Pubmed du 26/09/19

jeudi 26 septembre 2019

1. Austin C, Curtin P, Curtin A, Gennings C, Arora M, Tammimies K, Isaksson J, Willfors C, Bolte S. Dynamical properties of elemental metabolism distinguish attention deficit hyperactivity disorder from autism spectrum disorder. Transl Psychiatry ;2019 (Sep 25) ;9(1):238.

Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are neurodevelopmental conditions of overlapping etiologies and phenotypes. For ASD, we recently reported altered elemental metabolic patterns in the form of short and irregular zinc and copper cycles. Here, we extend the application of these biomarkers of prenatal and early postnatal elemental metabolism to distinguish between individuals diagnosed with ADHD and/or ASD and neurotypical controls. We recruited twins discordant for ADHD, ASD and other neurodevelopmental diagnoses from national twin studies in Sweden (N = 74) diagnosed according to DSM-5 clinical consensus and standardized psychiatric instruments. Detailed temporal profiles of exposure to 10 metals over the prenatal and early childhood periods were measured using tooth biomarkers. We used recurrence quantification analysis (RQA) to characterize properties of cyclical metabolic patterns of these metals. Regularity (determinism) and complexity (entropy) of elemental cycles was consistently reduced in ADHD for cobalt, lead, and vanadium (determinism : cobalt, beta = -0.03, P = 0.017 ; lead, beta = -0.03, P = 0.016 ; and vanadium, beta = -0.03, P = 0.01. Entropy : cobalt, beta = -0.13, P = 0.017 ; lead, beta = -0.18, P = 0.016 ; and vanadium, beta = -0.15, P = 0.008). Further, we found elemental pathways and dynamical features specific to ADHD vs ASD, and unique characteristics associated with ADHD/ASD combined presentation. Dysregulation of cyclical processes in elemental metabolism during prenatal and early postnatal development not only encompasses pathways shared by ADHD and ASD, but also comprise features specific to either condition.

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2. Ferreira JFB, Batista JS, Fantin C. Screening for FMR1 expanded alleles in patients with Autism Spectrum Disorders in Manaus, Northern Brazil. An Acad Bras Cienc ;2019 ;91(3):e20180882.

Fragile X Syndrome (FXS) is a neurodevelopmental disorder caused by dynamic mutations of a CGG repetition segment in an X chromosome’s single gene. It is considered the leading hereditary cause of both Autism Spectrum Disorders and Intellectual Disability. Some authors suggest that all individuals diagnosed with some of these latter conditions to be clinically and molecularly trialled for FXS due to the high levels of comorbidity between both conditions and also due to the variable expressiveness of this syndrome. This study has focused on verifying the presence of FMR1 expanded alleles since there is a lack of information about this kind of mutation in autism patients from the northern region of Brazil. The presence of large alleles for this gene could offer new therapeutic or pharmacological methods for the treatment of these patients. Both the presence and the frequency of CGG expansions were verified in 90 autism males by molecular analysis. Four of them had intermediate alleles and four others presented premutated alleles. Premutation carriers are on the propensity of developing the late onset Fragile X-associated tremor/ataxia syndrome. No full mutation alleles were found. Further studies are necessary to obtain more accurate statistical data about this kind of dynamic mutation.

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3. Gazestani VH, Pramparo T, Nalabolu S, Kellman BP, Murray S, Lopez L, Pierce K, Courchesne E, Lewis NE. A perturbed gene network containing PI3K-AKT, RAS-ERK and WNT-beta-catenin pathways in leukocytes is linked to ASD genetics and symptom severity. Nat Neurosci ;2019 (Oct) ;22(10):1624-1634.

