A Novel Glycolipid Biosurfactant Confers Grazing Resistance upon Pantoea ananatis BRT175 against the Social Amoeba Dictyostelium discoideum
Bibliographic record
Abstract
Pantoea is a versatile genus of bacteria with both plant- and animal-pathogenic strains, some of which have been suggested to cause human infections. There is, however, limited knowledge on the potential determinants used for host association and pathogenesis in animal systems. In this study, we used the model host Dictyostelium discoideum to show that isolates of Pantoea ananatis exhibit differential grazing susceptibility, with some being resistant to grazing by the amoebae. We carried out a high-throughput genetic screen of one grazing-resistant isolate, P. ananatis BRT175, using the D. discoideum pathosystem to identify genes responsible for the resistance phenotype. Among the 26 candidate genes involved in grazing resistance, we identified rhlA and rhlB, which we show are involved in the biosynthesis of a biosurfactant that enables swarming motility in P. ananatis BRT175. Using liquid chromatography-mass spectrometry (LC-MS), the biosurfactant was shown to be a glycolipid with monohexose-C10-C10 as the primary congener. We show that this novel glycolipid biosurfactant is cytotoxic to the amoebae and is capable of compromising cellular integrity, leading to cell lysis. The production of this biosurfactant may be important for bacterial survival in the environment and could contribute to the establishment of opportunistic infections. IMPORTANCE The genetic factors used for host interaction by the opportunistic human pathogen Pantoea ananatis are largely unknown. We identified two genes that are important for the production of a biosurfactant that confers grazing resistance against the social amoeba Dictyostelium discoideum. We show that the biosurfactant, which exhibits cytotoxicity toward the amoebae, is a glycolipid that incorporates a hexose rather than rhamnose. The production of this biosurfactant may confer a competitive advantage in the environment and could potentially contribute to the establishment of opportunistic infections.
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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".