Autism mutations rewire protein interaction networks to drive neurodevelopmental pathology.

Multi-omic data integration improves the resolution of the molecular etiology of autism in a mouse model.

RNA-sequencing, 3-dimensional protein-centric chromatin conformation, and whole genome DNA methylation sequencing approaches are used to investigate hippocampal tissue from an ASD mouse model to determine if multi-omic data integration improves the resolution of key molecular pathways contributing to the complex ASD phenotype.

X-linked SYTL4 missense variant disrupts RAB27A-dependent vesicle trafficking and synaptic transmission in autism.

The findings implicate disrupted SYTL4-RAB27A-dependent vesicle trafficking in ASD pathogenesis and identify SYTL4 and RAB27A as previously unrecognized contributors to autism-associated synaptic deficits and behavior.

Molecules and Cells

Identification of EIF4A1 as a candidate molecular indicator of autism spectrum disorder using integrative bioinformatic and biological methods.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental condition with complex genetic and molecular mechanism. Identifying reliable molecular biomarkers remains a critical challenge. In this study, we integrated mRNA expression profiles from five post-mortem brain tissue GEO datasets to identify ASD-associated genes. Following batch effect correction, differentially expressed genes (DEGs) were analysed and Weighted Gene Co-expression Network Analysis (WGCNA) was performed to screen genes correlated with ASD. Then, five machine learning algorithms - Random Forest, LASSO, Boruta, CatBoost, and LightGBM - were applied to screen hub genes. Lastly, alterations of the hub gene(s) were investigated with a maternal immune activation (MIA) rat model using poly I:C by measuring mRNA expression of the hub genes in the rat nucleus accumbens (NAc) and caudate putamen (CPu). A total of 30 DEGs and 54 WGCNA module genes were identified, yielding 29 key candidates by intersecting these two gene sets. EIF4A1 (Eukaryotic Translation Initiation Factor 4A1) was the sole gene consistently ranked among the top five by all five machine learning algorithms. Analysis of the integrated dataset confirmed that EIF4A1 mRNA expression was significantly elevated in ASD subjects. Finally, using the MIA rat model of ASD, we found that EIF4A1 mRNA expression was significantly down-regulated in the NAc and CPu, and this deficit was rescued by treatment with the antipsychotics olanzapine or risperidone. In conclusion, the present study positions EIF4A1 as a promising candidate molecular indicator with potential implications for understanding disease mechanisms and developing targeted interventions of ASD.

Shared molecular dysregulation across neuropsychiatric disorders reveals candidate therapeutic targets.

A cross-disorder transcriptomic framework was applied to identify shared molecular dysregulation across ASD, BD, and SCZ and identified 20 high-confidence recurrent genes, with pronounced transcriptional convergence observed between BD and SCZ.

Mapping lncRNAs onto multilevel mRNA co‑expression modules in autism.

Autism spectrum disorder (ASD) involves heterogeneous genetic and transcriptomic alterations, but how these changes are organized across hierarchical co-expression scales and linked to long noncoding RNAs (lncRNAs) remains incompletely understood. Here, we present Minimum Span Clustering Network (MSCN), an unsupervised, deterministic framework that constructs traceable multilevel mRNA co-expression hierarchies without requiring soft-thresholding powers, fixed module numbers, cut heights, or stochastic initialization. Applied to two independent ASD brain transcriptome cohorts, MSCN reveals hierarchical gene modules across four resolution levels, enabling the detection of transcriptional patterns ranging from low-level, specific signals to high-level, broader biological pathways. We uncovered modules enriched in neuronal/axonal, developmental, and immune-related pathways, reflecting interconnected neurodevelopmental and immune-dysregulation programs in ASD. Cross-method comparisons showed that MSCN complements WGCNA and MEGENA by preserving biologically concordant modules while providing an explicit parent-child hierarchy; simulation and benchmark analyses supported comparable module recovery, preservation, and enrichment performance. By mapping lncRNAs to MSCN-derived mRNA modules, we identified lncRNAs associated with ASD-related mRNA modules and evaluated them as candidate statistical mediators in downstream transcription factor (TF)-lncRNA-mRNA analyses. This analysis identified over 11,000 candidate TF-lncRNA-mRNA axes, including 644 axes involving 46 SFARI score 1 or 2 genes, four of which were syndromic genes, and 12 named lncRNA mediators. External transcriptomic evaluation further supported 789 axes, including representative HIF1A-STXBP5-AS1-CADPS, E2F1-PART1-SCN2A, and RELA-RFPL1S-GRIN2A relationships. Together, these findings establish MSCN as a scalable framework for decomposing ASD mRNA co-expression architecture, provide a hypothesis-generating resource linking coding and noncoding transcriptomic alterations, and help prioritize these relationships for future validation.