Cited by 12Ubiquitin and proteasome pathwaysAdvanced Proteomics Techniques and ApplicationsProtein Degradation and Inhibitors

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Proteome Birthdating Reveals Age-Selectivity of Protein Ubiquitination

Open access

Published version of a preprint

Abstract

Abstract Within a cell, proteins have distinct and highly variable half-lives. As a result, the molecular ages of proteins can range from seconds to years. How the age of a protein influences its environmental interactions is a largely unexplored area of biology. To investigate the age-selectivity of cellular pathways, we developed a methodology termed “proteome birthdating” that barcodes proteins based on their time of synthesis. We demonstrate that this approach provides accurate measurements of protein turnover kinetics without the requirement for multiple kinetic time points. As a first use case of the birthdated proteome, we investigated the age distribution of the human ubiquitinome. Our results indicate that the vast majority of ubiquitinated proteins in a cell consist of newly synthesized proteins and that these young proteins constitute the bulk of the degradative flux through the proteasome. Rapidly ubiquitinated nascent proteins are enriched in cytosolic subunits of large protein complexes. Conversely, proteins destined for the secretory pathway and vesicular transport have older ubiquitinated populations. Our data also identified a smaller subset of very old ubiquitinated cellular proteins that do not appear to be targeted to the proteasome for rapid degradation. Together, our data provide an age census of the human ubiquitinome and establish proteome birthdating as a robust methodology for investigating the protein age-selectivity of diverse cellular pathways. Significance Statement Cellular proteins have widely different ages - whereas some have been recently synthesized, others have existed in the cell for days or even years. How a protein’s age influences its functions and interactions is largely unknown because it is difficult to globally differentiate proteins based on their time of synthesis. To address this challenge, we developed an analytical method named “proteome birthdating” that can partition cellular proteins into multiple discernible age groups. As an example application, we used proteome birthdating to examine the protein age-selectivity of the ubiquitin proteasome system, a major protein degradation pathway in eukaryotes. Our results show that proteins destined for degradation by this pathway consist of either particularly young or particularly old proteins, with the former being the predominant population. Together, our results establish proteome birthdating as a useful approach for analyzing the turnover of proteins and investigating the functional consequences of their age.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.mcpro.2024.100791

