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Deberneh HM, Abdelrahman DR, Verma SK, Linares JJ, Murton AJ, Russell WK, Kuyumcu-Martinez MN, Miller BF, Sadygov RG. Quantifying label enrichment from two mass isotopomers increases proteome coverage for in vivo protein turnover using heavy water metabolic labeling. Commun Chem 2023; 6:72. [PMID: 37069333 PMCID: PMC10110577 DOI: 10.1038/s42004-023-00873-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 03/31/2023] [Indexed: 04/19/2023] Open
Abstract
Heavy water metabolic labeling followed by liquid chromatography coupled with mass spectrometry is a powerful high throughput technique for measuring the turnover rates of individual proteins in vivo. The turnover rate is obtained from the exponential decay modeling of the depletion of the monoisotopic relative isotope abundance. We provide theoretical formulas for the time course dynamics of six mass isotopomers and use the formulas to introduce a method that utilizes partial isotope profiles, only two mass isotopomers, to compute protein turnover rate. The use of partial isotope profiles alleviates the interferences from co-eluting contaminants in complex proteome mixtures and improves the accuracy of the estimation of label enrichment. In five different datasets, the technique consistently doubles the number of peptides with high goodness-of-fit characteristics of the turnover rate model. We also introduce a software tool, d2ome+, which automates the protein turnover estimation from partial isotope profiles.
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Affiliation(s)
- Henock M Deberneh
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, USA
| | - Doaa R Abdelrahman
- Department of Surgery, The University of Texas Medical Branch, Galveston, TX, USA
- Sealy Center on Aging, The University of Texas Medical Branch, Galveston, TX, USA
| | - Sunil K Verma
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, USA
| | - Jennifer J Linares
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, USA
| | - Andrew J Murton
- Department of Surgery, The University of Texas Medical Branch, Galveston, TX, USA
- Sealy Center on Aging, The University of Texas Medical Branch, Galveston, TX, USA
| | - William K Russell
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, USA
| | - Muge N Kuyumcu-Martinez
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, USA
- Department of Neuroscience, Cell Biology and Anatomy, The University of Texas Medical Branch, Galveston, TX, USA
- Department of Molecular Physiology and Biological Physics, The University of Virginia, Charlottesville, VA, USA
| | - Benjamin F Miller
- Oklahoma Medical Research Foundation, Oklahoma Nathan Shock Center, Oklahoma Center for Geosciences, Harold Hamm Diabetes Center, Oklahoma City, OK, USA
- Oklahoma City Veterans Association, Oklahoma City, OK, USA
| | - Rovshan G Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, Galveston, TX, USA.
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2
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Lignieres L, Sénécaut N, Dang T, Bellutti L, Hamon M, Terrier S, Legros V, Chevreux G, Lelandais G, Mège RM, Dumont J, Camadro JM. Extending the Range of SLIM-Labeling Applications: From Human Cell Lines in Culture to Caenorhabditis elegans Whole-Organism Labeling. J Proteome Res 2023; 22:996-1002. [PMID: 36748112 PMCID: PMC9990122 DOI: 10.1021/acs.jproteome.2c00699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
The simple light isotope metabolic-labeling technique relies on the in vivo biosynthesis of amino acids from U-[12C]-labeled molecules provided as the sole carbon source. The incorporation of the resulting U-[12C]-amino acids into proteins presents several key advantages for mass-spectrometry-based proteomics analysis, as it results in more intense monoisotopic ions, with a better signal-to-noise ratio in bottom-up analysis. In our initial studies, we developed the simple light isotope metabolic (SLIM)-labeling strategy using prototrophic eukaryotic microorganisms, the yeasts Candida albicans and Saccharomyces cerevisiae, as well as strains with genetic markers that lead to amino-acid auxotrophy. To extend the range of SLIM-labeling applications, we evaluated (i) the incorporation of U-[12C]-glucose into proteins of human cells grown in a complex RPMI-based medium containing the labeled molecule, considering that human cell lines require a large number of essential amino-acids to support their growth, and (ii) an indirect labeling strategy in which the nematode Caenorhabditis elegans grown on plates was fed U-[12C]-labeled bacteria (Escherichia coli) and the worm proteome analyzed for 12C incorporation into proteins. In both cases, we were able to demonstrate efficient incorporation of 12C into the newly synthesized proteins, opening the way for original approaches in quantitative proteomics.
