The Role of LAG3 in Antibody Responses To Kidney Transplantation
Bibliographic record
Abstract
Abstract The role of lymphocyte activation gene-3 (LAG3) in T cell functions is well studied, however its role in humoral immune responses remains poorly characterized. The goal of this study was to test the role of recipient LAG3 in a mouse model of renal allograft rejectionNaive B6.LAG3−/− mice have elevated numbers of follicular and memory T cells, and plasma cells compared to WT mice, as well as increased frequencies of memory T cells reactive to H-2Dd, H-2Ds, H-2Dq and H-2Dk alloantigens. When kidneys were transplanted from C3H donors to B6 WT and LAG3−/− mice, all C3H kidney allografts survived for > 60d in WT recipients, whereas LAG3−/− recipients rapidly reject allografts and have elevated serum creatinine levels at d14 posttransplant. Graft histology at rejection revealed minimal T cell infiltration, diffuse C4d staining, atrophic peritubular capillaries, endothelial swelling and edema characteristic of antibody mediated rejection (AMR). Compared to WT, LAG3−/− recipients had elevated frequencies of anti-donor IFNγ producing T cells and increased levels of donor specific antibody (DSA) against MHC-I and MHC-II. Depletion of CD8 T cells in LAG3−/− recipients did not alter rejection kinetics, while B cell depletion significantly extended C3H kidney allograft survival, suggesting the predominant role of alloantibody rather than T cell mediated rejection in these mice. However, neither T nor B cell conditional knockout recipients rejected the allograft demonstrating LAG3 expression on both cell types is necessary to mediate rejection. These findings demonstrate that LAG3 regulates both T and B cell functions in response to kidney allografts, and is an attractive therapeutic target for the prevention of AMR. Supported by grants from NIH (P01AI087586-10)
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".