Weighted Gene Co‐Expression Network Analysis Identifies Gender Specific Modules and Hub Genes Related to Metabolism and Inflammation in Response to an Acute Lipid Challenge

Fatima, Attia; Connaughton, Ruth M.; Weiser, Anna; Murphy, Aoife M; O'Grada, Colm; Ryan, Mary Morgan; Brennan, Lorraine; Ó’Gaora, Peadar; Roche, Helen M.

doi:10.1002/mnfr.201700388

Cited by 17 publications

(10 citation statements)

References 56 publications

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“…(11) , noted a significant difference in sex in response to a 6-month intervention replacing standard margarines and spreads with products enriched with α-linoleic acid, with a greater increase in the EPA content of plasma phospholipids in females compared to males after 6 months. Sex differences such as this are not unique and have been previously reported in other studies (26,27) . McMorrow et al .…”

Section: Phenotypic Variation Influencing Response To Nutrition Intersupporting

confidence: 82%

Personalised nutrition – phenotypic and genetic variation in response to dietary intervention

Gibney

2019

Proc. Nutr. Soc.

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Personalised nutrition allows individual differences in dietary, lifestyle, anthropometry, phenotype and/or genomic profile to be used to direct specific dietary advice. For personalised nutrition advice to be effective both sides need to be considered; firstly, that factors influencing variation in response to dietary intervention are identified and appropriate advice can be derived and secondly; that these are then used effectively in the provision of nutrition advice, resulting in a positive dietary and/or lifestyle behaviour change. There is considerable evidence demonstrating genetic and phenotypic influence on the biological response to the consumption of nutrients and bioactives. However, findings are often mixed, with studies often investigating at the level of a single nutrient/bioactive and/or a single genetic/phenotypic variation, meaning the derivation of specific advice at a dietary level in an individual/group of individuals can be complex. Similarly, the impact of using this information to derive personalised advice is also mixed, with some studies demonstrating no effectiveness and others showing a significant impact. The present paper will outline examples of phenotypic and genetic variation influencing response to nutritional interventions, and will consider how they could be used in the provision of personalised nutrition.

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Section: Phenotypic Variation Influencing Response To Nutrition Intersupporting

confidence: 82%

Personalised nutrition – phenotypic and genetic variation in response to dietary intervention

Gibney

2019

Proc. Nutr. Soc.

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“…Briefly, WGCNA is designed to uncover highly correlated gene modules and to relate gene clusters to one another and to sample traits. In recent studies, it has been verified in previous sample characteristics such as disease status (Yan et al, 2018), gender (Fatima et al, 2018), age (Maffei et al, 2017), and BMI (Wang W. et al, 2017). It was also proved that it was a reliable and promising instrument for cardiovascular diseases’ clinical diagnosis (Chen et al, 2016) and cardiomyocyte differentiation (Liu et al, 2017).…”

Section: Discussionmentioning

confidence: 68%

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

Chen

Long

et al. 2018

Front. Physiol.

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As one common disease causing young people to die suddenly due to cardiac arrest, arrhythmogenic right ventricular cardiomyopathy (ARVC) is a disorder of heart muscle whose progression covers one complicated gene interaction network that influence the diagnosis and prognosis of it. In our research, differentially expressed genes (DEGs) were screened, and we established a weighted gene coexpression network analysis (WGCNA) and gene set net correlations analysis (GSNCA) for identifying crucial genes as well as pathways related to ARVC pathogenic mechanism (n = 12). In the research, the results demonstrated that there were 619 DEGs in total between non-failing donor myocardial samples and ARVC tissues (FDR < 0.05). WGCNA analysis identified the two gene modules (brown and turquoise) as being most significantly associated with ARVC state. Then the ARVC-related four key biological pathways (cytokine–cytokine receptor interaction, chemokine signaling pathway, neuroactive ligand receptor interaction, and JAK-STAT signaling pathway) and four hub genes (CXCL2, TNFRSF11B, LIFR, and C5AR1) in ARVC samples were further identified by GSNCA method. Finally, we used t-test and receiver operating characteristic (ROC) curves for validating hub genes, results showed significant differences in t-test and their AUC areas all greater than 0.8. Together, these results revealed that the new four hub genes as well as key pathways that might be involved into ARVC diagnosis. Even though further experimental validation is required for the implication by association, our findings demonstrate that the computational methods based on systems biology might complement the traditional gene-wide approaches, as such, might offer a new insight in therapeutic intervention within rare diseases of people like ARVC.

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“…[ 15–17 ] Study 2 (the MECHE study) also used postprandial testing (response at 4h post‐challenge) to evaluate the transcriptome (human whole‐genome GeneChip microarray, in PBMCs) and serum lipid profile (Orbitrap LC‐MS, identification of a selection of lipids) of thirteen men with different metabolic phenotypes (obese/non‐obese) to an oral lipid tolerance test (OLTT). [ 18–21 ] Study 3 (the MARIS study) used a randomized controlled parallel study design to evaluate an 8 week dietary intervention, testing three dietary patterns: a Western‐type diet high in saturated fatty acids (SFA diet), a Western‐type diet high in MUFA from olive oil (MUFA diet), and a Mediterranean‐type diet (MED diet) with equivalent MUFA amounts to the MUFA diet. [ 22 ] The transcriptome (human whole‐genome GeneChip microarray, in PBMCs) and serum metabolome (NMR, identification of a selection of metabolites from various classes) were assessed in 39 overweight or obese men and women using fasting samples collected after a 2 week run‐in period (SFA diet) and after the dietary interventions.…”

Section: Methodsmentioning

confidence: 99%

Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies

Burton-Pimentel

Pimentel

Hughes

et al. 2021

Molecular Nutrition Food Res

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Scope Combining different “omics” data types in a single, integrated analysis may better characterize the effects of diet on human health. Methods and results The performance of two data integration tools, similarity network fusion tool (SNFtool) and Data Integration Analysis for Biomarker discovery using Latent variable approaches for “Omics” (DIABLO; MixOmics), in discriminating responses to diet and metabolic phenotypes is investigated by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non‐obese subjects (n = 13; study 2); and an 8‐week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improve sample classification. For studies 2 and 3, the value of SNF integration depends on the dietary groups being compared, while DIABLO discriminates samples well but does not perform better than transcriptomic data alone. Conclusion The integration of associated “omics” datasets can help clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size.

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Weighted Gene Co‐Expression Network Analysis Identifies Gender Specific Modules and Hub Genes Related to Metabolism and Inflammation in Response to an Acute Lipid Challenge

Cited by 17 publications

References 56 publications

Personalised nutrition – phenotypic and genetic variation in response to dietary intervention

Personalised nutrition – phenotypic and genetic variation in response to dietary intervention

Identification of Crucial Genes and Pathways in Human Arrhythmogenic Right Ventricular Cardiomyopathy by Coexpression Analysis

Discriminating Dietary Responses by Combining Transcriptomics and Metabolomics Data in Nutrition Intervention Studies

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