The role of phagocytosis-dependent activation of TFEB in the clearance of salmonella
Bibliographic record
Abstract
During phagocytosis, macrophages engulf and sequester pathogens into phagosomes. Phagosomes then fuse with acidic and degradative lysosomes to degrade the internalized pathogen. We previously demonstrated that phagocytosis of IgG-opsonized particles and non-opsonized E.coli causes activation of the Transcription Factor EB (TFEB), which enhances the expression of lysosomal genes, increases the degradative capacity of lysosomes and boosts bactericidal activity. However, pathogens like Salmonella typhimurium have evolved mechanisms to evade and/or alter phagosome maturation to promote their own survival. We investigated: i) whether pathogens like Salmonella can alter TFEB activation and ii) whether phagocytosis-dependent activation of TFEB can counteract the pathogenicity of microorganisms. Here, we show that non-viable (heat-killed) S. typhimurium, pathogenic (EHEC and UPEC) and non-pathogenic E.coli (DH5α) all caused TFEB nuclear translocation in RAW macrophages, while strikingly live S. typhimurium maintained TFEB in the cytosol in the first hours post-infection. By contrast, Salmonella mutants for ΔsifA, ΔsopD2, ΔphoP all triggered TFEB activation in the first hour of infection. However, Salmonella infection eventually triggered a steady increase in nuclear TFEB after 4 h of infection, suggesting a more complex interplay between TFEB and Salmonella infection. We dissected the importance of TFEB activation towards Salmonella survivability by pre-activating TFEB before infection within WT macrophages and macrophages with a CRISPR-based deletion of TFEB. Our work suggests that Salmonella actively interferes with TFEB signaling in order to enhance its own survival. These results could provide insight into using TFEB as a target for the clearance of infections.
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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".