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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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MethodLayers of regulation of cell-cycle gene expression in the budding yeastSaccharomyces cerevisiae
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“[10,11] even in a very short time frame [12].”
BackgroundKrásný, L., Bland, P. J., Burns, J., Lima, N. C., Harrison, P. T., Pacini, L., Elms, M. L., Ning, J., Martínez, V. G., Yu, Y.-R., et al. (2020) · Disease Models & Mechanisms · doi:10.1242/dmm.044586 · Cited by 19
“org) including libraries for human [15], fruit fly [16], 5 zebrafish [17] and yeast [18].”
BackgroundA predictive model of gene expression using a deep learning framework
Xie, R., Quitadamo, A., Cheng, J., & Shi, X. M. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822599 · Cited by 21
Percy, A. J., Yang, J., Chambers, A. G., Mohammed, Y., Miliotis, T., & Borchers, C. H. (2016) · Advances in experimental medicine and biology · doi:10.1007/978-3-319-41448-5_24 · Cited by 21
Genetic Basis of Haloperidol Resistance in Saccharomyces cerevisiae Is Complex and Dose Dependent
Wang, X., & Kruglyak, L. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004894 · Cited by 19
Genome-wide mapping of cellular traits using yeast
Parts, L. (2014) · Yeast · doi:10.1002/yea.3010 · Cited by 21
“The quantification methodology ranges from 2D gels for abundant proteins to mass spectrometry of tryptic peptides in a global (de Godoy et al., 2008; Lu et al., 2007; Marguerat et al., 2012), intermediate (Venable et al., 2004) or focused (Picotti et al., 2013) fashion.”
MethodProteomic Studies Related to Genetic Determinants of Variability in Protein Concentrations
Horvatovich, P. L., Franke, L., & Bischoff, R. (2013) · Journal of Proteome Research · doi:10.1021/pr400765y · Cited by 18
Towards lager beer aroma improvement via selective amino acid release by proteases during mashing
Lin, C. L., Petersen, M. A., Mauch, A., & Gottlieb, A. (2022) · Journal of the Institute of Brewing · doi:10.1002/jib.682 · Cited by 17
Computational Prediction of Protein Complexes from Protein Interaction Networks
Srihari, S., Yong, C. H., & Wong, L. (2017) · doi:10.1145/3064650 · Cited by 17
Nitric oxide prevents Aft1 activation and metabolic remodeling in frataxin-deficient yeast
Alsina, D., Ros, J., & Tamarit, J. (2017) · Redox Biology · doi:10.1016/j.redox.2017.09.001 · Cited by 14
Feng, T., Jie, M., Deng, K., Yang, J., & Jiang, H. (2024) · Clinica Chimica Acta · doi:10.1016/j.cca.2024.119675 · Cited by 16
An Overview of Mass Spectrometry-Based Methods for Functional Proteomics
O’Neill, J. R. (2018) · Methods in molecular biology · doi:10.1007/978-1-4939-8814-3_13 · Cited by 15
Systematic Assessment of the Effect of Internal Library in Targeted Analysis of SWATH-MS
Zhong, C., Wu, R., Chen, X., Wu, S., Shuai, J., & Han, J. (2019) · Journal of Proteome Research · doi:10.1021/acs.jproteome.9b00669 · Cited by 13
Mass-Spectrometry-Based Near-Complete Draft of the Saccharomyces cerevisiae Proteome
Gao, Y., Ping, L., Duong, D. M., Zhang, C., Dammer, E. B., Li, Y., Chen, P., Chang, L., Gao, H., Wu, J., et al. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.0c00721 · Cited by 16
Splice variants in the proteome: a promising and challenging field to targeted drug discovery
Tavares, R., Scherer, N. D. M., Ferreira, C. G., Costa, F. F., & Passetti, F. (2014) · Drug Discovery Today · doi:10.1016/j.drudis.2014.11.002 · Cited by 14
Paulitschke, V., Eichhoff, O. M., Cheng, P. F., Levesque, M. P., & Höeller, C. (2016) · Current Opinion in Oncology · doi:10.1097/cco.0000000000000261 · Cited by 11
Natural gene expression variation studies in yeast
Thompson, D. A., & Cubillos, F. A. (2016) · Yeast · doi:10.1002/yea.3210 · Cited by 15
The promise of targeted proteomics for quantitative network biology
