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Multilayered proteomics reveals molecular switches dictating ligand-dependent EGFR trafficking

Nature Structural & Molecular Biology volume 23, pages 608–618 (2016)Cite this article

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Abstract

A fascinating conundrum in cell signaling is how stimulation of the same receptor tyrosine kinase with distinct ligands generates specific outcomes. To decipher the functional selectivity of EGF and TGF-α, which induce epidermal growth factor receptor (EGFR) degradation and recycling, respectively, we devised an integrated multilayered proteomics approach (IMPA). We analyzed dynamic changes in the receptor interactome, ubiquitinome, phosphoproteome, and late proteome in response to both ligands in human cells by quantitative MS and identified 67 proteins regulated at multiple levels. We identified RAB7 phosphorylation and RCP recruitment to EGFR as switches for EGF and TGF-α outputs, controlling receptor trafficking, signaling duration, proliferation, and migration. By manipulating RCP levels or phosphorylation of RAB7 in EGFR-positive cancer cells, we were able to switch a TGF-α-mediated response to an EGF-like response or vice versa as EGFR trafficking was rerouted. We propose IMPA as an approach to uncover fine-tuned regulatory mechanisms in cell signaling.

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Figure 1: EGFR trafficking and responses depend on biased ligands.
Figure 2: Multilayered proteomics of EGFR signaling shows ligand-specific EGFR regulation.
Figure 3: Analysis of dynamic EGFR signaling indicates ligand-dependent regulation of phosphorylation and ubiquitination.
Figure 4: Biased EGFR ligands differentially promote the cross-talk between phosphorylation and ubiquitination.
Figure 5: RAB7 phosphorylation on Y183 is important for EGF-dependent EGFR degradation and outcomes.
Figure 6: RCP promotes TGF-α-dependent EGFR recycling.
Figure 7: RCP mediates TGF-α-dependent cellular responses.
Figure 8: Model of EGFR trafficking, signaling, and responses, based on this study.

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Acknowledgements

The authors thank all laboratory members for fruitful discussions, especially J. Daniel for critical input on the manuscript. We thank Y. Chen (Shanghai Medical College), M. Zerial (Max Planck Institute of Molecular Cell Biology and Genetics), F. Senic-Matuglia and B. Goud (Institut Curie), B. van Deurs (University of Copenhagen), and U. Cavallaro (European Institute of Oncology) for providing reagents. Work at The Novo Nordisk Foundation Center for Protein Research (CPR) is funded in part by a generous donation from the Novo Nordisk Foundation (grant no. NNF14CC0001). The proteomics technology developments applied were part of a project that has received funding from the European Union's Horizon 2020 Research and Innovation Programme under grant agreement 686547 (J.V.O.). The work was supported by the 7th Framework Programme of the European Union (201648-PROSPECTS, 262067- PRIME-XS and 290257-UPStream to J.V.O.); the Danish Research Council (research career program FSS Sapere Aude to J.V.O.); the Junior Group Leader Fellowship by the Lundbeck Foundation (to B.B.); and the Marie Curie IEF and EMBO Long-Term postdoctoral fellowships (to C.F.).

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  1. Kristoffer T G Rigbolt

    Present address: Present address: Gubra, Horsholm, Denmark.,

Authors and Affiliations

  1. Proteomics Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Chiara Francavilla, Moreno Papetti, Anna-Kathrine Pedersen, Jon O Sigurdsson & Jesper V Olsen

  2. Department of Biochemistry and Molecular Biology, Center for Experimental Bioinformatics, University of Southern Denmark, Odense, Denmark

    Kristoffer T G Rigbolt & Blagoy Blagoev

  3. Protein Structure and Function Program, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Giuseppe Cazzamali

  4. Protein Imaging Platform, Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark

    Gopal Karemore

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Contributions

M.P. performed the experiments shown in Figures 2e,f, 3e,f, 5e, and 6b,h, and Supplementary Figure 7a,b,g,h. K.T.G.R. contributed to data analysis shown in Figures 2c, 3a–d, 4, 6a,c, and 7a, and Supplementary Figures 2,3,4. J.O.S. performed the proteome experiment described in Figures 2c and 7a, and Supplementary Figure 2a. G.C. prepared the RAB7-mutant construct. A.-K.P. performed the experiments shown in Supplementary Figure 1a, and Figures 6a,b and 2g. G.K. contributed to quantification of immunofluorescence experiments. B.B. edited the manuscript and discussed the results. C.F. generated and analyzed the data shown in the remaining figures. C.F. and J.V.O. designed the experiments, critically evaluated the results, supervised M.P. and J.O.S., and wrote the manuscript.

Corresponding authors

Correspondence to Chiara Francavilla or Jesper V Olsen.

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The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Endogenous EGFR trafficking and responses depend on biased ligands.

