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A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis
Open accessAbstract
Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.
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In the citing paper
“Studies in model organisms have shown that variation in mRNA and protein expression levels are often uncorrelated (5-8).”
From Impact of regulatory variation from RNA to protein (2015) · cites this paper as [7]
In the citing paper
“The advance in quantitative proteomics allows a genome-wide map of pQTL model organisms (Foss et al., 2007; Picotti et al., 2013).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Picotti et al., 2013
In the citing paper
“Picotti et al. used a nearly complete map of yeast proteome and genetic variation data to identify strong genetic effects of protein abundance, and to demonstrate epistatic interactions affecting protein levels (Picotti et al., 2013).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Picotti et al., 2013
In the citing paper
“Using precise MS measurement of peptides over a large number of samples, recent pQTL analyses revealed complex genetic influence on the levels of proteins in yeast (Picotti et al., 2013) and mouse (Holdt et al., 2013).”
From Integrative Analysis of Multi-omics Data for Discovery and Functional Studies of Complex Human Diseases (2016) · cites this paper as Picotti et al., 2013
In the citing paper
“Human biology is tightly linked to proteins, and mass spectrometry-based proteomics has established a strong linkage between phenotype and protein-level biology (1, 2).”
From The Blood Proteoform Atlas: A reference map of proteoforms in human hematopoietic cells (2022) · cites this paper as [1]
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Simicevic, J., Schmid, A. W., Gilardoni, P. A., Zoller, B., Raghav, S. K., Krier, I., Gubelmann, C., Lisacek, F., Naef, F., Moniatte, M., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2441 · Cited by 83
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“Comparison with transcriptome data (14) demonstrated that up to 60% of proteomic changes could not be predicted from the dynamics of their transcripts, therefore, emphasizing the importance of studying proteins directly (20,21).”
BackgroundThe future of whole-cell modeling
Macklin, D. N., Ruggero, N. A., & Covert, M. W. (2014) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2014.01.012 · Cited by 86
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“strategy and technologies have been applied to yeast and mammalian cells [81,82], showing a sharp delay of proteomic research on filamentous fungi when compared to other model systems [79,83,84].”
MethodCurrent trends in quantitative proteomics – an update
Li, H., Han, J., Pan, J., Liu, T., Parker, C. E., & Borchers, C. H. (2017) · Journal of Mass Spectrometry · doi:10.1002/jms.3932 · Cited by 78
The quest for tolerant varieties: the importance of integrating “omics” techniques to phenotyping
Zivy, M., Wienkoop, S., Renaut, J., Pinheiro, C., Goulas, E., & Carpentier, S. (2015) · Frontiers in Plant Science · doi:10.3389/fpls.2015.00448 · Cited by 76
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Tandem mass spectral libraries of peptides and their roles in proteomics research
Shao, W., & Lam, H. (2016) · Mass Spectrometry Reviews · doi:10.1002/mas.21512 · Cited by 70
“Lastly, the key limitation of spectral library searching, that it can only identify previously observed peptides, can be partially overcome by extending libraries with spectra from synthetic peptides (Picotti et al., 2013) and various spectrum prediction methods.”
BackgroundMethodSelf-citationEmerging mass spectrometry techniques for the direct analysis of microbial colonies
Fang, J., & Dorrestein, P. C. (2014) · Current Opinion in Microbiology · doi:10.1016/j.mib.2014.06.014 · Cited by 70
Huang, R., Chen, Z., He, L., He, N., Xi, Z., Li, Z., Deng, Y., & Zeng, X. (2017) · Theranostics · doi:10.7150/thno.20797 · Cited by 69
“One of the applications of the shotgun approach is to generate spectral libraries for mass spectrometric reference maps [41, 42].”
