Pubmed du 06/04/25
1. Contestabile A, Kojovic N, Casarotto G, Delavari F, Hagmann P, Schaer M, Bellone C. Translational research approach to social orienting deficits in autism: the role of superior colliculus-ventral tegmental pathway. Mol Psychiatry. 2025.
Autism Spectrum Disorder (ASD) is characterized by impairments in social interaction and repetitive behaviors. A key characteristic of ASD is a decreased interest in social interactions, which affects individuals’ ability to engage with their social environment. This study explores the neurobiological basis of these social deficits, focusing on the pathway between the Superior Colliculus (SC) and the Ventral Tegmental Area (VTA). Adopting a translational approach, our research used Shank3 knockout mice (Shank3(-/-)), which parallel a clinical cohort of young children with ASD, to investigate these mechanisms. We observed consistent deficits in social orienting across species. In children with ASD, fMRI analyses revealed a significant decrease in connectivity between the SC and VTA. Additionally, using miniscopes in mice, we identified a reduction in the frequency of calcium transients in SC neurons projecting to the VTA, accompanied by changes in neuronal correlation and intrinsic cellular properties. Notably, the interneuronal correlation in Shank3(-/-) mice and the functional connectivity of the SC to VTA pathway in children with ASD correlated with the severity of social deficits. Our findings underscore the potential of the SC-VTA pathway as a biomarker for ASD and open new avenues for therapeutic interventions, highlighting the importance of early detection and targeted treatment strategies.
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2. Loomis S, Silva DG, Savopoulos R, Cilia J, Li J, Davis MD, Virley D, Foley A, Loro E, McCreary AC. BEHAVIORAL AND TRANSCRIPTOMIC EFFECTS OF A NOVEL CANNABINOID ON A RAT VALPROIC ACID MODEL OF AUTISM. Neuropharmacology. 2025: 110450.
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by impaired social communication, restricted interests, repetitive behavior and irritability. Exposure to valproic acid (VPA) during pregnancy has been shown to increase the risk of autism in children and has led to the development of the in-utero VPA rat model that elicits neurodevelopmental autistic-like features. Offspring exhibit behavioral and neurobiological alterations modelling ASD symptoms. We performed a behavioral and molecular assessment in a rat in-utero VPA model treated with a novel botanical cannabinoid, JZP541. Male offspring from dams treated with VPA were tested acutely and sub-chronically with JZP541 (10, 30, or 100 mg/kg, intraperitoneally). A behavioral testing battery was performed, and brain frontal cortex and hippocampus used for RNA sequencing. In utero exposure to VPA resulted in progeny showing behavioral phenotypes characteristic of ASD. JZP541 attenuated these deficits in social, stereotypic, hyperactivity and irritability behavior in a dose-dependent fashion. VPA exposure was associated with a substantial transcriptional dysregulation impacting multiple key biological processes in a tissue-dependent manner. The expression profiles were integrated with publicly available datasets of autism-associated genes to support the validity of the model used and to focus on the effects of treatment on known autism-relevant transcriptional targets. This approach indicated a strong and dose-dependent reduction of the autism-associated gene expression signature in brain samples from animals dosed with JZP541. Our findings demonstrate JZP541 was able to ameliorate ASD associated behavioral deficits, and this was supported by improvements in putative transcriptional biomarkers of ASD.
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3. Teter OM, McQuade A, Hagan V, Liang W, Dräger NM, Sattler SM, Holmes BB, Castillo VC, Papakis V, Leng K, Boggess S, Nowakowski TJ, Wells J, Kampmann M. CRISPRi-based screen of autism spectrum disorder risk genes in microglia uncovers roles of ADNP in microglia endocytosis and synaptic pruning. Mol Psychiatry. 2025.
