Discovery of a New Natural Product and a Deactivation of a Quorum Sensing System by Culturing a “Producer” Bacterium With a Heat-Killed “Inducer” Culture
Bibliographic record
Abstract
Herein we describe a modified bacterial culture methodology as a tool to discover new natural products via supplementing actinomycete fermentation media with autoclaved cultures of “inducer” microbes. Using seven actinomycetes and four inducer microbes, we detected 28 metabolites that were induced in UHPLC-HRESIMS-based analysis of bacterial fermentations. Metabolomic analysis indicated that each inducer elicited a unique response from the actinomycetes and that some chemical responses were specific to each inducer-producer combination. Among these 28 metabolites, hydrazidomycin D, a new hydrazide-containing natural product was isolated from the pair Streptomyces sp. RKBH-B178 and Mycobacterium smegmatis. This result validated the effectiveness of the strategy in discovering new natural products. From the same set of induced metabolites, an in-depth investigation of a fermentation of Streptomyces sp. RKBH-B178 and autoclaved Pseudomonas aeruginosa led to the discovery of a glucuronidated analogue of the pseudomonas quinolone signal (PQS). We demonstrated that RKBH-B178 is able to biotransform the P. aeruginosa quorum sensing molecules, 2-heptyl-4-quinolone (HHQ) and Pseudomonas quinolone signal (PQS), to form PQS-GlcA. Further, PQS-GlcA was shown to have poor binding affinity to PqsR, the innate receptor of HHQ and PQS.
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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 | 0.001 |
| 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".