Effect of aerobic exercise on brain metabolite profiles in mice models of methamphetamine addiction: LC-MS-based metabolomics study

doi:10.21203/rs.3.rs-2462908/v1

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Effect of aerobic exercise on brain metabolite profiles in mice models of methamphetamine addiction: LC-MS-based metabolomics study

https://doi.org/10.21203/rs.3.rs-2462908/v1

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In recent years, an increasing number of studies have used metabolomics techniques to uncover the underlying mechanisms of diseases, including substance abuse. Although physical exercise has been considered as a potential treatment for methamphetamine (MA) abuse, there is still a lack of studies regarding exercise modulating the metabolic profile of the brain following MA use. This untargeted metabolomics study, based on liquid chromatography-mass spectrometry (LC-MS), investigated the effects of aerobic exercise on MA-induced metabolism in mice brain. We found that 2 weeks of treadmill training modulates MA-induced metabolic disturbances in the brain. Among these metabolites, 129 metabolites with significant differences were identified under MA induction, and 32 metabolites were significantly affected by exercise. The relevant differential metabolites were mainly enriched in glycerophospholipid metabolism, steroid hormone biosynthesis and degradation, and renin-angiotensin system pathways. To our knowledge, this is the first study to use LC-MS approach to explore the effects of aerobic exercise on MA-induced metabolic profiles in the brain. The findings of this study provide new insights into exercise therapy for MA use.

methamphetamine

aerobic exercise

metabolomics

brain

substance abuse

Methamphetamine (MA) abuse is a global public health problem[1, 2], causing serious medical burden and social impact[3, 4]. MA is a highly addictive, widely abused amphetamine-type psychostimulant[1]. Its high lipid solubility enables it to cross the blood-brain barrier (BBB) and reach the brain rapidly[5], causing neurotoxicity such as high temperature, neuroinflammation, and oxidative stress, resulting in brain and central nervous system damage [6–9]. Clinical trials have observed that MA abuse can lead to reactions such as hypertension, emotional agitation, behavioral disinhibition, and anxiety in individuals[10–13]. This is accompanied by extremely complex and intense withdrawal symptoms[14], such as anhedonia[15], aggression[16], sleep disturbance[17], and strong cravings after drug discontinuation[18]. Evidence suggested that addiction-related symptoms and neurological damage are due to drug-induced metabolic disturbances[19].

Physical activity is widely considered an intervention for the treatment of substance use disorders[20]. Clinical studies indicated that regular exercise can help MA-dependent patients reduce drug cravings[21], and improve physical, mental, and social health[22, 23]. Meanwhile, exercise can also improve the brain function of MA-dependent patients, such as improving cognitive impairment[24] and strengthening inhibitory control functions[25]. Moreover, exercise can alleviate MA-induced neurotoxicity[26], stabilize blood-brain barrier integrity[27], and improve MA-induced reductions in striatal dopaminergic markers[28]. Despite the growing evidence showing that exercise improves the symptoms and mechanisms of MA dependence, it remains unclear regarding the mechanism of how exercise modulates MA-induced metabolic homeostasis in the brain. Metabolomics has the advantages of high sensitivity and specificity[29] and can discover potential biomarkers by studying the perturbation of metabolic state, which can help to reveal the underlying mechanism of diseases[19, 30]. Currently, this emerging method can be used to identify changes in metabolites affected by addictive drugs[19]. Clinical studies have found that compared with non-drug users, MA abusers have disturbances in serum metabolism, including phenylalanine metabolism, glyoxylate and dicarboxylate metabolism, and alanine, aspartate and glutamate metabolism, which may partially explain the oxidative stress and neuroinflammatory changes induced by MA[31]. Furthermore, metabolomics studies based on magnetic resonance imaging and positron emission tomography suggested that brain volume is affected in MA-dependent individuals, and the major metabolites in brain tissue are abnormally represented, including N-acetylasparate and total creatine[32]. Preclinical experiments have also provided evidence for metabolic disorders in MA addiction. MA-administered rats exhibited rapid and marked changes in energy metabolism, nervous system, and membrane lipid metabolism[33, 34], inducing neurotoxicity and locomotor sensitization, depression-, and anxiety-like behaviors[35]. Human brain tissue and cerebrospinal fluid are not readily available due to ethical and safety constraints[36]. Although brain tissue can be selected for metabolomic analysis in animal models, the current evidence for brain metabolomic studies on MA administration is still limited[37].

In the field of drug addiction, conditioned place preference(CPP) is an effective and commonly used method to verify drug dependence status through behavioral changes in animals[38, 39]. Changes in CPP caused by MA abuse can be mapped to multiple brain regions such as the nucleus accumbens[40], hippocampus[41], and hypothalamus[42], and are also associated with changes in the trends of addiction-related neurotransmitters such as dopamine and glutamate[43, 44]. Based on the above evidence, we hypothesize that exercise can modulate the metabolic homeostasis of the MA-induced brain. In this study, we used the MA-induced mice model. MA-induced metabolome in mouse brain tissue was investigated based on liquid chromatography-tandem mass spectrometry (LC-MS) and the metabolite profiles of exercise and non-exercise MA-treated mice were compared. These biomarkers and their enrichment pathways may explain changes in brain function in MA addiction, and provide new insights into the field of exercise therapy for MA addiction.

2.1 Animals

Male C57BL/6J mice (8-week-old, body weight 18–22 g) used in this study were purchased from Chengdu Dashuo Experimental Animals Co. Ltd. (Chengdu, China) and bred in the animal laboratory of Chengdu Sport University. All mice were allowed to feed and water ad libitum, ensured a 12-hour light/dark cycle, a temperature of 22 ± 2°C, and humidity of 52 ± 2%, and were reared adaptively for 1 week. All experimental procedures were approved by the Academic Committee of Chengdu Sport University (No: 2022-56) and followed the National Guidelines for the Care and Use of Laboratory Animals.

2.2 Experimental designs

Eighteen mice were randomly assigned to the control group (C group, n = 6), methamphetamine control group (N-MA group, n = 6), and methamphetamine exercise group (E-MA group, n = 6). Mice in group C were injected with normal saline, and group N-MA and group E-MA were modeled for MA addiction according to previous literature[45]. MA was obtained from the Sichuan Key Laboratory of Intelligent Police, Sichuan Police College (Luzhou, China), and dissolved in 0.9% NaCl (saline). Mice received intraperitoneal injection of MA (1 mg/kg) or an equal volume of 0.9% saline at 8 am every day for 7 consecutive days, and the model was validated by condition place preference (CPP). Afterward, mice in the C group and N-MA group were kept in cages for 2 weeks without any drug or any intervention. While mice in the E-MA group were exercised on a treadmill (SA101, SANS, Jiangsu, China). The exercise protocol was based on previous studies and modified according to actual conditions [46]. Briefly, we performed exercise training at moderate intensity (12 m/min, 1 hour per day) for 2 weeks (When mice stop exercising, make sure they continue to exercise by gently touching their tail). After the 2-week exercise intervention, all animals were anesthetized with 0.5% sodium pentobarbital. After dissection according to the brain atlas, the brain (excluding the cerebellum) was excised and immediately put into liquid nitrogen for the preparation of subsequent analysis. The experimental flow is presented in Fig. 1.

