Sensitive detection of plasma interferon regulatory factor-5 (IRF5) by solid-phase proximity ligation assay validates IRF5 high and low subgroups in patients with systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVES: Interferon regulatory factor 5 (IRF5) plays a central role in interferon-mediated inflammation and is implicated in autoimmune diseases, including systemic lupus erythematosus (SLE). Detecting IRF5 in plasma is challenging due to its low circulating levels, highlighting the need for a highly sensitive and quantitative assay. This study aimed to develop such an assay, validate the existence of previously identified IRF5 high and low subgroups in SLE, and support the potential of IRF5 as a biomarker in precision medicine. METHOD: We established a solid-phase proximity ligation assay (SP-PLA) for IRF5 detection using a commercially available polyclonal antibody, which was benchmarked against two in-house recombinant antibodies. A linear calibration curve was generated using recombinant IRF5 protein (range 0.01-100 pg/µL), with a limit of detection between 0.01 and 0.05 pg/µL. EDTA-plasma samples from three SLE subgroups (n = 25 per group) were analysed. RESULTS: IRF5 was detectable in all SLE plasma samples using SP-PLA (mean 0.63 pg/µL; sd 1.92 pg/µL; maximum 13.54 pg/µL). The IRF5 high SLE subgroup showed significantly higher IRF5 plasma levels than the IRF5 low SLE subgroup (Dunn's post-hoc test, adjusted p < 0.05). CONCLUSION: The SP-PLA enabled sensitive and specific detection of low-level IRF5 in plasma and confirmed the presence of IRF5 high and low subgroups in SLE using a quantitative method. These findings support the potential of IRF5 as a biomarker, but validation in independent cohorts is required. The assay may facilitate patient stratification in future research and precision medicine approaches targeting the interferon pathway.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".