Docosahexaenoic and Eicosapentaenoic Acids Prevent Altered-Muc2 Secretion Induced by Palmitic Acid by Alleviating Endoplasmic Reticulum Stress in LS174T Goblet Cells
Bibliographic record
Abstract
Diets high in saturated fatty acids (FA) represent a risk factor for the development of obesity and associated metabolic disorders, partly through their impact on the epithelial cell barrier integrity. We hypothesized that unsaturated FA could alleviate saturated FA-induced endoplasmic reticulum (ER) stress occurring in intestinal secretory goblet cells, and consequently the reduced synthesis and secretion of mucins that form the protective mucus barrier. To investigate this hypothesis, we treated well-differentiated human colonic LS174T goblet cells with palmitic acid (PAL)—the most commonly used inducer of lipotoxicity in in vitro systems—or n-9, n-6, or n-3 unsaturated fatty acids alone or in co-treatment with PAL, and measured the impact of such treatments on ER stress and Muc2 production. Our results showed that only eicosapentaenoic (EPA) and docosahexaenoic (DHA) acids protect goblet cells against ER stress-mediated altered Muc2 secretion induced by PAL, whereas neither linolenic acid nor n-9 and n-6 FA are able to provide such protection. We conclude that EPA and DHA could represent potential therapeutic nutrients against the detrimental lipotoxicity of saturated fatty acids, associated with type 2 diabetes and obesity or inflammatory bowel disease. These in vitro data remain to be explored in vivo in a context of dietary obesity.
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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".