Evidence map›Paper›PMID 36165806›Full record

ArticleJournal of proteome research2023

TurnoveR: A Skyline External Tool for Analysis of Protein Turnover in Metabolic Labeling Studies.

Nathan Basisty, Nicholas Shulman, Cameron Wehrfritz, Alexandra N Marsh, Samah Shah, Jacob Rose, Scott Ebert, Matthew Miller, Dao-Fu Dai, Peter S Rabinovitch and 4 more

Abstract read
In one paragraph

Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Flexible Quality Control for Protein Turnover Rates Using d2ome.International journal of molecular sciences · 2023
    Article
  7. Article
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

14 authors.

Nathan BasistyBuck Institute for Research on Aging, Novato, California 94945, United States.ORCID 0000-0001-6173-1139
Nicholas ShulmanDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.
Cameron WehrfritzBuck Institute for Research on Aging, Novato, California 94945, United States.
Alexandra N MarshDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0003-2142-6978
Samah ShahBuck Institute for Research on Aging, Novato, California 94945, United States.
Jacob RoseBuck Institute for Research on Aging, Novato, California 94945, United States.
Scott EbertDivision of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, Minnesota 55905, United States.
Matthew MillerDivision of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, Minnesota 55905, United States.
Dao-Fu DaiDepartment of Pathology, University of Iowa, Iowa City, Iowa 52242, United States.
Peter S RabinovitchDepartment of Pathology, University of Washington, Seattle, Washington 98195, United States.
Christopher M AdamsDivision of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, Minnesota 55905, United States.
Michael J MacCossDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.ORCID 0000-0003-1853-0256
Brendan MacLeanDepartment of Genome Sciences, University of Washington, Seattle, Washington 98195, United States.
Birgit SchillingBuck Institute for Research on Aging, Novato, California 94945, United States.ORCID 0000-0001-9907-2749

Funding

SPECIFICITY OF OXYGEN DNA DAMAGE AND MUTAGENESISP01AG001751 · NIA · UNIVERSITY OF WASHINGTON · PI RABINOVITCH, PETER S · 1985 to 2021
$29.6M
University of Washington Nathan Shock Center of Excellence in the Basic Biology of AgingP30AG013280 · NIA · UNIVERSITY OF WASHINGTON · PI Michael MacCoss · 1995 to 2026
$27.1M
TrainingP41GM103533 · NIGMS · UNIVERSITY OF WASHINGTON · PI YATES III, JOHN R · 2012 to 2021
$20.7M
Seattle Quant: A Resource for the Skyline Software EcosystemR24GM141156 · NIGMS · UNIVERSITY OF WASHINGTON · PI Michael MacCoss · 2021 to 2026
$6.7M
Medical Scientist Training ProgramT32GM139776 · NIGMS · UNIVERSITY OF IOWA · PI Gordon F Buchanan · 2021 to 2026
$6.4M
Quantitative Proteomics to Develop Robust Senescence-Related Biomarkers for AgingU01AG060906 · NIA · BUCK INSTITUTE FOR RESEARCH ON AGING · PI SCHILLING, BIRGIT · 2018 to 2022
$4.0M
Novel Signaling Pathways Underlying Skeletal Muscle AtrophyR01AG060637 · NIA · UNIVERSITY OF IOWA · PI ADAMS, CHRISTOPHER M · 2018 to 2022
$2.6M
Molecular pathogenesis of skeletal muscle atrophyR01AR071762 · NIAMS · UNIVERSITY OF IOWA · PI ADAMS, CHRISTOPHER M · 2017 to 2021
$2.4M
The role of energy sensor signaling in mitochondrial cardiomyopathyK08HL145138 · NHLBI · UNIVERSITY OF IOWA · PI DAI, DAO-FU · 2019 to 2023
$803k
TripleTOF 5600 High Resolution Mass SpectrometerS10OD016281 · OD · BUCK INSTITUTE FOR RESEARCH ON AGING · PI GIBSON, BRADFORD WAYNE · 2013 to 2013
$597k
Proteomic Pipelines for the Quantification of Abundance and Turnover of Post-Translationally Modified Proteins in Aging StudiesZIAAG000347 · NIA · NATIONAL INSTITUTE ON AGING · PI BASISTY, NATHAN · 2021 to 2025
$298k
Quantitating, Targeting, and Phenotyping In Vivo Cellular Senescence in HumansK99AG065484 · NIA · BUCK INSTITUTE FOR RESEARCH ON AGING · PI BASISTY, NATAN BOHDONIVICH · 2020 to 2021
$158k
NHLBI NIH HHS K08 HL145138NIAMS NIH HHS R01 AR071762NIA NIH HHS K99 AG065484NIA NIH HHS P01 AG001751NIA NIH HHS P30 AG013280NIA NIH HHS R01 AG060637NIA NIH HHS U01 AG060906NIGMS NIH HHS P41 GM103533NIGMS NIH HHS R24 GM141156NIGMS NIH HHS T32 GM139776NIH HHS S10 OD016281
6 · The paper itself

Abstract

In spite of its central role in biology and disease, protein turnover is a largely understudied aspect of most proteomic studies due to the complexity of computational workflows that analyze in vivo turnover rates. To address this need, we developed a new computational tool, TurnoveR, to accurately calculate protein turnover rates from mass spectrometric analysis of metabolic labeling experiments in Skyline, a free and open-source proteomics software platform. TurnoveR is a straightforward graphical interface that enables seamless integration of protein turnover analysis into a traditional proteomics workflow in Skyline, allowing users to take advantage of the advanced and flexible data visualization and curation features built into the software. The computational pipeline of TurnoveR performs critical steps to determine protein turnover rates, including isotopologue demultiplexing, precursor-pool correction, statistical analysis, and generation of data reports and visualizations. This workflow is compatible with many mass spectrometric platforms and recapitulates turnover rates and differential changes in turnover rates between treatment groups calculated in previous studies. We expect that the addition of TurnoveR to the widely used Skyline proteomics software will facilitate wider utilization of protein turnover analysis in highly relevant biological models, including aging, neurodegeneration, and skeletal muscle atrophy.

Indexed as

ProteomicsSoftwareIsotope LabelingMass SpectrometryProteolysisWorkflowagingmass spectrometrymetabolic labelingprotein degradationprotein synthesisprotein turnoverproteostasisquantitative proteomicsSkylinestable isotope labeling

Identifiers

PMID36165806
PMCPMC10066879

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.