Innate Immune Collectin Surfactant Protein D Simultaneously Binds Both Neutrophil Extracellular Traps and Carbohydrate Ligands and Promotes Bacterial Trapping
Bibliographic record
Abstract
Neutrophils release DNA-based extracellular traps to capture and kill bacteria. The mechanism(s) and proteins that promote neutrophil extracellular trap (NET)-mediated bacterial trapping are not clearly established. Surfactant protein D (SP-D) is an innate immune collectin present in many mucosal surfaces. We hypothesized that SP-D can bind both the pathogens and NETs to augment NET-mediated bacterial trapping. To test this hypothesis, we used LPS and Pseudomonas aeruginosa pneumonia mouse models and performed in vivo and ex vivo assays. In this study, we show that NETs are produced by the neutrophils recruited to the airways in response to the bacterial ligand. Notably, NETs are detected as short fragments of DNA-protein complexes in the airways as opposed to the long stringlike structures seen in ex vivo cultures. SP-D recognizes both the short NET fragments and the long NET DNA structures. SP-D-NET copurification studies further show that SP-D can simultaneously recognize NETs and carbohydrate ligands in vivo. Similar to the LPS model, soluble DNA-protein complexes and increased amounts of SP-D are detected in the murine model of P. aeruginosa pneumonia. We then tested the effect of SP-D on NET-mediated trapping of P. aeruginosa by means of Western blots, fluorescence microscopy, and scanning electron microscopy. Results of these experiments show that SP-D microagglutinates P. aeruginosa and allows an efficient bacterial trapping by NETs. Collectively, these findings provide a unique biological relevance for SP-D-DNA interactions and places SP-D as an important innate immune protein that promotes bacterial trapping by NETs during neutrophil-mediated host 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 | 0.000 |
| 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".