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 43

  1. Doherty, 2006, Protein turnover on the scale of the proteome, Expert Rev. Proteomics, 3, 97, 10.1586/14789450.3.1.97 · DOI
  2. Ross, 2021, Proteome turnover in the spotlight: approaches, applications, and Perspectives, Mol. Cell. Proteomics, 20, 10.1074/mcp.R120.002190 · DOI
  3. Fornasiero, 2018, Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions, Nat. Commun., 9, 4230, 10.1038/s41467-018-06519-0 · DOI
  4. Hasper, 2023, Turnover and replication analysis by isotope labeling (TRAIL) reveals the influence of tissue context on protein and organelle lifetimes, Mol. Syst. Biol., 19, 10.15252/msb.202211393 · DOI
  5. Swovick, 2021, Interspecies differences in proteome turnover kinetics are correlated with life Spans and Energetic Demands, Mol. Cell. Proteomics, 20, 10.1074/mcp.RA120.002301 · DOI · Paper page
  6. Wang, 2013, A cotranslational ubiquitination pathway for quality control of misfolded proteins, Mol. Cell, 50, 368, 10.1016/j.molcel.2013.03.009 · DOI
  7. Schubert, 2000, Rapid degradation of a large fraction of newly synthesized proteins by proteasomes, Nature, 404, 770, 10.1038/35008096 · DOI
  8. Kim, 2011, Systematic and quantitative assessment of the ubiquitin-modified proteome, Mol. Cell, 44, 325, 10.1016/j.molcel.2011.08.025 · DOI
  9. Brandman, 2012, A ribosome-bound quality control complex triggers degradation of nascent peptides and signals translation stress, Cell, 151, 1042, 10.1016/j.cell.2012.10.044 · DOI
  10. Reits, 2000, The major substrates for TAP in vivo are derived from newly synthesized proteins, Nature, 404, 774, 10.1038/35008103 · DOI
  11. Goldberg, 2003, Protein degradation and protection against misfolded or damaged proteins, Nature, 426, 895, 10.1038/nature02263 · DOI · Paper page
  12. Alves-Rodrigues, 1998, Ubiquitin, cellular inclusions and their role in neurodegeneration, Trends Neurosci., 21, 516, 10.1016/S0166-2236(98)01276-4 · DOI · Paper page
  13. Sitte, 1998, Proteasome-dependent degradation of oxidized proteins in MRC-5 fibroblasts, FEBS Lett., 440, 399, 10.1016/S0014-5793(98)01495-1 · DOI
  14. Jayaraj, 2020, Functional Modules of the proteostasis network, Cold Spring Harb. Perspect. Biol., 12, 10.1101/cshperspect.a033951 · DOI
  15. Claydon, 2012, Proteome dynamics: revisiting turnover with a global perspective, Mol. Cell. Proteomics, 11, 1551, 10.1074/mcp.O112.022186 · DOI
  16. Hinkson, 2011, The dynamic state of protein turnover: it's about time, Trends Cell Biol., 21, 293, 10.1016/j.tcb.2011.02.002 · DOI
  17. Basisty, 2020, Accumulation of "old proteins" and the critical need for MS-based protein turnover measurements in aging and longevity, Proteomics, 20, 10.1002/pmic.201800403 · DOI
  18. Basisty, 2018, Protein turnover in aging and longevity, Proteomics, 18, 10.1002/pmic.201700108 · DOI
  19. Hebert, 2013, Neutron-encoded mass signatures for multiplexed proteome quantification, Nat. Methods, 10, 332, 10.1038/nmeth.2378 · DOI
  20. Merrill, 2014, NeuCode labels for relative protein quantification, Mol. Cell. Proteomics, 13, 2503, 10.1074/mcp.M114.040287 · DOI
  21. Zhang, 2017, Proteome-wide modulation of degradation dynamics in response to growth arrest, Proc. Natl. Acad. Sci. U. S. A., 114, E10329, 10.1073/pnas.1710238114 · DOI
  22. Kong, 2017, MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry-based proteomics, Nat. Methods, 14, 513, 10.1038/nmeth.4256 · DOI
  23. Chambers, 2012, A cross-platform toolkit for mass spectrometry and proteomics, Nat. Biotechnol., 30, 918, 10.1038/nbt.2377 · DOI
  24. Chen, 2015, Quantitative proteomics using SILAC: principles, applications, and developments, Proteomics, 15, 3175, 10.1002/pmic.201500108 · DOI
  25. Welle, 2016, Time-resolved analysis of proteome dynamics by Tandem mass Tags and stable isotope labeling in cell culture (TMT-SILAC) Hyperplexing, Mol. Cell. Proteomics, 15, 3551, 10.1074/mcp.M116.063230 · DOI
  26. Zhang, 2016, Global analysis of cellular protein flux quantifies the selectivity of basal autophagy, Cell Rep., 14, 2426, 10.1016/j.celrep.2016.02.040 · DOI
  27. Zhang, 2014, Kinetics of precursor labeling in stable isotope labeling in cell cultures (SILAC) experiments, Anal. Chem., 86, 11334, 10.1021/ac503067a · DOI
  28. Xu, 2010, Global analysis of lysine ubiquitination by ubiquitin remnant immunoaffinity profiling, Nat. Biotechnol., 28, 868, 10.1038/nbt.1654 · DOI
  29. Wagner, 2012, Proteomic analyses reveal divergent ubiquitylation site patterns in murine tissues, Mol. Cell. Proteomics, 11, 1578, 10.1074/mcp.M112.017905 · DOI
  30. Steger, 2022, Ubiquitinomics: history, methods, and applications in basic research and drug discovery, Proteomics, 22, 10.1002/pmic.202200074 · DOI
  31. Heil, 2023, Evaluating the performance of the Astral mass analyzer for quantitative proteomics using data-Independent acquisition, J. Proteome Res., 22, 3290, 10.1021/acs.jproteome.3c00357 · DOI
  32. Slavov, 2021, Single-cell protein analysis by mass spectrometry, Curr. Opin. Chem. Biol., 60, 1, 10.1016/j.cbpa.2020.04.018 · DOI · Paper page
  33. Brunner, 2022, Ultra-high sensitivity mass spectrometry quantifies single-cell proteome changes upon perturbation, Mol. Syst. Biol., 18, 10.15252/msb.202110798 · DOI · Paper page
  34. McShane, 2016, Kinetic analysis of protein stability reveals age-dependent degradation, Cell, 167, 803, 10.1016/j.cell.2016.09.015 · DOI
  35. Hammaren, 2022, Protein-peptide turnover profiling reveals the order of PTM addition and removal during protein maturation, Nat. Commun., 13, 7431, 10.1038/s41467-022-35054-2 · DOI
  36. Hipp, 2019, The proteostasis network and its decline in ageing, Nat. Rev. Mol. Cell Biol., 20, 421, 10.1038/s41580-019-0101-y · DOI · Paper page
  37. Zhu, 2022, Pulse labeling reveals the tail end of protein folding by proteome profiling, Cell Rep., 40, 10.1016/j.celrep.2022.111096 · DOI · Paper page
  38. Akopian, 2013, Signal recognition particle: an essential protein-targeting machine, Annu. Rev. Biochem., 82, 693, 10.1146/annurev-biochem-072711-164732 · DOI
  39. Liao, 2022, Non-proteolytic ubiquitylation in cellular signaling and human disease, Commun. Biol., 5, 114, 10.1038/s42003-022-03060-1 · DOI
  40. Verhoef, 2002, Aggregate formation inhibits proteasomal degradation of polyglutamine proteins, Hum. Mol. Genet., 11, 2689, 10.1093/hmg/11.22.2689 · DOI
  41. Lamark, 2012, Aggrephagy: selective disposal of protein aggregates by macroautophagy, Int. J. Cell Biol., 2012, 736905, 10.1155/2012/736905 · DOI
  42. Basisty, 2018, Stable isotope labeling reveals novel insights into ubiquitin-mediated protein aggregation with age, calorie restriction, and rapamycin treatment, J. Gerontol. A. Biol. Sci. Med. Sci., 73, 561, 10.1093/gerona/glx047 · DOI
  43. SCHIMKE, 1970, Regulation of protein degradation in mammalian tissues, Mamm. Protein Metab., 4, 177, 10.1016/B978-0-12-510604-7.50010-4 · DOI

