Revealing the cellular degradome by mass spectrometry analysis of proteasome-cleaved peptides


Cellular function is critically regulated through degradation of substrates by the proteasome. To enable direct analysis of naturally cleaved proteasomal peptides under physiological conditions, we developed mass spectrometry analysis of proteolytic peptides (MAPP), a method for proteasomal footprinting that allows for capture, isolation and analysis of proteasome-cleaved peptides. Application of MAPP to cancer cell lines as well as primary immune cells revealed dynamic modulation of the cellular degradome in response to various stimuli, such as proinflammatory signals. Further, we performed analysis of minute amounts of clinical samples by studying cells from the peripheral blood of patients with systemic lupus erythematosus (SLE). We found increased degradation of histones in patient immune cells, thereby suggesting a role of aberrant proteasomal degradation in the pathophysiology of SLE. Thus, MAPP offers a broadly applicable method to facilitate the study of the cellular-degradation landscape in various cellular conditions and diseases involving changes in proteasomal degradation, including protein aggregation diseases, autoimmunity and cancer.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Establishing proteasomal profiling by MAPP.
Figure 2: MAPP-identified peptides reveal properties of endogenous proteasome cleavage.
Figure 3: MAPP profiles distinguish PBMCs from patients with SLE and from healthy individuals.


  1. 1

    Rock, K.L. et al. Inhibitors of the proteasome block the degradation of most cell proteins and the generation of peptides presented on MHC class I molecules. Cell 78, 761–771 (1994).

    CAS  PubMed  Google Scholar 

  2. 2

    Schmidt, M. & Finley, D. Regulation of proteasome activity in health and disease. Biochim. Biophys. Acta 1843, 13–25 (2014).

    CAS  PubMed  Google Scholar 

  3. 3

    Hershko, A. & Ciechanover, A. The ubiquitin system for protein degradation. Annu. Rev. Biochem. 61, 761–807 (1992).

    CAS  PubMed  Google Scholar 

  4. 4

    Finley, D., Chen, X. & Walters, K.J. Gates, channels, and switches: elements of the proteasome machine. Trends Biochem. Sci. 41, 77–93 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

    Rock, K.L., York, I.A., Saric, T. & Goldberg, A.L. Protein degradation and the generation of MHC class I-presented peptides. Adv. Immunol. 80, 1–70 (2002).

    CAS  PubMed  Google Scholar 

  6. 6

    Rock, K.L., Reits, E. & Neefjes, J. Present yourself! By MHC class I and MHC class II molecules. Trends Immunol. 37, 724–737 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Rape, M. & Jentsch, S. Taking a bite: proteasomal protein processing. Nat. Cell Biol. 4, E113–E116 (2002).

    CAS  PubMed  Google Scholar 

  8. 8

    Aebersold, R. & Mann, M. Mass-spectrometric exploration of proteome structure and function. Nature 537, 347–355 (2016).

    CAS  Google Scholar 

  9. 9

    Doherty, M.K., Hammond, D.E., Clague, M.J., Gaskell, S.J. & Beynon, R.J. Turnover of the human proteome: determination of protein intracellular stability by dynamic SILAC. J. Proteome Res. 8, 104–112 (2009).

    CAS  PubMed  Google Scholar 

  10. 10

    Yen, H.-C.S., Xu, Q., Chou, D.M., Zhao, Z. & Elledge, S. J. Global protein stability profiling in mammalian cells. Science 322, 918–923 (2008).

    CAS  PubMed  Google Scholar 

  11. 11

    Savitski, M.M. et al. Multiplexed proteome dynamics profiling reveals mechanisms controlling protein homeostasis. Cell 173, 260–274.e25 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Kim, W. et al. Systematic and quantitative assessment of the ubiquitin-modified proteome. Mol. Cell 44, 325–340 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Mathieson, T. et al. Systematic analysis of protein turnover in primary cells. Nat. Commun. 9, 689 (2018).

    PubMed  PubMed Central  Google Scholar 

  14. 14

    McShane, E. et al. Kinetic analysis of protein stability reveals age-dependent degradation. Cell 167, 803–815.e21 (2016).

    CAS  PubMed  Google Scholar 

  15. 15

    Eden, E. et al. Proteome half-life dynamics in living human cells. Science 331, 764–768 (2011).

