Determining and interpreting protein lifetimes in mammalian tissues
2022; Elsevier BV; Volume: 48; Issue: 2 Linguagem: Inglês
10.1016/j.tibs.2022.08.011
ISSN1362-4326
AutoresEugenio F. Fornasiero, Jeffrey N. Savas,
Tópico(s)Adipose Tissue and Metabolism
ResumoRobust proteome homeostasis (i.e., proteostasis) is crucial for organismal health because proteome imbalance and the accumulation of damaged molecules have negative effects on nearly all biological processes.It has become clear that protein half-life data in mammals provide vital information at the whole-proteome level for understanding dynamic phenotypic changes across scales.Although methods and analysis frameworks for determining protein half-lives in vivo at the whole-proteome level are becoming more popular, they require careful customization depending on the biological question.Samples obtained from metabolic labeling schemes can be used to provide spatial turnover information through mass spectrometry imaging technologies such as matrix-assisted laser desorption ionization (MALDI) and nanoscale secondary ion mass spectrometry (NanoSIMS).Protein abundance and turnover can be measured using similar mass spectrometry-based approaches but are fundamentally different and provide valuable and complementary insights. 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. 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. The complex nature of mammalian tissues presents several analytical challenges for studying in vivo protein turnover (see Glossary). However, recent advances in liquid chromatography with tandem mass spectrometry (LC-MS/MS) and proteomic data analysis have made high-throughput studies of protein turnover in vivo a reality [1.Price J.C. et al.Analysis of proteome dynamics in the mouse brain.Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 14508-14513Crossref PubMed Scopus (261) Google Scholar, 2.Bomba-Warczak E. et al.Long-lived mitochondrial cristae proteins in mouse heart and brain.J. Cell Biol. 2021; 220e202005193Crossref PubMed Scopus (21) Google Scholar, 3.Alevra M. et al.A mass spectrometry workflow for measuring protein turnover rates in vivo.Nat. Protoc. 2019; 14: 3333-3365Crossref PubMed Scopus (20) Google Scholar, 4.Fornasiero E.F. et al.Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions.Nat. 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The results of these studies have begun to revolutionize our understanding of proteome fidelity and proteostasis. In this opinion, we highlight the importance of measuring protein turnover and protein half-lives in vivo in whole mammals and why correctly interpreting these results is crucial for advancing the field. Specifically, we summarize leading analytical strategies, discuss recent discoveries, and disambiguate terms used to describe proteome-wide measurements of protein turnover. The strengths and weaknesses associated with commonly used experimental designs are also presented while highlighting recent mechanistic insights gained from studying protein turnover in vivo. Efficient protein degradation and robust protein turnover are crucial for maintaining organ homeostasis. Accordingly, impaired protein turnover plays a key role in numerous human disorders, diseases, and during aging [9.Basisty N. et al.Protein turnover in aging and longevity.Proteomics. 2018; 18e1700108Crossref Scopus (58) Google Scholar]. Historically, most early studies of protein half-lives have focused on tracking individual proteins, but currently a constellation of large-scale approaches can be used to monitor the turnover rate of several thousand proteins in a single experiment [10.Ross A.B. et al.Proteome turnover in the spotlight: approaches, applications, and perspectives.Mol. Cell. Proteomics. 2021; 20100016Abstract Full Text Full Text PDF Google Scholar] (Table 1). Although these are exciting times for large-scale studies of protein turnover, an under-appreciation of what is being measured in vivo and the impact of these findings has emerged. In our opinion, it is crucial to emphasize the importance of careful experimental design, precise terminology, and accurate data interpretation.Table 1Compendium of turnover studies of particular relevance to mammalian tissues and whole animals, organized from the oldest to the most recent studiesaAbbreviations: AD, Alzheimer's disease; AHA, l-azidohomoalanine; ELLPs, extremely long-lived proteins; NanoSIMS, nanoscale secondary ion mass spectrometry; NUPs, nucleoporins; SILT, stable isotope labeling tandem MS.NumberFirst author and yearLabelAnimalLabeling paradigm(s)Data analysis strategyConsiderationsBiological relevanceRefs1Wu200415NRattus norvegicusShort pulses15N absolute quantificationPioneering work that introduced 15N for metabolic labeling of mammalsFeasibility study[19.Wu C.C. et al.Metabolic labeling of mammalian organisms with stable isotopes for quantitative proteomic analysis.Anal. 