1. Adebayo OL, Dewenter I, Rinne L, Golubiani G, Solomonia R, Müller M. {{Intensified mitochondrial hydrogen peroxide release occurs in all brain regions, affects male as well as female Rett mice, and constitutes a life-long burden}}. {Archives of biochemistry and biophysics}. 2020: 108666.
The neurodevelopmental disorder Rett syndrome (RTT) affects mostly females. Upon an apparently normal initial development, cognitive impairment, irregular breathing, motor dysfunction, and epilepsy occur. The complex pathogenesis includes, among others, mitochondrial impairment, redox imbalance, and oxidative damage. As these arise already in neonatal Rett mice, they were proposed contributors of disease progression. Several mitochondrial studies in RTT used either full brains or selected brain regions only. Here, we mapped brain-wide mitochondria-related ROS generation. Using sophisticated multi-sample spectrofluorimetry, H(2)O(2) release by isolated mitochondria was quantified in a coupled reaction of Amplex UltraRed and horseradish peroxidase. All brain regions and the entire lifespan were characterized in male and female mice. In WT mice, mitochondrial H(2)O(2) release was usually highest in cortex and lowest in hippocampus. Maximum rates occurred at postnatal day (PD) 10 and they slightly declined with further maturation. Already at PD 10, male and female Rett mice showed exaggerated mitochondrial H(2)O(2) releases in first brain regions and persistent brain-wide increases from PD 50 on. Interestingly, female Rett mice were more intensely affected than male Rett mice, with their brainstem, midbrain and hippocampus being most severely struck. In conclusion, we used a reliable multi-sample cuvette-based assay on mitochondrial ROS release to perform brain-wide analyzes along the entire lifespan. Mitochondrial H(2)O(2) release in Rett mice is intensified in all brain regions, includes hemizygous males and heterozygous females, and affects all maturational stages. Therefore, intensified mitochondrial H(2)O(2) release seriously needs to be considered throughout RTT pathogenesis and may constitute a potential therapeutic target.
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2. Albajara Sáenz A, Van Schuerbeek P, Baijot S, Septier M, Deconinck N, Defresne P, Delvenne V, Passeri G, Raeymaekers H, Slama H, Victoor L, Willaye E, Peigneux P, Villemonteix T, Massat I. {{Disorder-specific brain volumetric abnormalities in Attention-Deficit/Hyperactivity Disorder relative to Autism Spectrum Disorder}}. {PLoS One}. 2020; 15(11): e0241856.
The overlap/distinctiveness between Attention-Deficit/Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD) has been increasingly investigated in recent years, particularly since the DSM-5 allows the dual diagnosis of ASD and ADHD, but the underlying brain mechanisms remain unclear. Although both disorders are associated with brain volumetric abnormalities, it is necessary to unfold the shared and specific volume abnormalities that could contribute to explain the similarities and differences in the clinical and neurocognitive profiles between ADHD and ASD. In this voxel-based morphometry (VBM) study, regional grey matter volumes (GMV) were compared between 22 children with ADHD, 18 children with ASD and 17 typically developing (TD) children aged 8 to 12 years old, controlling for age and total intracranial volume. When compared to TD children or children with ASD, children with ADHD had a larger left precuneus, and a smaller right thalamus, suggesting that these brain abnormalities are specific to ADHD relative to ASD. Overall, this study contributes to the delineation of disorder-specific structural abnormalities in ADHD and ASD.
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3. Alper M. {{Improving Research on Screen Media, Autism, and Families of Young Children}}. {JAMA Pediatr}. 2020.
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4. Alpert JS. {{Autism: A Spectrum Disease}}. {The American journal of medicine}. 2020.
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5. Ash RT, Park J, Suter B, Zoghbi HY, Smirnakis SM. {{Excessive formation and stabilization of dendritic spine clusters in the MECP2 duplication syndrome mouse model of autism}}. {eNeuro}. 2020.
