Identification of Morphine and Heroin-Treatment in Mice Using Metabonomics

Lu, Wuhuan; Zhang, Ran; Wei, Sheng; Feng, Luohua; Xu, Peng; Wang, Youmei; Xie, Yuan; Xu, Hui; Wang, Guangji; Aa, Jiye

doi:10.3390/metabo11090607

Cited by 8 publications

(13 citation statements)

References 39 publications

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“…Alpha‐ketoglutarate acid has been thought to determine the rate of the citric acid cycle in the TCA cycle 28 and involve in amino acid formation. The alpha‐ketoglutarate acid has been found elevated in blood of patients with bipolar disorder, 29 schizophrenia 30 and Alzheimer's disease 31 but reduced in schizophrenia patients 25 and heroin‐exposed mice 27 . Taken together, our findings indicated that L‐aspartic acid, L‐glutamic acid and taurine are stable biomarkers that might be potential clinical evaluator for the diagnosis and treatment of METH addiction, while the alpha‐ketoglutarate acid and citric acid are useful indicators to reflect METH‐disrupted energy metabolism in the blood.…”

Section: Discussionmentioning

confidence: 99%

“…In urine of METH‐induced rats, the alpha‐ketoglutarate acid and citric acid were decreased 24 . The citric acid has also shown at a reduced levels in blood of schizophrenia patients, 25 but increased in heroin‐exposed rats 26 and mice 27 . Alpha‐ketoglutarate acid has been thought to determine the rate of the citric acid cycle in the TCA cycle 28 and involve in amino acid formation.…”

Section: Discussionmentioning

confidence: 99%

“…24 The citric acid has also shown at a reduced levels in blood of schizophrenia patients, 25 but increased in heroin-exposed rats 26 and mice. 27 Alphaketoglutarate acid has been thought to determine the rate of the citric acid cycle in the TCA cycle 28 and involve in amino acid formation. The alpha-ketoglutarate acid has been found elevated in blood of patients with bipolar disorder, 29 schizophrenia 30 and Alzheimer's disease 31 but reduced in schizophrenia patients 25 and heroin-exposed mice.…”

Section: Sex-specific Alteration Of Serum Metabolitesmentioning

confidence: 99%

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Sex‐specific metabolic signatures in methamphetamine addicts

et al. 2022

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Methamphetamine (METH) is a commonly abused addictive psychostimulant, and METH-induced neurotoxic and behavioural deficits are in a sex-specific manner.However, there is lack of biomarkers to evaluate METH addiction in clinical practice, especially for gender differences. We utilized ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) to detect the serum metabolomics in METH addicts and controls, specially exploring the sex-specific metabolic alterations by METH abuse. We found that many differently expressed metabolites in METH addicts related to metabolisms of amino acid, energy, vitamin and neurological disorders. Further, METH abuse caused different patterns of metabolomics in a sex-specific manner. As to amino acid metabolism, L-phenylalanine, Ltryptophan and L-histidine in serum of male addicts and betaine in serum of female addicts were significantly changed by METH use. In addition, it seemed that purine and pyrimidine-related metabolites (e.g., xanthosine and adenosine 5 0 -monophosphate) in male and the metabolites of hormone (e.g., cortisol) and folate biosynthesis (e.g., 7,8-dihydrobiopterin and 4-hydroxybenzoic acid) in female were more sensitive to METH addiction. Our findings revealed that L-glutamic acid, L-aspartic acid, alphaketoglutarate acid and citric acid may be potential biomarkers for monitoring METH addiction in clinic. Considering sex-specific toxicity by METH, the metabolites of purine and pyrimidine metabolism in male and those of stress-related hormones in female may be used to facilitate the accurate diagnosis and treatment for METH addicts of different genders.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Sex-specific Alteration Of Serum Metabolitesmentioning

confidence: 99%

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Sex‐specific metabolic signatures in methamphetamine addicts

et al. 2022

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“…Samples were prepared according to the established protocol in [13,22]. Briefly, methanol solution with internal standard compound (myristic-1,2-13 C2 acid, 2.5 μg/mL) Figure 1.…”

Section: Sample Preparation For Gc/ms Analysis and Compound Identific...mentioning

confidence: 99%

“…These methods have limitations on personnel willingness, location, and instrument. In our previous studies [20], metabolomics could reflect changes in the metabolic state of metabolic disease in different periods, including morphine, heroin [21][22][23], and methamphetamine [13], which altered metabolic status during periods of exposure, withdrawal, and relapse in serum or urine. These studies suggest that metabolomics has the ability to reflect changes in the metabolic rate at different times of drug use through changes in the peripheral circulation.…”

Section: Introductionmentioning

confidence: 99%

Identification of Biomarkers for Methamphetamine Exposure Time Prediction in Mice Using Metabolomics and Machine Learning Approaches

et al. 2022

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Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg−1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography–mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.

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