• Users Online: 659
  • Home
  • Print this page
  • Email this page
Home About us Editorial board Ahead of print Current issue Search Archives Submit article Instructions Subscribe Contacts Login 


 
 Table of Contents  
ORIGINAL ARTICLE
Year : 2016  |  Volume : 2  |  Issue : 2  |  Page : 91-97

A Preliminary Urinary Metabolomics Study of Sprague-Dawley Rats after Short-term Ketamine Administration by Proton Nuclear Magnetic Resonance Spectroscopy


1 Department of Forensic Toxicological Analysis, West China School of Pharmacy, Sichuan University, Chengdu, Sichuan, China
2 Department of Forensic Toxicological Analysis, West China School of Basic Science and Forensic Medicine, Sichuan University, Chengdu, Sichuan, China

Date of Web Publication16-Jun-2016

Correspondence Address:
Linchuan Liao
8th Floor, Fa Yi Building, No. 16, Section 3, Renmin Nan Road, Chengdu, Sichuan 610041
China
Login to access the Email id

Source of Support: None, Conflict of Interest: None


DOI: 10.4103/2349-5014.184192

Rights and Permissions
  Abstract 


Drug abuse has become a global problem. The mass spectrometry-based metabolic consequences of ketamine administration in anesthesia and therapy have been well studied, but to the best of our knowledge, metabolomic studies of ketamine abuse based on nuclear magnetic resonance (NMR) spectroscopy are still lacking. In this study, twenty Sprague–Dawley rats were randomly assigned into two groups: a control group (n = 10) and a ketamine group (n = 10). The animals in the ketamine group received intraperitoneal injections of ketamine twice daily at 12-h intervals at progressively increasing doses over a period of 9 days, while the control group received an equal volume of saline. The urine samples were collected for 24 h at days 0, 1, 3, 5, 7, and 9 for the metabolomics study. The metabolic changes in urine after short-term ketamine administration were analyzed by proton NMR coupled with multivariate statistical analysis. The results indicated that short-term ketamine exposure led to significant alterations of the metabolites in the urine of the rats. Specifically, 1,3,7-trimethyluric acid, 1,3-dimethyluric acid, acetoacetic acid, acetylglycine, creatine, sarcosine, dimethylglycine, glycine, and theobromine were significantly increased in the urine. Significant changes were also found in metabolites related to antioxidant and energy metabolism, including acetoacetic acid, succinate, 1,3,7-trimethyluric acid, 1,3-dimethyluric acid, creatine, and taurine. Our findings indicated that short-term ketamine administration leads to disorder of energy metabolism and oxidative stress. In addition, the modified metabolites identified could serve as the new biological markers and potential biological indices reflecting the underlying mechanism of ketamine abuse.

Keywords: Biomarker, ketamine, metabolomics, proton nuclear magnetic resonance


How to cite this article:
Lu X, Tang Q, Ye Y, Guo R, Chen F, Dai X, Yan Y, Liao L. A Preliminary Urinary Metabolomics Study of Sprague-Dawley Rats after Short-term Ketamine Administration by Proton Nuclear Magnetic Resonance Spectroscopy. J Forensic Sci Med 2016;2:91-7

How to cite this URL:
Lu X, Tang Q, Ye Y, Guo R, Chen F, Dai X, Yan Y, Liao L. A Preliminary Urinary Metabolomics Study of Sprague-Dawley Rats after Short-term Ketamine Administration by Proton Nuclear Magnetic Resonance Spectroscopy. J Forensic Sci Med [serial online] 2016 [cited 2019 Nov 19];2:91-7. Available from: http://www.jfsmonline.com/text.asp?2016/2/2/91/184192




  Introduction Top


Ketamine was first synthesized as an anesthetic for animals and humans in 1962.[1] The sedative, analgesic, and amnesic properties of the drug have been well characterized, leading to its use as a recreational drug.[2] However, when administered at subanesthetic doses, ketamine can induce a variety of damages with respect to both mental health and bodily functions, such as heart, cystitis, and kidney dysfunction.[3],[4] Recently, the number of illegal ketamine abusers has grown rapidly, which has become a major concern in many countries.

