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      UPLC-QTOFMS-based metabolomic analysis of the serum of hypoxic preconditioning mice

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          Abstract

          Hypoxic preconditioning (HPC) is well-known to exert a protective effect against hypoxic injury; however, the underlying molecular mechanism remains unclear. The present study utilized a serum metabolomics approach to detect the alterations associated with HPC. In the present study, an animal model of HPC was established by exposing adult BALB/c mice to acute repetitive hypoxia four times. The serum samples were collected by orbital blood sampling. Metabolite profiling was performed using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOFMS), in conjunction with univariate and multivariate statistical analyses. The results of the present study confirmed that the HPC mouse model was established and refined, suggesting significant differences between the control and HPC groups at the molecular levels. HPC caused significant metabolic alterations, as represented by the significant upregulation of valine, methionine, tyrosine, isoleucine, phenylalanine, lysophosphatidylcholine (LysoPC; 16:1), LysoPC (22:6), linoelaidylcarnitine, palmitoylcarnitine, octadecenoylcarnitine, taurine, arachidonic acid, linoleic acid, oleic acid and palmitic acid, and the downregulation of acetylcarnitine, malate, citrate and succinate. Using MetaboAnalyst 3.0, a number of key metabolic pathways were observed to be acutely perturbed, including valine, leucine and isoleucine biosynthesis, in addition to taurine, hypotaurine, phenylalanine, linoleic acid and arachidonic acid metabolism. The results of the present study provided novel insights into the mechanisms involved in the acclimatization of organisms to hypoxia, and demonstrated the protective mechanism of HPC.

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          Most cited references34

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          Metabolomics--the link between genotypes and phenotypes.

          Metabolites are the end products of cellular regulatory processes, and their levels can be regarded as the ultimate response of biological systems to genetic or environmental changes. In parallel to the terms 'transcriptome' and proteome', the set of metabolites synthesized by a biological system constitute its 'metabolome'. Yet, unlike other functional genomics approaches, the unbiased simultaneous identification and quantification of plant metabolomes has been largely neglected. Until recently, most analyses were restricted to profiling selected classes of compounds, or to fingerprinting metabolic changes without sufficient analytical resolution to determine metabolite levels and identities individually. As a prerequisite for metabolomic analysis, careful consideration of the methods employed for tissue extraction, sample preparation, data acquisition, and data mining must be taken. In this review, the differences among metabolite target analysis, metabolite profiling, and metabolic fingerprinting are clarified, and terms are defined. Current approaches are examined, and potential applications are summarized with a special emphasis on data mining and mathematical modelling of metabolism.
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            Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression.

            Multiple, complex molecular events characterize cancer development and progression. Deciphering the molecular networks that distinguish organ-confined disease from metastatic disease may lead to the identification of critical biomarkers for cancer invasion and disease aggressiveness. Although gene and protein expression have been extensively profiled in human tumours, little is known about the global metabolomic alterations that characterize neoplastic progression. Using a combination of high-throughput liquid-and-gas-chromatography-based mass spectrometry, we profiled more than 1,126 metabolites across 262 clinical samples related to prostate cancer (42 tissues and 110 each of urine and plasma). These unbiased metabolomic profiles were able to distinguish benign prostate, clinically localized prostate cancer and metastatic disease. Sarcosine, an N-methyl derivative of the amino acid glycine, was identified as a differential metabolite that was highly increased during prostate cancer progression to metastasis and can be detected non-invasively in urine. Sarcosine levels were also increased in invasive prostate cancer cell lines relative to benign prostate epithelial cells. Knockdown of glycine-N-methyl transferase, the enzyme that generates sarcosine from glycine, attenuated prostate cancer invasion. Addition of exogenous sarcosine or knockdown of the enzyme that leads to sarcosine degradation, sarcosine dehydrogenase, induced an invasive phenotype in benign prostate epithelial cells. Androgen receptor and the ERG gene fusion product coordinately regulate components of the sarcosine pathway. Here, by profiling the metabolomic alterations of prostate cancer progression, we reveal sarcosine as a potentially important metabolic intermediary of cancer cell invasion and aggressivity.
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              MetaboAnalyst 2.0—a comprehensive server for metabolomic data analysis

