Potential Regulator of KIM-1 Mediated Autophagy
Bibliographic record
Abstract
Background: Kidney injury molecule 1 (KIM-1) is a phosphatidylserine receptor expressed on injured proximal tubule epithelial cells (PTECs) during acute kidney injury (AKI). KIM-1 mediates the phagocytic clearance of apoptotic cells by PTECs and targets the engulfed apoptotic corpses to autophagosomes by triggering autophagy. However, the detailed signaling mechanisms are poorly understood. This study aims to uncover KIM-1-interacting proteins that regulate autophagy in PTECs. Methods: KIM-1 immunoprecipitates from human PTECs (HK-2), which endogenously express KIM-1, stimulated with apoptotic cells were separated using SDS-PAGE. The bands of putative KIM-1 interacting proteins were visualized using Coomassie blue staining and analyzed using liquid chromatography with tandem mass spectrometry. Proteins were identified using PEAKS DB proteomics software, which uses de novo sequencing assisted database searching. Protein-protein interactions were confirmed using western blot analysis. Results: The immunoprecipitation of KIM-1 was confirmed by both western blot and mass spectrometry analyses, in which KIM-1 was only present in the anti-KIM-1 group and absent in the IgG control. We identified the phosphoinositide-3-kinase regulatory subunit 4 (PIK3R4) with a PEAKS peptide score of 99.1 (minimum threshold at 26.7) and a sequence coverage of 6%. Western blot analysis showed that PIK3R4 co-immunoprecipitated with KIM-1 in both apoptotic cells-stimulated and unstimulated HK-2 cells. Conclusions: PIK3R4 interacts with KIM-1 constitutively in PTECs. It has previously been implicated in the regulation of autophagy including autophagosome formation and endocytic trafficking but not in PTECs. Further investigation will elucidate whether PIK3R4 is required for KIM-1 mediated autophagy.Figure 1.: Co-immunoprecipitation of KIM-1 and PIK3R4 from HK-2 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".