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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".