Abstract
Protein production and degradation are tightly regulated to prevent cellular structures from accumulating damage and to allow their correct functioning. A key aspect of this regulation is the protein half-life, corresponding to the time in which half of a specific protein population is exchanged with respect to its initial state. Proteome-wide techniques to investigate protein half-lives in vivo are emerging. Recently, we have established and thoroughly tested a metabolic labeling approach using 13C lysine (Lys(6)) for measuring protein lifetimes in mice. The approach is based on the fact that different proteins will incorporate a metabolic label at a rate that is dependent on their half-life. Using amino acid pool modeling and mass spectrometry, it is possible to measure the fraction of newly synthesized proteins and determine protein half-lives. In this chapter, we show how to extend this approach to zebrafish (Danio rerio), using a commercially available fish diet based on the stable isotope labeling by amino acids in cell culture (SILAC) technology. We describe the methods for labeling animals and subsequently use mass spectrometry to determine the lifetimes of a large number of proteins. In the mass spectrometry workflow proposed here, we have implemented the BoxCar data acquisition approach for increasing sample coverage and optimize machine use. To establish the proteome library used in the BoxCar approach, we recommend performing an in-solution digestion followed by peptide fractionation through basic reversed-phase chromatography. Overall, this chapter extends the use of current proteome technologies for the quantification of protein turnover to zebrafish and similar organisms and permits the study of germline changes following specific manipulations.
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References
Sala AJ, Bott LC, Morimoto RI (2017) Shaping proteostasis at the cellular, tissue, and organismal level. J Cell Biol 216:1231–1241. https://doi.org/10.1083/jcb.201612111
Basisty N, Meyer JG, Schilling B (2018) Protein turnover in aging and longevity. Proteomics 18. https://doi.org/10.1002/pmic.201700108
Schwanhäusser B, Gossen M, Dittmar G, Selbach M (2009) Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9:205–209. https://doi.org/10.1002/pmic.200800275
Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455:58–63. https://doi.org/10.1038/nature07228
Fornasiero EF, Mandad S, Wildhagen H, Alevra M, Rammner B, Keihani S, Opazo F, Urban I, Ischebeck T, Sakib MS, Fard MK, Kirli K, Centeno TP, Vidal RO, Rahman R-U, Benito E, Fischer A, Dennerlein S, Rehling P, Feussner I, Bonn S, Simons M, Urlaub H, Rizzoli SO (2018) Precisely measured protein lifetimes in the mouse brain reveal differences across tissues and subcellular fractions. Nat Commun 9:4230. https://doi.org/10.1038/s41467-018-06519-0
Lau E, Cao Q, Ng DCM, Bleakley BJ, Dincer TU, Bot BM, Wang D, Liem DA, Lam MPY, Ge J (2016) A large dataset of protein dynamics in the mammalian heart proteome. Sci Data 3:160015. https://doi.org/10.1038/sdata.2016.15
Mann M, Kulak NA, Nagaraj N, Cox J (2013) The coming age of complete, accurate, and ubiquitous proteomes. Mol Cell 49:583–590. https://doi.org/10.1016/j.molcel.2013.01.029
Aebersold R, Mann M (2003) Mass spectrometry-based proteomics. Nature 422:198–207. https://doi.org/10.1038/nature01511
Humphrey TJ, Davies DD (1975) A new method for the measurement of protein turnover. Biochem J 148:119–127. https://doi.org/10.1042/bj1480119
Dietz WH, Wolfe MH, Wolfe RR (1982) A method for the rapid determination of protein turnover. Metabolism 31:749–754. https://doi.org/10.1016/0026-0495(82)90070-1
Schoenheimer R, Rittenberg D (1935) Deuterium as an indicator in the study of intermediary metabolism. Science 82:156–157. https://doi.org/10.1126/science.82.2120.156
Toyama BH, Savas JN, Park SK, Harris MS, Ingolia NT, Yates JR, Hetzer MW (2013) Identification of long-lived proteins reveals exceptional stability of essential cellular structures. Cell 154:971–982. https://doi.org/10.1016/j.cell.2013.07.037