Metabolomics is an emerging and rapidly growing discipline that permits the simultaneous evaluation of multiple metabolites related to changes in pathological processes and relevant biochemical pathways. The nuclear magnetic resonance (NMR) spectroscopy technique is the most widely used approach to generate metabolomic datasets and is uniquely suitable for the detection of a large range of endogenous metabolites in urine, serum, and the whole organism.[5] For example, methamphetamine administration induced a significant disturbance in neurotransmitters, oxidative stress, and membrane disruption in the hippocampus, nucleus accumbens, and prefrontal cortex.[6]

In this study, proton NMR (1 H NMR) spectroscopic methods combined with the multivariate analysis methods of principal component analysis (PCA), partial least square-discriminant analysis (PLS-DA), and orthogonal signal correction (OSC) were employed to explore the metabolic profile in the urine of rats after ketamine exposure. Our results indicated that short-term ketamine administration induced significant metabolite alterations. These modified metabolites may serve as the biological indices underlying the mechanism of ketamine abuse.


  Materials and Methods Top


Animals

Twenty male adult Sprague-Dawley rats (200 ± 20 g, purchased from the Laboratory Animal Center of Sichuan University) were housed individually in metabolic cages and acclimatized for 7 days before beginning the experiment under standard conditions (12/12-h light-dark cycle, lights on 7:00 a.m.; room temperature 23 ± 2°C; humidity 50 ± 10%; food and water adlibitum). The rats were randomly assigned into two groups: a control group (n = 10) and a ketamine group (n = 10). Animals in the ketamine group received intraperitoneal injections of ketamine twice daily at 12-h intervals at progressively increasing doses of 6–14 mg/kg over a period of 9 days (days 1–5:6–14 mg/kg with a progressive increase by 2 mg/kg/day; days 6–9: 14 mg/kg). The control group received the same volume of vehicle throughout the 9-day experimental period. All rats were weighed daily to adjust the dosage of ketamine administration. The urine samples were harvested for 24 h on days 0, 1, 3, 5, 7, and 9 into 0.1 mL of 1% sodium azide solution over ice and then centrifuged for 10 min at 4°C. The supernatant was separately stored at −80°C until analysis. All the animal treatments in this study were conducted in accordance with the guidelines established by the Association for Assessment and Accreditation of Laboratory Animal Care.

Test substances

Ketamine hydrochloride with a purity of 99.7% was provided by the Sichuan Public Security Department and was diluted with saline to 2 mg/mL. Sodium (3-trimethylsilyl)-2, 2, 3, 3-tetradeuteriopropionate (TSP) was purchased from Merck (Darmstadt, Germany); D2O (99.9% D) was obtained from CIL (Andover, U.S.A); and disodium hydrogen phosphate and sodium dihydrogen phosphate were both analytical grade and purchased from Bodi (Tianjin, China).

Sample preparation

An aliquot of 200 μL buffer (0.02M Na2 HPO4-NaH2 PO4, pH = 7.4) was added to a 400 μL urine sample and the mixture was vortexed for 30 s. After centrifugation at 12,000 ×g for 10 min at 4°C, 550 μL of the supernatant was mixed with 50 μL of D2O and 10 μL of 0.1% TSP solution for 30 s and centrifuged under the same condition described above. The final supernatant was pipetted into a 5-mm NMR tube for 1 H NMR detection.

Proton nuclear magnetic resonance spectroscopy measurement of urine

All the 1 H NMR spectral data were acquired on a Bruker-Av II 600 MHz spectrometer (Bruker, Switzerland) at 300 K. Spectra were acquired over 128 scans using a conventional presaturation pulse sequence for solvent suppression based on the start of the nuclear overhauser effect of spectroscopy pulse sequence (relaxation delay = 2 s; mixing time = 150 ms; spectral width = 8 kHz; time domain = 64,000 data points; and solvent presaturation was applied during the relaxation delay and mixing time). The free induction delay values were weighted by an exponential function with a 0.3-Hz line-broadening factor prior to Fourier transformation. The D2O and TSP provided the deuterium lock signal for the NMR spectrometer and the chemical shift reference (δ0.0), respectively. Quality control samples were analyzed to investigate the reproducibility of the metabolic features before the formal sample analysis and between every ten urine samples.