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Mass Spectrom. 2007; 18: 997-1006Crossref PubMed Scopus (59) Google Scholar]4Price201015NMus musculusSeveral short pulsesExponential fittingPioneering work designed to define the half-lives of rapid-turnover proteinsTissue comparison, half-life determination[1.Price J.C. et al.Analysis of proteome dynamics in the mouse brain.Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 14508-14513Crossref PubMed Scopus (261) Google Scholar]5Kasumov20112H2OR. norvegicusSeveral short pulsesExponential fittingPioneering work that introduced 2H2O labeling for protein turnover studiesEffects of feeding on albumin synthesis[56.Kasumov T. et al.Measuring protein synthesis using metabolic 2H labeling, high-resolution mass spectrometry, and an algorithm.Anal. Biochem. 2011; 412: 47-55Crossref PubMed Scopus (57) Google Scholar]6Savas201215NR. norvegicusGenerational pulse and chase15N/14N abundancePioneering work to identify intracellular ELLPsIdentification of NUPs and histones as ELLPs[27.Savas J.N. et al.Extremely long-lived nuclear pore proteins in the rat brain.Science. 2012; 335: 942Crossref PubMed Scopus (229) Google Scholar]7Price20122H2OH. sapiensDrinking 2H20 administration (single pulse)Kinetic model to account for precursor enrichmentPioneering work for the analysis of protein lifetimes in humansProtein turnover values in human plasma[51.Price J.C. et al.Measurement of human plasma proteome dynamics with 2H2O and liquid chromatography tandem mass spectrometry.Anal. Biochem. 2012; 420: 73-83Crossref PubMed Scopus (80) Google Scholar]8Guan201215NModeling/data analysisSeveral short pulsesSeveral computational approachesPioneering work dealing with compartment modeling for mammalian turnover studiesSimple exponential decays are not appropriate for whole animals[15.Guan S. et al.Compartment modeling for mammalian protein turnover studies by stable isotope metabolic labeling.Anal. Chem. 2012; 84: 4014-4021Crossref PubMed Scopus (35) Google Scholar]9Toyama201315NR. norvegicusGenerational pulse and chase15N/14N abundanceDetailed work characterizing the long-lived proteomeDetailed analysis of nuclear pore complexes[26.Toyama B.H. et al.Identification of long-lived proteins reveals exceptional stability of essential cellular structures.Cell. 2013; 154: 971-982Abstract Full Text Full Text PDF PubMed Scopus (377) Google Scholar]10Lam20142H2OM. musculusH. sapiensSeveral short pulsesExponential fittingExtensive work that also provided data on turnover of the human plasma proteomeStudy of isoproterenol effects on heart remodeling[64.Lam M.P.Y. et al.Protein kinetic signatures of the remodeling heart following isoproterenol stimulation.J. Clin. Invest. 2014; 124: 1734-1744Crossref PubMed Scopus (66) Google Scholar]11McClatchy2015AHAM. musculusShort pulsesEnrichment of AHA (click and biotinylation)Work aimed at identifying the newly synthesized proteomeIntroduces a method for newly synthesized proteins in tissues[22.McClatchy D.B. et al.Pulsed azidohomoalanine labeling in mammals (PALM) detects changes in liver-specific LKB1 knockout mice.J. Proteome Res. 2015; 14: 4815-4822Crossref PubMed Scopus (57) Google Scholar]12Karunadharma 20152H3-leucineM. musculusSeveral short pulsesExponential fittingSeveral conditions and tissues were analyzed in parallel (respiratory chain)Tissue comparison for mitochondrial proteins[32.Karunadharma P.P. et al.Respiratory chain protein turnover rates in mice are highly heterogeneous but strikingly conserved across tissues, ages, and treatments.FASEB J. 2015; 29: 3582-3592Crossref PubMed Scopus (46) Google Scholar]13Hammond201613C6-lysineMyodes glareolus (bank vole)Several short pulsesExponential fittingTurnover in a small rodent (bank vole) by cross-species matching to mouseAnalytical methodology may contribute to variance in turnover[6.Hammond D.E. et al.Proteome dynamics: tissue variation in the kinetics of proteostasis in intact animals.Mol. Cell. Proteomics. 