1. Ahmad B, Dumbuya JS, Tang JX, Li W, Chen X, Lu J. Rett syndrome: Pathogenicity and regulation of MECP2 (human) and Mecp2 (mouse) genes and their protein products through various molecular mechanisms. Mutat Res Rev Mutat Res;2025 (Jul 17);796:108553.

Rett syndrome was first described over 50 years ago as an unusual clinical entity. Mutations in the X-linked MECP2 gene are the primary causes of Rett syndrome. The unstructured MeCP2 protein adopts various functional conformations, complicating its study. Researchers have investigated the pathogenicity and regulation of MECP2 through mechanisms such as apoptosis, mitophagy, the PI3K/AKT/mTOR pathway, BMP signaling, NF-kB, STAT3, and the Wnt/β-catenin pathway. These mechanisms have not been reviewed in such detail before. Summarizing these pathways is essential for facilitating further exploration by researchers; therefore, we have comprehensively summarized these pathways.

Lien vers le texte intégral (Open Access ou abonnement)

2. Erdogan MA, Gurbuz O, Bozkurt MF, Erbas O. Retraction Note: Prenatal Exposure to COVID-19 mRNA Vaccine BNT162b2 Induces Autism-Like Behaviors in Male Neonatal Rats: Insights into WNT and BDNF Signaling Perturbations. Neurochem Res;2025 (Jul 19);50(4):237.

Lien vers le texte intégral (Open Access ou abonnement)

3. Hagerman R. Bringing fragile X-associated neuropsychiatric disorders into the phenotypic fold of premutation conditions. Brain;2025 (Jul 18)

Lien vers le texte intégral (Open Access ou abonnement)

4. Muris P, Otgaar H, Donkers F, Ollendick T, Deckers A. Caught in the Web of the Net? Part II: A Motivation-Based Developmental Psychopathology Model for the Aberrant Internet Use in (Young) People with Autism Spectrum Disorder. Clin Child Fam Psychol Rev;2025 (Jul 18)

In Part I (Muris et al. in Clinical Child and Family Psychology Review 22:549-561, 2025), we provided meta-analytic evidence showing that individuals with autism spectrum disorder (ASD) or high levels of autistic traits exhibit higher rates of problematic internet use (PIU), but paradoxically have lower levels of social media use compared to typically developing individuals or those with lower levels of autistic traits. The current theoretical article introduces a motivation-based developmental psychopathology model aimed at clarifying the motives behind the atypical internet and social media use observed in people with ASD or with high levels of autistic traits. We argue that excessive online activities, such as gaming and watching videos, can be understood through specific social, coping, and enhancement motives for internet use, which are especially prominent in ASD due to disorder-specific characteristics such as narrow interests and challenges in face-to-face interactions. In contrast, when it comes to social media use, these three motives operate differently, leading individuals with ASD to exhibit lower motivation to engage in online social interactions compared to neurotypical individuals. The current article emphasizes adolescence as a critical developmental period where internet use can easily become maladaptive and explores the role of parents in regulating the online behaviors of young people with ASD. Finally, the clinical implications of the model are briefly discussed.

Lien vers le texte intégral (Open Access ou abonnement)

5. Pruett JR, Jr., Todorov AA, Hawks ZW, Talovic M, Nishino T, Petersen SE, Davis S, Stahl L, Botteron KN, Constantino JN, Dager SR, Elison JT, Estes AM, Evans AC, Gerig G, Girault JB, Hazlett H, MacIntyre L, Marrus N, McKinstry RC, Pandey J, Schultz RT, Shannon WD, Shen MD, Snyder AZ, Styner M, Wolff JJ, Zwaigenbaum L, Piven J. Brain functional connectivity correlates of autism diagnosis and familial liability in 24-month-olds. J Neurodev Disord;2025 (Jul 18);17(1):40.

