Dataset Information

Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops.

ABSTRACT: Oilseed crops are one of the most important sources of vegetable oils for food and industry. Nutritional and technical properties of vegetable oil are primarily determined by its fatty acid (FA) composition. The content and composition of FAs in plants are commonly determined using gas chromatography-mass spectrometry (GS-MS) or gas chromatography-flame ionization detection (GC-FID) techniques. In the present work, we applied ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) technique to FA profiling of sunflower and rapeseed seeds and compared this method with the GC-FID technique. GC-FID detected 11 FAs in sunflower and 13 FAs in rapeseed, while UPLC-MS appeared to be more sensitive, detecting about 2.5 times higher numbers of FAs in both plants. In addition to even-chain FAs, UPLC-MS was able to detect odd-chain FAs. The longest FA detected using GC-FID was an FA with 24 carbon atoms, whereas UPLC-MS could reveal the presence of longer FAs with the tails of up to 28 carbon atoms. Based on our results, we may conclude that UPLC-MS has great potential to be used for the assessment of FA profiles of oil crops.

SUBMITTER: Chernova A

PROVIDER: S-EPMC6408914 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops.

Chernova Alina A Mazin Pavel P Goryunova Svetlana S Goryunov Denis D Demurin Yakov Y Gorlova Lyudmila L Vanyushkina Anna A Mair Waltraud W Anikanov Nikolai N Yushina Ekaterina E Pavlova Anna A Martynova Elena E Garkusha Sergei S Mukhina Zhanna Z Savenko Elena E Khaitovich Philipp P

PeerJ 20190306

Oilseed crops are one of the most important sources of vegetable oils for food and industry. Nutritional and technical properties of vegetable oil are primarily determined by its fatty acid (FA) composition. The content and composition of FAs in plants are commonly determined using gas chromatography-mass spectrometry (GS-MS) or gas chromatography-flame ionization detection (GC-FID) techniques. In the present work, we applied ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) te ...[more]

PMID: 30863679

Similar Datasets

Project description:BackgroundParkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.MethodsWe presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.ResultsA list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.ConclusionsThis study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.

Dataset Information

Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops.

Publications

Ultra-performance liquid chromatography-mass spectrometry for precise fatty acid profiling of oilseed crops.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets