Untargeted Metabolomics Using UHPLC-HRMS Reveals Metabolic Changes of Fresh-Cut Potato during Browning Process

Liu, Baohong; Fu, Yingjie; Xi, Hui; Liu, Shan; Zhao, Wuduo; Wu, Fan; Wang, Dingzhong; Sun, Shihao

doi:10.3390/molecules28083375

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…The last one works only on .raw files generated by Xcalibur software and Orbitrap mass spectrometers but provides automated compound annotation via the mzCloud database [24]. Compound Discoverer was used in various untargeted metabolomics studies for, e.g., compound iden-tification in ethanol extract of plants [25][26][27][28], comparing samples of propolis [29] and lentil seed coats [30], determining the metabolic changes during the browning process [31], the analysis of biological fluid samples [32,33], autism studies, [34] etc.…”

Section: Introductionmentioning

confidence: 99%

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

Chernonosov,

Mednova,

Levchuk

et al. 2024

Metabolites

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Depressive disorder is a multifactorial disease that is based on dysfunctions in mental and biological processes. The search for biomarkers can improve its diagnosis, personalize therapy, and lead to a deep understanding of the biochemical processes underlying depression. The purpose of this work was a metabolomic analysis of blood serum to classify patients with depressive disorders and healthy individuals using Compound Discoverer software. Using high-resolution mass spectrometry, blood plasma samples from 60 people were analyzed, of which 30 were included in a comparison group (healthy donors), and 30 were patients with a depressive episode (F32.11) and recurrent depressive disorder (F33.11). Differences between patient and control groups were identified using the built-in utilities in Compound Discoverer software. Compounds were identified by their accurate mass and fragment patterns using the mzCloud database and tentatively identified by their exact mass using the ChemSpider search engine and the KEGG, ChEBI, FDA UNII-NLM, Human Metabolome and LipidMAPS databases. We identified 18 metabolites that could divide patients with depressive disorders from healthy donors. Of these, only two compounds were tentatively identified using the mzCloud database (betaine and piperine) based on their fragmentation spectra. For three compounds ((4S,5S,8S,10R)-4,5,8-trihydroxy-10-methyl-3,4,5,8,9,10-hexahydro-2H-oxecin-2-one, (2E,4E)-N-(2-hydroxy-2-methylpropyl)-2,4-tetradecadienamide and 17α-methyl-androstan-3-hydroxyimine-17β-ol), matches were found in the mzCloud database but with low score, which could not serve as reliable evidence of their structure. Another 13 compounds were identified by their exact mass in the ChemSpider database, 9 (g-butyrobetaine, 6-diazonio-5-oxo-L-norleucine, 11-aminoundecanoic acid, methyl N-acetyl-2-diazonionorleucinate, glycyl-glycyl-argininal, dilaurylmethylamine, 12-ketodeoxycholic acid, dicetylamine, 1-linoleoyl-2-hydroxy-sn-glycero-3-PC) had only molecular formulas proposed, and 4 were unidentified. Thus, the use of Compound Discoverer software alone was not sufficient to identify all revealed metabolites. Nevertheless, the combination of the found metabolites made it possible to divide patients with depressive disorders from healthy donors.

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Section: Introductionmentioning

confidence: 99%

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

Chernonosov,

Mednova,

Levchuk

et al. 2024

Metabolites

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“…components, including 21 flavonoids (1,9, 11, 12, 14, 15, 17, 19, 20, 21, 22, 24, 26, 27, 29, 32, 34, 36, 40, 41 and 48), 4 alkaloids(3,5,23,58), 7 fatty acids (4, 7, 8, 43, 45, 50 and 53), 3 phenols (6, 31 and 35), 2 phenylpropanoids (10 and 13), 1 ester(16), and several other compounds(46). A total of 33 non-volatile components were identified in the branches, including 6 flavonoids(28,32,33,39,41,54), 15 alkaloids(2, 5, 23, 25, 37, 38, 42, 47, 49, 51, 52, 59, 61, 63 and 65), 4 fatty acids(7, 44, 45 and 57), 1 phenylpropanoid (30), 3 esters(16, 56 and 66), and several other compounds…”

mentioning

confidence: 99%

Comparative Analysis of Chemical Composition of Zanthoxylum myriacanthum Branches and Leaves by GC-MS and UPLC-Q-Orbitrap HRMS, and Evaluation of Their Antioxidant Activities

Zhang

Dai

et al. 2023

Molecules

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Zanthoxylum myriacanthum Wall. ex Hook. f., a plant belonging to the Rutaceae family and the Zanthoxylum genus, is extensively utilized for its medicinal properties and as a culinary seasoning in China and Southeast Asian countries. However, the chemical composition and biological activities of Z. myriacanthum branches and leaves remain insufficiently explored. In this study, the volatile and non-volatile components of Z. myriacanthum branches and leaves were analyzed using GC-MS and UPLC-Q-Orbitrap HRMS techniques. A total of 78 volatile compounds and 66 non-volatile compounds were identified. The volatile compounds were predominantly terpenoids and aliphatic compounds, while the non-volatile compounds were primarily flavonoids and alkaloids. The branches contained 52 volatile compounds and 33 non-volatile compounds, whereas the leaves contained 48 volatile compounds and 40 non-volatile compounds. The antioxidant activities of the methanol extracts from Z. myriacanthum branches and leaves were evaluated using ABTS and DPPH free-radical-scavenging assays, both of which demonstrated certain antioxidant activity. The methanol extract of leaves demonstrated significantly higher antioxidant activity compared to that of the branches, possibly due to the higher presence of flavonoids and phenols in the leaves, with IC50 values of 7.12 ± 0.257 μg/mL and 1.22 × 102 ± 5.01 μg/mL for ABTS and DPPH, respectively. These findings enhance our understanding of the chemical composition and antioxidant potential of Z. myriacanthum. The plant holds promise as a natural source of antioxidants for applications in pharmaceuticals, cosmetics, and functional foods. Further research can explore its broader biological activities and potential applications.

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Endogenous compound chenodeoxycholic acid in potatoes can delay enzymatic browning by inducing antioxidant capacity

Feng,

Liu,

Dong

et al. 2024

Postharvest Biology and Technology

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Untargeted Metabolomics Using UHPLC-HRMS Reveals Metabolic Changes of Fresh-Cut Potato during Browning Process

Cited by 3 publications

References 52 publications

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

Untargeted Plasma Metabolomic Profiling in Patients with Depressive Disorders: A Preliminary Study

Comparative Analysis of Chemical Composition of Zanthoxylum myriacanthum Branches and Leaves by GC-MS and UPLC-Q-Orbitrap HRMS, and Evaluation of Their Antioxidant Activities

Endogenous compound chenodeoxycholic acid in potatoes can delay enzymatic browning by inducing antioxidant capacity

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