TY - JOUR
T1 - Timepoint Selection Strategy for in Vivo Proteome Dynamics from Heavy Water Metabolic Labeling and LC-MS
AU - Sadygov, Vugar R.
AU - Zhang, William
AU - Sadygov, Rovshan G.
N1 - Funding Information:
WH was supported by UTMB High School Summer Biomedical Research Program. This research was supported in part by the NIGMS of NIH under the award number R01GM112044.
Publisher Copyright:
Copyright © 2020 American Chemical Society.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Protein homeostasis, proteostasis, is essential for healthy cell functioning and is dysregulated in many diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled online to mass spectrometry (LC-MS) is a powerful high-throughput technique to study proteome dynamics in vivo. Longer labeling duration and dense timepoint sampling (TPS) of tissues provide accurate proteome dynamics estimations. However, the experiments are expensive, and they require animal housing and care, as well as labeling with stable isotopes. Often, the animals are sacrificed at selected timepoints to collect tissues. Therefore, it is necessary to optimize TPS for a given number of sampling points and labeling duration and target a specific tissue of study. Currently, such techniques are missing in proteomics. Here, we report on a formula-based stochastic simulation strategy for TPS for in vivo studies with heavy water metabolic labeling and LC-MS. We model the rate constant (lognormal), measurement error (Laplace), peptide length (gamma), relative abundance of the monoisotopic peak (beta regression), and the number of exchangeable hydrogens (gamma regression). The parameters of the distributions are determined using the corresponding empirical probability density functions from a large-scale dataset of murine heart proteome. The models are used in the simulations of the rate constant to minimize the root-mean-square error (rmse). The rmse for different TPSs shows structured patterns. They are analyzed to elucidate common features in the patterns.
AB - Protein homeostasis, proteostasis, is essential for healthy cell functioning and is dysregulated in many diseases. Metabolic labeling with heavy water followed by liquid chromatography coupled online to mass spectrometry (LC-MS) is a powerful high-throughput technique to study proteome dynamics in vivo. Longer labeling duration and dense timepoint sampling (TPS) of tissues provide accurate proteome dynamics estimations. However, the experiments are expensive, and they require animal housing and care, as well as labeling with stable isotopes. Often, the animals are sacrificed at selected timepoints to collect tissues. Therefore, it is necessary to optimize TPS for a given number of sampling points and labeling duration and target a specific tissue of study. Currently, such techniques are missing in proteomics. Here, we report on a formula-based stochastic simulation strategy for TPS for in vivo studies with heavy water metabolic labeling and LC-MS. We model the rate constant (lognormal), measurement error (Laplace), peptide length (gamma), relative abundance of the monoisotopic peak (beta regression), and the number of exchangeable hydrogens (gamma regression). The parameters of the distributions are determined using the corresponding empirical probability density functions from a large-scale dataset of murine heart proteome. The models are used in the simulations of the rate constant to minimize the root-mean-square error (rmse). The rmse for different TPSs shows structured patterns. They are analyzed to elucidate common features in the patterns.
KW - LC-MS
KW - conditional independence of relative abundance and number of exchangeable hydrogens
KW - distribution of turnover rates
KW - error of relative abundances
KW - heavy water metabolic labeling
KW - isotope distributions
KW - model of protein degradation rate constant
KW - protein turnover
KW - stochastic simulations
KW - timepoint selection
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U2 - 10.1021/acs.jproteome.0c00023
DO - 10.1021/acs.jproteome.0c00023
M3 - Article
C2 - 32183509
AN - SCOPUS:85084185266
SN - 1535-3893
VL - 19
SP - 2105
EP - 2112
JO - Journal of Proteome Research
JF - Journal of Proteome Research
IS - 5
ER -