FADS1 rs174550 genotype and high linoleic acid diet modify plasma PUFA phospholipids in a dietary intervention study

Introduction: Fatty acid desaturase 1 (FADS1) gene encodes for delta-5 desaturase enzyme which is needed in conversion of linoleic acid (LA) to arachidonic acid (AA). Recent studies have shown that response to dietary PUFAs differs between the genotypes in circulating fatty acids. However, interactions between the FADS1 genotype and dietary LA on overall metabolism have not been studied. Objectives: We aimed to examine the interactions of FADS1 rs174550 genotypes (TT and CC) and high-LA diet to identify plasma metabolites that respond differentially to dietary LA according to the FADS1 genotype. Methods: A total of 59 men (TT n = 26, CC n = 33) consumed a sunflower oil supplemented diet for 4\ua0weeks. Daily dose of 30, 40, or 50\ua0ml was calculated based on body mass index. It resulted in 17–28\ua0g of LA on top of the usual daily intake. Fasting plasma samples at the beginning and at the end of the intervention were analyzed with LC–MS/MS non-targeted metabolomics method. Results: At the baseline, the carriers of FADS1 rs174550-TT genotype had higher abundance of long-chain PUFA phospholipids compared to the FADS1 rs174550-CC one. In response to the high-LA diet, LA phospholipids and long-chain acylcarnitines increased and lysophospholipids decreased in fasting plasma similarly in both genotypes. LysoPE (20:4), LysoPC (20:4), and PC (16:0_20:4) decreased and cortisol increased in the carriers of rs174550-CC genotype; however, these genotype–diet interactions were not significant after correction for multiple testing. Conclusion: Our findings show that both FADS1 rs174550 genotype and high-LA diet modify plasma phospholipid composition. Trial registration: The study was registered to ClinicalTrials: NCT02543216, September 7, 2015 (retrospectively registered)

FADS1 rs174550 genotype and high linoleic acid diet modify plasma PUFA phospholipids in a dietary intervention study

Introduction Fatty acid desaturase 1 (FADS1) gene encodes for delta-5 desaturase enzyme which is needed in conversion of linoleic acid (LA) to arachidonic acid (AA). Recent studies have shown that response to dietary PUFAs differs between the genotypes in circulating fatty acids. However, interactions between the FADS1 genotype and dietary LA on overall metabolism have not been studied.Objectives We aimed to examine the interactions of FADS1 rs174550 genotypes (TT and CC) and high-LA diet to identify plasma metabolites that respond differentially to dietary LA according to the FADS1 genotype.Methods A total of 59 men (TT n = 26, CC n = 33) consumed a sunflower oil supplemented diet for 4 weeks. Daily dose of 30, 40, or 50 ml was calculated based on body mass index. It resulted in 17-28 g of LA on top of the usual daily intake. Fasting plasma samples at the beginning and at the end of the intervention were analyzed with LC-MS/MS non-targeted metabolomics method.Results At the baseline, the carriers of FADS1 rs174550-TT genotype had higher abundance of long-chain PUFA phospholipids compared to the FADS1 rs174550-CC one. In response to the high-LA diet, LA phospholipids and long-chain acylcarnitines increased and lysophospholipids decreased in fasting plasma similarly in both genotypes. LysoPE (20:4), LysoPC (20:4), and PC (16:0_20:4) decreased and cortisol increased in the carriers of rs174550-CC genotype; however, these genotype-diet interactions were not significant after correction for multiple testing.Conclusion Our findings show that both FADS1 rs174550 genotype and high-LA diet modify plasma phospholipid composition.</p

“Notame”: Workflow for non-targeted LC-MS metabolic profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

“Notame”: Workflow for non-targeted LC-MS metabolic profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting

The FADS1 rs174550 Genotype Modifies the n-3 and n-6 PUFA and Lipid Mediator Responses to a High Alpha-Linolenic Acid and High Linoleic Acid Diets

Scope: The fatty acid composition of plasma lipids, which is associated with biomarkers and risk of non-communicable diseases, is regulated by dietary polyunsaturated fatty acids (PUFAs) and variants of fatty acid desaturase (FADS). We investigated the interactions between dietary PUFAs and FADS1 rs174550 variant.Methods and results: Participants (n = 118), homozygous for FADS1 rs174550 variant (TT and CC) followed a high alpha-linolenic acid (ALA, 5 percent of energy (E-%)) or a high linoleic acid (LA, 10 E-%) diet during an 8-week randomized controlled intervention. Fatty acid composition of plasma lipids and PUFA-derived lipid mediators were quantified by gas and liquid chromatography mass spectrometry, respectively. The high-LA diet increased the concentration of plasma LA, but not its lipid mediators. The concentration of plasma arachidonic acid decreased in carriers of CC and remained unchanged in the TT genotype. The high-ALA diet increased the concentration of plasma ALA and its cytochrome P450-derived epoxides and dihydroxys, and cyclooxygenase-derived monohydroxys. Concentrations of plasma eicosapentaenoic acid and its mono- and dihydroxys increased only in TT genotype carriers.Conclusions: These findings suggest the potential for genotype-based recommendations for PUFA consumption, resulting in modulation of bioactive lipid mediators which can exert beneficial effects in maintaining health.</p

"Notame": Workflow for Non-Targeted LC-MS Metabolic Profiling

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting