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Integrative proteomics reveals an increase in non-degradative ubiquitylation in activated CD4+ T cells

Nature Immunology volume 20, pages 747–755 (2019)Cite this article

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Abstract

Despite gathering evidence that ubiquitylation can direct non-degradative outcomes, most investigations of ubiquitylation in T cells have focused on degradation. Here, we integrated proteomic and transcriptomic datasets from primary mouse CD4+ T cells to establish a framework for predicting degradative or non-degradative outcomes of ubiquitylation. Di-glycine remnant profiling was used to reveal ubiquitylated proteins, which in combination with whole-cell proteomic and transcriptomic data allowed prediction of protein degradation. Analysis of ubiquitylated proteins identified by di-glycine remnant profiling indicated that activation of CD4+ T cells led to an increase in non-degradative ubiquitylation. This correlated with an increase in non-proteasome-targeted K29, K33 and K63 polyubiquitin chains. This study revealed over 1,200 proteins that were ubiquitylated in primary mouse CD4+ T cells and highlighted the relevance of non-proteasomally targeted ubiquitin chains in T cell signaling.

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Fig. 1: Proteasome inhibitor MG132 prevents robust T cell activation.
Fig. 2: Quantitative mass spectrometry reproducibly identifies over 5,500 proteins in the CD4+ T cell proteome during TCR stimulation.
Fig. 3: Di-glycine remnant enrichment reveals that ubiquitylation is associated with increases or decreases in total protein abundance during TCR stimulation.
Fig. 4: RNA-seq data reveal disparate changes in RNA and corresponding protein abundances during TCR stimulation.
Fig. 5: Ubiquitin targets multiple proteins within the TCR pathway and elicits both degradative and non-degradative outcomes.
Fig. 6: Integrating ubiquitin proteomics, WCP and RNA-seq data reveals a prevalence of non-degradative ubiquitylation.

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Data availability

The RNA-seq data in this publication have been deposited in NCBI’s Gene Expression Omnibus54 and are accessible through GEO Series accession number GSE128154. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE55 partner repository with the dataset identifier PXD012831.

Code availability

The data were analyzed using standard algorithms for data manipulation, quantification and statistical analysis. The implementation of the analysis was performed using the R software. The scripts are available from the corresponding author upon request or can be accessed via GitHub, https://github.com/JosephDybas/TcellReceptorProteomics.

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Acknowledgements

The authors wish to acknowledge the helpful discussion and methods development insight from H. Fazelinia and D. Taylor at the Children’s Hospital of Philadelphia. This work was supported by NIH grants to P.M.O. (grant nos. R01 AI093566 and R01 AI114515), and an NRSA to C.E.O. (grant no. F31 CA180300).

Author information

Author notes

  1. These authors contributed equally: J. M. Dybas, C. E. O’Leary.

Authors and Affiliations

  1. Division of Protective Immunity, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Joseph M. Dybas & Paula M. Oliver

  2. Cell Pathology Division, Department of Pathology and Laboratory Medicine, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Joseph M. Dybas, Claire E. O’Leary, Hua Ding, Lynn A. Spruce, Steven H. Seeholzer & Paula M. Oliver

  3. Department of Biomedical and Health Informatics, The Children’s Hospital of Philadelphia, Philadelphia, PA, USA

    Joseph M. Dybas

  4. Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Paula M. Oliver

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Contributions

J.M.D. and C.E.O. performed experiments, analyzed data and/or assembled figures. H.D., L.A.S. and S.H.S. generated the proteomics data. J.M.D. and C.E.O. wrote the manuscript. P.M.O. conceived and guided the project and edited the manuscript. All authors read/edited the manuscript.

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Correspondence to Paula M. Oliver.

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Integrated supplementary information

Supplementary Figure 1 Proteasome inhibitor MG132 prevents appropriate T cell activation.

a, Flow cytometry measurement of surface CD44 mean fluorescence intensity (MFI) fold change during TCR stimulation in CD4+ T cells rested or restimulated for 4 h with CD3+CD28 antibody coated beads (1:1 cell:bead ratio). Restimulated cells were untreated or treated with MG132 or cycloheximide (CHX) for the entire 4 h stimulation. b, Flow cytometry measurement of surface CD3ε-γ MFI fold change during TCR stimulation in CD4 + T cells, as described in (a). a, b MFI fold changes are normalized to intensity in unstimulated T cells within the same experiment. Data are compiled from 2 independent experiments comprising 8 mice, mean shown ± SEM. P values calculated by one-way ANOVA with Holm–Sidak test for multiple comparisons, **P < 0.01***P < 0.001, ****P < 0.0001. c, Flow diagram showing expression of surface CD69 in CD4+ T cells rested or restimulated for 4 h using CD3+CD28 antibody coated beads (3:1 cell:bead ratio). A representative plot of activation achieved in three WCP experiments is shown (minimum 65% activation across three experiments). Previously gated on live singlets, CD3 + CD4 + . d, Gating strategy for flow cytometry analysis.

