Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway.
Network inference from glycoproteomics data reveals new reactions in the IgG glycosylation pathway.
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DOI:
10.1038/s41467-017-01525-0
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发表时间:
2017-11-14
影响因子:
16.6
通讯作者:
Krumsiek J
中科院分区:
文献类型:
--
作者:
Benedetti E;Pučić-Baković M;Keser T;Wahl A;Hassinen A;Yang JY;Liu L;Trbojević-Akmačić I;Razdorov G;Štambuk J;Klarić L;Ugrina I;Selman MHJ;Wuhrer M;Rudan I;Polasek O;Hayward C;Grallert H;Strauch K;Peters A;Meitinger T;Gieger C;Vilaj M;Boons GJ;Moremen KW;Ovchinnikova T;Bovin N;Kellokumpu S;Theis FJ;Lauc G;Krumsiek J
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. IgG glycosylation is an important factor in immune function, yet the molecular details of protein glycosylation remain poorly understood. The data-driven approach presented here uses large-scale plasma IgG mass spectrometry measurements to infer new biochemical reactions in the glycosylation pathway.
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影响因子:
--
作者:
Krumsiek J;Suhre K;Illig T;Adamski J;Theis FJ
通讯作者:
Theis FJ
影响因子:
2.9
作者:
BROCKHAUSEN, I;CARVER, JP;SCHACHTER, H
通讯作者:
SCHACHTER, H
影响因子:
4.8
作者:
Hassinen, Antti;Pujol, Francois M.;Kellokumpu, Sakari
通讯作者:
Kellokumpu, Sakari
影响因子:
4.4
作者:
Do, Kieu Trinh;Kastenmueller, Gabi;Krumsiek, Jan
通讯作者:
Krumsiek, Jan
DOI:
10.1073/pnas.59.2.491
发表时间:
1968-01-01
影响因子:
11.1
作者:
BREW, K;VANAMAN, TC;HILL, RL
通讯作者:
HILL, RL