An investigation of saccharide complexation by aqueous oxoacids. Can oxoacids mediate A1-2 quorum sensing in bacteria?
Bibliographic record
Abstract
The goal of this investigation was to determine whether it is plausible for bacterial AI-2 quorum sensing to be selectively regulated by environmentally available oxoacids. In particular, we investigated the ability of H2CO3 and H4SiO4 to bind molecules structurally analogous to the AI-2 signaling compound (a hydration product of (4S)-4,5-dihydroxy-2,3-pentanedione, S-DPD) which is known to be bound to H3BO3 in the AI-2 receptor site of V. harveyi. We report the first ever evidence of mono- and di-ester linked complexes formed spontaneously between carbonic acid and aqueous polyhydroxy hydrocarbons, providing support for the hypothesis that a complex between H2CO3 and some S-DPD derivative regulates AI-2 quorum sensing in S. gordonii. The carbonate centre in these novel complexes retains three-fold coordination. Additionally, we compared the binding affinity of silicic acid to that of boric acid and carbonic acid to several different alcohols and saccharides, and determined that the formation constants generally increase as H2CO3 < H4SiO4 < H3BO3. It seems entirely plausible, therefore, that silicic acid could modulate AI-2 quorum sensing in Sirich environments such as soil solutions. \nFinally, we determined that stannic acid is also complexed by a range of polyhydroxy hydrocarbons in aqueous solution and characterized of structures of many of the resulting mono-, bis- and tris-ligand complexes.
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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) | 0.001 | 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".