2.3 CPP test

The experimental procedures for CPP are based on previous reports with minor modifications [47, 42]. The CPP apparatus was purchased from Jiangsu SANS Company (Jiangsu, China), including two compartments of the same size on the left (black striped wall) and right (white wall) (15cm*17cm*20cm) and a central corridor (6cm*15cm*20cm). There was a camera on top of each compartment, and all data was recorded by a computerized video tracking system. The experimental process was an unbiased apparatus and a balancing procedure, which consisted of four phases:

Adaptation phase (Day 1): Mice were placed from the central corridor and allowed to walk freely in the CPP apparatus for 10 minutes, to ensure that all mice were acclimated to the environment.

Baseline phase (Day 2): Each mouse was placed in the central hallway and allowed to explore freely in both compartments for 10 minutes, and the time spent in each compartment was recorded. The aim was to determine whether there was an intrinsic preference in mice and to identify drug-related compartments.

Conditioning phase (Day 3–9): Firstly, the mice in the model group were injected with methamphetamine (1 mg/kg), and then placed in the drug-related compartment (assumed to be the right compartment) for 30 minutes. Control experiments were performed 8 hours later on the same day. Experimental animals were injected with saline and restrained in a non-medicated compartment for 30 minutes. To ensure the balance of the experiment, the saline control group was injected with an equal volume of saline.

Testing phase (Day 10): Drug injection was stopped and mice were placed in the central corridor and allowed to freely explore both compartments for 10 minutes. Then, the time spent in each compartment was recorded. The preference score was defined as the time spent in the drug-associated compartment during the test phase minus the time spent in the drug-associated compartment during the baseline phase. [48].

2.4 Metabolite profiling

LC-MS analysis was based on Dionex U3000 ultra-high performance liquid chromatography-tandem Q exactive plus high-resolution mass spectrometer system (Thermo Fisher Scientific, Waltham, MA, USA). All solvents and chemicals were analytical or chromatographic grade. Water, methanol, acetonitrile, and formic acid were purchased from Thermo Fisher Scientific. L-2-Chlorophenylalanine was purchased from Shanghai HC Biotechnology Co., Ltd. (Shanghai, China). Samples were processed in the following steps. Samples were thawed in an ice bath to minimize degradation. Tissue samples were accurately weighed (30 mg) and transferred to a 1.5 ml Eppendorf tube. TwentyµL of L-2 chlorophenylalanine (0.06 mg/ml) was dissolved in methanol as an internal standard, after which 400 µL of a mixture of methanol and water (4/1, vol/vol) was added. After samples were pre-cooled at -20°C refrigerators for 2 min, put in a grinder and grind (60 Hz, 2 min). The whole sample was extracted in an ice-water bath for 10 min, followed by standing at -20°C for 30 min. The extract was centrifuged for 10 min (13,000 rpm, 4°C), and 300 µL of the supernatant was loaded into an LC-MS injection vial to evaporate. After that, 300 µL methanol-water (1/4, vol/vol) was reconstituted (vortex for 30 sec, sonicate for 3 min) and allowed to stand at -20 ℃ for 2 hours. They were then centrifuged for 10 min (13,000 rpm, 4°C), pipetted with a syringe with 150 µL of the supernatant, filtered with a 0.22 µm organic-phase pinhole filter, transferred to an LC injection vial, and stored at -80°C until LC-MS analysis was performed. Quality control samples were prepared by mixing equal volumes of extracts from all samples.

2.5 Statistical analysis

CPP data were statistically analyzed using SPSS 26.0 (IBM SPSS Statistics) and graphed using GraphPad PRISM 9.0. The place preference of mice in the baseline and test phases for the left and right compartments and the place preferences of the two groups of mice in the test phase E-MA and N-MA were compared using independent samples t-tests. Results are expressed as mean ± standard, and p༜0.05 is considered statistically significant.

The LC-MS raw data were subjected to baseline filtering, peak identification, integration, retention time correction, peak alignment, and normalization by metabolomics processing software Progenesis QI v2.3 (Nonlinear Dynamics, Newcastle, UK). In this study, univariate analysis and multivariate analysis were combined to identify the differential metabolites. Multivariate statistical analysis was used to distinguish the overall differences in metabolic profiles between groups and to find differential metabolites between groups. Unsupervised principal component analysis (PCA) was used to observe the overall distribution between samples and the stability of the entire analysis process. The orthogonal partial least squares discriminant analysis (OPLS-DA) can maximize the difference between different groups within the model.

In addition, we used a 200- response permutation testing(RPT) to examine the model quality. In the RPT test, the established OPLS-DA model is considered reliable if the intercept of the Q2 regression line is negative. Variable important in projection (VIP) is determined according to the OPLS-DA model. Univariate statistical analysis was performed using the Student’s t test for data with parametric distributions to compare metabolites between the two groups. Among these metabolites, metabolites with p < 0.05 in the T-test were considered as differentially expressed metabolites. On this basis, metabolites with VIP > 1 in OPLS-DA were considered potential biomarkers. Kyoto encyclopedia of genes and genomes (KEGG) was used to identify potentially deranged metabolic pathways (http://www.genome.jp/kegg/).

3.1 CPP result

The CPP data showed that there was no statistical difference in the time spent by the mice in the left and right compartments during the baseline phase (P = 0.36), thus excluding an intrinsic location preference. We identified the right compartment as the target compartment for MA administration. The results of the test phase showed that after the MA adjustment, the mice's place preference scores in the right compartment (119.10 ± 42.26) were significantly higher than those in the left compartment (-146.99 ± 49.16) after the drug was stopped (P༜0.01), indicating that the modeling of MA dependence was successful. There was no statistical difference in the CPP scores in the right compartment between the mice randomly assigned to the E-MA group and the N-MA group (P = 0.36). (Fig. 2)

3.2 MA-induced metabolic profile changes

After preprocessing and normalizing the data, we performed statistical analysis on 8864 metabolic signatures. PCA1 in the PCA plot showed that there were significant differences in metabolic characteristics between the N-MA group and the C group (Fig. 3A). Afterward, OPLS-DA was used to more clearly explain the differences between the two groups (Fig. 3B). And the RPT results of this study R2=(0.0, 0.932), Q2=(0.0, -0.15) ensure the reliability of the model (Fig. 3C).

Then we initiated a search for potential metabolites responsible for the differences between groups. Thresholds were set at VIP༞1 and p༜0.05, and a total of 129 differential metabolites were identified (Table 1). Compared with the C group, 80 metabolites were significantly increased in the N-MA group (red dots), and 49 metabolites were significantly decreased (blue dots) (Fig. 3D). The enrichment analysis of 129 different metabolites was carried out through the KEGG database, and 24 pathways were obtained (p༜0.05) (Fig. 3E), including glucagon signaling pathway; sphingolipid signaling pathway; renin secretion; glycerophospholipid metabolism; alanine, aspartate and glutamate metabolism, etc.

3.3 Exercise regulates MA-induced metabolic signatures

Furthermore, we attempted to explore the effect of aerobic exercise on MA-induced brain metabolic disorders. The PCA plot is shown in Fig. 4A, and the metabolic differences between the E-MA group and the N-MA group were visualized in the PCA2. The differences between the two groups can be explained more clearly in OPLS-DA (Fig. 4B). In addition, the RPT results of this study R2=(0.0, 0.95), Q2=(0.0, -0.12) further ensure the reliability of this model (Fig. 4C). After that, the thresholds were set to p < 0.05 and VIP > 1, and a total of 32 differential metabolites were screened (Table 2). Compared with the N-MA group, 13 metabolites were significantly increased in the E-MA group (red dots) and 19 metabolites were significantly decreased (blue dots) (Fig. 4D). Finally, enrichment analysis (p༜0.05) of the screened differential metabolites was carried out through the KEGG database, and five pathways were obtained (Fig. 4E), including purine metabolism; Pentose phosphate pathway; Glycosaminoglycan biosynthesis - heparan sulfate/heparin; Choline metabolism in cancer; and Glycosaminoglycan biosynthesis - chondroitin sulfate / dermatan sulfate.