Data reduction and pattern recognition analysis

All the NMR spectra were manually rephased, baseline- corrected, and then data-reduced to 166 segments, each with a 0.04-ppm width spectral window ranging from 0.5 to 9.5 ppm, using MestReNova 6.1.1 software (Mestrelab Research SL, Spain). The segments of 6.5–4.2 ppm in the spectra were removed to eliminate the carbamide signal and the artifacts of the residual water resonance. The area for each segmented region was calculated. The datasets were mean-centered prior to PCA, PLS-DA, and OSC-PLS analyses with the SIMCA-P11.0 software (Umetrics, USA) package. Two-dimensional score plots were employed to visualize the separation of the samples, and the corresponding loading plots were used to identify the spectral variable contribution to the position of the spectra that were altered because of ketamine treatment.1 H NMR chemical shifts and assignments of endogenous metabolites were conducted and identified according to the literature and with reference to the Human Metabolome Database.[7],[8]


  Results Top


Proton nuclear magnetic resonance spectra and statistical analysis

Typical 1 H NMR spectra of the water extracts of the urine from rats after ketamine exposure at different time points are shown in [Figure 1], with the major metabolites in the corresponding regions numbered. In all datasets, PCA, PLS-DA, and OSC-PLS were employed to uncover the latent biochemical information from the 1 H NMR spectra. PCA, an unsupervised pattern recognition (PR) method, was initially used to analyze the NMR spectra. However, PCA of the urine NMR spectra showed no significant separation for the first two principal components with partial overlap between certain groups. The supervised PR method PLS-DA was subsequently applied, and the PLS-DA score plots displayed slight separation between the control and ketamine groups. Therefore, OSC was applied to the data followed by the PLS-DA analysis. After application of OSC, the two groups were well separated in the OSC-PLS score plots [Figure 2]. Cross-validation was employed to evaluate the model [Figure 3]. In general, R2 Y describes how well the data in the training sets are mathematically reproduced, which varies between 0 and 1, where 1 indicates a model with a perfect fit. Q2 Y represents the predictability of the original model, and values >0.9 and >0.5 are considered indicative of excellent and good predictive abilities, respectively. Q2 Y intercepts below zero indicate valid models. Moreover, the high values of R2 Y and Q2 Y obtained in this study indicated that the PLS models after application of OSC showed good or excellent fitness and predictive abilities, respectively [Table 1] and [Figure 3].
Figure 1: Typical 600 MHz Carr–Purcell–Meiboom–Gill proton nuclear magnetic resonance spectra of the urine from rats from different administration days in ketamine group and control group

Click here to view
Figure 2: Orthogonal signal correction-partial least square scores plots and its corresponding loading plots of urine from rats in different administration days. control; ketamine. A,B: 1 day; C,D: 3 days; E,F: 5 days; G,H: 7days; I,J: 9 days

Click here to view
Figure 3: Validation plots of the partial least square models after application of orthogonal signal correction

Click here to view
Table 1: Summary of partial least square parameters after application of orthogonal signal correction for assessing the quality of groups of control and ketamine

Click here to view


Metabolic changes in ketamine-treated rats

In the comparison of the control and ketamine groups, OSC-PLS was applied to the datasets, and the score plots of OSC-PLS and its corresponding loading plots at different days are shown in [Figure 2]. Compared with the control group, 1, 3, 7-trimethyluric acid, 1,3-dimethyluric acid, acetoacetic acid, acetylglycine, creatine, sarcosine, dimethylglycine, glycine, and theobromine increased after repeated ketamine administration. By contrast, a decrease in 3-methyladenine, hypotaurine, malonic acid, succinate, and L-cysteine was observed because of ketamine exposure [Table 2].
Table 2: Summary of the variations from urine metabolites in rats

