Chemokines and eicosanoids fuel the hyperinflammation within the lungs of patients with severe COVID-19
Notice bibliographique
Résumé
BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to a variety of clinical outcomes, ranging from the absence of symptoms to severe acute respiratory disease and ultimately death. A feature of patients with severe coronavirus disease 2019 (COVID-19) is the abundance of inflammatory cytokines in the blood. Elevated levels of cytokines are predictive of infection severity and clinical outcome. In contrast, studies aimed at defining the driving forces behind the inflammation in lungs of subjects with severe COVID-19 remain scarce.ObjectiveOur aim was to analyze and compare the plasma and bronchoalveolar lavage (BAL) fluids of patients with severe COVID-19 (n = 45) for the presence of cytokines and lipid mediators of inflammation (LMIs).MethodsCytokines were measured by using Luminex multiplex assay, and LMIs were measured by using liquid chromatography–tandem mass spectrometry.ResultsWe revealed high concentrations of numerous cytokines, chemokines, and LMIs in the BAL fluid of patients with severe COVID-19. Of the 13 most abundant mediators in BAL fluid, 11 were chemokines, with CXCL1 and CXCL8 being 200 times more abundant than IL-6 and TNF-α. Eicosanoid levels were also elevated in the lungs of subjects with severe COVID-19. Consistent with the presence chemotactic molecules, BAL fluid samples were enriched for neutrophils, lymphocytes, and eosinophils. Inflammatory cytokines and LMIs in plasma showed limited correlations with those present in BAL fluid, arguing that circulating inflammatory molecules may not be a reliable proxy of the inflammation occurring in the lungs of patients with severe COVID-19.ConclusionsOur findings indicate that hyperinflammation of the lungs of patients with severe COVID-19 is fueled by excessive production of chemokines and eicosanoids. Therapeutic strategies to dampen inflammation in patients with COVID-19 should be tailored accordingly. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can lead to a variety of clinical outcomes, ranging from the absence of symptoms to severe acute respiratory disease and ultimately death. A feature of patients with severe coronavirus disease 2019 (COVID-19) is the abundance of inflammatory cytokines in the blood. Elevated levels of cytokines are predictive of infection severity and clinical outcome. In contrast, studies aimed at defining the driving forces behind the inflammation in lungs of subjects with severe COVID-19 remain scarce. Our aim was to analyze and compare the plasma and bronchoalveolar lavage (BAL) fluids of patients with severe COVID-19 (n = 45) for the presence of cytokines and lipid mediators of inflammation (LMIs). Cytokines were measured by using Luminex multiplex assay, and LMIs were measured by using liquid chromatography–tandem mass spectrometry. We revealed high concentrations of numerous cytokines, chemokines, and LMIs in the BAL fluid of patients with severe COVID-19. Of the 13 most abundant mediators in BAL fluid, 11 were chemokines, with CXCL1 and CXCL8 being 200 times more abundant than IL-6 and TNF-α. Eicosanoid levels were also elevated in the lungs of subjects with severe COVID-19. Consistent with the presence chemotactic molecules, BAL fluid samples were enriched for neutrophils, lymphocytes, and eosinophils. Inflammatory cytokines and LMIs in plasma showed limited correlations with those present in BAL fluid, arguing that circulating inflammatory molecules may not be a reliable proxy of the inflammation occurring in the lungs of patients with severe COVID-19. Our findings indicate that hyperinflammation of the lungs of patients with severe COVID-19 is fueled by excessive production of chemokines and eicosanoids. Therapeutic strategies to dampen inflammation in patients with COVID-19 should be tailored accordingly.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,027 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».