3.4 Comparison of MA and exercise in metabolic changes

We listed 6 differential metabolites showing opposite trends under exercise and MA induction in Table 3. Among them, the expression levels PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0), Dehydroepiandrosterone (DHEA), PI(20:4(5Z,8Z,11Z,14Z)/0:0), p-hydroxyfosinoprilat, PHODiA-PA in the N-MA group were increased, and the expression level of PC(24:0/P-18:1(11Z)) was lower compared to C group. Exercise brings the opposite trend. Notably, the pathway analysis showed that these identified differential metabolites were mainly involved in glycerophospholipid metabolism, steroid hormone biosynthesis and degradation, and renin-angiotensin system pathways. Moreover, there were 8 differential metabolites with the same change trend under exercise and MA induction (Table 4). Both N-MA and E-MA decreased the expression of ribose 1-phosphate; 5'-CMP; adenosine 3',5'-diphosphate; deoxyguanosine diphosphate (dGDP) and increased the expression of xanthosine; D-Glycerate 3-phosphate; 3-Methyl sulfolene; penciclovir.

In this study, we used the MA-induced mice model of addiction and explored the regulation of MA-dependent metabolic homeostasis in brain tissue by aerobic exercise based on LC-MS. This was the first study to our knowledge of using LC-MS to investigate the effects of exercise on MA-dependent brain metabolism. Our findings suggested that aerobic exercise can alter the expression of metabolites involved in glycerophospholipid metabolism, biosynthesis and metabolism of steroid hormones, and the renin-angiotensin system. Among them, the expression of PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0), DHEA, PI(20:4(5Z,8Z,11Z,14Z)/0:0), p-hydroxyfosinoprilat, PHODiA-PA, and PC(24:0/P-18:1(11Z)) were reversed after exercise intervention, suggesting that these six metabolites have the potential to be the biomarkers for exercise treatment of MA use.

A growing number of studies have suggested that glycerophospholipid metabolites could serve as markers for the assessment of drug addiction[33, 49, 50], and our findings demonstrated that this recommendation is equally applicable to MA addiction. Previous studies have shown that direct damage from MA exposure is associated with vascular toxicity[51], decreased BBB tight junctions and increased permeability[52], direct neurotoxic to neurons, and neuroinflammation[53]. The abnormal glycerophospholipid metabolism observed in this study may partially explain the occurrence of these injuries. Glycerophospholipids(GPLs) are the most common and abundant phospholipids in the body and the major component of membranes[54]. They play an important role in the dynamics of synaptic membranes and cooperate with synapsins to promote the exocytic and endocytosis of synaptic vesicles[55]. In addition, glycerophospholipid homeostasis has been recognized as a key factor in shaping neuronal morphology[56]. Glycerophospholipid metabolites are involved to regulate many biological processes as second messengers to ensure normal ion homeostasis in neurons and glial cells[57]. It is worth noting that dysregulated glycerophospholipid metabolism may lead to adverse effect on nerve damage and inflammation[58]. Therefore, the disruption of BBB, central nervous system synaptic signaling by MA, and the development of MA-induced neuronal injury and inflammation may be due to the disruption of glycerophospholipid metabolism.

Phosphatidylcholine (PC), phosphatidylinositol (PI), and phosphatidic acid (PA) are the major members of the glycerophospholipid family[59]. PC is the most abundant lipid class in the brain, playing a role in signaling during neuronal differentiation[60] and the recovery of neuronal differentiation under pathological conditions. Meanwhile, studies have found that normally expressed PC has anti-inflammatory and antioxidant properties[60], But both abnormally high and abnormally low PC levels can affect the body's energy metabolism and lead to disease development[61]. Furthermore, although PI is a small fraction of glycerophospholipids, it is extremely important for the nervous system[62]. PI has a key role in regulating synaptic function, including vesicle trafficking in the presynaptic and receptor modulation in the postsynaptic [63]. Moreover, PI and its phosphorylated forms also play an important role in cell signaling and membrane trafficking[64]. PA is the precursor of all GPLs, an important component of GPL homeostasis during intracellular transport[65], and PA has received increasing attention as a second messenger and regulator of membrane shape [66]. This study found that aerobic exercise can regulate the expression of the above three types of glycerophospholipids to normal levels. We speculate that the important role of exercise in maintaining BBB function, protecting neuronal function, reducing the inflammatory response and reducing nerve damage, and improving synaptic plasticity may be carried out by regulating glycerophospholipid metabolic homeostasis[67, 28]. However, the detailed mechanism by which exercise affects drug addiction through abnormal glycerophospholipids metabolism is currently not fully understood.

DHEA is defined as a "neurosteroid" when produced in the brain and acts as a modulator affecting inhibitory and excitatory neurotransmitters [68]. Endogenous DHEA levels in the brain appear to be positively correlated with drug abuse in studies involving addiction. Chronic cocaine administration leads to increased brain DHEA levels in animal models[69]. Clinical studies have also found that DHEA levels in the serum of smokers are up-regulated compared to healthy populations[70]. The evidence supports the findings of this study that MA use leads to increased levels of DHEA in the brain. Notably, increased brain endogenous DHEA is reported as a compensatory protective mechanism to counteract cravings for substance abuse and promote recovery(67). This may be related to the activity of dopamine, sigma-1 receptors, glutamate receptors, and gamma-aminobutyric acid type A receptors(68). Accumulating evidence revealed that taking MA can lead to cognitive impairment and decision-making impairment (69), which is one of the important reasons for the high relapse rate in MA. While overexpressed endogenous DHEA can inhibit hippocampal-based functions, which may affect the encoding and processing of space and cues[71]. In addition, the study found that DHEA levels were down-regulated after 6 weeks of substance withdrawal[70], which is consistent with the observations in our study that exercise tendsed to normalize endogenous DHEA levels in the brain. In conclusion, MA-induced elevation of DHEA levels in the brain appears to be a hallmark of brain injury, and exercise could modulate it.

The renin-angiotensin system (RAS) in the brain exhibits pleiotropic properties and can be involved in neuroprotection and cognition, blood pressure regulation, stress, depression, alcohol addiction, and pain regulation[72]. RAS mainly exerts its important functions through the angiotensin-converting enzyme(ACE)[73]. A previous study found that p-hydroxyfosinoprilat can inhithe ACE activity[74]. When ACE is inhibited, the expression of the unconventional enkephalin Met-enkephalin-Arg-Phe is enhanced in the nucleus accumbens of mice, which in turn activates endogenous µ-opioid receptors, causing a cell type-specific long-term depression of glutamate release onto medium spiny projection neurons expressing the dopamine-1 receptor[75]. In addition, RAS may also be involved in maintaining BBB integrity in MA-addicted brains[76]. The observed MA-induced elevated expression of p-hydroxyfosinoprilat in the mice brain may be a protective mechanism for resistance to brain injury and degradation of enkephalins by ACE. The study also found that physical exercise can adjust the expression of p-hydroxyfosinoprilat to normal levels, which may explain the mitigation of this damage, but the specific mechanism of this change is still unknown. Future studies are needed to explore the cechanisms of RAS in the treatment of methaphetamine-dependent.

