Humoral immunity to lung antigens early post-transplant confers risk for chronic lung allograft dysfunction
Bibliographic record
Abstract
BACKGROUND: Autoantibodies and de novo donor HLA-specific antibodies (dnDSA) may contribute to chronic lung allograft dysfunction (CLAD). However, the breadth of reactivities against self-antigens and their association with CLAD has been underexamined. In a single-center study, we screened lung transplant (LTx) recipients for novel autoantibodies at transplant and 6 months post-LTx, assessed dnDSA exposure, and CLAD-free survival. METHODS: Serum samples were collected from 89 crossmatch-negative bilateral LTx recipients at the time of LTx and 6 months post-LTx, before a CLAD diagnosis, for autoantibody screening using a custom antigen microarray. RESULTS: Patients who developed CLAD by 5 years post-LTx demonstrated a decrease in average IgG reactivity, but no decrease in IgM reactivity when measured at 6 months post-LTx. IgG anti-tropoelastin, SP-D, and thyroglobulin autoantibodies were significantly elevated 6 months post-LTx in patients who developed CLAD by 5 years, compared to those who remained CLAD-free at 5 years. In contrast, patients who remained CLAD-free at 5 years had elevated levels of IgG anti-CENP-B at both timepoints and PM/SCL100 at 6 months post-LTx, suggesting these may confer protection. Exposure to autoantibodies against lung-enriched targets and dnDSA conferred increased CLAD risk. CONCLUSIONS: We identified novel autoantibodies associated with CLAD-free survival, bolstering the independent relationship between autoantibodies and CLAD. We also identified autoantibody signatures that are associated with a marked increase in CLAD risk. Exposure to lung-enriched targets and dnDSA may have a reciprocal amplifying effect that lies on a tissue-specific mechanistic pathway leading to CLAD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".