Abstract
We describe a proteomic screening approach based on the concept of sentinel proteins, biological markers whose change in abundance characterizes the activation state of a given cellular process. Our sentinel assay simultaneously probed 188 biological processes in Saccharomyces cerevisiae exposed to a set of environmental perturbations. The approach can be applied to analyze responses to large sets of uncharacterized perturbations in high throughput.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
$259.00 per year
only $21.58 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
References
Aebersold, R. & Mann, M. Nature 422, 198–207 (2003).
Wolf-Yadlin, A., Hautaniemi, S., Lauffenburger, D.A. & White, F.M. Proc. Natl. Acad. Sci. USA 104, 5860–5865 (2007).
Nahnsen, S., Bielow, C., Reinert, K. & Kohlbacher, O. Mol. Cell. Proteomics 12, 549–556 (2013).
Picotti, P. & Aebersold, R. Nat. Methods 9, 555–566 (2012).
Gillet, L.C. et al. Mol. Cell. Proteomics 11, O111.016717 (2012).
Klionsky, D.J., Cuervo, A.M. & Seglen, P.O. Autophagy 3, 181–206 (2007).
Cherkasova, V.A. Methods 40, 234–242 (2006).
Stoevesandt, O. & Taussig, M.J. Proteomics 7, 2738–2750 (2007).
Bairoch, A. et al. Nucleic Acids Res. 33, D154–D159 (2005).
Saito, H. & Posas, F. Genetics 192, 289–318 (2012).
Dumlao, D.S., Hertz, N. & Clarke, S. Biochemistry 47, 698–709 (2008).
Morano, K.A., Grant, C.M. & Moye-Rowley, W.S. Genetics 190, 1157–1195 (2012).
Erkine, A.M., Magrogan, S.F., Sekinger, E.A. & Gross, D.S. Mol. Cell. Biol. 19, 1627–1639 (1999).
Nakashima, A., Sato, T. & Tamanoi, F. J. Cell Sci. 123, 777–786 (2010).
Urban, J. et al. Mol. Cell 26, 663–674 (2007).
Fernández-García, P., Peláez, R., Herrero, P. & Moreno, F. J. Biol. Chem. 287, 42151–42164 (2012).
Homann, M.J., Poole, M.A., Gaynor, P.M., Ho, C.T. & Carman, G.M. J. Bacteriol. 169, 533–539 (1987).
Soulard, A. et al. Mol. Biol. Cell 21, 3475–3486 (2010).
Griffioen, G. et al. Mol. Cell. Biol. 21, 511–523 (2001).
Garay-Arroyo, A., Colmenero-Flores, J.M., Garciarrubio, A. & Covarrubias, A.A. J. Biol. Chem. 275, 5668–5674 (2000).
Michnick, S.W. Curr. Opin. Biotechnol. 14, 610–617 (2003).
Altvater, M. et al. Mol. Syst. Biol. 8, 628 (2012).
Bisson, N. et al. Nat. Biotechnol. 29, 653–658 (2011).
Drabovich, A.P., Pavlou, M.P., Dimitromanolakis, A. & Diamandis, E.P. Mol. Cell. Proteomics 11, 422–434 (2012).
Picotti, P., Bodenmiller, B., Mueller, L.N., Domon, B. & Aebersold, R. Cell 138, 795–806 (2009).
Sherman, F. Methods Enzymol. 350, 3–41 (2002).
Wang, M. et al. Mol. Cell. Proteomics 11, 492–500 (2012).
Costenoble, R. et al. Mol. Syst. Biol. 7, 464 (2011).
Bodenmiller, B. et al. Sci. Signal. 3, rs4 (2010).
Dephoure, N. & Gygi, S.P. Sci. Signal. 5, rs2 (2012).
Nagaraj, N. et al. Mol. Cell. Proteomics 11, M111.013722 (2012).