To the best of our knowledge, this is the first study based on the LC-MS technique to explore the effects of aerobic exercise on MA-induced brain metabolic profiles. This study not only elucidated the expression profile of MA-induced brain metabolites but also demonstrated that aerobic exercise could modulate this change. In addition, differential metabolites affected by both MA and exercise were mainly enriched in glycerophospholipid metabolism, steroid hormone biosynthesis and degradation, and the renin-angiotensin system. These metabolites and pathways may play a key role in the treatment of MA use by exercise, worthy of attention. In conclusion, the findings of this study provide new theoretical support for exercise treatment of MA use.

Accession Numbers

The metabolomic datasets generated and analyzed during the current study are available in the ‘Baidu Netdisk’ (https://pan.baidu.com/s/1nPdoYfWtTXdCZxxTLyJYsA) with extraction code cdsu.

Ethics approval

The animal study was reviewed and approved by the Ethics Committee of Chengdu Sports University (Permit No. 2022 [56] ). All efforts were made to reduce the number and suffering of animals to a minimum.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data and materials

All data generated or analyzed during this study are included in this published article and its supplementary information files.

Competing interests

The authors declare no competing interests.

Funding

This study was funded by the Sichuan Province Drug Rehabilitation Administration (Grant No. XLZT-19FFC040002), the Humanities and Social Sciences Project of the Ministry of Education (Grant No. 21XJA890001), and the Sports Medicine Key Laboratory of Sichuan Province (Grant No. 2022-A046).

Authors' contributions

Jisheng Xu: Experimental intervention, Writing - Original Draft, and Data Curation.

Xue Li: Funding acquisition, Resources, Project administration, and Supervision.

Jisheng Xu and Xue Li: Conceived and designed the study.

Zhicheng Zhu: Visualization, Formal analysis;

Yu Jin, Changling Wei, and Yi Wang: Writing - Review & Editing

All authors have read and agreed to the published version of the manuscript.

Acknowledgements

Not applicable.

