Discovery of Cryptic Natural Products Using High-Throughput Elicitor Screening on Agar Media
Bibliographic record
Abstract
It is now well-established that microbial genomes carry sparingly expressed biosynthetic gene clusters (BGCs) that need to be induced in order to characterize their products. To do so, we herein subjected two well-known producers, Burkholderia plantarii and Burkholderia gladioli, to high-throughput elicitor screening (HiTES), an emerging approach for accessing the products of these “cryptic” BGCs. Both organisms have previously been examined extensively in liquid cultures. We therefore applied HiTES on agar and found several novel natural products that are only produced in this format and not in liquid cultures. Most notably we found two metabolites, termed burkethyl A and B, that contain an unusual m -ethylbenzoyl group and for which we identified the cognate BGC using bioinformatic and genetic studies. Our results indicate that agar-based HiTES is a promising approach for natural product discovery and are in line with the notion that even “drained” strains remain sources of new metabolites as long as alternative approaches are employed.
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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) | 0.001 | 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".