Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children
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Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children. / Nygaard, Ulrikka; Nielsen, Annelaura Bach; Dungu, Kia Hee Schultz; Drici, Lylia; Holm, Mette; Ottenheijm, Maud Eline; Nielsen, Allan Bybeck; Glenthøj, Jonathan Peter; Schmidt, Lisbeth Samsø; Cortes, Dina; Jørgensen, Inger Merete; Mogensen, Trine Hyrup; Schmiegelow, Kjeld; Mann, Matthias; Vissing, Nadja Hawwa; Wewer Albrechtsen, Nicolai J.
In: Communications Biology , Vol. 7, No. 1, 688, 2024.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children
AU - Nygaard, Ulrikka
AU - Nielsen, Annelaura Bach
AU - Dungu, Kia Hee Schultz
AU - Drici, Lylia
AU - Holm, Mette
AU - Ottenheijm, Maud Eline
AU - Nielsen, Allan Bybeck
AU - Glenthøj, Jonathan Peter
AU - Schmidt, Lisbeth Samsø
AU - Cortes, Dina
AU - Jørgensen, Inger Merete
AU - Mogensen, Trine Hyrup
AU - Schmiegelow, Kjeld
AU - Mann, Matthias
AU - Vissing, Nadja Hawwa
AU - Wewer Albrechtsen, Nicolai J.
N1 - Publisher Copyright: © The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
U2 - 10.1038/s42003-024-06370-8
DO - 10.1038/s42003-024-06370-8
M3 - Journal article
C2 - 38839859
AN - SCOPUS:85195332433
VL - 7
JO - Communications Biology
JF - Communications Biology
SN - 2399-3642
IS - 1
M1 - 688
ER -
ID: 395085279