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Affiliation(s)
- Laurent Lignieres
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Nicolas Sénécaut
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Tien Dang
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Laura Bellutti
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Marion Hamon
- Institut de Biologie Physico-Chimique, F-75005 Paris, France
| | - Samuel Terrier
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Véronique Legros
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Guillaume Chevreux
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Gaëlle Lelandais
- Institut de Biologie Intégrative de la Cellule, F-91190 Gif-sur-Yvette, France
| | - René-Marc Mège
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
| | - Julien Dumont
- Université Paris Cité, CNRS, Institut Jacques Monod, F-75013 Paris, France
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Fornasiero EF, Savas JN. Determining and interpreting protein lifetimes in mammalian tissues. Trends Biochem Sci 2023; 48:106-118. [PMID: 36163144 PMCID: PMC9868050 DOI: 10.1016/j.tibs.2022.08.011] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 01/26/2023]
Abstract
The orchestration of protein production and degradation, and the regulation of protein lifetimes, play a central role in the majority of biological processes. Recent advances in proteomics have enabled the estimation of protein half-lives for thousands of proteins in vivo. What is the utility of these measurements, and how can they be leveraged to interpret the proteome changes occurring during development, aging, and disease? This opinion article summarizes leading technical approaches and highlights their strengths and weaknesses. We also disambiguate frequently used terminology, illustrate recent mechanistic insights, and provide guidance for interpreting and validating protein turnover measurements. Overall, protein lifetimes, coupled to estimates of protein levels, are essential for obtaining a deep understanding of mammalian biology and the basic processes defining life itself.
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Affiliation(s)
- Eugenio F Fornasiero
- Department of Neuro-Sensory Physiology, University Medical Center Göttingen, 37073 Göttingen, Germany.
| | - Jeffrey N Savas
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
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Sénécaut N, Poulain P, Lignières L, Terrier S, Legros V, Chevreux G, Lelandais G, Camadro JM. Quantitative Proteomics in Yeast : From bSLIM and Proteome Discoverer Outputs to Graphical Assessment of the Significance of Protein Quantification Scores. Methods Mol Biol 2022; 2477:275-292. [PMID: 35524123 DOI: 10.1007/978-1-0716-2257-5_16] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Simple light isotope metabolic labeling (bSLIM) is an innovative method to accurately quantify differences in protein abundance at the proteome level in standard bottom-up experiments. The quantification process requires computation of the ratio of intensity of several isotopologs in the isotopic cluster of every identified peptide. Thus, appropriate bioinformatic workflows are required to extract the signals from the instrument files and calculate the required ratio to infer peptide/protein abundance. In a previous study (Sénécaut et al., J Proteome Res 20:1476-1487, 2021), we developed original open-source workflows based on OpenMS nodes implemented in a KNIME working environment. Here, we extend the use of the bSLIM labeling strategy in quantitative proteomics by presenting an alternative procedure to extract isotopolog intensities and process them by taking advantage of new functionalities integrated into the Minora node of Proteome Discoverer 2.4 software. We also present a graphical strategy to evaluate the statistical robustness of protein quantification scores and calculate the associated false discovery rates (FDR). We validated these approaches in a case study in which we compared the differences between the proteomes of two closely related yeast strains.
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Affiliation(s)
- Nicolas Sénécaut
- Mitochondria, Metals, and Oxidative Stress Group, Institut Jacques Monod, Université de Paris-CNRS, Paris, France
| | - Pierre Poulain
- Mitochondria, Metals, and Oxidative Stress Group, Institut Jacques Monod, Université de Paris-CNRS, Paris, France
| | - Laurent Lignières
- ProteoSeine@IJM, Institut Jacques Monod, Université de Paris-CNRS, Paris, France
| | - Samuel Terrier
- ProteoSeine@IJM, Institut Jacques Monod, Université de Paris-CNRS, Paris, France
| | - Véronique Legros
- ProteoSeine@IJM, Institut Jacques Monod, Université de Paris-CNRS, Paris, France
| | - Guillaume Chevreux
- ProteoSeine@IJM, Institut Jacques Monod, Université de Paris-CNRS, Paris, France
| | - Gaëlle Lelandais
- Institute for Integrative Biology of the Cell (I2BC), CEA, CNRS, Université Paris-Saclay, Gif-sur-Yvette, France
| | - Jean-Michel Camadro
- Mitochondria, Metals, and Oxidative Stress Group, Institut Jacques Monod, Université de Paris-CNRS, Paris, France.
