Novel Anti-bacterial Activities of β-defensin 1 in Human Platelets: Suppression of Pathogen Growth and Signaling of Neutrophil Extracellular Trap Formation
Bibliographic record
Abstract
Human β-defensins (hBD) are antimicrobial peptides that curb microbial activity. Although hBD's are primarily expressed by epithelial cells, we show that human platelets express hBD-1 that has both predicted and novel antibacterial activities. We observed that activated platelets surround Staphylococcus aureus (S. aureus), forcing the pathogens into clusters that have a reduced growth rate compared to S. aureus alone. Given the microbicidal activity of β-defensins, we determined whether hBD family members were present in platelets and found mRNA and protein for hBD-1. We also established that hBD-1 protein resided in extragranular cytoplasmic compartments of platelets. Consistent with this localization pattern, agonists that elicit granular secretion by platelets did not readily induce hBD-1 release. Nevertheless, platelets released hBD-1 when they were stimulated by α-toxin, a S. aureus product that permeabilizes target cells. Platelet-derived hBD-1 significantly impaired the growth of clinical strains of S. aureus. hBD-1 also induced robust neutrophil extracellular trap (NET) formation by target polymorphonuclear leukocytes (PMNs), which is a novel antimicrobial function of β-defensins that was not previously identified. Taken together, these data demonstrate that hBD-1 is a previously-unrecognized component of platelets that displays classic antimicrobial activity and, in addition, signals PMNs to extrude DNA lattices that capture and kill bacteria.
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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) | 0.000 | 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 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".