The role of the biofilm of Porphyromonas gingivalis in driving amyloid beta secretion and aggregation
Bibliographic record
Abstract
Abstract Background Over the past five years, the hypothesis linking periodontitis to Alzheimer’s disease (AD) has gained significant traction even though little is known about the underlying mechanisms driving this association. Periodontitis is caused by the dysbiosis of the dental biofilm, an extra‐cellular matrix produced by bacteria, which is mostly caused by the Gram‐negative bacterium Porphyromonas gingivalis. P. gingivalis has been found to infect the brain of AD patients and drive amyloid beta (Aβ) production while biofilm components can be identified interwoven with Aβ plaques1,2. We therefore hypothesize that P. gingivalis drives Aβ production in neural cells which co‐aggregates with bacterial biofilm to expedite plaque formation and downstream neuroinflammation. Method To assess if the biofilm of co‐aggregation of Aβ and the matrix was then analyzed by fluorescence microscopy, atomic force microscopy (AFM) and thioflavin T (ThT) fluorescence assays. To examine whether P. gingivalis stimulates Aβ production, we infected BE(2)‐M17 human neuroblastomas at a mean of infection of 1:100 before collecting cells and supernatant proteins to observe the cell response through protein and transcription assays. Results When co‐cultured, P. gingivalis and Aβ1‐42 co‐aggregated in plaque‐like structures, Interestingly, colocalization analysis revealed that Aβ1‐42 particularly associate with the extracellular component of biofilm (Figure 1). ThT assays and AFM imaging demonstrated faster aggregation of Aβ1‐42 when co‐incubated with fragments of P. gingivalis biofilm for 8 h, which suggests a significant cross‐seeding phenomenon between the two components (Figure 2). Furthermore, infection of human neuroblastomas resulted in a significant increase in Aβ production that could be detected by western blot, thus offering a workable model to study the link between AD and periodontitis (Figure 3). Conclusion Our project offers significant insights into the relationship between periodontitis and AD, allowing us to understand how P. gingivalis triggers Aβ production and how its biofilm drives plaque aggregation. This research greatly contributes to a better understanding of external AD risk factors and how to mediate their impacts on neurodegeneration. 1Dominy et al. Science Advances (2019) 2Mirzaei et al. Microbial Pathogenesis (2020)
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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 | 0.001 |
| 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".