TRANSCRIPTOMIC ANALYSIS REVEALS A SYNERGY OF HYDROXYCHLOROQUINE AND GLUCOCORTICOIDS IN MODULATING B CELL-RELATED IMMUNE PROCESSES
Notice bibliographique
Résumé
PV113 / #260 Poster Topic: AS12 - Genetics, Epigenetics, Transcriptomics Background/Purpose Hydroxychloroquine (HCQ), glucocorticoids (GC), and their combination are common treatments in autoimmune rheumatic diseases. Their effects at the molecular level and potential synergistic effects remain unclear, which formed the scope of this work, including investigation of specific gene sets that are involved in this potential interaction. Methods We analyzed bulk RNA sequencing data from 591 samples from the PRECISESADS project.[1] The patients were diagnosed with systemic lupus erythematosus (SLE), Sjögren’s disease (SjD), undifferentiated connective tissue disease (UCTD), and mixed connective tissue disease (MCTD), and were grouped into 4 categories based on treatment status: no current exposure to HCQ or GC (off-treatment; n = 244), on HCQ (n = 198), on GC (n = 47), or on HCQ and GC combined (n = 102) (Table 1). Patients were not on any immunosuppressive treatment at the time of sampling. We performed differential gene expression (DGE) analysis across treatment groups (HCQ, GC, and HCQ+GC) with the off-treatment group of patients serving as the comparator. Overrepresentation analysis (ORA) was conducted on genes with amplified effects (absolute log 2 fold change (FC) < 0.5 for the individual treatments; absolute log 2 FC > 0.5 for the combination treatment), using Chaussabel’s gene set modules to identify enriched pathways.[2] The activity of the modules was estimated using gene set variation analysis (GSVA). Statistical comparisons of gene activities within modules between treatment groups (GC vs. HCQ+GC) for steroid dosages (low, medium, high) were conducted using the Mann-Whitney U test. Table 1. Number of samples per patient diagnosis and treatment category. Results Combination of HCQ and GC generated a synergistic molecular response, with a higher number of differentially expressed genes and greater effect size compared to the individual treatments, regarding both differentially overexpressed and downregulated genes. The ORA of genes with amplified effect size pointed to numerous immune-related pathways, consistent with the GSEA analysis results. Notably, B cell-related gene proliferation and activity modules were significantly suppressed (ORA adj. p-value < 0.05, GSEA adj. p-value < 0.05) in the group of patients on combination treatment. The B cell proliferation module was significantly lowered by the addition of HCQ to GC at a daily average dose of 4-6 mg of prednisone equivalents compared to GC alone at the same doses ( p = 0.014 ). Pathways related to DNA damage and DNA replication were also reduced. Conclusions The combination of HCQ and GC results in a synergistic molecular response. ORA and GSEA revealed significant involvement of immune-related pathways, with a notable suppression of B cell-related gene modules. While the suppression of the B cell proliferation module was significantly amplified by the addition of HCQ to GC treatment, the influence of GC dosage requires further investigation. Overall, these findings suggest a synergy at the molecular level when HCQ and GC are administered concurrently in combined regimens, enhancing the modulation of key immune processes. References: [1.] Barturen G. Arthritis Rheumatol 2021;73:1073-85. [2.] Rinchai D. Bioinformatics 2021;37:2382-9.
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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) | 0,001 | 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 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 ».