Autism-associated genetic mutations may perturb the balance between stability and plasticity of synaptic connections in the brain. Here we report an increase in the formation and stabilization of dendritic spines in the cerebral cortex of the mouse model of MECP2-duplication syndrome, a high-penetrance form of syndromic autism. Increased stabilization is mediated entirely by spines that form cooperatively in 10-micron clusters and is observable across multiple cortical areas both spontaneously and following motor training. Excessive stability of dendritic spine clusters could contribute to behavioral rigidity and other phenotypes in syndromic autism.Significance Statement The inflexible repetitive behaviors, « insistence on sameness, » and at times exceptional learning abilities seen in autism imply a defect in the neural processes underlying learning and memory, potentially affecting the balance between stability and plasticity of synaptic connections in the brain. Here we report a pathological bias toward stability of newly formed dendritic spines in the MECP2-duplication mouse model of autism. Enhanced spine stability is mediated entirely by spines aggregating within 10 µm of each other, in clusters. Enhanced clustered spine stability is observable in multiple brain areas both at rest and during motor training. The results suggest that some phenotypes of autism could arise from abnormal consolidation of clustered synaptic connections.
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6. Barokova MD, Hassan S, Lee C, Xu M, Tager-Flusberg H. {{A Comparison of Natural Language Samples Collected From Minimally and Low-Verbal Children and Adolescents With Autism by Parents and Examiners}}. {Journal of speech, language, and hearing research : JSLHR}. 2020: 1-11.
Purpose We aimed to compare the speech of parents and examiners as they elicited language samples from minimally and low-verbal (MLV) children and adolescents with autism spectrum disorder (ASD), while following the same semi-structured elicitation protocol, Eliciting Language Samples for Analysis-Adolescents (ELSA-A). We also compared the speech elicited from the MLV children/adolescents by their parents at home and by trained examiners in the lab and assessed the feasibility of parents collecting language samples at home. Method Thirty-three (five female, 28 male) MLV children and adolescents with ASD between the ages of 6;6 and 19;7 (years;months) participated. All participants were administered standardized assessments, and a trained examiner collected an ELSA-A language sample from them in the lab. The parents of 22 of the children/adolescents collected an ELSA-A sample at home. All language samples were transcribed following standard procedures, and measures of expressive language were extracted to assess the quantity of speech, its syntactic complexity, and lexical diversity. At the end of the study, parents filled out a feedback survey about their experiences collecting ELSA-A. Results On average, parents produced twice as much speech as trained examiners during ELSA-A. However, their speech did not differ in syntactic complexity or lexical diversity. When with their parents, the MLV children/adolescents also produced twice as much speech than with trained examiners. In addition, their samples were more lexically diverse. Overall, parents elicited longer language samples but administered fewer of the ELSA-A activities. Nevertheless, the majority of parents rated the experience of collecting language samples at home favorably. Conclusions When parents collect language samples at home, their older MLV children/adolescents with ASD produce more speech and engage in more back-and-forth verbal interactions than when with trained examiners. Because parent-elicited language samples allow for a richer assessment of children’s expressive language abilities, future studies should focus on identifying ways to encourage parents to collect data at home.
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7. Carpita B, Muti D, Cremone IM, Fagiolini A, Dell’Osso L. {{Eating disorders and autism spectrum: links and risks}}. {CNS spectrums}. 2020: 1-40.
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8. Chew L, Sun KL, Sun W, Wang Z, Rajadas J, Flores RE, Arnold E, Jo B, Fung LK. {{Association of serum allopregnanolone with restricted and repetitive behaviors in adult males with autism}}. {Psychoneuroendocrinology}. 2020; 123: 105039.