Courtney KE, Ray LA (2014) Methamphetamine: an update on epidemiology, pharmacology, clinical phenomenology, and treatment literature. Drug and alcohol dependence 143:11-21. doi:10.1016/j.drugalcdep.2014.08.003
Darke S, Kaye S, Duflou J (2017) Methamphetamine-related death is an under-addressed public health problem. Addiction (Abingdon, England) 112 (12):2204-2205. doi:10.1111/add.14035
Jones CM, Compton WM, Mustaquim D (2020) Patterns and Characteristics of Methamphetamine Use Among Adults - United States, 2015-2018. MMWR Morbidity and mortality weekly report 69 (12):317-323. doi:10.15585/mmwr.mm6912a1
Hong SJ, Shen BY, Sun RJ, Yang GM, Duan CM, Nie QY, Zhang CB, Dong WJ, Yu H, Wang S, Liu PL, Wen PY, Li LH (2021) Current Situation of Methamphetamine Abuse and Related Research Progress. Fa yi xue za zhi 37 (6):763-775. doi:10.12116/j.issn.1004-5619.2021.310202
Northrop NA, Yamamoto BK (2015) Methamphetamine effects on blood-brain barrier structure and function. Frontiers in neuroscience 9:69. doi:10.3389/fnins.2015.00069
Kiyatkin EA, Sharma HS (2009) Acute methamphetamine intoxication: brain hyperthermia, blood-brain barrier, brain edema, and morphological cell abnormalities. International review of neurobiology 88:65-100. doi:10.1016/s0074-7742(09)88004-5
Pimentel E, Sivalingam K, Doke M, Samikkannu T (2020) Effects of Drugs of Abuse on the Blood-Brain Barrier: A Brief Overview. Frontiers in neuroscience 14:513. doi:10.3389/fnins.2020.00513
Northrop NA, Yamamoto BK (2012) Persistent neuroinflammatory effects of serial exposure to stress and methamphetamine on the blood-brain barrier. Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology 7 (4):951-968. doi:10.1007/s11481-012-9391-y
Sajja RK, Rahman S, Cucullo L (2016) Drugs of abuse and blood-brain barrier endothelial dysfunction: A focus on the role of oxidative stress. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism 36 (3):539-554. doi:10.1177/0271678x15616978
Kevil CG, Goeders NE, Woolard MD, Bhuiyan MS, Dominic P, Kolluru GK, Arnold CL, Traylor JG, Orr AW (2019) Methamphetamine Use and Cardiovascular Disease. Arteriosclerosis, thrombosis, and vascular biology 39 (9):1739-1746. doi:10.1161/atvbaha.119.312461
Prakash MD, Tangalakis K, Antonipillai J, Stojanovska L, Nurgali K, Apostolopoulos V (2017) Methamphetamine: Effects on the brain, gut and immune system. Pharmacol Res 120:60-67. doi:10.1016/j.phrs.2017.03.009
Basedow LA, Kuitunen-Paul S, Wiedmann MF, Ehrlich S, Roessner V, Golub Y (2021) Verbal learning impairment in adolescents with methamphetamine use disorder: a cross-sectional study. BMC psychiatry 21 (1):166. doi:10.1186/s12888-021-03169-3
Ru Q, Xiong Q, Zhou M, Chen L, Tian X, Xiao H, Li C, Li Y (2019) Withdrawal from chronic treatment with methamphetamine induces anxiety and depression-like behavior in mice. Psychiatry research 271:476-483. doi:10.1016/j.psychres.2018.11.072
Paulus MP, Stewart JL (2020) Neurobiology, Clinical Presentation, and Treatment of Methamphetamine Use Disorder: A Review. JAMA psychiatry 77 (9):959-966. doi:10.1001/jamapsychiatry.2020.0246
Abbruscato TJ, Trippier PC (2018) DARK Classics in Chemical Neuroscience: Methamphetamine. ACS chemical neuroscience 9 (10):2373-2378. doi:10.1021/acschemneuro.8b00123
Uhlmann A, Ipser JC, Wilson D, Stein DJ (2018) Social cognition and aggression in methamphetamine dependence with and without a history of psychosis. Metabolic brain disease 33 (2):559-568. doi:10.1007/s11011-017-0157-3
Vrajová M, Šlamberová R, Hoschl C, Ovsepian SV (2021) Methamphetamine and sleep impairments: neurobehavioral correlates and molecular mechanisms. Sleep 44 (6). doi:10.1093/sleep/zsab001
Zhao D, Zhang M, Tian W, Cao X, Yin L, Liu Y, Xu TL, Luo W, Yuan TF (2021) Neurophysiological correlate of incubation of craving in individuals with methamphetamine use disorder. Molecular psychiatry 26 (11):6198-6208. doi:10.1038/s41380-021-01252-5
Ghanbari R, Sumner S (2018) Using Metabolomics to Investigate Biomarkers of Drug Addiction. Trends in molecular medicine 24 (2):197-205. doi:10.1016/j.molmed.2017.12.005
Zhang L, Yuan TF (2019) Exercise and substance abuse. International review of neurobiology 147:269-280. doi:10.1016/bs.irn.2019.07.007
Wang D, Zhou C, Chang YK (2015) Acute exercise ameliorates craving and inhibitory deficits in methamphetamine: An ERP study. Physiology & behavior 147:38-46. doi:10.1016/j.physbeh.2015.04.008
Zhu D, Jiang M, Xu D, Schöllhorn WI (2020) Long-Term Effects of Mind-Body Exercises on the Physical Fitness and Quality of Life of Individuals With Substance Use Disorder-A Randomized Trial. Frontiers in psychiatry 11:528373. doi:10.3389/fpsyt.2020.528373
Zhu Z, Li X, Li M, Wan H (2021) Effect of Tai Chi on Physical and Psychological Intervention of ATS Dependents of Different Genders: A Meta-analysis. China Sport Sci Technol 57 (06):53-66. doi:10.16470/j.csst.2021062
Zhang K, Zhang Q, Jiang H, Du J, Zhou C, Yu S, Hashimoto K, Zhao M (2018) Impact of aerobic exercise on cognitive impairment and oxidative stress markers in methamphetamine-dependent patients. Psychiatry research 266:328-333. doi:10.1016/j.psychres.2018.03.032
Wang D, Zhou C, Zhao M, Wu X, Chang YK (2016) Dose-response relationships between exercise intensity, cravings, and inhibitory control in methamphetamine dependence: An ERPs study. Drug and alcohol dependence 161:331-339. doi:10.1016/j.drugalcdep.2016.02.023
Park M, Levine H, Toborek M (2016) Exercise protects against methamphetamine-induced aberrant neurogenesis. Scientific reports 6:34111. doi:10.1038/srep34111
Toborek M, Seelbach MJ, Rashid CS, András IE, Chen L, Park M, Esser KA (2013) Voluntary exercise protects against methamphetamine-induced oxidative stress in brain microvasculature and disruption of the blood-brain barrier. Mol Neurodegener 8:22. doi:10.1186/1750-1326-8-22
Morais APD, Pita IR, Fontes-Ribeiro CA, Pereira FC (2018) The neurobiological mechanisms of physical exercise in methamphetamine addiction. CNS neuroscience & therapeutics 24 (2):85-97. doi:10.1111/cns.12788
Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nature reviews Molecular cell biology 17 (7):451-459. doi:10.1038/nrm.2016.25
Rinschen MM, Ivanisevic J, Giera M, Siuzdak G (2019) Identification of bioactive metabolites using activity metabolomics. Nature reviews Molecular cell biology 20 (6):353-367. doi:10.1038/s41580-019-0108-4
Li X, Li K, Zhu Z, Jin Y, Gao Z, Xu J, Zhang L (2022) Exercise Regulates the Metabolic Homeostasis of Methamphetamine Dependence. Metabolites 12 (7). doi:10.3390/metabo12070606
Chang L, Alicata D, Ernst T, Volkow N (2007) Structural and metabolic brain changes in the striatum associated with methamphetamine abuse. Addiction (Abingdon, England) 102 Suppl 1:16-32. doi:10.1111/j.1360-0443.2006.01782.x
Kim S, Jang WJ, Yu H, Ryu IS, Jeong CH, Lee S (2019) Integrated Non-targeted and Targeted Metabolomics Uncovers Dynamic Metabolic Effects during Short-Term Abstinence in Methamphetamine Self-Administering Rats. Journal of proteome research 18 (11):3913-3925. doi:10.1021/acs.jproteome.9b00363
Caspani G, Sebők V, Sultana N, Swann JR, Bailey A (2022) Metabolic phenotyping of opioid and psychostimulant addiction: A novel approach for biomarker discovery and biochemical understanding of the disorder. Br J Pharmacol 179 (8):1578-1606. doi:10.1111/bph.15475
Zhang KK, Chen LJ, Li JH, Liu JL, Wang LB, Xu LL, Yang JZ, Li XW, Xie XL, Wang Q (2022) Methamphetamine Disturbs Gut Homeostasis and Reshapes Serum Metabolome, Inducing Neurotoxicity and Abnormal Behaviors in Mice. Frontiers in microbiology 13:755189. doi:10.3389/fmicb.2022.755189
Association WM (2013) World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Jama 310 (20):2191-2194. doi:10.1001/jama.2013.281053
Kim M, Jang WJ, Shakya R, Choi B, Jeong CH, Lee S (2019) Current Understanding of Methamphetamine-Associated Metabolic Changes Revealed by the Metabolomics Approach. Metabolites 9 (10). doi:10.3390/metabo9100195
Napier TC, Herrold AA, de Wit H (2013) Using conditioned place preference to identify relapse prevention medications. Neuroscience and biobehavioral reviews 37 (9 Pt A):2081-2086. doi:10.1016/j.neubiorev.2013.05.002
Tzschentke TM (1998) Measuring reward with the conditioned place preference paradigm: a comprehensive review of drug effects, recent progress and new issues. Progress in neurobiology 56 (6):613-672. doi:10.1016/s0301-0082(98)00060-4
Shang Q, Wang J, Xi Z, Gao B, Qian H, An R, Shao G, Liu H, Li T, Liu X (2022) Mechanisms underlying microRNA-222-3p modulation of methamphetamine-induced conditioned place preference in the nucleus accumbens in mice. Psychopharmacology. doi:10.1007/s00213-022-06183-9
