Effects of Gut Microbiota Alterations on Motor, Gastrointestinal, and Behavioral Phenotype in a Mouse Model of Parkinson’s Disease
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is a multi-system disorder consisting of not only classic motor symptoms but also a variety of non-motor symptoms including gastrointestinal (GI) dysfunction and mood disorders. The gut microbiota has been suggested to play a role in modulating PD motor and non-motor features, although the causality and mechanisms behind these proposed interactions remains largely understudied. OBJECTIVE: In this study, we aimed to provide in-depth characterization of an established mouse model of PD (transgenic (TG) SNCA A53T) and experimentally address how changes to the gut microbiota impact the PD-like phenotype. METHODS: We profiled the PD-like phenotype of transgenic mice through a panel of motor, GI, and behavioral tests. We then investigated how antibiotic treatment or gut microbial community transfer (via cohousing with wild-type mice) impacted the PD-like phenotype. RESULTS: We found that this mouse model demonstrated early (6 weeks of age) motor symptoms when compared to a wild-type control mouse strain. Transgenic mice also exhibited early GI dysfunction, as well as behavioral alterations, including reduced anxiety-like behavior, and increased depression-like and apathy-like behavior. Compared to wild-type mice, the transgenic fecal microbiota was less diverse and compositionally distinct. Interestingly, drastic alterations to the gut microbiota, through antibiotic treatment or cohousing with wild-type mice, had a minimal effect on the motor, GI, and behavioral phenotype of transgenic mice. CONCLUSION: We concluded that this mouse model effectively recapitulates motor and non-motor features of PD; however, the gut microbiota appears to exhibit a minor impact on the pathophysiology of this PD model.
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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 | 0.001 |
| 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".