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Benedetti, Elisa; Pucic-Bakovic, Maja; Keser, Toma; Wahl, Annika; Hassinen, Antti; Yang, Jeong-Yeh; Liu, Lin; Trbojevic-Akmacic, Irena; Razdorov, Genadij; Stambuk, Jerko; Klaric, Lucija; Ugrina, Ivo; Selman, Maurice H. J.; Wuhrer, Manfred; Rudan, Igor; Polasek, Ozren; Hayward, Caroline; Grallert, Harald; Strauch, Konstantin; Peters, Annette; Meitinger, Thomas; Gieger, Christian; Vilaj, Marija; Boons, Geert-Jan; Moremen, Kelley W.; Ovchinnikova, Tatiana; Bovin, Nicolai; Kellokumpu, Sakari; Theis, Fabian J.; Lauc, Gordan; Krumsiek, Jan (2017): Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway. In: Nature Communications, Vol. 8, 1483
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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.