Accelerated receptor shedding inhibits kidney injury molecule-1 (KIM-1)-mediated efferocytosis
Bibliographic record
Abstract
Efficient clearance of apoptotic cells (efferocytosis) prevents inflammation and permits repair following tissue injury. Kidney injury molecule-1 (KIM-1) is a receptor for phosphatidylserine, an "eat-me" signal exposed on the surface of apoptotic cells that marks them for phagocytic clearance. KIM-1 is upregulated on proximal tubule epithelial cells (PTECs) during ischemic acute kidney injury (AKI), enabling efferocytosis by surviving PTECs. KIM-1 is spontaneously cleaved at its ectodomain region to generate a soluble fragment that serves a sensitive and specific biomarker for AKI, but the biological relevance of KIM-1 shedding is unknown. Here, we sought to determine how KIM-1 shedding might regulate efferocytosis. Using cells that endogenously and exogenously express KIM-1, we found that hydrogen peroxide-mediated oxidative injury or PMA treatment accelerated KIM-1 shedding in a dose-dependent manner. KIM-1 shedding was also accelerated when apoptotic cells were added. Accelerated shedding or the presence of excess soluble KIM-1 in the extracellular milieu significantly inhibited efferocytosis. We also identified that TNF-α-converting enzyme (TACE or ADAM17) mediates both the spontaneous and PMA-accelerated shedding of KIM-1. While accelerated shedding inhibited efferocytosis, we found that spontaneous KIM-1 cleavage does not affect the phagocytic efficiency of PTECs. Our results suggest that KIM-1 shedding is accelerated by worsening cellular injury, and excess soluble KIM-1 competitively inhibits efferocytosis. These findings may be important in AKI when there is severe cellular injury.
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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) | 0.001 | 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".