<i>Salmonella</i> Typhimurium exploits the reticulophagy/ERphagy receptor RETREG1 to promote infection
Bibliographic record
Abstract
Macroautophagy/autophagy is a key catabolic-recycling pathway that can selectively target damaged organelles or invading pathogens for degradation. The selective autophagic degradation of the endoplasmic reticulum, called reticulophagy/ERphagy, controls ER size and degradation of misfolded protein aggregates. RETREG1/FAM134B is an ERphagy receptor that acts by inducing ER membrane curvature and scission through oligomerization. Interestingly, RETREG1 has also been implicated in the cellular response against pathogen infection. Multiple microbes have developed strategies to inhibit ERphagy by targeting RETREG1. In a recent study, we characterized an unidentified mechanism of bacterial-mediated inhibition of ERphagy. Specifically, we found that Salmonella enterica Serovar Typhimurium, a well-known intracellular pathogen that continues to be a major cause of foodborne infections worldwide, inhibits ERphagy by specifically targeting the activity of RETREG1, leading to a pronounced increase in Salmonella burden. We show that Salmonella prevents RETREG1 oligomerization, which is required for efficient ERphagy. Conversely, Salmonella-mediated ERphagy blockage can be bypassed by promoting RETREG1 oligomerization, which recovers ERphagy levels. Salmonella infection also decreases RETREG1 phosphorylation and acetylation, previously reported to be requisite steps in RETREG1-driven ERphagy. Furthermore, in vivo analysis of retreg1 knockout mice infected with Salmonella reveals increased intestinal damage and bacterial levels. Our results provide insights into the interplay between ERphagy and bacterial infection, highlighting a key role for RETREG1 in innate immunity.
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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) | 0.001 | 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) | 0.001 | 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".