Approximately 50% of patients with the genetic disease tuberous sclerosis complex present with autism spectrum disorder. Although a number of studies have investigated the link between autism and ...tuberous sclerosis complex, the etiology of autism spectrum disorder in these patients remains unclear. Abnormal cerebellar function during critical phases of development could disrupt functional processes in the brain, leading to development of autistic features. Accordingly, the authors review the potential role of cerebellar dysfunction in the pathogenesis of autism spectrum disorder in tuberous sclerosis complex. The authors also introduce conditional knockout mouse models of Tsc1 and Tsc2 that link cerebellar circuitry to the development of autistic-like features. Taken together, these preclinical and clinical investigations indicate the cerebellum has a profound regulatory role during development of social communication and repetitive behaviors.
Deficits in social cognition are the defining characteristic of autism spectrum disorder (ASD). Social cognition requires the integration of several neural circuits in a time-sensitive fashion, so ...impairments in social interactions could arise as a result of alterations in network connectivity. Electroencephalography (EEG) has revealed abnormalities in event related potentials (ERPs) evoked by auditory and visual sensory stimuli in humans with ASD, indicating disruption of neural connectivity. Similar abnormalities in sensory-evoked ERPs have been observed in animal models of ASD, suggesting that ERPs have the potential to provide a translational biomarker of the disorder. People with ASD also have abnormal ERPs in response to auditory and visual social stimuli, demonstrating functional disruption of the social circuit. To assess the integrity of the social circuit and characterize biomarkers of circuit dysfunction, novel EEG paradigms that use social stimuli to induce ERPs should be developed for use in animal models. The identification of a socially-relevant ERP that is consistent in animal models and humans would facilitate the development of pharmacological treatment strategies for the social impairments in ASD and other neuropsychiatric disorders.
Single gene disorders of the autophagy pathway are an emerging, novel and diverse group of multisystem diseases in children. Clinically, these disorders prominently affect the central nervous system ...at various stages of development, leading to brain malformations, developmental delay, intellectual disability, epilepsy, movement disorders, and neurodegeneration, among others. Frequent early and severe involvement of the central nervous system puts the paediatric neurologist, neurogeneticist, and neurometabolic specialist at the forefront of recognizing and treating these rare conditions. On a molecular level, mutations in key autophagy genes map to different stages of this highly conserved pathway and thus lead to impairment in isolation membrane (or phagophore) and autophagosome formation, maturation, or autophagosome-lysosome fusion. Here we discuss 'congenital disorders of autophagy' as an emerging subclass of inborn errors of metabolism by using the examples of six recently identified monogenic diseases: EPG5-related Vici syndrome, beta-propeller protein-associated neurodegeneration due to mutations in WDR45, SNX14-associated autosomal-recessive cerebellar ataxia and intellectual disability syndrome, and three forms of hereditary spastic paraplegia, SPG11, SPG15 and SPG49 caused by SPG11, ZFYVE26 and TECPR2 mutations, respectively. We also highlight associations between defective autophagy and other inborn errors of metabolism such as lysosomal storage diseases and neurodevelopmental diseases associated with the mTOR pathway, which may be included in the wider spectrum of autophagy-related diseases from a pathobiological point of view. By exploring these emerging themes in disease pathogenesis and underlying pathophysiological mechanisms, we discuss how congenital disorders of autophagy inform our understanding of the importance of this fascinating cellular pathway for central nervous system biology and disease. Finally, we review the concept of modulating autophagy as a therapeutic target and argue that congenital disorders of autophagy provide a unique genetic perspective on the possibilities and challenges of pathway-specific drug development.