Matsumoto, M., & Nakayama, K. I. (2018) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2018.02.014 · Cited by 14
Tailored Use of Targeted Proteomics in Plant-Specific Applications
Rödiger, A., & Baginsky, S. (2018) · Frontiers in Plant Science · doi:10.3389/fpls.2018.01204 · Cited by 15
Peptide barcoding for establishment of new types of genotype–phenotype linkages
Miyamoto, K., Aoki, W., Ohtani, Y., Miura, N., Aburaya, S., Matsuzaki, Y., Kajiwara, K., Kitagawa, Y., & Ueda, M. (2019) · PLoS ONE · doi:10.1371/journal.pone.0215993 · Cited by 14
Targeted proteomics of myofilament phosphorylation and other protein posttranslational modifications
Ramirez‐Correa, G. A., Martinez‐Ferrando, M. I., Zhang, P., & Murphy, A. M. (2014) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201400034 · Cited by 13
Matsuda, F., Ogura, T., Tomita, A., Hirano, I., & Shimizu, H. (2014) · Journal of Bioscience and Bioengineering · doi:10.1016/j.jbiosc.2014.06.010 · Cited by 13
specL—an R/Bioconductor package to prepare peptide spectrum matches for use in targeted proteomics
Panse, C., Trachsel, C., Grossmann, J., & Schlapbach, R. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv105 · Cited by 13
“MS/MS spectral libraries can be downloaded from publicly available databases [8, 9, 10].”
BackgroundQuantitative proteomics of model organisms
Feng, Y., Cappelletti, V., & Picotti, P. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.09.004 · Cited by 12
Self-citationSpatial top-down proteomics for the functional characterization of human kidney
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“Mass spectrometry (MS) is a powerful tool to directly characterize proteins; however, routine bottom-up proteomics (BUP) cannot fully capture the molecular identity of proteoforms [4].”
Hakobyan, A., Liesack, W., & Glatter, T. (2018) · Journal of Proteome Research · doi:10.1021/acs.jproteome.8b00216 · Cited by 12
Protein turnover measurement using selected reaction monitoring-mass spectrometry (SRM-MS)
Holman, S. W., Hammond, D. E., Simpson, D. M., Waters, J., Hurst, J. L., & Beynon, R. J. (2016) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2015.0362 · Cited by 12
“However, repositories of optimal product ions to monitor for specific peptides expedite this stage significantly [29].”
BackgroundBird, S. S., Stavrovskaya, I. G., Gathungu, R. M., Tousi, F., & Kristal, B. S. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-2257-4_36 · Cited by 11
An extensive library of surrogate peptides for all human proteins
Mohammed, Y., & Borchers, C. H. (2015) · Journal of Proteomics · doi:10.1016/j.jprot.2015.07.025 · Cited by 11
Definition of a RACK1 Interaction Network in Drosophila melanogaster Using SWATH-MS
Kühn, L., Majzoub, K., Einhorn, E., Chicher, J., Pompon, J., Imler, J., Hammann, P., & Meignin, C. (2017) · G3 Genes Genomes Genetics · doi:10.1534/g3.117.042564 · Cited by 12
“DIA results in comprehensive high resolution data with qualitative confirmation and no tedious method development (Bisson et al. 2011; Chang et al. 2012; Picotti and Aebersold 2012; Picotti et al. 2013; Selevsek et al. 2015).”
BackgroundPeptide barcoding for one-pot evaluation of sequence–function relationships of nanobodies
Matsuzaki, Y., Aoki, W., Miyazaki, T., Aburaya, S., Ohtani, Y., Kajiwara, K., Koike, N., Minakuchi, H., Miura, N., Kadonosono, T., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-01019-6 · Cited by 11
A genome-to-proteome map reveals how natural variants drive proteome diversity and shape fitness
Jakobson, C. M., Hartl, J., Trébulle, P., Mülleder, M., Jarosz, D. F., & Ralser, M. (2025) · Science · doi:10.1126/science.adu3198 · Cited by 14
Label-Free Quantitative Proteomics in Yeast
Léger, T., Garcia, C., Videlier, M., & Camadro, J. (2015) · Methods in molecular biology · doi:10.1007/978-1-4939-3079-1_16 · Cited by 12
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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