A Glycoproteome Data Mining Strategy for Characterizing Structural Features of Altered Glycans with Thymic Involution.
Gespeichert in:
| Titel: | A Glycoproteome Data Mining Strategy for Characterizing Structural Features of Altered Glycans with Thymic Involution. |
|---|---|
| Autoren: | Zhang Z; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Wu Y; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Hou K; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Zhang Y; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Chen L; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Yang M; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Jin Z; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Xu Y; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Zhang Y; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Cai Y; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Zhao J; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China., Sun S; Laboratory for Disease Glycoproteomics, College of Life Sciences, Northwest University, Xi'an, 710069, P. R. China. |
| Quelle: | Advanced science (Weinheim, Baden-Wurttemberg, Germany) [Adv Sci (Weinh)] 2025 Oct; Vol. 12 (38), pp. e02013. Date of Electronic Publication: 2025 Jul 24. |
| Publikationsart: | Journal Article |
| Sprache: | English |
| Info zur Zeitschrift: | Publisher: WILEY-VCH Country of Publication: Germany NLM ID: 101664569 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2198-3844 (Electronic) Linking ISSN: 21983844 NLM ISO Abbreviation: Adv Sci (Weinh) Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Weinheim : WILEY-VCH, [2014]- |
| MeSH-Schlagworte: | Polysaccharides*/metabolism , Polysaccharides*/chemistry , Thymus Gland*/metabolism , Data Mining*/methods , Proteome*/metabolism , Proteomics*/methods , Glycoproteins*/metabolism, Animals ; Mice ; Glycosylation |
| Abstract: | Glycosylation plays an important role in regulating innate and adaptive immunity. With promising advances in structural and site-specific glycoproteomics, how to thoroughly extract important information from these multi-dimensional data has become another unresolved issue. The present study reports a comprehensive data mining strategy to systematically extract overall and altered glycan features from quantitative glycoproteome data. By applying the strategy to investigation of thymic involution, the study not only presents a high-resolution glycoproteome map of the mouse thymus, displaying distinct glycan structure patterns among immune-relevant cellular components, but also uncovers four major altered glycan features associated with thymic involution, including elevated LacdiNAc mainly on the MHC class I complex, increased sialoglycans that perform multiple immune functions, down-regulated bisecting glycans mostly linked to a sole GlcNAc branch, as well as possible shifts of glycan structures at the same glycosites. Regulatory network analyses further reveal the coordinated interactions of altered glycans with upstream regulators, including glycosyltransferases, glycosidases, and glycan-binding proteins, as well as downstream signaling pathways. These data offer valuable resources for future functional studies on glycosylation and the mechanistic investigation of thymic involution, supporting the strategy as a powerful tool for in-depth mining of structural and site-specific glycoproteome data from various biomedical samples. (© 2025 The Author(s). Advanced Science published by Wiley‐VCH GmbH.) |
| References: | Nat Rev Mol Cell Biol. 2020 Dec;21(12):729-749. (PMID: 33087899) Nat Commun. 2022 Apr 7;13(1):1900. (PMID: 35393418) Curr Opin Chem Biol. 2023 Feb;72:102238. (PMID: 36525809) Annu Rev Immunol. 2020 Apr 26;38:365-395. (PMID: 31986070) Nat Methods. 2021 Dec;18(12):1515-1523. (PMID: 34824474) Mol Cell Proteomics. 2022 Apr;21(4):100214. (PMID: 35183770) Front Microbiol. 2020 Jan 10;10:2909. (PMID: 32010066) Cancer Immunol Res. 2014 Nov;2(11):1023-33. (PMID: 25367977) Curr Opin Immunol. 2012 Feb;24(1):92-8. (PMID: 22285892) Redox Biol. 2025 Apr;81:103584. (PMID: 40073759) Science. 2007 Jun 1;316(5829):1349-53. (PMID: 17540904) Nucleic Acids Res. 2013 Jan;41(Database issue):D1063-9. (PMID: 23203882) Plant Physiol. 2019 Jul;180(3):1436-1449. (PMID: 31061102) Nucleic Acids Res. 2019 Jan 8;47(D1):D1211-D1217. (PMID: 30252093) Nat Commun. 2025 Mar 07;16(1):2292. (PMID: 40055327) Anal Chem. 2023 Jan 12;:. (PMID: 36633944) Adv Sci (Weinh). 