Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Willems, E; Gloerich, J; Suppers, A; van, der, Flier, M; van, den, Heuvel, LP; van, de, Kar, N; Philipsen, RHLA; van, Dael, M; Kaforou, M; Wright, VJ; Herberg, JA; Torres, FM; Levin, M; de, Groot, R; van, Gool, AJ; Lefeber, DJ; Wessels, HJCT; de, Jonge, MI, , PERFORM, consortium.
Impact of infection on proteome-wide glycosylation revealed by distinct signatures for bacterial and viral pathogens.
iScience. 2023; 26(8): 107257
Doi: 10.1016/j.isci.2023.107257
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- Study Group Mitglieder der Med Uni Graz:
- Bauchinger Sebastian
- Baumgart Hinrich
- Benesch Martin
- Binder Alexander
- Eber Ernst
- Gallistl Siegfried
- Gores Gunther
- Haidl Harald
- Hauer Almuthe
- Keldorfer Markus
- Kohlfürst Daniela
- Kohlmaier Benno
- Krenn Larissa
- Leitner Manuel
- Löffler Sabine
- Niedrist Tobias Josef
- Nordberg Gudrun
- Pfleger Andreas
- Pfurtscheller Klaus
- Pilch Heidemarie
- Pölz Lena
- Rajic Glorija
- Roedl Siegfried
- Sagmeister Manfred Gerald
- Schweintzger Nina
- Skrabl-Baumgartner Andrea
- Sperl Matthias
- Stampfer Laura
- Strenger Volker
- Till Holger
- Trobisch Andreas
- Zenz Werner
- Zurl Christoph Johann
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- Abstract:
- Mechanisms of infection and pathogenesis have predominantly been studied based on differential gene or protein expression. Less is known about posttranslational modifications, which are essential for protein functional diversity. We applied an innovative glycoproteomics method to study the systemic proteome-wide glycosylation in response to infection. The protein site-specific glycosylation was characterized in plasma derived from well-defined controls and patients. We found 3862 unique features, of which we identified 463 distinct intact glycopeptides, that could be mapped to more than 30 different proteins. Statistical analyses were used to derive a glycopeptide signature that enabled significant differentiation between patients with a bacterial or viral infection. Furthermore, supported by a machine learning algorithm, we demonstrated the ability to identify the causative pathogens based on the distinctive host blood plasma glycopeptide signatures. These results illustrate that glycoproteomics holds enormous potential as an innovative approach to improve the interpretation of relevant biological changes in response to infection.