/ fronto-central sharp activity, but not truly epileptiform NA Fast background activity and left centro-parietal spikes 2 y / slow and disorganized back ground with multifocal epilepti form discharges Patient 1 Patient 2 Patient 3 Patient 4 Patient 5a Previously reported Orenstein et al. Butler et al. Age / sex 5.5 m / F 32 m / F 5 y / F 17 y / M 13 y / F 10 m / M 11 y / F Genomic change (Chr4, GRCh37) g.46305482A>G g.46264131G>C g.46305545A>G g.46264027G>T g.46264027G>T g.46263999T>G g.46264127G>T Base change, amino acid change (NM_000807.2) c.851T>C c.871C>G c.788T>C c.975C>A c.975C>A c.1003A>C c.875C>A p.(Val284Ala) p.(Leu291Val) p.(Met263Thr) p.(Phe325Leu) p.(Phe325Leu) p.(Asn335His) p.(Thr292Lys) Inheritance De novo De novo De novo Father (mosaic) Father (mosaic) De novo De novo CADD score 24.2 23.9 26.2 26.3 26.3 24.5 31 Pregnancy / neonatal mensurations N / N excess fetal hiccuping / N N / N N / N N/N N / N NA Neonatal findings Hypotonia, failure to thrive, respiratory distress Severe hypotonia Hypotonia, respiratory distress Jaundice NA Severe hypotonia, neonatal hypothermia NA Age of walking Too young Not able Not able 14 m Around 12 m Too young Not able Current language ability Too young Not able Very low Decreased ability Slightly decreased Too young Not able Cognitive development Severe Profound Severe Moderate Mild/moderate Severe Profound ASD Too young NA Yes Yes No Too young NA Age at seizure onset 1 m 1 d 2 d 17 y 2 y 10 w 6 w Seizure type at onset Ocular revulsion Ocular version, focal clonic seizures Focal clonic Generalized tonic-clonic Complex partial Mastication and blinking Clustered focal seizures, spasms Seizure types during disease course Absences, clonic Behavioral arrests, clonic, tonic Spasm, clonic Generalized tonic-clonic Complex partial Hypertonus, grima cing and facial flushing, partial tonic with clonic eyelid movements Tonic, tonic-clonic and myoclonic Status epilepticus No Yes Yes No Yes NA NA Frequency of seizures, max 20/d 15/d 10/d Only had 3 NA At least once daily NA Current AETs VPA, PHT PB, CLB, CLN, VPA,TPM Sabril, hydrocortisone LTG OXC TPM, GVG VPA, PB, CLB Pharmoresistance Yes Yes Yes No with treatment No with treatment Partial response Yes Head circumference, SD −2 −3.75 0 0 0 0 Microcephaly Neurological examination Hypotonia, pyramidal syndrome Severe hypotonia, reduced reflexes, cortical visual impairment Hypotonia Features of autism N Severe hypotonia, choreiform movements Severe hypotonia, spasticity, cortical visual impairment RMI: age / main abnormalities 3 m / N 23 m / generalized brain atrophy, abnormal myelination; CC thin and short 2 y / N 17 y / N 13 y / N 10 w / N 9 y / hypomyelination EEG: age / main abnormalities 2 m / isolated spike, rapid rythm 28 m / multifocal spikes right and left hemisphere; generalized spikes and polyspikes 8 d / discontinous, diffuse spike 17 y / normal, bilateral frontal/ fronto-central sharp activity, but not truly epileptiform NA Fast background activity and left centro-parietal spikes 2 y / slow and disorganized back ground with multifocal epilepti form discharges Antiepileptic treatments (AETs): CBZ = carbamazepine, CLB = clobazam, CLN = clonazepam, GVG = vigabatrin, LTG = lamotrigine, OXC = oxcarbamazepine, PB = phenobarbital, PHT = phenytoin, TPM = topiramate, VPA = sodium valproate. ASD = autism spectrum disorders; CC = corpus callosum; d = day; F = female; M = male; m = months; N = normal; NA = not available; w = weeks; y = years. aPatient 4’s sister. View Large Table 1 Clinical and genetic summary of the patients with GABRA2 variants Patient 1 Patient 2 Patient 3 Patient 4 Patient 5a Previously reported Orenstein et al. Butler et al. Age / sex 5.5 m / F 32 m / F 5 y / F 17 y / M 13 y / F 10 m / M 11 y / F Genomic change (Chr4, GRCh37) g.46305482A>G g.46264131G>C g.46305545A>G g.46264027G>T g.46264027G>T g.46263999T>G g.46264127G>T Base change, amino acid change (NM_000807.2) c.851T>C c.871C>G c.788T>C c.975C>A c.975C>A c.1003A>C c.875C>A p.(Val284Ala) p.(Leu291Val) p.(Met263Thr) p.(Phe325Leu) p.(Phe325Leu) p.(Asn335His) p.