Click here to view



  Discussion Top


Oxidative stress

In a previous study, 1, 3, 7-trimethyluric acid and 1,3-dimethyluric acid showed an effect of the scavenging of hydroxyl radicals and inhibition of lipid peroxidation; however, 1, 3, 7-trimethyluric acid and its analogs could not only very efficiently scavenge hydroxyl radicals but also displayed protective effects against tertbutylhyroperoxide-induced lipid peroxidation in vitro.[9] In addition, creatine is considered to have direct antioxidant effects.[10] N-acetylcysteine is the acetylated variant of L-cysteine, which was found to have decreased after ketamine exposure in this study. N-acetylcysteine is a precursor in the formation of the antioxidant glutathione and is known as a metabolite capable of stimulating glutathione synthesis, and plays roles both in promoting detoxification and as a free-radical scavenger.[11] Taurine is considered a neuroprotective chemical, showing effects of apoptosis inhibition, calcium modulation, and antioxidant properties.[12],[13],[14] For example, taurine was shown to protect C6 cells from morphine-induced apoptosis and oxidative damage, and could decrease the level of oxidative stress induced by acute ethanol exposure in the zebrafish brain.[13],[15] Therefore, the increases of 1, 3, 7-trimethyluric acid, 1,3-dimethyluric acid, creatine, and taurine in our study suggest that repeated ketamine exposure can cause oxidative stress and may reflect a protective mechanism as well as biological indices of ketamine abuse.

Energy metabolism

As ketone bodies, acetoacetic acid with β-hydroxybutyrate produced by the liver can replace as much as 70% of the glucose as the fuel when carbohydrate intake is low,[16] and was found to be increased following ketamine exposure in this experiment. In addition to its anti-oxygenation role discussed above, creatine is thought to have multifaceted roles in the brain. Apart from being implicated in brain osmoregulation, it has recently been associated with energy homeostasis.[17] Therefore, dysregulation of creatine may indicate an energetic shift, suggesting the up-regulation of metabolic activity and down-regulation of energy storage capacity. Furthermore, the observed decrease in succinate, an intermediate of the tricarboxylic acid cycle, revealed that ketamine administration could induce energy metabolism disorder. Sinhorin et al.[18] found that succinate administration at subconvulsant doses caused significant oxidative damage and behavioral effects through N-methyl-d-aspartate (NMDA) receptor-mediated mechanisms, which is in accordance with the observed elevation of 1, 3, 7-trimethyluric acid, 1,3-dimethyluric acid, creatine, and taurine in this study, as discussed above. In addition, through activation of the NMDA receptor, the accumulation of succinate might reflect a response to the excitotoxicity caused by succinate dehydrogenase inhibitors.[19] It has been reported that ketamine has a neuroprotective effect in brain injury, indicating that the decrease of succinate may be a protective mechanism.[20],[21]

Comparison with previous findings

Our results are quite distinct from a previous ketamine metabolomics study based on MS. Glycine and ribitol, the only two altered urinary metabolites found in common among the previous and present studies, were decreased following ketamine administration in the previous study [22] and showed the opposite tendency in our study. We consider that these differences are mostly attributed to the dosage of ketamine employed in the two studies. Wen et al.[22] reported that the rats in the ketamine group continued receiving a daily intraperitoneal injection of ketamine (50 mg/kg) for 14 days to explore the anesthetic effect of ketamine. Rats in the ketamine group presented with cataleptic immobility within 1 min after administration, followed by ataxia (head and body swaying) after about 15 min, subsequently falling over and staying still for approximately 1 h until recovery. However, in our experiment, the animals received intraperitoneal injections of ketamine twice daily at 12-h intervals at progressively increasing doses of 6–14 mg/kg, with the aim of exploring the “recreational” effect of ketamine as a common club drug. The rats in this study did not present ataxia and a limp, although restlessness and dysphoria were observed. Moreover, the analytical principle of NMR and gas chromatography (GC)-MS are markedly different, with advantages and disadvantages for both techniques. NMR is not destructive and requires minimal sample preparation. Nevertheless, the high detection limits and complicated spectra interpretation restrict its application to mild or highly concentrated metabolites. By contrast, GC-MS provides high sensitivity (depending on the coupled analyzer), but derivatization is necessary in the process of sample preparation, which is time-consuming and could introduce errors and biases.[23] Consequently, the data acquired from both NMR used in our study and GC-MS in the previous work are supplementary and can increase the metabolite coverage.


