A study in collaboration with Rob Beynon's group at the University of Liverpool (https://www.liverpool.ac.uk/centre-for-proteome-research/) has been accepted at Molecular and Cellular Proteomics.
In this paper our teams performed a controlled comparison of heavy water vs amino acid labeling for measuring protein turnover rates across four different tissues (heart, liver, kidney, muscle) in the mouse.
Amino acid labeling is commonly used as a metabolic protein precursor in animal models to measure in vivo protein turnover rate, but there are usually delays in how fast the labeled amino acid precursors become available for protein synthesis. This difference is likely dependent on the metabolism of the tissue too, and will delay the label incorporation into the measured protein, especially for proteins with very short half-life.
We compared different kinetic models, and methods to derive the precursor kinetic parameters used to adjust half-life measurement, and compared the result to heavy water labeling which does not suffer from the same precursor delay problem.
After some method refinement, we were able to show that both methods produced similar turnover rates. We provide some guidelines and best practice for correcting for precursor kinetics. We also describe an original Pythons software, Riana, which can be used to perform mass spectrometry data integration and kinetics curve fitting for both amino acid and heavy water studies.
Read the open access paper here! https://www.mcponline.org/article/S1535-9476(22)00060-3/fulltext