Signal Inhibitory Receptor on Leukocytes-1 Limits the Formation of Neutrophil Extracellular Traps, but Preserves Intracellular Bacterial Killing
Bibliographic record
Abstract
In response to microbial invasion, neutrophils release neutrophil extracellular traps (NETs) to trap and kill extracellular microbes. Alternatively, NET formation can result in tissue damage in inflammatory conditions and may perpetuate autoimmune disease. Intervention strategies that are aimed at modifying pathogenic NET formation should ideally preserve other neutrophil antimicrobial functions. We now show that signal inhibitory receptor on leukocytes-1 (SIRL-1) attenuates NET release by human neutrophils in response to distinct triggers, including opsonized Staphylococcus aureus and inflammatory danger signals. NET release has different kinetics depending on the stimulus, and rapid NET formation is independent of NADPH oxidase activity. In line with this, we show that NET release and reactive oxygen species production upon challenge with opsonized S. aureus require different signaling events. Importantly, engagement of SIRL-1 does not affect bacterially induced production of reactive oxygen species, and intracellular bacterial killing by neutrophils remains intact. Thus, our studies define SIRL-1 as an intervention point of benefit to suppress NET formation in disease while preserving intracellular antimicrobial defense.
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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 | 0.000 | 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 | 0.001 |
| 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".