Literature DB >> 27932276

The why and how of amino acid analytics in cancer diagnostics and therapy.

Friederike Manig1, Konstantin Kuhne2, Cläre von Neubeck3, Uwe Schwarzenbolz4, Zhanru Yu5, Benedikt M Kessler5, Jens Pietzsch6, Leoni A Kunz-Schughart7.   

Abstract

Pathological alterations in cell functions are frequently accompanied by metabolic reprogramming including modifications in amino acid metabolism. Amino acid detection is thus integral to the diagnosis of many hereditary metabolic diseases. The development of malignant diseases as metabolic disorders comes along with a complex dysregulation of genetic and epigenetic factors affecting metabolic enzymes. Cancer cells might transiently or permanently become auxotrophic for non-essential or semi-essential amino acids such as asparagine or arginine. Also, transformed cells are often more susceptible to local shortage of essential amino acids such as methionine than normal tissues. This offers new points of attacking unique metabolic features in cancer cells. To better understand these processes, highly sensitive methods for amino acid detection and quantification are required. Our review summarizes the main methodologies for amino acid detection with a particular focus on applications in biomedicine and cancer, provides a historical overview of the methodological pre-requisites in amino acid analytics. We compare classical and modern approaches such as the combination of gas chromatography and liquid chromatography with mass spectrometry (GC-MS/LC-MS). The latter is increasingly applied in clinical routine. We therefore illustrate an LC-MS workflow for analyzing arginine and methionine as well as their precursors and analogs in biological material. Pitfalls during protocol development are discussed, but LC-MS emerges as a reliable and sensitive tool for the detection of amino acids in biological matrices. Quantification is challenging, but of particular interest in cancer research as targeting arginine and methionine turnover in cancer cells represent novel treatment strategies.
Copyright © 2016 Elsevier B.V. All rights reserved.

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Year:  2016        PMID: 27932276     DOI: 10.1016/j.jbiotec.2016.12.001

Source DB:  PubMed          Journal:  J Biotechnol        ISSN: 0168-1656            Impact factor:   3.307


  8 in total

1.  A cost-effective approach to produce 15N-labelled amino acids employing Chlamydomonas reinhardtii CC503.

Authors:  Jesús Nicolás Carcelén; Juan Manuel Marchante-Gayón; Pablo Rodríguez González; Luis Valledor; María Jesús Cañal; José Ignacio García Alonso
Journal:  Microb Cell Fact       Date:  2017-08-18       Impact factor: 5.328

2.  Identification of Potential Biomarkers in Association With Progression and Prognosis in Epithelial Ovarian Cancer by Integrated Bioinformatics Analysis.

Authors:  Jinhui Liu; Huangyang Meng; Siyue Li; Yujie Shen; Hui Wang; Wu Shan; Jiangnan Qiu; Jie Zhang; Wenjun Cheng
Journal:  Front Genet       Date:  2019-10-24       Impact factor: 4.599

3.  Dual role of ER stress in response to metabolic co-targeting and radiosensitivity in head and neck cancer cells.

Authors:  Oleg Chen; Friederike Manig; Loreen Lehmann; Nagwa Sorour; Steffen Löck; Zhanru Yu; Anna Dubrovska; Michael Baumann; Benedikt M Kessler; Oleh Stasyk; Leoni A Kunz-Schughart
Journal:  Cell Mol Life Sci       Date:  2020-11-23       Impact factor: 9.261

4.  CircMYH9 drives colorectal cancer growth by regulating serine metabolism and redox homeostasis in a p53-dependent manner.

Authors:  Xin Liu; Yunze Liu; Zhao Liu; Changwei Lin; Fanchao Meng; Lei Xu; Xiuzhong Zhang; Chong Zhang; Penbo Zhang; Shuai Gong; Nai Wu; Zeqiang Ren; Jun Song; Yi Zhang
Journal:  Mol Cancer       Date:  2021-09-08       Impact factor: 27.401

5.  Diagnostic value of plasma tryptophan and symmetric dimethylarginine levels for acute kidney injury among tacrolimus-treated kidney transplant patients by targeted metabolomics analysis.

Authors:  Feng Zhang; Qinghua Wang; Tianyi Xia; Shangxi Fu; Xia Tao; Yan Wen; Shen'an Chan; Shouhong Gao; Xiaojuan Xiong; Wansheng Chen
Journal:  Sci Rep       Date:  2018-10-02       Impact factor: 4.379

6.  Separation of 9-Fluorenylmethyloxycarbonyl Amino Acid Derivatives in Micellar Systems of High-Performance Thin-Layer Chromatography and Pressurized Planar Electrochromatography.

Authors:  Beata Polak; Adam Traczuk; Sylwia Misztal
Journal:  Sci Rep       Date:  2019-11-19       Impact factor: 4.379

7.  The serum amino acid profile in COVID-19.

Authors:  Alptug Atila; Handan Alay; Mehmet Emrah Yaman; Tugrul Cagri Akman; Elif Cadirci; Burak Bayrak; Saffet Celik; Nihal Efe Atila; Aycan Mutlu Yaganoglu; Yucel Kadioglu; Zekai Halıcı; Emine Parlak; Zafer Bayraktutan
Journal:  Amino Acids       Date:  2021-10-04       Impact factor: 3.520

8.  Presence of human breast cancer xenograft changes the diurnal profile of amino acids in mice.

Authors:  Rubens Paula Junior; Nathália Martins Sonehara; Bruna Victorasso Jardim-Perassi; Akos Pal; Yasmin Asad; Luiz Gustavo Almeida Chuffa; Roger Chammas; Florence I Raynaud; Debora A P C Zuccari
Journal:  Sci Rep       Date:  2022-01-19       Impact factor: 4.379

  8 in total

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