2016; 15: 1204-1219Abstract Full Text Full Text PDF PubMed Scopus (23) Google Scholar]14Lau20162H2OM. musculusSeveral short pulsesExponential fittingComprehensive dataset of half-lives in the heartHeart hypertrophy studied across six mouse strains[58.Lau E. et al.A large dataset of protein dynamics in the mammalian heart proteome.Sci. Data. 2016; 3160015Crossref PubMed Scopus (55) Google Scholar]15Rahman201615N and 2H2OModeling/data analysisSeveral short pulsesSeveral approaches including a stochastic modelOne- and two-compartment models were used to analyze data from other studiesModels can be independent of the labeling isotope[16.Rahman M. et al.Gaussian process modeling of protein turnover.J. Proteome Res. 2016; 15: 2115-2122Crossref PubMed Scopus (18) Google Scholar]16Naylor20172H2OModeling/data analysisShort pulsesExponential fittingThe described software platform simplifies analysisTurnover rates are consistent across studies[60.Naylor B.C. et al.DeuteRater: a tool for quantifying peptide isotope precision and kinetic proteomics.Bioinformatics. 2017; 33: 1514-1520PubMed Google Scholar]17Fornasiero201813C6-lysine13C6-15N4-arginineM. musculusSeveral pulses including pulse and chaseExponential fitting/global modelingComprehensive dataset of protein half-lives in the brain and in other tissues, including cell sorting and fractionationProteins have a reduced turnover at synapses Environmental enrichment changes specific lifetimes[4.Fornasiero E.F. et al.Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions.Nat. Commun. 2018; 9: 4230Crossref PubMed Scopus (159) Google Scholar]18Basisty20182H3-leucineM. musculusSeveral short pulsesExponential fittingAnalysis of the antibody-enriched ubiquitinome at different mouse ages and fed with different dietsAging increases bulk protein ubiquitinationAggregated proteins are older[7.Basisty N.B. et al.Stable isotope labeling reveals novel insights into ubiquitin-mediated protein aggregation with age, calorie restriction, and rapamycin treatment.J. Gerontol. A Biol. Sci. Med. Sci. 2018; 73: 561-570Crossref PubMed Scopus (17) Google Scholar]19Heo201813C6-lysineM. musculusShort pulse and chaseTurnover ratiosAnalysis of brain synaptic proteinsProteins have reduced turnover at synapses[31.Heo S. et al.Identification of long-lived synaptic proteins by proteomic analysis of synaptosome protein turnover.Proc. Natl. Acad. Sci. U. S. A. 2018; 115: E3827-E3836Crossref PubMed Scopus (100) Google Scholar]20Lau20182H2OM. musculusSeveral short pulsesExponential fittingIntegrated omics: transcript abundance; protein abundance and turnoverIntegrated omics provides several gene candidates for heart hypertrophy[35.Lau E. et al.Integrated omics dissection of proteome dynamics during cardiac remodeling.Nat. Commun. 2018; 9: 120Crossref PubMed Scopus (53) Google Scholar]21Sadygov20182H2OM. musculus and modeling/data analysisShort pulsesNonlinear fitting with outlier detection and removalThe software platform simplifies the analysis of protein lifetimesAnalysis of fatty liver disease reveals changes in ribosomal proteins[59.Sadygov R.G. et al.d2ome, software for in vivo protein turnover analysis using heavy water labeling and LC-MS, reveals alterations of hepatic proteome dynamics in a mouse model of NAFLD.J. Proteome Res. 2018; 17: 3740-3748Crossref PubMed Scopus (32) Google Scholar]22Ko201815NM. musculusShort pulseDifference in labeling ratiosAimed at understanding the effects of peripheral nerve injury on protein turnoverPeripheral nerve injury induces faster turnover of defined synaptic proteins[37.Ko H.-G. et al.Rapid turnover of cortical NCAM1 regulates synaptic reorganization after peripheral nerve injury.Cell Rep. 2018; 22: 748-759Abstract Full Text Full Text PDF PubMed Scopus (30) Google Scholar]23Alevra201913C6-lysineProtocol with data analysisShort pulsesExponential fitting/global modelingDetailed indications for determining protein lifetimesProtocol covering aspects of lifetime measurements[3.Alevra M. et al.A mass spectrometry workflow for measuring protein turnover rates in vivo.Nat. Protoc. 