Alcoholism Clinical and Experimental ResearchNeurosciencePeer-reviewed journal article
Assessing the role of long-noncoding RNA in nucleus accumbens in subjects with alcohol dependence
Gowon O. McMichael, John Drake, Eric VornholtiD, Kellen G. CresswelliD, Vernell S. Williamson, Chris ChatzinakosiD, Mohammed Mamdani, Siddharth P. HariharaniD, Kenneth S. KendleriD, Michael F. MilesiD, Gursharan KalsiiD, Brien P. RileyiD, Mikhail G. DozmoroviD, Silviu‐Alin BacanuiD, Vladimir I. VladimiroviD
Abstract
Recently, long noncoding RNA (lncRNA) were implicated in the etiology of alcohol dependence (AD). As lncRNA provide another layer of complexity to the transcriptome, assessing their expression in the brain is the first critical step towards understanding lncRNA functions in AD. To that end, we profiled the expression of lncRNA and protein coding genes (PCG) in nucleus accumbens (NAc) from 41 subjects with AD and 41 controls. At false discovery rate (FDR) of 5%, we identified 69 and 309 differentially expressed lncRNA and PCG genes, respectively. Using co-expression network analyses, we identified three lncRNA and five PCG modules significantly correlated with AD at Bonferroni adj. p[≤]0.05. To better understand lncRNA functions, we integrated the lncRNA and PCG hubs from the significant AD modules; at FDR of 5%, we identified 3 151 positive and 2 255 negative correlations supporting the functional role of lncRNA in the development of AD. Gene enrichment analysis revealed that PCG significantly correlated with lncRNA are, among others, enriched for neuronal and immune related processes. To highlight the mechanisms, by which genetic variants contribute to AD, we integrated lncRNA and PCG hubs with genome-wide SNP data. At FDR[≤]0.3, we identified 276 expression quantitative trait loci (eQTL), affecting the expression of 20 and 256 lncRNA and PCG hubs, respectively. Our study is the first to profile lncRNA expression in nucleus accumbens in a large postmortem alcohol brain sample and our results may provide novel insights into the regulation of the brain transcriptome across disease.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 18
- FWCI
- 1.03
- Field percentile
- top 30%
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