Cai J, Che X, Xu T, Luo Y, Yin M, Lu X, Wu C, Yang J (2022) Repeated oxytocin treatment during abstinence inhibited context- or restraint stress-induced reinstatement of methamphetamine-conditioned place preference and promoted adult hippocampal neurogenesis in mice. Experimental neurology 347:113907. doi:10.1016/j.expneurol.2021.113907
Liu M, Tan X, Liu E, Hang Z, Song R, Mu S, Han W, Yue Q, Sun J (2022) Inactivation of the Lateral Hypothalamus Attenuates Methamphetamine-Induced Conditioned Place Preference through Regulation of Kcnq3 Expression. International journal of molecular sciences 23 (13). doi:10.3390/ijms23137305
Zhu L, Wu F, Yan Z, He L, Wang S, Hu H, Goh ELK, Zhu Y, Guan F, Chen T (2022) A novel microRNA, novel-m009C, regulates methamphetamine rewarding effects. Molecular psychiatry. doi:10.1038/s41380-022-01651-2
Busceti CL, Ginerete RP, Di Menna L, D'Errico G, Cisani F, Di Pietro P, Imbriglio T, Bruno V, Battaglia G, Fornai F, Monn JA, Pittaluga A, Nicoletti F (2021) Behavioural and biochemical responses to methamphetamine are differentially regulated by mGlu2 and mGlu3 metabotropic glutamate receptors in male mice. Neuropharmacology 196:108692. doi:10.1016/j.neuropharm.2021.108692
Wang ZY, Guo LK, Han X, Song R, Dong GM, Ma CM, Wu N, Li J (2021) Naltrexone attenuates methamphetamine-induced behavioral sensitization and conditioned place preference in mice. Behavioural brain research 399:112971. doi:10.1016/j.bbr.2020.112971
Cheng T, Huang XD, Hu XF, Wang SQ, Chen K, Wei JA, Yan L, So KF, Yuan TF, Zhang L (2021) Physical exercise rescues cocaine-evoked synaptic deficits in motor cortex. Molecular psychiatry 26 (11):6187-6197. doi:10.1038/s41380-021-01336-2
Huston JP, Silva MAdS, Topic B, Müller CP (2013) What's conditioned in conditioned place preference? Trends in Pharmacological Sciences 34 (3):162-166. doi:https://doi.org/10.1016/j.tips.2013.01.004
Thorn DA, Winter JC, Li J-X (2012) Agmatine attenuates methamphetamine-induced conditioned place preference in rats. European Journal of Pharmacology 680 (1):69-72. doi:https://doi.org/10.1016/j.ejphar.2012.01.037
Lin Y, Gu H, Jiang L, Xu W, Liu C, Li Y, Qian X, Li D, Li Z, Hu J, Zhang H, Guo W, Zhao Y, Cen X (2017) Cocaine modifies brain lipidome in mice. Molecular and cellular neurosciences 85:29-44. doi:10.1016/j.mcn.2017.08.004
Wang-Sattler R, Yu Y, Mittelstrass K, Lattka E, Altmaier E, Gieger C, Ladwig KH, Dahmen N, Weinberger KM, Hao P, Liu L, Li Y, Wichmann HE, Adamski J, Suhre K, Illig T (2008) Metabolic profiling reveals distinct variations linked to nicotine consumption in humans--first results from the KORA study. PloS one 3 (12):e3863. doi:10.1371/journal.pone.0003863
Rodriguez EA, Yamamoto BK (2021) Toxic Effects of Methamphetamine on Perivascular Health: Co-morbid Effects of Stress and Alcohol Use Disorders. Current neuropharmacology 19 (12):2092-2107. doi:10.2174/1570159x19666210803150023
Northrop NA, Halpin LE, Yamamoto BK (2016) Peripheral ammonia and blood brain barrier structure and function after methamphetamine. Neuropharmacology 107:18-26. doi:10.1016/j.neuropharm.2016.03.018
Castellano P, Nwagbo C, Martinez LR, Eugenin EA (2016) Methamphetamine compromises gap junctional communication in astrocytes and neurons. Journal of neurochemistry 137 (4):561-575. doi:10.1111/jnc.13603
Farooqui AA, Horrocks LA, Farooqui T (2000) Glycerophospholipids in brain: their metabolism, incorporation into membranes, functions, and involvement in neurological disorders. Chemistry and physics of lipids 106 (1):1-29. doi:10.1016/s0009-3084(00)00128-6
Mochel F (2018) Lipids and synaptic functions. Journal of inherited metabolic disease 41 (6):1117-1122. doi:10.1007/s10545-018-0204-1
Ziegler AB, Tavosanis G (2019) Glycerophospholipids - Emerging players in neuronal dendrite branching and outgrowth. Developmental biology 451 (1):25-34. doi:10.1016/j.ydbio.2018.12.009
Farooqui AA, Yang HC, Rosenberger TA, Horrocks LA (1997) Phospholipase A2 and its role in brain tissue. Journal of neurochemistry 69 (3):889-901. doi:10.1046/j.1471-4159.1997.69030889.x
Farooqui AA, Horrocks LA, Farooqui T (2007) Modulation of inflammation in brain: a matter of fat. Journal of neurochemistry 101 (3):577-599. doi:10.1111/j.1471-4159.2006.04371.x
Wang R, Li B, Lam SM, Shui G (2020) Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression. Journal of genetics and genomics = Yi chuan xue bao 47 (2):69-83. doi:10.1016/j.jgg.2019.11.009
Tan W, Zhang Q, Dong Z, Yan Y, Fu Y, Liu X, Zhao B, Duan X (2020) Phosphatidylcholine Ameliorates LPS-Induced Systemic Inflammation and Cognitive Impairments via Mediating the Gut-Brain Axis Balance. Journal of agricultural and food chemistry 68 (50):14884-14895. doi:10.1021/acs.jafc.0c06383
Kumar A, Sundaram K, Mu J, Dryden GW, Sriwastva MK, Lei C, Zhang L, Qiu X, Xu F, Yan J, Zhang X, Park JW, Merchant ML, Bohler HCL, Wang B, Zhang S, Qin C, Xu Z, Han X, McClain CJ, Teng Y, Zhang HG (2021) High-fat diet-induced upregulation of exosomal phosphatidylcholine contributes to insulin resistance. Nature communications 12 (1):213. doi:10.1038/s41467-020-20500-w
Xiao C, Rossignol F, Vaz FM, Ferreira CR (2021) Inherited disorders of complex lipid metabolism: A clinical review. Journal of inherited metabolic disease 44 (4):809-825. doi:10.1002/jimd.12369
Balla T (2013) Phosphoinositides: tiny lipids with giant impact on cell regulation. Physiological reviews 93 (3):1019-1137. doi:10.1152/physrev.00028.2012
Raiborg C, Schink KO, Stenmark H (2013) Class III phosphatidylinositol 3-kinase and its catalytic product PtdIns3P in regulation of endocytic membrane traffic. The FEBS journal 280 (12):2730-2742. doi:10.1111/febs.12116
Hermansson M, Hokynar K, Somerharju P (2011) Mechanisms of glycerophospholipid homeostasis in mammalian cells. Progress in lipid research 50 (3):240-257. doi:10.1016/j.plipres.2011.02.004
Ammar MR, Kassas N, Bader MF, Vitale N (2014) Phosphatidic acid in neuronal development: a node for membrane and cytoskeleton rearrangements. Biochimie 107 Pt A:51-57. doi:10.1016/j.biochi.2014.07.026
Małkiewicz MA, Małecki A, Toborek M, Szarmach A, Winklewski PJ (2020) Substances of abuse and the blood brain barrier: Interactions with physical exercise. Neuroscience and biobehavioral reviews 119:204-216. doi:10.1016/j.neubiorev.2020.09.026
Pérez-Neri I, Montes S, Ojeda-López C, Ramírez-Bermúdez J, Ríos C (2008) Modulation of neurotransmitter systems by dehydroepiandrosterone and dehydroepiandrosterone sulfate: Mechanism of action and relevance to psychiatric disorders. Progress in Neuro-Psychopharmacology and Biological Psychiatry 32 (5):1118-1130. doi:https://doi.org/10.1016/j.pnpbp.2007.12.001
Maayan R, Lotan S, Doron R, Shabat-Simon M, Gispan-Herman I, Weizman A, Yadid G (2006) Dehydroepiandrosterone (DHEA) attenuates cocaine-seeking behavior in the self-administration model in rats. European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology 16 (5):329-339. doi:10.1016/j.euroneuro.2005.10.002
Oncken C, Prestwood K, Cooney JL, Unson C, Fall P, Kulldorff M, Raisz LG (2002) Effects of smoking cessation or reduction on hormone profiles and bone turnover in postmenopausal women. Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco 4 (4):451-458. doi:10.1080/1462220021000018399
Farooqi NAI, Scotti M, Yu A, Lew J, Monnier P, Botteron KN, Campbell BC, Booij L, Herba CM, Séguin JR, Castellanos-Ryan N, McCracken JT, Nguyen TV (2019) Sex-specific contribution of DHEA-cortisol ratio to prefrontal-hippocampal structural development, cognitive abilities and personality traits. Journal of neuroendocrinology 31 (2):e12682. doi:10.1111/jne.12682
Urmila A, Rashmi P, Nilam G, Subhash B (2021) Recent Advances in the Endogenous Brain Renin-Angiotensin System and Drugs Acting on It. Journal of the renin-angiotensin-aldosterone system : JRAAS 2021:9293553. doi:10.1155/2021/9293553
Santos RAS, Oudit GY, Verano-Braga T, Canta G, Steckelings UM, Bader M (2019) The renin-angiotensin system: going beyond the classical paradigms. American journal of physiology Heart and circulatory physiology 316 (5):H958-h970. doi:10.1152/ajpheart.00723.2018
Shionoiri H, Naruse M, Minamisawa K, Ueda S, Himeno H, Hiroto S, Takasaki I (1997) Fosinopril. Clinical pharmacokinetics and clinical potential. Clinical pharmacokinetics 32 (6):460-480. doi:10.2165/00003088-199732060-00003
Trieu BH, Remmers BC, Toddes C, Brandner DD, Lefevre EM, Kocharian A, Retzlaff CL, Dick RM, Mashal MA, Gauthier EA, Xie W, Zhang Y, More SS, Rothwell PE (2022) Angiotensin-converting enzyme gates brain circuit-specific plasticity via an endogenous opioid. Science (New York, NY) 375 (6585):1177-1182. doi:10.1126/science.abl5130
Guo S, Shan S, Wu H, Hao H, Li Z (2021) Recombinant water stress protein 1 (Re-WSP1) suppresses colon cancer cell growth through the miR-539/β-catenin signaling pathway. Molecular biology reports 48 (11):7059-7065. doi:10.1007/s11033-021-06549-w