    CAS  PubMed  Google Scholar 

  16. 16

    Mann, M. & Kelleher, N.L. Precision proteomics: the case for high resolution and high mass accuracy. Proc. Natl. Acad. Sci. USA 105, 18132–18138 (2008).

    CAS  PubMed  Google Scholar 

  17. 17

    Aebersold, R. et al. How many human proteoforms are there? Nat. Chem. Biol. 14, 206–214 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Larance, M., Ahmad, Y., Kirkwood, K.J., Ly, T. & Lamond, A.I. Global subcellular characterization of protein degradation using quantitative proteomics. Mol. Cell. Proteomics 12, 638–650 (2013).

    CAS  PubMed  Google Scholar 

  19. 19

    Cardozo, C., Vinitsky, A., Hidalgo, M.C., Michaud, C. & Orlowski, M. A 3,4-dichloroisocoumarin-resistant component of the multicatalytic proteinase complex. Biochemistry 31, 7373–7380 (1992).

    CAS  PubMed  Google Scholar 

  20. 20

    Nussbaum, A.K. et al. Cleavage motifs of the yeast 20S proteasome beta subunits deduced from digests of enolase 1. Proc. Natl. Acad. Sci. USA 95, 12504–12509 (1998).

    CAS  PubMed  Google Scholar 

  21. 21

    Zhou, F. et al. Genome-scale proteome quantification by DEEP SEQ mass spectrometry. Nat. Commun. 4, 2171 (2013).

    PubMed  PubMed Central  Google Scholar 

  22. 22

    Feist, E., Burmester, G.-R. & Krüger, E. The proteasome: victim or culprit in autoimmunity. Clin. Immunol. 172, 83–89 (2016).

    CAS  PubMed  Google Scholar 

  23. 23

    Ullrich, O. et al. Poly-ADP ribose polymerase activates nuclear proteasome to degrade oxidatively damaged histones. Proc. Natl. Acad. Sci. USA 96, 6223–6228 (1999).

    CAS  PubMed  Google Scholar 

  24. 24

    Cervantes-Laurean, D., Roberts, M.J., Jacobson, E.L. & Jacobson, M.K. Nuclear proteasome activation and degradation of carboxymethylated histones in human keratinocytes following glyoxal treatment. Free Radic. Biol. Med. 38, 786–795 (2005).

    CAS  PubMed  Google Scholar 

  25. 25

    Perl, A. Oxidative stress in the pathology and treatment of systemic lupus erythematosus. Nat. Rev. Rheumatol. 9, 674–686 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Wang, C.H., Wu, S.B., Wu, Y.T. & Wei, Y.H. Oxidative stress response elicited by mitochondrial dysfunction: implication in the pathophysiology of aging. Exp. Biol. Med. (Maywood) 238, 450–460 (2013).

    Google Scholar 

  27. 27

    Cadenas, E. & Davies, K.J. Mitochondrial free radical generation, oxidative stress, and aging. Free Radic. Biol. Med. 29, 222–230 (2000).

    CAS  PubMed  Google Scholar 

  28. 28

    Kisselev, A.F., Kaganovich, D. & Goldberg, A.L. Binding of hydrophobic peptides to several non-catalytic sites promotes peptide hydrolysis by all active sites of 20 S proteasomes: evidence for peptide-induced channel opening in the alpha-rings. J. Biol. Chem. 277, 22260–22270 (2002).

    CAS  PubMed  Google Scholar 

  29. 29

    Schmidtke, G., Emch, S., Groettrup, M. & Holzhütter, H.-G. Evidence for the existence of a non-catalytic modifier site of peptide hydrolysis by the 20 S proteasome. J. Biol. Chem. 275, 22056–22063 (2000).

    CAS  PubMed  Google Scholar 

  30. 30

    Liepe, J. et al. Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes. eLife 4, e07545 (2015).

    PubMed  Google Scholar 

  31. 31

    Schaab, C., Geiger, T., Stoehr, G., Cox, J. & Mann, M. Analysis of high accuracy, quantitative proteomics data in the MaxQB Database. Mol. Cell. Proteomics 11, M111.014068 (2012).

    PubMed  PubMed Central  Google Scholar 

  32. 32

    Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative proteomic analysis of eleven common cell lines reveals ubiquitous but varying expression of most proteins. Mol. Cell. Proteomics 11, M111.014050 (2012).