BackgroundModified MuDPIT Separation Identified 4488 Proteins in a System-wide Analysis of Quiescence in Yeast
Webb, K. J., Xu, T., Park, S. K., & Yates, J. R. (2013) · Journal of Proteome Research · doi:10.1021/pr400027m · Cited by 67
Bludau, I., Heusel, M., Frank, M., Rosenberger, G., Hafen, R., Banaei‐Esfahani, A., van Drogen, A., Collins, B. C., Gstaiger, M., & Aebersold, R. H. (2020) · Nature Protocols · doi:10.1038/s41596-020-0332-6 · Cited by 61
Self-citationKusebauch, U., Deutsch, E. W., Campbell, D. S., Sun, Z., Farrah, T., & Moritz, R. L. (2014) · Current Protocols in Bioinformatics · doi:10.1002/0471250953.bi1325s46 · Cited by 63
“…on which the data were acquired, the number of runs performed, the number of proteins and peptides measured, the presence of isotope-labeled peptides to quantify endogenous peptides, the presence of mProphet (Reiter et al., 2011) scores, and the publication in which the study is described.”
BackgroundMethodInfluentialSelf-citationTargeted Proteomics for Studying Pathogenic Bacteria
Saleh, S., Staes, A., Deborggraeve, S., & Gevaert, K. (2019) · PROTEOMICS · doi:10.1002/pmic.201800435 · Cited by 67
Bottom-Up Proteomics (2013–2015): Keeping up in the Era of Systems Biology
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Champion, M. M., Williams, E. A., Pinapati, R. S., & Champion, P. A. D. (2014) · Journal of Proteome Research · doi:10.1021/pr500484w · Cited by 61
Paulo, J. A., O’Connell, J. D., Gaun, A., & Gygi, S. P. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-07-0499 · Cited by 57
“cerevisiae via mass spectrometry–based techniques (King et al., 2006; Nagaraj et al., 2012; Picotti et al., 2013; Webb et al., 2013; Hebert et al., 2014; Paulo and Gygi, 2014).”
ResultFaria, S. S., Morris, C. F. M., Silva, A. R., da Fonseca, M. P., Forget, P., Castro, M. S., & Fontes, W. (2017) · Frontiers in Oncology · doi:10.3389/fonc.2017.00013 · Cited by 63
“A classical targeted proteomics assay requires specific MS instruments like the triple quadrupole analyzer, where the first and third quadrupoles work as m/z filters, while the second quadrupole acts as a collision cell (146).”
BackgroundMethodChoi, S. B., Zamarbide, M., Manzini, M. C., & Nemes, P. (2016) · Journal of the American Society for Mass Spectrometry · doi:10.1007/s13361-016-1532-8 · Cited by 58
Quantitative isoform-profiling of highly diversified recognition molecules
Schreiner, D., Simicevic, J., Ahrné, E., Schmidt, A., & Scheiffele, P. (2015) · eLife · doi:10.7554/elife.07794 · Cited by 59
Self-citationWhat are the 100 most cited fungal genera?
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“cataloged natural differences across strains, in transcript abundance [37, 45, 46], 119 protein abundance [47-49], metabolism [50-52], and growth in various 120 environments [32, 36, 37, 42, 52-54].”
BackgroundMulti-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers
Guo, T., Li, L., Zhong, Q., Rupp, N. J., Charmpi, K., Wong, C. E., Wagner, U., Rueschoff, J. H., Jochum, W., Fankhauser, C. D., et al. (2018) · Life Science Alliance · doi:10.26508/lsa.201800042 · Cited by 52
Self-citationTargeted proteomics reveals compositional dynamics of 60S pre‐ribosomes after nuclear export
Altvater, M., Chang, Y., Melnik, A., Occhipinti, L., Schütz, S., Rothenbusch, U., Picotti, P., & Panse, V. G. (2012) · Molecular Systems Biology · doi:10.1038/msb.2012.63 · Cited by 54
Self-citationEvolution of Gene Regulation during Transcription and Translation
Wang, Z., Sun, X., Zhao, Y., Guo, X., Jiang, H., Li, H., & Gu, Z. (2015) · Genome Biology and Evolution · doi:10.1093/gbe/evv059 · Cited by 56
“…than Transcription during Evolution To illustrate the relationship between transcriptome and translatome with respect to the protein product, we first explored the correlation of our transcriptome and translatome data with the whole-cell proteome data in S. cerevisiae (Picotti et al. 2013).”