(a) Lysates from HeLa cells stimulated for 2 min. with increasing concentration of ligands were immunoblotted as indicated. (b) Object-based co-localization (see Online Methods) of the experiment shown in Fig. 1d. Values in the graph represent the median ± SD of four images from independent experiments. *, p value<0.02 (Wilcoxon test with alpha 0.05). These results were consistent with those shown in Fig. 1c. (c) Quantification (see Online Methods) of the presence of EGFR in endocytic markers-positive regions upon EGF- or TGF-α-stimulation. Values in the graph represent the means ± SD. of three experiments. A.U., arbitrary units.*, p value<0.01. (d) Representative images from (c), showing the presence of EGF- or TGF-α-stimulated EGFR (red) in intracellular markers (green)-positive regions at different time intervals. Activated EGFR is in blue. Bar, 5 μm. See also Supplementary Data Set 1 for uncropped gels.

Source data

Supplementary Figure 2 Assessment of the multilayered proteomics dataset shows overall data quality.

(a) Experimental design of MS-based quantitative analysis of EGF- and TGF-α-induced signaling dynamics and changes in protein abundance. (b) The correlation of each of the five data sets shows good reproducibility between biological replicates. The distribution of peptide mass error (c) and peptide intensity (d) shows that most of the peptides were identified with high mass accuracy (mass error less than 2 p.p.m.) and high intensity. (e) Number of identified sites and proteins at each time point. See Fig.3a for number of regulated sites at each time point. (f) Distribution of Serine (Ser), Threonine (Thr) and Tyrosine (Tyr) phosphorylated sites identified in this study. (g) Distribution of phosphopeptides with one, two, or more phosphorylated sites. (h) Distribution of ubiquitylated peptides with one, two, or more ubiquitylated sites.

Supplementary Figure 3 Clustering and S-score analyses reveal ligand-dependent regulation of phosphorylation and ubiquitination.

(a) Left, clustering based on fuzzy c-means of the 330 phosphorylation sites and 52 ubiquitylated sites quantified at all time points. Right, number of sites in each cluster. Each cluster included both phosphorylated and ubiquitylated sites whose SILAC ratios were as similar as possible within the same cluster and as dissimilar as possible between clusters (see Online Methods). However, sites with similar patterns were not necessarily co-regulated and sites could belong to more than one cluster. For instance, phosphorylated tyrosine 998 on EGFR belonged to cluster 4 in case of EGF-stimulated cells and to cluster 1 in case of TGF-α-treated samples. This indicates a different regulation based on experimental conditions. Ubiquitylated lysine K867 on EGFR belonged to cluster 4 in case of EGF-stimulated cells, showing similar regulation as tyrosine 998. On the contrary, it was found in cluster 5 in TGF-α-treated samples, suggesting again context-dependent regulation. (b) GO terms enrichment analysis of clustering shown in (a). (c) Phosphorylation and ubiquitylation sites were portioned in five equally sized groups based on S-score (Rigbolt, K.T. et al. Sci Signal 4, rs3, 2011), and each group was tested for over-represented GO Biological Process terms compared to the sites from the remaining groups. Enriched GO terms for the most stimulus-specific sites are found at the left (S score<40) whereas events common to both stimuli increase towards the right (S score >40). The results are color-coded to indicate the S-score percentile bins where the GO term was found over-represented. GO terms with S-score <40, whose members are shown in (d), are highlighted in the magenta box. (d) Overview of the 66 ubiquitylated proteins that are differentially regulated between EGF and TGF-α, color-coded based on the clustering shown in (a). Two proteins have been found to be specifically regulated by TGF-α in the late proteome analysis (magenta font).

Supplementary Figure 4 The cross-talk between phosphorylation and ubiquitination is stimulus dependent.

Heat maps of the 25 doubly modified proteins shown in Fig.4d-e and color-coded according to regulation upon EGF or TGF-α. Phosphorylated proteins are shown on the left and ubiquitylated proteins on the right. The validated candidate Rab7A is shown in magenta.

Supplementary Figure 5 RAB7 phosphorylation on Y183 is enhanced by EGF.

(a) Experimental design of MS-based quantitative analysis of EGF- or TGF-α-dependent phosphoproteome in HeLa cells stimulated for 1, 8, 90 min. (b) The correlation between EGF vs. TGF-α-stimulated cells in two biological replicates shows an overall similar phosphoproteome. The proteins whose phosphorylated site is differentially regulated upon one or the other stimulus are highlighted in colors (blue, EGF regulated; green, TGF-α regulated). (c, d) Quantitation by MS of Erk1 and Erk2 Y202/T204-containing doubly phosphorylated peptide (c) and Rab7 Y183 phosphorylated peptide (d) upon EGF (blue) or TGF-α (green) stimulation. Values are the median ± SD of two replicates. A.U., arbitrary units In (d) the values of the experiment shown in Supplementary Table 2 are also reported for comparison. Data presented in (c) confirmed the temporal profiles of Erk activation shown in Fig. 2d-f and in Supplementary Tables 2 and 6.

