Tctex‐1, a novel interaction partner of Kidney Injury Molecule‐1, is required for efferocytosis
Bibliographic record
Abstract
Kidney injury molecule-1 (KIM-1) is a phosphatidylserine receptor that is specifically upregulated on proximal tubular epithelial cells (PTECs) during acute kidney injury and mitigates tissue damage by mediating efferocytosis (the phagocytic clearance of apoptotic cells). The signaling molecules that regulate efferocytosis in TECs are not well understood. Using a yeast two-hybrid screen, we identified the dynein light chain protein, Tctex-1, as a novel KIM-1-interacting protein. Immunoprecipitation and confocal imaging studies suggested that Tctex-1 associates with KIM-1 in cells at baseline, but, dissociates from KIM-1 within 90 min of initiation of efferocytosis. Interfering with actin or microtubule polymerization interestingly prevented the dissociation of KIM-1 from Tctex-1. Moreover, the subcellular localization of Tctex-1 changed from being microtubule-associated to mainly cytosolic upon expression of KIM-1. Short hairpin RNA-mediated silencing of endogenous Tctex-1 in cells significantly inhibited efferocytosis to levels comparable to that of knock down of KIM-1 in the same cells. Importantly, Tctex-1 was not involved in the delivery of KIM-1 to the cell-surface. On the other hand, KIM-1 expression significantly inhibited the phosphorylation of Tctex-1 at threonine 94 (T94), a post-translational modification which is known to disrupt the binding of Tctex-1 to dynein on microtubules. In keeping with this, we found that KIM-1 bound less efficiently to the phosphomimic (T94E) mutant of Tctex-1 compared to wild type Tctex-1. Surprisingly, expression of Tctex-1 T94E did not influence KIM-1-mediated efferocytosis. Our studies uncover a previously unknown role for Tctex-1 in KIM-1-dependent efferocytosis in epithelial cells.
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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 | 0.001 |
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 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".