The 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.
Abstract
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.
Autism spectrum disorder (ASD) is classically conceptualized as a dysconnectivity syndrome. However, most studies have examined structural and functional connectivity in isolation, leaving the coupling mechanisms between brain structure and function, particularly the contribution of white matter, poorly understood. To systematically characterize connectome pathology in ASD, we analyzed multimodal imaging data from 580 participants (240 with ASD, 340 typical controls) in the Autism Brain Imaging Data Exchange II dataset. We constructed multilayer brain networks integrating gray and white matter layers and quantified topological alterations using multiplex clustering and participation coefficients. Imaging-transcriptomic analysis was performed using the Allen Human Brain Atlas to link network changes to molecular pathways. The results revealed widespread whole-brain topological reorganization in white matter multiplex networks, involving the corpus callosum and major fiber tracts, with gene expression enriched in immune regulation and cellular metabolism pathways. The gray matter multiplex networks exhibited localized hyper-clustering centered on the cortico-striatum-thalamic-cortical circuit and the default mode network associated with genes implicated in cell adhesion and synaptic transmission. Notably, no significant group differences were observed in the multiplex participation coefficient, which indexes cross-layer integration, suggesting that the overall cross-layer connectivity distribution remains relatively stable. These findings delineate the co-occurring patterns of gray matter hyper-clustering and widespread white matter topological alterations in ASD and establish a multilevel framework bridging macroscopic connectome disruptions to the underlying molecular mechanisms, offering an integrated perspective on ASD heterogeneity.
Yingzhuo Wan, Hairong Xiao, Hanrui Chen et al.· Progress in Neuro-psychophar...· 0 citations
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.
Carolina D Alberca, Kwangmoon Park, Ligia A. Papale et al.· Molecular Psychiatry· 0 citations
This study integrated mRNA expression profiles from five post-mortem brain tissue GEO datasets to identify ASD-associated genes and found that EIF4A1 mRNA expression was significantly elevated in ASD subjects and rescued by treatment with the antipsychotics olanzapine or risperidone.
FMRI data from 162 ASD and 175 TD adolescents are analyzed to demonstrate statistical associations among altered spatial-functional properties, clinical severity, and transcriptomic profiles related to synaptic signaling, mitochondrial processes, and glial-related functions in ASD, providing a complementary spatial perspective on large-scale functional organization.
Jun Pan, Heng Zhang, Yiran Zhai et al.· Frontiers in Neuroscience· 0 citations
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent impairments in social communication, restricted interests, and repetitive behaviors. This narrative review synthesizes advances in machine learning applications to ASD genomic research through May 2026, spanning gene expression analysis, whole-exome sequencing (WES), non-coding variant interpretation, multi-omics integration, single-cell transcriptomics, epigenetic profiling, and gut microbiome analysis. A purposive, thematic literature synthesis approach was employed, allowing broad coverage of emerging methodological innovations and biological insights. We critically evaluate state-of-the-art deep learning architectures-including the Separate Translated Autism Research Neural Network and SHapley Additive exPlanations-based explainable artificial intelligence frameworks. Reported discrimination across the field varies widely, from receiver operating characteristic-area under the curve (ROC-AUC) values near 0.66 to implausibly perfect values of 1.00; the best-validated specialized genomic architecture achieves only modest discrimination (ROC-AUC≈0.73). We emphasize that interpretability and predictive performance are orthogonal properties: specialized architectures yield biologically interpretable feature attributions despite modest discriminative power; and several extreme AUC values in the literature are, in our assessment, more consistent with overfitting or data leakage than with genuine signal, although the primary reports did not always provide the information needed to definitively attribute them. Key themes include: (1) identification of differentially expressed genes through meta-analysis of transcriptomic data; (2) validation of predictive gene features from large-scale WES; (3) detection of non-coding regulatory mutations affecting synaptic transmission pathways; (4) discovery of gut microbiome signatures associated with ASD classification; and (5) discovery of data-driven subtypes enabling precision medicine stratification. Critical challenges include population bias toward European ancestry, socioeconomic ascertainment bias, modest predictive effect sizes, conflation of association with causation, and gaps between computational prediction and clinical utility. Future directions emphasize multi-modal data integration, diverse cohort expansion, engagement with neurodiversity perspectives, and regulatory science development.
Zahra Sadr, Bita Fallahpour, Alireza Alireza Dastgheib et al.· Medeniyet Medical Journal· 0 citations
RNA splicing shapes neuronal identity and disease risk, yet current maps lack the developmental resolution and depth to resolve this complexity. Here, we integrate deep long-read RNA sequencing and proteomics in induced pluripotent stem cell-derived cortical neurons to generate a high-resolution proteogenomic atlas of human neuron development. We identify 182,371 mRNA isoforms (over half previously unknown) and provide direct peptide evidence for the translation of hundreds of novel protein-coding sequences. Population genetics demonstrates that variants affecting novel exons and splice sites are under negative selection, underscoring the potential significance of these isoforms. During neuronal maturation, we observe that autism risk genes undergo dynamic isoform switching, including microexon inclusion and intron retention, that remodel key protein domains and regulatory regions. Furthermore, we uncover widespread, long-range coordination between alternative transcript processing events, including transcription start sites, exon splicing, and polyadenylation. Finally, our atlas enables variant reinterpretation in autism, highlighting the value of an isoform-centric view for interpreting pathogenic variation in neurodevelopment. Alternative splicing expands neuronal diversity, but many brain RNA isoforms remain uncharacterized. Here, the authors generate a high‑resolution proteogenomic atlas of human neuron development, uncovering thousands of novel isoforms and revealing dynamic splicing changes in autism risk genes.
N. Xu, Katherine M. Rynard, E. Radley et al.· Nature Communications· 0 citations
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