Amylase Binding to Oral Streptococci: A Key Interaction for Human Oral Microbial Ecology, Adaptation and Fitness
Bibliographic record
Abstract
The interaction between human salivary alpha-amylase (HSAmy) and amylase-binding oral streptococci (ABS) helps determine the bacteria that colonize the oral cavity by establishing dental biofilms. Streptococci are important pioneer species of the oral cavity and influence oral health as well as common diseases such as dental caries. Various oral streptococcal species express distinct amylase-binding proteins, among which amylase-binding protein A (AbpA), encoded by the abpA gene in Streptococcus gordonii and several other species, which is the most extensively studied. Amylase binding facilitates microbial adhesion to host surfaces and biofilm formation and enables bacteria to harness the host’s amylase enzymatic activity at their cell surface, enhancing their capacity to metabolize dietary starch for nutritional gain. Additionally, amylase binding may also influence bacterial cell division and stress tolerance by engaging novel bacterial signaling pathways. From an evolutionary perspective, both Neanderthals and modern humans exhibit functional adaptations in nutrient metabolism, including selection for salivary amylase-binding oral streptococci, highlighting the importance of microbial co-adaptation in response to host diet. Further research is warranted to elucidate the broader roles of amylase binding to bacteria in host-bacterial signaling, bacterial cell division and fitness and the evolutionary trajectory of the oral microbiome.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".