Polymorphisms in HAVCR1 Alter KIM-1-Mediated Phagocytosis
Bibliographic record
Abstract
Background: During renal ischemia reperfusion injury (IRI), necrotic cells, and apoptotic tubular epithelial cells (TECs) undergoing secondary necrosis, release their immunogenic contents into the extracellular milieu, exacerbating inflammation. Kidney Injury Molecule -1 (KIM-1) is a cell-surface glycoprotein upregulated on TECs during acute kidney injury (AKI). We previously uncovered that KIM-1 protects against renal IRI by enabling TECs to bind and engulf dying neighbouring cells, limiting inflammation and tissue damage. The gene encoding KIM-1 (HAVCR1) is highly polymorphic, but the relevance of human KIM-1 polymorphisms in renal IRI has not been studied. We hypothesized that HAVCR1 variant expressing TECs would have decreased phagocytic activity in vitro. Methods: Using site-directed mutagenesis, we generated constructs for 3 high-frequency HAVCR1 coding variants in addition to an expression plasmid-encoding wild-type KIM-1 (pcDNA3-KIM-1). We then expressed the pcDNA3 vector, or HAVCR1variants in HEK-293 cells using stable transfection. Results: We report that all 3 variants had altered cell surface KIM-1 expression compared to the wild-type. Importantly, the phagocytic uptake of apoptotic cells was significantly reduced in HEK-293 cells expressing each of the KIM-1 variants compared to those expressing wild-type KIM-1, indicating that mutations in these coding regions contribute to a functional impairment of KIM-1 activity. Conclusions: This is the first study suggesting that human polymorphic variants in HAVCR1 may have consequences on the functional role of the KIM-1 protein in the kidney. This work strengthens the plausibility of a biological role for KIM-1 during AKI. Funding: Government Support - Non-U.S.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".