BACKGROUND: fcMRI correlates of autism spectrum disorder (ASD) diagnosis and familial liability were studied in 24-month-olds at high (older affected sibling) and low familial likelihood for ASD. METHODS: fcMRI comparisons of high-familial-likelihood (HL) ASD-positive (HLP, N = 23) and ASD-negative (HLN, N = 91), and low-likelihood ASD-negative (LLN, N = 27) 24-month-olds from the Infant Brain Imaging Study (IBIS) Network were conducted, employing object oriented data analysis (OODA), support vector machine (SVM) classification, and network-level fcMRI enrichment analyses. RESULTS: OODA (alpha = 0.0167, 3 comparisons) revealed differences in HLP and LLN fcMRI matrices (p = 0.012), but none for HLP versus HLN (p = 0.047) nor HLN versus LLN (p = 0.225). SVM distinguished HLP from HLN (accuracy = 99%, PPV = 96%, NPV = 100%), based on connectivity involving many networks. SVM accurately classified (non-training) LLN subjects with 100% accuracy. Enrichment analyses identified a cross-group fcMRI difference in the posterior cingulate default mode network 1 (pcDMN1)- temporal default mode network (tDMN) pair (p = 0.0070). Functional connectivity for implicated connections in these networks was consistently lower in HLP and HLN than in LLN (p = 0.0461 and 0.0004). HLP did not differ from HLN (p = 0.2254). Secondary testing showed HL children with low ASD behaviors still differed from LLN (p = 0.0036). CONCLUSIONS: 24-month-old high-familial-likelihood infants show reduced intra-DMN connectivity, a potential neural finding related to familial liability, while widely distributed functional connections correlate with ASD diagnosis.

Lien vers le texte intégral (Open Access ou abonnement)

6. Sterner EF, Greve A, Knolle F. Temporal stability of semantic predictions in subclinical autistic and schizotypal personality traits. Schizophrenia (Heidelb);2025 (Jul 19);11(1):103.

Language impairments are core symptoms of both schizophrenia and autism spectrum disorders and have been linked to deficits in predictive language processing. While altered use of semantic predictions have been reported in both conditions, little is known whether semantic predictions are stable over time. The goal of this study was therefore to investigate the temporal stability of semantic prior beliefs focusing on individuals with schizotypal and autistic traits. 115 participants, assessed for subclinical schizotypal (SPQ(5ls); mean = 77.99, SD = 39.31) and autistic traits (AQ; mean = 15.67, SD = 6.01), completed an auditory stability paradigm at two timepoints to investigate the temporal stability of semantic predictions. At timepoint one, consisting of one session, participants listened to 240 sentence beginnings varying in predictability (e.g., high: « The swimmer jumped into the… »; low: « The child hid the toy under the… ») and provided a prediction for each sentence-final word. Timepoint two, consisting of two sessions, each session comprising of 120 old and 120 new sentences. In addition to final-word predictions, sentence recall was assessed to examine the influence of memory on prediction stability. Generalized linear mixed models revealed that higher predictability led to greater temporal stability of semantic predictions. Importantly, increasing schizotypal and autistic traits were associated with reduced stability, particularly in highly predictable contexts where stable predictions typically facilitate efficient language processing. While poorer sentence recall was linked to greater instability, especially in medium- and low-predictability contexts, it did not account for the reduced stability observed in relation to schizotypal and autistic traits. These findings suggest that individuals with higher schizotypal and autistic traits struggle to form stable, lasting semantic predictions, which may contribute to difficulties in efficient language processing.

Lien vers le texte intégral (Open Access ou abonnement)

7. Tafolla M, Benrey N, Rosen N, Lerner J, Lord C. Autism Assessment with English-Spanish Bilingual Individuals in the United States. J Autism Dev Disord;2025 (Jul 19)

PURPOSE: There is limited clinical guidance on best autism assessment practices for bilingual individuals. This study aimed to examine whether Spanish-English bilingual participants display varying levels of autism symptoms on the Autism Diagnostic Observation Schedule, Second Edition (ADOS-2) when it is administered in English compared to Spanish, and whether these differences are associated with participants’ dominant language. Furthermore, we explored how often participants met the ADOS-2 autism cutoff scores on both the Spanish and English administrations and compared percentages. We then used generalized linear models with random effects to examine whether the language of ADOS-2 administration (English or Spanish) predicted autism severity scores, depending on participants’ Spanish exposure or usage [1-99%], while controlling for sex, verbal IQ, and autism diagnosis. METHOD: A total of 94 community-referred English-Spanish bilingual participants (age range = 1.5 years- 44.6 years) from predominantly low-income households were included, all with existing diagnoses of autism or other neurodevelopmental or mental health conditions. RESULTS: We found that, on average, the ADOS-2 yields similar severity scores when it is administered in Spanish and English with bilingual individuals. Additionally, language of the ADOS-2 administration does not predict severity scores regardless of percentage of Spanish use or exposure. CONCLUSION: We discuss how findings from this study can inform clinical practice in autism assessment for bilingual individuals, while acknowledging that language is only one aspect of culturally sensitive assessment and must be considered when working with bilingual families.

