Unexpected Participation of Intercalated Cells in Renal Inflammation and Acute Kidney Injury
Bibliographic record
Abstract
Epithelial cells constitute the 1st line of defense against pathogens, and their participation in innate immunity is rapidly emerging. In this mini-review, we discuss the noncanonical role of renal intercalated cells (ICs) in pathogen defense and in the initiation of sterile inflammation. This last function has strong implications in the onset of acute kidney injury (AKI), a potentially fatal medical complication that is seen in hospitalized patients. AKI is associated with inflammation, and it is often diagnosed only after the kidneys have suffered significant and often irreversible damage. While examining the regulation of proton secretion by type A ICs (A-ICs), we unexpectedly found high expression of the pro-inflammatory purinergic receptor P2Y14 in these cells. This receptor is located on the apical surface of A-ICs and binds UDP-glucose (UDP-Glc), a danger-associated molecular pattern molecule released from injured cells that is filtered by the glomeruli and is concentrated in the collecting duct lumen. UDP-Glc activates P2Y14 in A-ICs and triggers the production of chemokines that attract pro-inflammatory immune cells into the kidney stroma and aggravate ischemia-induced proximal tubule injury. Inhibition of P2Y14 or deletion of its gene specifically in ICs in a murine model of ischemia-reperfusion injury attenuated these effects. Thus, together with their previously recognized role in pathogen defense, A-ICs are now recognized as sensors and mediators of renal sterile inflammation that participate in the onset of AKI. Blocking the UDP-Glc/P2Y14 pathway in A-ICs provides new insights into the development of novel AKI therapeutics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".