Table 1

Differential metabolites screened from the N-MA and control groups
NO.	Metabolite	p Value	VIP	Fold Change	Variations versus controls
1	Adenosine	0.027	29.103	0.256	↓
2	Psilocybin	0.013	16.409	0.557	↓
3	PE(0:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z))	0.017	15.318	2.575	↑
4	LysoPC(0:0/16:0)	0.038	14.020	1.708	↑
5	1-Pentanesulfenothioic acid	0.007	11.427	1.444	↑
6	PC(18:1(11Z)/0:0)	0.030	11.171	1.903	↑
7	Adenosine 3'-monophosphate	0.005	11.133	0.308	↓
8	Oxidized glutathione	0.009	10.776	0.515	↓
9	PS(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	0.001	10.690	2.711	↑
10	Mahaleboside	0.032	10.226	0.619	↓
11	LysoPE(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	0.017	9.542	2.969	↑
12	Pyrrolidonecarboxylic acid	0.012	9.494	0.807	↓
13	LysoPC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	0.015	8.706	3.258	↑
14	Prostaglandin E2 p-benzamidophenyl ester	0.001	8.629	2.017	↑
15	Adenosine monophosphate	0.005	8.497	0.285	↓
16	LysoPC(20:4(8Z,11Z,14Z,17Z)/0:0)	0.018	8.454	2.647	↑
17	Choline	0.024	8.366	1.230	↑
18	LysoPE(0:0/20:4(8Z,11Z,14Z,17Z))	0.037	8.329	2.085	↑
19	Guanidylic acid (guanosine monophosphate)	0.010	8.045	0.341	↓
20	Guanosine 3'-monophosphate	0.008	8.034	0.326	↓
21	Hypoxanthine	0.038	7.531	1.260	↑
22	Citric acid	0.022	7.061	1.262	↑
23	p-hydroxyfosinoprilat	0.000	6.772	2.858	↑
24	Pyridine N-oxide glucuronide	0.008	6.549	2.377	↑
25	Isocitrate	0.029	5.888	1.214	↑
26	Calcimycin	0.003	5.743	2.159	↑
27	PI(20:4(5Z,8Z,11Z,14Z)/0:0)	0.001	5.657	2.036	↑
28	L-Carnitine	0.024	5.438	0.791	↓
29	LysoPE(20:4(5Z,8Z,11Z,14Z)/0:0)	0.026	5.226	2.469	↑
30	PE(18:1(9Z)/0:0)	0.040	5.176	1.601	↑
31	Uridine diphosphate-N-acetylglucosamine	0.002	4.957	0.619	↓
32	LysoPE(18:0/0:0)	0.047	4.775	1.560	↑
33	Adenylsuccinic acid	0.006	4.678	0.162	↓
34	PC(18:1(9Z)/0:0)	0.041	4.646	1.979	↑
35	Benthiavalicarb isopropyl	0.015	4.526	0.237	↓
36	D-Gluconic acid Mn(II) salt	0.003	4.419	0.321	↓
37	FAPy-adenine	0.033	4.407	0.273	↓
38	L-Acetylcarnitine	0.018	4.380	0.645	↓
39	Dehydroepiandrosterone (DHEA)	0.036	4.126	2.597	↑
40	N2,N2-Dimethylguanosine	0.041	4.095	0.183	↓
41	(R)-(+)-2-Pyrrolidone-5-carboxylic acid	0.010	4.061	1.510	↑
42	PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	0.017	4.034	3.717	↑
43	Glucose-uridine-C1,5'-diphosphate	0.032	4.014	0.569	↓
44	PC(20:4(5Z,8Z,11Z,14Z)/0:0)	0.019	3.934	2.890	↑
45	Glycolic acid	0.020	3.474	1.766	↑
46	Xanthine	0.008	3.417	1.568	↑
47	3-Methyl sulfolene	0.007	3.390	1.344	↑
48	p-CHLOROPHENYLALANINE	0.040	3.340	0.937	↓
49	PG(18:3(6Z,9Z,12Z)/0:0)	0.023	3.061	2.151	↑
50	4'-Demethyldeoxypodophyllotoxin	0.029	2.963	1.687	↑
51	LysoPE(18:1(9Z)/0:0)	0.043	2.824	1.645	↑
52	Uridine	0.046	2.789	1.279	↑
53	5-Hydroxy-N-formylkynurenine	0.031	2.707	0.246	↓
54	PHODiA-PA	0.002	2.616	2.569	↑
55	2,3,4-Trihydroxybutanoic acid	0.045	2.557	1.498	↑
56	PS-PE	0.001	2.478	2.476	↑
57	1-(2-methoxy-6Z-heptadecenyl)-sn-glycero-3-phosphoethanolamine	0.047	2.440	1.594	↑
58	Tiocarbazil	0.046	2.331	1.642	↑
59	Norepinephrine sulfate	0.011	2.311	2.146	↑
60	Metam-sodium	0.035	2.290	0.708	↓
61	C16 Sphinganine	0.023	2.262	0.871	↓
62	DL-Acetylcarnitine	0.040	2.197	0.706	↓
63	2,4-Diamino-6,7-dimethoxyquinazoline	0.018	2.188	1.305	↑
64	PC(22:4(7Z,10Z,13Z,16Z)/0:0)	0.014	2.082	3.391	↑
65	Dimethoate	0.034	2.071	0.239	↓
66	PC(2:0/1:0)[U]	0.049	2.028	2.656	↑
67	LysoPI(16:0/0:0)	0.030	2.009	2.483	↑
68	Phosphoribosyl-AMP	0.006	2.000	0.419	↓
69	Uracil	0.013	1.998	1.304	↑
70	Ribose 1-phosphate	0.020	1.986	0.886	↓
71	Thioperamide	0.035	1.962	2.071	↑
72	Thiabendazole	0.042	1.931	0.939	↓
73	Adenosine 3',5'-diphosphate	0.008	1.876	0.707	↓
74	PIM1(17:0/18:1(9Z))	0.025	1.861	9.323	↑
75	LysoPC(16:1(9Z))	0.011	1.826	1.653	↑
76	PC(24:0/P-18:1(11Z))	0.024	1.822	0.869	↓
77	LysoPC(20:3(5Z,8Z,11Z)/0:0)	0.011	1.818	2.985	↑
78	D-Tagatose 1,6-bisphosphate	0.015	1.788	1.877	↑
79	Uridine monophosphate (UMP)	0.011	1.780	0.324	↓
80	Isopropalin	0.036	1.735	1.797	↑
81	3b,16a-Dihydroxyandrostenone sulfate	0.048	1.696	2.890	↑
82	MG(0:0/20:4(5Z,8Z,11Z,14Z)/0:0)	0.001	1.655	1.722	↑
83	Succinyladenosine	0.036	1.651	0.781	↓
84	Caffeic acid 3-sulfate	0.008	1.638	2.054	↑
85	5-(2'-Carboxyethyl)-4,6-Dihydroxypicolinate	0.020	1.609	1.248	↑
86	Aminoparathion	0.040	1.606	1.417	↑
87	1-Oleoylglycerophosphoserine	0.010	1.567	2.641	↑
88	Deoxyguanosine diphosphate (dGDP)	0.001	1.559	0.685	↓
89	LysoPC(18:2(9Z,12Z))	0.043	1.544	1.803	↑
90	Diethyl disulfide	0.000	1.413	0.749	↓
91	Retinylphosphate mannose	0.013	1.387	1.799	↑
92	FAD	0.001	1.384	0.875	↓
93	Tetracenomycin D1	0.010	1.372	2.931	↑
94	5'-CMP	0.000	1.370	0.687	↓
95	LysoPE(0:0/18:2(9Z,12Z))	0.025	1.357	2.291	↑
96	GDP-L-fucose	0.007	1.341	0.689	↓
97	Isosorbide Dinitrate	0.033	1.332	1.487	↑
98	Tazobactam	0.039	1.322	2.204	↑
99	Tetrahydrodipicolinate	0.005	1.309	0.626	↓
100	Dibutyl sulfide	0.021	1.307	1.322	↑
101	Hirsutin	0.000	1.291	6.223	↑
102	PG(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	0.015	1.277	2.922	↑
103	Suprofen	0.043	1.269	1.442	↑
104	Sphinganine	0.025	1.239	0.894	↓
105	5β-Chola-3,8(14),11-trien-24-oic Acid	0.036	1.239	4.146	↑
106	alpha-D-Glucose 1,6-bisphosphate	0.010	1.217	1.679	↑
107	Ethyl 3-mercaptobutyrate	0.034	1.216	0.813	↓
108	7b-Hydroxy-3-oxo-5b-cholanoic acid	0.038	1.215	2.328	↑
109	2,7-Anhydro-alpha-N-acetylneuraminic acid	0.032	1.205	1.206	↑
110	Imidazoleacetic acid riboside	0.022	1.201	0.693	↓
111	N-(2,3-Dihydroxybenzoyl)-L-serine	0.017	1.193	1.657	↑
112	Myristic acid	0.028	1.189	0.758	↓
113	PE(18:2(9Z,12Z)/0:0)	0.033	1.186	2.724	↑
114	gamma-Glutamylalanine	0.035	1.148	0.776	↓
115	D-Glycerate 3-phosphate	0.029	1.108	2.097	↑
116	Penciclovir	0.039	1.099	1.576	↑
117	N-Acetyl-b-neuraminic acid	0.017	1.098	0.611	↓
118	CDP-ethanolamine	0.047	1.080	0.674	↓
119	DPA	0.039	1.066	2.617	↑
120	8,11,14-Eicosatrienoic acid	0.024	1.063	1.122	↑
121	Mangold's acid	0.026	1.056	1.373	↑
122	2-Hydroxy-6-ketononatrienedioate	0.035	1.053	0.719	↓
123	1-O-(2R-methoxy-hexadecyl)-sn-glycerol	0.002	1.047	2.111	↑
124	SULFAMONOMETHOXINE	0.001	1.045	0.729	↓
125	Xanthosine	0.005	1.030	2.011	↑
126	1-Acetoxy-2-hydroxy-16-heptadecen-4-one	0.001	1.027	0.574	↓
127	PI(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	0.003	1.021	0.514	↓
128	D-Erythrose 4-phosphate	0.040	1.016	1.271	↑
129	2-Hydroxyadenine	0.012	1.003	0.374	↓