- ProteoSeine@IJM, Institut Jacques Monod, Université de Paris-CNRS, Paris, France.
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Sénécaut N, Alves G, Weisser H, Lignières L, Terrier S, Yang-Crosson L, Poulain P, Lelandais G, Yu YK, Camadro JM. Novel Insights into Quantitative Proteomics from an Innovative Bottom-Up Simple Light Isotope Metabolic (bSLIM) Labeling Data Processing Strategy. J Proteome Res 2021; 20:1476-1487. [PMID: 33573382 PMCID: PMC8459934 DOI: 10.1021/acs.jproteome.0c00478] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Simple light isotope metabolic labeling (SLIM labeling) is an innovative method to quantify variations in the proteome based on an original in vivo labeling strategy. Heterotrophic cells grown in U-[12C] as the sole source of carbon synthesize U-[12C]-amino acids, which are incorporated into proteins, giving rise to U-[12C]-proteins. This results in a large increase in the intensity of the monoisotope ion of peptides and proteins, thus allowing higher identification scores and protein sequence coverage in mass spectrometry experiments. This method, initially developed for signal processing and quantification of the incorporation rate of 12C into peptides, was based on a multistep process that was difficult to implement for many laboratories. To overcome these limitations, we developed a new theoretical background to analyze bottom-up proteomics data using SLIM-labeling (bSLIM) and established simple procedures based on open-source software, using dedicated OpenMS modules, and embedded R scripts to process the bSLIM experimental data. These new tools allow computation of both the 12C abundance in peptides to follow the kinetics of protein labeling and the molar fraction of unlabeled and 12C-labeled peptides in multiplexing experiments to determine the relative abundance of proteins extracted under different biological conditions. They also make it possible to consider incomplete 12C labeling, such as that observed in cells with nutritional requirements for nonlabeled amino acids. These tools were validated on an experimental dataset produced using various yeast strains of Saccharomyces cerevisiae and growth conditions. The workflows are built on the implementation of appropriate calculation modules in a KNIME working environment. These new integrated tools provide a convenient framework for the wider use of the SLIM-labeling strategy.
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Affiliation(s)
- Nicolas Sénécaut
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Gelio Alves
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland 20894, United States
| | | | - Laurent Lignières
- ProteoSeine@IJM, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Samuel Terrier
- ProteoSeine@IJM, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Lilian Yang-Crosson
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Pierre Poulain
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
| | - Gaëlle Lelandais
- Institut de Biologie Intégrative de la Cellule, 91190 Orsay, France
| | - Yi-Kuo Yu
- National Center for Biotechnology Information, NLM, NIH, Bethesda, Maryland 20894, United States
| | - Jean-Michel Camadro
- ≪ Mitochondria, Metals, and Oxidative Stress ≫ Group, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
- ProteoSeine@IJM, Université de Paris-CNRS, Institut Jacques Monod, 75013 Paris, France
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Sadygov RG. Using Heavy Mass Isotopomers for Protein Turnover in Heavy Water Metabolic Labeling. J Proteome Res 2021; 20:2035-2041. [PMID: 33661639 DOI: 10.1021/acs.jproteome.0c00873] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Metabolic labeling followed by LC-MS-based proteomics is a powerful tool to study proteome dynamics in high-throughput experiments both in vivo and in vitro. High mass resolution and accuracy allow differentiation in isotope profiles and the quantification of partially labeled peptide species. Metabolic labeling duration introduces a time domain in which the gradual incorporation of labeled isotopes is recorded. Different stable isotopes are used for labeling. Labeling with heavy water has advantages because it is cost-effective and easy to use. The protein degradation rate constant has been modeled using exponential decay models for the relative abundances of mass isotopomers. The recently developed closed-form equations were applied to study the analytic behavior of the heavy mass isotopomers in the time domain of metabolic labeling. The predictions from the closed-form equations are compared with the practices that have been used to extract degradation rate constants from the time-course profiles of heavy mass isotopomers. It is shown that all mass isotopomers, except for the monoisotope, require data transformations to obtain the exponential depletion, which serves as a basis for the rate constant model. Heavy mass isotopomers may be preferable choices for modeling high-mass peptides or peptides with a high number of labeling sites. The results are also applicable to stable isotope labeling with other atom-based labeling agents.
