NETosing Neutrophils Activate Complement Both on Their Own NETs and Bacteria via Alternative and Non-alternative Pathways
Bibliographic record
Abstract
Neutrophils deposit antimicrobial proteins, such as myeloperoxidase and proteases on chromatin, which they release as neutrophil extracellular traps (NETs). Neutrophils also carry key components of the complement alternative pathway (AP) such as properdin or complement factor P (CFP), complement factor B (CFB), and C3. However, the contribution of these complement components and complement activation during NET formation in the presence and absence of bacteria is poorly understood. We studied complement activation on NETs and a Gram-negative opportunistic bacterial pathogen Pseudomonas aeruginosa (PA01, PAKwt, and PAKgfp). Here, we show that anaphylatoxin C5a, formyl-methionyl-leucyl-phenylalanine (fMLP) and phorbol myristate acetate (PMA), which activates NADPH oxidase, induce the release of CFP, CFB, and C3 from neutrophils. In response to PMA or P. aeruginosa, neutrophils secrete CFP, deposit it on NETs and bacteria, and induce the formation of terminal complement complexes (C5b-9). A blocking anti-CFP antibody inhibited AP-mediated but not non-AP-mediated complement activation on NETs and P. aeruginosa. Therefore, NET-mediated complement activation occurs via both AP- and non AP-based mechanisms, and AP-mediated complement activation during NETosis is dependent on CFP. These findings suggest that neutrophils could use their "AP tool kit" to readily activate complement on NETs and Gram-negative bacteria, such as P. aeruginosa, whereas additional components present in the serum help to fix non-AP-mediated complement both on NETs and bacteria. This unique mechanism may play important roles in host defense and help to explain specific roles of complement activation in NET-related diseases.
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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) | 0.004 | 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".