Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children

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Standard

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

Harvard

Nygaard, U, Nielsen, AB, Dungu, KHS, Drici, L, Holm, M, Ottenheijm, ME, Nielsen, AB, Glenthøj, JP, Schmidt, LS, Cortes, D, Jørgensen, IM, Mogensen, TH, Schmiegelow, K, Mann, M, Vissing, NH & Wewer Albrechtsen, NJ 2024, 'Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children', Communications Biology , vol. 7, no. 1, 688. https://doi.org/10.1038/s42003-024-06370-8

APA

Nygaard, U., Nielsen, A. B., Dungu, K. H. S., Drici, L., Holm, M., Ottenheijm, M. E., Nielsen, A. B., Glenthøj, J. P., Schmidt, L. S., Cortes, D., Jørgensen, I. M., Mogensen, T. H., Schmiegelow, K., Mann, M., Vissing, N. H., & Wewer Albrechtsen, N. J. (2024). Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children. Communications Biology , 7(1), [688]. https://doi.org/10.1038/s42003-024-06370-8

Vancouver

Nygaard U, Nielsen AB, Dungu KHS, Drici L, Holm M, Ottenheijm ME et al. Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children. Communications Biology . 2024;7(1). 688. https://doi.org/10.1038/s42003-024-06370-8

Author

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. / Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children. In: Communications Biology . 2024 ; Vol. 7, No. 1.

Bibtex

@article{959d44154fd045edb500caee3a6adc5b,

title = "Proteomic profiling reveals diagnostic signatures and pathogenic insights in multisystem inflammatory syndrome in children",

abstract = "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.",

author = "Ulrikka Nygaard and Nielsen, {Annelaura Bach} and Dungu, {Kia Hee Schultz} and Lylia Drici and Mette Holm and Ottenheijm, {Maud Eline} and Nielsen, {Allan Bybeck} and Glenth{\o}j, {Jonathan Peter} and Schmidt, {Lisbeth Sams{\o}} and Dina Cortes and J{\o}rgensen, {Inger Merete} and Mogensen, {Trine Hyrup} and Kjeld Schmiegelow and Matthias Mann and Vissing, {Nadja Hawwa} and {Wewer Albrechtsen}, {Nicolai J.}",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

doi = "10.1038/s42003-024-06370-8",

language = "English",

volume = "7",

journal = "Communications Biology",

issn = "2399-3642",

publisher = "nature publishing group",

number = "1",

}

RIS

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.

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

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