(Thr292Lys) Inheritance De novo De novo De novo Father (mosaic) Father (mosaic) De novo De novo CADD score 24.2 23.9 26.2 26.3 26.3 24.5 31 Pregnancy / neonatal mensurations N / N excess fetal hiccuping / N N / N N / N N/N N / N NA Neonatal findings Hypotonia, failure to thrive, respiratory distress Severe hypotonia Hypotonia, respiratory distress Jaundice NA Severe hypotonia, neonatal hypothermia NA Age of walking Too young Not able Not able 14 m Around 12 m Too young Not able Current language ability Too young Not able Very low Decreased ability Slightly decreased Too young Not able Cognitive development Severe Profound Severe Moderate Mild/moderate Severe Profound ASD Too young NA Yes Yes No Too young NA Age at seizure onset 1 m 1 d 2 d 17 y 2 y 10 w 6 w Seizure type at onset Ocular revulsion Ocular version, focal clonic seizures Focal clonic Generalized tonic-clonic Complex partial Mastication and blinking Clustered focal seizures, spasms Seizure types during disease course Absences, clonic Behavioral arrests, clonic, tonic Spasm, clonic Generalized tonic-clonic Complex partial Hypertonus, grima cing and facial flushing, partial tonic with clonic eyelid movements Tonic, tonic-clonic and myoclonic Status epilepticus No Yes Yes No Yes NA NA Frequency of seizures, max 20/d 15/d 10/d Only had 3 NA At least once daily NA Current AETs VPA, PHT PB, CLB, CLN, VPA,TPM Sabril, hydrocortisone LTG OXC TPM, GVG VPA, PB, CLB Pharmoresistance Yes Yes Yes No with treatment No with treatment Partial response Yes Head circumference, SD −2 −3.75 0 0 0 0 Microcephaly Neurological examination Hypotonia, pyramidal syndrome Severe hypotonia, reduced reflexes, cortical visual impairment Hypotonia Features of autism N Severe hypotonia, choreiform movements Severe hypotonia, spasticity, cortical visual impairment RMI: age / main abnormalities 3 m / N 23 m / generalized brain atrophy, abnormal myelination; CC thin and short 2 y / N 17 y / N 13 y / N 10 w / N 9 y / hypomyelination EEG: age / main abnormalities 2 m / isolated spike, rapid rythm 28 m / multifocal spikes right and left hemisphere; generalized spikes and polyspikes 8 d / discontinous, diffuse spike 17 y / normal, bilateral frontal/ fronto-central sharp activity, but not truly epileptiform NA Fast background activity and left centro-parietal spikes 2 y / slow and disorganized back ground with multifocal epilepti form discharges Patient 1 Patient 2 Patient 3 Patient 4 Patient 5a Previously reported Orenstein et al. Butler et al. Age / sex 5.5 m / F 32 m / F 5 y / F 17 y / M 13 y / F 10 m / M 11 y / F Genomic change (Chr4, GRCh37) g.46305482A>G g.46264131G>C g.46305545A>G g.46264027G>T g.46264027G>T g.46263999T>G g.46264127G>T Base change, amino acid change (NM_000807.2) c.851T>C c.871C>G c.788T>C c.975C>A c.975C>A c.1003A>C c.875C>A p.(Val284Ala) p.(Leu291Val) p.(Met263Thr) p.(Phe325Leu) p.(Phe325Leu) p.(Asn335His) p.(Thr292Lys) Inheritance De novo De novo De novo Father (mosaic) Father (mosaic) De novo De novo CADD score 24.2 23.9 26.2 26.3 26.3 24.5 31 Pregnancy / neonatal mensurations N / N excess fetal hiccuping / N N / N N / N N/N N / N NA Neonatal findings Hypotonia, failure to thrive, respiratory distress Severe hypotonia Hypotonia, respiratory distress Jaundice NA Severe hypotonia, neonatal hypothermia NA Age of walking Too young Not able Not able 14 m Around 12 m Too young Not able Current language ability Too young Not able Very low Decreased ability Slightly decreased Too young Not able Cognitive development Severe Profound Severe Moderate Mild/moderate Severe Profound ASD Too young NA Yes Yes No Too young NA Age at seizure onset 1 m 1 d 2 d 17 y 2 y 10 w 6 w Seizure type at onset Ocular revulsion Ocular version, focal clonic seizures Focal clonic Generalized tonic-clonic Complex partial Mastication and blinking Clustered focal seizures, spasms Seizure types during disease course Absences, clonic Behavioral arrests, clonic, tonic Spasm, clonic Generalized tonic-clonic Complex partial Hypertonus, grima cing and facial flushing, partial tonic with clonic eyelid movements Tonic, tonic-clonic and myoclonic Status epilepticus No Yes Yes No Yes NA NA Frequency of seizures, max 20/d 15/d 10/d Only had 3 NA At least once daily NA Current AETs VPA, PHT PB, CLB, CLN, VPA,TPM Sabril, hydrocortisone