Longitudinal plasma proteomics reveals biomarkers of alveolar-capillary barrier disruption in critically ill COVID-19 patients.
Erik DuijvelaarJack S GisbyJames E PetersHarm Jan BogaardJurjan AmanPublished in: Nature communications (2024)
The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between viral cytopathic effects and a dysregulated host immune response. In critically ill patients, imatinib treatment demonstrated potential for reducing invasive ventilation duration and mortality. Here, we perform longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predict critical illness development. Next to dysregulation of inflammation, critical illness is characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuates protein perturbations associated with inflammation and extracellular matrix turnover. These proteomic alterations are contextualised using external pulmonary RNA-sequencing data of deceased COVID-19 patients and imatinib-treated Syrian hamsters. Together, we show that alveolar capillary barrier disruption in critical COVID-19 is reflected in the plasma proteome, and is attenuated with imatinib treatment. This study comprises a secondary analysis of both clinical data and plasma samples derived from a clinical trial that was registered with the EU Clinical Trials Register (EudraCT 2020-001236-10, https://www.clinicaltrialsregister.eu/ctr-search/trial/2020-001236-10/NL ) and Netherlands Trial Register (NL8491, https://www.trialregister.nl/trial/8491 ).
Keyphrases
- clinical trial
- sars cov
- extracellular matrix
- coronavirus disease
- immune response
- respiratory failure
- study protocol
- phase ii
- phase iii
- oxidative stress
- chronic kidney disease
- chronic myeloid leukemia
- end stage renal disease
- machine learning
- emergency department
- randomized controlled trial
- open label
- pulmonary hypertension
- newly diagnosed
- inflammatory response
- combination therapy
- mass spectrometry
- cardiovascular disease
- artificial intelligence
- dendritic cells
- extracorporeal membrane oxygenation
- risk assessment
- toll like receptor
- deep learning
- prognostic factors
- big data