Cited by 15Glycosylation and Glycoproteins Researchvaccines and immunoinformatics approachesAdvanced Proteomics Techniques and Applications

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Unraveling the glycosylated immunopeptidome with HLA-Glyco

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Abstract

Recent interest in targeted therapies has been sparked by the study of MHC-associated peptides (MAPs) that undergo post-translational modifications (PTMs), particularly glycosylation. In this study, we introduce a fast computational workflow that merges the MSFragger-Glyco search algorithm with a false discovery rate control for glycopeptide analysis from mass spectrometry-based immunopeptidome data. By analyzing eight large-scale publicly available studies, we find that glycosylated MAPs are predominantly presented by MHC class II. Here, we present HLA-Glyco, a comprehensive resource containing over 3,400 human leukocyte antigen (HLA) class II N-glycopeptides from 1,049 distinct protein glycosylation sites. This resource provides valuable insights, including high levels of truncated glycans, conserved HLA-binding cores, and differences in glycosylation positional specificity between HLA allele groups. We integrate the workflow within the FragPipe computational platform and provide HLA-Glyco as a free web resource. Overall, our work provides a valuable tool and resource to aid the nascent field of glyco-immunopeptidomics.

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Research works printed but not linked: 58

  1. Costa, A. F., Campos, D., Reis, C. A. & Gomes, C. Targeting glycosylation: a new road for cancer drug discovery. Trends Cancer 6, 757–766 (2020). · DOI
  2. Thomas, D., Rathinavel, A. K. & Radhakrishnan, P. Altered glycosylation in cancer: a promising target for biomarkers and therapeutics. Biochim. Biophys. Acta BBA Rev. Cancer 1875, 188464 (2021). · DOI
  3. Wang, M., Zhu, J., Lubman, D. M. & Gao, C. Aberrant glycosylation and cancer biomarker discovery: a promising and thorny journey. Clin. Chem. Lab. Med. 57, 407–416 (2019). · DOI
  4. Mereiter, S., Balmaña, M., Campos, D., Gomes, J. & Reis, C. A. Glycosylation in the era of cancer-targeted therapy: where are we heading? Cancer Cell 36, 6–16 (2019). · DOI
  5. Mangalaparthi, K. K. et al. Digging deeper into the immunopeptidome: characterization of post-translationally modified peptides presented by MHC I. J. Proteins Proteom. 12, 151–160 (2021). · DOI
  6. Mei, S. et al. Immunopeptidomic analysis reveals that deamidated HLA-bound peptides arise predominantly from deglycosylated precursors. Mol. Cell. Proteom. 19, 1236–1247 (2020). · DOI
  7. Carra, G. Selective association of a 22–38 kDa glycoprotein with MHC class II DP antigen on activated human lymphocytes at the plasma membrane. Mol. Immunol. 33, 269–278 (1996). · DOI
  8. Malaker, S. A. et al. Identification and characterization of complex glycosylated peptides presented by the MHC Class II processing pathway in Melanoma. J. Proteome Res. 16, 228–237 (2017). · DOI
  9. Olvera, A. et al. Does antigen glycosylation impact the HIV-specific T cell immunity? Front. Immunol. 11, 573928 (2021). · DOI
  10. Xu, Y., Sette, A., Sidney, J., Gendler, S. J. & Franco, A. Tumor‐associated carbohydrate antigens: A possible avenue for cancer prevention. Immunol. Cell Biol. 83, 440–448 (2005). · DOI
  11. Housseau, F. et al. N-linked carbohydrates in tyrosinase are required for its recognition by human MHC class II-restricted CD4+ T cells. Eur. J. Immunol. 31, 2690–2701 (2001). · DOI
  12. Li, H. et al. Identification of an N-linked glycosylation in the C4 region of HIV-1 envelope gp120 that is critical for recognition of neighboring CD4 T cell epitopes. J. Immunol. 180, 4011–4021 (2008). · DOI
  13. Thaysen-Andersen, M., Packer, N. H. & Schulz, B. L. Maturing glycoproteomics technologies provide unique structural insights into the N-glycoproteome and its regulation in health and disease. Mol. Cell. Proteom. 15, 1773–1790 (2016). · DOI
  14. Shen, J. et al. StrucGP: de novo structural sequencing of site-specific N-glycan on glycoproteins using a modularization strategy. Nat. Methods 18, 921–929 (2021). · DOI
  15. Zeng, W.-F., Cao, W.-Q., Liu, M.-Q., He, S.-M. & Yang, P.-Y. Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3. Nat. Methods 18, 1515–1523 (2021). · DOI
  16. Fang, Z. et al. Glyco-Decipher enables glycan database-independent peptide matching and in-depth characterization of site-specific N-glycosylation. Nat. Commun. 13, 1900 (2022). · DOI
