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Unraveling Gender-specific Lipids and Flavor Volatiles in Giant Salamander () Livers Via Lipidomics and GC-IMS

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Journal Food Chem X
Date 2024 Sep 17
PMID 39286042
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Abstract

To uncover the relationships between lipid components and flavor volatiles, distinctness in lipid components and odor substances in giant salamander livers of different genders were comparatively characterized through UPLC-Q Exactive-MS lipidomics and gas chromatography-ion migration spectrometry (GC-IMS). A total of 2171 and 974 lipid metabolites were detected in positive and negative ion modes, respectively. Triglycerides (TG) and phosphatidylcholines (PC) are the most abundant types of lipids. TG level in male livers was higher than that in female livers ( < 0.05), whereas PC level showed no marked variation ( > 0.05). Additionally, a total of 51 volatile components were detected through GC-IMS. Ketones (42.18 % ∼ 45.44 %) and alcohols (24.19 % ∼ 26.50 %) were the predominant categories, and their relative contents were higher in female livers. Finally, 30 differential lipid metabolites and 12 differential odor substances were screened and could be used as distinguishing labels in giant salamander livers of different genders. Correlation analysis indicated that PS(36:2e), TG(48:13), ZyE(37:6), and ZyE(33:6) correlated positively with 3-methyl butanal, 3-hydroxy-2-butanone, and 2-methyl-1-propanol ( < 0.05), but adversely linked with 1-penten-3-one, and 1-octen-3-one ( < 0.01). By three-fold cross-validation, prediction accuracies of these differential lipids and volatile compounds for gender recognition based on random forest model were 100 % and 92 %, respectively. These findings might not only add knowledge on lipid and volatile profiles in giant salamander livers as affected by genders, but also provide clues for their gender recognition.

Citing Articles

Sex-Specific Lipid Profiles and Flavor Volatiles in Giant Salamander () Tails Revealed by Lipidomics and GC-IMS.

Zhao S, Yu J, Xi L, Kong X, Pei J, Jiang P Foods. 2024; 13(19).

PMID: 39410083 PMC: 11476126. DOI: 10.3390/foods13193048.

References
1.
Zheng X, Pan F, Naumovski N, Wei Y, Wu L, Peng W . Precise prediction of metabolites patterns using machine learning approaches in distinguishing honey and sugar diets fed to mice. Food Chem. 2023; 430:136915. DOI: 10.1016/j.foodchem.2023.136915. View

2.
Liu Z, Zhao M, Wang X, Li C, Liu Z, Shen X . Investigation of oyster Crassostrea gigas lipid profile from three sea areas of China based on non-targeted lipidomics for their geographic region traceability. Food Chem. 2022; 386:132748. DOI: 10.1016/j.foodchem.2022.132748. View

3.
Colon-Crespo L, Herrera-Hernandez D, Holness H, Furton K . Determination of VOC marker combinations for the classification of individuals by gender and race/ethnicity. Forensic Sci Int. 2016; 270:193-199. DOI: 10.1016/j.forsciint.2016.09.011. View

4.
Pei J, Chen D, Jin W, Geng J, Wang W, Zhang S . Structure and mode of action of a novel antibacterial peptide from the blood of Andrias davidianus. Lett Appl Microbiol. 2019; 69(5):312-317. DOI: 10.1111/lam.13219. View

5.
Zhang T, Xu J, Wang Y, Xue C . Health benefits of dietary marine DHA/EPA-enriched glycerophospholipids. Prog Lipid Res. 2019; 75:100997. DOI: 10.1016/j.plipres.2019.100997. View