Dietary linoleic acid interacts with FADS1 genetic variability to modulate HDL-cholesterol and obesity-related traits

Dumont, Julie; Goumidi, Louisa; Grenier‐Boley, Benjamin; Cottel, Dominique; Marécaux, Nadine; Montaye, Michèle; Wagner, Anne; Arveiler, Dominique; Simon, Chantal; Ferrières, Jean; Ruidavets, Jean‐Bernard; Amouyel, Philippe; Dallongeville, Jean; Meirhaeghe, Aline

doi:10.1016/j.clnu.2017.07.012

Cited by 30 publications

(40 citation statements)

References 37 publications

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“…The cluster is built about a 283 kb region on chromosome 11 that contains the DAGLA, MYRF, TMEM258, RP11-467L20, FADS1, FADS2, FADS3 , and BEST1 genes (Fig 2b; S3 Table). Consistent with this observation, genetic variations in the FADS1 – FADS3 region have previously been associated with alterations in the synthesis of PUFAs 32 , inflammatory bowel diseases 33 , cholesterol levels and BMI 34 , coronary artery disease and type 2 diabetes 35 , and colorectal cancer 36 .…”

Section: Resultssupporting

confidence: 62%

Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

Fadason

Schierding

Lumley

et al. 2018

Preprint

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Clinical studies of non-communicable diseases identify multimorbidities that reflect our relatively limited fixed metabolic capacity. Despite the fact that we have ∼24000 genes, we do not understand the genetic pathways that contribute to the development of multimorbid non-communicable disease. We created a “multimorbidity atlas” of traits based on pleiotropy of spatially regulated genes using convex biclustering. Using chromatin interaction and expression Quantitative Trait Loci (eQTL) data, we analysed 20,782 variants (p < 5 × 10−6) associated with 1,351 phenotypes, to identify 16,248 putative eQTL-eGene pairs that are involved in 76,013 short- and long-range regulatory interactions (FDR < 0.05) in different human tissues. Convex biclustering of eGenes that are shared between phenotypes identified complex inter-relationships between nominally different phenotype associated SNPs. Notably, the loci at the centre of these inter-relationships were subject to complex tissue and disease specific regulatory effects. The largest cluster, 40 phenotypes that are related to fat and lipid metabolism, inflammatory disorders, and cancers, is centred on the FADS1-FADS3 locus (chromosome 11). Our novel approach enables the simultaneous elucidation of variant interactions with genes that are drivers of multimorbidity and those that contribute to unique phenotype associated characteristics.

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Section: Resultssupporting

confidence: 62%

Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

Fadason

Schierding

Lumley

et al. 2018

Preprint

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“…A less likely option is that the 18:2 backbone may arise from a higher 18:2n-6 intake, which is linked to weight gain in mice 47 and affects obesity-related traits 48 . Since we do not have detailed dietary data, we could not evaluate this hypothesis.…”

Section: Discussionmentioning

confidence: 99%

An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children

Hellmuth

Kirchberg

Brandt

et al. 2019

Sci Rep

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Childhood obesity prevalence is rising in countries worldwide. A variety of etiologic factors contribute to childhood obesity but little is known about underlying biochemical mechanisms. We performed an individual participant meta-analysis including 1,020 pre-pubertal children from three European studies and investigated the associations of 285 metabolites measured by LC/MS-MS with BMI z-score, height, weight, HOMA, and lipoprotein concentrations. Seventeen metabolites were significantly associated with BMI z-score. Sphingomyelin (SM) 32:2 showed the strongest association with BMI z-score (P = 4.68 × 10−23) and was also closely related to weight, and less strongly to height and LDL, but not to HOMA. Mass spectrometric analyses identified SM 32:2 as myristic acid containing SM d18:2/14:0. Thirty-five metabolites were significantly associated to HOMA index. Alanine showed the strongest positive association with HOMA (P = 9.77 × 10−16), while acylcarnitines and non-esterified fatty acids were negatively associated with HOMA. SM d18:2/14:0 is a powerful marker for molecular changes in childhood obesity. Tracing back the origin of SM 32:2 to dietary source in combination with genetic predisposition will path the way for early intervention programs. Metabolic profiling might facilitate risk prediction and personalized interventions in overweight children.

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“…GO enrichment analysis for down regulated genes was implemented. Enriched genes such as ACADL (acyl-CoA dehydrogenase long chain) [168], AGT (angiotensinogen) [169], FADS1 [170], PDK4 [171], PER2 [172] and SLC27A2 [173] were liable for progression of obesity. Enriched genes such as ACSS2 [174] and HSD17B12 [175] were diagnosed with growth of various cancer types such as gastric cancer and ovarian cancer, but these genes may be involved in development of obesity associated cancer.…”

Section: Construction Of Target Genes -Mirna Regulatory Networkmentioning

confidence: 99%

Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

Joshi¹,

Vastrad²,

Joshi³

et al. 2020

Preprint

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Background Obesity is the most common metabolic disorder worldwide. Its progression rate has remained high in recent years. ObjectivesTherefore, the aim of this study was to diagnose important differentially expressed genes (DEGs) associated in its development, which may be used as novel biomarkers or potential therapeutic targets for obesity. MethodsThe gene expression profile of E-MTAB-6728 was downloaded from the database. After screening DEGs in each ArrayExpress dataset, we further used the robust rank aggregation method to diagnose 876 significant DEGs including 438 up regulated and 438 down regulated genes. Pathway enrichment analyses and Gene Ontology (GO) were performed by online tool ToppCluster. These DEGs were shown to be significantly enriched in different obesity related pathways and GO functions. Then, the mentha, miRNet and NetworkAnalyst databases were used to construct the protein–protein interaction network, target genes - miRNA regulatory network and target genes - TF regulatory network. The module analysis was performed by the PEWCC1 plug‐in of Cytoscape based on the whole PPI network.Results We finally filtered out HSPA8, ESR1, YWHAH, RPL14, SOD2, BTG2, LYZ and EFNA1 hub genes. Hub genes were validated by ICH analysis, Receiver operating curve (ROC) analysis and RT-PCR. The robust DEGs linked with the development of obesity were screened through the ArrayExpress database, and integrated bioinformatics analysis was conducted. ConclusionsOur study provides reliable molecular biomarkers for screening and diagnosis, prognosis as well as novel therapeutic targets for obesity.

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Dietary linoleic acid interacts with FADS1 genetic variability to modulate HDL-cholesterol and obesity-related traits

Cited by 30 publications

References 37 publications

Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

Chromatin interactions and expression quantitative trait loci reveal genetic drivers of multimorbidities

An individual participant data meta-analysis on metabolomics profiles for obesity and insulin resistance in European children

Distinct Molecular Mechanisms Analysis of Obesity Based on Gene Expression Profiles

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