G protein α<sub>12</sub>(Gα<sub>12</sub>) is a negative regulator of kidney injury molecule-1-mediated efferocytosis
Bibliographic record
Abstract
Kidney injury molecule-1 (KIM-1) is a receptor for the “eat me” signal, phosphatidylserine, on apoptotic cells. The specific upregulation of KIM-1 by injured tubular epithelial cells (TECs) enables them to clear apoptotic cells (also known as efferocytosis), thereby protecting from acute kidney injury. Recently, we uncovered that KIM-1 binds directly to the α-subunit of heterotrimeric G12protein (Gα12) and inhibits its activation by reactive oxygen species during renal ischemia-reperfusion injury (Ismail OZ, Zhang X, Wei J, Haig A, Denker BM, Suri RS, Sener A, Gunaratnam L. Am J Pathol 185: 1207–1215, 2015). Here, we investigated the role that Gα12plays in KIM-1-mediated efferocytosis by TECs. We showed that KIM-1 remains bound to Gα12and suppresses its activity during phagocytosis. When we silenced Gα12expression using small interefering RNA, KIM-1-mediated engulfment of apoptotic cells was increased significantly; in contrast overexpression of constitutively active Gα12( QLGα12) resulted in inhibition of efferocytosis. Inhibition of RhoA, a key effector of Gα12, using a chemical inhibitor or expression of dominant-negative RhoA, had the same effect as inhibition of Gα12on efferocytosis. Consistent with this, silencing Gα12suppressed active RhoA in KIM-1-expressing cells. Finally, using primary TECs from Kim-1+/+and Kim-1−/−mice, we confirmed that engulfment of apoptotic cells requires KIM-1 expression and that silencing Gα12enhanced efferocytosis by primary TECs. Our data reveal a previously unknown role for Gα12in regulating efferocytosis and that renal TECs require KIM-1 to mediate this process. These results may have therapeutic implications given the known harmful role of Gα12in acute kidney 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) | 0.000 | 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".