Autism Spectrum Disorders (ASD) are a set of neurodevelopmental disorders with complex biology. The identification of ASD risk genes from exome-wide association studies and de novo variation analyses has enabled mechanistic investigations into how ASD-risk genes alter development. Most functional genomics studies have focused on the role of these genes in neurons and neural progenitor cells. However, roles for ASD risk genes in other cell types are largely uncharacterized. There is evidence from postmortem tissue that microglia, the resident immune cells of the brain, appear activated in ASD. Here, we used CRISPRi-based functional genomics to systematically assess the impact of ASD risk gene knockdown on microglia activation and phagocytosis. We developed an iPSC-derived microglia-neuron coculture system and high-throughput flow cytometry readout for synaptic pruning to enable parallel CRISPRi-based screening of phagocytosis of beads, synaptosomes, and synaptic pruning. Our screen identified ADNP, a high-confidence ASD risk genes, as a modifier of microglial synaptic pruning. We found that microglia with ADNP loss have altered endocytic trafficking, remodeled proteomes, and increased motility in coculture.
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4. Younes S, Mourad N, Haddad C, Saadeh D, Sacre H, Malhab SB, Mayta S, Hamzeh N, Salloum Y, Rahal M, Salameh P. A cross-sectional study of public knowledge and stigma towards autism spectrum disorder in Lebanon. Sci Rep. 2025; 15(1): 11680.
Autism Spectrum Disorder (ASD) is a neurodevelopmental condition that requires public understanding to foster acceptance and reduce stigma. This study aimed to evaluate ASD knowledge and stigma among the Lebanese population and the factors influencing them. An online cross-sectional study was conducted between February and July 2022 among Lebanese adults. Participants completed a self-administered structured questionnaire that comprised a sociodemographic section and two validated scales serving the study’s purpose (The Autism Stigma and Knowledge Questionnaire (ASK-Q) and The Autism Social Distance Scale). A total of 949 participants filled out the questionnaire. More than half of them had adequate knowledge of autism diagnosis and symptoms (57.9%), while only 6.6% and 9.6% showed adequate knowledge of its etiology and treatment, respectively. Additionally, 83.4% of the participants did not endorse stigma toward autism. The multivariate analysis taking the knowledge total score and subscales as the dependent variables showed that declaring prior knowledge of autism was significantly associated with better knowledge (Beta = 1.38) and higher stigma (Beta = 0.26). Furthermore, a higher knowledge score (Beta = – 0.06) and a declared prior knowledge of autism (Beta = – 0.62) were significantly associated with lower autism social distance. These findings highlight the need for targeted awareness campaigns to address knowledge gaps and further reduce stigma in Lebanon.
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5. Yuwattana W, Saeliw T, van Erp ML, Poolcharoen C, Kanlayaprasit S, Trairatvorakul P, Chonchaiya W, Hu VW, Sarachana T. Machine learning of clinical phenotypes facilitates autism screening and identifies novel subgroups with distinct transcriptomic profiles. Sci Rep. 2025; 15(1): 11712.
Autism spectrum disorder (ASD) presents significant challenges in diagnosis and intervention due to its diverse clinical manifestations and underlying biological complexity. This study explored machine learning approaches to enhance ASD screening accuracy and identify meaningful subtypes using clinical assessments from AGRE database integrated with molecular data from GSE15402. Analysis of ADI-R scores from a large cohort of 2794 individuals demonstrated that deep learning models could achieve exceptional screening accuracy of 95.23% (CI 94.32-95.99%). Notably, comparable performance was maintained using a streamlined set of just 27 ADI-R sub-items, suggesting potential for more efficient diagnostic tools. Clustering analyses revealed three distinct subgroups identifiable through both clinical symptoms and gene expression patterns. When ASD were grouped based on clinical features, stronger associations emerged between symptoms and underlying molecular profiles compared to grouping based on gene expression alone. These findings suggest that starting with detailed clinical observations may be more effective for identifying biologically meaningful ASD subtypes than beginning with molecular data. This integrated approach combining clinical and molecular data through machine learning offers promising directions for developing more precise screening methods and personalized intervention strategies for individuals with ASD.
