Nanoscale Dynamics of Streptococcal Adhesion to AGE-Modified Collagen
Bibliographic record
Abstract
The adhesion of initial colonizers such as Streptococcus mutans to collagen is critical for dentinal and root caries progression. One of the most described pathological and aging-associated changes in collagen—including dentinal collagen—is the generation of advanced glycation end-products (AGEs) such as methylglyoxal (MGO)–derived AGEs. Despite previous reports suggesting that AGEs alter bacterial adhesion to collagen, the biophysics driving oral streptococcal attachment to MGO-modified collagen remains largely understudied. Thus, the aim of this work was to unravel the dynamics of the initial adhesion of S. mutans to type I collagen in the presence and absence of MGO-derived AGEs by employing bacterial cell force spectroscopy with atomic force microscopy (AFM). Type I collagen gels were treated with 10 mM MGO to induce AGE formation, which was characterized with microscopy and enzyme-linked immunosorbent assay. Subsequently, AFM cantilevers were functionalized with living S. mutans UA 159 or Streptococcus sanguinis SK 36 cells and probed against collagen surfaces to obtain force curves displaying bacterial attachment in real time, from which the adhesion force, number of events, Poisson analysis, and contour and rupture lengths for each individual detachment event were computed. Furthermore, in silico computer simulation docking studies between the relevant S. mutans UA 159 collagen-binding protein SpaP and collagen were computed, in the presence and absence of MGO. Overall, results showed that MGO modification increased both the number and adhesion force of single-unbinding events between S. mutans and collagen, without altering the contour or rupture lengths. Both experimental and in silico simulations suggest that this effect is due to increased specific and nonspecific forces and interactions between S. mutans UA 159 and MGO-modified collagen substrates. In summary, these results suggest that collagen alterations due to aging and glycation may play a role in early bacterial adherence to oral tissues, associated with conditions such as aging or chronic hyperglycemia, among others.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".