Ultradeep N-glycoproteome atlas of mouse reveals spatiotemporal signatures of brain aging and neurodegenerative diseases.

Journal: Nature communications
Published Date:

Abstract

The current depth of site-specific N-glycoproteomics is insufficient to fully characterize glycosylation events in biological samples. Herein, we achieve an ultradeep and precision analysis of the N-glycoproteome of mouse tissues by integrating multiple workflows. The largest N-glycoproteomic dataset to date is established on mice, which contains 91,972 precursor glycopeptides, 62,216 glycoforms, 8939 glycosites and 4563 glycoproteins. The database consists of 6.8 million glyco-spectra (containing oxonium ions), among which 160,928 spectra is high-quality with confident N-glycopeptide identifications. The large-scale and high-quality dataset enhances the performance of current artificial intelligence models for glycopeptide tandem spectrum prediction. Using this ultradeep dataset, we observe tissue specific microheterogeneity and functional implications of protein glycosylation in mice. Furthermore, the region-resolved brain N-glycoproteomes for Alzheimer's Diseases, Parkinson Disease and aging mice reveal the spatiotemporal signatures and distinct pathological functions of the N-glycoproteins. A comprehensive database resource of experimental N-glycoproteomic data from this study and previous literatures is further established. This N-glycoproteome atlas serves as a promising tool for revealing the role of protein glycosylation in biological systems.

Authors

  • Pan Fang
    Industry Solutions Research and Development, Alibaba Cloud Computing, Hangzhou, 330110, China.
  • Xiangming Yu
    MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Institute of Molecular Enzymology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • MengYang Ding
    MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Institute of Molecular Enzymology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Cong Qifei
    Department of Neurology and Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China.
  • Hongyu Jiang
    Institute of Electronic Engineering, China Academy of Engineering Physics, Mianyang 621000, China.
  • Qi Shi
    MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Institute of Molecular Enzymology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Weiwei Zhao
  • Weimin Zheng
    Department of Radiology, Aerospace Center Hospital, Beijing 100049, China.
  • Yingning Li
    MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Institute of Molecular Enzymology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Zixiang Ling
    MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Key Laboratory of Pathogen Bioscience and Anti-infective Medicine, Institute of Molecular Enzymology, School of Life Sciences, Suzhou Medical College of Soochow University, Suzhou, China.
  • Wei-Jun Kong
    School of Chemical Science and Engineering, Tongji University, Shanghai, China.
  • Pengyuan Yang
    Department of Chemistry, Shanghai Stomatological Hospital, and Institutes of Biomedical Sciences, Fudan University, Shanghai, 200000, China.
  • Huali Shen
    Institutes of Biomedical Sciences, Fudan University, Shanghai, 200032, P. R. China. shenhuali@fudan.edu.cn.