    PubMed  Google Scholar 

  33. 33

    Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).

    PubMed  Google Scholar 

  34. 34

    Murakami, Y. et al. Ornithine decarboxylase is degraded by the 26S proteasome without ubiquitination. Nature 360, 597–599 (1992).

    CAS  PubMed  Google Scholar 

  35. 35

    Vlashi, E. et al. In vivo imaging, tracking, and targeting of cancer stem cells. J. Natl. Cancer Inst. 101, 350–359 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Philips, G.K. & Atkins, M. Therapeutic uses of anti-PD-1 and anti-PD-L1 antibodies. Int. Immunol. 27, 39–46 (2015).

    CAS  PubMed  Google Scholar 

  37. 37

    McKinney, E.F., Lee, J.C., Jayne, D.R.W., Lyons, P.A. & Smith, K.G.C. T-cell exhaustion, co-stimulation and clinical outcome in autoimmunity and infection. Nature 523, 612–616 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. 38

    Yang, W., Chen, P.W., Li, H., Alizadeh, H. & Niederkorn, J.Y. PD-L1: PD-1 interaction contributes to the functional suppression of T-cell responses to human uveal melanoma cells in vitro. Invest. Opthalmol. Vis. Sci. 49, 2518–2525 (2008).

    Google Scholar 

  39. 39

    Tan, E.M. et al. The 1982 revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 25, 1271–1277 (1982).

    CAS  PubMed  Google Scholar 

  40. 40

    Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    CAS  Google Scholar 

  41. 41

    Bassani-Sternberg, M., Pletscher-Frankild, S., Jensen, L.J. & Mann, M. Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation. Mol. Cell. Proteomics 14, 658–673 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    CAS  PubMed  Google Scholar 

  43. 43

    Mi, H. et al. The PANTHER database of protein families, subfamilies, functions and pathways. Nucleic Acids Res. 33, D284–D288 (2005).

    CAS  PubMed  Google Scholar 

  44. 44

    Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Google Scholar 

  45. 45

    Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes. Nucleic Acids Res. 36, D646–D650 (2008).

    CAS  PubMed  Google Scholar 

  46. 46

    Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes--2009. Nucleic Acids Res. 38, D497–D501 (2010).

    CAS  PubMed  Google Scholar 

  47. 47

    Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

    You, Y., Jang, I., Lee, K., Kim, H. & Lee, K. An approach for a substitution matrix based on protein blocks and physicochemical properties of amino acids through PCA. Interdiscip. Bio Cent. 6, 3.1–3.10 (2014).

    Google Scholar 

  49. 49

    Vizca&no, J.A. et al. 2016 update of the PRIDE database and its related tools. Nucleic Acids Res. 44, D447–D456 (2016).

    Google Scholar 

Download references


We thank K. Tanaka and S. Murata (University of Tokyo) for the anti-PSMA1 hybridoma, and J. Bar and I. Kamer (Sheba Medical Center) for the anti-PD-1 monoclonal antibody Nivolumab. We thank members of the Merbl laboratory and M. Sharon for critically reviewing the manuscript; S. Itzkovitz, M. Askenazi, S. Shen-Orr, I. Amit and E. Elinav for scientific advice; and M. Kupervaser and A. Savidor for analytic support. This project received funding from Merck KGaA; the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme (grant agreement 677748); the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation (grant 12/7751); Rothschild Caesarea; The Peter & Patricia Gruber Foundation (Gruber award); Adiel Eleanor, Carla Hunter and Andre Schub (Y.M.); and the Intramural Research Program at NIAMS/NIH ZIA AR041199 (C.C.-R. and M.J.K.). Y.M. is supported as the incumbent Leonard and Carol Berall Career Development Chair.

Author information




Y.M., H.W.-L., A.E.-L., A.U., D.S., M.P.K., N.N. and I.R. designed and performed experiments and interpreted the data. Y.M., A.J., H.W.-L., A.K., B.D., V.F.-Y. and L.Z. performed and interpreted computational analyses. Y.M., A.E.-L. and A.J. wrote the manuscript. H.W.-L., D.E., D.M. and Y.L. performed MS analysis. C.C.-R. and M.J.K. administered the human SLE research and provided clinical advice. Y.M. conceived the concept and oversaw the study.