LTG OXC TPM, GVG VPA, PB, CLB Pharmoresistance Yes Yes Yes No with treatment No with treatment Partial response Yes Head circumference, SD −2 −3.75 0 0 0 0 Microcephaly Neurological examination Hypotonia, pyramidal syndrome Severe hypotonia, reduced reflexes, cortical visual impairment Hypotonia Features of autism N Severe hypotonia, choreiform movements Severe hypotonia, spasticity, cortical visual impairment RMI: age / main abnormalities 3 m / N 23 m / generalized brain atrophy, abnormal myelination; CC thin and short 2 y / N 17 y / N 13 y / N 10 w / N 9 y / hypomyelination EEG: age / main abnormalities 2 m / isolated spike, rapid rythm 28 m / multifocal spikes right and left hemisphere; generalized spikes and polyspikes 8 d / discontinous, diffuse spike 17 y / normal, bilateral frontal/ fronto-central sharp activity, but not truly epileptiform NA Fast background activity and left centro-parietal spikes 2 y / slow and disorganized back ground with multifocal epilepti form discharges Antiepileptic treatments (AETs): CBZ = carbamazepine, CLB = clobazam, CLN = clonazepam, GVG = vigabatrin, LTG = lamotrigine, OXC = oxcarbamazepine, PB = phenobarbital, PHT = phenytoin, TPM = topiramate, VPA = sodium valproate. ASD = autism spectrum disorders; CC = corpus callosum; d = day; F = female; M = male; m = months; N = normal; NA = not available; w = weeks; y = years. aPatient 4’s sister. View Large Patient 4 showed language delay without motor delay and was diagnosed with autism spectrum disorder at 18 months of age and presented a regression of acquisitions at 2.5 years. He had moderate intellectual disability, without other neurological symptoms. At 17 years, he exhibited generalized tonic-clonic seizures that were treated by lamotrigine. His sister also had mild/moderate intellectual disability with seizures beginning at the age of 2 years, but without autistic features. Both patients had the heterozygous GABRA2 c.975C>A p.(Phe325Leu) variant. Analysis of parental samples showed that the variant was also present at a low level in the blood of the father, indicating paternal mosaicism. All four variants are absent from GnomAD, predicted damaging by PolyPhen-2 and have a CADD score between 23.9 and 26.2. They alter amino acids highly conserved during evolution. Two variants [p.(Val284Ala) and p.(Leu291Val)] are located in the second transmembrane domain of the protein aligning the pore of the receptor while the other two are located in the first p.(Met263Thr) and third p.(Phe325Leu) transmembrane domain (Fig. 1A and B). Figure 1 View largeDownload slide Novel GABRA2 variants in epileptic encephalopathy and intellectual disability with seizures. (A) GABRA2 cDNA structure showing positions of exon boundaries and four newly identified variants p.(Met263Thr), p.(Val284Ala), p.(Leu291Val), and p.(Phe325Leu) (top). Two previously reported variants p.(Thr292Lys) and p.(Asn335His) are also depicted. Protein structure of the α2 subunit including positions of the signal peptide, extracellular N-terminus, transmembrane domains M1-M4 and intracellular M3-M4 domain (bottom). (B) Schematic illustrating the putative structure of the α2 subunit containing all so far identified disease variants, one each in M1 and M3 segment, three in the M2 segment and one in the intracellular M3-M4 loop. A predicted structure of the pentameric GABAA receptor with M2 segments forming the channel pore is shown on the right. (C) Average normalized GABA-induced currents recorded from Xenopus laevis oocytes expressing GABRA2 wild-type (WT) and different variants upon application of 1 mM GABA. All current amplitudes were normalized to the mean amplitude of the wild-type recorded on the same day and pooled from different experiments. Data are presented as box-and-whisker plots, indicating the median, interquartile ranges, and minimum and maximum values. Plus symbol indicates mean values which are as follows: wild-type 1.00 (n = 169), Met263Thr 0.33 (n = 45), Val284Ala 0.66 (n = 27), Leu291Val 0.05 (n = 45), Phe325Leu 0.60 (n = 69); *P < 0.05, ****P < 0.0001. (D) Dose-response curves were obtained from the responses to application of different GABA concentrations (in µM: 