Startseite Spontaneous dehydration side reactions of core amino acids in GC-MS sample preparation: the role of drying conditions
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Spontaneous dehydration side reactions of core amino acids in GC-MS sample preparation: the role of drying conditions

  • Anastasia E. Karnaeva ORCID logo EMAIL logo , Dmitry A. Korzhenevskii ORCID logo und Alexey M. Nesterenko ORCID logo
Veröffentlicht/Copyright: 27. Februar 2025

Abstract

The metabolomic and metabolic flux analyses often require accurate measurement of amino acids, which can be effectively conducted using GC-MS analysis after preliminarily trimethylsilyl (TMS) derivatization. Among the four central amino acids (glutamate, alanine, serine and aspartate) taken for the study, glutamate has the most peculiar behaviour because of pyroglutamic acid formation. We have discovered that spontaneous non-enzymatic cyclisation occurs only if we dry glutamate from aqueous solution before starting TMS derivatization in pyridine. We investigated various factors during sample preparation and injection into the GC that could affect glutamate cyclisation, and the decisive factor was the presence of water in the sample, which could catalyse the glutamate cyclization process. Very important practical advice was demonstrated that drying from a water-methanol solution (1:1, v/v) instead of drying from water solution significantly prevents the formation of pyroglutamate. Brief research of three other amino-acids indicate the similar problems for alanine and serine amino acids: formations of anhydrides likely happened during drying from pure water solutions.


Corresponding author: Anastasia E. Karnaeva, Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, Moscow, Russia, e-mail:

Award Identifier / Grant number: 124041900012-4

  1. Research ethics: Not applicable.

  2. Informed consent: Not applicable.

  3. Author contributions: Anastasia E. Karnaeva: conceptualization, methodology, investigation, validation, visualization, writing-original draft preparation. Aleksey M. Nesterenko: supervision; conceptualization, visualization, writing-reviewing and editing. Dmitry A. Korzhenevskii: writing-reviewing and editing.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: The authors state no conflict of interest.

  6. Research funding: This research was supported by the Ministry of Science and Higher Education of the Russian Federation (№ 124041900012-4).

  7. Data availability: The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Supplementary Material

This article contains supplementary material (https://doi.org/10.1515/pac-2024-0328).


Received: 2024-11-20
Accepted: 2025-02-18
Published Online: 2025-02-27
Published in Print: 2025-04-28

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