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Peer-reviewedCC BY 4.0Molecular Psychiatry

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

  • Carolina D. Alberca1
  • Kwangmoon Park2
  • Ligia A. Papale1
  • Andy Madrid1
  • Phillip E. Bergmann1
  • Sündüz Keleş2,3
  • Reid S. Alisch1
  1. 1Department of Neurological Surgery, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792 USA
  2. 2Department of Statistics, University of Wisconsin, Medical Sciences Center, 1300 University Ave Room 1220, Madison, WI 53706 USA
  3. 3Department of Biostatistics and Medical Informatics, University of Wisconsin School of Medicine and Public Health, 600 Highland Ave, Madison, WI 53792 USA
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Abstract

Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder with complex molecular etiology. Since genetic causes account for less than 50% of ASD cases, novel approaches are required to overcome the challenges of characterizing genes and molecular pathways linking risk alleles to phenotype. Here we use RNA-sequencing, 3-dimensional protein-centric chromatin conformation (Hi-ChIP), and whole genome DNA methylation sequencing approaches to investigate hippocampal tissue from an ASD mouse model (Cntnap2 knockout [Cntnap2 KO]) to determine if multi-omic data integration improves the resolution of key molecular pathways contributing to the complex ASD phenotype. Each -omic dataset individually identified disruptions in numerous genes and pathways, providing broad ASD-related insights, such as 1) 699 downregulated genes with an enrichment of neuronal ontological terms; 2) unique chromatin interactions in Cntnap2 KO mice; and 3) that most differentially methylated genes (1220/1659) have a neuronal function. The multi-omic data integration reduced the heterogeneity and refined the data to 43 genes with links to ASD (e.g., Zbtb18, Cttnbp2, Gabbr2). A pathways analysis of these 43 genes identified one gene ontological term: regulation of neuronal synaptic plasticity. These findings are consistent with large scale gene expression studies of human ASD postmortem brain tissue, suggesting that multi-omic data integration can be used to achieve a greater resolution of the heterogeneous ASD molecular etiology.

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Cite

BibTeXRIS
BibTeX
@article{alberca2026multiomic,
  title = {{Multi-omic data integration improves the resolution of the molecular etiology of autism in a mouse model}},
  author = {Carolina D. Alberca and Kwangmoon Park and Ligia A. Papale and Andy Madrid and Phillip E. Bergmann and Sündüz Keleş and Reid S. Alisch},
  journal = {Molecular Psychiatry},
  year = {2026},
  month = jul,
  doi = {10.1038/s41380-026-03772-4},
  url = {https://doi.org/10.1038/s41380-026-03772-4},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Multi-omic data integration improves the resolution of the molecular etiology of autism in a mouse model
AU  - Carolina D. Alberca
AU  - Kwangmoon Park
AU  - Ligia A. Papale
AU  - Andy Madrid
AU  - Phillip E. Bergmann
AU  - Sündüz Keleş
AU  - Reid S. Alisch
PY  - 2026
DA  - 2026/07/25
JO  - Molecular Psychiatry
DO  - 10.1038/s41380-026-03772-4
UR  - https://doi.org/10.1038/s41380-026-03772-4
AB  - Autism spectrum disorder (ASD) is a multifactorial neurodevelopmental disorder with complex molecular etiology. Since genetic causes account for less than 50% of ASD cases, novel approaches are required to overcome the challenges of characterizing genes and molecular pathways linking risk alleles to phenotype. Here we use RNA-sequencing, 3-dimensional protein-centric chromatin conformation (Hi-ChIP), and whole genome DNA methylation sequencing approaches to investigate hippocampal tissue from an ASD mouse model (Cntnap2 knockout [Cntnap2 KO]) to determine if multi-omic data integration improves the resolution of key molecular pathways contributing to the complex ASD phenotype. Each -omic dataset individually identified disruptions in numerous genes and pathways, providing broad ASD-related insights, such as 1) 699 downregulated genes with an enrichment of neuronal ontological terms; 2) unique chromatin interactions in Cntnap2 KO mice; and 3) that most differentially methylated genes (1220/1659) have a neuronal function. The multi-omic data integration reduced the heterogeneity and refined the data to 43 genes with links to ASD (e.g., Zbtb18, Cttnbp2, Gabbr2). A pathways analysis of these 43 genes identified one gene ontological term: regulation of neuronal synaptic plasticity. These findings are consistent with large scale gene expression studies of human ASD postmortem brain tissue, suggesting that multi-omic data integration can be used to achieve a greater resolution of the heterogeneous ASD molecular etiology.
SN  - 1359-4184
SN  - 1476-5578
ER  -