Integrative analysis identifies IL-6/JUN/MMP-9 pathway destroyed blood-brain-barrier in autism mice via machine learning and bioinformatic analysis

Abstract

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by social communication deficits and restricted, repetitive behaviors. Growing evidence implicates neuroinflammation-induced blood-brain barrier (BBB) dysfunction as a key pathogenic mechanism in ASD, although the underlying molecular pathways remain poorly understood. This study aimed to identify critical genes linking BBB function and neuroinflammatory activation, with the ultimate goal of evaluating potential therapeutic targets. Through integrative analysis combining differential gene expression profiling with three machine learning algorithms - Least Absolute Shrinkage and Selection Operator (LASSO) regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and RandomForest combined with eXtreme Gradient Boosting (XGBoost) - we identified four hub genes, with JUN emerging as a core regulator. JUN demonstrated strong associations with both BBB integrity and microglial activation in ASD pathogenesis. Using a maternal immune activation (MIA) mouse model of ASD, we observed significant downregulation of cortical tight junction proteins ZO-1 and occludin, confirmed through immunofluorescence and qPCR analysis. Bioinformatics analysis revealed a close correlation between JUN and IL-6/MMP-9 signaling in ASD-associated microglial activation. These findings were validated in vivo, with immunofluorescence and qPCR demonstrating elevated IL-6 and MMP-9 expression in ASD mice. Pharmacological intervention using ventricular JNK inhibitor administration effectively downregulated JUN and MMP-9 expression. In vitro studies using IL-6-stimulated BV-2 microglial cells replicated these findings, showing JNK inhibitor-mediated suppression of JUN and MMP-9 upregulation. These results collectively identify the IL-6/JUN/MMP-9 pathway as a specific mediator of barrier dysfunction in ASD, representing a promising target for personalized therapeutic interventions.

Fig. 1

Flow chart.

Fig. 2. Machine learning to identify hub genes in ASD.

A Volcano plots of DEGs in GSE28521, |log2 fold change (FC)|>0.2 and p < 0.05 as selection criteria. B 39 intersection genes of DEGs and Tight junction related genes. C Protein-protein internet(PPI) of 39 intersection genes. D LASSO regression algorithm selected 6 genes. E 17 hub genes were screened out by the XGB method. F 10 genes were extracted by using the SVM-RFE algorithm. G Venn network to intersect 3 gene subsets selected by machine learning. H The expression of 4 hub genes in ASD and TD (n = 13). Significant differences are indicated by **P < 0.01, ***P < 0.01, compared with ASD and TD group. I ROC curve of 4 hub gene.

Fig. 3. JUN is widely expressed in the brain and is mainly derived from microglia.

A The single-cell sequencing results of ASD in four hub genes on the BBB. B The expression of JUN in the frontal cortex (n = 16) and cerebellum (n = 10) in GSE28521. C, D The expression of JUN in the different brain regions of the HPA(C) and FANTOM5(D) human brain RNA-Seq dataset. E, F Expression of JUN in the human brain of single-cell RNA sequencing of HPA dataset. Error bars indicate as means ± SD, *P < 0.05, **P < 0.01, compared with ASD and TD group.

Fig. 4. BBB impairment and JUN upregulation in ASD.

A Schematic diagram of modeling a mouse model of ASD with maternal immune activation. Embryonic day 12.5 (E12.5 d). B The movement trajectories in the open field test (OFT) (n = 6). C Time spent in the corner and center of OFT experitment (n = 6). D The number of marbles buried in the Marble buried experiment(n = 6). E Social time in three-chamber social interaction test (n = 6). F Relative protein levels of ZO-1 and occludin in control and poly(I: C) group (n = 6). G Relative protein levels of IBA1 and JUN in control and poly(I: C) group (n = 6). Scale bars, 20 μm. Scale bars, 20 μm. Error bars indicate as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001 compared with control and poly(I: C) group, unpaired t-test.

Fig. 5. IL-6 and MMP-9 upregulation in ASD.

A 326 intersection genes of autism and microglia activation-related genes. B Top 20 genes with the highest closeness in the inner loop. C Top 20 genes with the highest betweenness centrality in the inner loop. D Top 10 networks of transcription factors with target genes (red) and protein-protein interaction networks (PPI, blue) with MMP-9 in the GenDoma database. E Correlation between the expression levels of JUN and MMP-9 in each brain region of the FANTOM dataset (R = 0.7001, P = 0.0053). F Relative protein levels of MMP-9 in control and poly(I: C) group (n = 6). G Relative mRNA levels of IL-6 and MMP-9 in control and poly(I: C) group (n = 6). Scale bars, 20 μm. Error bars indicate as means ± SD, **P < 0.01, ***P < 0.001 compared with control and poly(I: C) group, unpaired t-test.

Fig. 6. IL-6 upregulate the JUN/MMP-9 pathway.

A Ventricular administration of JNK inhibitor for 3 days significantly altered the protein expression levels of JUN and MMP-9 across four experimental groups (n = 6). B The mRNA expression of JUN and MMP-9 was assessed by RT-qPCR in BV-2 cells after 10 ng/ml IL-6 or 5 μM/ml JNK inhibitor for 24 h (n = 4). Scale bars, 20 μm. Error bars indicate as means ± SD, *P < 0.05, **P < 0.01, ***P < 0.001, One-way ANOVA.