analysis of protein turnover rates in skyline with the
TRANSCRIPT
Analysis of Protein Turnover Rates in Skyline with the TurnoveR External Tool
Nathan Basisty, PhDTranslational Geroproteomics Unit, NIH
Skyline User MeetingOctober 27th, 2021
Basisty et al. Proteomics. 2018
Metabolic Labeling and Mass Spectrometry Enable Comprehensive Measurement of Protein Turnover Rates
Ad lib Calorie Restricted (CR)
Metabolic Labeling and Mass Spectrometry Enable Comprehensive Measurement of Protein Turnover Rates
TurnoveR
Basisty et al. Unpublished
Mike MacCoss(UW)
Brendan MacLean(UW)
Nicholas Shulman(UW)
Ali Marsh
Skyline Document
Populate Skyline w/ all isotope permutations
Labeling experiment and MS
TurnoveR
1. Isotope analysis
2. Regressions3. Statistics4. Reports
Workflow for TurnoveR: A Skyline External Tool for the Analysis of Protein Turnover from Metabolic Labeling Studies
p < 0.0001p < 0.0001
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
Workflow for TurnoveR: A Skyline External Tool for the Analysis of Protein Turnover from Metabolic Labeling Studies
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
TurnoveR Calculates “True Isotope Distributions”
Unlabeled
0 LH 1 LH 2 LH
36% 48% 16%
M M + 3 M + 61 Leucine 2 Leucine
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
TurnoveR Calculates “True Isotope Distributions”
Unlabeled
0 LH 1 LH 2 LH
36% 48% 16%
M M + 3 M + 61 Leucine 2 Leucine
Brauman et al. 1966. Least Squares Analysis and Simplification of Multi-Isotope Mass Spectra
Unlabeled
0 LH 1 LH 2 LH
39% 47% 14%
M M + 3 M + 61 Leucine 2 Leucine
Natural isotope correction prevents systematic overestimation of heavy label enrichment
36% 48% 16%
Precursor pool correction is required for accurate calculation of turnoverLL = light leucineLH = heavy leucine
LL
LHLH
LL
LLProtein synthesis
40% heavy labeled
40% newly synthesized?
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
LH
LH
LH
LH
LH
LHLH
LH
LH
LH
LH
LH
100% of precursor leucines are heavy
LL = light leucineLH = heavy leucine
LL
LHLH
LL
LLProtein synthesis
40% heavy labeled
40% newly synthesized
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
Precursor pool correction is required for accurate calculation of turnover
New protein is synthesized from a mixture of heavy and light leucine
LL
LHLH
LL
LLProtein synthesis
40% heavy labeled
LL = light leucineLH = heavy leucine
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
LL
LH
LL
LL
LL
LLLL
LL
LL
LH
LH
LH
40% of precursor leucines are heavy
Precursor pool correction is required for accurate calculation of turnover
New protein is synthesized from a mixture of heavy and light leucine
LL
LHLH
LL
LLProtein synthesis
40% heavy labeled
100% newly synthesized
LL = light leucineLH = heavy leucine
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
LL
LH
LL
LL
LL
LLLL
LL
LL
LH
LH
LH
40% of precursor leucines are heavy
Precursor pool correction is required for accurate calculation of turnover
Always overestimated
Half-life without precursor RIA = 12.9 days
Half-life with precursor RIA = 6 days‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
Hsieh E, Shulman N … MacCoss. 2012. Molecular & Cellular Proteomics
Precursor pool correction is required for accurate calculation of turnover
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
Precursor pool correction is required for accurate calculation of turnover
CTL = ControlCR = Calorie Restricted
‘true’ isotope distributions
precursor pool enrichment
% newly synthesized
Turnover regressions (slopes, half-lives)
Statistical comparisons (Treated vs untreated)
TurnoveR Computational Pipeline
Precursor pool correction is required for accurate calculation of turnover
p = 0.0164 p < 0.0001
p < 0.0001 p = 0.0031
CTL = ControlCR = Calorie Restricted
Questions to Address w/ Protein Turnover
• Which proteins turnover during age-related muscle atrophy?
• How does acylation affect protein turnover?
• Is longer half-life a biomarker for interventions that extend lifespan?
• What is the turnover of insoluble proteins during aging?
Christopher Adams(Mayo Clinic) Scott EbertMatthew Miller
(Gadd45a, p21)
ATF4 May Modulate Atrophy by Altering Protein Turnover
Altered turnover of: Mitochondrial proteins (IDH2)Myofibrillar proteins
AcknowledgementsSchilling Lab (Buck):Birgit SchillingJacob RoseCameron WehrfritzSandip PatelSamah ShahFrancesco NeriAmy O’BroinJoanna BonsPierre-Yves DesprezChristina King
MacCoss Lab: (Univ. of Washington)Michael MacCossBrendan MacLeanNicholas ShulmanAlexandra Marsh
Funding:1K99AG065484-01A1, U01AG060906, Glenn Foundation
Rabinovitch Lab: (Univ. of Washington)Peter RabinovitchYing Ann ChiaoPabalu KarunadharmaDao-Fu DaiEllen QuarlesJeannie Fredrickson
Translational Geroproteomics Unit (TGU)Nathan [email protected]
Hiring Postdocs!
Adams Lab: (Mayo Clinic)Christopher AdamsScott EbertMatthew Miller Translational Geroproteomics Unit (NIA)
Anjana RamReema Banarjee
Thank you, Nat Bruce!