Autism spectrum disorder (ASD) has been associated with imbalance between excitatory and inhibitory (E/I) neurotransmission systems, as well as with neuroinflammation. Sitting at the crossroads between E/I imbalance and neuroinflammation is a class of endogenous hormones known as neurosteroids. Current literature points to dysregulated steroid metabolism and atypical neurosteroid levels in ASD as early as in utero. However, due to the complexity of neurosteroid metabolomics, including possible sex differences, the impact of neurosteroids on ASD symptomatology remains unclear. In this study, we assessed neurosteroid levels and ASD symptom severity of 21 males with ASD and 20 full-scale-IQ-matched typically developing (TD) males, all aged 18-39. Using liquid chromatography-tandem mass spectrometry, concentrations of allopregnanolone, cortisol, dehydroepiandrosterone, progesterone, and testosterone were measured in saliva and serum. With the exception of cortisol’s, all neurosteroids’ concentrations were found to have ASD vs. TD group differences in distribution, where one group was normally distributed and the other non-normally distributed. Serum allopregnanolone levels in males with ASD were found to negatively correlate with clinician-rated measures of restricted and repetitive behavior measures (ADOS-2 RRB and ADI-R RRSB domain scores). Additionally, lower serum allopregnanolone levels were found to predict more negative camouflaging scores, which represent greater differences in self- and clinician-rated symptom severity, of both ASD symptomatology overall and repetitive behaviors in particular. Taken together, our findings demonstrate that in adult males with ASD, decreased serum allopregnanolone levels are associated with more severe restricted and repetitive behaviors and with less insight into the severity of these behaviors.
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9. Chiurazzi P, Kiani AK, Miertus J, Paolacci S, Barati S, Manara E, Stuppia L, Gurrieri F, Bertelli M. {{Genetic analysis of intellectual disability and autism}}. {Acta bio-medica : Atenei Parmensis}. 2020; 91(13-s): e2020003.
BACKGROUND AND AIM: Intellectual disability (ID) and autism spectrum disorders (ASD) are neurodevelopmental conditions that often co-exist and affect children from birth, impacting on their cognition and adaptive behaviour. Social interaction and communication ability are also severely impaired in ASD. Almost 1-3% of the population is affected and it has been estimated that approximately 30% of intellectual disability and autism is caused by genetic factors. The aim of this review is to summarize monogenic conditions characterized by intellectual disability and/or autism for which the causative genes have been identified. METHODS AND RESULTS: We identified monogenic ID/ASD conditions through PubMed and other NCBI databases. Many such genes are located on the X chromosome (>150 out of 900 X-linked protein-coding genes), but at least 2000 human genes are estimated to be involved in ID/ASD. We selected 174 genes (64 X-linked and 110 autosomal) for an NGS panel in order to screen patients with ID and/or ASD, after fragile X syndrome and significant Copy Number Variants have been excluded. CONCLUSIONS: Accurate clinical and genetic diagnosis is required for precise treatment of these disorders, but due to their genetic heterogeneity, most cases remain undiagnosed. Next generation sequencing technologies have greatly enhanced the identification of new genes associated with intellectual disability and autism, ultimately leading to the development of better treatment options.
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10. Desoete A, Warreyn P. {{Introduction to the Special Issue: Mathematical abilities in developmental disabilities}}. {Res Dev Disabil}. 2020; 107: 103805.
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11. Farahani M, Rezaei-Tavirani M, Zali A, Zamanian-Azodi M. {{Systematic Analysis of Protein-Protein and Gene-Environment Interactions to Decipher the Cognitive Mechanisms of Autism Spectrum Disorder}}. {Cellular and molecular neurobiology}. 2020.
Autism spectrum disorder (ASD), a heterogeneous neurodevelopmental disorder resulting from both genetic and environmental risk factors, is manifested by deficits in cognitive function. Elucidating the cognitive disorder-relevant biological mechanisms may open up promising therapeutic approaches. In this work, we mined ASD cognitive phenotype proteins to construct and analyze protein-protein and gene-environment interaction networks. Incorporating the protein-protein interaction (PPI), human cognition proteins, and connections of autism-cognition proteins enabled us to generate an autism-cognition network (ACN). With the topological analysis of ACN, important proteins, highly clustered modules, and 3-node motifs were identified. Moreover, the impact of environmental exposures in cognitive impairment was investigated through chemicals that target the cognition-related proteins. Functional enrichment analysis of the ACN-associated modules and chemical targets revealed biological processes involved in the cognitive deficits of ASD. Among the 17 identified hub-bottlenecks in the ACN, PSD-95 was recognized as an important protein through analyzing the module and motif interactions. PSD-95 and its interacting partners constructed a cognitive-specific module. This hub-bottleneck interacted with the 89 cognition-related 3-node motifs. The identification of gene-environment interactions indicated that most of the cognitive-related proteins interact with bisphenol A (BPA) and valproic acid (VPA). Moreover, we detected significant expression changes of 56 cognitive-specific genes using four ASD microarray datasets in the GEO database, including GSE28521, GSE26415, GSE18123 and GSE29691. Our outcomes suggest future endeavors for dissecting the PSD-95 function in ASD and evaluating the various environmental conditions to discover possible mechanisms of the different levels of cognitive impairment.
