Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship.

Carbonyl steroids Derivatization H/D exchange Quantitative structure-retention relationship UPLC-Q-exactive-MS/MS

Journal

Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
ISSN: 1873-376X
Titre abrégé: J Chromatogr B Analyt Technol Biomed Life Sci
Pays: Netherlands
ID NLM: 101139554

Informations de publication

Date de publication:
15 Jul 2023
Historique:
received: 06 04 2023
revised: 09 05 2023
accepted: 30 05 2023
medline: 11 7 2023
pubmed: 13 6 2023
entrez: 13 6 2023
Statut: ppublish

Résumé

Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.

Identifiants

pubmed: 37311272
pii: S1570-0232(23)00186-1
doi: 10.1016/j.jchromb.2023.123776
pii:
doi:

Substances chimiques

Deuterium AR09D82C7G
Steroids 0

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

123776

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Yinyu Wei (Y)

Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.

Yi Sun (Y)

Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.

Shuailong Jia (S)

Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, China.

Pan Yan (P)

Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410028, China.

Chaomei Xiong (C)

Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.

Meiling Qi (M)

Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.

Chenxi Wang (C)

Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.

Zhifeng Du (Z)

Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China. Electronic address: duzhifeng@hust.edu.cn.

Hongliang Jiang (H)

Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China. Electronic address: jianghongliang@hust.edu.cn.

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Classifications MeSH