Hundreds of genes are implicated in autism spectrum disorder (ASD), but the mechanisms through which they contribute to ASD pathophysiology remain elusive. Here we analyzed leukocyte transcriptomics from 1- to 4-year-old male toddlers with ASD or typical development from the general population. We discovered a perturbed gene network that includes highly expressed genes during fetal brain development. This network is dysregulated in human induced pluripotent stem cell-derived neuron models of ASD. High-confidence ASD risk genes emerge as upstream regulators of the network, and many risk genes may impact the network by modulating RAS-ERK, PI3K-AKT and WNT-beta-catenin signaling pathways. We found that the degree of dysregulation in this network correlated with the severity of ASD symptoms in the toddlers. These results demonstrate how the heterogeneous genetics of ASD may dysregulate a core network to influence brain development at prenatal and very early postnatal ages and, thereby, the severity of later ASD symptoms.

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4. Glinton KE, Elsea SH. Untargeted Metabolomics for Autism Spectrum Disorders : Current Status and Future Directions. Front Psychiatry ;2019 ;10:647.

Autism spectrum disorders (ASDs) are a group of neurodevelopment disorders characterized by childhood onset deficits in social communication and interaction. Although the exact etiology of most cases of ASDs is unknown, a portion has been proposed to be associated with various metabolic abnormalities including mitochondrial dysfunction, disorders of cholesterol metabolism, and folate abnormalities. Targeted biochemical testing like plasma amino acid and acylcarnitine profiles have demonstrated limited utility in helping to diagnose and manage such patients. Untargeted metabolomics has emerged, however, as a promising tool in screening for underlying biochemical abnormalities and managing treatment and as a means of investigating possible novel biomarkers for the disorder. Here, we review the principles and methodology behind untargeted metabolomics, recent pilot studies utilizing this technology, and areas in which it may be integrated into the care of children with this disorder in the future.

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5. Hariyani N, Soebekti RH, Setyowati D, Bramantoro T, Palupi LS, Oktarina, Putriana E. Factors influencing the severity of dental caries among Indonesian children with autism spectrum disorder - a pilot study. Clin Cosmet Investig Dent ;2019 ;11:227-233.

Aim : To assess the prevalence of dental caries among autism spectrum disorders (ASD) children in Surabaya, Indonesia, and to explore the association between oral health-related behaviors comprising toothbrushing, snacking and dental visiting and the severity of caries. Methods : The prevalence and severity of caries were assessed using DMF-T/dmf-t and PUFA/pufa indexes for mixed dentitions. The parents or carers’ responses were collected to identify the oral health-related behaviors that were associated with caries severity. Results : This study shows that almost 79% of ASD children in Surabaya were experiencing caries and 47.1% had untreated caries with pulp involvement or more severe conditions. Oral health behaviour factors associated with the severity of dental caries experienced by children with autism were brushing frequency (Beta [95% CI] =2.46 [0.09-4.83] and 2.03 [0.78-3.28]), brushing time (Beta [95% CI] =3.76 [1.32-6.20] and 2.03 [0.74-3.32]) and cleansing after snack (B [95% CI] = 2.04 [0.20-3.89] and 1.21 [0.23-2.18]) in DMF-T/dmf-t and PUFA/pufa indexes, respectively. Conclusions : The prevalence of caries among children with autism in Surabaya was high. The severity was associated with brushing and snacking behaviors. Caries in children with autism should be paid attention, and research in modifying daily activities are needed to allow them to adopt more healthy oral health-related behaviors.

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6. Jyonouchi H, Geng L, Toruner GA, Rose S, Bennuri SC, Frye RE. Serum microRNAs in ASD : Association With Monocyte Cytokine Profiles and Mitochondrial Respiration. Front Psychiatry ;2019 ;10:614.

