Literature DB >> 27983783

Metabolic Pathway Extension Approach for Metabolomic Biomarker Identification.

Lin Wang1, Hui Ye1, Di Sun1, Tuo Meng1, Lijuan Cao1, Mengqiu Wu1, Min Zhao1, Yun Wang1, Baoqiang Chen1, Xiaowei Xu1, Guangji Wang1, Haiping Hao1.   

Abstract

Discovery of metabolomic biomarkers represents an important task in disease diagnosis and therapy. Although the development of various analytical tools and online libraries facilitates the identification of biomarkers, the fast and reliable identification of new biomarkers that are not included in databases still represents a major bottleneck in the field of metabolomics. Here, we developed a metabolic pathway extension (MPE) approach to the fast characterization of metabolomic biomarkers. This approach was proposed based on a core concept that the whole metabolome is built from a limited number of initial metabolites via various kinds and multiple steps of metabolic reactions, and thus, theoretically, the whole metabolome might be mapped from the initial metabolites and metabolic reactions. Carnitine was used as an example of initial metabolites to validate this concept and the usefulness of MPE approach. The intragastric dosing of carnitine to mice induced a significant alternation of a total of 97 metabolites. Mass differences between each pair of metabolites were calculated and then matched with those of typical metabolic pathways automatically by an in-house developed program. Diagnostic ions and neutral losses were used for validating the matches. With this approach, 93 out of a total of 97 metabolites were putatively identified, while only half of them could be traced from the currently available online database. The MPE approach was further validated by applying to the identification of carnitine-associated biomarkers in a typical mice model of fasting, and extended to the development of bile acids submetabolome. Our study indicates that the MPE approach is highly useful for rapid and reliable identification of metabolically and structurally associated biomarkers.

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Year:  2016        PMID: 27983783     DOI: 10.1021/acs.analchem.6b03757

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  4 in total

1.  Butyrate Suppresses the Proliferation of Colorectal Cancer Cells via Targeting Pyruvate Kinase M2 and Metabolic Reprogramming.

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Journal:  Mol Cell Proteomics       Date:  2018-05-08       Impact factor: 5.911

2.  Functional Metabolomics and Chemoproteomics Approaches Reveal Novel Metabolic Targets for Anticancer Therapy.

Authors:  Chang Shao; Wenjie Lu; Haiping Hao; Hui Ye
Journal:  Adv Exp Med Biol       Date:  2021       Impact factor: 2.622

3.  Metabolism of five diterpenoid lactones from Dioscorea bulbifera tubers in zebrafish.

Authors:  Wei Shi; Jie Ling; Li-Long Jiang; Dong-Sheng Zhao; Ling-Li Wang; Zi-Tian Wu; Ping Li; Ying-Jie Wei; Hui-Jun Li
Journal:  RSC Adv       Date:  2018-02-19       Impact factor: 4.036

Review 4.  Advances in decomposing complex metabolite mixtures using substructure- and network-based computational metabolomics approaches.

Authors:  Mehdi A Beniddir; Kyo Bin Kang; Grégory Genta-Jouve; Florian Huber; Simon Rogers; Justin J J van der Hooft
Journal:  Nat Prod Rep       Date:  2021-11-17       Impact factor: 13.423

  4 in total

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