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Personalized phosphoproteomics identifies functional signaling


Protein phosphorylation dynamically integrates environmental and cellular information to control biological processes. Identifying functional phosphorylation amongst the thousands of phosphosites regulated by a perturbation at a global scale is a major challenge. Here we introduce ‘personalized phosphoproteomics’, a combination of experimental and computational analyses to link signaling with biological function by utilizing human phenotypic variance. We measure individual subject phosphoproteome responses to interventions with corresponding phenotypes measured in parallel. Applying this approach to investigate how exercise potentiates insulin signaling in human skeletal muscle, we identify both known and previously unidentified phosphosites on proteins involved in glucose metabolism. This includes a cooperative relationship between mTOR and AMPK whereby the former directly phosphorylates the latter on S377, for which we find a role in metabolic regulation. These results establish personalized phosphoproteomics as a general approach for investigating the signal transduction underlying complex biology.

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Fig. 1: Phosphoproteomes of extensive physiological perturbations retain unique subject-specific signatures.
Fig. 2: Individualized analysis of human phosphoproteomes defines invariable and variable phosphosites across humans.
Fig. 3: Insulin signaling in human skeletal muscle is rewired by exercise.
Fig. 4: Personalized phosphoproteomics prioritizes functional, phenotype-associated signaling.
Fig. 5: An independent validation cohort replicates personalized phosphoproteomics findings.
Fig. 6: mTORC1 phosphorylates AMPKα2 S377 to promote cellular proliferation during glucose withdrawal without inhibition of AMPK.

Data availability

RAW data, MaxQuant and Spectronaut output tables have been deposited in the PRIDE proteomeXchange repository ( with accession no. PXD027198. Processed data can be explored online at

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We thank S. Kurdyukov, A. Zadoorian, I. Beck Nielsen, B. Bolmgren and L. Hansen for technical assistance. We thank E. Pickering and members of the Metabolic Systems Biology group, Pfizer Cardiovascular and Metabolic Diseases Research Unit and the Molecular Physiology section of the University of Copenhagen for feedback and discussions. AMPKα double-knockout MEFs were kindly provided by B. Viollet. This research was facilitated by access to Sydney Mass Spectrometry, a core research facility at the University of Sydney. This work was funded by: Pfizer, Inc.; Australian Government Research Training Program Scholarship and the University of Sydney Val Street Scholarship (to E.J.N.); ARC Laureate Fellowship (no. FL200100096 to D.E.J.); NHMRC Project Grant (no. APP1122376 to B.L.P.); APP1161262 (to J.S.O.); ARC Discovery Project Grant (no. DP180101682 to J.P.); Danish Council for In-dependent Research Medical Sciences (no. FSS 8020-00288 to J.F.P.W.); Ministry of Culture Denmark (no. FPK 2016-0027 to J.F.P.W.); The Novo Nordisk Foundation (no. NNF16OC 0023046 to J.F.P.W.); The University of Sydney University of Copenhagen Partnership Collaboration Awards (to J.F.P.W. and D.E.J.); the Victorian Government’s Operational Infrastructure Support Program (to J.S.O.); a research grant from the Danish Diabetes Academy, which is funded by the Novo Nordisk Foundation, grant number NNF17SA0031406 (J.O.).

Author information

Authors and Affiliations



E.J.N., S.J.H., D.E.J., J.F.P.W. and C.P. conceptualized the study. J.R.H., S.J.H., E.J.N., B.L.P., G.Y., N.X.Y.L., K.R.M., J.O., J.M.K., K.H., E.A.R., B.K. and J.F.P.W. conducted investigations. E.J.N., S.J.H., J.R.H., J.F.P.W., B.L.P., E.A.R., B.K. and J.P. were responsible for methodology. E.J.N. performed formal analysis. E.J.N. and S.J.H. were responsible for visualization. Software was the responsibility of E.J.N. and S.J.H. N.X.Y.L., J.S.O. and D.E.J. oversaw resources. Project administration was performed by C.P., D.E.J., J.F.P.W. and S.J.H. Funding was acquired by D.E.J., J.F.P.W. and C.P. S.J.H., D.E.J., J.F.P.W., C.P. and J.P. supervised the project. E.J.N., S.J.H. and D.E.J. wrote the original draft. Writing, review and editing were undertaken by all authors.