Our previous research has shown that purified peripheral blood monocytes (PRMo) from individuals who are diagnosed with autism spectrum disorders (ASDs) and have innate immune abnormalities reveal altered interleukin-1ss (IL-1ss)/IL-10 ratios. We also found, in separate studies, that microRNA (miRNA) expression in PBMo and mitochondrial respiration in peripheral blood mononuclear cells (PBMCs) differed in the IL-1ss/IL-10-based ASD subgroups. This study explored whether serum miRNAs are associated with both altered innate immune responses and changes in mitochondrial respiration as a link of regulatory mechanisms for these two common abnormalities in ASD subjects. Serum miRNA levels were examined by high-throughput deep sequencing in ASD and non-ASD control sera with concurrent measurement of PBMo cytokine production and mitochondrial respiration by PBMCs. ASD samples were examined as a whole group and with respect to the previously defined IL-1ss/IL-10-based ASD subgroups (high, normal, and low groups). Serum miRNA levels differed between the overall ASD sera (N = 116) and non-ASD control sera (N = 35) and also differed across the IL-1ss/IL-10-based ASD subgroups. Specifically, miRNA levels were increased and decreased in eight and nine miRNAs, respectively, in the high-ratio ASD subgroup (N = 48). In contrast, the low- (N = 25) and normal- (N = 43) ratio ASD subgroups only showed decreased miRNAs levels (18 and 10 miRNAs, respectively). Gene targets of the altered miRNAs in the high and/or low IL-1beta/IL-10 ratio ASD subgroups were enriched in pathways critical for monocyte functions and metabolic regulation. Gene targets of the altered miRNAs in all the ASD subgroups were enriched in pathways of neuronal development and synaptic plasticity, along with cell proliferation/differentiation. ASD subgroup-specific associations were observed between serum miRNA expression and IL-1ss/IL-10 ratios, mitochondrial respiration, and monocyte cytokine profiles (IL-10, CCL2, and TNF-alpha). In summary, our results indicate that serum levels of select miRNAs may serve as promising biomarkers for screening and monitoring changes in innate immunity and mitochondrial respiration in ASD.

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7. Lee SH, Maenner MJ, Heilig CM. A comparison of machine learning algorithms for the surveillance of autism spectrum disorder. PLoS One ;2019 ;14(9):e0222907.

OBJECTIVE : The Centers for Disease Control and Prevention (CDC) coordinates a labor-intensive process to measure the prevalence of autism spectrum disorder (ASD) among children in the United States. Random forests methods have shown promise in speeding up this process, but they lag behind human classification accuracy by about 5%. We explore whether more recently available document classification algorithms can close this gap. MATERIALS AND METHODS : Using data gathered from a single surveillance site, we applied 8 supervised learning algorithms to predict whether children meet the case definition for ASD based solely on the words in their evaluations. We compared the algorithms’ performance across 10 random train-test splits of the data, using classification accuracy, F1 score, and number of positive calls to evaluate their potential use for surveillance. RESULTS : Across the 10 train-test cycles, the random forest and support vector machine with Naive Bayes features (NB-SVM) each achieved slightly more than 87% mean accuracy. The NB-SVM produced significantly more false negatives than false positives (P = 0.027), but the random forest did not, making its prevalence estimates very close to the true prevalence in the data. The best-performing neural network performed similarly to the random forest on both measures. DISCUSSION : The random forest performed as well as more recently available models like the NB-SVM and the neural network, and it also produced good prevalence estimates. NB-SVM may not be a good candidate for use in a fully-automated surveillance workflow due to increased false negatives. More sophisticated algorithms, like hierarchical convolutional neural networks, may not be feasible to train due to characteristics of the data. Current algorithms might perform better if the data are abstracted and processed differently and if they take into account information about the children in addition to their evaluations. CONCLUSION : Deep learning models performed similarly to traditional machine learning methods at predicting the clinician-assigned case status for CDC’s autism surveillance system. While deep learning methods had limited benefit in this task, they may have applications in other surveillance systems.

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8. Mandy W. Social camouflaging in autism : Is it time to lose the mask ?. Autism ;2019 (Sep 25):1362361319878559.

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9. McLane RD, Schmitt LM, Pedapati EV, Shaffer RC, Dominick KC, Horn PS, Gross C, Erickson CA. Peripheral Amyloid Precursor Protein Derivative Expression in Fragile X Syndrome. Front Integr Neurosci ;2019 ;13:49.

