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Record W4390199130 · doi:10.1002/alz.073279

The role of the biofilm of Porphyromonas gingivalis in driving amyloid beta secretion and aggregation

2023· article· en· W4390199130 on OpenAlexaff
David Dumoulin, Tamàs Fülöp, Pascale B. Beauregard

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsPorphyromonas gingivalisBiofilmThioflavinMicrobiologyExtracellular matrixBacteriaSecretionFluorescence microscopeChemistryNeuroinflammationAmyloid betaAtomic force microscopyExtracellularBiologyInflammationCell biologyFluorescenceImmunologyMedicineMaterials sciencePathologyAlzheimer's diseaseBiochemistryNanotechnologyDisease

Abstract

fetched live from OpenAlex

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)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.274
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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