New powerful tool to bring personalised medicine closer

Scientists have developed a powerful tool for determining the inherent biological differences between individuals, an advance that may lead to personalised medicine for treating disorders such as diabetes and obesity.
One of the biggest obstacles in successfully treating metabolic disorders such as diabetes, obesity, fatty liver etc, is the variation in the way patients respond to medication.
The key to this variation lies in the inherent biological differences between individuals, which cannot all be explained genetically.
At the same time, this variation makes it very difficult to develop "standard" treatments for certain diseases.
Scientists at ETH Zurich (ETHZ) and Ecole Polytechnique Federale de Lausanne (EPFL) in Switzerland developed a strategy that can define metabolic differences between individuals, paving the way for precision medicine.

Looking at 40 different mice strains, the researchers successfully connected the variation between individuals' genomes to the variation between their proteomes - their full set of proteins.

"There is a black box between a patient's genome and their disease. What we have done here is find a way to fill the black box by obtaining information on the patient's proteome," said Johan Auwerx from EPFL.
The scientists used protein data from mice, which they obtained from a new mass spectrometry technique.
They measured 2,600 different proteins from tissue samples of 40 mice strains, all of which came from the same two ancestors, and were genetically related to each other.

The mice were divided into groups of representing each of the 40 strains, and the groups were fed either a high-fat diet - essentially junk food - or a healthy, low-fat diet.
Over a few weeks, the scientists charted the mice's physiological data, and tested how fast they could gain weight on the junk-food diet and lose weight by exercising.
Despite their similar genetic make-up, the mice on the high-fat diet showed varied responses to diet and exercise. For example, some developed metabolic disorders like fatty liver, while others did not.
The researchers combined the physiological data with data for their genome, their proteome, and their transcriptome, which is essentially their full set of RNAs - another biological "layer" in the black box.

Through this combination, the scientists were able to better understand the role several proteins play when it comes to metabolising fat and producing energy from it.
"The aim here is to be able to customise medical intervention for each patient based on their individual biological makeup, the 'black box'," said Auwerx.
The study was published in the journal Science.