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The plasma proteome differentiates the multisystem inflammatory syndrome in children (MIS-C) from children with SARS-CoV-2 negative sepsis.
Patel, Maitray A; Fraser, Douglas D; Daley, Mark; Cepinskas, Gediminas; Veraldi, Noemi; Grazioli, Serge.
Afiliación
  • Patel MA; Epidemiology and Biostatistics, Western University, N6A 3K7, London, ON, Canada.
  • Fraser DD; Lawson Health Research Institute, N6C 2R5, London, ON, Canada. douglas.fraser@lhsc.on.ca.
  • Daley M; Children's Health Research Institute, N6C 4V3, London, ON, Canada. douglas.fraser@lhsc.on.ca.
  • Cepinskas G; Pediatrics, Western University, N6A 3K7, London, ON, Canada. douglas.fraser@lhsc.on.ca.
  • Veraldi N; Clinical Neurological Sciences, Western University, N6A 3K7, London, ON, Canada. douglas.fraser@lhsc.on.ca.
  • Grazioli S; Physiology & Pharmacology, Western University, N6A 3K7, London, ON, Canada. douglas.fraser@lhsc.on.ca.
Mol Med ; 30(1): 51, 2024 Apr 17.
Article en En | MEDLINE | ID: mdl-38632526
ABSTRACT

BACKGROUND:

The Multi-System Inflammatory Syndrome in Children (MIS-C) can develop several weeks after SARS-CoV-2 infection and requires a distinct treatment protocol. Distinguishing MIS-C from SARS-CoV-2 negative sepsis (SCNS) patients is important to quickly institute the correct therapies. We performed targeted proteomics and machine learning analysis to identify novel plasma proteins of MIS-C for early disease recognition.

METHODS:

A case-control study comparing the expression of 2,870 unique blood proteins in MIS-C versus SCNS patients, measured using proximity extension assays. The 2,870 proteins were reduced in number with either feature selection alone or with a prior COMBAT-Seq batch effect adjustment. The leading proteins were correlated with demographic and clinical variables. Organ system and cell type expression patterns were analyzed with Natural Language Processing (NLP).

RESULTS:

The cohorts were well-balanced for age and sex. Of the 2,870 unique blood proteins, 58 proteins were identified with feature selection (FDR-adjusted P < 0.005, P < 0.0001; accuracy = 0.96, AUC = 1.00, F1 = 0.95), and 15 proteins were identified with a COMBAT-Seq batch effect adjusted feature selection (FDR-adjusted P < 0.05, P < 0.0001; accuracy = 0.92, AUC = 1.00, F1 = 0.89). All of the latter 15 proteins were present in the former 58-protein model. Several proteins were correlated with illness severity scores, length of stay, and interventions (LTA4H, PTN, PPBP, and EGF; P < 0.001). NLP analysis highlighted the multi-system nature of MIS-C, with the 58-protein set expressed in all organ systems; the highest levels of expression were found in the digestive system. The cell types most involved included leukocytes not yet determined, lymphocytes, macrophages, and platelets.

CONCLUSIONS:

The plasma proteome of MIS-C patients was distinct from that of SCNS. The key proteins demonstrated expression in all organ systems and most cell types. The unique proteomic signature identified in MIS-C patients could aid future diagnostic and therapeutic advancements, as well as predict hospital length of stays, interventions, and mortality risks.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis / COVID-19 Límite: Child / Humans Idioma: En Revista: Mol Med Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sepsis / COVID-19 Límite: Child / Humans Idioma: En Revista: Mol Med Asunto de la revista: BIOLOGIA MOLECULAR Año: 2024 Tipo del documento: Article País de afiliación: Canadá Pais de publicación: Reino Unido