Reduction of chronic allograft nephropathy by inhibition of extracellular signal-regulated kinase 1 and 2 signaling
Bibliographic record
Abstract
Chronic allograft nephropathy (CAN), the most common cause of late kidney allograft failure, is not effectively prevented by immunosuppressive regimens. Activation of extracellular signal-regulated kinases 1 and 2 (ERK1/2) via MEK mediates actions of various growth factors, including transforming growth factor (TGF)-beta1, which plays a key role in CAN. Hence, we tested the therapeutic potential of MEK-ERK1/2 signaling disruption to prevent CAN. Kidneys from C57BL/6J (H-2(b)) mice were transplanted to bilaterally nephrectomized BALB/c (H-2(d)) mice. At 14 days after transplantation, the recipients were subjected to 28 days of treatment with the MEK inhibitor CI-1040. All six CI-1040-treated allografts survived, while two of seven grafts in the vehicle-treated group were lost. At the end of the experiment, the function and structure of grafts in the CI-1040-treated group were significantly preserved, as indicated by lower levels of serum creatinine or blood urea nitrogen than in the vehicle-treated group [30 +/- 6 vs. 94 +/- 39 microM creatinine (P = 0.0015) and 22 +/- 8 vs. 56 +/- 25 mM BUN (P = 0.0054)] and reduced CAN in the CI-1040-treated group compared with vehicle controls (CAN score = 4.2 vs. 10.3, P = 0.0119). The beneficial effects induced by CI-1040 were associated with reduction of ERK1/2 phosphorylation and TGFbeta1 levels in grafts. Also, CI-1040 potently suppressed not only TGFbeta biosynthesis in kidney cell cultures but also antiallograft immune responses in vitro and in vivo. Our data suggest that interference of MEK-ERK1/2 signaling with a pharmacological agent (e.g., CI-1040) has therapeutic potential to prevent CAN in kidney transplantation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".