Surface motility favors co-dependent interaction between <i>Pseudomonas aeruginosa</i> and <i>Burkholderia cenocepacia</i>
Bibliographic record
Abstract
Abstract Interactions between different bacterial species shape bacterial communities and their environments. The opportunistic pathogens Pseudomonas aeruginosa and Burkholderia cenocepacia both can colonize the lungs of individuals affected by cystic fibrosis. Using the social surface behavior called swarming motility as a study model of interactions, we noticed intricate interactions between B. cenocepacia K56-2 and P. aeruginosa PA14. While strain K56-2 does not swarm under P. aeruginosa favorable swarming conditions, co-inoculation with a non-motile PA14 flagellum-less Δ fliC mutant restored spreading for both strains. We show that P. aeruginosa provides the wetting agent rhamnolipids allowing K56-2 to perform swarming motility, while aflagellated PA14 seems able to «hitchhike» along with K56-2 cells in the swarming colony. Importance Pseudomonas aeruginosa and Burkholderia cenocepacia are important opportunistic pathogens often found together in the airways of persons with cystic fibrosis. Laboratory co-culture of both species often ends with one taking over the other. We used a surface motility assay to study the social interactions between population of these bacterial species. Under our conditions, B. cenocepacia cannot swarm without supplementation of the wetting agent produced by P. aeruginosa . In a mixed colony of both species, an aflagellated mutant of P. aeruginosa provides the necessary wetting agent to B. cenocepacia , allowing both bacteria to swarm and colonize a surface. We highlight this peculiar interaction where both bacteria set aside their antagonistic tendencies to cooperate.
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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 | 0.001 |
| 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".