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Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction

Overview of attention for article published in BMC Systems Biology, June 2015
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Title
Gene network analysis shows immune-signaling and ERK1/2 as novel genetic markers for multiple addiction phenotypes: alcohol, smoking and opioid addiction
Published in
BMC Systems Biology, June 2015
DOI 10.1186/s12918-015-0167-x
Pubmed ID
Authors

Cielito C. Reyes-Gibby, Christine Yuan, Jian Wang, Sai-Ching J. Yeung, Sanjay Shete

Abstract

Addictions to alcohol and tobacco, known risk factors for cancer, are complex heritable disorders. Addictive behaviors have a bidirectional relationship with pain. We hypothesize that the associations between alcohol, smoking, and opioid addiction observed in cancer patients have a genetic basis. Therefore, using bioinformatics tools, we explored the underlying genetic basis and identified new candidate genes and common biological pathways for smoking, alcohol, and opioid addiction. Literature search showed 56 genes associated with alcohol, smoking and opioid addiction. Using Core Analysis function in Ingenuity Pathway Analysis software, we found that ERK1/2 was strongly interconnected across all three addiction networks. Genes involved in immune signaling pathways were shown across all three networks. Connect function from IPA My Pathway toolbox showed that DRD2 is the gene common to both the list of genetic variations associated with all three addiction phenotypes and the components of the brain neuronal signaling network involved in substance addiction. The top canonical pathways associated with the 56 genes were: 1) calcium signaling, 2) GPCR signaling, 3) cAMP-mediated signaling, 4) GABA receptor signaling, and 5) G-alpha i signaling. Cancer patients are often prescribed opioids for cancer pain thus increasing their risk for opioid abuse and addiction. Our findings provide candidate genes and biological pathways underlying addiction phenotypes, which may be future targets for treatment of addiction. Further study of the variations of the candidate genes could allow physicians to make more informed decisions when treating cancer pain with opioid analgesics.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 13 17%
Student > Master 11 14%
Researcher 9 12%
Student > Ph. D. Student 9 12%
Student > Doctoral Student 7 9%
Other 14 18%
Unknown 13 17%
Readers by discipline Count As %
Medicine and Dentistry 16 21%
Psychology 12 16%
Biochemistry, Genetics and Molecular Biology 8 11%
Neuroscience 8 11%
Agricultural and Biological Sciences 7 9%
Other 13 17%
Unknown 12 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 January 2016.
All research outputs
#6,670,000
of 8,755,605 outputs
Outputs from BMC Systems Biology
#635
of 876 outputs
Outputs of similar age
#158,203
of 224,195 outputs
Outputs of similar age from BMC Systems Biology
#21
of 26 outputs
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