Neutrophils use selective autophagy receptor p62/SQSTM1 to target <i>Staphylococcus aureus</i> for degradation <i>in vivo</i> in zebrafish
Bibliographic record
Abstract
Abstract Autophagy leads to degradation of cellular components and has an important role in restricting intracellular pathogens. Autophagy receptors, including p62, target invading intracellular pathogens to the autophagy pathway for degradation. Staphylococcus aureus is a significant pathogen of humans and often life-threatening in the immunocompromised. Increasing evidence demonstrates that S. aureus is an intracellular pathogen of immune cells and may use neutrophils as proliferative niche but the intracellular fate of S. aureus following phagocytosis by neutrophils has not previously been analysed in vivo. In vitro , p62 is able to co-localise with intracellular Staphylococcus aureus , but whether p62 is beneficial or detrimental in host defence against S. aureus in vivo had not been determined. Here we use zebrafish to determine the fate and location of S. aureus within neutrophils throughout infection. We show that Lc3 and p62 recruitment to phagocytosed S. aureus is altered depending on the bacterial location within the neutrophil. We also show rapid Lc3 marking of bacterial phagosomes within neutrophils which may be associated with subsequent bacterial degradation. Finally, we find that p62 is important for controlling cytosolic bacteria demonstrating for the first time a key role of p62 in autophagic control of S. aureus in neutrophils.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".