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12. Heffler KF, Bennett DS, Subedi K. {{Improving Research on Screen Media, Autism, and Families of Young Children-Reply}}. {JAMA Pediatr}. 2020.
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13. Jacobs GR, Voineskos AN, Hawco C, Stefanik L, Forde NJ, Dickie EW, Lai MC, Szatmari P, Schachar R, Crosbie J, Arnold PD, Goldenberg A, Erdman L, Ameis SH. {{Integration of brain and behavior measures for identification of data-driven groups cutting across children with ASD, ADHD, or OCD}}. {Neuropsychopharmacology}. 2020.
Autism spectrum disorder (ASD), obsessive-compulsive disorder (OCD) and attention-deficit/hyperactivity disorder (ADHD) are clinically and biologically heterogeneous neurodevelopmental disorders (NDDs). The objective of the present study was to integrate brain imaging and behavioral measures to identify new brain-behavior subgroups cutting across these disorders. A subset of the data from the Province of Ontario Neurodevelopmental Disorder (POND) Network was used including participants with different NDDs (aged 6-16 years) that underwent cross-sectional T1-weighted and diffusion-weighted magnetic resonance imaging (MRI) scanning on the same 3T scanner, and behavioral/cognitive assessments. Similarity Network Fusion was applied to integrate cortical thickness, subcortical volume, white matter fractional anisotropy (FA), and behavioral measures in 176 children with ASD, ADHD or OCD with complete data that passed quality control. Normalized mutual information was used to determine top contributing model features. Bootstrapping, out-of-model outcome measures and supervised machine learning were each used to examine stability and evaluate the new groups. Cortical thickness in socio-emotional and attention/executive networks and inattention symptoms comprised the top ten features driving participant similarity and differences between four transdiagnostic groups. Subcortical volumes (pallidum, nucleus accumbens, thalamus) were also different among groups, although white matter FA showed limited differences. Features driving participant similarity remained stable across resampling, and the new groups showed significantly different scores on everyday adaptive functioning. Our findings open the possibility of studying new data-driven groups that represent children with NDDs more similar to each other than others within their own diagnostic group. Future work is needed to build on this early attempt through replication of the current findings in independent samples and testing longitudinally for prognostic value.
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14. Kim SS. {{Recent Trends in Autism Spectrum Disorder Research using Text Mining of PubMed: Importance of Early Detection}}. {Clinical and experimental pediatrics}. 2020.
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15. Lin E, Balogh R, Chung H, Dobranowski K, Durbin A, Volpe T, Lunsky Y. {{Looking across health and healthcare outcomes for people with intellectual and developmental disabilities and psychiatric disorders: population-based longitudinal study}}. {The British journal of psychiatry : the journal of mental science}. 2020: 1-7.
