Systematic analysis of nutrigenomic effects of polyphenols related to cardiometabolic health in humans – Evidence from untargeted mRNA and miRNA studies

Abstract

Cardiovascular and metabolic disorders present major causes of mortality in the ageing population. Polyphenols present in human diets possess cardiometabolic protective properties, however their underlying molecular mechanisms in humans are still not well identified. Even though preclinical and in vitro studies advocate that these bioactives can modulate gene expression, most studies were performed using targeted approaches. With the objective to decipher the molecular mechanisms underlying polyphenols cardiometabolic preventive properties in humans, we performed integrative multi-omic bioinformatic analyses of published studies which reported improvements of cardiometabolic risk factors following polyphenol intake, together with genomic analyses performed using untargeted approach. We identified 5 studies within our criteria and nearly 5000 differentially expressed genes, both mRNAs and miRNAs, in peripheral blood cells. Integrative bioinformatic analyses (e.g. pathway and gene network analyses, identification of transcription factors, correlation of gene expression profiles with those associated with diseases and drug intake) revealed that these genes are involved in the processes such as cell adhesion and mobility, immune system, metabolism, or cell signaling. We also identified 27 miRNAs known to regulate processes such as cell cytoskeleton, chemotaxis, cell signaling, or cell metabolism. Gene expression profiles negatively correlated with expression profiles of cardiovascular disease patients, while a positive correlation was observed with gene expression profiles following intake of drugs against cardiometabolic disorders. These analyses further advocate for health protective effects of these bioactives against age-associated diseases. In conclusion, polyphenols can exert multi-genomic modifications in humans and use of untargeted methods coupled with bioinformatic analyses represent the best approach to decipher molecular mechanisms underlying healthy-ageing effects of these bioactives.