Abstract
Pulsed stable isotope labeling by amino acids in cell culture (pulsed SILAC or pSILAC) allows to monitor and quantify the de novo synthesis of proteins in an unbiased fashion on a proteome-wide scale. The high applicability of this metabolic labeling technique has been demonstrated for the identification of posttranscriptional changes in gene expression on the proteome level, in particular those caused by microRNAs. The application of pSILAC allows the selective quantification of newly synthesized proteins and thus the detection of differences in protein translation. This is of particular interest in the case of microRNA-mediated regulations, which characteristically cause rather modest decreases in protein amounts that may be difficult to detect by other proteomic methods. Here, we describe a detailed protocol for using pSILAC to track miRNA-mediated changes in protein expression, using the p53-induced miR-34a microRNA as a prototypic example of microRNA-mediated regulations.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Boisvert FM, Ahmad Y, Gierlinski M et al (2012) A quantitative spatial proteomics analysis of proteome turnover in human cells. Mol Cell Proteomics 11(M111):011429
Huo Y, Iadevaia V, Yao Z et al (2012) Stable isotope-labelling analysis of the impact of inhibition of the mammalian target of rapamycin on protein synthesis. Biochem J 444:141–151
Zhang L, Zhao H, Blagg BS, Dobrowsky RT (2012) C-terminal heat shock protein 90 inhibitor decreases hyperglycemia-induced oxidative stress and improves mitochondrial bioenergetics in sensory neurons. J Proteome Res 11:2581–2593
Martin BR, Wang C, Adibekian A et al (2012) Global profiling of dynamic protein palmitoylation. Nat Methods 9:84–89
Zee BM, Levin RS, Dimaggio PA, Garcia BA (2010) Global turnover of histone post-translational modifications and variants in human cells. Epigenetics Chromatin 3:22–31
Kraft-Terry SD, Gendelman HE (2011) Proteomic biosignatures for monocyte-macrophage differentiation. Cell Immunol 271:239–255
Kaller M, Liffers ST, Oeljeklaus S et al (2011) Genome-wide characterization of miR-34a induced changes in protein and mRNA expression by a combined pulsed SILAC and microarray analysis. Mol Cell Proteomics 10(M111):010462
Maragkakis M, Alexiou P, Papadopoulos GL et al (2009) Accurate microRNA target prediction correlates with protein repression levels. BMC Bioinform 10:295
Patron JP, Fendler A, Bild M et al (2012) MiR-133b targets antiapoptotic genes and enhances death receptor-induced apoptosis. PLoS One 7:e35345
Schwanhausser B, Gossen M, Dittmar G et al (2009) Global analysis of cellular protein translation by pulsed SILAC. Proteomics 9:205–209
Selbach M, Schwanhausser B, Thierfelder N et al (2008) Widespread changes in protein synthesis induced by microRNAs. Nature 455:58–63
Fabian MR, Sonenberg N, Filipowicz W (2010) Regulation of mRNA translation and stability by microRNAs. Annu Rev Biochem 79:351–379
Friedman RC, Farh KK, Burge CB et al (2009) Most mammalian mRNAs are conserved targets of microRNAs. Genome Res 19:92–105
Ebert MS, Sharp PA (2012) Roles for microRNAs in conferring robustness to biological processes. Cell 149:515–524
Ong SE, Blagoev B, Kratchmarova I et al (2002) Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics 1:376–386
Baek D, Villen J, Shin C et al (2008) The impact of microRNAs on protein output. Nature 455:64–71
Vinther J, Hedegaard MM, Gardner PP et al (2006) Identification of miRNA targets with stable isotope labeling by amino acids in cell culture. Nucleic Acids Res 34:e107
Epanchintsev A, Jung P, Menssen A et al (2006) Inducible microRNA expression by an all-in-one episomal vector system. Nucleic Acids Res 34:e119
Hermeking H (2012) MicroRNAs in the p53 network: micromanagement of tumour suppression. Nat Rev Cancer 12:613–626
Krek A, Grun D, Poy MN et al (2005) Combinatorial microRNA target predictions. Nat Genet 37:495–500
Kertesz M, Iovino N, Unnerstall U et al (2007) The role of site accessibility in microRNA target recognition. Nat Genet 39:1278–1284
Miranda KC, Huynh T, Tay Y et al (2006) A pattern-based method for the identification of MicroRNA binding sites and their corresponding heteroduplexes. Cell 126:1203–1217
Alexiou P, Maragkakis M, Papadopoulos GL et al (2009) Lost in translation: an assessment and perspective for computational microRNA target identification. Bioinformatics 25:3049–3055
Reimers M, Carey VJ (2006) Bioconductor: an open source framework for bioinformatics and computational biology. Methods Enzymol 411:119–134
Saeed AI, Bhagabati NK, Braisted JC et al (2006) TM4 microarray software suite. Methods Enzymol 411:134–193
Djuranovic S, Nahvi A, Green R (2011) A parsimonious model for gene regulation by miRNAs. Science 331:550–553
Guo H, Ingolia NT, Weissman JS et al (2010) Mammalian microRNAs predominantly act to decrease target mRNA levels. Nature 466:835–840
Chi SW, Zang JB, Mele A et al (2009) Argonaute HITS-CLIP decodes microRNA-mRNA interaction maps. Nature 460:479–486
Hafner M, Landthaler M, Burger L et al (2010) Transcriptome-wide identification of RNA-binding protein and microRNA target sites by PAR-CLIP. Cell 141:129–141
Nonne N, Ameyar-Zazoua M, Souidi M et al (2010) Tandem affinity purification of miRNA target mRNAs (TAP-Tar). Nucleic Acids Res 38:e20
Orom UA, Lund AH (2007) Isolation of microRNA targets using biotinylated synthetic microRNAs. Methods 43:162–165
Jackstadt R, Menssen A, Hermeking H (2013) Genome-wide analysis of c-MYC-regulated mRNAs and miRNAs, c-MYC DNA-binding by next generation sequencing. Methods Mol Biol 1012:145–185
Hünten S, Siemens H, Kaller M et al (2013) The p53/microRNA network in cancer: experimental and bioinformatics approaches. In: Schmitz U, Wolkenhauser O, Julio V (eds) miRNA cancer regulation: advanced concepts, bioinformatics and systems biology tools. Springer, New York, NY, pp 77–102
Cox J, Matic I, Hilger M et al (2009) A practical guide to the MaxQuant computational platform for SILAC-based quantitative proteomics. Nat Protoc 4:698–705
Cox J, Neuhauser N, Michalski A et al (2011) Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res 10:1794–1805
Bradford MM (1976) A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72:248–254
Bornkamm GW, Berens C, Kuklik-Roos C et al (2005) Stringent doxycycline-dependent control of gene activities using an episomal one-vector system. Nucleic Acids Res 33:e137
Welch C, Chen Y, Stallings RL (2007) MicroRNA-34a functions as a potential tumor suppressor by inducing apoptosis in neuroblastoma cells. Oncogene 26:5017–5022
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this protocol
Cite this protocol
Kaller, M., Oeljeklaus, S., Warscheid, B., Hermeking, H. (2014). Identification of MicroRNA Targets by Pulsed SILAC. In: Warscheid, B. (eds) Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC). Methods in Molecular Biology, vol 1188. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1142-4_23
Download citation
DOI: https://doi.org/10.1007/978-1-4939-1142-4_23
Published:
Publisher Name: Humana Press, New York, NY
Print ISBN: 978-1-4939-1141-7
Online ISBN: 978-1-4939-1142-4
eBook Packages: Springer Protocols