Pubmed du 11/04/20

samedi 11 avril 2020

1. Brede J, Babb C, Jones C, Elliott M, Zanker C, Tchanturia K, Serpell L, Fox J, Mandy W. "For Me, the Anorexia is Just a Symptom, and the Cause is the Autism" : Investigating Restrictive Eating Disorders in Autistic Women. J Autism Dev Disord. 2020.

Autistic women are overrepresented among people in treatment for Anorexia Nervosa (AN). The current study aimed to : (1) better understand how AN develops and persists in autistic individuals from the perspective of autistic women, parents and healthcare professionals ; (2) derive a theoretical model of restrictive eating difficulties in autism. We conducted 44 semi-structured interviews and used Thematic Analysis to identify patterns of meaning across the data. Themes related to sensory sensitivities, social interaction and relationships, sense of self and identity, difficulties with emotions, thinking styles, and a need for control and predictability. We developed a model of potential autism-specific mechanisms underlying restrictive eating difficulties. This study generated novel insights, which have the potential to inform treatment adaptations following empirical testing.

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2. Del Valle Rubido M, Hollander E, McCracken JT, Shic F, Noeldeke J, Boak L, Khwaja O, Sadikhov S, Fontoura P, Umbricht D. Exploring Social Biomarkers in High-Functioning Adults with Autism and Asperger’s Versus Healthy Controls : A Cross-Sectional Analysis. J Autism Dev Disord. 2020.

Biomarkers for autism spectrum disorder (ASD) are lacking but would facilitate drug development for the core deficits of the disorder. We evaluated markers proposed for characterization of differences in social communication and interaction in adults with ASD versus healthy controls (HC) for utility as biomarkers. Data pooled from an observational study and baseline data from a placebo-controlled study were analyzed. Between-group differences were observed in eye-tracking tasks for activity monitoring, biomotion, human activity preference, composite score (p = 0.0001-0.037) and pupillometry (various tasks, p = 0.017-0.05). Impaired olfaction was more common in the ASD sample versus HC (p = 0.018). Our preliminary results suggest the potential use for stratification and response sub-analyses outcome-prediction of specific eye-tracking tasks, pupillometry and olfaction tests in ASD trials.

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3. Fujioka T, Tsuchiya KJ, Saito M, Hirano Y, Matsuo M, Kikuchi M, Maegaki Y, Choi D, Kato S, Yoshida T, Yoshimura Y, Ooba S, Mizuno Y, Takiguchi S, Matsuzaki H, Tomoda A, Shudo K, Ninomiya M, Katayama T, Kosaka H. Developmental changes in attention to social information from childhood to adolescence in autism spectrum disorders : a comparative study. Mol Autism. 2020 ; 11(1) : 24.

BACKGROUND : Elucidating developmental changes in the symptoms of autism spectrum disorder (ASD) is important to support individuals with ASD. However, no report has clarified the developmental changes in attention to social information for a broad age range. The aim of this study was to investigate the developmental changes in attention to social information from early childhood to adolescence in individuals with ASD and typically developed (TD) children. METHODS : We recruited children with ASD (n = 83) and TD participants (n = 307) between 2 and 18 years of age. Using the all-in-one-eye-tracking system, Gazefinder, we measured the percentage fixation time allocated to areas of interest (AoIs) depicted in movies (the eyes and mouth in movies of a human face with/without mouth motion, upright and inverted biological motion in movies showing these stimuli simultaneously, people and geometry in preference paradigm movies showing these stimuli simultaneously, and objects with/without finger-pointing in a movie showing a woman pointing toward an object). We conducted a three-way analysis of variance, 2 (diagnosis : ASD and TD) by 2 (sex : male and female) by 3 (age group : 0-5, 6-11, and 12-18 years) and locally weighted the scatterplot smoothing (LOESS) regression curve on each AoI. RESULTS : In the face stimuli, the percentage fixation time to the eye region for the TD group increased with age, whereas the one for the ASD group did not. In the ASD group, the LOESS curves of the gaze ratios at the eye region increased up to approximately 10 years of age and thereafter tended to decrease. For the percentage fixation time to the people region in the preference paradigm, the ASD group gazed more briefly at people than did the TD group. LIMITATIONS : It is possible that due to the cross-sectional design, the degree of severity and of social interest might have differed according to the subjects’ age. CONCLUSIONS : There may be qualitative differences in abnormal eye contact in ASD between individuals in early childhood and those older than 10 years.

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4. Gladfelter A, Barron KL. How Children with Autism Spectrum Disorder, Developmental Language Disorder, and Typical Language Learn to Produce Global and Local Semantic Features. Brain Sci. 2020 ; 10(4).

