Author
Listed:
- Elisa Benedetti
(Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health)
- Maja Pučić-Baković
(Genos Glycoscience Research Laboratory)
- Toma Keser
(University of Zagreb)
- Annika Wahl
(Institute of Epidemiology 2, Research Unit Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health
Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health)
- Antti Hassinen
(University of Oulu)
- Jeong-Yeh Yang
(University of Georgia)
- Lin Liu
(University of Georgia)
- Irena Trbojević-Akmačić
(Genos Glycoscience Research Laboratory)
- Genadij Razdorov
(Genos Glycoscience Research Laboratory)
- Jerko Štambuk
(Genos Glycoscience Research Laboratory)
- Lucija Klarić
(Genos Glycoscience Research Laboratory
Usher Institute of Population Health Sciences and Informatics, University of Edinburgh
Institute of Genetics and Molecular Medicine, University of Edinburgh)
- Ivo Ugrina
(University of Zagreb
University of Split
Intellomics Ltd.)
- Maurice H. J. Selman
(Leiden University Medical Center)
- Manfred Wuhrer
(Leiden University Medical Center)
- Igor Rudan
(Usher Institute of Population Health Sciences and Informatics, University of Edinburgh)
- Ozren Polasek
(University of Split School of Medicine
Gen-info Ltd.)
- Caroline Hayward
(Institute of Genetics and Molecular Medicine, University of Edinburgh)
- Harald Grallert
(Institute of Epidemiology 2, Research Unit Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health
Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health
German Center for Diabetes Research (DZD))
- Konstantin Strauch
(Institute of Genetic Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health
Institute of Medical Informatics, Biometry and Epidemiology, Chair of Genetic Epidemiology, Ludwig-Maximilians Universität)
- Annette Peters
(Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health)
- Thomas Meitinger
(Institute of Human Genetics, Helmholtz Zentrum München—German Research Center for Environmental Health)
- Christian Gieger
(Institute of Epidemiology 2, Research Unit Molecular Epidemiology, Helmholtz Zentrum München—German Research Center for Environmental Health
Institute of Epidemiology 2, Helmholtz Zentrum München—German Research Center for Environmental Health)
- Marija Vilaj
(Genos Glycoscience Research Laboratory)
- Geert-Jan Boons
(University of Georgia
Utrecht Institute for Pharmaceutical Sciences, and Bijvoet Center for Biomolecular Research, Utrecht University)
- Kelley W. Moremen
(University of Georgia)
- Tatiana Ovchinnikova
(Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences)
- Nicolai Bovin
(Shemyakin and Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences)
- Sakari Kellokumpu
(University of Oulu)
- Fabian J. Theis
(Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health
Technical University Munich)
- Gordan Lauc
(Genos Glycoscience Research Laboratory
University of Zagreb)
- Jan Krumsiek
(Institute of Computational Biology, Helmholtz Zentrum München—German Research Center for Environmental Health
German Center for Diabetes Research (DZD))
Abstract
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.
Suggested Citation
Elisa Benedetti & Maja Pučić-Baković & Toma Keser & Annika Wahl & Antti Hassinen & Jeong-Yeh Yang & Lin Liu & Irena Trbojević-Akmačić & Genadij Razdorov & Jerko Štambuk & Lucija Klarić & Ivo Ugrina & , 2017.
"Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway,"
Nature Communications, Nature, vol. 8(1), pages 1-15, December.
Handle:
RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_s41467-017-01525-0
DOI: 10.1038/s41467-017-01525-0
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