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      Toxicology of tramadol following chronic exposure based on metabolomics of the cerebrum in mice

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          Abstract

          Tramadol is an opioid used as an analgesic for treating moderate or severe pain. The long-term use of tramadol can induce several adverse effects. The toxicological mechanism of tramadol abuse is unclear. Metabolomics is a very useful method for investigating the toxicology of drug abuse. We investigated the impact of chronic tramadol administration on the cerebrum of mice, focusing on the metabolites after tramadol administration. The mice received 20 or 50 mg/kg body weight tramadol dissolved in physiological saline daily for 5 weeks via oral gavage. Compared with the control group, the low dose tramadol group showed seven potential biomarkers, including gamma-hydroxybutyric acid, succinate semialdehyde, and methylmalonic acid, which were either up- or down-regulated. Compared with the control group, the high dose tramadol group showed ten potential biomarkers, including gamma-hydroxybutyric acid, glutamine, and O-phosphorylethanolamine, which were either up- or down-regulated. The up-regulated gamma-hydroxybutyric acid and the down-regulated succinate semialdehyde revealed that the neurotransmitter system was disrupted after tramadol abuse. Compared with the low dose tramadol group, there were twenty-nine potential biomarkers in the high dose tramadol group, mainly related to the pentose phosphate pathway and glycerophospholipid metabolism. In conclusion, metabolomics in the tramadol abuse group demonstrated that long-term tramadol abuse can result in oxidative damage, inflammation, and disruption of the GABA neurotransmitter system, which will help to elucidate the toxicology of tramadol abuse.

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          Effect of Opioid vs Nonopioid Medications on Pain-Related Function in Patients With Chronic Back Pain or Hip or Knee Osteoarthritis Pain

          Limited evidence is available regarding long-term outcomes of opioids compared with nonopioid medications for chronic pain.
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            Partial least squares: a versatile tool for the analysis of high-dimensional genomic data.

            Partial least squares (PLS) is an efficient statistical regression technique that is highly suited for the analysis of genomic and proteomic data. In this article, we review both the theory underlying PLS as well as a host of bioinformatics applications of PLS. In particular, we provide a systematic comparison of the PLS approaches currently employed, and discuss analysis problems as diverse as, e.g. tumor classification from transcriptome data, identification of relevant genes, survival analysis and modeling of gene networks and transcription factor activities.
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              Livestock metabolomics and the livestock metabolome: A systematic review

              Metabolomics uses advanced analytical chemistry techniques to comprehensively measure large numbers of small molecule metabolites in cells, tissues and biofluids. The ability to rapidly detect and quantify hundreds or even thousands of metabolites within a single sample is helping scientists paint a far more complete picture of system-wide metabolism and biology. Metabolomics is also allowing researchers to focus on measuring the end-products of complex, hard-to-decipher genetic, epigenetic and environmental interactions. As a result, metabolomics has become an increasingly popular “omics” approach to assist with the robust phenotypic characterization of humans, crop plants and model organisms. Indeed, metabolomics is now routinely used in biomedical, nutritional and crop research. It is also being increasingly used in livestock research and livestock monitoring. The purpose of this systematic review is to quantitatively and objectively summarize the current status of livestock metabolomics and to identify emerging trends, preferred technologies and important gaps in the field. In conducting this review we also critically assessed the applications of livestock metabolomics in key areas such as animal health assessment, disease diagnosis, bioproduct characterization and biomarker discovery for highly desirable economic traits (i.e., feed efficiency, growth potential and milk production). A secondary goal of this critical review was to compile data on the known composition of the livestock metabolome (for 5 of the most common livestock species namely cattle, sheep, goats, horses and pigs). These data have been made available through an open access, comprehensive livestock metabolome database (LMDB, available at http://www.lmdb.ca). The LMDB should enable livestock researchers and producers to conduct more targeted metabolomic studies and to identify where further metabolome coverage is needed.
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                Author and article information

                Contributors
                gaolina0114@sina.com
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                7 July 2020
                7 July 2020
                2020
                : 10
                : 11130
                Affiliations
                [1 ]ISNI 0000 0000 9678 1884, GRID grid.412449.e, School of Forensic Medicine, , China Medical University, ; No. 77 Puhe Road, Shenbei New District, Shenyang, 110014 China
                [2 ]ISNI 0000 0000 9678 1884, GRID grid.412449.e, School of Fundamental Sciences, , China Medical University, ; Shenyang, 110014 China
                Article
                67974
                10.1038/s41598-020-67974-8
                7341866
                42eb755e-bdd4-46a2-a9ad-787f2595cbdd
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 March 2020
                : 17 June 2020
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                © The Author(s) 2020

                Uncategorized
                biochemistry,molecular biology
                Uncategorized
                biochemistry, molecular biology

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