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Ikonnikova A, Morozova A, Antonova O, Ochneva A, Fedoseeva E, Abramova O, Emelyanova M, Filippova M, Morozova I, Zorkina Y, Syunyakov T, Andryushchenko A, Andreuyk D, Kostyuk G, Gryadunov D. Evaluation of the Polygenic Risk Score for Alzheimer's Disease in Russian Patients with Dementia Using a Low-Density Hydrogel Oligonucleotide Microarray. Int J Mol Sci 2023; 24:14765. [PMID: 37834213 PMCID: PMC10572681 DOI: 10.3390/ijms241914765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The polygenic risk score (PRS), together with the ɛ4 allele of the APOE gene (APOE-ɛ4), has shown high potential for Alzheimer's disease (AD) risk prediction. The aim of this study was to validate the model of polygenic risk in Russian patients with dementia. A microarray-based assay was developed to identify 21 markers of polygenic risk and ɛ alleles of the APOE gene. This case-control study included 348 dementia patients and 519 cognitively normal volunteers. Cerebrospinal fluid (CSF) amyloid-β (Aβ) and tau protein levels were assessed in 57 dementia patients. PRS and APOE-ɛ4 were significant genetic risk factors for dementia. Adjusted for APOE-ɛ4, individuals with PRS corresponding to the fourth quartile had an increased risk of dementia compared to the first quartile (OR 1.85; p-value 0.002). The area under the curve (AUC) was 0.559 for the PRS model only, and the inclusion of APOE-ɛ4 improved the AUC to 0.604. PRS was positively correlated with tTau and pTau181 and inversely correlated with Aβ42/Aβ40 ratio. Carriers of APOE-ɛ4 had higher levels of tTau and pTau181 and lower levels of Aβ42 and Aβ42/Aβ40. The developed assay can be part of a strategy for assessing individuals for AD risk, with the purpose of assisting primary preventive interventions.
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Affiliation(s)
- Anna Ikonnikova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Anna Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Olga Antonova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Alexandra Ochneva
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
| | - Elena Fedoseeva
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Olga Abramova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Marina Emelyanova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Marina Filippova
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
| | - Irina Morozova
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
| | - Yana Zorkina
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Basic and Applied Neurobiology, V. Serbsky Federal Medical Research Centre of Psychiatry and Narcology, Kropotkinsky per. 23, 119034 Moscow, Russia
| | - Timur Syunyakov
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- International Centre for Education and Research in Neuropsychiatry (ICERN), Samara State Medical University, 443016 Samara, Russia
| | - Alisa Andryushchenko
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
| | - Denis Andreuyk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Economy Faculty, M.V. Lomonosov Moscow State University, 119991 Moscow, Russia
| | - Georgy Kostyuk
- Mental-Health Clinic No. 1 Named after N.A. Alekseev, Zagorodnoe Highway 2, 115191 Moscow, Russia; (A.M.); (A.O.); (O.A.); (I.M.); (Y.Z.); (T.S.); (A.A.); (D.A.); (G.K.)
- Department of Psychiatry, Federal State Budgetary Educational Institution of Higher Education “Moscow State University of Food Production”, Volokolamskoye Highway 11, 125080 Moscow, Russia
| | - Dmitry Gryadunov
- Center for Precision Genome Editing and Genetic Technologies for Biomedicine, Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia; (O.A.); (E.F.); (M.E.); (M.F.); (D.G.)
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Abstract
To maintain energy supply to the brain, a direct energy source called adenosine triphosphate (ATP) is produced by oxidative phosphorylation and aerobic glycolysis of glucose in the mitochondria and cytoplasm. Brain glucose metabolism is reduced in many neurodegenerative diseases, including Alzheimer's disease (AD), where it appears presymptomatically in a progressive and region-specific manner. Following dysregulation of energy metabolism in AD, many cellular repair/regenerative processes are activated to conserve the energy required for cell viability. Glucose metabolism plays an important role in the pathology of AD and is closely associated with the tricarboxylic acid cycle, type 2 diabetes mellitus, and insulin resistance. The glucose intake in neurons is from endothelial cells, astrocytes, and microglia. Damage to neurocentric glucose also damages the energy transport systems in AD. Gut microbiota is necessary to modulate bidirectional communication between the gastrointestinal tract and brain. Gut microbiota may influence the process of AD by regulating the immune system and maintaining the integrity of the intestinal barrier. Furthermore, some therapeutic strategies have shown promising therapeutic effects in the treatment of AD at different stages, including the use of antidiabetic drugs, rescuing mitochondrial dysfunction, and epigenetic and dietary intervention. This review discusses the underlying mechanisms of alterations in energy metabolism in AD and provides potential therapeutic strategies in the treatment of AD.