Supplementary Figure 6 RAB7 phosphorylation on Y183 influences the RAB7 interactome.

(a) Experimental design of MS-based quantitative analysis of EGF-stimulated cells transfected with Rab7 or its mutant. (b) The correlation of the two biological replicates shows good reproducibility. (c) Ion eXtracted chromatogram of the wild type and Y→F mutant Rab7 peptide. (d) Full-scan MS (left) and representative MS/MS spectrum (right) of the ubiquitylated K191 peptide of wt and mutant Rab7. (d) Table with information of the chemical composition of the Rab7 peptides. (e) List of the significant interactors of mutant Rab7 with a role in endocytosis upon EGF stimulation. (f) Lysates from not-stimulated HeLa cells transfected with Rab7-GFP were immunoblotted as indicated. See also Supplementary Data Set 1 for uncropped gels.

Supplementary Figure 7 The phosphorylation and ubiquitination patterns on EGFR and EGFR interactors are ligand dependent.

(a, b) Lysates from stimulated cells were immunoprecipitated and immunoblotted with the indicated antibodies. pY, anti-phosphorylated Tyr antibody. pY1045, anti-phosphorylated Tyr1045 antibody. This antibody recognizes Y1069 on EGFR sequence. pY1068, anti-phosphorylated Tyr1068 antibody. This antibody recognizes Y1092 on EGFR sequence. P4D1, antibody recognizing both polyubiquitin and monoubiquitin (Haglund, K. et al., Nat Cell Biol 5, 461-6 2003). FK1, antibody recognizing only polyubiquitin (both as ubiquitin monomers and polymers) but not single ubiquitin monomers. Since EGFR was immunoreactive with P4D1 but not with FK1 antibody, we concluded that the receptor was monoubiquitinated. Monoubiquitination has a role in lysosomal degradation, protein-protein interaction or recruitment to a certain sub-cellular compartment (Komander, D. & Rape, M. Annu Rev Biochem 81, 203-29 2012). K48, antibody recognizing ubiquitin linked to Lysine 48, a marker for proteasomal degradation or transcription factor regulation Komander, D. & Rape, M. Annu Rev Biochem 81, 203-29 2012). K63, antibody recognizing ubiquitin linked to Lysine 63, a marker for lysosomal degradation, signaling, receptor trafficking or protein-protein interaction3. (c) Lysates from stimulated HeLa cells were immunoblotted as indicated before immunoprecipitation. (d) Summary of the estimated occupancy of phosphorylated tyrosine and ubiquitylated lysine residues on EGFR at different time points. (e) Top, lysates from stimulated HeLa cells were immunoblotted as indicated before immunoprecipitation of HRS (also known as HGS). Bottom, log2 ratio of the difference between EGF and TGF-α of the identified phosphorylated tyrosine residues on HRS at 8 min. stimulation. These data together indicate that HRS was more phosphorylated on tyrosine residues upon EGF than upon TGF-α stimulation. (f) Overview of the PTM regulation on the EGFR interactors identified by MS. Each square indicates a time point and the color-scale represents the difference between the regulation upon EGF divided by the regulation upon TGF-α stimulation. (g, h) Lysates from stimulated HeLa cells were immunoprecipitated and immunoblotted with the indicated antibodies. See also Supplementary Data Set 1 for uncropped gels.

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Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–7 (PDF 2193 kb)

Supplementary Data Set 1

Uncropped versions of all western blots (PDF 698 kb)

Supplementary Table 1

Summary of the data from two biological replicates of HeLa cells stimulated with either EGF or TGF-α for 1, 8, 40 and 90 min. (XLSX 1888 kb)

Supplementary Table 2

Summary of the data from two biological replicates of HeLa cells stimulated with either EGF or TGF-α for 1, 8, 40 and 90 min. (XLSX 434 kb)

Supplementary Table 3

Summary of the EGFR interactome data. From the 855 identified proteins all common contaminants including keratins, ribosomal and proteasomal proteins have been filtered out. (XLSX 22 kb)

Supplementary Table 4

Summary of the late proteome data. List of identified and quantified proteins. (XLSX 1641 kb)

Supplementary Table 5

Summary of non-redundant proteins. Proteins regulated in one or more datasets are indicated with a plus. (XLSX 347 kb)

Supplementary Table 6

Summary of the data from two biological replicates of HeLa cells stimulated with either EGF or TGF-α for 1, 8, and 90 min. (XLSX 76 kb)

Supplementary Table 7

List of antibodies used in this study. (XLSX 16 kb)

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Francavilla, C., Papetti, M., Rigbolt, K. et al. Multilayered proteomics reveals molecular switches dictating ligand-dependent EGFR trafficking. Nat Struct Mol Biol 23, 608–618 (2016). https://doi.org/10.1038/nsmb.3218

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