Synthetic cystic fibrosis sputum medium diminishes <i>Burkholderia cenocepacia</i> antifungal activity against <i>Aspergillus fumigatus</i> independently of phenylacetic acid production
Bibliographic record
Abstract
Phenylacetic acid (PAA), an intermediate of phenylalanine degradation, is emerging as a signal molecule in microbial interactions with the host. In this work, we explore the presence of phenylalanine and PAA catabolism in 3 microbial pathogens of the cystic fibrosis (CF) lung microbiome: Pseudomonas aeruginosa, Burkholderia cenocepacia, and Aspergillus fumigatus. While in silico analysis of B. cenocepacia J2315 and A. fumigatus Af293 genome sequences showed complete pathways from phenylalanine to PAA, the P. aeruginosa PAO1 genome lacked several coding genes for phenylalanine and PAA catabolic enzymes. High-performance liquid chromatography analysis of supernatants from B. cenocepacia K56-2 detected PAA when grown in Luria-Bertani medium but not in synthetic cystic fibrosis sputum medium (SCFM). However, we were unable to identify PAA production by A. fumigatus or P. aeruginosa in any of the conditions tested. The inhibitory effect of B. cenocepacia on A. fumigatus growth was evaluated using agar plate interaction assays. Inhibition of fungal growth by B. cenocepacia was lessened in SCFM but this effect was not dependent on bacterial production of PAA. In summary, while we demonstrated PAA production by B. cenocepacia, we were not able to link this metabolite with the B. cenocepacia - A. fumigatus microbial interaction in CF nutritional conditions.
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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".