| Title |
Metabolic profiling of urine and blood plasma in rat models of drug addiction on the basis of morphine, methamphetamine, and cocaine-induced conditioned place preference
|
|---|---|
| Published in |
Analytical & Bioanalytical Chemistry, August 2013
|
| DOI | 10.1007/s00216-013-7234-1 |
| Pubmed ID | |
| Authors |
Kei Zaitsu, Izuru Miyawaki, Kiyoko Bando, Hiroshi Horie, Noriaki Shima, Munehiro Katagi, Michiaki Tatsuno, Takeshi Bamba, Takako Sato, Akira Ishii, Hitoshi Tsuchihashi, Koichi Suzuki, Eiichiro Fukusaki |
| Abstract |
The metabolic profiles of urine and blood plasma in drug-addicted rat models based on morphine (MOR), methamphetamine (MA), and cocaine (COC)-induced conditioned place preference (CPP) were investigated. Rewarding effects induced by each drug were assessed by use of the CPP model. A mass spectrometry (MS)-based metabolomics approach was applied to urine and plasma of MOR, MA, and COC-addicted rats. In total, 57 metabolites in plasma and 70 metabolites in urine were identified by gas chromatography-MS. The metabolomics approach revealed that amounts of some metabolites, including tricarboxylic acid cycle intermediates, significantly changed in the urine of MOR-addicted rats. This result indicated that disruption of energy metabolism is deeply relevant to MOR addiction. In addition, 3-hydroxybutyric acid, L-tryptophan, cystine, and n-propylamine levels were significantly changed in the plasma of MOR-addicted rats. Lactose, spermidine, and stearic acid levels were significantly changed in the urine of MA-addicted rats. Threonine, cystine, and spermidine levels were significantly increased in the plasma of COC-addicted rats. In conclusion, differences in the metabolic profiles were suggestive of different biological states of MOR, MA, and COC addiction; these may be attributed to the different actions of the drugs on the brain reward circuitry and the resulting adaptation. In addition, the results showed possibility of predict the extent of MOR addiction by metabolic profiling. This is the first study to apply metabolomics to CPP models of drug addiction, and we demonstrated that metabolomics can be a multilateral approach to investigating the mechanism of drug addiction. |
Mendeley readers
The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Japan | 1 | 1% |
| United States | 1 | 1% |
| Unknown | 80 | 98% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 16 | 20% |
| Student > Doctoral Student | 10 | 12% |
| Researcher | 8 | 10% |
| Student > Master | 7 | 9% |
| Professor | 6 | 7% |
| Other | 14 | 17% |
| Unknown | 21 | 26% |
| Readers by discipline | Count | As % |
|---|---|---|
| Chemistry | 15 | 18% |
| Pharmacology, Toxicology and Pharmaceutical Science | 8 | 10% |
| Agricultural and Biological Sciences | 8 | 10% |
| Neuroscience | 6 | 7% |
| Biochemistry, Genetics and Molecular Biology | 5 | 6% |
| Other | 14 | 17% |
| Unknown | 26 | 32% |
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 09 August 2013.
All research outputs
#22,834,739
of 25,460,914 outputs
Outputs from Analytical & Bioanalytical Chemistry
#7,570
of 9,646 outputs
Outputs of similar age
#186,956
of 209,795 outputs
Outputs of similar age from Analytical & Bioanalytical Chemistry
#69
of 73 outputs
Altmetric has tracked 25,460,914 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 9,646 research outputs from this source. They receive a mean Attention Score of 3.1. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 209,795 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 73 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.