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Gene Expression Evidence for Remodeling of Lateral Hypothalamic Circuitry in Cocaine Addiction
Serge H. Ahmed, Robert Lutjens, Lena D. van der Stap, Dusan Lekic, Vincenzo Romano-Spica, Marisela Morales, George F. Koob, Vez Repunte-Canonigo, Pietro Paolo Sanna and Floyd E. Bloom
Proceedings of the National Academy of Sciences of the United States of America
Vol. 102, No. 32 (Aug. 9, 2005), pp. 11533-11538
Published by: National Academy of Sciences
Stable URL: http://www.jstor.org/stable/3376301
Page Count: 6
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By using high-density oligonucleotide arrays, we profiled gene expression in reward-related brain regions of rats that developed escalated cocaine intake after extended access to cocaine (6 h per day). Rats allowed restricted daily access to cocaine (only 1 h) that displayed a stable level of cocaine intake and cocaine naive rats were used for controls. Four analysis methods were compared: Affymetrix MICROARRAY SUITE 4 and MICROARRAY SUITE 5, which use perfect-match-minus-mismatch models, and DCHIP and RMA, which use perfect-match-only models to generate expression values. Results were validated by RT-PCR in individual animals from an independent replication of the experiment. A small number of genes was associated with escalated cocaine intake (ESC genes). Unexpectedly, of the brain regions examined [prefrontal cortex, nucleus accumbens, septum, lateral hypothalamus (LH), amygdala, and ventral tegmental area], the LH was the most transcriptionally responsive in escalation of cocaine intake. Most of the ESC genes identified are also expressed during synaptogenesis and synaptic plasticity and include genes that code for several presynaptic and postsynaptic proteins involved in neurotransmission. These results suggest that LH intrinsic circuitry undergoes a structural reorganization during escalation of cocaine use. This remodeling of LH circuitry could contribute to the chronic deficit in reward function that has been hypothesized to drive the transition to drug addiction. Results also support the value of using multiple analysis strategies to identify the most robust changes in gene expression and to compensate for the biases that affect each strategy.
Proceedings of the National Academy of Sciences of the United States of America © 2005 National Academy of Sciences