Corresponding authors

Correspondence to David E. James, Jørgen F. P. Wojtaszewski or Sean J. Humphrey.

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Competing interests

C.P. was an employee of Pfizer during the study. The other authors declare no competing interests.

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Peer review information Nature Biotechnology thanks the anonymous reviewers for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Quality control and subject-specific human phosphoproteome variation.

a, Correlation matrix of the technical replicates. b, Correlation matrix of the biological replicates. c, Quantified and regulated phosphopeptides, phosphosites and phosphoproteins. d-e, PCA of median normalised phosphosites quantified in all ground state samples. Points are numbered by subject and coloured by exercised (blue) and non-exercised (grey) legs. f-g, PCA of the subset of the phosphosites from d-e, with matched protein data quantified in all samples. h-i, PCA of the sites in f-g, normalized by their respective total protein levels by subtracting the median normalised protein log2 intensities from the median normalised phosphosite log2 intensities. Abundance of phosphorylated j, 14-3-3γ Y133, k, ROCK2 T1374 and l, ATP Synthase β S415 compared to their total protein abundances at ground state. m, Glucose uptake of the non-exercised (grey) and exercised (blue) legs during a hyperinsulinemic-euglycemic clamp expressed as area under the curve (two-sided paired t-test).

Extended Data Fig. 2 Exercise-and insulin regulated phosphosites, and comparison with phosphoproteomes measured immediately after acute exercise.

a, Volcano plots of the phosphosites regulated by exercise and insulin (two-way repeated measures ANOVA with planned contrasts). b, Upset plot, quantifying the regulated phosphosites (BH padj < 0.05 and fold change > 1.5) shared in the different conditions. c, Quantified phosphosites in this study compared to Hoffman et al. 2015. d, Regulated phosphosites by exercise recovery in this study, not considering the insulin-stimulated states, compared to Hoffman et al. 2015. For this study, regulated is padj < 0.05 and fold change > 1.5. For Hoffman et al., regulated is considered as padj < 0.05. e, Enrichment of kinase substrates in the ground state exercise-regulated phosphosites in this study compared to Hoffman et al. regulated phosphosites.

Extended Data Fig. 3 Patterns of signalling in response to exercise and insulin.

a, Hypothetical profiles that fit the groupings in Fig. 3 and Supplementary Table 4. b, Quantification of phosphosites in the groupings, with the proportion of phosphosites with an interaction between exercise and insulin shaded. Sites were considered to have an interaction if the interaction term padj < 0.05 and fold change >1.5.

Extended Data Fig. 4 Personalised phosphoproteomics extracts glucose uptake-associated phosphosites.

a, Ranks of phosphosites that passed filtering for glucose uptake correlations ordered by glucose uptake correlation significance, ANOVA significance and magnitude fold change. Kendall’s rank correlation (τ) was calculated on the -log10p and absolute log2FC values. b, Gene Ontology enrichment of the glucose uptake-associated phosphosites, (Fisher’s exact test), filtered for pathways with BH-padj < 0.05. Pearson’s correlation and BH padj of phosphorylated c, GYS1 S649 and d, VAMP2 S61 with glucose uptake, with prior exercise (blue outline) and insulin (orange centre). e, Overrepresentation of kinase substrates in positive glucose uptake-associated sites (Fisher’s exact test, BH-padj). f, The distribution of kinase substrates by correlation coefficient with glucose uptake (asterisk indicates empirical p < 0.05 from a Kolmogorov-Smirnov test ordered by correlation p-value). g, Quantification of Akt substrates in the classes defined in Fig. 3. Profiles of Akt regulatory sites h, T308 i, S473 and j, substrate GSK3β S9 k, Pearson’s correlation and BH padj of phosphorylated LPIN1 S889 with glucose uptake. l, LPIN1 sequence aligned to mTORC1 motif. m, Pearson’s correlation and BH padj of AGPAT1 protein levels with glucose uptake AUC, in ground state exercised (blue) or not exercised (grey) legs. Correlations and Kolmogorov-Smirnov tests were two-sided. Fisher’s exact tests were one-sided.

