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U of Texas Transcriptome Study Reveals Gene Networks Related to Alcoholism


NEW YORK (GenomeWeb) – A transcriptome sequencing study from researchers at the University of Texas has identified new gene co-expression networks associated with alcoholism. 

Existing studies have shown that the risk of developing alcoholism is equally determined by genes and the environment. "This study is trying to identify which genes are changing and how our brains are changing in response to chronic alcohol abuse," said lead author Sean Farris, a post-doctoral research at the Waggoner Center for Alcohol and Addiction Research at the University of Texas. "We can see how the genes are being differentially regulated and it's related to how much the person has been drinking," he said.

The study was published this week in Molecular Psychiatry.

Examining samples from the pre-frontal cortex of 16 individuals who had demonstrated chronic alcohol abuse, the researchers used RNA sequencing to uncover gene expression profiles. RNA samples were prepared using the Thermo Fisher Life Technologies RiboMinus Eukaryote Kit, processed using the ABI whole transcriptome library preparation kit, and sequenced on the ABI SOLiD 4 system. The reads were processed by the Texas Advanced Computing Center.

Statistical analysis of these profiles using the weighted gene co-expression network analysis package in R revealed 38 scale-free gene networks in the alcoholics' brain samples, some comprising more than 1,000 genes. In the five gene networks most strongly correlated to lifetime alcohol consumption, the correlation coefficient was greater than r=0.6. In many cases, the more the individual drank, the more that gene network was expressed. "They're operating differently in an alcoholic brain," Farris said.

Sitting at the hub of some of these networks were genes that have already been shown to play a role in alcoholism, including those coding for gamma-aminobutyric acid and glutamate receptors. The identified gene expression modules were cross-referenced with GWAS for alcohol dependence and one of the groups of gene networks was linked to genes containing SNPs associated with alcohol dependence. And the researchers are especially interested in the other genes in the networks linked to these hub genes, many of which haven't been studied before for a role in alcohol dependence.

"Hub genes have been shown, in cancer, to be really involved in biology. We know they already have functional relevance, so this helps us give confidence that these other genes need to be studied," Farris said.

He added that his colleagues are already moving on to study these genes in the laboratory and are validating the biological mechanisms through which they affect alcoholism.

Farris said that the study was only the second to use RNA-seq to find gene expression networks related to alcoholism. While similar research had been done using microarrays, it required researchers to know the exact sequences of RNA they were looking for. RNA-seq allowed them to sidestep that issue, giving them expanded genetic expression profiles. 

"Next-generation sequencing is complementing previous work we have done using microarrays, but it allows us to get a better picture of what's happening," Farris said.