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
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".