Corresponding authors

Correspondence to Avital Eisenberg-Lerner or Yifat Merbl.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 MAPP Immuno-purification protocol robustly captures proteasome complexes

(a) Proteasomes were immunoprecipitated from HEK293 cells using a dose curve of an antibody targeting PSMA1. The precipitate was separated on SDS-PAGE and analyzed by western blot probed with a pan α-20s proteasome antibody. Shown is representative of two individual dose curves. (b) PSMA1 was immunoprecipitated according to the MAPP protocol and the immune complex was separated by SDS-PAGE and analyzed by western blot probed with antibodies against the immunoprecipitated PSMA1, as well as co-precipitated 20S subunits of the proteasome PSMA1-7 PSMB5 and PSMB7 and against the 19S subunits PSMD6 and PSMD11. Mock: empty beads; TCE: Total cell extract. (c) MAPP conditions were calibrated to evaluate non-specific binding of cellular peptides to empty beads (mock), versus α-PSMA1-conjugated beads (MAPP), or without addition of protease inhibitors (- Protease Inhibitors). The number of unique peptides significantly decreased when using empty beads and significantly increased when no protease inhibitors was used. Bars: mean ±standard deviation (Welch’s corrected one tailed t test, *p = 0.0230, *p = 0.0348, **p = 0.0077). (d) The intensity and identity of peptides identified from an immunoprecipitation using α-PSMA1 antibody and α-PSMA4, an antibody against a different subunit of the proteasome, correlated with one another (R2 = 0.738). n = 4375 peptides. (e) A representative pull down of GAPDH was separated by SDS-PAGE and analyzed by western blot. TCE: total cell extract.

Supplementary Figure 2 Peptides captured and identified by MAPP are produced in a proteasome dependent manner.

(a) Extracted ion chromatograms of three peptides identified by MAPP from cells under basal conditions or following treatment with the proteasome inhibitor epoxomicin (1μM, 4 hours). M+1 and M+2 represent first and second isotopes of the precursor ion respectively. (b) The fold changes in intensity of peptides identified by MAPP between untreated cells (UT) and cells treated with Velcade (50nM, 4 hours) were ranked and displayed as a bar diagram. Those with a 2-fold change or greater were marked in orange, and those that decreased by 2-fold or more were marked in blue. Of the 3505 peptides identified by MAPP, 2463 peptides or 70% of the total were reduced by at least 2 fold in intensity. (c) The abundance of PSMA1 in the MAPP protein fraction normalized to the mean abundance (Log 2 normalized ratio of LFQ intensities; Normalization was done to the median of the isotype control; Bars: mean + range of the triplicates). (d) Protein abundance was determined by standard proteomics in untreated cells (UT) or following cycloheximide treatment (100μg/ml, 2 hours). Bars: mean ± standard deviation.

Supplementary Figure 3 Peptides from known proteasomal substrates are detected by MAPP.

(a) Cells were transfected with ZsGreen fused to the proteasomal degradation domain of mouse ornithine decarboxylase, and were either treated with the proteasome inhibitor epoxomicin (1μM, 4 hours) or left untreated, and analyzed by MAPP. As control, untransfected cells were analyzed. Shown are extracted fragment ion chromatograms of the parallel reaction monitoring mass spectrometry for two peptides which were ZsGreen degradation products. The peaks were not detected in cells treated with epoxomicin or in untransfected cells (UT).

Supplementary Figure 4 Bioinformatics processing of MAPP identified peptides.

(a) Bioinformatics cutoff of raw MAPP peptides allowed peptides that had at least two valid intensities out of three independent biological replicates (“replicates filtering”), and included razor peptides which belong to a unique MaxQuant “protein group”. To eliminate background peptides, we considered only peptides that were detected at least 2-fold higher in the PSMA1 immunoprecipitate than in the mock beads control (“beads filtering”, 440 peptides were removed at this stage. The shown filtering process was performed on HEK293 in biological triplicates. (b) Twenty-two percent of the proteins identified by MAPP were not detected in parallel analysis of the sample using standard proteomics.

Supplementary Figure 5 Proteins specifically identified by MAPP differ in biochemical properties from those identified by standard proteomics.