A local processing bias, often considered a cognitive style unique to autism spectrum disorder (ASD), may influence the types of semantic features acquired by children with ASD and could contribute to weaknesses in word learning. Children with developmental language disorder (DLD) also struggle to learn semantic aspects of words, but this cognitive style has not been ascribed to children with DLD. The purpose of this study was to explore whether global-local processing differences influence the type of semantic features children with ASD, DLD, and their neurotypical peers learn to produce when learning new words. Novel word definitions produced by 36 school-aged children (12 with ASD, 12 with DLD, and 12 with typical language) who participated in an extended word-learning paradigm were used to extract newly learned semantic features. These semantic features were then coded for global and local attributes and analyzed to detect whether there were differences between groups. Results indicated that the children with ASD and DLD produced more global, rather than local, semantic features in their definitions than the children with typical language. An over-reliance on global, rather than local, features in children with ASD and DLD may reflect deficits in depth of word knowledge.

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5. Kshatri A, Cerrada A, Gimeno R, Bartolome-Martin D, Rojas P, Giraldez T. Differential regulation of BK channels by fragile X mental retardation protein. The Journal of general physiology. 2020 ; 152(6).

Fragile X mental retardation protein (FMRP) is an RNA-binding protein prominently expressed in neurons. Missense mutations or complete loss of FMRP can potentially lead to fragile X syndrome, a common form of inherited intellectual disability. In addition to RNA regulation, FMRP was also proposed to modulate neuronal function by direct interaction with the large conductance Ca2+- and voltage-activated potassium channel (BK) beta4 regulatory subunits (BKbeta4). However, the molecular mechanisms underlying FMRP regulation of BK channels were not studied in detail. We have used electrophysiology and super-resolution stochastic optical reconstruction microscopy (STORM) to characterize the effects of FMRP on pore-forming BKalpha subunits, as well as the association with regulatory subunits BKbeta4. Our data indicate that, in the absence of coexpressed beta4, FMRP alters the steady-state properties of BKalpha channels by decreasing channel activation and deactivation rates. Analysis using the Horrigan-Aldrich model revealed alterations in the parameters associated with channel opening (L0) and voltage sensor activation (J0). Interestingly, FMRP also altered the biophysical properties of BKalphabeta4 channels favoring channel opening, although not as dramatically as BKalpha. STORM experiments revealed clustered multi-protein complexes, consistent with FMRP interacting not only to BKalphabeta4 but also to BKalpha. Lastly, we found that a partial loss-of-function mutation in FMRP (R138Q) counteracts many of its functional effects on BKalpha and BKalphabeta4 channels. In summary, our data show that FMRP modulates the function of both BKalpha and BKalphabeta4 channels.

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6. Kuo SS, Eack SM. Meta-Analysis of Cognitive Performance in Neurodevelopmental Disorders during Adulthood : Comparisons between Autism Spectrum Disorder and Schizophrenia on the Wechsler Adult Intelligence Scales. Frontiers in psychiatry. 2020 ; 11 : 187.

Autism Spectrum Disorder (ASD) and schizophrenia are neurodevelopmental disorders which show substantial cognitive heterogeneity in adulthood, yet it remains unclear whether cognitive profiles may overlap across these diagnoses. Thus, the aim of this review was to summarize comparisons between ASD and schizophrenia on nonsocial cognition in adulthood. To minimize between-study heterogeneity in a relatively small literature, subtest scaled scores from the Wechsler Adult Intelligence Scale were compared between ASD (N=190) and schizophrenia (N=260) in six studies comprising a total of 450 participants. Meta-analyses of 11 subtests indicated that participants with ASD demonstrated significantly better performance than schizophrenia for visuospatial perception and reasoning and problem solving (Hedge’s g=0.636), as well as visual attention and organization (g=0.433-0.475). Participants with ASD also demonstrated better performance than those with schizophrenia for working memory (g=0.334) and language (g=0.275), and generally comparable performance on processing speed and verbal comprehension. These findings were largely stable across age, sex, intelligence quotient (IQ), intellectual disability, scale version, and age- and sex-matching. Overall, ASD and schizophrenia showed striking differences in visuospatial perception and reasoning and problem solving, small differences in working memory and language, and substantial overlap in processing speed and verbal comprehension. These cognitive profiles were generally stable from adolescence to middle adulthood. To our knowledge, this is the first review to summarize comparisons of nonsocial cognition in verbal adults with ASD or schizophrenia. These findings are consistent with and substantially extend prior meta-analyses of case-control studies for ASD and schizophrenia (8, 9), which also suggest that, in comparison to neurotypical controls, ASD demonstrates smaller cognitive impairments than schizophrenia across most cognitive domains, particularly working memory, visuospatial learning/memory, and language. Our findings therefore highlight the importance of comparing cognition transdiagnostically to inform the etiologies of these neurodevelopmental disorders and to refine shared and unique targets for remediating cognition.

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7. Rzepka-Migut B, Paprocka J. Efficacy and Safety of Melatonin Treatment in Children with Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder-A Review of the Literature. Brain Sci. 2020 ; 10(4).

Autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD) are neurodevelopmental disorders with disturbed melatonin secretion profile and sleep problems. The growing incidence of ASD and ADHD inspires scientists to research the underlying causes of these conditions. The authors focused on two fundamental aspects, the first one being the presentation of the role of melatonin in ASD and ADHD and the second of the influence of melatonin treatment on sleep disorders. The authors present the use of melatonin both in the context of causal and symptomatic treatment and discuss melatonin supplementation : Dosage patterns, effectiveness, and safety. Sleep disorders may have a different clinical picture, so the assessment of exogenous melatonin efficacy should also refer to a specific group of symptoms. The review draws attention to the wide range of doses of melatonin used in supplementation and the need to introduce unified standards especially in the group of pediatric patients.

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8. Sbardella D, Tundo GR, Cunsolo V, Grasso G, Cascella R, Caputo V, Santoro AM, Milardi D, Pecorelli A, Ciaccio C, Di Pierro D, Leoncini S, Campagnolo L, Pironi V, Oddone F, Manni P, Foti S, Giardina E, De Felice C, Hayek J, Curatolo P, Galasso C, Valacchi G, Coletta M, Graziani G, Marini S. Defective proteasome biogenesis into skin fibroblasts isolated from Rett syndrome subjects with MeCP2 non-sense mutations. Biochimica et biophysica acta Molecular basis of disease. 2020 : 165793.

Rett Syndrome (RTT) is a rare X-linked neurodevelopmental disorder which affects about 1 : 10000 live births. In >95% of subjects RTT is caused by a mutation in Methyl-CpG binding protein-2 (MECP2) gene, which encodes for a transcription regulator with pleiotropic genetic/epigenetic activities. The molecular mechanisms underscoring the phenotypic alteration of RTT are largely unknown and this has impaired the development of therapeutic approaches to alleviate signs and symptoms during disease progression. A defective proteasome biogenesis into two skin primary fibroblasts isolated from RTT subjects harbouring non-sense (early-truncating) MeCP2 mutations (i.e., R190fs and R255X) is herewith reported. Proteasome is the proteolytic machinery of Ubiquitin Proteasome System (UPS), a pathway of overwhelming relevance for post-mitotic cells metabolism. Molecular, transcription and proteomic analyses indicate that MeCP2 mutations down-regulate the expression of one proteasome subunit, alpha7, and of two chaperones, PAC1 and PAC2, which bind each other in the earliest step of proteasome biogenesis. Furthermore, this molecular alteration recapitulates in neuron-like SH-SY5Y cells upon silencing of MeCP2 expression, envisaging a general significance of this transcription regulator in proteasome biogenesis.

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9. Wang Y, Kou Y, Meng D. Network Structure Analysis Identifying Key Genes of Autism and Its Mechanism. Computational and mathematical methods in medicine. 2020 ; 2020 : 3753080.

Identifying the key genes of autism is of great significance for understanding its pathogenesis and improving the clinical level of medicine. In this paper, we use the structural parameters (average degree) of gene correlation networks to identify genes related to autism and study its pathogenesis. Based on the gene expression profiles of 82 autistic patients (the experimental group, E) and 64 healthy persons (the control group, C) in NCBI database, spearman correlation networks are established, and their average degrees under different thresholds are analyzed. It is found that average degrees of C and E are basically separable at the full thresholds. This indicates that there is a clear difference between the network structures of C and E, and it also suggests that this difference is related to the mechanism of disease. By annotating and enrichment analysis of the first 20 genes (MD-Gs) with significant difference in the average degree, we find that they are significantly related to gland development, cardiovascular development, and embryogenesis of nervous system, which support the results in Alter et al.’s original research. In addition, FIGF and CSF3 may play an important role in the mechanism of autism.

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10. Zhuang J, Dvornek NC, Li X, Ventola P, Duncan JS. Invertible Network for Classification and Biomarker Selection for ASD. Medical image computing and computer-assisted intervention : MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention. 2019 ; 11766 : 700-8.

Determining biomarkers for autism spectrum disorder (ASD) is crucial to understanding its mechanisms. Recently deep learning methods have achieved success in the classification task of ASD using fMRI data. However, due to the black-box nature of most deep learning models, it’s hard to perform biomarker selection and interpret model decisions. The recently proposed invertible networks can accurately reconstruct the input from its output, and have the potential to unravel the black-box representation. Therefore, we propose a novel method to classify ASD and identify biomarkers for ASD using the connectivity matrix calculated from fMRI as the input. Specifically, with invertible networks, we explicitly determine the decision boundary and the projection of data points onto the boundary. Like linear classifiers, the difference between a point and its projection onto the decision boundary can be viewed as the explanation. We then define the importance as the explanation weighted by the gradient of prediction w.r.t the input, and identify biomarkers based on this importance measure. We perform a regression task to further validate our biomarker selection : compared to using all edges in the connectivity matrix, using the top 10% important edges we generate a lower regression error on 6 different severity scores. Our experiments show that the invertible network is both effective at ASD classification and interpretable, allowing for discovery of reliable biomarkers.

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