Poulsen, J.W. et al. J. Proteomics 75, 3886–3897 (2012).
Soufi, B. et al. Mol. Biosyst. 5, 1337–1346 (2009).
Vogel, C., Silva, G.M. & Marcotte, E.M. Mol. Cell. Proteomics 10, M111.009217 (2011).
Gasch, A.P. et al. Mol. Biol. Cell 11, 4241–4257 (2000).
Su, L.J. et al. Dis. Model. Mech. 3, 194–208 (2010).
Franceschini, A. et al. Nucleic Acids Res. 41, D808–D815 (2013).
Ashburner, M. et al. Nat. Genet. 25, 25–29 (2000).
UniProt Consortium. Nucleic Acids Res. 41, D43–D47 (2013).
Di Girolamo, F., Righetti, P.G., Soste, M., Feng, Y. & Picotti, P. J. Proteomics 89, 215–226 (2013).
Elias, J.E. & Gygi, S.P. Nat. Methods 4, 207–214 (2007).
Lam, H. et al. Nat. Methods 5, 873–875 (2008).
MacLean, B. et al. Bioinformatics 26, 966–968 (2010).
Escher, C. et al. Proteomics 12, 1111–1121 (2012).
Picotti, P. et al. Nat. Methods 7, 43–46 (2010).
Kriegel, T.M., Rush, J., Vojtek, A.B., Clifton, D. & Fraenkel, D.G. Biochemistry 33, 148–152 (1994).
Behlke, J. et al. Biochemistry 37, 11989–11995 (1998).
Holt, L.J. et al. Science 325, 1682–1686 (2009).
Ahn, S.H. et al. Cell 120, 25–36 (2005).
Ahn, S.H., Henderson, K.A., Keeney, S. & Allis, C.D. Cell Cycle 4, 780–783 (2005).
Choi, M. et al. Bioinformatics 10.1093/bioinformatics/btu305 (2 May 2014).
Chang, C.Y. et al. Mol. Cell. Proteomics 11, M111.014662 (2012).
Acknowledgements
P.P. is supported by a European Research Council Starting Grant (ERC-2013-StG–337965), a 'Foerderungsprofessur' grant from the Swiss National Science Foundation (PP00P3_133670), an EU Seventh Framework Program Reintegration grant (FP7-PEOPLE-2010-RG-277147) and a Promedica Stiftung (2-70669-11). M.S. is supported by a Natural Sciences and Engineering Research Council of Canada postgraduate scholarship D award (PGSD3-403808-2011). R.H. is supported by the Operational Program, Research and Development for Innovations (CZ.1.05/2.1.00/03.0124) and by a short-term EMBO fellowship (ASTF 475-20130). C.v.M. and S.W. acknowledge support by the SystemX.ch initiative. We thank our colleagues with experience in different aspects of yeast biology for their help: M. Peter, M. Kijanska, R. Deschant, S. Saad, A. Schreiber, C. Kraft and R. Loewith. We also thank M. Choi for assistance with MSstats and R. Shamir for insightful discussions. We are grateful to A. Bairoch and the UniProt team, as their excellent manual annotation of protein information in UniProt drastically accelerated our sentinel selection step. We thank the Peter laboratory (ETH Zurich) for the yeast strains used in this study and for sharing general lab reagents and equipment. We also thank Biognosys AG for assistance with scheduling SRM analyses using their iRT Kit.
Author information
Authors and Affiliations
Contributions
M.S., R.H. and P.P. designed the experiments. M.S., R.H., A. Melnik., T.W. and M.T. performed experiments and analyzed the data. S.W. and C.v.M. designed and implemented the prediction strategy for computationally suggesting sentinel proteins. P.B. and A. Maiolica contributed to MS measurements. M.S., R.H. and P.P. wrote the manuscript. P.P. conceived of and supervised the project.
Corresponding author
Ethics declarations
Competing interests
P.P. is a scientific advisor of the company Biognosys AG, which commercializes targeted proteomics assays.
