Autophagy-ER stress crosstalk controls mucus secretion and susceptibility to gut inflammation
Bibliographic record
Abstract
Mucus secretion from colonic goblet cells is an important host defense mechanism against the harsh lumenal environment. Yet how mucus secretion is regulated is not well understood. We discovered that constitutive activation of macroautophagy/autophagy via BECN1 (beclin 1) relieves endoplasmic reticulum (ER) stress in goblet cells, which in turn produce a thicker and less penetrable mucus barrier. Pharmacological reduction of the ER stress or activation of the unfolded protein response (UPR) in mice, regardless of autophagy activation, lead to excess mucus secretion. This regulation of mucus secretion by ER stress is microbiota-dependent and requires the activity of the intracellular sensor NOD2 (nucleotide-binding oligomerization domain containing 2). Excess mucus production in the colon alters the gut microbiota and protects from chemical- and infection-driven inflammation. Our findings provide new insights into the mechanisms by which autophagy regulates mucus secretion and susceptibility to intestinal inflammation.Abbreviations:BECN1– Beclin 1; ER– endoplasmic reticulum; UPR – unfolded protein response; NOD2 – nucleotide-binding oligomerization domain containing 2; IBD– inflammatory bowel disease; BCL2- B cell leukemia/lymphoma 2; TUDCA- tauroursodeoxycholic acid; ATG16L1– autophagy related 16 like 1; LRRK2- leucine-rich repeat kinase 2.
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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".