Macrophage Uptake of Necrotic Cell DNA Activates the AIM2 Inflammasome to Regulate a Proinflammatory Phenotype in CKD
Bibliographic record
Abstract
Nonmicrobial inflammation contributes to CKD progression and fibrosis. Absent in melanoma 2 (AIM2) is an inflammasome-forming receptor for double-stranded DNA. AIM2 is expressed in the kidney and activated mainly by macrophages. We investigated the potential pathogenic role of the AIM2 inflammasome in kidney disease. In kidneys from patients with diabetic or nondiabetic CKD, immunofluorescence showed AIM2 expression in glomeruli, tubules, and infiltrating leukocytes. In a mouse model of unilateral ureteral obstruction (UUO), Aim2 deficiency attenuated the renal injury, fibrosis, and inflammation observed in wild-type (WT) littermates. In bone marrow chimera studies, UUO induced substantially more tubular injury and IL-1 β cleavage in Aim2 −/− or WT mice that received WT bone marrow than in WT mice that received Aim2 −/− bone marrow. Intravital microscopy of the kidney in LysM(gfp/gfp) mice 5–6 days after UUO demonstrated the significant recruitment of GFP + proinflammatory macrophages that crawled along injured tubules, engulfed DNA from necrotic cells, and expressed active caspase-1. DNA uptake occurred in large vacuolar structures within recruited macrophages but not resident CX 3 CR1 + renal phagocytes. In vitro , macrophages that engulfed necrotic debris showed AIM2-dependent activation of caspase-1 and IL-1 β , as well as the formation of AIM2 + ASC specks. ASC specks are a hallmark of inflammasome activation. Cotreatment with DNaseI attenuated the increase in IL-1 β levels, confirming that DNA was the principal damage-associated molecular pattern in this process. Therefore, the activation of the AIM2 inflammasome by DNA from necrotic cells drives a proinflammatory phenotype that contributes to chronic injury in the kidney.
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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 | 0.001 | 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 | 0.000 |
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".