  17. Dengjel, J., Rammensee, H.-G. & Stevanovic, S. Glycan side chains on naturally presented MHC class II ligands. J. Mass Spectrom. 40, 100–104 (2005). · DOI
  18. Parker, R. et al. Mapping the SARS-CoV-2 spike glycoprotein-derived peptidome presented by HLA class II on dendritic cells. Cell Rep. 35, 109179 (2021). · DOI
  19. Kong, A. T., Leprevost, F. V., Avtonomov, D. M., Mellacheruvu, D. & Nesvizhskii, A. I. MSFragger: ultrafast and comprehensive peptide identification in mass spectrometry–based proteomics. Nat. Methods 14, 513 (2017). · DOI
  20. Polasky, D. A., Yu, F., Teo, G. C. & Nesvizhskii, A. I. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat. Methods 17, 1125–1132 (2020). · DOI
  21. Ott, P. A. et al. An immunogenic personal neoantigen vaccine for patients with melanoma. Nature 547, 217–221 (2017). · DOI · Paper page
  22. Oliveira, G. et al. Landscape of helper and regulatory antitumour CD4+ T cells in melanoma. Nature 605, 532–538 (2022). · DOI
  23. Axelrod, M. L., Cook, R. S., Johnson, D. B. & Balko, J. M. Biological consequences of MHC-II expression by tumor cells in cancer. Clin. Cancer Res. 25, 2392–2402 (2019). · DOI
  24. Tay, R. E., Richardson, E. K. & Toh, H. C. Revisiting the role of CD4+ T cells in cancer immunotherapy—new insights into old paradigms. Cancer Gene Ther. 28, 5–17 (2021). · DOI
  25. Johnson, A. M. et al. Cancer cell–intrinsic expression of MHC class II regulates the immune microenvironment and response to anti–PD-1 therapy in lung adenocarcinoma. J. Immunol. 204, 2295–2307 (2020). · DOI
  26. Mukherjee, S., Sanchez-Bernabeu, A., Demmers, L. C., Wu, W. & Heck, A. J. R. The HLA ligandome comprises a limited repertoire of O-GlcNAcylated antigens preferentially associated With HLA-B*07:02. Front. Immunol. 12, 796584 (2021). · DOI
  27. Geiszler, D. J. et al. PTM-shepherd: analysis and summarization of post-translational and chemical modifications from open search results. Mol. Cell. Proteom. 20, 100018 (2021). · DOI
  28. Polasky, D. A., Geiszler, D. J., Yu, F. & Nesvizhskii, A. I. Multiattribute glycan identification and FDR control for glycoproteomics. Mol. Cell. Proteom. 21, 100205 (2022). · DOI
  29. Bassani-Sternberg, M. et al. Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry. Nat. Commun. 7, 13404 (2016). · DOI · Paper page
  30. Marcu, A. et al. HLA ligand atlas: a benign reference of HLA-presented peptides to improve T-cell-based cancer immunotherapy. J. Immunother. Cancer 9, e002071 (2021). · DOI · Paper page
  31. Newey, A. et al. Immunopeptidomics of colorectal cancer organoids reveals a sparse HLA class I neoantigen landscape and no increase in neoantigens with interferon or MEK-inhibitor treatment. J. Immunother. Cancer 7, 309 (2019). · DOI
  32. Chong, C. et al. Integrated proteogenomic deep sequencing and analytics accurately identify non-canonical peptides in tumor immunopeptidomes. Nat. Commun. 11, 1293 (2020). · DOI
  33. Chong, C. et al. High-throughput and sensitive immunopeptidomics platform reveals profound interferonγ-mediated remodeling of the human leukocyte antigen (HLA) ligandome. Mol. Cell. Proteom. 17, 533–548 (2018). · DOI
  34. Ramarathinam, S. H., Ho, B. K., Dudek, N. L. & Purcell, A. W. HLA class II immunopeptidomics reveals that co‐inherited HLA‐allotypes within an extended haplotype can improve proteome coverage for immunosurveillance. Proteomics 21, 2000160 (2021). · DOI
  35. Forlani, G. et al. CIITA-transduced glioblastoma cells uncover a rich repertoire of clinically relevant tumor-associated HLA-II antigens. Mol. Cell. Proteom. 20, 100032 (2021). · DOI
  36. Racle, J. et al. Robust prediction of HLA class II epitopes by deep motif deconvolution of immunopeptidomes. Nat. Biotechnol. 37, 1283–1286 (2019). · DOI
  37. York, W. S. et al. GlyGen: computational and informatics resources for glycoscience. Glycobiology 30, 72–73 (2020). · DOI
  38. Colley, K. J. et al. Cellular Organization of Glycosylation. 41–49 (Cold Spring Harbor Laboratory Press, 2015). https://doi.org/10.1101/glycobiology.3e.004. · DOI
  39. Werdelin, O., Meldal, M. & Jensen, T. Processing of glycans on glycoprotein and glycopeptide antigens in antigen-presenting cells. Proc. Natl Acad. Sci. USA 99, 9611–9613 (2002). · DOI
  40. Kario, E., Tirosh, B., Ploegh, H. L. & Navon, A. N-linked glycosylation does not impair proteasomal degradation but affects class I major histocompatibility complex presentation. J. Biol. Chem. 283, 244–254 (2008). · DOI
