Precise Estimation of In Vivo Protein Turnover Rates

Jonathon J. O’Brien, Vikram Narayan, Yao Wong, Phillip Seitzer, Celeste M. Sandoval, Nicole Haste, Megan Smith, Ramin Rad, Aleksandr Gaun, Adam Baker, Matthew Kukurugya, Baby Martin-McNulty, Chunlian Zhang, Ganesh Kolumam, Carmela Sidrauski, Vladimir Jojic, Fiona McAllister, Bryson Bennett, Rochelle Buffenstein
2020 · doi:10.1101/2020.11.10.377440
preprint OA: closed CC-BY-4.0
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This paper developed a novel method for precisely estimating protein turnover rates in living organisms using stable isotope labeling and mass spectrometry.

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Abstract

Isotopic labeling with deuterium oxide (D 2 O) is a common technique for estimating in vivo protein turnover, but its use has been limited by two long-standing problems: (1) identifying non-monoisotopic peptides; and (2) estimating protein turnover rates in the presence of dynamic amino acid enrichment. In this paper, we present a novel experimental and analytical framework for solving these two problems. Peptides with high probabilities of labeling in many amino acids present fragmentation spectra that frequently do not match the theoretical spectra used in standard identification algorithms. We resolve this difficulty using a modified search algorithm we call Conditional Ion Distribution Search (CIDS). Increased identifications from CIDS along with direct measurement of amino acid enrichment and statistical modeling that accounts for heterogeneous information across peptides, dramatically improves the accuracy and precision of half-life estimates. We benchmark the approach in cells, where near-complete labeling is possible, and conduct an in vivo experiment revealing, for the first time, differences in protein turnover between mice and naked mole-rats commensurate with their disparate longevity.

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License: CC-BY-4.0