              First released in 2009, MetaboAnalyst (www.metaboanalyst.ca) was a relatively simple web server designed to facilitate metabolomic data processing and statistical analysis. With continuing advances in metabolomics along with constant user feedback, it became clear that a substantial upgrade to the original server was necessary. MetaboAnalyst 2.0, which is the successor to MetaboAnalyst, represents just such an upgrade. MetaboAnalyst 2.0 now contains dozens of new features and functions including new procedures for data filtering, data editing and data normalization. It also supports multi-group data analysis, two-factor analysis as well as time-series data analysis. These new functions have also been supplemented with: (i) a quality-control module that allows users to evaluate their data quality before conducting any analysis, (ii) a functional enrichment analysis module that allows users to identify biologically meaningful patterns using metabolite set enrichment analysis and (iii) a metabolic pathway analysis module that allows users to perform pathway analysis and visualization for 15 different model organisms. In developing MetaboAnalyst 2.0 we have also substantially improved its graphical presentation tools. All images are now generated using anti-aliasing and are available over a range of resolutions, sizes and formats (PNG, TIFF, PDF, PostScript, or SVG). To improve its performance, MetaboAnalyst 2.0 is now hosted on a much more powerful server with substantially modified code to take advantage the server’s multi-core CPUs for computationally intensive tasks. MetaboAnalyst 2.0 also maintains a collection of 50 or more FAQs and more than a dozen tutorials compiled from user queries and requests. A downloadable version of MetaboAnalyst 2.0, along detailed instructions for local installation is now available as well.
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                Author and article information

                Journal
                Mol Med Rep
                Mol Med Rep
                Molecular Medicine Reports
                D.A. Spandidos
                1791-2997
                1791-3004
                November 2017
                13 September 2017
                13 September 2017
                : 16
                : 5
                : 6828-6836
                Affiliations
                [1 ]Institute of Medicine and Hygienic Equipment for High Altitude Region, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P.R. China
                [2 ]Key Laboratory of High Altitude Medicine, Ministry of Education, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P.R. China
                [3 ]The Key Laboratory of High Altitude Medicine, People's Liberation Army, Third Military Medical University, Chongqing 400038, P.R. China
                [4 ]Department of High Altitude Military Hygiene, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P.R. China
                [5 ]Department of Pathophysiology and High Altitude Physiology, College of High Altitude Military Medicine, Third Military Medical University, Chongqing 400038, P.R. China
                [6 ]Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, Nanjing, Jiangsu 210009, P.R. China
                [7 ]Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, China Pharmaceutical University, Nanjing, Jiangsu 210009, P.R. China
                Author notes
                Correspondence to: Dr Wenting Liao, Department of Pharmaceutical Analysis, School of Pharmacy, China Pharmaceutical University, 24 Tong Jia Xiang, Nanjing, Jiangsu 210009, P.R. China, E-mail: lwting84@ 123456163.com
                Professor Yu-Qi Gao, Institute of Medicine and Hygienic Equipment for High Altitude Region, College of High Altitude Military Medicine, Third Military Medical University, 30 Gao Tan Yan Street, Chongqing 400038, P.R. China, E-mail: gaoy66@ 123456yahoo.com
                Article
                mmr-16-05-6828
                10.3892/mmr.2017.7493
                5865841
                28901489
                f085ca2e-3800-4382-b0e4-0fdeadc223dd
                Copyright: © Liu et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 15 March 2017
                : 24 July 2017
                Categories
                Articles

                hypoxic preconditioning,metabolomics,ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry,metabolic pathway

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