(a) Identified proteins were subdivided into three categories: those identified solely by MAPP (denoted as [A]), those identified both in standard proteomics and MAPP (denoted as shared [B]) and those identified solely through standard proteomics (denoted [C]). The expected average protein abundance and turnover are listed for each group. (b) Protein turnover rate calculations adapted from Schwanhäusser et al.1 where P indicates protein abundance and δ is the protein degradation rate. The amount of protein degraded per hour, or Protein Turnover, is the product of δ and P (c) Protein abundance inferred from the deep proteomics of HEK293 cells deposited in the MaxQB database2,3 were applied to the three groups described in A (n = 65 [A], 779 [B], 3250 [C] proteins). (d) The protein turnover calculated from a dataset generated by Larance et al4. was applied to the three groups described in a using calculations described in b (n = 32 [A], 753 [B], 2509 [C] proteins). (e) Endogenous proteasome-cleaved peptides demonstrated a varied distribution along the sequence of their source protein, with higher peptide frequencies of the N- and C-termini, while tryptic-digestion showed a uniform frequency (protein length was normalized to 100%). (f) Correlation of amino acid composition within peptides identified by either MAPP or standard proteomics analysis (39,700 and 330,127 peptides, respectively).1. Schwanhäusser, B. et al. Global quantification of mammalian gene expression control. Nature 473, 337–342 (2011).2. Schaab, C., Geiger, T., Stoehr, G., Cox, J. & Mann, M. Analysis of High Accuracy, Quantitative Proteomics Data in the MaxQB Database. Mol. Cell. Proteomics 11, M111.014068 (2012).3. Geiger, T., Wehner, A., Schaab, C., Cox, J. & Mann, M. Comparative Proteomic Analysis of Eleven Common Cell Lines Reveals Ubiquitous but Varying Expression of Most Proteins. Mol. Cell. Proteomics 11, M111.014050 (2012).4. Larance, M., Ahmad, Y., Kirkwood, K. J., Ly, T. & Lamond, A. I. Global Subcellular Characterization of Protein Degradation Using Quantitative Proteomics. Mol. Cell. Proteomics 12, 638–650 (2012).

Supplementary Figure 6 Perturbations of cellular steady state change MAPP- identified proteins and intensities.

HEK293 cells were treated with TNFα + IFNγ (20ng/ml, 24 hours, denoted T+I), or left untreated (denoted UT) and analyzed using both MAPP and standard proteomics. (a) The proteins identified by both approaches were annotated into 23 different protein classes (based on Panther annotations). Similar percentages of protein classes were classified to the proteins identified by either MAPP or standard proteomics analyses. (b) The percent of MAPP-identified proteins in each protein class (as annotated in Panther) which were also identified by standard proteomics. A red line is indicated at the 70% threshold used. (c) The percent of proteins in each class that differed by 2-fold or more in intensity between cells treated with TNFα + IFNγ to untreated cells was determined. In most of the protein classes, the percentage of the proteins that changed in intensity between the examined conditions was greater for proteins identified by MAPP in comparison to standard proteomics (d) The ranked fold change between cells treated with TNFα + IFNγ (T+I) and untreated cells (UT) in MAPP (top, n = 1172) or standard proteomics (bottom, n = 5009). (e+f) HEK293 cells were treated with TNFα + IFNγ (20ng/ml, 24 hours, denoted T+I), or left untreated (denoted UT) and analyzed using both MAPP and standard proteomics. Proteins that are annotated in the CORUM1,2 database to be part of the (e) spliceosome complex (f) ribosome complex are marked in green or blue, respectively. (g) Proteins annotated to be part of the ubiquitin proteasome system (UB components) in the KEGG database are marked in teal.1. Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes. Nucleic Acids Res. 36, D646–50 (2008).2. Ruepp, A. et al. CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Res. 38, D497–D501 (2010).

Supplementary Figure 7 MAPP highlights changes in the proteolytic landscape in response to pro-inflammatory cytokine stimulation.

