Research Highlights

• Subject Category:
  • Genetics

Published online: 19 December 2007 | doi:10.1038/nchina.2007.272

Drug addiction: The ultimate gene list

John Fox

Introduction

Drug addiction is known to be influenced by genetics. Scientists have used a number of strategies to identify genes linked to addiction, but because there are so many genes and signalling pathways involved, data derived from any single strategy might be biased. By integrating data from different studies made over the past three decades, Liping Wei and co-workers at Peking University in Beijing¹ have come up with a gene list that provides a more complete picture of drug addiction.

The researchers surveyed the scientific literature between the year 1976 and 2006, and found thousands of data that linked various human genes to addiction; these data were derived through single-gene strategies, microarrays, proteomics, or genetic studies. They then developed a molecular network called knowledge base for addiction-related genes (KARG), which connected these genes and allowed them to resolve pathways that were statistically important.

Five different pathways, which are common to four addictive drugs and may underlie shared reward and addictive actions, were identified. Three of these pathways — namely, long-term potentiation, the MAPK (mitogen-activated protein kinase) signalling pathway and neuroactive ligand–receptor interaction — have been linked to addiction in previous studies. More importantly, the significance of the other two pathways in drug addiction — namely the GnRH signalling pathway and gap junctions — was not previously recognized. The researchers plan to investigate the roles of these pathways in future experimental studies.

The authors of this work are from:
Center for Bioinformatics, National Laboratory of Protein Engineering and Plant Genetic Engineering, College of Life Sciences, Peking University, Beijing, China.

Reference

1. Li, C. Y., Mao, X. & Wei, L. Genes and (common) pathways underlying addiction revealed by combining and analyzing candidate gene lists from multiple technology platforms. PLoS Comput. Biol. doi: 10.1371/journal.pcbi.0040002 (2007). | Article |