2025 Oct;12(38):e02013. (PMID: 40704636) Elife. 2021 Mar 09;10:. (PMID: 33687326) Adv Exp Med Biol. 2021;1325:341-373. (PMID: 34495544) Elife. 2020 Aug 25;9:. (PMID: 32840480) Gastroenterology. 2020 Jan;158(1):95-110. (PMID: 31626754) Cell Death Differ. 2025 Jul;32(7):1181-1199. (PMID: 40045035) Anal Chem. 2015;87(10):5181-8. (PMID: 25945896) Trends Cell Biol. 2019 Dec;29(12):929-939. (PMID: 31662235) Nat Methods. 2021 Aug;18(8):921-929. (PMID: 34341581) Curr Protoc Bioinformatics. 2012 Dec;Chapter 13:13.20.1-13.20.14. (PMID: 23255153) Nucleic Acids Res. 2023 Jan 6;51(D1):D638-D646. (PMID: 36370105) J Biol Chem. 1982 Sep 10;257(17):10235-42. (PMID: 6213618) Genome Res. 2003 Nov;13(11):2498-504. (PMID: 14597658) J R Soc Interface. 2021 Mar;18(176):20200982. (PMID: 33726544) Mol Cell Proteomics. 2011 Oct;10(10):O111.009381. (PMID: 21784994) Nat Methods. 2020 Nov;17(11):1133-1138. (PMID: 33106676) Science. 2020 Feb 21;367(6480):. (PMID: 32079746) Nat Commun. 2023 Jul 8;14(1):4046. (PMID: 37422459) Nat Methods. 2020 Nov;17(11):1125-1132. (PMID: 33020657) Oxid Med Cell Longev. 2020 Feb 5;2020:7986071. (PMID: 32089780) Immunol Lett. 2007 May 15;110(1):65-73. (PMID: 17467811) Acta Biochim Biophys Sin (Shanghai). 2024 Aug 8;56(8):1221-1233. (PMID: 39126246) J Mol Histol. 2008 Aug;39(4):417-26. (PMID: 18642095) Life Sci. 2020 Feb 1;242:117242. (PMID: 31891723) Mol Oncol. 2022 Jun;16(11):2135-2152. (PMID: 34855283) Front Med. 2023 Apr;17(2):304-316. (PMID: 36580234) Immun Ageing. 2020 Jan 20;17:2. (PMID: 31988649) Science. 2001 Mar 23;291(5512):2370-6. (PMID: 11269318) Nat Protoc. 2018 Jul;13(7):1632-1661. (PMID: 29988108) Trends Biochem Sci. 2021 Apr;46(4):284-300. (PMID: 33349503) Nat Biotechnol. 2016 Jan;34(1):84-8. (PMID: 26571101) Proc Natl Acad Sci U S A. 2017 Aug 29;114(35):E7255-E7261. (PMID: 28808029) Front Immunol. 2020 Jan 31;10:3099. (PMID: 32082299) |
| Grant Information: | 2019YFA0905200 National Key Research and Development Program of China; 23JHZ006 Shaanxi Fundamental Science Research Project for Chemistry and Biology; 2024JC-TBZC-06 Natural Science Foundation of Shaanxi Province; 2023-JC-ZD-46 Natural Science Foundation of Shaanxi Province; 22374117 National Natural Science Foundation of China |
| Contributed Indexing: | Keywords: data mining; glycan structures; glycoproteomics; multi‐omics integration; thymic involution |
| Substance Nomenclature: | 0 (Polysaccharides) 0 (Proteome) 0 (Glycoproteins) |
| Entry Date(s): | Date Created: 20250724 Date Completed: 20251014 Latest Revision: 20251016 |
| Update Code: | 20251016 |
| PubMed Central ID: | PMC12520470 |
| DOI: | 10.1002/advs.202502013 |
| PMID: | 40704636 |
| Datenbank: | MEDLINE |
Full Text 
Full Text Finder 
Nájsť tento článok vo Web of Science | Abstract: | Glycosylation plays an important role in regulating innate and adaptive immunity. With promising advances in structural and site-specific glycoproteomics, how to thoroughly extract important information from these multi-dimensional data has become another unresolved issue. The present study reports a comprehensive data mining strategy to systematically extract overall and altered glycan features from quantitative glycoproteome data. By applying the strategy to investigation of thymic involution, the study not only presents a high-resolution glycoproteome map of the mouse thymus, displaying distinct glycan structure patterns among immune-relevant cellular components, but also uncovers four major altered glycan features associated with thymic involution, including elevated LacdiNAc mainly on the MHC class I complex, increased sialoglycans that perform multiple immune functions, down-regulated bisecting glycans mostly linked to a sole GlcNAc branch, as well as possible shifts of glycan structures at the same glycosites. Regulatory network analyses further reveal the coordinated interactions of altered glycans with upstream regulators, including glycosyltransferases, glycosidases, and glycan-binding proteins, as well as downstream signaling pathways. These data offer valuable resources for future functional studies on glycosylation and the mechanistic investigation of thymic involution, supporting the strategy as a powerful tool for in-depth mining of structural and site-specific glycoproteome data from various biomedical samples.<br /> (© 2025 The Author(s). Advanced Science published by Wiley‐VCH GmbH.) |
|---|---|
| ISSN: | 2198-3844 |
| DOI: | 10.1002/advs.202502013 |