  Conclusion Top


Collectively, by using a coupled approach of 1 H NMR-based metabolomic analysis and multivariate statistical approaches, we have identified profoundly perturbed metabolites in the urine of rats after short-term ketamine exposure. Our research provides supplemental data to those obtained in a previous MS-based ketamine metabolomics study, thus increasing the metabolite coverage. In addition, the modified metabolites identified could be new biological markers and may serve as biological indices underlying the mechanisms of ketamine abuse.

Financial support and sponsorship

The Project of the National Natural Sciences Foundation of China (81373239, 30973369).

Conflicts of interest

There are no conflicts of interest.

 
  References Top

1.
Domino EF, Chodoff P, Corssen G. Pharmacologic effects of CI-581, a new dissociative anesthetic, in man. Clin Pharmacol Ther 1965;6:279-91.  Back to cited text no. 1
[PUBMED]    
2.
Fendrich M, Wislar JS, Johnson TP, Hubbell A. A contextual profile of club drug use among adults in Chicago. Addiction 2003;98:1693-703.  Back to cited text no. 2
    
3.
Oxley JD, Cottrell AM, Adams S, Gillatt D. Ketamine cystitis as a mimic of carcinoma in situ. Histopathology 2009;55:705-8.  Back to cited text no. 3
    
4.
Morgan CJ, Muetzelfeldt L, Curran HV. Consequences of chronic ketamine self-administration upon neurocognitive function and psychological wellbeing: A 1-year longitudinal study. Addiction 2010;105:121-33.  Back to cited text no. 4
    
5.
Nicholson JK, Connelly J, Lindon JC, Holmes E. Metabonomics: A platform for studying drug toxicity and gene function. Nat Rev Drug Discov 2002;1:153-61.  Back to cited text no. 5
    
6.
Bu Q, Lv L, Yan G, Deng P, Wang Y, Zhou J, et al. NMR-based metabonomic in hippocampus, nucleus accumbens and prefrontal cortex of methamphetamine-sensitized rats. Neurotoxicology 2013;36:17-23.  Back to cited text no. 6
    
7.
Gao H, Xiang Y, Sun N, Zhu H, Wang Y, Liu M, et al. Metabolic changes in rat prefrontal cortex and hippocampus induced by chronic morphine treatment studied ex vivo by high resolution 1H NMR spectroscopy. Neurochem Int 2007;50:386-94.  Back to cited text no. 7
    
8.
Wishart DS, Jewison T, Guo AC, Wilson M, Knox C, Liu Y, et al. HMDB 3.0 – The human metabolome database in 2013. Nucleic Acids Res 2013;41:D801-7.  Back to cited text no. 8
    
9.
Bhat VB, Sridhar GR, Madyastha KM. Efficient scavenging of hydroxyl radicals and inhibition of lipid peroxidation by novel analogues of 1,3,7-trimethyluric acid. Life Sci 2001;70:381-93.  Back to cited text no. 9
    
10.
Guidi C, Potenza L, Sestili P, Martinelli C, Guescini M, Stocchi L, et al. Differential effect of creatine on oxidatively-injured mitochondrial and nuclear DNA. Biochim Biophys Acta 2008;1780:16-26.  Back to cited text no. 10
    
11.
Chandramani Shivalingappa P, Jin H, Anantharam V, Kanthasamy A, Kanthasamy A. N-acetyl cysteine protects against methamphetamine-induced dopaminergic neurodegeneration via modulation of redox status and autophagy in dopaminergic cells. Parkinsons Dis 2012;2012:424285.  Back to cited text no. 11
    