1, 3, 10, 30, 100, 300 and 1000). EC50 values in µM: wild-type 18.8 ± 1.4 (n = 9), Met263Thr 7.2 ± 2.6 (n = 8), Val284Ala 9 ± 3 (n = 9), Leu291Val 67 ± 24 (n = 10), Phe325Leu 14.1 ± 2.5 (n = 69); P < 0.05 for wild-type versus Met263Thr. Hill coefficient values: wild-type 1.48 ± 0.14, Met263Thr 0.96 ± 0.17, Val284Ala 1.20 ± 0.10, Leu291Val 1.03 ± 0.06 (n = 45), Phe325Leu 0.90 ± 0.10 (n = 69); P < 0.05 for wild-type versus Met263Thr and wild-type versus Phe325Leu. Significance was tested using Kruskal-Wallis test with Dunn’s multiple comparison test. Figure 1 View largeDownload slide Novel GABRA2 variants in epileptic encephalopathy and intellectual disability with seizures. (A) GABRA2 cDNA structure showing positions of exon boundaries and four newly identified variants p.(Met263Thr), p.(Val284Ala), p.(Leu291Val), and p.(Phe325Leu) (top). Two previously reported variants p.(Thr292Lys) and p.(Asn335His) are also depicted. Protein structure of the α2 subunit including positions of the signal peptide, extracellular N-terminus, transmembrane domains M1-M4 and intracellular M3-M4 domain (bottom). (B) Schematic illustrating the putative structure of the α2 subunit containing all so far identified disease variants, one each in M1 and M3 segment, three in the M2 segment and one in the intracellular M3-M4 loop. A predicted structure of the pentameric GABAA receptor with M2 segments forming the channel pore is shown on the right. (C) Average normalized GABA-induced currents recorded from Xenopus laevis oocytes expressing GABRA2 wild-type (WT) and different variants upon application of 1 mM GABA. All current amplitudes were normalized to the mean amplitude of the wild-type recorded on the same day and pooled from different experiments. Data are presented as box-and-whisker plots, indicating the median, interquartile ranges, and minimum and maximum values. Plus symbol indicates mean values which are as follows: wild-type 1.00 (n = 169), Met263Thr 0.33 (n = 45), Val284Ala 0.66 (n = 27), Leu291Val 0.05 (n = 45), Phe325Leu 0.60 (n = 69); *P < 0.05, ****P < 0.0001. (D) Dose-response curves were obtained from the responses to application of different GABA concentrations (in µM: 1, 3, 10, 30, 100, 300 and 1000). EC50 values in µM: wild-type 18.8 ± 1.4 (n = 9), Met263Thr 7.2 ± 2.6 (n = 8), Val284Ala 9 ± 3 (n = 9), Leu291Val 67 ± 24 (n = 10), Phe325Leu 14.1 ± 2.5 (n = 69); P < 0.05 for wild-type versus Met263Thr. Hill coefficient values: wild-type 1.48 ± 0.14, Met263Thr 0.96 ± 0.17, Val284Ala 1.20 ± 0.10, Leu291Val 1.03 ± 0.06 (n = 45), Phe325Leu 0.90 ± 0.10 (n = 69); P < 0.05 for wild-type versus Met263Thr and wild-type versus Phe325Leu. Significance was tested using Kruskal-Wallis test with Dunn’s multiple comparison test. To investigate their functional impact, we expressed the four variants in Xenopus laevis oocytes and recorded GABA-induced currents using automated and manual two microelectrode voltage clamp technique as described previously (Forster et al., 2001; Li et al., 2017; Niturad et al., 2017). To this end, cRNA encoding wild-type or mutant α2 subunits was co-injected with β3 and γ2 cRNAs in a 1:1:2 ratio. The application of 1 mM GABA for 15 s was used to assess the maximal current response at the holding potential of −70 mV. Each current was normalized to the mean value of the wild-type current recorded on the same day so that the data from different experiments could be pooled. All four variants caused a significant reduction in current amplitudes compared to the wild-type recorded on the same day and under the same conditions (Fig. 1C). Application of increasing GABA concentrations (in µM: 1, 3, 10, 30, 100, 300 and 1000) was used to obtain dose-response curves (Fig. 1D). The current response to each GABA concentration was normalized to the maximum response evoked by 1 mM GABA. The normalized GABA responses of each cell were fitted with the four-parameter logistic equation to estimate the EC50 value, representing the concentration of the agonist at which half of the maximum response is achieved, and the Hill coefficient, which reflects the steepness of the dose response curve. Only p.