(a) Schematic of experiment. HEK293 cells were grown for 42 hours after seeding and then stimulated with TNFα + IFNγ for the time indicated. All cells were collected 48 hours after seeding. (b-f) HEK293 cells were treated with TNFα + IFNγ (20ng/ml, T+I) for 1, 2 or 6 hours, or left untreated (UT) and analyzed using both MAPP and standard proteomics. (b) Directed clustering of MAPP-identified protein intensities after stimulation of cells with TNFα + IFNγ for 1, 2, or 6 hours normalized to 0 hours. Only proteins which changed in intensity by at least 2-fold in at least one time point are displayed (city-block distance function on median values distance matrix, Normalized Log2 transformed intensity ratios). (c) Directed clustering of the changes in intensity of proteins identified by MAPP (Log 2 transformed ratio of LFQ intensities at 1 or 2 hours of treatment normalized to 0 hours) reveals four groups of proteins that either increase or decrease over two hours of treatment with TNFα + IFNγ (T+I) (d) The abundance of selected proteins from these four clusters, known to be involved in inflammatory signals, as identified by standard proteomics and MAPP (left, Log2 ratio of standard proteomics LFQ intensities normalized to 0h; right, Log 2 normalized ratio of MAPP LFQ intensities after 1, 2 or 6 hours of TNFα + IFNγ to 0 hours). (e) Examples of the intensities of two of the proteins listed in (d), ZFAND5 and CNBP, as identified by either standard proteomics or MAPP (Log2 Normalized LFQ intensity ratios between the indicated time point and 0h). (f) A network of the 127 proteins found in the four clusters in (d). Proteins are organized by functional groups, node color indicates the highest differential ratio of abundance between MAPP after 1 or 2 hours of TNFα + IFNγ stimulation (T+I) to 0 hours (UT) (Log 2 normalized LFQ intensity ratio). Node border thickness indicates abundance of the given proteins as detected by standard proteomics (Log 10 normalized LFQ intensity). Interactions are indicated by lines between nodes (STRING interaction confidence score is indicated by line width).1. Takada, H. et al. Role of {SODD} in Regulation of Tumor Necrosis Factor Responses. Mol. Cell. Biol. 23, 4026–4033 (2003).2. Huang, J. et al. ZNF216 Is an A20-like and IκB Kinase γ-Interacting Inhibitor of NFκB Activation. J. Biol. Chem. 279, 16847–16853 (2004).3. Liscovitch, N. & French, L. Differential co-expression between α-synuclein and IFN-γ signaling genes across development and in parkinson's disease. {PLoS} {ONE} 9, e115029 (2014).4. Lee, E. et al. CNBP acts as a key transcriptional regulator of sustained expression of interleukin-6. Nucleic Acids Res. 45, 3280–3296 (2017).5. Yan, W. et al. System-based proteomic analysis of the interferon response in human liver cells. Genome Biol. 5, R54 (2004).6. JUNG, Y. et al. Hypoxia-inducible factor induction by tumour necrosis factor in normoxic cells requires receptor-interacting protein-dependent nuclear factor kappaB activation. Biochem. J. 370, 1011–1017 (2003).7. Yeh, J. E. et al. Granulin, a novel STAT3-interacting protein, enhances STAT3 transcriptional function and correlates with poorer prognosis in breast cancer. Genes Cancer 6, 153–68 (2015).

Supplementary Figure 8 Identification of patient-specific proteolytic signatures of T-cells exhaustion in the PD-1 immune-checkpoint.

(a) IL-7Rα and PD-1 levels of CD8+ T-cells purified from human blood were evaluated by flow cytometry analysis after activation with CD3/CD28 and following 7 days of co-culture with a Melanoma-derived cell line. Cells were first gated for lymphocytes followed by a single cell gate (grey box). n= 7700 cells (PD1), 12,249 cells (CD127). (b) Pearson correlation coefficients between MAPP proteins across the examined time points (lower triangle, at exhaustion, T0, and 30 minutes or 6 hours after PD-1 blockade), and across individuals (upper triangle, P1, P2, P3). n=1293 proteins for each pairwise comparison. (c) Clustering the intensities of MAPP proteins (which were detected in at least two individuals) across the temporal response to PD-1 blockade (0, 30’, 6h) revealed common proteolytic signatures (P1, P2, P3); Euclidean distance function, log10 intensity values) (d) Volcano plot representation of the relative degradation intensities of proteins detected by MAPP in cytotoxic CD8+ T cells, either at time zero (T0, exhaustion) and 6 hours after reactivation by PD-1 blockade. Median values represent at least 2 individuals, and color codes for the degree of fold change between T0 and 6 hours; Two sided t test, no correction for multiple comparisons. (e) Schematic representation of PD-1 related signaling pathways, adapted from the T-cell receptor signaling (KEGG) pathway. Proteins that were detected by MAPP as actively degraded, in either of the time points, are labeled in a blue oval including components of T cell activation, cytoskeleton organization and ubiquitination (f) Changes detected in protein abundance and degradation signatures of selected proteins, related to the PD-1 signaling pathways, at either exhaustion, or after 30 minutes or 6 hours of PD-1 blockade. Values represent mean intensities (log 10) error bars indicate standard deviation.1. Martinez, G. J. et al. The Transcription Factor NFAT Promotes Exhaustion of Activated CD8+ T Cells. Immunity 42, 265–278 (2015).2. Radu, M., Semenova, G., Kosoff, R. & Chernoff, J. PAK- signalling during the development and progression of cancer. Nat. Rev. Cancer 14, 13–25 (2014).3. Venuprasad, K. Cbl-b and Itch: Key Regulators of Peripheral T-cell Tolerance. Cancer Res. 70, 3009–3012 (2010).