BACKGROUND: Intellectual and developmental disabilities (IDDs) and psychiatric disorders frequently co-occur. Although each has been associated with negative outcomes, their combined effect has rarely been studied. AIMS: To examine the likelihood of five negative health and healthcare outcomes for adults with IDD and mental health/addiction disorders (MHAs), both separately and together. For each outcome, demographic, clinical and system-level factors were also examined. METHOD: Linked administrative data-sets were used to identify adults in Ontario, Canada, with IDD and MHA (n = 29 476), IDD-only (n = 35 223) and MHA-only (n = 727 591). Five outcomes (30-day readmission, 30-day repeat ED visit, delayed discharge, long-term care admission and premature mortality) were examined by logistic regression models with generalised estimating equation or survival analyses. For each outcome, crude (disorder groups only) and complete (adding biosocial covariates) models were run using a general population reference group. RESULTS: The IDD and MHA group had the highest proportions across outcomes for both crude and complete models. They had the highest adjusted ratios for readmissions (aOR 1.93, 95%CI 1.88-1.99), repeat ED visit (aOR 2.00, 95%CI 1.98-2.02) and long-term care admission (aHR 12.19, 95%CI 10.84-13.71). For delayed discharge, the IDD and MHA and IDD-only groups had similar results (aOR 2.00 (95%CI 1.90-2.11) and 2.21 (95%CI 2.07-2.36). For premature mortality, the adjusted ratios were similar for all groups. CONCLUSIONS: Poorer outcomes for adults with IDD, particularly those with MHA, suggest a need for a comprehensive, system-wide approach spanning health, disability and social support.
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16. Meera SS, Donovan K, Wolff JJ, Zwaigenbaum L, Elison JT, Kinh T, Shen MD, Estes AM, Hazlett HC, Watson LR, Baranek GT, Swanson MR, John TS, Burrows CA, Schultz RT, Dager SR, Botteron KN, Pandey J, Piven J. {{Towards a Data Driven Approach to Screen for Autism Risk at 12 Months of Age}}. {J Am Acad Child Adolesc Psychiatry}. 2020.
OBJECTIVE: This study aimed to develop a classifier for infants at 12 months of age based on a parent-report measure (the First Year Inventory v.2.0; FYI), to: (1) classify infants at elevated risk, above and beyond that attributable to familial risk status for ASD; and, (2) serve as a starting point to refine an approach for risk estimation in population samples. METHOD: Fifty-four high familial risk (HR) infants later diagnosed with ASD (HR-ASD), 183 HR infants not diagnosed with ASD at 24 months of age (HR-Neg), and 72 low risk controls participated in the study. All infants contributed FYI data at 12 months of age and had a diagnostic assessment for ASD at age 24 months. A data-driven, cross-validated analytic approach was used to develop a classifier to determine screening accuracy (e.g. sensitivity) of the FYI to classify HR-ASD and HR-Neg. RESULTS: The newly developed FYI classifier had an estimated sensitivity of 0.71 (95% CI: 0.50, 0.91) and specificity of 0.72 (95% CI: 0.49, 0.91). CONCLUSION: This classifier demonstrates the potential to improve current screening for ASD risk at 12 months of age in infants already at elevated familial risk for ASD, increasing opportunities for detection of autism risk in infancy. Findings from this study highlight the utility of combining parent-report measures with machine learning approaches.
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17. Rydzewska E, Dunn K, Cooper SA. {{Umbrella systematic review of systematic reviews and meta-analyses on comorbid physical conditions in people with autism spectrum disorder}}. {The British journal of psychiatry : the journal of mental science}. 2020: 1-10.
BACKGROUND: Comorbid physical conditions may be more common in people with autism spectrum disorder (ASD) than other people. AIMS: To identify what is and what is not known about comorbid physical conditions in people with ASD. METHOD: We undertook an umbrella systematic review of systematic reviews and meta-analyses on comorbid physical conditions in people with ASD. Five databases were searched. There were strict inclusion/exclusion criteria. We undertook double reviewing for eligibility, systematic data extraction and quality assessment. Prospective PROSPERO registration: CRD42015020896. RESULTS: In total, 24 of 5552 retrieved articles were included, 15 on children, 1 on adults, and 8 both on children and adults. Although the quality of included reviews was good, most reported several limitations in the studies they included and considerable heterogeneity. Comorbid physical conditions are common, and some are more prevalent than in the general population: sleep problems, epilepsy, sensory impairments, atopy, autoimmune disorders and obesity. Asthma is not. However, there are substantial gaps in the evidence base. Fewer studies have been undertaken on other conditions and some findings are inconsistent. CONCLUSIONS: Comorbid physical conditions occur more commonly in people with ASD, but the evidence base is slim and more research is needed. Some comorbidities compound care if clinicians are unaware, for example sensory impairments, given the communication needs of people with ASD. Others, such as obesity, can lead to an array of other conditions, disadvantages and early mortality. It is essential that potentially modifiable physical conditions are identified to ensure people with ASD achieve their best outcomes. Heightening clinicians’ awareness is important to aid in assessments and differential diagnoses, and to improve healthcare.