(Leu291Val) showed a significant change in EC50. Even though three variants appeared to have increased sensitivity to GABA concentrations ≤10 µM compared with the wild-type, after correction for the overall current reduction measured for each variant (Fig. 1C), the difference was predicted to be significant only for the p.(Val284Ala) at concentrations of 3 µM (P < 0.01) and 10 µM (P < 0.05) (data not shown). In addition, for two of the variants, p.(Met263Thr) and p.(Phe325Leu), we found significantly reduced Hill coefficient values. These data show that all four variants caused a loss of function, with the most severe impact seen for the p.(Leu291Val) variant. In addition to diminishing GABA-induced currents, p.(Met263Thr) and p.(Phe325Leu) variants may reduce cooperative binding of the ligand. Lastly, p.(Val284Ala), showed the least prominent impact on the current amplitudes but also an increased response to low GABA concentrations. This suggests that different molecular pathological mechanisms may contribute to the observed clinical outcomes associated with these variants. Altogether, we identified four heterozygous missense variants in GABRA2 and provided a functional screen that yielded results compatible with a loss of function mechanism. Three variants occurred de novo in three unrelated families while the fourth variant, present in two affected siblings, was inherited from their mosaic father. Genes encoding GABAA-R have been associated with a broader spectrum of epileptic syndromes and rare variants in genes encoding GABA-R can either be associated with monogenic epileptic syndromes or constitute risk factors for the genetically complex generalized epilepsies (May et al., 2018). Variants in GABRA3, GABRB1 and GABRA5 have been only associated with severe phenotypes so far (Lien et al., 2016; Niturad et al., 2017; Butler et al., 2018), whereas variants in most other genes including GABRA1, GABRB2, GABRB3 and GABRG2 are associated with a broader clinical spectrum, ranging from EOEE, moderate/mild intellectual disability with epilepsy, to benign familial epilepsy without intellectual disability as illustrated by variants in GABRG2 and GABRA1 (Baulac et al., 2001; Wallace et al., 2001; Boillot et al., 2015; Epi4K Consortium, 2016; Johannesen et al., 2016; Kang and Macdonald, 2016; Hamdan et al., 2017; Zou et al., 2017; Heyne et al., 2018). Variants in GABRA2 have been associated with a severe epileptic encephalopathy phenotype in five of six unrelated patients described to date. However, in this study, Patients 4 and 5 have a less severe phenotype, with pharmacoresponsive epilepsy, autism spectrum disorder and moderate intellectual disability while all other patients show severe to profound intellectual disability. The variant itself could define the phenotype; however, other genetic and/or environmental factors may also contribute to disorder severity. Our study therefore confirms that de novo GABRA2 variants can lead to severe forms of EE and expands the clinical spectrum to patients with mild/moderate intellectual disability, autistic disorders, and late-onset epilepsy, as previously described for genes encoding others GABAA receptor subunits and others genes encoding ion channels, such as SCN1A, SCN2A or HCN1 (Gambardella and Marini, 2009; Wolff et al., 2017; Marini et al., 2018). Data availability The data that support the findings of this study are available from the corresponding author, upon reasonable request. Acknowledgements We thank the patients and their family for their participation in this study. We thank Claude Estrade, Sabina Karagic, Elodie Lejeune, Corinne Mach and Valérie Olin for the technical assistance. Funding The research generating these results was funded by the Agence National de la Recherche (ANR EUHFAUTISM), the “Investissements d’avenir” program ANR-10-IAIHU-06 (IHU-A-ICM), the Bio-Psy labex, INSERM, the Assistance Publique des hôpitaux de Paris (AP-HP), the National Health and Medical Research Council of Australia (Program grant 10915693), and the Pierre and Marie Curie University. Competing interests K.W. is an employee of GeneDx, Inc., a wholly owned subsidiary of OPKO Health, Inc. 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