Evidence map›Paper›PMID 29414762›Full record

ArticleMolecular & cellular proteomics : MCP2018

Peptide Level Turnover Measurements Enable the Study of Proteoform Dynamics.

Jana Zecha, Chen Meng, Daniel Paul Zolg, Patroklos Samaras, Mathias Wilhelm, Bernhard Kuster

Abstract read
In one paragraph

Article in Molecular & cellular proteomics : MCP, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers.

0numbers the graph read from it
0cells of the map it votes in
72citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

72 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Mass spectrometry proteomics for studying mitostasis.Protein science : a publication of the Protein Society · 2026
    Review
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Proteostasis of immune checkpoint receptors.The Biochemical journal · 2025
    Review
  16. Article
  17. Article
  18. Article
  19. Article
  20. Article

12 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jana ZechaFrom the ‡Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), 85354 Freising, Germany.
Chen MengFrom the ‡Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), 85354 Freising, Germany.
Daniel Paul ZolgFrom the ‡Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), 85354 Freising, Germany.
Patroklos SamarasFrom the ‡Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), 85354 Freising, Germany.
Mathias WilhelmFrom the ‡Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), 85354 Freising, Germany.
Bernhard KusterFrom the ‡Chair of Proteomics and Bioanalytics, Technical University of Munich (TUM), 85354 Freising, Germany; kuster@tum.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The coordination of protein synthesis and degradation regulating protein abundance is a fundamental process in cellular homeostasis. Today, mass spectrometry-based technologies allow determination of endogenous protein turnover on a proteome-wide scale. However, standard dynamic SILAC (Stable Isotope Labeling in Cell Culture) approaches can suffer from missing data across pulse time-points limiting the accuracy of such analysis. This issue is of particular relevance when studying protein stability at the level of proteoforms because often only single peptides distinguish between different protein products of the same gene. To address this shortcoming, we evaluated the merits of combining dynamic SILAC and tandem mass tag (TMT)-labeling of ten pulse time-points in a single experiment. Although the comparison to the standard dynamic SILAC method showed a high concordance of protein turnover rates, the pulsed SILAC-TMT approach yielded more comprehensive data (6000 proteins on average) without missing values. Replicate analysis further established that the same reproducibility of turnover rate determination can be obtained for peptides and proteins facilitating proteoform resolved investigation of protein stability. We provide several examples of differentially turned over splice variants and show that post-translational modifications can affect cellular protein half-lives. For example, N-terminally processed peptides exhibited both faster and slower turnover behavior compared with other peptides of the same protein. In addition, the suspected proteolytic processing of the fusion protein FAU was substantiated by measuring vastly different stabilities of the cleavage products. Furthermore, differential peptide turnover suggested a previously unknown mechanism of activity regulation by post-translational destabilization of cathepsin D as well as the DNA helicase BLM. Finally, our comprehensive data set facilitated a detailed evaluation of the impact of protein properties and functions on protein stability in steady-state cells and uncovered that the high turnover of respiratory chain complex I proteins might be explained by oxidative stress.

Indexed as

Enzyme StabilityHalf-LifeHeLa CellsHumansIsotope LabelingNADH DehydrogenaseOxidative StressPeptidesProtein BiosynthesisProteolysisProteomeProteomicsReproducibility of ResultsNADH DehydrogenasePeptidesProteome

Identifiers

PMID29414762
PMCPMC5930408

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.