Cyanide Toxicity to Burkholderia cenocepacia Is Modulated by Polymicrobial Communities and Environmental Factors
Bibliographic record
Abstract
Microbes within polymicrobial communities can establish positive and negative interactions that have the potential to influence the overall behavior of the community. Pseudomonas aeruginosa and species of the Burkholderia cepacia complex (Bcc) can co-exist in the lower airways, however several studies have shown that P. aeruginosa can effectively kill the Bcc in vitro, for which hydrogen cyanide (HCN) was recently proposed to play a critical role. Here we show that modification of the environment (i.e., culture medium), long-term genetic adaptation of P. aeruginosa to the cystic fibrosis (CF) lung, or the addition of another bacterial species to the community can alter the sensitivity of Burkholderia cenocepacia to P. aeruginosa toxins. We specifically demonstrate that undefined rich media leads to higher susceptibility of B. cenocepacia to P. aeruginosa toxins like cyanide as compared to a synthetic medium (SCFM), that mimics the CF lung nutritional content. Overall, our study shows that the polymicrobial environment can have profound effects on negative interactions mediated by P. aeruginosa against B. cenocepacia. In fact, evolved P. aeruginosa or the presence of other species such as Staphylococcus aureus can directly abolish the direct competition mediated by cyanide and consequently maintaining a higher level of species diversity within the community.
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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".