Grapefruit Juice Flavanones Modulate the Expression of Genes Regulating Inflammation, Cell Interactions and Vascular Function in Peripheral Blood Mononuclear Cells of Postmenopausal Women

Irena Krga, Karla Fabiola Corral-Jara, Nicolas Barber-Chamoux, Claude Dubray, Christine Morand, Dragan Milenkovic, Irena Krga, Karla Fabiola Corral-Jara, Nicolas Barber-Chamoux, Claude Dubray, Christine Morand, Dragan Milenkovic

Abstract

Grapefruit is a rich source of flavanones, phytochemicals suggested excreting vasculoprotective effects. We previously showed that flavanones in grapefruit juice (GFJ) reduced postmenopausal women's pulse-wave velocity (PWV), a measure of arterial stiffness. However, mechanisms of flavanone action in humans are largely unknown. This study aimed to decipher molecular mechanisms of flavanones by multi-omics analysis in PBMCs of volunteers consuming GFJ and flavanone-free control drink for 6 months. Modulated genes and microRNAs (miRNAs) were identified using microarrays. Bioinformatics analyses assessed their functions, interactions and correlations with previously observed changes in PWV. GFJ modified gene and miRNA expressions. Integrated analysis of modulated genes and miRNA-target genes suggests regulation of inflammation, immune response, cell interaction and mobility. Bioinformatics identified putative mediators of the observed nutrigenomic effect (STAT3, NF-κB) and molecular docking demonstrated potential binding of flavanone metabolites to transcription factors and cell-signaling proteins. We also observed 34 significant correlations between changes in gene expression and PWV. Moreover, global gene expression was negatively correlated with gene expression profiles in arterial stiffness and hypertension. This study revealed molecular mechanisms underlying vasculoprotective effects of flavanones, including interactions with transcription factors and gene and miRNA expression changes that inversely correlate with gene expression profiles associated with cardiovascular risk factors.

Clinical trial registration: [ClinicalTrials.gov], identifier [NCT01272167].

Keywords: bioinformatics; flavanone; genomics; naringin; vascular function.

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2022 Krga, Corral-Jara, Barber-Chamoux, Dubray, Morand and Milenkovic.

Figures

FIGURE 1
FIGURE 1
A flow-chart describing steps implemented in the genomic and bioinformatic analysis.
FIGURE 2
FIGURE 2
A 2D principal component analysis plot of the genomic profiles of PBMCs from volunteers after consumption of flavanones in GFJ and same volunteers after control drink consumption.
FIGURE 3
FIGURE 3
Functional ontology and network analyses of differentially expressed genes. (A) Network of over-represented gene ontologies of identified differentially expressed genes identified using ShinyGO tool. Two nodes are connected if they share 20% or more genes. Darker nodes are more significantly enriched gene sets. Bigger nodes represent larger gene sets. Thicker edges represent more overlapped genes. (B) Pie chart of significantly over-represented functional gene networks identified using text-mining approach from Metacore. Pie chart represent functional groups of the identified networks, with section proportional to the number of networks in each group. Gene networks of each functional group are represented together with the number of genes in each network. *p < 0.05, **p < 0.01, and ****p < 0.0001.
FIGURE 4
FIGURE 4
Functional pathway analysis of differentially expressed genes presented as a histogram of significantly over-represented pathways and networks of genes present in pathways of functional groups. Yellow bars depict p-values of the identified significant pathways, and orange dots the number of genes in each pathway. Histogram data obtained using BioCarta (†), Kyoto Encyclopedia of Genes and Genomes (*) and NCI Pathways (#) databases through GeneTrail2. Networks of genes present in pathways of functional groups obtained using Cytoscape.
FIGURE 5
FIGURE 5
In silico docking analysis of interactions between naringin metabolites and potential transcription factors and cell signaling proteins. (A) Naringenin 4’-O-glucuronide to STAT3; (B) naringenin-7-O-glucuronide to STAT3; (C) naringenin-7-O-glucuronide to Jak2; (D) naringenin 4’-O-glucuronide to ERK1(MAPK3); (E) naringenin-7-O-glucuronide to Akt.
FIGURE 6
FIGURE 6
The effects of flavanones in GFJ on miRNA expression in PBMCs with a functional analysis of differentially modulated miRNAs. (A) Fold-changes for the identified differentially expressed miRNAs following the consumption of GFJ; (B) Network presentation of miRNAs and potential target genes; (C) Functional pathway analysis of miRNAs target genes. The orange histogram bars depict p-values of the identified significant pathways, and brown dots the number of genes in each pathway. Histogram data obtained using BioCarta (†), Kyoto Encyclopedia of Genes and Genomes (*) and NCI Pathways (#) databases through GeneTrail2.
FIGURE 7
FIGURE 7
Integration analysis of differentially expressed protein-coding genes and miRNAs. (A) Network of the identified putative transcription factors, differentially expressed miRNAs, differentially expressed genes and the identified putative gene targets of differentially expressed miRNAs; (B) Comparison of pathways identified from differentially expressed genes and target genes of differentially expressed miRNAs. The size of each rectangle is proportional to the number of genes in the pathway. Black letters: pathways identified from differentially expressed protein-coding genes; white letters: pathways from differentially expressed miRNAs; red letters: pathways in common for both protein-coding and miRNA genes.
FIGURE 8
FIGURE 8
Pearson correlation analysis between modulation in the expression of genes and changes in pulse wave velocity of the same volunteers. (A) Correlation plot indicating positive and negative correlations, (B) correlation plot between pulse wave velocity and VCL; (C) correlation plot between pulse wave velocity and RAET1K; (D) correlation plot between pulse-wave velocity and PLEKHG4B; (E) correlation plot between pulse wave velocity and SMPD2. All p-values <0.05.
FIGURE 9
FIGURE 9
Correlations between global gene expression profile from volunteers who consumed grapefruit juice and gene expression profile associated with an increase in (A) arterial stiffness and (B) hypertension and arterial stiffness obtained from Gene Expression Omnibus (GEO) repository.
FIGURE 10
FIGURE 10
Schematic presentation of genomic modifications induced by consumption of flavanones in grapefruit juice and its impact on vascular health.

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