TRANSCRIPTOMIC ANALYSIS REVEALS A SYNERGY OF HYDROXYCHLOROQUINE AND GLUCOCORTICOIDS IN MODULATING B CELL-RELATED IMMUNE PROCESSES
Bibliographic record
Abstract
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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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) | 0.001 | 0.000 |
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".