BackgroundUnderstanding the molecular diversity of synapses
van Oostrum, M., & Schuman, E. M. (2025) · Nature Reviews Neuroscience · doi:10.1038/s41583-024-00888-w · Cited by 49
Zhang, C., Qi, Y., Ma, H.-X., Li, W., Dai, L., & Xiao, D. (2015) · Journal of Industrial Microbiology & Biotechnology · doi:10.1007/s10295-015-1583-z · Cited by 54
“[15] mapped almost entirely the proteome of S.”
BackgroundMass spectrometry-based proteomic quest for diabetes biomarkers
Shao, S., Guo, T., & Aebersold, R. H. (2014) · Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics · doi:10.1016/j.bbapap.2014.12.012 · Cited by 55
“The S/MRM assay (reference map) is defined as the collection of fragment ion spectra of peptides corresponding to predicted protein sequences based on the genome [70], containing all essential coordinates of informative peptides.”
BackgroundSelf-citationProtein degradation corrects for imbalanced subunit stoichiometry in OST complex assembly
Mueller, S., Wåhlander, Å., Selevsek, N., Otto, C., Ngwa, E. M., Poljak, K., Frey, A. D., Aebi, M., & Gauss, R. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-03-0168 · Cited by 51
Metadata
- Authors
- Picotti, P., Clément-Ziza, M., Lam, H., Campbell, D. S., Schmidt, A., Deutsch, E. W., Röst, H., Sun, Z., Rinner, O., Reiter, L., Shen, Q., Michaelson, J. J., Frei, A., Alberti, S., Kusebauch, U., Wollscheid, B., Moritz, R. L., Beyer, A., & Aebersold, R.
- Affiliations
- ETH Zurich (CH)
- Hong Kong University of Science and Technology (HK)
- Institute for Systems Biology (US)
- University of Basel (CH)
- Chinese Academy of Sciences (CN)
- Biotechnologisches Zentrum (DE)
- Max Planck Institute of Molecular Cell Biology and Genetics (DE)
- University of Zurich (CH)
- Corresponding author
- Picotti, Paola
- Beyer, Andreas
- Aebersold, Ruedi
- ORCID
- Venue
- Nature
- Published
- 2013-01-20
- Volume
- 494
- Issue
- 7436
- Pages
- 266-270
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC3951219
- OpenAlex
- W2065096062
- Cited by
- 318
- References
- 73
- Citations by year
- 2026: 2 · 2025: 10 · 2024: 7 · 2023: 10 · 2022: 18 · 2021: 14 · 2020: 10 · 2019: 30 · 2018: 24 · 2017: 44 · 2016: 31 · 2015: 44 · 2014: 40 · 2013: 31
- FWCI
- 21.82
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Max Planck Institute for Plasma Physics (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
- Funders
- Université du Luxembourg
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (133670, 107679)
- National Institutes of Health (n01hv28179-12-0-0)
- National Institute of Standards and Technology
- National Heart, Lung, and Blood Institute (N01 HV028179, N01-HV-28179)
- National Human Genome Research Institute (RC2HG005805)
- National Institute of General Medical Sciences (P50GM076547)
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Fungal and yeast genetics research
- Keywords
- proteome reference map, protein abundance, shotgun proteomics, targeted proteomics, genetic polymorphism, protein stoichiometry, selection pressure, yeast strain
- MeSH terms
- Polymorphism, Genetic, Reference Values, Saccharomyces cerevisiae, Selection, Genetic, Mass Spectrometry, Peptide Library, Proteome, Saccharomyces cerevisiae Proteins, Quantitative Trait Loci, Proteomics
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