Abstract Background Tuberous sclerosis complex is a multisystem genetic disorder with a range of physical manifestations that require evaluation, surveillance, and management. Individuals with ...tuberous sclerosis complex also have a range of behavioral, psychiatric, intellectual, academic, neuropsychologic, and psychosocial difficulties. These may represent the greatest burden of the disease. Around 90% of individuals with tuberous sclerosis complex will have some of these difficulties during their lifetime, yet only about 20% ever receive evaluation and treatment. The Neuropsychiatry Panel at the 2012 Tuberous Sclerosis Complex International Consensus Conference expressed concern about the significant “treatment gap” and about confusion regarding terminology relating to the biopsychosocial difficulties associated with tuberous sclerosis complex. Methods The Tuberous Sclerosis Complex Neuropsychiatry Panel coined the term TAND—tuberous sclerosis complex-associated neuropsychiatric disorders—to bring together these multidimensional manifestations of the disorder, and recommended annual screening for TAND. In addition, the Panel agreed to develop a TAND Checklist as a guide for screening. Results Here, we present an outline of the conceptualization of TAND, rationale for the structure of the TAND Checklist, and include the full US English version of the TAND Checklist. Conclusion We hope that the unified term TAND and the TAND Checklist will raise awareness of the importance of tuberous sclerosis complex-associated neuropsychiatric disorders and of the major burden of disease associated with it, provide a shared language and a simple tool to describe and evaluate the different levels of TAND, alert clinical teams and families or individuals of the importance of screening, assessment, and treatment of TAND, and provide a shared framework for future studies of tuberous sclerosis complex-associated neuropsychiatric disorders.
Effective fall-detection and classification systems are vital in mitigating severe medical and economical consequences of falls to people in the fall risk groups. One class of such systems is based ...on wearable sensors. While there is a vast amount of academic work on this class of systems, not much effort has been devoted to the investigation of effective and robust algorithms and like-for-like comparison of state-of-the-art algorithms using a sufficiently large dataset. In this article, fall-direction classification algorithms are presented and compared on an extensive dataset, comprising a total of 1600 fall trials. Eight machine learning classifiers are implemented for fall-direction classification into four basic directions (forward, backward, right, and left). These are, namely, Bayesian decision making (BDM), least squares method (LSM), k-nearest neighbor classifier (k-NN), artificial neural networks (ANNs), support vector machines (SVMs), decision-tree classifier (DTC), random forest (RF), and adaptive boosting or AdaBoost (AB). BDM achieves perfect classification, followed by k-NN, SVM, and RF. Data acquired from only a single motion sensor unit, worn at the waist of the subject, are processed for experimental verification. Four of the classifiers (BDM, LSM, k-NN, and ANN) are modified to handle the presence of data from an unknown class and evaluated on the same dataset. In this robustness analysis, ANN and k-NN yield accuracies above 96.2%. The results obtained in this study are promising in developing real-world fall-classification systems as they enable fast and reliable classification of fall directions.
•Employed a fall data set with 1600 simulated fall trials.•Classified falls into four basic directions: forward-backward-right-left.•Implemented and compared accuracy & runtimes of eight machine learning classifiers.•Conducted robustness analysis that includes falls with undefined directions.•Used four of the eight machine learning classifiers and achieved >96% accuracy.
We present a novel heuristic fall-detection algorithm based on combining double thresholding of two simple features with fuzzy logic techniques. We extract the features from the acceleration and ...gyroscopic data recorded from a waist-worn motion sensor unit. We compare the proposed algorithm to 15 state-of-the-art heuristic fall-detection algorithms in terms of five performance metrics and runtime on a vast benchmarking fall dataset that is publicly available. The dataset comprises recordings from 2880 short experiments (1600 fall and 1280 non-fall trials) with 16 participants. The proposed algorithm exhibits superior average accuracy (98.45%), sensitivity (98.31%), and F-measure (98.59%) performance metrics with a runtime that allows real-time operation. Besides proposing a novel heuristic fall-detection algorithm, this work has comparative value in that it provides a fair comparison on the relative performances of a considerably large number of existing heuristic algorithms with the proposed one, based on the same dataset. The results of this research are encouraging in the development of fall-detection systems that can function in the real-world for reliable and rapid fall detection.
