2. Academic Validation
  3. Unraveling pathogenesis and potential biomarkers for autism spectrum disorder associated with HIF1A pathway based on machine learning and experiment validation

Unraveling pathogenesis and potential biomarkers for autism spectrum disorder associated with HIF1A pathway based on machine learning and experiment validation

  • Neurobiol Dis. 2025 Jan:204:106763. doi: 10.1016/j.nbd.2024.106763.
Jinru Cui 1,  Heli Li 1,  Cong Hu 1,  Feiyan Zhang 1,  Yunjie Li 1,  Ying Weng 2,  Liping Yang 1,  Yingying Li 2,  Minglan Yao 2,  Hao Li 3,  Xiaoping Luo 2,  Yan Hao 4
Affiliations

Affiliations

  • 1 Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
  • 2 Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
  • 3 Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
  • 4 Division of Child Healthcare, Department of Pediatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. Electronic address: haoyaner@163.com.
PMID: 39657846 DOI: 10.1016/j.nbd.2024.106763
Abstract

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with a high social burden and limited treatments. Hypoxic condition of the brain is considered an important pathological mechanism of ASD. HIF1A is a key participant in brain hypoxia, but its contribution to the pathophysiological landscape of ASD remains unclear.

Methods: ASD-related datasets were obtained from GEO database, and HIF1A-related genes from GeneCards. Co-expression module analysis identified module genes, which were intersected with HIF1A-related genes to identify common genes. Machine learning identified hub genes from intersection genes and PPI networks were constructed to explore relationships among hub and HIF1A. Single-cell RNA Sequencing analyzed hub gene distribution across cell clusters. ASD mouse model was created by inducing maternal immune activation (MIA) with poly(I:C) injections, verified through behavioral tests. Validation of HIF1A pathway and hub genes was confirmed through Western Blot, qPCR, and immunofluorescence in ASD mice and microglia BV-2 cells.

Results: Using CEMiTool and GeneCards, 45 genes associated with ASD and HIF1A pathway were identified. Machine learning identified CDKN1A, ETS2, LYN, and SLC16A3 as potential ASD diagnostic markers. Single-cell Sequencing pinpointed activated microglia as key immune cells. Behavioral tests showed MIA offspring mice exhibited typical ASD-like behaviors. Immunofluorescence confirmed the activation of microglia and HIF1A pathway in frontal cortex of ASD mice. Additionally, IL-6 contributed to ASD by activating JUN/HIF1A pathway, affecting CDKN1A, LYN, and SLC16A3 expression in microglia.

Conclusions: HIF1A-related genes CDKN1A, ETS2, LYN, and SLC16A3 are strong diagnostic markers for ASD and the activation of IL-6/JUN/HIF1A pathway in microglia contributes to the pathogenesis of ASD.

Keywords

Autism spectrum disorder; Frontal cortex; HIF1A; IL-6; Machine learning; Microglia.

Figures
Products
  • Cat. No.
    Product Name
    Description
    Target
    Research Area
  • HY-15617
    98.92%, JNK Inhibitor
    JNK