Methionine and Its Hydroxyl Analogues Improve Stem Cell Activity To Eliminate Deoxynivalenol-Induced Intestinal Injury by Reactivating Wnt/β-Catenin Signaling
Bibliographic record
Abstract
The intestinal epithelium is derived from intestinal stem cells (ISCs) and has direct contact with nutrients and toxins. However, whether methionine (Met) or a methionine hydroxyl analogue (2-hydroxy-4-(methylthio)butanoic acid (HMB)) can alleviate deoxynivalenol (DON)-induced intestinal injury remains unknown. Mice were treated orally with Met or HMB on days 1-11 and with DON on days 4-8. On day 12, the mice were sacrificed, and the jejunum was collected for crypt isolation and culture. Mouse enteroids were treated with DON and Met or HMB ex vivo. The results showed that Met and HMB increased the average daily feed intake and average daily gain of the mice. Met and HMB also improved the jejunal structure and barrier integrity and promoted ISC expansion, as indicated by the increased enteroid formation efficiency and area, under DON-induced injury conditions. In addition, DON-induced decreases in ISC activity were rescued Wnt/β-catenin signaling reactivation by Met or HMB in vivo and ex vivo. Collectively, our findings reveal that Met and HMB alleviated DON-induced intestinal injury by improving ISC expansion and reactivating Wnt/β-catenin signaling. Our study thus provides a nutritional intervention for intestinal diseases involving Wnt/β-catenin signaling.
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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".