The impact of dextran sodium sulphate and probiotic pre-treatment in a murine model of Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: Recent work has established that Parkinson's disease (PD) patients have an altered gut microbiome, along with signs of intestinal inflammation. This could help explain the high degree of gastric disturbances in PD patients, as well as potentially be linked to the migration of peripheral inflammatory factors into the brain. To our knowledge, this is the first study to examine microbiome alteration prior to the induction of a PD murine model. METHODS: We presently assessed whether pre-treatment with the probiotic, VSL #3, or the inflammatory inducer, dextran sodium sulphate (DSS), would influence the PD-like pathology provoked by a dual hit toxin model using lipopolysaccharide (LPS) and paraquat exposure. RESULTS: While VSL #3 has been reported to have anti-inflammatory effects, DSS is often used as a model of colitis because of the gut inflammation and the breach of the intestinal barrier that it induces. We found that VSL#3 did not have any significant effects (beyond a blunting of LPS paraquat-induced weight loss). However, the DSS treatment caused marked changes in the gut microbiome and was also associated with augmented behavioral and inflammatory outcomes. In fact, DSS markedly increased taxa belonging to the Bacteroidaceae and Porphyromonadaceae families but reduced those from Rikencellaceae and S24-7, as well as provoking colonic pro-inflammatory cytokine expression, consistent with an inflamed gut. The DSS also increased the impact of LPS plus paraquat upon microglial morphology, along with circulating lipocalin-2 (neutrophil marker) and IL-6. Yet, neither DSS nor VSL#3 influenced the loss of substantia nigra dopamine neurons or the astrocytic and cytoskeleton remodeling protein changes that were provoked by the LPS followed by paraquat treatment. CONCLUSIONS: These data suggest that disruption of the intestinal integrity and the associated microbiome can interact with systemic inflammatory events to promote widespread brain-gut changes that could be relevant for PD and at the very least, suggestive of novel neuro-immune communication.
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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".