Fragile X syndrome (FXS) is the most common inherited form of intellectual disability and is associated with increased risk for autism spectrum disorder (ASD), anxiety, ADHD, and epilepsy. While our understanding of FXS pathophysiology has improved, a lack of validated blood-based biomarkers of disease continues to impede bench-to-bedside efforts. To meet this demand, there is a growing effort to discover a reliable biomarker to inform treatment discovery and evaluate treatment target engagement. Such a marker, amyloid-beta precursor protein (APP), has shown potential dysregulation in the absence of fragile X mental retardation protein (FMRP) and may therefore be associated with FXS pathophysiology. While APP is best understood in the context of Alzheimer disease, there is a growing body of evidence suggesting the molecule and its derivatives play a broader role in regulating neuronal hyperexcitability, a well-characterized phenotype in FXS. To evaluate the viability of APP as a peripheral biological marker in FXS, we conducted an exploratory ELISA-based evaluation of plasma APP-related species involving 27 persons with FXS (mean age : 22.0 +/- 11.5) and 25 age- and sex-matched persons with neurotypical development (mean age : 21.1 +/- 10.7). Peripheral levels of both Abeta(1-40) and Abeta(1-42) were increased, while sAPPalpha was significantly decreased in persons with FXS as compared to control participants. These results suggest that dysregulated APP processing, with potential preferential beta-secretase processing, may be a readily accessible marker of FXS pathophysiology.

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10. Melo C, Ruano L, Jorge J, Pinto Ribeiro T, Oliveira G, Azevedo L, Temudo T. Prevalence and determinants of motor stereotypies in autism spectrum disorder : A systematic review and meta-analysis. Autism ;2019 (Sep 25):1362361319869118.

Stereotypies are frequently reported in people with autism spectrum disorder (ASD) but remain one of the less explained phenomena. We aimed to describe, through a systematic review and a meta-analysis, the prevalence of motor stereotypies in ASD and study the factors that influence this prevalence. Our literature search included MEDLINE, Scopus, and PsycINFO databases. Quality and risk of bias were assessed. Thirty-seven studies were included and the median prevalence of motor stereotypies in ASD was 51.8%, ranging from 21.9% to 97.5%. The most frequent determinants associated with a higher number of stereotypies in ASD were a younger age, lower intelligence quotient, and a greater severity of ASD. Moreover, gender did not seem to influence the prevalence of stereotypies. Meta-analytic analysis showed that lower IQ and autism diagnosis (independent of IQ) are associated with a higher prevalence of motor stereotypies (odds ratio = 2.5 and 4.7, respectively). Limitations of the reviewed literature include the use of convenience samples, with small sizes and heterogeneous inclusion criteria, and the predominance of high-functioning autism individuals.

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11. Merbler AM, Byiers BJ, Hoch J, Dimian AC, Barney CC, Feyma TJ, Beisang AA, Bartolomucci A, Symons FJ. Preliminary Evidence That Resting State Heart Rate Variability Predicts Reactivity to Tactile Stimuli in Rett Syndrome. J Child Neurol ;2019 (Sep 25):883073819875915.

Patients with Rett syndrome may manifest altered pain perception/experience and are vulnerable to conditions associated with chronic pain. Pain response is difficult to measure, however, because of severe communicative impairment. There is also documented autonomic dysfunction, including decreased heart rate variability. Given the relation between pain and the autonomic nervous system, we tested the feasibility of using resting heart rate variability to predict nonverbal pain/discomfort behavior during a standardized modified quantitative sensory test in Rett syndrome. All stimulus applications resulted in increased behavioral reactivity compared to baseline, with repeated von Frey significantly greater than all other stimuli. Resting heart rate variability predicted behavioral reactivity to repeated von Frey. These preliminary findings provide feasibility evidence for an integrated autonomic-sensory measurement approach and are consistent at a construct level with preclinical evidence in Rett syndrome. Further work is needed to determine how heart rate variability changes during stimulus application.

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12. Rosenblau G, O’Connell G, Heekeren HR, Dziobek I. Neurobiological mechanisms of social cognition treatment in high-functioning adults with autism spectrum disorder. Psychol Med ;2019 (Sep 25):1-11.

