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Elfrink, H. L.

Publications and source records attributed to Elfrink, H. L..

2 recordsLinked to original sources

Aging selectively dampens oscillation of lipid abundance in white and brown adipose tissue

Lipid metabolism is under the control of the circadian system and circadian dysregulation has been linked to obesity and dyslipidemia. These factors and outcomes have also been associated to, or affected by, the process of aging. Here, we investigated whether murine white (WAT) and brown (BAT) adipose tissue lipids exhibit rhythmicity and if this is affected by aging. To this end, we have measured the 24 hour lipid profiles of WAT and BAT using a global lipidomics analysis of >1100 lipids. We observed rhythmicity in nearly all lipid classes including glycerolipids, glycerophospholipids, sterol lipids and sphingolipids. Overall, ~22% of the analyzed lipids were considered rhythmic in WAT and BAT. Despite a general accumulation of lipids upon aging the fraction of oscillating lipids decreased in both tissues to 14% and 18%, respectively. Diurnal profiles of lipids in BAT appeared to depend on the lipid acyl chain length and this specific regulation was lost in aged mice. Our study revealed how aging affects the rhythmicity of lipid metabolism and could contribute to the quest for targets that improve diurnal lipid homeostasis to maintain cardiometabolic health during aging.

biochemistry

Metabolomics and lipidomics in C. elegans using a single sample preparation

Comprehensive metabolomic and lipidomic mass spectrometry methods are in increasing demand, for instance in research related to nutrition and aging. The nematode C. elegans is a key model organism in these fields, due to the large repository of available C. elegans mutants and their convenient natural lifespan. Here, we describe a robust and sensitive analytical method for the semi-quantitative analysis of >100 polar (metabolomics) and >1000 apolar (lipidomics) metabolites in C. elegans, using a single sample preparation. Our method is capable of reliably detecting a wide variety of biologically relevant metabolic aberrations in, for instance, glycolysis and the TCA cycle, pyrimidine metabolism and complex lipid biosynthesis. In conclusion, we provide a powerful analytical tool that maximizes metabolic data yield from a single sample.Competing Interest StatementThe authors have declared no competing interest.View Full Text

biochemistry