  41. Purcell, A. W., Driel, I. R. & Gleeson, P. A. Impact of glycans on T‐cell tolerance to glycosylated self‐antigens. Immunol. Cell Biol. 86, 574–579 (2008). · DOI
  42. Petersen, J., Purcell, A. W. & Rossjohn, J. Post-translationally modified T cell epitopes: immune recognition and immunotherapy. J. Mol. Med. 87, 1045 (2009). · DOI
  43. Gfeller, D. & Bassani-Sternberg, M. Predicting antigen presentation—what could we learn from a million peptides? Front. Immunol. 9, 1716 (2018). · DOI
  44. Kaabinejadian, S. et al. Accurate MHC motif deconvolution of immunopeptidomics data reveals a significant contribution of DRB3, 4 and 5 to the total DR immunopeptidome. Front. Immunol. 13, 835454 (2022). · DOI
  45. Reynisson, B., Alvarez, B., Paul, S., Peters, B. & Nielsen, M. NetMHCpan-4.1 and NetMHCIIpan-4.0: improved predictions of MHC antigen presentation by concurrent motif deconvolution and integration of MS MHC eluted ligand data. Nucleic Acids Res. 48, W449–W454 (2020). · DOI · Paper page
  46. Kacen, A. et al. Post-translational modifications reshape the antigenic landscape of the MHC I immunopeptidome in tumors. Nat. Biotechnol. 41, 239–251 (2023). · DOI
  47. Symonds, P. et al. Citrullinated epitopes identified on tumour MHC class II by peptide elution stimulate both regulatory and Th1 responses and require careful selection for optimal anti-tumour responses. Front. Immunol. 12, 764462 (2021). · DOI
  48. Marino, F. et al. Extended O-GlcNAc on HLA class-I-bound peptides. J. Am. Chem. Soc. 137, 10922–10925 (2015). · DOI
  49. Lu, L., Riley, N. M., Shortreed, M. R., Bertozzi, C. R. & Smith, L. M. O-Pair Search with MetaMorpheus for O-glycopeptide characterization. Nat. Methods 17, 1133–1138 (2020). · DOI
  50. Perez-Riverol, Y. et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 47, D442–D450 (2019). · DOI
  51. Craig, R., Cortens, J. P. & Beavis, R. C. Open source system for analyzing, validating, and storing protein identification data. J. Proteome Res. 3, 1234–1242 (2004). · DOI
  52. Kessner, D., Chambers, M., Burke, R., Agus, D. & Mallick, P. ProteoWizard: open source software for rapid proteomics tools development. Bioinformatics 24, 2534–2536 (2008). · DOI · Paper page
  53. The UniProt Consortium. UniProt: the universal protein knowledgebase. Nucleic Acids Res. 45, D158–D169 (2017). · DOI · Paper page
  54. Teo, G. C., Polasky, D. A., Yu, F. & Nesvizhskii, A. I. Fast deisotoping algorithm and its implementation in the MSFragger search engine. J. Proteome Res. 20, 498–505 (2021). · DOI
  55. Yu, F. et al. Identification of modified peptides using localization-aware open search. Nat. Commun. 11, 4065 (2020). · DOI
  56. da Veiga Leprevost, F. et al. Philosopher: a versatile toolkit for shotgun proteomics data analysis. Nat. Methods 17, 869–870 (2020). · DOI
  57. Ma, K., Vitek, O. & Nesvizhskii, A. I. A statistical model-building perspective to identification of MS/MS spectra with PeptideProphet. BMC Bioinformatics 13, S1 (2012). · DOI
  58. Nesvizhskii, A. I., Keller, A., Kolker, E. & Aebersold, R. A Statistical Model for Identifying Proteins by Tandem Mass Spectrometry. Anal. Chem. 75, 4646–4658 (2003). · DOI

Metadata

Authors
Bedran, G., Polasky, D. A., Hsiao, Y., Yu, F., da Veiga Leprevost, F., Alfaro, J. A., Cieslik, M., & Nesvizhskii, A. I.
Affiliations
  • University of Michigan (US)
  • University of Victoria (CA)
Corresponding author
Nesvizhskii, Alexey I.
Venue
Nature Communications
Published
2023-06-12
Volume
14
Issue
1
Pages
3461-3461
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4380361693
Cited by
15
References
2
Citations by year
2026: 3 · 2025: 6 · 2024: 4 · 2023: 2
FWCI
1.30
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Edinburgh Research Explorer (University of Edinburgh) (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • U.S. Department of Health & Human Services | NIH | National Cancer Institute (U24-CA210967, U24-CA271037)
  • EC | Horizon 2020 Framework Programme (101017453)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Glycosylation and Glycoproteins Research
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
Keywords
glycopeptide analysis, post-translational modification, glycosylation sites, false discovery rate control, FragPipe
MeSH terms
Algorithms, Genes, MHC Class II, Glycopeptides, Glycosylation, Humans, Protein Processing, Post-Translational

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