Integrated supplementary information
Supplementary Figure 1 Ape1, example of a protein degradation sentinel.
Mature Ape1 (mApe1, blue) is produced from precursor Ape1 (pApe1, grey) when the propeptide region containing a vacuolar targeting sequence (red) is cleaved by proteinase B (Pep4). Cleavage, and thus the production of mApe1, is induced upon the activation of autophagy. The ratio of tApe1/pApe1 can be used to report on autophagy. A decrease or no regulation of pApe1 abundance combined with an increase in tApe1 abundance (i.e. a tApe1/pApe1 ratio >1) indicates a shift toward mApe1 and thus activation of autophagy. NH2 and COOH indicate the amino- and carboxy-termini of Ape1, respectively. In this study, pApe1 and mApe1 were counted as two protein species and quantified by SRM. For quantification, six peptides were retained from total Ape1 (tApe1) that are present in both forms, and pApe1 was represented by a peptide spanning the cleavage site. Detection of the peptide spanning the cleavage site could not be confirmed by the heavy-isotope labelled surrogate peptide spike-in experiment (Online Methods ) in the chosen conditions, but should be targeted in future experiments involving different sets of samples and conditions.
Supplementary information
Supplementary Text and Figures
Supplementary Figure1 and Supplementary Note (PDF 661 kb)
Protein-based sentinels
Biologically validated (A-grade) and predicted (B-grade) protein sentinels, which report on the activity of a specific biological process are listed and were selected as described in the Online Methods. The same protein may appear in mulitiple rows if it serves as a sentinel for multiple processess. Open reading frame (ORF) names are according to the Yeast Genome Database (www.yeastgenome.org). The reported “Sentinel Induction” and “Sentinel Function” information is from the Uniprot database (www.uniprot.org) and additional information is from the Yeast Genome Database. (XLSX 118 kb)
Phosphorylation-based sentinels
Phosphopeptides containing literature-cited phosphosites which report on the activation state of a specific biological process are listed. The same phosphopeptide may appear in mulitiple rows if it serves as a sentinel for multiple processess. A given phosphosite may also be represented by multiple peptides (e.g. missed cleavage or multiply phosphorylated forms). Open reading frame (ORF) names are according to the Yeast Genome Database (www.yeastgenome.org). Phosphorylated residues are labelled with [Pho]. “Included in the Sentinel Fingerprint Assay” indicates that the target was monitored in the perturbation experiment presented in this study and the associated SRM assay coordinates are in Supplementary Table 3. Sentinel phosphopeptides that were not detected in this study are also listed and should be targeted for quantification in other experiments based on this method. Where possible, an assay specific to the particular target residue was used (“Assay specificity” column, termed “p-site”), otherwise it is specific to the phosphorylated state of the peptide. The “Sentinel Role Reference” indicates the bibliographic reference where information on the sentinel role of the phosphopeptide can be found. Additional references extracted from the Uniprot database (www.uniprot.org, “Function”, “Post-translational modifications” and “Enzyme regulation” fields) provide related useful information for each phosphorylation-based sentinel. (XLSX 107 kb)
Complete set of SRM assays used in the sentinel fingerprint assay
The following tables (multiple worksheets) contain the SRM assay coordinates used to measure all peptides and proteins related to Figures 2 and 3, arranged by sentinel type (worksheet titles). (XLSX 124 kb)
Sentinel protein quantification data across eight perturbation experiments
