Gene expression differences associated with alcohol use disorder in human brain

Abstract

Excessive alcohol consumption is a leading cause of preventable death worldwide. To improve understanding of neurobiological mechanisms associated with alcohol use disorder (AUD) in humans, we compared gene expression data from deceased individuals with and without AUD across two addiction-relevant brain regions: the nucleus accumbens (NAc) and dorsolateral prefrontal cortex (DLPFC). Bulk RNA-seq data from NAc and DLPFC (N ≥50 with AUD, ≥46 non-AUD) were analyzed for differential gene expression using modified negative binomial regression adjusting for technical and biological covariates. The region-level results were meta-analyzed with those from an independent dataset (N_NAc = 28 AUD, 29 non-AUD; N_PFC = 66 AUD, 77 non-AUD). We further tested for heritability enrichment of AUD-related phenotypes, gene co-expression networks, gene ontology enrichment, and drug repurposing. We identified 176 differentially expressed genes (DEGs; 12 in both regions, 78 in NAc only, 86 in DLPFC only) for AUD in our new dataset. After meta-analyzing with published data, we identified 476 AUD DEGs (25 in both regions, 29 in NAc only, 422 in PFC only). Of these DEGs, 17 were significant when looked up in GWAS of problematic alcohol use or drinks per week. Gene co-expression analysis showed both concordant and unique gene networks across brain regions. We also identified 29 and 436 drug compounds that target DEGs from our meta-analysis in NAc and PFC, respectively. This study identified robust AUD-associated DEGs, contributing novel neurobiological insights into AUD and highlighting genes targeted by known drug compounds, generating opportunity for drug repurposing to treat AUD.

Fig. 1

Overview of analysis workflow.

Fig. 2. Individual dataset and meta-analysis differential gene expression results.

A Volcano plots of the differentially expressed genes (FDR < 0.05) by AUD status in the LIBD datasets. B Volcano plots of the differentially expressed genes (FDR < 0.05) by AUD status in the NSW-TRC datasets. For both (A) and (B), blue triangles represent significant downregulated genes and red triangles represent significantly upregulated genes. Genes not significantly differentially expressed are colored grey. C UpSet plots displaying the gene overlap count between the per-dataset DEGs and meta-analysis DEGs, by brain region.

Fig. 3. GSEA significant (FDR < 0.05) terms plotted by p-value and database.

Terms were clustered by semantic similarity. One term per cluster is labeled (term with lowest p-value).