Beiersdorf makes new skin cell metabolism discovery to aid anti-ageing development via Cosmetics Design EU
21 September 2015
By Andrew McDougall+, 15-Sep-2015
Beiersdrof researchers have found that skin cells can protect themselves against stress even faster and more holistically than previously thought, and this discovery will lead to the development of new anti-ageing products.
Together with the Swiss Federal Institute of Technology in Zurich (ETH), Beirsdorf scientists, Dr Janosch Hildebrand and Dr Marc Winnefeld, completed the four-year research project into how skin cells use the metabolism to protect themselves against UV-induced or oxidative stress.
The study was published in the journal Molecular Cell , and Beiersdorf explains that the next step will be to develop new ways to protect the skin against stress and ageing.
“The project results will be used to develop new strategies to protect skin cells against stress and to treat
environmentally-induced skin ageing,” says Dr Winnefeld.
Project manager, Dr Hildebrand, explains that the team set out on its research to find out more how skin cells benefit from their metabolism and were able to unearth new information.
“We discovered a previously unknown mechanism: the ‘First Line Response.’ It enables skin cells to counteract stress
caused by, for example, solar radiation or oxidative stress within minutes,” he says.
Hildebrand explains that this mechanism is important because it delivers key molecules to the cell which are needed for acute detoxification and repair.
In order to understand this mechanism and to holistically analyse skin metabolism, the scientists used a new metabolomics technology that enabled them to examine over 800 metabolites simultaneously.
“In addition to the new technology, systems biology analysis tools were also used,” continues Winnefeld, Head of the Lab for Special & Aged Skin.
“By using these techniques we were able to investigate the complex relationships between biological processes like
metabolic pathways and repair mechanisms. Here, we were able to analyse big data sets by combining biology and