Lien vers le texte intégral (Open Access ou abonnement)

8. Wang Y, Chen Z, Song P, Lam GY, Kang X, Wong PCM, Geng X. Diagnosis-informed neuro-subtyping reveals subgroups of autism spectrum disorder with reliable and distinct functional connectivity profiles. Prog Neuropsychopharmacol Biol Psychiatry;2025 (Jul 16):111452.

BACKGROUND: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder characterized by heterogeneous symptoms and neurobiological features, which hinders the identification of reliable biomarkers. Until recently, ASD neuro-subtyping has emerged to detect neural features in each subgroup. METHODS: We implemented neuro-subtyping of ASD using a semi-supervised clustering method, HeterogeneitY through DiscRiminative Analysis (HYDRA), guided by the labeling information of ASD/controls, together with a multi-scale dimension reduction method of high-dimensional input features. Functional connectivity was estimated as neural features for subtyping subjects from a large dataset with ~2000 subjects. Systematic evaluation of clustering performance was conducted and the semi-supervised approach was compared with unsupervised K-means, commonly used for neuro-subtyping, combined with different types of feature reduction methods. RESULTS: We successfully detected two clusters, the hyper-connectivity subtype and hypo-connectivity subtype, each exhibiting distinct connectivity patterns between and within large networks, with high reliability. The semi-supervised clustering approach demonstrated superior performance compared to the unsupervised approach. We observed cluster effect on functional connectivities, for instance, the hyper-connectivity cluster shows hyper-connectivity within major large networks and hyper/hypo-connectivities between networks, such as hyper-connectivity between default mode and attention networks, and hypo-connectivity between default mode and visual/auditory networks. In contrast, the hypo-connectivity cluster displayed the opposite connectivity patterns. Furthermore, we found varying correlations between connectivities and main symptoms of ASD across subtypes. CONCLUSIONS: Our findings indicate that the semi-supervised approach has the potential to subtype ASD into distinct and reliable clusters. The clusters effectively differentiate heterogeneous neural markers based on functional connectivity patterns, meanwhile establish distinct neurobehavioral relationships across each subtype, which is a critical step towards developing individualized diagnosis and treatment strategies in the future.

Lien vers le texte intégral (Open Access ou abonnement)

9. Zhang C, He Q, Bennett AN, Pu Y, Wang T, Katie Chan KH. Repositioning Drugs for Autism Spectrum Disorder: An Integrated Network Analysis of Blood and Brain Tissue Key Driver Genes. Eur J Pharmacol;2025 (Jul 16):177963.

BACKGROUND: Autism spectrum disorder (ASD) is a complex neurological condition marked by social, communication, and behavioral challenges. Current treatments are limited, with few approved drugs. This study used network analysis of key driver genes from blood and brain tissues to identify potential therapeutic drugs for ASD. METHOD: We examined Gene Expression Omnibus (GEO) data from postmortem brain (GSE28521) and blood leukocyte (GSE42133) samples to find differentially expressed genes. Key driver genes were identified using weighted key driver analysis, supported by literature, knockout mouse model databases, and enrichment analysis. Drug repositioning was performed with PharmOmics and Connectivity Map (CMap) platforms. RESULTS: In blood samples, 204 key driver genes were discovered, associated with cell cycle regulation and stress response. In brain samples, 290 key driver genes focused on ribosomal activity and protein production. An integrated protein-protein interaction (PPI) network identified 16 shared key driver genes, demonstrating common disease signatures including RNA metabolism, protein regulation, SLIT and ROBO signaling, and antiviral pathways. Drug repositioning revealed 23 potential drugs for ASD, with sulpiride and everolimus demonstrating promise, and 19 drugs exhibiting neurological significance, including six with substantial blood-brain barrier permeability. CONCLUSIONS: Our study reveals both tissue-specific and shared molecular signatures of ASD in blood and brain tissues through PPI network analysis. We identified 16 key driver genes and 23 potential therapeutic drugs. Additionally, we discovered twelve novel key driver genes associated with ASD, emphasizing their roles in neurological and immune functions. These findings enhance our understanding of the molecular basis of ASD and suggest new therapeutic possibilities.

Lien vers le texte intégral (Open Access ou abonnement)