Clusterin regulates macrophage expansion, polarization and phagocytic activity in response to inflammation in the kidneys
Bibliographic record
Abstract
Abstract Clusterin (CLU) is a multifunctional protein localized extracellularly and intracellularly. Although CLU‐knockout (KO) mice are more susceptible to renal ischemia‐reperfusion injury (IRI), the mechanisms underlying the actions of CLU in IRI are not fully understood. Macrophages are key regulators of IRI severity and tissue repair. Therefore, we investigated the role of CLU in macrophage polarization and phagocytosis. Renal IRI was induced in wild‐type (WT) or CLU‐KO C57BL/6 mice by clamping the renal pedicles for 30 min at 32°C. Peritoneal macrophages were activated via an intraperitoneal injection of lipopolysaccharide (LPS). Renal tissue damage was examined using histology, whereas leukocyte phenotypes were assessed using flow cytometry and immunohistochemistry. We found that monocytes/macrophages expressed the CLU protein that was upregulated by hypoxia. The percentages of macrophages (F4/80 + , CD11b + or MAC3 + ) infiltrating the kidneys of WT mice were significantly less than those in CLU‐KO mice after IRI. The M1/M2 phenotype ratio of the macrophages in WT kidneys decreased at day 7 post‐IRI when the injury was repaired, whereas that in KO kidneys increased consistently as tissue injury persisted. In response to LPS stimulation, WT mice produced fewer M1 macrophages, but not M2, than the control did. Phagocytosis was stimulated by CLU expression in macrophages compared with the CLU null controls and by the exogenous CLU protein. In conclusion, CLU suppresses macrophage infiltration and proinflammatory M1 polarization during the recovery period following IRI, and enhances phagocytic activity, which may be partly responsible for tissue repair in the kidneys of WT mice after injury.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".