12.
Wu H, Jin Y, Wei J, Jin H, Sha D, Wu JY. Mode of action of taurine as a neuroprotector. Brain Res 2005;1038:123-31.  Back to cited text no. 12
    
13.
Zhou J, Li Y, Yan G, Bu Q, Lv L, Yang Y, et al. Protective role of taurine against morphine-induced neurotoxicity in C6 cells via inhibition of oxidative stress. Neurotox Res 2011;20:334-42.  Back to cited text no. 13
    
14.
Oja SS, Saransaari P. Pharmacology of taurine. Proc West Pharmacol Soc 2007;50:8-15.  Back to cited text no. 14
    
15.
Rosemberg DB, da Rocha RF, Rico EP, Zanotto-Filho A, Dias RD, Bogo MR, et al. Taurine prevents enhancement of acetylcholinesterase activity induced by acute ethanol exposure and decreases the level of markers of oxidative stress in zebrafish brain. Neuroscience 2010;171:683-92.  Back to cited text no. 15
    
16.
Tremblay S, Ouellet R, Rodrigue S, Langlois R, Bénard F, Cunnane SC. Automated synthesis of 11C-acetoacetic acid, a key alternate brain fuel to glucose. Appl Radiat Isot 2007;65:934-40.  Back to cited text no. 16
    
17.
Zhang X, Liu H, Wu J, Zhang X, Liu M, Wang Y. Metabonomic alterations in hippocampus, temporal and prefrontal cortex with age in rats. Neurochem Int 2009;54:481-7.  Back to cited text no. 17
[PUBMED]    
18.
Sinhorin VD, Roehrs C, Pasin JS, Bellé NA, Rubin MA, Mello CF. Succinate causes oxidative damage through N-methyl-D-aspartate-mediated mechanisms. Brain Res 2005;1051:66-71.  Back to cited text no. 18
    
19.
Roehrs C, Garrido-Sanabria ER, Da Silva AC, Faria LC, Sinhorin VD, Marques RH, et al. Succinate increases neuronal post-synaptic excitatory potentials in vitro and induces convulsive behavior through N-methyl-d-aspartate-mediated mechanisms. Neuroscience 2004;125:965-71.  Back to cited text no. 19
    
20.
Panzer O, Moitra V, Sladen RN. Pharmacology of sedative-analgesic agents: Dexmedetomidine, remifentanil, ketamine, volatile anesthetics, and the role of peripheral Mu antagonists. Anesthesiol Clin 2011;29:587-605, vii.  Back to cited text no. 20
    
21.
Brown BP, Kang SC, Gawelek K, Zacharias RA, Anderson SR, Turner CP, et al. In vivo and in vitro ketamine exposure exhibits a dose-dependent induction of activity-dependent neuroprotective protein in rat neurons. Neuroscience 2015;290:31-40.  Back to cited text no. 21
    
22.
Wen C, Zhang M, Ma J, Hu L, Wang X, Lin G. Urine metabolomics in rats after administration of ketamine. Drug Des Devel Ther 2015;9:717-22.  Back to cited text no. 22
    
23.
Mastrangelo A, Ferrarini A, Rey-Stolle F, García A, Barbas C. From sample treatment to biomarker discovery: A tutorial for untargeted metabolomics based on GC-(EI)-Q-MS. Anal Chim Acta 2015;900:21-35.  Back to cited text no. 23
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2]



 

Top
 
 
  Search
 
Similar in PUBMED
   Search Pubmed for
   Search in Google Scholar for
 Related articles
Access Statistics
Email Alert *
Add to My List *
* Registration required (free)

 
  In this article
Abstract
Introduction
Materials and Me...
Results
Discussion
Conclusion
References
Article Figures
Article Tables

 Article Access Statistics
    Viewed1634    
    Printed82    
    Emailed0    
    PDF Downloaded187    
    Comments [Add]    

Recommend this journal


[TAG2]
[TAG3]
[TAG4]