Reciprocal copy number variations (CNVs) of 16p11.2 are associated with a wide spectrum of neuropsychiatric and neurodevelopmental disorders. Here, we use human induced pluripotent stem cells ...(iPSCs)-derived dopaminergic (DA) neurons carrying CNVs of 16p11.2 duplication (16pdup) and 16p11.2 deletion (16pdel), engineered using CRISPR-Cas9. We show that 16pdel iPSC-derived DA neurons have increased soma size and synaptic marker expression compared to isogenic control lines, while 16pdup iPSC-derived DA neurons show deficits in neuronal differentiation and reduced synaptic marker expression. The 16pdel iPSC-derived DA neurons have impaired neurophysiological properties. The 16pdel iPSC-derived DA neuronal networks are hyperactive and have increased bursting in culture compared to controls. We also show that the expression of RHOA is increased in the 16pdel iPSC-derived DA neurons and that treatment with a specific RHOA-inhibitor, Rhosin, rescues the network activity of the 16pdel iPSC-derived DA neurons. Our data suggest that 16p11.2 deletion-associated iPSC-derived DA neuron hyperactivation can be rescued by RHOA inhibition.
The circadian timing system synchronizes cellular function by coordinating rhythmic transcription via a transcription-translational feedback loop. How the circadian system regulates gene expression ...at the translational level remains a mystery. Here, we show that the key circadian transcription factor BMAL1 associates with the translational machinery in the cytosol and promotes protein synthesis. The mTOR-effector kinase, ribosomal S6 protein kinase 1 (S6K1), an important regulator of translation, rhythmically phosphorylates BMAL1 at an evolutionarily conserved site. S6K1-mediated phosphorylation is critical for BMAL1 to both associate with the translational machinery and stimulate protein synthesis. Protein synthesis rates demonstrate circadian oscillations dependent on BMAL1. Thus, in addition to its critical role in circadian transcription, BMAL1 is a translation factor that links circadian timing and the mTOR signaling pathway. More broadly, these results expand the role of the circadian clock to the regulation of protein synthesis.
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•The circadian protein BMAL1 rhythmically interacts with the translational machinery•BMAL1 is a substrate of the mTOR-effector kinase S6K1•BMAL1 regulates circadian rhythms of protein synthesis
BMAL1 rhythmically interacts with translational machinery, promoting protein synthesis in response to mTOR signaling. These findings connect circadian timing to the control of protein production.
Epileptic encephalopathies represent a particularly severe form of epilepsy, associated with cognitive and behavioral deficits, including impaired social-communication and restricted, repetitive ...behaviors that are the hallmarks of autism spectrum disorder (ASD). With the advent of next-generation sequencing, the genetic landscape of epileptic encephalopathies is growing and demonstrates overlap with genes separately implicated in ASD. However, many questions remain about this connection, including whether epileptiform activity itself contributes to the development of ASD symptomatology. In this review, we compiled a database of genes associated with both epileptic encephalopathy and ASD, limiting our purview to Mendelian disorders not including inborn errors of metabolism, and we focused on the connection between ASD and epileptic encephalopathy rather than epilepsy broadly. Our review has four goals: to (1) discuss the overlapping presentations of ASD and monogenic epileptic encephalopathies; (2) examine the impact of the epilepsy itself on neurocognitive features, including ASD, in monogenic epileptic encephalopathies; (3) outline many of the genetic causes responsible for both ASD and epileptic encephalopathy; (4) provide an illustrative example of a final common pathway that may be implicated in both ASD and epileptic encephalopathy. We demonstrate that autistic features are a common association with monogenic epileptic encephalopathies. Certain epileptic encephalopathy syndromes, like infantile spasms, are especially linked to the development of ASD. The connection between seizures themselves and neurobehavioral deficits in these monogenic encephalopathies remains open to debate. Finally, advances in genetics have revealed many genes that overlap in ties to both ASD and epileptic encephalopathy and that play a role in diverse central nervous system processes. Increased attention to the autistic features of monogenic epileptic encephalopathies is warranted for both researchers and clinicians alike.
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