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Alcohol abuse linked to newly identified neurogenetic network.

Researchers at The University of Texas at Austin have identified a network of genes that appear to work together in determining alcohol dependence. The findings, which could lead to future treatments and therapies for alcoholics and possibly help doctors screen for alcoholism, are being published in the journal Molecular Psychiatry.

By comparing patterns of genetic code from the brain tissue of alcoholics and nonalcoholics, the researchers discovered a particular set of genes co-expressed together in the individuals who had consumed the most alcohol. Specifically, certain sets of genes were strongly linked as networks in alcoholics, but not in nonalcoholics.

This provides the most comprehensive picture to date of the gene sets that drive alcohol dependence.  The medical community now has a much clearer picture of where specific traits related to alcohol dependence overlap with specific expressions in genetic code.

Scientists have known for some time that genetics play a role in alcoholism and addiction and that the tendency for dependence to be genetically linked is more complicated than the presence or absence of any one gene. The new research, however, represents the first time scientists used revolutionary bioinformatics technology of RNA sequencing to identify the specific group of different genes that, expressed together, are highly correlated with alcohol dependence.

The team hopes their model can serve as a type of Wikipedia of alcohol dependence, helping to break down the complexities of alcohol dependence and becoming a reference for future research into drug therapies.

Only three drugs have approval from the Food and Drug Administration to treat alcoholism, and none offers a silver bullet in helping people dependent on alcohol end their addiction. The identification of genetic factors and networks in the brains of alcoholics gives drug researchers more information to work from and may one day allow for better screenings to evaluate a person’s risk factors for alcohol dependence, possibly even before the onset of heavy drinking.

Source:  University of Texas at Austin

 

Network diagrams for group 1 gene coexpression modules associated with lifetime alcohol consumption. Visualization demonstrates the interconnection of genes for the top 10% of module members with |r=0.80| correlation strength. Relative size of the depicted networks is based on the size of the four modules. Size of the nodes reflects the module connectivity ranking for individual genes. The degree of node opacity is proportional to the correlation with lifetime consumption, with alcohol hubs defined as those genes within the top 10% of alcohol-associated genes in our human sample. Networks were further overlapped using a prior meta-analysis of alcohol-drinking behavior in mice45 to identify potential points of convergent validity in rodent models.  Transcriptome organization for chronic alcohol abuse in human brain.  Mayfield et al 2014.
Network diagrams for group 1 gene coexpression modules associated with lifetime alcohol consumption. Visualization demonstrates the interconnection of genes for the top 10% of module members with |r=0.80| correlation strength. Relative size of the depicted networks is based on the size of the four modules. Size of the nodes reflects the module connectivity ranking for individual genes. The degree of node opacity is proportional to the correlation with lifetime consumption, with alcohol hubs defined as those genes within the top 10% of alcohol-associated genes in our human sample. Networks were further overlapped using a prior meta-analysis of alcohol-drinking behavior in mice45 to identify potential points of convergent validity in rodent models. Transcriptome organization for chronic alcohol abuse in human brain. Mayfield et al 2014.

Michelle Petersen View All

Michelle is a health industry veteran who taught and worked in the field before training as a science journalist.

Featured by numerous prestigious brands and publishers, she specializes in clinical trial innovation--expertise she gained while working in multiple positions within the private sector, the NHS, and Oxford University.

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