Literature DB >> 28084684

Secretome analysis to elucidate metalloprotease-dependent ectodomain shedding of glycoproteins during neuronal differentiation.

Kazuya Tsumagari1,2, Kyoko Shirakabe3, Mayu Ogura2, Fuminori Sato1, Yasushi Ishihama2, Atsuko Sehara-Fujisawa1.   

Abstract

Many membrane proteins are subjected to limited proteolyses at their juxtamembrane regions, processes referred to as ectodomain shedding. Shedding ectodomains of membrane-bound ligands results in activation of downstream signaling pathways, whereas shedding those of cell adhesion molecules causes loss of cell-cell contacts. Secreted proteomics (secretomics) using high-resolution mass spectrometry would be strong tools for both comprehensive identification and quantitative measurement of membrane proteins that undergo ectodomain shedding. In this study, to elucidate the ectodomain shedding events that occur during neuronal differentiation, we establish a strategy for quantitative secretomics of glycoproteins released from differentiating neuroblastoma cells into culture medium with or without GM6001, a broad-spectrum metalloprotease inhibitor. Considering that most of transmembrane and secreted proteins are N-glycosylated, we include a process of N-glycosylated peptides enrichment as well as isotope tagging in our secretomics workflow. Our results show that differentiating N1E-115 neurons secrete numerous glycosylated polypeptides in metalloprotease-dependent manners. They are derived from cell adhesion molecules such as NCAM1, CADM1, L1CAM, various transporters and receptor proteins. These results show the landscape of ectodomain shedding and other secretory events in differentiating neurons and/or during axon elongation, which should help elucidate the mechanism of neurogenesis and the pathogenesis of neurological disorders.
© 2017 Molecular Biology Society of Japan and John Wiley & Sons Australia, Ltd.

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Year:  2017        PMID: 28084684     DOI: 10.1111/gtc.12466

Source DB:  PubMed          Journal:  Genes Cells        ISSN: 1356-9597            Impact factor:   1.891


  3 in total

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Journal:  Sci Rep       Date:  2019-06-18       Impact factor: 4.379

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Journal:  Sci Rep       Date:  2018-11-16       Impact factor: 4.379

  3 in total

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