An Anti-Phospholipase A2 Receptor Quantitative Immunoassay and Epitope Analysis in Membranous Nephropathy Reveals Different Antigenic Domains of the Receptor
Bibliographic record
Abstract
The phospholipase A2 receptor (PLA2R) was recently discovered as a target autoantigen in patients with idiopathic membranous nephropathy (IMN). Published evidence suggests that the autoantibodies directed towards a conformation dependent epitope are currently effectively detected by a cell based assay (CBA) utilizing indirect immunofluorescence (IIF) on tissue culture cells transfected with the PLA2R cDNA. Limitations of such IIF-CBA assays include observer dependent subjective evaluation of semi-quantitative test results and the protocols are not amenable to high throughput diagnostic testing. We developed a quantitative, observer independent, high throughput capture immunoassay for detecting PLA2R autoantibodies on an addressable laser bead immunoassay (ALBIA) platform. Since reactive domains of PLA2R (i.e. epitopes) could be used to improve diagnostic tests by using small peptides in various high throughput diagnostic platforms, we identified PLA2R epitopes that bound autoantibodies of IMN patients. These studies confirmed that inter-molecular epitope spreading occurs in IMN but use of the cognate synthetic peptides in immunoassays was unable to conclusively distinguish between IMN patients and normal controls. However, combinations of these peptides were able to effectively absorb anti-PLA2R reactivity in IIF-CBA and an immunoassay that employed a lysate derived from HEK cells tranfected with and overexpressing PLA2R. While we provide evidence of intermolecular epitope spreading, our data indicates that in addition to conformational epitopes, human anti-PLA2R reactivity in a commercially available CBA and an addressable laser bead immunoassay is significantly absorbed by peptides representing epitopes of PLA2R.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".