2019; 14: 3333-3365Crossref PubMed Scopus (20) Google Scholar]24McClatchy2020AHAM. musculusShort pulse and chaseExponential fittingUse of AHA for determining degradation dynamics in different tissuesSubcellular localization and activity influence protein stability[8.McClatchy D.B. et al.Quantitative analysis of global protein stability rates in tissues.Sci. Rep. 2020; 10: 15983Crossref PubMed Scopus (10) Google Scholar]25Bomba-Warczak202115NM. musculusSeveral pulses (up to 4 months)15N/14N abundancePulse labeling shows that some mitochondrial proteins are exceptionally long-livedOxidative phosphorylation complexes are preserved with low subunit exchange[2.Bomba-Warczak E. et al.Long-lived mitochondrial cristae proteins in mouse heart and brain.J. Cell Biol. 2021; 220e202005193Crossref PubMed Scopus (21) Google Scholar]26Krishna202115NM. musculusPulse and chase15N/14N abundanceNanoSIMS confirmation that some mitochondrial proteins are exceptionally long-livedCOX7C contributes to oxidative phosphorylation complex assembly[25.Krishna S. et al.Identification of long-lived proteins in the mitochondria reveals increased stability of the electron transport chain.Dev. Cell. 2021; 56: 2952-2965Abstract Full Text Full Text PDF PubMed Scopus (14) Google Scholar]27Hark202115NM. musculusGenerational pulse and chase15N/14N abundanceThree genetic models of AD were analyzedThe turnover of synaptic vesicle-associated proteins is altered in AD[34.Hark T.J. et al.Pulse-chase proteomics of the App knockin mouse models of alzheimer's disease reveals that synaptic dysfunction originates in presynaptic terminals.Cell Syst. 2021; 12: 141-158Abstract Full Text Full Text PDF PubMed Scopus (24) Google Scholar]28Chepyala202113C6-lysineM. musculusSeveral short pulsesExponential fittingThree settings can be used for calculating lifetimesDirect measurements of lysine pools improve the data[17.Chepyala S.R. et al.JUMPt: comprehensive protein turnover modeling of in vivo pulse SILAC data by ordinary differential equations.Anal. Chem. 2021; 93: 13495-13504Crossref PubMed Scopus (6) Google Scholar]29Rolfs202113C6-lysineM. musculusSeveral short pulsesExponential fittingAnalysis of half-lives across five mouse tissuesPostnatal tissue development complicates the analysis of results[65.Rolfs Z. et al.An atlas of protein turnover rates in mouse tissues.Nat. Commun. 2021; 12: 6778Crossref PubMed Scopus (16) Google Scholar]30Kluever202213C6-lysineM. musculusSeveral short pulsesExponential fittingAnalysis of mean protein lifetimes in aged mouse brainAged brain proteins last longer than young proteins[33.Kluever V. et al.Protein lifetimes in aged brains reveal a proteostatic adaptation linking physiological aging to neurodegeneration.Sci. Adv. 2022; 8: eabn4437Crossref PubMed Scopus (12) Google Scholar]31Hammond20222H2O and 13C6-lysineM. musculusSeveral short pulsesExponential fittingComparison of different labeling strategiesThe amino acid labels tested are suited for turnover studies[44.Hammond D.E. et al.Harmonizing labeling and analytical strategies to obtain protein turnover rates in intact adult animals.Mol. Cell. Proteomics. 2022; 21100252Abstract Full Text Full Text PDF PubMed Scopus (9) Google Scholar]a Abbreviations: AD, Alzheimer's disease; AHA, l-azidohomoalanine; ELLPs, extremely long-lived proteins; NanoSIMS, nanoscale secondary ion mass spectrometry; NUPs, nucleoporins; SILT, stable isotope labeling tandem MS. Open table in a new tab Proteostasis refers to the processes that ensure the delicate balance of protein production, maintenance, and degradation that are vital for cellular and tissue function. Although the general concepts underlying protein synthesis and degradation are well understood, the terms describing these processes are sometimes used ambiguously. In this section we would like to bring clarity by providing a common set of terms for the field. Cells contain proteins with abundances that roughly vary from thousands to tens of millions of copies [11.Edfors F. et al.Gene-specific correlation of RNA and protein levels in human cells and tissues.Mol. Syst. Biol. 2016; 12: 883Crossref PubMed Scopus (255) Google Scholar]. For an individual protein, the protein lifetime encompasses the entire time from synthesis (i.e., birth) to degradation (i.e., death). Historically, protein renewal (i.e., the replacement of old proteins) has been quantified in terms of average protein half-life. However, protein half-lives in mammals cannot be reliably calculated for long-lived proteins (LLPs). Because for some LLPs the mean protein lifetime is years or even decades, even small differences in labeling ratio will greatly influence these values. Often the term meaning 'protein lifetime' is used interchangeably with 'protein half-life' and, because this is not strictly correct, we advise to use the latter whenever