Extended Data Fig. 5 An independent cohort replicates glucose uptake associations.

a, Correlation coefficients for the common sites that passed filtering to test for correlation to glucose uptake in both studies (significant regulation and quantification in ≥ 50% of the samples, discovery cohort: Pearson’s correlation, validation cohort: Kendall’s rank correlation) are plotted. Green points are reported mTOR/p70S6K substrates. b, Phosphosites that passed filtering to correlate with glucose uptake (regulated, quantified ≥ 50% samples) were ordered by -log10p of their correlation to glucose uptake, their ANOVA significance, or their magnitude fold change. Enrichment was calculated with Kolmogorov-Smirnov test with empirical p-values. c, Enrichment of Gene Ontology biological processes in the glucose uptake-associated phosphosites, calculated by Fisher’s exact test, filtered for pathways with BH padj < 0.05. d, Enrichment of kinase substrates amongst phosphosites positively correlated with glucose uptake (padj < 0.05, r > 0, Fisher’s exact test). e, Glucose uptake Kendall’s rank correlation coefficients and BH padj of phosphosites grouped by kinase. Asterisks indicate Kolmogorov-Smirnov test empirical p-value < 0.05. Correlations and Kolmogorov-Smirnov tests were two-sided. Fisher’s exact tests were one-sided.

Extended Data Fig. 6 mTORC1 phosphorylates AMPKα2 S377.

a, Repeated measures correlation profile of AMPK S377 (two-sided, BH-adjusted p-value). b, Quantification of phosphosites on AMPK from in vitro kinase assays with mTORC1 and different combinations of AMPK subunits. The γ subunit was kept as AMPKγ1. For each condition, n = 1. c, AMPK inhibitory site profiles in human skeletal muscle. d, Sequence alignment of AMPKα2 S377 across different species. S. cerevisiae SNF1 was used in the alignment. e, AMPKα2β2γ1 structure from PDB 6b2e. AMPKα2 residues 377–398 were not modelled so are included as a dashed line. AMPKα2 S377 is approximated as immediately adjacent to residue 376 and indicated in green. f, Real-time proliferation analysis of AMPKα1/2 KO MEF cells re-expressing WT or S377A AMPKα2 in media with 25 mM glucose. g, AMPK substrate phosphosite profiles in human skeletal muscle. h, mTORC1 substrate phosphosite profiles in human skeletal muscle. Data are presented as the mean +/− SEM represented by shading.

Supplementary information

Supplementary Information

Supplementary Note 1.

Reporting Summary

Supplementary Tables

Table 1: The post-exercise and insulin-regulated human skeletal muscle phosphoproteome, and associations with leg glucose uptake. Table 2: Inter- and intrasubject phosphoproteome CVs. Table 3: Comparison of 4-h post-exercise phosphoproteomes with those measured immediately after acute exercise. Table 4: Insulin-regulated, potentiated, emergent and divergent phosphosites with exercise. Table 5: Discovery and validation cohort subject characteristics. Table 6: Glucose uptake AUC and proteome correlations. Table 7: Validation cohort glucose uptake associations with discovery cohort glucose-uptake-associated phosphosites. Table 8: Glucose uptake associations of the combined discovery and validation cohorts. Table 9: Detection of AMPK phosphosites from mTORC1 in vitro kinase assays.

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Needham, E.J., Hingst, J.R., Parker, B.L. et al. Personalized phosphoproteomics identifies functional signaling. Nat Biotechnol 40, 576–584 (2022).

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