Metadata

Authors
Meadow, M. E., Broas, S., Hoare, M., Alimohammadi, F., Welle, K. A., Swovick, K., Hryhorenko, J. R., Martinez, J. C., Biashad, S. A., Seluanov, A., Gorbunova, V., Buchwalter, A., & Ghaemmaghami, S.
Affiliations
  • University of Rochester Medicine (US)
  • University of Rochester (US)
  • University of California, San Francisco (US)
Corresponding author
Ghaemmaghami, Sina
Venue
Molecular & Cellular Proteomics
Publisher
Elsevier BV
Published
2024-07
Volume
23
Issue
7
Pages
100791
ISSN
1535-9476 (ISSN-L)
OpenAlex
W4398762793
Cited by
12
References
3
Citations by year
2026: 6 · 2025: 5 · 2024: 1
FWCI
1.63
Type
journal article
Language
en
Open access
Locations
  • Molecular & Cellular Proteomics (publishedVersion, cc-by-nc-nd)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • National Institutes of Health (1s10od025242-01)
  • National Institute on Aging (R01AG046320, R01AG027237, P01AG047200, R37AG046320, P01AG051449)
  • National Institute of General Medical Sciences (R35GM119502)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Ubiquitin and proteasome pathways
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords
protein half-life, ubiquitination, ubiquitinome, proteasomal degradation, nascent protein, secretory pathway, vesicle trafficking, protein complexes, protein degradation
MeSH terms
Humans, Proteome, Ubiquitin, Proteomics, Proteasome Endopeptidase Complex, Ubiquitination, Ubiquitinated Proteins, Proteolysis

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review