Assembling the jigsaw puzzle of drug addiction

Using an integrative meta-analysis approach, researchers from the Center for Bioinformatics at Peking University in Beijing have assembled the most comprehensive gene atlas underlying drug addiction and identified five molecular pathways common to four different addictive drugs. This novel paper appears in PLoS Computational Biology on January 4, 2008.

Drug addiction is a serious worldwide problem with strong genetic and environmental influences. So far different technologies have revealed a variety of genes and biological processes underlying addiction. However, individual technology can be biased and render only an incomplete picture. Studying individual or a small number of genes is like looking at pieces of a jigsaw puzzle - only when you gather most of the pieces from different places and arrange them together in an orderly fashion do interesting patterns emerge.

The team, led by Liping Wei, surveyed scientific literature published in the past 30 years and collected 2,343 items of evidence linking genes and chromosome regions to addiction based on single-gene strategies, microarray, proteomics, or genetic studies. They made this gene atlas freely available in the first online molecular database for addiction, named KARG (http://karg.cbi.pku.edu.cn), with extensive annotations and friendly web interface.

Assembling the pieces of evidence together, the authors identified 18 molecular pathways that are statistically enriched in the addiction-related genes. They then identified five pathways that are common to addiction to four different substances. These common pathways may underlie shared rewarding and response mechanisms and may be targets for effective treatments for a wide range of addictive disorders.

CITATION: Li CY, Mao X, Wei L (2008) Genes and (common) pathways underlying drug addiction. PLoS Comput Biol 4(1): e2. doi:10.1371/journal.pcbi.0040002

CONTACT:
Liping Wei
Center for Bioinformatics,
College of Life Sciences,
Peking University,
Beijing,
People's Republic of China
weilp@mail.cbi.pku.edu.cn

Disclaimer

This press release refers to an upcoming article in PLoS Computational Biology. The release is provided by the article authors. Any opinions expressed in this release or article are the personal views of the journal staff and/or article contributors, and do not necessarily represent the views or policies of PLoS. PLoS expressly disclaims any and all warranties and liability in connection with the information found in the releases and articles and your use of such information.

PLoS Computational Biology (www.ploscompbiol.org) features works of exceptional significance that further our understanding of living systems at all scales through the application of computational methods. All works published in PLoS Computational Biology are open access. Everything is immediately available subject only to the condition that the original authorship and source are properly attributed. Copyright is retained by the authors. The Public Library of Science uses the Creative Commons Attribution License.

The Public Library of Science (PLoS) is a non-profit organization of scientists and physicians committed to making the world's scientific and medical literature a freely available public resource. For more information, visit http://www.plos.org.