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Li Q, Lv X, Jin F, Liao K, Gao L, Xu J. Associations of Polygenic Risk Score for Late-Onset Alzheimer's Disease With Biomarkers. Front Aging Neurosci 2022; 14:849443. [PMID: 35493930 PMCID: PMC9047857 DOI: 10.3389/fnagi.2022.849443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Accepted: 03/14/2022] [Indexed: 11/25/2022] Open
Abstract
Late-onset Alzheimer's disease (LOAD) is a common irreversible neurodegenerative disease with heterogeneous genetic characteristics. Identifying the biological biomarkers with the potential to predict the conversion from normal controls to LOAD is clinically important for early interventions of LOAD and clinical treatment. The polygenic risk score for LOAD (AD-PRS) has been reported the potential possibility for reliably identifying individuals with risk of developing LOAD recently. To investigate the external phenotype changes resulting from LOAD and the underlying etiology, we summarize the comprehensive associations of AD-PRS with multiple biomarkers, including neuroimaging, cerebrospinal fluid and plasma biomarkers, cardiovascular risk factors, cognitive behavior, and mental health. This systematic review helps improve the understanding of the biomarkers with potential predictive value for LOAD and further optimizing the prediction and accurate treatment of LOAD.
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Affiliation(s)
- Qiaojun Li
- School of Information Engineering, Tianjin University of Commerce, Tianjin, China
- *Correspondence: Qiaojun Li
| | - Xingping Lv
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Fei Jin
- Department of Molecular Imaging, Qingdao Central Hospital, Qingdao University, Qingdao, China
| | - Kun Liao
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Liyuan Gao
- School of Sciences, Tianjin University of Commerce, Tianjin, China
| | - Jiayuan Xu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
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Zhou Y, Xie Z, Zhang Z, Yang J, Chen M, Chen F, Ma Y, Chen C, Peng Q, Zou L, Gao J, Xu Y, Kuang Y, Zhu M, You D, Yu J, Wang K. Plasma metabolites changes in male heroin addicts during acute and protracted withdrawal. Aging (Albany NY) 2021; 13:18669-18688. [PMID: 34282053 PMCID: PMC8351709 DOI: 10.18632/aging.203311] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 06/25/2021] [Indexed: 12/26/2022]
Abstract
BACKGROUND Heroin addiction and withdrawal have been associated with an increased risk for infectious diseases and psychological complications. However, the changes of metabolites in heroin addicts during withdrawal remain largely unknown. METHODS A total of 50 participants including 20 heroin addicts with acute abstinence stage, 15 with protracted abstinence stage and 15 healthy controls, were recruited. We performed metabolic profiling of plasma samples based on ultraperformance liquid chromatography coupled to tandem mass spectrometry to explore the potential biomarkers and mechanisms of heroin withdrawal. RESULTS Among the metabolites analyzed, omega-6 polyunsaturated fatty acids (linoleic acid, dihomo-gamma-linolenic acid, arachidonic acid, n-6 docosapentaenoic acid), omega-3 polyunsaturated fatty acids (docosahexaenoic acid, docosapentaenoic acid), aromatic amino acids (phenylalanine, tyrosine, tryptophan), and intermediates of the tricarboxylic acid cycle (oxoglutaric acid, isocitric acid) were significantly reduced during acute heroin withdrawal. Although majority of the metabolite changes could recover after months of withdrawal, the levels of alpha-aminobutyric acid, alloisoleucine, ketoleucine, and oxalic acid do not recover. CONCLUSIONS In conclusion, the plasma metabolites undergo tremendous changes during heroin withdrawal. Through metabolomic analysis, we have identified links between a framework of metabolic perturbations and withdrawal stages in heroin addicts.