All sentinels that were detected in at least one condition are shown. Quantification of normalized SRM peak areas is displayed as abundance fold change (FC) in a perturbation sample compared to the matching control sample. If the peptide was not detected (nd) in both the perturbation sample and in the associated control, as determined by manual inspection in Skyline, the quantification was assigned a “n/a” value. For the protein-sentinel measurements, peak areas were exported from Skyline and, for transitions below the average background level (1500), the peak areas were assigned to the average background level. Fold change (FC) cut-offs were calculated based on the number of biological replicates, peptides and transitions used in the measurement while retaining a statistical power > 0.8 (see Online Methods). A quantification result which did not meet both the FC and p-value cut-offs was deemed non-significant. For phosphorylation-based sentinels (second worksheet), the phosphorylated residue is represented in the peptide sequence with a “[+80]” label, denoting the mass of HPO3. In Figure 2, a simplified module name clusters phosphorylation-based sentinels, whereas here a more detailed description is presented of how process activity and phosphorylation state are related for each sentinel. Abbreviations: HS30, heat shock 30 min; HS60, heat shock 60 min; HSR, heat shock recovery; OS, osmo-stress; OSA, osmo-stress adapted; R, rapamycin; AA/N, amino acid and nitrogen starvation; S, stationary phase; SE, standard error; DF, degree of freedom. ORF, open reading frame. (XLSX 251 kb)
Responses of yeast to eight environmental perturbations characterized using the sentinel fingerprint assay
This table contains the same data as Figure 2; however, a copy of the matrix is shown on the right side which has the measured fold changes visible. Fold change values were taken from the quantification results found in Supplementary Table 4 and used to colour the matrix according to the bins shown in Figure 2. (XLSX 73 kb)
Protein sentinel detection by targeted MS
To directly compare the sentinel detection rate of SWATH- and SRM-MS acquisition, one replicate of the proteome digests from yeast subjected to osmotic stress was used. The number of proteins successfully detected (“x”) by SWATH and SRM was compared. (XLSX 20 kb)
Success rate of the sentinel fingerprint assay
The responses that either agree or disagree with the literature were counted for A- and B-grade sentinels. Responses of sentinels were evaluated based on consulting the “Sentinel role” references in Supplementary Tables 1 and 2 as well as published reviews about the perturbations applied (see references below). Only sentinels for which the connection between the perturbation and the biological process the sentinel reports on was clear were included in the calculation. For predicted sentinels, information on the process they are associated to and the known behaviour of that process in the considered perturbation was used. (XLSX 20 kb)
Prediction of sentinel proteins
Each protein in the yeast proteome, based on the Saccharomyces Genome Database (http://www.yeastgenome.org/), was scored for meeting a set of sentinel criteria (column headers). The rationale behind selecting each criterion and an example of the scoring calculation for two proteins is provided in the Online Methods and Supplementary Note. The scores for each criterion were weighted and summed to an overall sentinel score (SCORE_combined). The 30 highest scoring proteins were then monitored by SRM across eight perturbation experiments (Fig. 2). Each cell contains a value for the sentinel criterion and the weighted score that was calculated for this value. Included, in brackets within the column header, is the maximum value for that sentinel criterion. (XLSX 1128 kb)
Rights and permissions
About this article
Cite this article
Soste, M., Hrabakova, R., Wanka, S. et al. A sentinel protein assay for simultaneously quantifying cellular processes. Nat Methods 11, 1045–1048 (2014). https://doi.org/10.1038/nmeth.3101
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/nmeth.3101
This article is cited by
-
Mapping specificity, cleavage entropy, allosteric changes and substrates of blood proteases in a high-throughput screen
Nature Communications (2021)
-
Systems NMR: single-sample quantification of RNA, proteins and metabolites for biomolecular network analysis
Nature Methods (2019)
-
Co-occurring KRAS mutation/LKB1 loss in non-small cell lung cancer cells results in enhanced metabolic activity susceptible to caloric restriction: an in vitro integrated multilevel approach
Journal of Experimental & Clinical Cancer Research (2018)
-
Cost-effective generation of precise label-free quantitative proteomes in high-throughput by microLC and data-independent acquisition
Scientific Reports (2018)
-
A large-scale targeted proteomics assay resource based on an in vitro human proteome
Nature Methods (2017)