Table 2

Differential metabolites screened from the E-MA and N-MA groups
NO.	Metabolite	p Value	VIP	Fold Change	Variations versus methamphetamine
1	PC(14:0/20:1(11Z))	0.034	68.468	1.389	↓
2	p-hydroxyfosinoprilat	0.023	4.175	0.769	↓
3	3-Methyl sulfolene	0.027	3.959	1.179	↑
4	PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	0.035	3.780	0.526	↓
5	Ribose 1-phosphate	0.013	3.729	0.824	↓
6	D-Glycerate 3-phosphate	0.025	3.632	2.979	↑
7	PI(20:4(5Z,8Z,11Z,14Z)/0:0)	0.002	3.593	0.837	↓
8	Dehydroepiandrosterone (DHEA)	0.010	2.806	0.714	↓
9	Adenosine 3',5'-diphosphate	0.037	2.804	0.606	↓
10	4-[(Hydroxymethyl)nitrosoamino]-1-(3-pyridinyl)-1-butanone	0.032	2.300	0.811	↓
11	Asparaginyl-Valine	0.002	2.255	0.835	↓
12	Deoxyguanosine diphosphate (dGDP)	0.008	2.091	0.553	↓
13	(9R,13R)-1a,1b-dihomo-jasmonic acid	0.002	1.986	0.950	↓
14	L-Iduronate 2-sulfate	0.013	1.723	1.390	↑
15	Guanosine tetraphosphate adenosine	0.003	1.715	2.574	↑
16	Butyric acid	0.013	1.507	1.319	↑
17	Hydroxymethylphosphonate	0.034	1.498	0.745	↓
18	PC(24:0/P-18:1(11Z))	0.029	1.493	1.080	↑
19	Procyanidin B1	0.024	1.399	1.245	↑
20	Penciclovir	0.020	1.382	1.288	↑
21	PHODiA-PA	0.049	1.323	0.805	↓
22	3-Mercapto-2-methyl-1-butanol	0.016	1.300	0.796	↓
23	Xanthosine	0.006	1.243	1.429	↑
24	3,5-Dinitrosalicylic acid	0.022	1.203	0.972	↓
25	Dieporeticenin	0.023	1.198	0.699	↓
26	Linoleoyl ethanolamide	0.030	1.189	0.803	↓
27	6-Thiourate	0.048	1.163	0.975	↓
28	Grepafloxacin	0.008	1.071	1.484	↑
29	(Z)-15-Oxo-11-eicosenoic acid	0.022	1.058	0.906	↓
30	5'-CMP	0.025	1.046	0.795	↓
31	Tetraphyllin B sulfate	0.048	1.038	4.496	↑
32	1-Propenyl 1-(propylsulfinyl)propyl disulfide	0.003	1.018	1.580	↑

Table 3

Metabolites with different trends under E-MA and N-MA
NO.	Metabolite	Variations with E-MA	Variations with N-MA
1	PC(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/0:0)	↓	↑
2	PI(20:4(5Z,8Z,11Z,14Z)/0:0)	↓	↑
3	p-hydroxyfosinoprilat	↓	↑
4	PHODiA-PA	↓	↑
5	Dehydroepiandrosterone (DHEA)	↓	↑
6	PC(24:0/P-18:1(11Z))	↑	↓

Table 4

Metabolites with the same trends under E-MA and N-MA
NO.	Metabolite	Variations with E-MA	Variations with N-MA
1	Ribose 1-phosphate	↓	↓
2	5'-CMP	↓	↓
3	Adenosine 3',5'-diphosphate	↓	↓
4	Deoxyguanosine diphosphate (dGDP)	↓	↓
5	Xanthosine	↑	↑
6	D-Glycerate 3-phosphate	↑	↑
7	3-Methyl sulfolene	↑	↑
8	Penciclovir	↑	↑

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Effect of aerobic exercise on brain metabolite profiles in mice models of methamphetamine addiction: LC-MS-based metabolomics study

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Abstract

Figures

1. Introduction

2. Methods

2.1 Animals

2.2 Experimental designs

2.3 CPP test

2.4 Metabolite profiling

2.5 Statistical analysis

3. Results

3.1 CPP result

3.2 MA-induced metabolic profile changes

3.3 Exercise regulates MA-induced metabolic signatures

3.4 Comparison of MA and exercise in metabolic changes

4. Discussion

5. Conclusions

Declarations

References

Tables

Supplementary Files

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