Integrated transcriptomic and clinical analysis of autism spectrum disorder reveals structured heterogeneity and links <i>Methyl‐CpG Binding Domain Protein 2</i> expression with symptom severity

AIM: Autism spectrum disorder (ASD) remains underexplored in Southeast Asia, with limited characterization of its molecular underpinnings and clinical heterogeneity. This study investigated transcriptomic variation and its relationship to clinical diversity in Thai children with ASD using integrated molecular and phenotypic analyses. METHOD: Peripheral blood transcriptomic profiling was performed in 150 ASD and 70 typically developing (TD) children to identify differentially expressed transcripts (DETs), followed by pathway enrichment, unsupervised molecular clustering, and integrative multiomics modeling. Clinical data from 200 ASD and 110 TD participants were analyzed in parallel to evaluate screening performance and derive phenotype-based subgroups. RESULTS: Differential expression analysis identified 1,407 DETs with significant enrichment of autism-associated genes. Pathway analysis consistently implicated dysregulation of protein-synthesis machinery. Unsupervised transcriptome-based clustering revealed three molecular subgroups characterized by translational, innate-immune, and interferon-associated signatures, which did not directly correspond to clinical severity, age, or cognitive level, indicating structured biological heterogeneity beyond symptom-based classification. Integration of transcriptomic and phenotypic data demonstrated moderate cross-domain correspondence. Among recurrent molecular signals, MBD2 showed consistent upregulation across subgroups and strong associations with social-interaction measures (r = 0.74). In parallel, clinical machine-learning models showed high within-cohort screening performance, while a refined 17-transcript panel achieved robust discriminative accuracy, supporting the complementary role of molecular features in ASD stratification. CONCLUSION: Transcriptomic stratification reveals biologically structured heterogeneity in ASD that is only partially reflected in clinical presentation, supporting a multilayered framework integrating molecular and phenotypic dimensions.

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Publication Details

Journal
Psychiatry and Clinical Neurosciences
Published
2026-04-14
DOI
https://doi.org/10.1111/pcn.70065
Citations
1
Primary Topic
Autism Spectrum Disorder Research
Type
article
Field-Weighted Citation Impact
18.92

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article

Integrated transcriptomic and clinical analysis of autism spectrum disorder reveals structured heterogeneity and links Methyl‐CpG Binding Domain Protein 2 expression with symptom severity

Thanit Saeliw, Songphon Kanlayaprasit, Natchaya Vanwong, Pon Trairatvorakul et al.
1 citations
Psychiatry and Clinical Neurosciences
Autism Spectrum Disorder Research
18.92
article

Integrated transcriptomic and clinical analysis of autism spectrum disorder reveals structured heterogeneity and links Methyl‐CpG Binding Domain Protein 2 expression with symptom severity

Thanit Saeliw, Songphon Kanlayaprasit, Natchaya Vanwong, Pon Trairatvorakul, Wasana Yuwattana, Weerasak Chonchaiya, Tewarit Sarachana, Valerie Wailin Hu, Chayanit Poolcharoen, Marlieke Lisanne van Erp, Kim‐Anh Lê Cao
article en
1 citations

Abstract

AIM: Autism spectrum disorder (ASD) remains underexplored in Southeast Asia, with limited characterization of its molecular underpinnings and clinical heterogeneity. This study investigated transcriptomic variation and its relationship to clinical diversity in Thai children with ASD using integrated molecular and phenotypic analyses. METHOD: Peripheral blood transcriptomic profiling was performed in 150 ASD and 70 typically developing (TD) children to identify differentially expressed transcripts (DETs), followed by pathway enrichment, unsupervised molecular clustering, and integrative multiomics modeling. Clinical data from 200 ASD and 110 TD participants were analyzed in parallel to evaluate screening performance and derive phenotype-based subgroups. RESULTS: Differential expression analysis identified 1,407 DETs with significant enrichment of autism-associated genes. Pathway analysis consistently implicated dysregulation of protein-synthesis machinery. Unsupervised transcriptome-based clustering revealed three molecular subgroups characterized by translational, innate-immune, and interferon-associated signatures, which did not directly correspond to clinical severity, age, or cognitive level, indicating structured biological heterogeneity beyond symptom-based classification. Integration of transcriptomic and phenotypic data demonstrated moderate cross-domain correspondence. Among recurrent molecular signals, MBD2 showed consistent upregulation across subgroups and strong associations with social-interaction measures (r = 0.74). In parallel, clinical machine-learning models showed high within-cohort screening performance, while a refined 17-transcript panel achieved robust discriminative accuracy, supporting the complementary role of molecular features in ASD stratification. CONCLUSION: Transcriptomic stratification reveals biologically structured heterogeneity in ASD that is only partially reflected in clinical presentation, supporting a multilayered framework integrating molecular and phenotypic dimensions.

Psychiatry and Clinical Neurosciences
Chulalongkorn University (TH), The University of Melbourne (AU), George Washington University (US), Melbourne Genomics Health Alliance (AU)
Royal Golden Jubilee (RGJ) Ph.D. Programme
Openalex Percentile: Top 1%
Autism Spectrum Disorder Research
18.92
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