Generation of catalytic antibodies is an intrinsic property of an individual’s immune system: a study on a large cohort of renal transplant patients
Bibliographic record
Abstract
Abstract The origin and physiopathological relevance of catalytic antibodies is not well understood owing to the fact that catalytic antibodies have been studied in relatively small cohorts of patients with rare diseases and/or without systematic follow-up. In the present study, we have followed the evolution of the levels of catalytic IgG in a large cohort of 100 renal transplant patients over a 2-year period. Prior to transplant, hydrolysis of the generic substrate PFR-MCA was greater for patients’ IgG than for a therapeutic preparation of pooled IgG from healthy donors (6.6±0.9 vs 0.65±0.03 fmol/min per pmol). Renal transplant was marked by a drastic decrease in levels of catalytic IgG over 3 months (6.6±0.9 vs 2.4±0.2 fmol/min/pmol; P<0.0001) followed by a steady increase at 12 months (3.2±0.3 fmol/min/pmol; P=0.015) and further at 24 months (5.1±0.6 fmol/min/pmol; P=0.004). When divided into quartiles based on the rates of IgG-mediated PFR-MCA hydrolysis measured in pre-transplant samples, the IgG catalytic activity in the upper quartile of patients was significantly high both pre-transplant (12.03±1.6 vs 2.7±0.2 fmol/min/pmol, P<0.0001) and 24 months post-transplant (6.8±1.2 vs 4.6±0.7, fmol/min/pmol, P=0.0004). Interestingly, IgG-mediated hydrolysis of a model protein substrate, pro-coagulant factor VIII, did not correlate with that of PFR-MCA prior transplantation, while it did 12 months post-transplant (P<0.0001, R2=0.4). Taken together, our results suggest that the level of circulating catalytic IgG under pathological conditions is an intrinsic property of each individual’s immune system, and that recovery of pre-transplant levels of catalytic IgG is accompanied by changes in the repertoire of target antigens.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".