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Comparative glycoproteomics of stem cells identifies new players in ricin toxicity

Abstract

Glycosylation, the covalent attachment of carbohydrate structures onto proteins, is the most abundant post-translational modification1. Over 50% of human proteins are glycosylated, which alters their activities in diverse fundamental biological processes2,3. Despite the importance of glycosylation in biology4, the identification and functional validation of complex glycoproteins has remained largely unexplored. Here we develop a novel quantitative approach to identify intact glycopeptides from comparative proteomic data sets, allowing us not only to infer complex glycan structures but also to directly map them to sites within the associated proteins at the proteome scale. We apply this method to human and mouse embryonic stem cells to illuminate the stem cell glycoproteome. This analysis nearly doubles the number of experimentally confirmed glycoproteins, identifies previously unknown glycosylation sites and multiple glycosylated stemness factors, and uncovers evolutionarily conserved as well as species-specific glycoproteins in embryonic stem cells. The specificity of our method is confirmed using sister stem cells carrying repairable mutations in enzymes required for fucosylation, Fut9 and Slc35c1. Ablation of fucosylation confers resistance to the bioweapon ricin5,6, and we discover proteins that carry a fucosylation-dependent sugar code for ricin toxicity. Mutations disrupting a subset of these proteins render cells ricin resistant, revealing new players that orchestrate ricin toxicity. Our comparative glycoproteomics platform, SugarQb, enables genome-wide insights into protein glycosylation and glycan modifications in complex biological systems.

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Figure 1: Glycoproteomics.
Figure 2: The glycoproteomes of hESCs and mESCs.
Figure 3: Detection of specific fucosylated proteins.
Figure 4: Validation of fucosylated proteins involved in ricin toxicity.

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Acknowledgements

We thank all members of our laboratories for discussions, Life Science Editors for editorial support, M. Novatchkova for RNA sequencing (RNA-seq) analysis, and J. Zuber for CRISPR/Cas9 vectors. K.M. is funded by the Austrian Science Fund (SFB F3402-B03, TRP308-N15, and I1469-B16). J.M.P. is supported by grants from IMBA, the Austrian Academy of Sciences, an ERC Advanced Grant, and an Era of Hope Innovator award. J.S. is a Wittgenstein Prize Fellow.

Author information

Authors and Affiliations

Authors

Contributions

J.S., J.T., and J.M.P. conceived the study. J.S. designed and performed glycoproteomics experiments and conceived the bio-informatic analysis algorithm. J.T. performed in vitro cell culture experiments. D.W. provided hESCs and U.E. mESCs. A.G., G.D. and F.D. programmed algorithms for glycoproteomics. L.M. provided Igf2r mutant lines. K.M. supervised glycoproteomics experiments. J.S., J.T., and J.M.P. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Josef M. Penninger.

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The authors declare no competing financial interests.

Additional information

Reviewer Information Nature thanks L. Wells and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Figure 1 Identification of N-glycopeptides and N-glycans.

a, Glycopeptide MS/MS raw data were pre-processed and identified using different MS/MS search engines: Mascot (orange), SEQUEST-HT (blue), and X!Tandem (green). The glycopeptide sequences identified by these algorithms were highly similar, both with respect to identities and to numbers. Data are representative of two independent mESC glycoproteomics experiments with similar results. b, The N-glycosylation sites identified in our study (orange) were mapped to published annotated N-glycosylation sites in Uniprot (grey) and those identified in ref. 7 (green). c, Negative-mode MALDI–TOF analysis of 2-aminobenzoic-acid-labelled N-glycans from mESCs directly correlated with the N-glycan profiles identified using our glycoproteomics approach.

Extended Data Figure 2 Quantitative proteomics.

Comparative proteomics of (a) Slc35c1 KO and (b) Fut9 KO versus their respective genetically repaired WT sister mESCs did not show significant changes in the overall protein expression as determined by quantitative proteomics. Differential peptide abundances are represented as scatter plots of the corresponding TMT-reporter ion intensities. Data are representative of two independent mESC glycoproteomics experiments with similar results. c, Coverage of the predicted (RNA-seq) mESC proteome by the quantitative proteomics data set.

Extended Data Figure 3 Stemness of KO mESC lines.

The parental control mESC line AN3.12, as well as mutant Hs2st1, Igf2r, Itgb1, Lamp1, Ly75, Slc39a14 mESC lines, were stained for the prototypic mESC markers Oct4, SSEA-1, and alkaline phosphatase. DAPI (4′,6-diamidino-2-phenylindole) is shown to visualize nuclei. Of note, no obvious growth defects or morphological phenotypes were observed. All mESCs were diploid as determined by Hoechst staining (not shown). Scale bars are indicated. The experiments were repeated three times. Each image is representative of five images.

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Stadlmann, J., Taubenschmid, J., Wenzel, D. et al. Comparative glycoproteomics of stem cells identifies new players in ricin toxicity. Nature 549, 538–542 (2017). https://doi.org/10.1038/nature24015

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