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Affiliation(s)
- Yong Zhou
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Zhenrong Xie
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Zunyue Zhang
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Jiqing Yang
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Minghui Chen
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Medical School, Kunming University of Science and Technology, Kunming 650032, Yunnan, China
| | - Fengrong Chen
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Yuru Ma
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Cheng Chen
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Yunnan Institute of Digestive Disease, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Qingyan Peng
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Lei Zou
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Jianyuan Gao
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Yu Xu
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Yunnan Institute of Digestive Disease, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Yiqun Kuang
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Mei Zhu
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Dingyun You
- School of Public Health, Kunming Medical University, Kunming 650032, Yunnan, China
| | - Juehua Yu
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Centre for Experimental Studies and Research, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China
| | - Kunhua Wang
- NHC Key Laboratory of Drug Addiction Medicine, Kunming Medical University, First Affiliated Hospital of Kunming Medical University, Kunming 650032, Yunnan, China.,Yunnan University, Kunming 650032, Yunnan, China
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The Microbiota-Gut-Brain Axis and Alzheimer's Disease: Neuroinflammation Is to Blame? Nutrients 2020; 13:nu13010037. [PMID: 33374235 PMCID: PMC7824474 DOI: 10.3390/nu13010037] [Citation(s) in RCA: 124] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Revised: 12/20/2020] [Accepted: 12/22/2020] [Indexed: 02/06/2023] Open
Abstract
For years, it has been reported that Alzheimer’s disease (AD) is the most common cause of dementia. Various external and internal factors may contribute to the early onset of AD. This review highlights a contribution of the disturbances in the microbiota–gut–brain (MGB) axis to the development of AD. Alteration in the gut microbiota composition is determined by increase in the permeability of the gut barrier and immune cell activation, leading to impairment in the blood–brain barrier function that promotes neuroinflammation, neuronal loss, neural injury, and ultimately AD. Numerous studies have shown that the gut microbiota plays a crucial role in brain function and changes in the behavior of individuals and the formation of bacterial amyloids. Lipopolysaccharides and bacterial amyloids synthesized by the gut microbiota can trigger the immune cells residing in the brain and can activate the immune response leading to neuroinflammation. Growing experimental and clinical data indicate the prominent role of gut dysbiosis and microbiota–host interactions in AD. Modulation of the gut microbiota with antibiotics or probiotic supplementation may create new preventive and therapeutic options in AD. Accumulating evidences affirm that research on MGB involvement in AD is necessary for new treatment targets and therapies for AD.
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Molinski J, Tadimety A, Burklund A, Zhang JXJ. Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning. Ann Biomed Eng 2020; 48:2377-2399. [PMID: 32816167 PMCID: PMC7785517 DOI: 10.1007/s10439-020-02593-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 08/11/2020] [Indexed: 02/07/2023]
Abstract
Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signature-based diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic 'fingerprints' rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies.
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Affiliation(s)
- John Molinski
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA
| | - Amogha Tadimety
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA
| | - Alison Burklund
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA
| | - John X J Zhang
- Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH, 03755, USA.
- Norris Cotton Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.
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Kim S, Jang WJ, Yu H, Ryu IS, Jeong CH, Lee S. Integrated Non-targeted and Targeted Metabolomics Uncovers Dynamic Metabolic Effects during Short-Term Abstinence in Methamphetamine Self-Administering Rats. J Proteome Res 2019; 18:3913-3925. [PMID: 31525931 DOI: 10.1021/acs.jproteome.9b00363] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
Persistent neurochemical disturbances by repeating drug reward and withdrawal lead to addiction. Particularly, drug withdrawal, usually starting within hours of the last dose, is considered as a critical step in the transition to addiction and a treatment clue. The aim of this study was to uncover metabolic effects associated with methamphetamine (MA) short-term abstinence using both non-targeted and targeted metabolomics. Metabolic alterations were investigated in rat plasma collected immediately after 16 days of MA self-administration and after 12 and 24 h of abstinence. Principal component analysis revealed that the highest level of separation occurred between the 24 h and saline (control) groups based on the significantly changed ion features, 257/320/333 and 331/409/388, in the SA/12 h/24 h groups in positive and negative modes of UPLC-QTOF-ESI-MS, respectively. Targeted metabolomics revealed dynamic changes in the biosynthesis/metabolism of amino acids, including the phenylalanine, tyrosine, and tryptophan biosynthesis and the valine, leucine, and isoleucine biosynthesis. Integrating non-targeted and targeted metabolomics data uncovered rapid and distinct changes in the metabolic pathways involved in energy metabolism, the nervous system, and membrane lipid metabolism. These findings provide essential knowledge of the dynamic metabolic effects associated with short-term MA abstinence and may help identify early warning signs of MA dependence.
