Bi-directional relationship between the biofilm of <i>Porphyromonas gingivalis</i> and the amyloid-beta peptide
Bibliographic record
Abstract
Abstract Periodontitis and Porphyromonas gingivalis infections are significant risk factors for the onset of Alzheimer’s disease (AD). Despite P. gingivalis relying on biofilm for its survival and virulence, the impact of the extracellular matrix on AD’s neuropathological hallmarks was never examined. In this study, we report a bidirectional relationship between the amyloid beta (Aβ) peptide, which plays a central role in AD, and the biofilm of P. gingivalis . Using multiple fluorescent markers for biofilm components, we observed that Aβ1-40 inhibited biofilm formation while Aβ1-42 increased extracellular matrix production. Also, using thioflavin T staining and atomic force microscopy, we observed co-aggregation between the biofilm and monomeric Aβ1-40, resulting in a quicker aggregation and significant changes in aggregate structures. Our findings propose mechanistic explanations for the role of P. gingivalis as a risk factor for AD and offer potential mechanisms for the microbial involvement in AD etiology. Importance While the etiology of Alzheimer’s disease has been studied extensively for the past 50 years, its exact causes remain unknown. Our current understanding is that the accumulation of multiple genetic and environmental risk factors would lead to the onset of the disease. Porphyromonas gingivalis is a bacterium that produces biofilm and elicits periodontitis, a chronic infection of the gums that constitutes a risk factor for Alzheimer’s disease. While studies have looked at the effects of P. gingivalis in triggering Alzheimer’s symptoms in animal models, none have explored the impact of the biofilm, which is ubiquitous to this bacterium. Our study seeks to bridge that gap by demonstrating a bi-directional relationship between the biofilm of P. gingivalis and amyloid beta, one of the brain lesions involved in Alzheimer’s. By understanding risk factors involved in Alzheimer’s and their impact, we hope to provide valuable knowledge on prevention and treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".