Supplementary Figure 2 Whole-cell proteomics of CD4+ T cells during TCR activation identifies upregulation of TCR-associated proteins and pathways.

a, Pairwise comparisons of protein abundance measured from WCP mass spectrometry experiments in CD4+ T cells rested or restimulated for 4 h with CD3 + CD28 antibody coated beads (3:1 cell:bead ratio). For label free quantification (exp 1), log2 normalized iBAQ values were used to represent protein abundance at rest or restimulation. For SILAC quantification (exp 2 and 3), log2 normalized “heavy” intensity was used for the resting cell protein abundance and “light” intensity was used for restimulated cell protein abundance. Correlation coefficients were calculated for all-by-all pairwise comparisons of three experiments, using the Pearson’s method. b, MetaCore (portal.genego.com) pathway enrichment within the significantly upregulated proteins identified in the WCP mass spectrometry experiments in CD4+ T cells unstimulated or stimulated, as described in (a). Analysis was performed on proteins exhibiting log2 fold change > 0 and p-value < 0.05, based on a two-tailed Students-t test. Enriched pathways were identified by FDR, based on a q-value calculation, performed by the MetaCore program. c, Histograms of log2 fold changes in protein abundance from WCP mass spectrometry experiments in CD4 + T cells unstimulated or stimulated, as described in (a). Log2 fold changes are compared for 1 h stimulation (gray) and 4 h stimulation (black). d, MS/MS counts from the label-free quantified WCP mass spectrometry experiments in CD4 + T cells rested or restimulated, as described in (a), for 1 or 4 h. MS/MS counts are used as a measure of protein abundance for proteins known to be induced upon CD4 + T cell activation. Rest (0 h) and 4 h data are reproduced from main Fig. 2d to provide for comparison with 1 h data.

Supplementary Figure 3 Di-glycine remnant proteomics during TCR stimulation is negligibly impacted by neddylation inhibition.

a, Intersection of identified di-glycine remnant peptides (left) and associated proteins (right) in two independent di-glycine remnant mass spectrometry experiments in CD4+ T cells unstimulated or stimulated for 4 h with CD3+CD28 antibody coated beads (3:1 cell:bead ratio). b, Western blot showing cullin 1 protein abundance in CD4 + T cells unstimulated or stimulated, as described in (a), with 1 h, 2 h or 4 h treatment with 1 uM concentration of neddylation inhibitor MLN4924. With no drug, a prominent neddylation band is seen at higher molecular weight, along with the native Cul1 band. The neddylation band is reduced with addition of MLN4924 neddylation inhibitor, at dose and times indicated. A representative blot of cullin abundance observed in three independent experiments is shown (n = 3 for addition of MLN4924 at 2 h of 4 h stimulation; for other time points n = 2). c, Flow cytometry measurement of CD69 upregulation on CD4 + T cells unstimulated or stimulated for 4 h with CD3+CD28 antibody coated beads (1:1 cell:bead ratio) and untreated (gray) or treated with addition of MLN4924 (1 uM) during the final 2 h (solid black line) or entire 4 h (dashed black line) of the 4-hour stimulation. A representative plot of distributions observed in two (2 h and 4 h treatment) or three (4 h treatment) independent experiments is shown. d, Image of the uncropped and unaltered blot obtained from the cullin 1 protein abundance experiment described in (b). The blot shows the lysates from two independent experiments run on a single gel (lanes 1–4 and 8–11).