Mandad S, Rahman R-U, Centeno TP, Vidal RO, Wildhagen H, Rammner B, Keihani S, Opazo F, Urban I, Ischebeck T, Kirli K, Benito E, Fischer A, Yousefi RY, Dennerlein S, Rehling P, Feussner I, Urlaub H, Bonn S, Rizzoli SO, Fornasiero EF (2018) The codon sequences predict protein lifetimes and other parameters of the protein life cycle in the mouse brain. Sci Rep 8:16913. https://doi.org/10.1038/s41598-018-35277-8
Zhang Y, Reckow S, Webhofer C, Boehme M, Gormanns P, Egge-Jacobsen WM, Turck CW (2011) Proteome scale turnover analysis in live animals using stable isotope metabolic labeling. Anal Chem 83:1665–1672. https://doi.org/10.1021/ac102755n
Price JC, Guan S, Burlingame A, Prusiner SB, Ghaemmaghami S (2010) Analysis of proteome dynamics in the mouse brain. Proc Natl Acad Sci U S A 107:14508–14513. https://doi.org/10.1073/pnas.1006551107
Schoenheimer R, Ratner S, Rittenberg D (2009) Studies in protein metabolism. Nutr Rev 40:23–26. https://doi.org/10.1111/j.1753-4887.1982.tb06822.x
Ong S-E, Mann M (2006) A practical recipe for stable isotope labeling by amino acids in cell culture (SILAC). Nat Protoc 1:2650–2660. https://doi.org/10.1038/nprot.2006.427
Krüger M, Moser M, Ussar S, Thievessen I, Luber CA, Forner F, Schmidt S, Zanivan S, Fässler R, Mann M (2008) SILAC mouse for quantitative proteomics uncovers kindlin-3 as an essential factor for red blood cell function. Cell 134:353–364. https://doi.org/10.1016/j.cell.2008.05.033
MacRae CA, Peterson RT (2015) Zebrafish as tools for drug discovery. Nat Rev Drug Discov 14:721–731. https://doi.org/10.1038/nrd4627
Lieschke GJ, Currie PD (2007) Animal models of human disease: zebrafish swim into view. Nat Rev Genet 8:353–367. https://doi.org/10.1038/nrg2091
Nolte H, Konzer A, Ruhs A, Jungblut B, Braun T, Krüger M (2014) Global protein expression profiling of zebrafish organs based on in vivo incorporation of stable isotopes. J Proteome Res 13:2162–2174. https://doi.org/10.1021/pr5000335
Geary B, Magee K, Cash P, Young IS, Whitfield PD, Doherty MK (2016) Determining synthesis rates of individual proteins in zebrafish (Danio rerio) with low levels of a stable isotope labelled amino acid. Proteomics 16:1398–1406. https://doi.org/10.1002/pmic.201500357
Gillet LC, Navarro P, Tate S, Röst H, Selevsek N, Reiter L, Bonner R, Aebersold R (2012) Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis. Mol Cell Proteomics 11:O111.016717. https://doi.org/10.1074/mcp.O111.016717
Meier F, Geyer PE, Virreira Winter S, Cox J, Mann M (2018) BoxCar acquisition method enables single-shot proteomics at a depth of 10,000 proteins in 100 minutes. Nat Methods 15:440–448. https://doi.org/10.1038/s41592-018-0003-5
Batth TS, Francavilla C, Olsen JV (2014) Off-line high-pH reversed-phase fractionation for in-depth phosphoproteomics. J Proteome Res 13:6176–6186. https://doi.org/10.1021/pr500893m
Alevra M, Mandad S, Ischebeck T, Urlaub H, Rizzoli SO, Fornasiero EF (2019) A mass spectrometry workflow for measuring protein turnover rates in vivo. Nat Protoc 14(12):3333–3365
Shevchenko A, Tomas H, Havlis J, Olsen JV, Mann M (2006) In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat Protoc 1:2856–2860. https://doi.org/10.1038/nprot.2006.468
Nakamura K, Aebersold R, Bairoch A, Dunn M, Celis J, Hanash S, Hochstrasser D, Humphrey-Smith I, James P, Klose J, LaBaer J, Langen H, Mann M, Parekh R, Patterson S, Pearce C, Poepstorff P, Simpson RJ, Tomlinson I, Tsugita A, Yates J (2004) From genome to proteome--aim of human proteomics. Seikagaku 76:1271–1274
Gilbert MJH, Zerulla TC, Tierney KB (2014) Zebrafish (Danio rerio) as a model for the study of aging and exercise: physical ability and trainability decrease with age. Exp Gerontol 50:106–113. https://doi.org/10.1016/j.exger.2013.11.013
Acknowledgments
This work was in part supported by an EMBO Long Term Fellowship and a HFSP Fellowship to E.F.F. (EMBO_LT_797_2012 and HFSP_LT000830/2013).
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Mandad, S., Kracht, G., Fornasiero, E.F. (2021). In Vivo Protein Lifetime Measurements Across Multiple Organs in the Zebrafish . In: Dosch, R. (eds) Germline Development in the Zebrafish. Methods in Molecular Biology, vol 2218. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-0970-5_23
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DOI: https://doi.org/10.1007/978-1-0716-0970-5_23
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