Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children
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Multisystem inflammatory syndrome in children (MIS-C) is a severe disease that emerged during the COVID-19 pandemic. Although recognized as an immune-mediated condition, the pathogenesis remains unresolved. Furthermore, the absence of a diagnostic test can lead to delayed immunotherapy. Using state-of-the-art mass-spectrometry proteomics, assisted by artificial intelligence (AI), we aimed to identify a diagnostic signature for MIS-C and to gain insights into disease mechanisms. We identified a highly specific 4-protein diagnostic signature in children with MIS-C. Furthermore, we identified seven clusters that differed between MIS-C and controls, indicating an interplay between apolipoproteins, immune response proteins, coagulation factors, platelet function, and the complement cascade. These intricate protein patterns indicated MIS-C as an immunometabolic condition with global hypercoagulability. Our findings emphasize the potential of AI-assisted proteomics as a powerful and unbiased tool for assessing disease pathogenesis and suggesting avenues for future interventions and impact on pediatric disease trajectories through early diagnosis.
Original language | English |
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Article number | 688 |
Journal | Communications Biology |
Volume | 7 |
Issue number | 1 |
Number of pages | 10 |
ISSN | 2399-3642 |
DOIs | |
Publication status | Published - 2024 |
Bibliographical note
Publisher Copyright:
© The Author(s) 2024.
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