Literature DB >> 27183212

Metabolomic study of aging in mouse plasma by gas chromatography-mass spectrometry.

Chan Seo1, Yun-Ho Hwang1, Youngbae Kim1, Bo Sun Joo2, Sung-Tae Yee1, Cheol Min Kim3, Man-Jeong Paik4.   

Abstract

Metabolomic analysis of aging was performed in plasma samples of young (8 weeks) and old (72 weeks) mice as ethoxycarbonyl/methoxime/tert-butyldimethylsilyl derivatives by gas chromatography-mass spectrometry (GC-MS). As new approaches, study of altered metabolism from aging was attempted by simultaneous profiling analysis of amino acids (AAs), organic acids (OAs) and fatty acids (FAs) by GC-MS in a single run combined with pattern analysis. As a result, 27 amino acids (AAs), 17 organic acids (OAs) and 24 fatty acids (FAs) were positively screened with large variations in plasma samples. Among altered metabolites, levels of six AAs (proline, methionine, 4-hydroxyproline, pipecolic acid, glutamic acid, α-aminoadipic acid) as neurotransmetters and nutrients, five OAs (2-hydroxybutyric acid, 2-hydroxyglutaric acid, cis-aconitic acid citric acid, isocitric acid) including intermediate metabolites in the TCA cycle, and three n-3 polyunsaturated FAs (PUFAs) of α-octadecatrienoic acid, eicosapentaenoic acid and docosahexaenoic acid as potential biomarkers were significantly different between young and old groups. Their levels were normalized to the corresponding mean values of the young group and then plotted into star symbol patterns, which were clearly distinct compared with numerical data and readily distinguishable for young and old groups. Thus, the present metabolomic screening and the star pattern recognition method might be useful for understanding the complexity of biochemical events in aging.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Aging; Amino acid; Fatty acid; Metabolomics; Organic acid

Mesh:

Substances:

Year:  2016        PMID: 27183212     DOI: 10.1016/j.jchromb.2016.04.052

Source DB:  PubMed          Journal:  J Chromatogr B Analyt Technol Biomed Life Sci        ISSN: 1570-0232            Impact factor:   3.205


  11 in total

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2.  Metabolomics Study of Isocaloric Different Dietary Patterns on the Life Span in Healthy Population.

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3.  Urinary Profile of Endogenous Gamma-Hydroxybutyric Acid and its Biomarker Metabolites in Healthy Korean Females: Determination of Age-Dependent and Intra-Individual Variability and Identification of Metabolites Correlated With Gamma-Hydroxybutyric Acid.

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Journal:  Front Pharmacol       Date:  2022-04-13       Impact factor: 5.988

4.  Cross-Platform Comparison of Untargeted and Targeted Lipidomics Approaches on Aging Mouse Plasma.

Authors:  Kévin Contrepois; Salah Mahmoudi; Baljit K Ubhi; Katharina Papsdorf; Daniel Hornburg; Anne Brunet; Michael Snyder
Journal:  Sci Rep       Date:  2018-12-10       Impact factor: 4.379

5.  Deoxysphingolipids and ether-linked diacylglycerols accumulate in the tissues of aged mice.

Authors:  Ayumi Ando; Masahiro Oka; Yoshinori Satomi
Journal:  Cell Biosci       Date:  2019-08-05       Impact factor: 7.133

6.  Effects of myocardial ischemia/reperfusion injury on plasma metabolomic profile during aging.

Authors:  Claudio de Lucia; Michela Piedepalumbo; Lu Wang; Fausto Carnevale Neto; Daniel Raftery; Erhe Gao; Domenico Praticò; Daniel E L Promislow; Walter J Koch
Journal:  Aging Cell       Date:  2020-12-29       Impact factor: 9.304

Review 7.  Proline metabolism and transport in retinal health and disease.

Authors:  Jianhai Du; Siyan Zhu; Rayne R Lim; Jennifer R Chao
Journal:  Amino Acids       Date:  2021-04-19       Impact factor: 3.520

8.  Circulating Metabolomic Analysis following Cecal Ligation and Puncture in Young and Aged Mice Reveals Age-Associated Temporal Shifts in Nicotinamide and Histidine/Histamine Metabolic Pathways.

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Journal:  Oxid Med Cell Longev       Date:  2021-09-02       Impact factor: 6.543

9.  Multi-omic rejuvenation of naturally aged tissues by a single cycle of transient reprogramming.

Authors:  Dafni Chondronasiou; Diljeet Gill; Lluc Mosteiro; Rocio G Urdinguio; Antonio Berenguer-Llergo; Mònica Aguilera; Sylvere Durand; Fanny Aprahamian; Nitharsshini Nirmalathasan; Maria Abad; Daniel E Martin-Herranz; Camille Stephan-Otto Attolini; Neus Prats; Guido Kroemer; Mario F Fraga; Wolf Reik; Manuel Serrano
Journal:  Aging Cell       Date:  2022-03-02       Impact factor: 9.304

10.  Comparative metabolomics of aging in a long-lived bat: Insights into the physiology of extreme longevity.

Authors:  Hope C Ball; Shiva Levari-Shariati; Lisa Noelle Cooper; Michel Aliani
Journal:  PLoS One       Date:  2018-05-01       Impact factor: 3.240

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