possible. In a steady-state situation, protein half-life is the point in time when the degradation of a population of old proteins is equal to the newly synthesized population of proteins. Thus, by definition, at steady-state, the mean protein lifetime is the same as the protein half-life (Box 1). Caution needs to be taken when non-steady-state conditions are the subject of investigation because the two measurements diverge. It is also important to emphasize that this terminology, in the context of LC-MS/MS measurements, reflects an average process (e.g., the mean protein lifetime, half-life, or turnover) of a pool of proteins with the same amino acid sequence. This is because these technologies typically do not achieve measurements at the level of single molecules and instead measure a population of peptides after protein extraction and trypsin digestion (Box 2).Box 1Analyzing and interpreting in vivo measurements of protein kineticsIn a steady-state situation, protein kinetics can be approximated by simple exponential decays (Figure I) in which the degradation and synthesis rate constants are equal. In reality, in a mammal the availability of precursor molecule might depend on the modalities by which the labels are provided (Box 2), further complicating half-life calculations.When interpreting the results of metabolic labeling, it is crucial that the age of the animal and the period of labeling are carefully considered. This is exemplified by the fact that some long-lived proteins (LLPs; such as extracellular matrix components and nuclear scaffolds in postmitotic cells) are synthesized at a specific age of the animal development [52.Long K.R. Huttner W.B. How the extracellular matrix shapes neural development.Open Biol. 2019; 9180216Crossref Scopus (118) Google Scholar,53.D'Angelo M.A. et al.Age-dependent deterioration of nuclear pore complexes causes a loss of nuclear integrity in postmitotic cells.Cell. 2009; 136: 284-295Abstract Full Text Full Text PDF PubMed Scopus (407) Google Scholar]. If the goal is to study protein half-lives, using relatively short labeling periods with heavy amino acids is often appropriate, especially for measuring the components of the proteome that are replaced frequently. However, if the goal is to identify LLPs, then multi-generation labeling followed by a chase with unlabeled food is probably more appropriate.Protein turnover measurements in isotopic labeling experiments are typically fractional, and the intact heavy and light labeled peptides are captured in the same MS1 scan. Thus, they are internally normalized within individual samples analyses and are not impacted by sample-to-sample technical variation. Attention must be exerted for the interpretation of these calculations because, depending on the workflow used, the fractional abundance of isotopically labeled proteins can reflect either an 'older' or a 'newer' population of proteins. However, bottom-up proteomic analysis depends on the ability to identify the peptide sequence in the MS2 scan. For some studies, both the heavy and light peptide pairs are identified, whereas in others only one isotopolog is selected for MS2 and the abundance is solely based on inferring its sequence based on the m/z values and peak intensities. MS-based imaging of metabolically labeled tissue sections can be achieved with MALDI and NanoSIMS, providing spatial information.Several labeling and analysis strategies have been deployed to investigate protein turnover and measure protein lifetimes in vivo. A problem that needs to be addressed when studying protein turnover in vivo is that amino acids (essential and non-essential) are recycled within animals to preserve energy and increase metabolic performance. All these approaches are based on theoretical predictions of kinetic influx and efflux of pools of amino acids and proteins, and several computational approaches are available [1.Price J.C. et al.Analysis of proteome dynamics in the mouse brain.Proc. Natl. Acad. Sci. U. S. A. 2010; 107: 14508-14513Crossref PubMed Scopus (261) Google Scholar,4.Fornasiero E.F. et al.Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions.Nat. Commun. 