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Affiliation(s)
- Suji Kim
- College of Pharmacy , Keimyung University , 1095 Dalgubeoldaero , Dalseo-gu, Daegu 42601 , Republic of Korea
| | - Won-Jun Jang
- College of Pharmacy , Keimyung University , 1095 Dalgubeoldaero , Dalseo-gu, Daegu 42601 , Republic of Korea
| | - Hyerim Yu
- New Drug Development Center , 123 Osongsaengmyeongro, Osong-eup , Heungdeok-gu, Cheongju , Chungcheongbuk-do 28160 , Republic of Korea
| | - In Soo Ryu
- Substance Abuse Pharmacology Group , Korea Institute of Toxicology , 141 Gajeong-ro , Yuseong-gu, Daegeon , 34114 , Republic of Korea
| | - Chul-Ho Jeong
- College of Pharmacy , Keimyung University , 1095 Dalgubeoldaero , Dalseo-gu, Daegu 42601 , Republic of Korea
| | - Sooyeun Lee
- College of Pharmacy , Keimyung University , 1095 Dalgubeoldaero , Dalseo-gu, Daegu 42601 , Republic of Korea
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Kim M, Jang WJ, Shakya R, Choi B, Jeong CH, Lee S. Current Understanding of Methamphetamine-Associated Metabolic Changes Revealed by the Metabolomics Approach. Metabolites 2019; 9:metabo9100195. [PMID: 31547093 PMCID: PMC6835349 DOI: 10.3390/metabo9100195] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 09/17/2019] [Accepted: 09/18/2019] [Indexed: 12/27/2022] Open
Abstract
Metabolomics is a powerful tool used in the description of metabolic system perturbations caused by diseases or abnormal conditions, and it usually involves qualitative and/or quantitative metabolome determination, accompanied by bioinformatics assessment. Methamphetamine is a psychostimulant with serious abuse potential and due to the absence of effective pharmacotherapy and a high recurrence potential, methamphetamine addiction is a grave issue. Moreover, its addiction mechanisms remain unclear, probably due to the lack of experimental models that reflect personal genetic variances and environmental factors determining drug addiction occurrence. The metabolic approach is only recently being used to study the metabolic effects induced by a variety of methamphetamine exposure statuses, in order to investigate metabolic disturbances related to the adverse effects and discover potential methamphetamine addiction biomarkers. To provide a critical overview of methamphetamine-associated metabolic changes revealed in recent years using the metabolomics approach, we discussed methamphetamine toxicity, applications of metabolomics in drug abuse and addiction studies, biological samples used in metabolomics, and previous studies on metabolic alterations in a variety of biological samples—including the brain, hair, serum, plasma, and urine—following methamphetamine exposure in animal studies. Metabolic alterations observed in animal brain and other biological samples after methamphetamine exposure were associated with neuronal and energy metabolism disruptions. This review highlights the significance of further metabolomics studies in the area of methamphetamine addiction research. These findings will contribute to a better understanding of metabolic changes induced by methamphetamine addiction progress and to the design of further studies targeting the discovery of methamphetamine addiction biomarkers and therapeutic targets.
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Affiliation(s)
- Minjeong Kim
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Won-Jun Jang
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Rupa Shakya
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Boyeon Choi
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Chul-Ho Jeong
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
| | - Sooyeun Lee
- College of Pharmacy, Keimyung University, 1095 Dalgubeoldaero, Dalseo-gu, Daegu 42601, Korea.
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