Supplementary Figure 4 RNA and protein abundance exhibit low correlation.

a, Log2 fold change in WCP protein abundance and RNA transcript abundance identified in WCP mass spectrometry and RNA-seq experiments, respectively, in CD4+ T cells rested or restimulated for 4 h with CD3+CD28 antibody coated beads (3:1 cell:bead ratio). WCP and RNA data is shown for all identified WCP proteins (gray) or significantly increased and decreased WCP proteins (red), along with the correlation coefficients for each group. Significantly increase and decreased proteins were classified by WCP log2 fold change p-value < 0.01, as calculated by a two-tailed student’s t-test. b, WCP protein abundance (measured by z-score on average of rested and restimulated CD4+ T cells) compared to RNA transcript abundance (measured by log10 transformed read count average of rested and restimulated CD4+ T cells) in CD4+ T cells rested or restimulated, as described in (a). WCP and RNA data is shown for all proteins (gray) or ubiquitylated proteins (cyan), identified in di-glycine remnant mass spectrometry experiments in CD4+ T cells rested or restimulated, as described in (a). a, b, Correlation coefficients were calculated using the Pearson’s method.

Supplementary Figure 5 Model predicting degradative or non-degradative ubiquitylation is assessed by immunoblot.

a, Ubiquitylation, WCP, and RNA-seq expression changes for all proteins exhibiting TCR-induced ubiquitylation ( > 25% increase) in CD4 + T cells unstimulated or stimulated for 4 h with CD3 + CD28 antibody coated beads (3:1 cell:bead ratio). Circle size corresponds to increase in ubiquitylation normalized log2 fold change. Proteins exhibiting consistent protein abundance and increased RNA are predicted to be degraded by ubiquitin (gray) while the remaining proteins are predicted to be non-degradative outcomes of ubiquitylation (red). Translucent blue filled circles indicate those proteins were tested for experimental validation of the prediction method. b, Western blot showing protein abundance of 7 selected proteins, from those described in (a), for CD4 + T cells unstimulated or stimulated with CD3 + CD28 antibody (plate-bound antibody, 5 μg/mL), for the indicated time-course. Cycloheximide (CHX) was added after 1 h of stimulation CD3 + CD28 antibody stimulation. Comparing protein levels at 1 h (no CHX) to 4 h with CHX added at 1 h suggests that LAT, MYCBP2 and SIN3B are significantly decreased, while GRAP, PKCθ, SNX18 and ZAP-70 remain stable. LC indicates loading control. Representative blots from three independent experiments are shown. c, Quantification of normalized intensity changes calculated for 4 h TCR stimulation with CHX added after 1 h compared to the protein intensity at 1 h, as described in (b). Decreases in protein levels with the addition of CHX (delta intensity < 1) for LAT, MYCBP2 and SIN3B indicate that these proteins are significantly decreased in abundance. Protein band intensity was normalized to corresponding loading control intensity. Mean fold changes ± sd of the three biological replicates are shown. Fold change of 1 indicates no change in abundance. Statistics were calculated using two-tailed, unpaired t-tests.

Supplementary Figure 6 Unaltered images of immunoblots.

a, Images of the uncropped and unaltered blots obtained from CD4 + T cell protein abundance experiments described in Supplementary Figure 6 and CD4 + T cell panTUBE experiments described in Fig. 5d. For cases in which multiple blots appear in one image, the relevant blot showing the analyzed antibody is indicated by a red box.

Supplementary Figure 7 Ubiquitin peptide abundance in di-glycine remnant proteome after 1 h of CD4+ T cell stimulation.

a, Relative abundance of ubiquitin lysine peptides identified in the ubiquitin proteome of CD4 + T cells stimulated with CD3 + CD28 antibody coated beads (3:1 cell:bead ratio) for 1 h. Relative abundance is represented by the z-score of the peptide abundance quantified by normalized intensity values from a single di-glycine immunoprecipitation experiment. b, Change in abundance of ubiquitin lysine peptides identified in the ubiquitin proteome of CD4 + T cells stimulated as described in (a). Log2 fold change ratios are determined from a single di-glycine immunoprecipitation mass spectrometry experiment.

Supplementary information

Supplementary Figs. 1–7

Reporting Summary

Supplementary Table 1: Whole cell proteome and RNA-seq data

Proteins quantified by log2 fold change during restimulation for the whole cell proteome (total protein abundance) and RNA-seq (transcript abundance) datasets.

Supplementary Table 2: K-ε-GG peptide data

K-ε-GG peptide abundance quantified by log2 fold change during restimulation.

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Dybas, J.M., O’Leary, C.E., Ding, H. et al. Integrative proteomics reveals an increase in non-degradative ubiquitylation in activated CD4+ T cells. Nat Immunol 20, 747–755 (2019). https://doi.org/10.1038/s41590-019-0381-6

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