New insights into immune mechanisms of antiperlecan/LG3 antibody production: Importance of T cells and innate B1 cells
Bibliographic record
Abstract
Autoantibodies against perlecan/LG3 (anti-LG3) have been associated with increased risks of delayed graft function, acute rejection, and reduced long-term survival. High titers of anti-LG3 antibodies have been found in de novo renal transplants recipients in the absence of allosensitizing or autoimmune conditions. Here, we seek to understand the pathways controlling anti-LG3 production prior to transplantation. Mice immunized with recombinant LG3 produce concomitantly IgM and IgG anti-LG3 antibodies suggesting a memory response. ELISpot confirmed the presence of LG3-specific memory B cells in nonimmunized mice. Purification of B1 and B2 subtypes identified peritoneal B1 cells as the major source of memory B cells reactive to LG3. Although nonimmunized CD4-deficient mice were found to express LG3-specific memory B cells, depletion of CD4 + T cells in wild type mice during immunization significantly decreased anti-LG3 production. These results demonstrate that B cell memory to LG3 is T cell independent but that production of anti-LG3 antibodies requires T cell help. Further supporting an important role for T cells in controlling anti-LG3 levels, we found that human renal transplant recipients show a significant decrease in anti-LG3 titers upon the initiation of CNI-based immunosuppression. Collectively, these results identify T cell targeting interventions as a means of reducing anti-LG3 levels in renal transplant patients.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".