Effects of Probiotic Supplementation on Short Chain Fatty Acids in the AppNL–G–F Mouse Model of Alzheimer’s Disease
Bibliographic record
Abstract
Background: The intestinal microbiota and its metabolites, particularly short-chain fatty acids (SCFAs), have been implicated in immune function, host metabolism, and even behavior. Objective: This study was performed to investigate whether probiotic administration influences levels of intestinal microbiota and their metabolites in a fashion that may attenuate brain changes in a mouse model of Alzheimer’s disease (AD). Methods: C57BL/6 wild-type (WT) mice were compared to AppNL–G–F mice. The animals were treated with either vehicle or probiotic (VSL#3) for 8 weeks. Fecal microbiome analysis along with Aβ, GFAP, Iba-1, c-Fos, and Ki-67 immunohisto-chemistry was done. SCFAs were analyzed in serum and brains using UPLC-MS/MS. Results: Probiotic (VSL#3) supplementation for 2 months resulted in altered microbiota in both WT and AppNL–G–F mice. An increase in serum SCFAs acetate, butyrate, and lactate were found in both genotypes following VSL#3 treatment. Propionate and isobutyrate were only increased in AppNL–G–F mice. Surprisingly, VSL#3 only increased lactate and acetate in brains of AppNL–G–F mice. No significant differences were observed between vehicle and VSL#3 fed AppNL–G–F hippocampal immunoreactivities of Aβ, GFAP, Iba-1, and Ki-67. However, hippocampal c-Fos staining increased in VSL#3 fed AppNL–G–F mice. Conclusion: These data demonstrate intestinal dysbiosis in the AppNL–G–F mouse model of AD. Probiotic VSL#3 feeding altered both serum and brain levels of lactate and acetate in AppNL–G–F mice correlating with increased expression of the neuronal activity marker, c-Fos.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".