Modeling gene-by-environment interaction in comorbid depression with alcohol use disorders via an integrated bioinformatics approach

Department of Psychiatry, University of Michigan, Ann Arbor, Michigan, USA.
BioData Mining (Impact Factor: 2.02). 02/2008; 1(1):2. DOI: 10.1186/1756-0381-1-2
Source: PubMed


Comorbidity of Major Depressive Disorder (depression) and Alcohol Use Disorders (AUD) is well documented. Depression, AUD, and the comorbidity of depression with AUD show evidence of genetic and environmental influences on susceptibility. We used an integrated bioinformatics approach, mining available data in multiple databases, to develop and refine a model of gene-by-environment interaction consistent with this comorbidity.
We established the validity of a genetic model via queries against NCBI databases, identifying and validating TNF (Tumor Necrosis Factor) and MTHFR (Methylenetetrahydrofolate Reductase) as candidate genes. We used the PDG-ACE algorithm (Prioritizing Disease Genes by Analysis of Common Elements) to show that TNF and MTHFR share significant commonality and that this commonality is consistent with a response to environmental exposure to ethanol. Finally, we used MetaCore from GeneGo, Inc. to model a gene-by-environment interaction consistent with the data.
TNF Alpha Converting Enzyme (TACE) activity is suppressed by ethanol exposure, resulting in reduced TNF signaling. TNF binds to TNF receptors, initiating signal transduction pathways that activate MTHFR expression. MTHFR is an essential enzyme in folate metabolism and reduced folate levels are associated with both AUD and depression. Integrating these pieces of information our model shows how excessive alcohol use would be expected to lead to reduced TNF signaling, reduced MTHFR expression, and increased susceptibility to depression.
The proposed model provides a novel hypothesis on the genetic etiology of comorbid depression with AUD, consistent with established clinical and biochemical data. This analysis also provides an example of how an integrated bioinformatics approach can maximize the use of available biomedical data to improve our understanding of complex disease.

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    • "This hypothesis is also consistent with our earlier work [4-8], where we found common underlying genetic etiology for related disease phenotypes. We also found in earlier work [6,7,9] that exploring this common underlying genetic etiology using a systems biology approach can lead to improved understanding of the related phenotypes and interactions among the genetic influences on them, and may point out potential clinically significant biomarkers or drug targets. "
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