The ER-phagy receptor FAM134B is targeted by Salmonella Typhimurium 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 (hereafter referred to as ER-phagy) is a homeostatic mechanism, controlling ER size, the removal of misfolded protein aggregates, and organelle damage. ER-phagy can also be stimulated by pathogen infection. However, the link between ER-phagy and bacterial infection remains poorly understood, as are the mechanisms evolved by pathogens to escape the effects of ER-phagy. Here, we show that Salmonella enterica serovar Typhimurium inhibits ER-phagy by targeting the ER-phagy receptor FAM134B, leading to a pronounced increase in Salmonella burden after invasion. Salmonella prevents FAM134B oligomerization, which is required for efficient ER-phagy. FAM134B knock-out raises intracellular Salmonella number, while FAM134B activation reduces Salmonella burden. Additionally, we found that Salmonella targets FAM134B through the bacterial effector SopF to enhance intracellular survival through ER-phagy inhibition. Furthermore, FAM134B knock-out mice infected with Salmonella presented severe intestinal damage and increased bacterial burden. These results provide mechanistic insight into the interplay between ER-phagy and bacterial infection, highlighting a key role for FAM134B in innate immunity. Here, Gatica et al show that FAM134B, which resides in the endoplasmic reticulum, can be targeted by and limited by Salmonella enterica serovar Typhimurium. This results in restriction of selective autophagy of the endoplasmic reticulum and enhances bacterial survival within host cells.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".