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Affiliation(s)
- Rovshan G Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University Boulevard, Galveston, Texas 77555, United States
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7
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Sadygov RG. Partial Isotope Profiles Are Sufficient for Protein Turnover Analysis Using Closed-Form Equations of Mass Isotopomer Dynamics. Anal Chem 2020; 92:14747-14753. [PMID: 33084301 DOI: 10.1021/acs.analchem.0c03343] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Metabolic labeling with atom-based heavy isotopes, followed by liquid chromatography coupled with mass spectrometry (LC-MS), has been a powerful technique for studies of proteome and metabolome. In proteomics, the protein turnover of thousands of proteins can be estimated from the gradual incorporation of 2H or 15N in the diet. Software tools have been developed to automate the estimation of protein turnover. Traditionally, the turnover has been estimated using the time course of the depletion of the normalized abundance of monoisotopes. While the bioinformatic aspects of peak detection and integration, time course modeling, and uncertainty estimation have progressed, mass isotopomer dynamics during label incorporation has only been modeled from approximate approaches or numerical simulations. We derive closed-form equations that describe the dynamics of mass isotopomers during metabolic labeling with an atom-based stable isotope. The derived equations create an alternative method for estimating label incorporation. They also provide opportunities for estimation of precursor-product relationships in species or systems where they are unknown. The equations are useful in bioinformatic tools for analyzing mass spectral data from metabolic labeling.
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Affiliation(s)
- Rovshan G Sadygov
- Department of Biochemistry and Molecular Biology, The University of Texas Medical Branch, 301 University of Blvd, Galveston, Texas 77555, United States
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Lelandais G, Denecker T, Garcia C, Danila N, Léger T, Camadro JM. Label-free quantitative proteomics in Candida yeast species: technical and biological replicates to assess data reproducibility. BMC Res Notes 2019; 12:470. [PMID: 31370875 PMCID: PMC6669970 DOI: 10.1186/s13104-019-4505-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Accepted: 07/20/2019] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVE Label-free quantitative proteomics has emerged as a powerful strategy to obtain high quality quantitative measures of the proteome with only a very small quantity of total protein extract. Because our research projects were requiring the application of bottom-up shotgun mass spectrometry proteomics in the pathogenic yeasts Candida glabrata and Candida albicans, we performed preliminary experiments to (i) obtain a precise list of all the proteins for which measures of abundance could be obtained and (ii) assess the reproducibility of the results arising respectively from biological and technical replicates. DATA DESCRIPTION Three time-courses were performed in each Candida species, and an alkaline pH stress was induced for two of them. Cells were collected 10 and 60 min after stress induction and proteins were extracted. Samples were analysed two times by mass spectrometry. Our final dataset thus comprises label-free quantitative proteomics results for 24 samples (two species, three time-courses, two time points and two runs of mass spectrometry). Statistical procedures were applied to identify proteins with differential abundances between stressed and unstressed situations. Considering that C. glabrata and C. albicans are human pathogens, which face important pH fluctuations during a human host infection, this dataset has a potential value to other researchers in the field.
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Affiliation(s)
- Gaëlle Lelandais
- CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Univ. Paris-Sud, Gif-Sur-Yvette, France.
| | - Thomas Denecker
- CEA, CNRS, Institute for Integrative Biology of the Cell (I2BC), Univ. Paris-Sud, Gif-Sur-Yvette, France
| | - Camille Garcia
- Mass Spectrometry Laboratory, CNRS, Institut Jacques Monod, UMR 7592, Université de Paris, 75205, Paris, France
| | - Nicolas Danila
- CNRS, Institut Jacques Monod (IJM), Univ. Paris Diderot, Paris, France
| | - Thibaut Léger
- Mass Spectrometry Laboratory, CNRS, Institut Jacques Monod, UMR 7592, Université de Paris, 75205, Paris, France
| | - Jean-Michel Camadro
- Mass Spectrometry Laboratory, CNRS, Institut Jacques Monod, UMR 7592, Université de Paris, 75205, Paris, France
- CNRS, Institut Jacques Monod (IJM), Univ. Paris Diderot, Paris, France
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