POS0237 ADULT-ONSET STILL’S DISEASE AND COVID 19 SHARE GENETIC EXPRESSION RELATED TO HYPERINFLAMMATION: DEFINING THE POSSIBLE ROLE OF THE SPECIALIZED PRO-RESOLVING LIPID MEDIATOR PROTECTIN D1 IN MODULATING MACROPHAGES POLARIZATION AND FUNCTION
Notice bibliographique
Résumé
Background COVID-19 and autoinflammatory diseases, such as Adult-onset Still’s Disease (AOSD), are characterized by a massive production and uncontrolled secretion of pro-inflammatory cytokines. The specialized pro-resolving lipid mediators (SPMs) family is one the most important processes counteracting hyperinflammation inducing tissue repair and homeostasis restoration. Among SPMs, Protectin D1 is able to exert antiviral features in animal models. Objectives The aim of this study was to compare the transcriptome of peripheral blood mononuclear cells (PBMCs) from patients with AOSD and COVID-19 and to evaluate the role of PD1 on those diseases, especially in modulating macrophages polarization. Methods This study enrolled patients with AOSD, COVID-19, and healthy donors (HD). Next-generation deep sequencing was performed on to identify the differences in PBMCs transcripts profiles. Plasma levels of PD1 were assessed by commercial ELISA kits. Monocytes-derived macrophages were polarized into M1 and M2 phenotype. We analyzed the effect of PD1 on macrophages differentiation. At 10 days, macrophages were analyzed for surface. expression of subtypes markers by flow cytometry. Cytokines production was measured in supernatants using Bio-Plex Pro Human Cytokine 17-plex Assay kit (Bio-Rad). Results All COVID-19 participants (n=21) were hospitalized. We found statistically significant higher ferritin levels between intensive care unit (ICU) and non-ICU patients (p=0.006), and higher procalcitonin (p=0.03). Five patients with AOSD, out of 13, had a systemic score (SS) ≥1. The most frequent manifestations were arthralgia (8/13), arthritis (5/13) and fever (3/13). Moreover, we found higher levels of ferritin and CRP in the AOSD SS ≥1 group as compared to AOSD SS=0 group. We also enrolled thirteen HD age- and sex-matched to AOSD patients. Transcriptome analysis of PBMCs from HD, AOSD, and COVID-19 patients is reported in Figure 1, showing the PCA plot (A), the pathway analysis by Pantherdb.org (B), the volcano plots representing differential expression analysis of genes in COVID-19 vs HD, AOSD vs HD, and COVID-19 vs AOSD (C), and the transcriptome heatmaps (D), showing that inflammation- and lipid catabolism-related genes are specifically dysregulated and those associated with monocytes phenotype and function were upregulated in COVID-19 and AOSD patients compared to HDs. Patients affected by COVID-19, hospitalized in intensive care unit (ICU), showed higher levels of PD1 compared to not-ICU hospitalized patients and HDs (ICU COVID-19 vs not-ICU COVID-19, p= 0.02; HDs vs ICU COVID-19, p= 0.0006). PD1 levels were increased in AOSD patients with SS ≥1 compared to patients with SS=0 (p=0.028) and HD (p=0.048). PD1 stimulation increased the expression of CD206 in M2 monocytes-derived macrophages from both COVID-19 and AOSD patients (COVID-19: untreated vs PD1, p=0.0098; AOSD: untreated vs PD1 p= 0.0234), whilst PD1 did not affect CD80 expression in both M1 and M2 macrophages and CD206 expression in M1 macrophages. A significant increase of both IL-10 and macrophage inflammatory protein (MIP-1β) was observed in M2 macrophages from COVID-19 patients after PD1 stimulation (IL-10, p= 0.03; MIP-1β, p= 0.003); the same results were found in AOSD patients (IL-10, p= 0.03; MIP-1β, p= 0.03). Conclusion PD1 can induce pro-resolutory programs in both AOSD and COVID-19 increasing M2 polarization and inducing their activity. PD1-treated M2 macrophages from AOSD and COVID-19 patients increased the production of IL-10, thus promoting anti-inflammation, and enhanced homeostatic restoration through MIP-1β production. References [1]P. Ruscitti et al, Ann. Rheum. Dis. 79 (2020) 1152–1155 [2]C.N. Serhan et al, Nat. Immunol. 6 (2005) 1191–1197 Acknowledgements: NIL. Disclosure of Interests luca navarini Speakers bureau: MSD, UCB, Eli Lilly, Novartis, Janssen-Cilag, AbbVie, Pfizer, BMS, Consultant of: Janssen, Marta Vomero: None declared, Damiano Currado: None declared, Onorina Berardicurti: None declared, Alice Biaggi: None declared, Annalisa Marino: None declared, Pietro Bearzi: None declared, Erika Corberi: None declared, Amelia Rigon: None declared, Luisa Arcarese: None declared, Alessandro Leuti: None declared, Marina Fava: None declared, Marta Fogolari: None declared, Alessia Mattei: None declared, Piero Ruscitti: None declared, Ilenia Di Cola: None declared, Federico Sambuco: None declared, Francesco Travaglino: None declared, silvia angeletti: None declared, Francesco Ursini: None declared, Erminia Mariani: None declared, Felice Eugenio Agrò: None declared, Paola Cipriani: None declared, Annamaria Iagnocco Speakers bureau: Abbvie, MSD, Alfasigma, Celltrion, BMS, Celgene, Eli-Lilly, Sanofi Genzyme, Pfizer, Galapagos, Gilead, Novartis, SOBI, Janssen, Grant/research support from: Pfizer, Abbvie, Raffaele Antonelli Incalzi Paid instructor for: MSD, GSK, Mauro Maccarrone Consultant of: InMed Pharmaceuticals, Jazz Pharmaceuticals, Roberto Giacomelli Speakers bureau: MSD, UCB, Eli Lilly, Novartis, Janssen-Cilag, AbbVie, Pfizer, BMS, Sobi, Grant/research support from: Sobi, Pfizer.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».