Total Synthesis, Isolation, Surfactant Properties, and Biological Evaluation of Ananatosides and Related Macrodilactone-Containing Rhamnolipids
Bibliographic record
Abstract
Rhamnolipids are a specific class of microbial surfactants, which hold great biotechnological and therapeutic potential. However, their exploitation at the industrial level is hampered because they are mainly produced by the opportunistic pathogen Pseudomonas aeruginosa. The non-human pathogenic bacterium Pantoea ananatis is an alternative producer of rhamnolipid-like metabolites containing glucose instead of rhamnose residues. Herein, we present the isolation, structural characterization, and total synthesis of ananatoside A, a 15-membered macrodilactone-containing glucolipid, and ananatoside B, its open-chain congener, from organic extracts of P. ananatis. Ananatoside A was synthesized through three alternative pathways involving either an intramolecular glycosylation, a chemical macrolactonization or a direct enzymatic transformation from ananatoside B. A series of diasteroisomerically pure (1®2), (1®3), and (1®4)-macrolactonized rhamnolipids were also synthesized through intramolecular glycosylation and their anomeric configurations as well as ring conformations were solved using molecular modeling in tandem with NMR studies. We show that ananatoside B is a more potent surfactant than its macrolide counterpart. We present evidence that macrolactonization of rhamnolipids enhances their cytotoxic and hemolytic potential, pointing towards a mechanism involving the formation of pores into the lipidic cell membrane. Lastly, we demonstrate that ananatoside A and ananatoside B as well as synthetic macrolactonized rhamnolipids can be perceived by the plant immune system, and that this sensing is more pronounced for a macrolide featuring a rhamnose moiety in its native 1C4 conformation. Altogether our results prove that macrolactonization of glycolipids can dramatically interfere with their surfactant properties and biological activity.
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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) | 0.001 | 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) | 0.000 | 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".