Supplementary Figure 9 MAPP separates PBMC samples from SLE and Healthy Patients more robustly than Standard Proteomics.

(a) Principal component analysis of the 250 most intense proteins from PBMCs of healthy individuals or SLE patients, as identified by standard proteomics. (b) 149 proteins were detected both by MAPP and standard proteomics. Principal component analysis of the protein intensities of these 149 proteins from PBMCs of healthy individuals or SLE patients, as identified by standard proteomics. (c) The average distance between all samples within a group (within) and each intergroup sample distance (between) was calculated for principle component analysis performed on a randomly selected group of 249 proteins from the proteomics identified set. This was performed 10,000 times and the distribution of the between/within ratio is plotted. The between within ratio of the 24 protein intensities identified by MAPP is significantly higher (p < 0.001), signifying the dataset characterizes the samples better than the corresponding standard proteomics sets.

Supplementary Figure 10 Examples of proteins which differ in MAPP of SLE Patients and Healthy Individuals.

(a + b) Scatterplot of protein intensities in the proteomics and MAPP datasets for (a) healthy individuals, n = 148 proteins or (b) SLE patients, n=143 proteins. The proteins which differ by 2-fold or greater in MAPP of SLE and healthy PBMCs are marked in blue/red. (c) The total protein abundance of histones as measured by standard proteomics, was not changed between healthy and SLE PBMCs. n = 26 (histones), 5067 (other). Line indicates mean. (d+e) The peptides identified in MAPP of PBMCs of healthy individuals (top) or SLE patients (bottom) which map to (c) HIST1H4A or (d) CHTF8 (residues 330-430), examples of histones or other nuclear proteins, respectively. Color scale indicates peptide intensity (Log10 transformed intensities).

Supplementary Figure 11 Different pathways are expressed in the proteomes of SLE Patients and Healthy Individuals.

Gene Set Enrichment Analysis (GSEA) was performed using the protein abundances in the proteomes of SLE Patients and Healthy Individuals as determined by standard proteomics. The HALLMARK pathways which passed the default GSEA FDR cutoff (25%) are presented. The normalized enrichment score indicates if the pathway is enriched in SLE patients (red bars, > 0) or healthy individuals (blue bars, < 0).1. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A. 102, 15545–50 (2005).

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–11 (PDF 1555 kb)

Life Sciences Reporting Summary (PDF 207 kb)

Supplementary Data 1

26S proteasome subunits identified in MAPP eluate. (XLSX 9 kb)

Supplementary Data 2

Peptides identified by MAPP of cells stimulated with TNFα and IFNγ or left untreated (XLSX 456 kb)

Supplementary Data 3

Proteins with increased degradation and unchanged abundance. (XLSX 16 kb)

Supplementary Data 4

Proteins clustered by degradation trend (XLSX 10 kb)

Supplementary Data 5

PD-1 blockade changed MAPP intensity by 2-fold or more in 168 proteins (XLSX 19 kb)

Supplementary Data 6

45 components of the ubiquitin system (XLSX 11 kb)

Supplementary Data 7

Patient information (XLSX 98 kb)

Supplementary Data 8

Peptides identified in MAPP of SLE patients and healthy individuals (XLSX 154 kb)

Supplementary Note

Extended Mass Spectrometry Methods (PDF 405 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wolf-Levy, H., Javitt, A., Eisenberg-Lerner, A. et al. Revealing the cellular degradome by mass spectrometry analysis of proteasome-cleaved peptides. Nat Biotechnol 36, 1110–1116 (2018).

Download citation

Further reading