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18. Sandbank M, Bottema-Beutel K, Woynaroski T. {{Intervention Recommendations for Children With Autism in Light of a Changing Evidence Base}}. {JAMA Pediatr}. 2020.
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19. Shih PY, Hsieh BY, Tsai CY, Lo CA, Chen BE, Hsueh YP. {{Autism-linked mutations of CTTNBP2 reduce social interaction and impair dendritic spine formation via diverse mechanisms}}. {Acta neuropathologica communications}. 2020; 8(1): 185.
Abnormal synaptic formation and signaling is one of the key molecular features of autism spectrum disorders (ASD). Cortactin binding protein 2 (CTTNBP2), an ASD-linked gene, is known to regulate the subcellular distribution of synaptic proteins, such as cortactin, thereby controlling dendritic spine formation and maintenance. However, it remains unclear how ASD-linked mutations of CTTNBP2 influence its function. Here, using cultured hippocampal neurons and knockin mouse models, we screen seven ASD-linked mutations in the short form of the Cttnbp2 gene and identify that M120I, R533* and D570Y mutations impair CTTNBP2 protein-protein interactions via divergent mechanisms to reduce dendritic spine density in neurons. R533* mutation impairs CTTNBP2 interaction with cortactin due to lack of the C-terminal proline-rich domain. Through an N-C terminal interaction, M120I mutation at the N-terminal region of CTTNBP2 also negatively influences cortactin interaction. D570Y mutation increases the association of CTTNBP2 with microtubule, resulting in a dendritic localization of CTTNBP2, consequently reducing the distribution of CTTNBP2 in dendritic spines and impairing the synaptic function of CTTNBP2. Finally, we generated heterozygous M120I knockin mice to mimic the genetic variation of patients and found they exhibit reduced social interaction. Our study elucidates that different ASD-linked mutations of CTTNBP2 result in diverse molecular deficits, but all have the similar consequence of synaptic impairment.
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20. Wang J, Wang L. {{Prediction and prioritization of autism-associated long non-coding RNAs using gene expression and sequence features}}. {BMC Bioinformatics}. 2020; 21(1): 505.
BACKGROUND: Autism spectrum disorders (ASD) refer to a range of neurodevelopmental conditions, which are genetically complex and heterogeneous with most of the genetic risk factors also found in the unaffected general population. Although all the currently known ASD risk genes code for proteins, long non-coding RNAs (lncRNAs) as essential regulators of gene expression have been implicated in ASD. Some lncRNAs show altered expression levels in autistic brains, but their roles in ASD pathogenesis are still unclear. RESULTS: In this study, we have developed a new machine learning approach to predict candidate lncRNAs associated with ASD. Particularly, the knowledge learnt from protein-coding ASD risk genes was transferred to the prediction and prioritization of ASD-associated lncRNAs. Both developmental brain gene expression data and transcript sequence were found to contain relevant information for ASD risk gene prediction. During the pre-training phase of model construction, an autoencoder network was implemented for a representation learning of the gene expression data, and a random-forest-based feature selection was applied to the transcript-sequence-derived k-mers. Our models, including logistic regression, support vector machine and random forest, showed robust performance based on tenfold cross-validations as well as candidate prioritization with hypothetical loci. We then utilized the models to predict and prioritize a list of candidate lncRNAs, including some reported to be cis-regulators of known ASD risk genes, for further investigation. CONCLUSIONS: Our results suggest that ASD risk genes can be accurately predicted using developmental brain gene expression data and transcript sequence features, and the models may provide useful information for functional characterization of the candidate lncRNAs associated with ASD.
