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Benedetti, E. ; Pučić-Baković, M.* ; Keser, T.* ; Wahl, A. ; Hassinen, A.* ; Yang, J.Y.* ; Liu, L.* ; Trbojević-Akmačić, I.* ; Razdorov, G.* ; Štambuk, J.* ; Klarić, L.* ; Ugrina, I.* ; Selman, M.H.J.* ; Wuhrer, M.* ; Rudan, I.* ; Polasek, O.* ; Hayward, C.* ; Grallert, H. ; Strauch, K. ; Peters, A. ; Meitinger, T. ; Gieger, C. ; Vilaj, M.* ; Boons, G.J.* ; Moremen, K.W.* ; Ovchinnikova, T.* ; Bovin, N.* ; Kellokumpu, S.* ; Theis, F.J. ; Lauc, G.* ; Krumsiek, J.

Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway.

Nat. Commun. 8:1483 (2017)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Immunoglobulin G (IgG) is a major effector molecule of the human immune response, and aberrations in IgG glycosylation are linked to various diseases. However, the molecular mechanisms underlying protein glycosylation are still poorly understood. We present a data-driven approach to infer reactions in the IgG glycosylation pathway using large-scale mass-spectrometry measurements. Gaussian graphical models are used to construct association networks from four cohorts. We find that glycan pairs with high partial correlations represent enzymatic reactions in the known glycosylation pathway, and then predict new biochemical reactions using a rule-based approach. Validation is performed using data from a GWAS and results from three in vitro experiments. We show that one predicted reaction is enzymatically feasible and that one rejected reaction does not occur in vitro. Moreover, in contrast to previous knowledge, enzymes involved in our predictions colocalize in the Golgi of two cell lines, further confirming the in silico predictions.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Asparagine-linked Oligosaccharides; Genome-wide Association; Substrate-specificity; Hen Oviduct; Human Blood; Hela-cells; Rat-liver; L-fucose; Sialyltransferase; Protein
Sprache englisch
Veröffentlichungsjahr 2017
HGF-Berichtsjahr 2017
ISSN (print) / ISBN 2041-1723
e-ISSN 2041-1723
Zeitschrift Nature Communications
Quellenangaben Band: 8, Heft: 1, Seiten: , Artikelnummer: 1483 Supplement: ,
Verlag Nature Publishing Group
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute of Epidemiology (EPI)
Institute of Human Genetics (IHG)
Institute of Genetic Epidemiology (IGE)
POF Topic(s) 30205 - Bioengineering and Digital Health
30202 - Environmental Health
30501 - Systemic Analysis of Genetic and Environmental Factors that Impact Health
90000 - German Center for Diabetes Research
Forschungsfeld(er) Enabling and Novel Technologies
Genetics and Epidemiology
PSP-Element(e) G-554100-001
G-503800-001
G-504091-002
G-504091-004
G-500700-001
G-504000-001
G-504100-001
G-501900-402
PubMed ID 29133956
DOI 10.1038/s41467-017-01525-0
WOS ID WOS:000415124000010
Erfassungsdatum 2017-11-22
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