最適化されたNMDAR自己抗体検出のためのNR1-4a LCBAおよびNR1/NR2融合FCBAの組み合わせ

Autoimmune encephalitis (AE) is a group of neurological disorders mediated by autoantibodies targeting proteins within the central nervous system, of which anti-N-methyl-d-aspartate receptor (NMDAR) encephalitis is the most common. The current Cell-Based Assay (CBA) that is primarily used to detect NMDAR antibodies in clinical settings, however, exhibits several limitations, including both false-positive and false-negative results. The aim of this research is to improve the sensitivity and specificity of NMDAR antibody detection through optimization of the CBA detection model.

In this research, recombinant vectors were constructed to express full-length, truncated, and fusion proteins of NMDA Receptor Subunit 1 (NR1) or/and NMDA Receptor Subunit 2B (NR2B). Six distinct models for CBA detection (Model I-Model VI) were established via transfection into CHO cells. A total of 36 serum (SER) and cerebrospinal fluid (CSF) samples from 18 anti-NMDAR patients were analyzed alongside 30 SER and CSF samples from individuals with other neurological disorders and 20 SER samples from healthy controls to systematically evaluate the detection efficacy across each model.

The findings indicate that the Model IV (NR1-4a single subunit) exhibited optimal diagnostic sensitivity(true positive identified) at 100% by live cell-based assay (L-CBA). In contrast, Model V (NR1-1a Amino Terminal Domain (ATD) fused with NR2B ATD) displayed the highest technical sensitivity(titer detection limit) by the fixed cell-based assay (F-CBA), which was significantly superior to the other models (P < 0.05). The combined testing of Models IV and V resulted in a substantial enhancement of the overall performance of the assay. Specifically, the sample diagnostic sensitivity of the SER increased from 83.3% of traditional detection method(Model I) to 100% and the technical sensitivity was improved with highly significant difference to Model I(SER: P < 0.0001; CSF: P < 0.05).

The combined detection established in this research markedly improved both accuracy and reliability in NMDAR antibody detection, effectively addressing the limitations associated with traditional detection methods. This optimized detection provides a robust foundation for early diagnosis and therapeutic intervention in cases of NMDAR encephalitis, thereby enhancing patient prognosis.