BACKGROUND : The promise of precision medicine for autism spectrum disorder (ASD) hinges on developing neuroscience-informed individualized interventions. Taking an important step in this direction, we investigated neuroplasticity in response to an ecologically-valid, computer-based social-cognitive training (SCOTT). METHODS : In an active control group design, 48 adults with ASD were randomly assigned to a 3-month SCOTT or non-social computer training. Participants completed behavioral tasks, a functional and structural magnetic resonance imaging session before and after the training period. RESULTS : The SCOTT group showed social-cognitive improvements on close and distant generalization tasks. The improvements scaled with reductions in functional activity and increases in cortical thickness in prefrontal regions. CONCLUSION : In sum, we provide evidence for the sensitivity of neuroscientific methods to reflect training-induced social-cognitive improvements in adults with ASD. These results encourage the use of neuroimaging data to describe and quantify treatment-related changes more broadly.

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13. Scandurra V, Emberti Gialloreti L, Barbanera F, Scordo MR, Pierini A, Canitano R. Neurodevelopmental Disorders and Adaptive Functions : A Study of Children With Autism Spectrum Disorders (ASD) and/or Attention Deficit and Hyperactivity Disorder (ADHD). Front Psychiatry ;2019 ;10:673.

Introduction : Autism spectrum disorder (ASD) and attention deficit and hyperactivity disorder (ADHD) are the two most common neurodevelopmental disorders observed in childhood. The DSM-5 accepts a combined diagnosis of ADHD and ASD, while the DSM-IV did not. The aim of this study was to identify and evaluate the adaptive profile of children and adolescents with a diagnosis of comorbid ADHD and ASD, in comparison with adaptive functioning in subjects with a diagnosis of only ASD or ADHD. Materials and Methods : Ninety-one children (77 boys, 14 girls), aging from 3.1 to 13.4 years (mean age : 8.3 +/- 7.2), who met the criteria for a diagnosis of ASD and/or ADHD were enrolled. A neuropsychological evaluation involving cognitive and adaptive assessment was conducted using the Autism Diagnostic Observation Schedule - Second Edition (ADOS-2), the Conners’ Parent Rating Scale - Revised : Long Version (CPRS-R), the Wechsler Intelligence Scale - Fourth Edition or the Griffiths Mental Developmental Scales - Extended Revised, the Vineland Adaptive Behaviour Scale - Second Edition (VABS-II). Conclusion : As to the adaptive skills in the three groups evaluated, a worse general profile was ascertained in the ASD and in ASD plus ADHD groups in comparison with respect to the ADHD-only group. With VABS-II evaluation, we found significant differences among the three groups across all domains and combined scores : Communication (F = 18.960 ; p < 0.001), Socialization (F = 25.410 ; p < 0.001), Daily Living Skills (F = 19.760 ; p < 0.001), Motor (F = 9.615 ; p < 0.001), and Adaptive behavior composite [ABC] (F = 29.370 ; p < 0.001). Implications of neurodevelopmental double diagnosis such as ASD plus ADHD are discussed.

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14. Shnier D, Voineagu MA, Voineagu I. Persistent homology analysis of brain transcriptome data in autism. J R Soc Interface ;2019 (Sep 27) ;16(158):20190531.

Persistent homology methods have found applications in the analysis of multiple types of biological data, particularly imaging data or data with a spatial and/or temporal component. However, few studies have assessed the use of persistent homology for the analysis of gene expression data. Here we apply persistent homology methods to investigate the global properties of gene expression in post-mortem brain tissue (cerebral cortex) of individuals with autism spectrum disorders (ASD) and matched controls. We observe a significant difference in the geometry of inter-sample relationships between autism and healthy controls as measured by the sum of the death times of zero-dimensional components and the Euler characteristic. This observation is replicated across two distinct datasets, and we interpret it as evidence for an increased heterogeneity of gene expression in autism. We also assessed the topology of gene-level point clouds and did not observe significant differences between ASD and control transcriptomes, suggesting that the overall transcriptome organization is similar in ASD and healthy cerebral cortex. Overall, our study provides a novel framework for persistent homology analyses of gene expression data for genetically complex disorders.

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