How to best measure protein turnover in complex systems
Hammond DE, Simpson DM, Franco C, Wright Muelas M, Waters J, Ludwig RW, Prescott MC, Hurst JL, Beynon RJ, Lau E. (2022) Harmonizing Labeling and Analytical Strategies to Obtain Protein Turnover Rates
in Intact Adult Animals. Mol Cell Proteomics. 2022 Jul;21(7):100252. doi: 10.1016/j.mcpro.2022.100252.
Changes in the abundance of individual proteins in the proteome can be elicited
by modulation of protein synthesis (the rate of input of newly synthesized
proteins into the protein pool) or degradation (the rate of removal of protein
molecules from the pool). A full understanding of proteome changes therefore
requires a definition of the roles of these two processes in proteostasis,
collectively known as protein turnover. Because protein turnover occurs even in
the absence of overt changes in pool abundance, turnover measurements
necessitate monitoring the flux of stable isotope-labeled precursors through the
protein pool such as labeled amino acids or metabolic precursors such as
ammonium chloride or heavy water. In cells in culture, the ability to manipulate
precursor pools by rapid medium changes is simple, but for more complex systems
such as intact animals, the approach becomes more convoluted. Individual methods
bring specific complications, and the suitability of different methods has not
been comprehensively explored. In this study, we compare the turnover rates of
proteins across four mouse tissues, obtained from the same inbred mouse strain
maintained under identical husbandry conditions, measured using either
[13C6]lysine or [2H2]O as the labeling precursor. We show that for long-lived
proteins, the two approaches yield essentially identical measures of the
first-order rate constant for degradation. For short-lived proteins, there is a
need to compensate for the slower equilibration of lysine through the precursor
pools. We evaluate different approaches to provide that compensation. We
conclude that both labels are suitable, but careful determination of precursor
enrichment kinetics in amino acid labeling is critical and has a considerable
influence on the numerical values of the derived protein turnover rates.
in Intact Adult Animals. Mol Cell Proteomics. 2022 Jul;21(7):100252. doi: 10.1016/j.mcpro.2022.100252.
Changes in the abundance of individual proteins in the proteome can be elicited
by modulation of protein synthesis (the rate of input of newly synthesized
proteins into the protein pool) or degradation (the rate of removal of protein
molecules from the pool). A full understanding of proteome changes therefore
requires a definition of the roles of these two processes in proteostasis,
collectively known as protein turnover. Because protein turnover occurs even in
the absence of overt changes in pool abundance, turnover measurements
necessitate monitoring the flux of stable isotope-labeled precursors through the
protein pool such as labeled amino acids or metabolic precursors such as
ammonium chloride or heavy water. In cells in culture, the ability to manipulate
precursor pools by rapid medium changes is simple, but for more complex systems
such as intact animals, the approach becomes more convoluted. Individual methods
bring specific complications, and the suitability of different methods has not
been comprehensively explored. In this study, we compare the turnover rates of
proteins across four mouse tissues, obtained from the same inbred mouse strain
maintained under identical husbandry conditions, measured using either
[13C6]lysine or [2H2]O as the labeling precursor. We show that for long-lived
proteins, the two approaches yield essentially identical measures of the
first-order rate constant for degradation. For short-lived proteins, there is a
need to compensate for the slower equilibration of lysine through the precursor
pools. We evaluate different approaches to provide that compensation. We
conclude that both labels are suitable, but careful determination of precursor
enrichment kinetics in amino acid labeling is critical and has a considerable
influence on the numerical values of the derived protein turnover rates.