Two transcriptionally and functionally distinct waves of neutrophils during mouse acute liver injury
Bibliographic record
Abstract
BACKGROUND: Neutrophils are key mediators of inflammation during acute liver injury (ALI). Emerging evidence suggests that they also contribute to injury resolution and tissue repair. However, the different neutrophil subsets involved in these processes and their kinetics are undefined. Herein, we characterized neutrophil kinetics and heterogeneity during ALI. METHODS: We used the carbon tetrachloride model of ALI and employed flow cytometry, tissue imaging, and quantitative RT-PCR to characterize intrahepatic neutrophils during the necroinflammatory early and late repair phases of the wound healing response to ALI. We FACS sorted intrahepatic neutrophils at key time points and examined their transcriptional profiles using RNA-sequencing. Finally, we evaluated neutrophil protein translation, mitochondrial function and metabolism, reactive oxygen species content, and neutrophil extracellular traps generation. RESULTS: We detected 2 temporarily distinct waves of neutrophils during (1) necroinflammation (at 24 hours after injury) and (2) late repair (at 72 hours). Early neutrophils were proinflammatory, characterized by: (1) upregulation of inflammatory cytokines, (2) activation of the noncanonical NF-κB pathway, (3) reduction of protein translation, (4) decreased oxidative phosphorylation, and (5) higher propensity to generate reactive oxygen species and neutrophil extracellular traps. In contrast, late neutrophils were prorepair and enriched in genes and pathways associated with tissue repair and angiogenesis. Finally, early proinflammatory neutrophils were characterized by the expression of a short isoform of C-X-C chemokine receptor 5, while the late prorepair neutrophils were characterized by the expression of C-X-C chemokine receptor 4. CONCLUSIONS: This study underscores the phenotypic and functional heterogeneity of neutrophils and their dual role in inflammation and tissue repair during ALI.
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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 | 0.001 |
| 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".