Forskning
March 14, 2014
An integrated multi-omics study revealed metabolic alterations underlying the effects of coffee consumption
S Takahashi et al, 2014, An integrated multi-omics study revealed metabolic alterations underlying the effects of coffee consumption, PLOS One, published online ahead of print.
ABSTRACT:
Many epidemiological studies have indicated that coffee consumption may reduce the risks of developing obesity and diabetes, but the underlying mechanisms of these effects are poorly understood. Our previous study revealed the changes on gene expression profiles in the livers of C57BL/6J mice fed a high-fat diet containing three types of coffee (caffeinated, decaffeinated and green unroasted coffee), using DNA microarrays. The results revealed remarkable alterations in lipid metabolism-related molecules which may be involved in the anti-obesity effects of coffee. We conducted the present study to further elucidate the metabolic alterations underlying the effects of coffee consumption through comprehensive proteomic and metabolomic analyses. Proteomics revealed an up-regulation of isocitrate dehydrogenase (a key enzyme in the TCA cycle) and its related proteins, suggesting increased energy generation. The metabolomics showed an up-regulation of metabolites involved in the urea cycle, with which the transcriptome data were highly consistent, indicating accelerated energy expenditure. The TCA cycle and the urea cycle are likely be accelerated in a concerted manner, since they are directly connected by mutually providing each other’s intermediates. The up-regulation of these pathways might result in a metabolic shift causing increased ATP turnover, which is related to the alterations of lipid metabolism. This mechanism may play an important part in the suppressive effects of coffee consumption on obesity, inflammation, and hepatosteatosis. This study newly revealed global metabolic alterations induced by coffee intake, providing significant insights into the association between coffee intake and the prevention of type 2 diabetes, utilizing the benefits of multi-omics analyses.
