Dietary Methionine Restriction Alleviates Cognitive Impairment in Alzheimer’s Disease Mice via Sex-Dependent Modulation on Gut Microbiota and Tryptophan Metabolism: A Multiomics Analysis
Bibliographic record
Abstract
Plant-based foods with low methionine contents have gained increasing interest for their potential health benefits, including neuroprotective effects. Methionine restriction (MR) linked to a plant-based diet has been shown to mitigate neurodegenerative diseases such as Alzheimer’s disease (AD) through mechanisms that involve the gut microbiota. In this study, a 16-week MR diet (0.17% methionine, w / w ) improved working memory and reduced neuronal damage exclusively in 4-month-old male APP/PS1 AD mice. Transcriptomic analysis revealed the activation of serum- and glucose-corticoid-regulated kinase 1 (SGK1) and peroxisome proliferator-activated receptor α (PPARα) pathways. Furthermore, metabolomics demonstrated increased serum indole-3-propionic acid (IPA) levels and an enhanced expression of gut barrier proteins Claudin-1 and MUC2 in male mice. MR significantly altered the gut microbiota composition, notably increasing indole-producing bacteria such as Lactobacillus reuteri ( L. reuteri ). Multiomics integration linked L. reuteri, IPA, and PPARα signaling to improved cognitive outcomes. Molecular docking and RT-qPCR analyses confirmed IPA’s interaction with PPARα, leading to the activation of neuroprotective targets ( Bdnf, Ppar α, Acsbg1, Scd2, and Scd3 ). These results highlight the role of methionine restriction in modulating gut microbiota and metabolites, offering a promising dietary approach to managing neurodegenerative diseases with sex-specific effects.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".