Assessing the Role of Long Noncoding RNA in Nucleus Accumbens in Subjects With Alcohol Dependence

• Published in: Alcoholism, clinical and experimental research Vol. 44; no. 12; pp. 2468 - 2480
• Main Authors: Drake, John, McMichael, Gowon O., Vornholt, Eric Sean, Cresswell, Kellen, Williamson, Vernell, Chatzinakos, Chris, Mamdani, Mohammed, Hariharan, Siddharth, Kendler, Kenneth S., Kalsi, Gursharan, Riley, Brien P., Dozmorov, Mikhail, Miles, Michael F., Bacanu, Silviu‐Alin, Vladimirov, Vladimir I.
• Format: Journal Article
• Language: English
• Published: England John Wiley and Sons Inc 01.12.2020
• Subjects: Alcoholism - genetics; Alcoholism - metabolism; Case-Control Studies; eQTLs; gene expression; Genome-Wide Association Study; Human and Animal Genetics; Humans; long noncoding RNA; nucleus accumbens; Nucleus Accumbens - metabolism; Oligonucleotide Array Sequence Analysis; Original; postmortem brain; Quantitative Trait Loci; RNA, Long Noncoding - genetics; RNA, Long Noncoding - metabolism; Transcriptome; long noncoding RNA; postmortem brain; eQTLs; nucleus accumbens; gene expression
• ISSN: 0145-6008; 1530-0277; 1530-0277
• DOI: 10.1111/acer.14479

Background Long noncoding RNA (lncRNA) have been implicated in the etiology of alcohol use. Since lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step toward understanding lncRNA functions in alcohol use and addiction. Thus, we sought to profile lncRNA expression in the nucleus accumbens (NAc) in a large postmortem alcohol brain sample. Methods LncRNA and protein‐coding gene (PCG) expressions in the NAc from 41 subjects with alcohol dependence (AD) and 41 controls were assessed via a regression model. Weighted gene coexpression network analysis was used to identify lncRNA and PCG networks (i.e., modules) significantly correlated with AD. Within the significant modules, key network genes (i.e., hubs) were also identified. The lncRNA and PCG hubs were correlated via Pearson correlations to elucidate the potential biological functions of lncRNA. The lncRNA and PCG hubs were further integrated with GWAS data to identify expression quantitative trait loci (eQTL). Results At Bonferroni adj. p‐value ≤ 0.05, we identified 19 lncRNA and 5 PCG significant modules, which were enriched for neuronal and immune‐related processes. In these modules, we further identified 86 and 315 PCG and lncRNA hubs, respectively. At false discovery rate (FDR) of 10%, the correlation analyses between the lncRNA and PCG hubs revealed 3,125 positive and 1,860 negative correlations. Integration of hubs with genotype data identified 243 eQTLs affecting the expression of 39 and 204 PCG and lncRNA hubs, respectively. Conclusions Our study identified lncRNA and gene networks significantly associated with AD in the NAc, coordinated lncRNA and mRNA coexpression changes, highlighting potentially regulatory functions for the lncRNA, and our genetic (cis‐eQTL) analysis provides novel insights into the etiological mechanisms of AD. Alcohol use disorder (AUD) is a debilitating disease with no reliable and efficacious treatment. However, studying the brain transcriptome may help elucidate the neuropathology of AUD. Using gene network approaches, we provide a glimpse into the complex interactions between coding and non‐coding RNA from patients with AUD and test the moderating effect of clinically relevant risk genetic elements on these interactions. Furthermore, genes and genetic elements associated with AUD were contextualized by examining past studies and conducting enrichment