2026

A novel surface plasmon resonance approach to assess anti-dsDNA antibody kinetics and disease severity in systemic lupus erythematosus.

Clinical & translational immunology Nagant C, Taghavi M, Ziani HBK, Ghorra N, Badot V, Parmentier S, Corazza F
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中文摘要

英文摘要

OBJECTIVES: Anti-dsDNA antibodies are established biomarkers in systemic lupus erythematosus (SLE), yet the clinical utility of conventional assays remains limited by variable analytical performance. We evaluated a novel Fibre-Optic Surface Plasmon Resonance (FO-SPR) biosensor for real-time profiling of anti-dsDNA antibody-antigen interactions and investigated whether kinetic parameters are associated with disease severity, particularly lupus nephritis (LN). METHODS: Sera from 26 SLE patients (including 12 with LN), 14 disease controls and 16 healthy donors were analysed using FO-SPR biosensors coated with biotinylated dsDNA. The sensors generated real-time kinetic profiles from which maximum binding response (B max), association rate and dissociation constant were computed. FO-SPR kinetic results were compared with a conventional chemiluminescent anti-dsDNA assay (CLIA) and correlated with disease activity and renal involvement. RESULTS: The FO-SPR platform enabled reliable detection of anti-dsDNA antibodies and clearly distinguished SLE patients with renal involvement: Eight of 12 LN patients showed B max values above the 90th percentile of the non-SLE control group. In these patients, kinetic parameters revealed high-affinity anti-dsDNA profiles characterised by significantly higher association rates and maximal shifts and lower dissociation constants. The AUC-ROC for SPR-derived affinity parameters was 0.82 (P = 0.006), outperforming the CLIA assay to discriminate lupus patients with versus without nephritis. Furthermore, FO-SPR binding responses correlated with disease activity score in LN. CONCLUSION: This study highlights the value of an innovative FO-SPR biosensor for real-time characterisation of anti-dsDNA antibody affinity. The kinetic profiles captured by this platform provide clinically relevant insights, particularly for distinguishing LN patients from other SLE phenotypes.

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