Deficiencies in methionine, tryptophan, and niacin remodels intestinal transcriptome and gut microbiota in female mice
Bibliographic record
Abstract
Caloric restriction is well-established as a robust intervention that may extend lifespan and improve metabolic health across species with effects that are increasingly attributed to both host metabolic remodeling and alterations in the gut microbiota. Recent studies suggest that restricting specific dietary components can replicate these benefits. While methionine and branched-chain amino acid restriction improve metabolism and modulate the gut microbiome, the effects of other nutrients remain unclear. Here, we explore the effects of methionine, tryptophan and niacin deprivation on host intestinal gene expression and gut microbiota using female murine models. Through transcriptomic analysis of the intestinal tissue, we found that transient dietary restriction of methionine, tryptophan, and niacin induced significant changes in intestinal gene expression, particularly in genes involved in oxidative phosphorylation and ATP production. Single-cell analysis revealed that dietary restriction of those nutrients led to an increase in intestinal immune cell populations. Gut microbiota profiling also revealed that transient deprivation of those nutrients resulted in changes in microbial composition, with an increased relative abundance of Lactobacillus species observed in some cases. Our findings highlight the potential of targeted nutrient restriction as a strategy to reprogram host-microbiome interactions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".