2018; 9: 4230Crossref PubMed Scopus (159) Google Scholar,15.Guan S. et al.Compartment modeling for mammalian protein turnover studies by stable isotope metabolic labeling.Anal. Chem. 2012; 84: 4014-4021Crossref PubMed Scopus (35) Google Scholar,17.Chepyala S.R. et al.JUMPt: comprehensive protein turnover modeling of in vivo pulse SILAC data by ordinary differential equations.Anal. Chem. 2021; 93: 13495-13504Crossref PubMed Scopus (6) Google Scholar,54.Claydon A.J. et al.Protein turnover: measurement of proteome dynamics by whole animal metabolic labelling with stable isotope labelled amino acids.Proteomics. 2012; 12: 1194-1206Crossref PubMed Scopus (60) Google Scholar,55.Doherty M.K. et al.Proteome dynamics in complex organisms: using stable isotopes to monitor individual protein turnover rates.Proteomics. 2005; 5: 522-533Crossref PubMed Scopus (137) Google Scholar]. It is important to underline that these approaches are based on assumptions which are necessary to allow mathematical modeling (see Table 1 in main text). The most common assumption is that the protein of interest does not change its level during the period analyzed, which may confound interpretation of the results.Box 2Labeling rodents with stable isotopes for studying protein turnoverStable isotopes can be incorporated into rodent proteins by metabolic labeling, similarly to what is done using stable isotope labeling with amino acids in culture (SILAC). In animals this is done with custom chow enriched with 'heavy' nitrogen (i.e., 15N) or carbon (i.e., 13C). The rodent chow is formulated without 'light' atoms for one or more defined molecular species, thus allowing specific labeling to be obtained.There are currently two practical strategies used to label proteins for experiments requiring stable isotope labeling in mammals (SILAM): (i) by providing 'heavy essential amino acids' such as lysine (e.g., 13C6-lysine) that cannot be synthesized and are thus solely provided from the diet and incorporated during protein biosynthesis and (ii) by providing 'labeled amino acid precursors' that are incorporated into biomolecules and proteins through enzymatic reactions occurring within cells. As an example, this is what is achieved when employing 15N diets. In this case 15N atoms are slowly incorporated into all the nitrogen-containing molecules such as amino acid sidechains and backbones. Because mice cannot efficiently incorporate 15N as a derivatized salt, historically the 15N diet is based on blue-green algae (i.e., Spirulina platensis) which can use 15N as the sole nitrogen source.An alternative strategy is to deliver heavy atoms by subcutaneous injection or in drinking water in the form of deuterium oxide (i.e., 2H2O) [56.Kasumov T. et al.Measuring protein synthesis using metabolic 2H labeling, high-resolution mass spectrometry, and an algorithm.Anal. Biochem. 2011; 412: 47-55Crossref PubMed Scopus (57) Google Scholar]. Owing to the large difference in relative mass with respect to 1H (protium), deuterium (2H) is the only stable isotope which exerts a sizeable 'kinetic isotope effect' that slows enzymatic reactions and results in toxicity at concentrations higher than 30% in animals and eukaryotic cells [57.Kushner D.J. et al.Pharmacological uses and perspectives of heavy water and deuterated compounds.Can. J. Physiol. Pharmacol. 1999; 77: 79-88Crossref PubMed Scopus (240) Google Scholar]. Nevertheless, because of its relatively low price, heavy water is an attractive solution for protein turnover studies not only in rodents but also in humans [51.Price J.C. et al.Measurement of human plasma proteome dynamics with 2H2O and liquid chromatography tandem mass spectrometry.Anal. Biochem. 2012; 420: 73-83Crossref PubMed Scopus (80) Google Scholar]. In practice, the toxicity issues are mitigated by using low concentrations of heavy water and relying on robust bioinformatic approaches for data interpretation [58.Lau E. et al.A large dataset of protein dynamics in the mammalian heart proteome.Sci. Data. 2016; 3160015Crossref PubMed Scopus (55) Google Scholar, 59.Sadygov R.G. et al.d2ome, software for in vivo protein turnover analysis using heavy water labeling and LC-MS, reveals alterations of hepatic proteome dynamics in a mouse model of NAFLD.J. Proteome Res. 2018; 17: 3740-3748Crossref PubMed Scopus (32) Google Scholar, 60.Naylor B.C. et al.DeuteRater: a tool for quantifying peptide isotope precision and kinetic proteomics.Bioinformatics. 2017; 33: 1514-1520PubMed Google Scholar].There are advantages and disadvantages to using global isotopic labeling (e.g., 15N-labeled essential amino acids; Figure I). Briefly, although atom-based tracers can provide reliable measurements of rel
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