Invading Bacterial Pathogens Activate Transcription Factor EB in Epithelial Cells through the Amino Acid Starvation Pathway of mTORC1 Inhibition
Bibliographic record
Abstract
Upon pathogen infection, intricate innate signaling cascades are induced to initiate the transcription of immune effectors, including cytokines and chemokines. Transcription factor EB (TFEB), a master regulator of lysosomal biogenesis and autophagy genes, was found recently to be a novel regulator of innate immunity in both Caenorhabditis elegans and mammals. Despite TFEB participating in critical mechanisms of pathogen recognition and in the transcriptional response to infection in mammalian macrophages, little is known about its roles in the infected epithelium or infected nonimmune cells in general. Here, we demonstrate that TFEB is activated in nonimmune cells upon infection with bacterial pathogens through a pathway dependent on mTORC1 inhibition and RAG-GTPase activity, reflecting the importance of membrane damage and amino acid starvation responses during infection. Additionally, we present data demonstrating that although TFEB does not affect bacterial killing or load in nonimmune cells, it alters the host transcriptome upon infection, thus promoting an antibacterial transcriptomic landscape. Elucidating the roles of TFEB in infected nonimmune cells and the upstream signaling cascade provides critical insight into understanding how cells recognize and respond to bacterial pathogens.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".