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21. Wang K, Li N, Xu M, Huang M, Huang F. {{Glyoxalase 1 Inhibitor Alleviates Autism-like Phenotype in a Prenatal Valproic Acid-Induced Mouse Model}}. {ACS chemical neuroscience}. 2020.
Autism spectrum disorder (ASD) is a severe neurological and developmental disorder that impairs a person’s ability to socialize and communicate and affects behavior. The number of patients diagnosed with ASD has risen rapidly. However, the pathophysiology of ASD is poorly understood, and drugs for ASD treatment are strikingly limited. This study aims to evaluate the roles of glyoxalase 1 (GLO1)-methylglyoxal (MG)-γ-aminobutyric acid (GABA) signaling in ASD using a valproic acid (VPA)-induced animal model of autism. The GLO1 levels were analyzed by RT-qPCR and Western blot assay, and MG levels were measured with a Methylglyoxal Assay Kit. The open-field and sniff duration tests were used to assess the interest and anxiety of VPA mice. The three-chamber, marble-burying, and tail-flick tests were applied to determine the sociability, repetitive behavior, and nociceptive threshold of VPA mice. Our results demonstrated that increased GLO1 and decreased MG were observed in VPA mice. Administration of S-p-bromobenzylglutathione cyclopentyl diester (BrBzGCp2), a GLO1 inhibitor, was beneficial for alleviating anxiety, reducing repetitive behavior, and improving the impaired sociability and nociceptive threshold of VPA mice. BrBzGCp2 treatment may be developed as a promising therapeutic strategy for patients with ASD.
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22. Wolff JJ, Piven J. {{Predicting Autism Risk in Infancy}}. {J Am Acad Child Adolesc Psychiatry}. 2020.
By definition, autism spectrum disorder (ASD) emerges during early childhood. However, despite longstanding recommendations for earlier identification and intervention, there has been relatively slow progress in lowering the average age of diagnosis and enrollment in treatment for affected children. This has been due to several factors, including the inadequacy of behavioral risk markers and clinical practice entailing a « wait to see » or « wait to fail » approach to identification. Converging evidence now suggests that brain changes precede changes in behavior in children with ASD. This work has led to the discovery of potential biomarkers of presymptomatic or prodromal risk which may be used to accurately identify children at ultra-high risk during the first year of life. Such findings raise the possibility of intervention prior to the consolidation of core autistic features and during a period of substantial neural plasticity. While these avenues of research suggest strong potential for eventual clinical application, they also raise new questions regarding content, dosage, and timing of intervention as well as ethical, legal, and social considerations concerning presymptomatic identification and treatment.
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23. Wu C, Zheng H, Wu H, Tang Y, Li F, Wang D. {{Age-related Brain Morphological Alteration of Medication-naive Boys With High Functioning Autism}}. {Academic radiology}. 2020.
RATIONALE AND OBJECTIVE: To investigate age-related brain morphological changes of boys with high functioning autism (HFA). MATERIALS AND METHODS: Forty-six medication-naive boys with HFA and 48 age-matched typically developing boys (4-12 years old) were included in this study. Structural brain images were processed with FreeSurfer to calculate the brain morphometric features including regional volume, surface area, average cortical thickness, and Gaussian curvature. General linear model was used to identify significant effects of diagnosis and age-by-diagnosis interaction. Correlations between age and the brain morphometric variables of significant clusters were explored. RESULTS: Primarily, most of the regions with statistically significant intergroup differences were located in the temporal lobe gyri. Importantly, the volume of bilateral superior temporal gyrus (STG) and the average cortical thickness of the right STG demonstrated significantly age-related intergroup differences. Further age-stratified analysis also revealed morphological alterations of STG among subgroups of preschool and school-aged children with or without HFA. CONCLUSION: The findings demonstrated abnormal age-related volume and cortical thickness atrophy of the STG in HFA children, which reflect brain development trajectories of ASD may initiate to diverge from early overgrowth in childhood period. The anatomical localization of specific brain regions would help us better understand the neurobiology alterations of HFA patients and indicate the effect of age should be carefully delineated and examined in future studies about HFA.
