Characterization of Variation in Natural Product Production Under Chemical Elicitation Using Parallel Stable Isotope Labeling
Bibliographic record
Abstract
Most microorganisms produce far fewer secondary metabolites under laboratory culture conditions than would be expected based on the number of biosynthetic gene clusters (BGCs) present in their genomes. One strategy for inducing secondary metabolite production is to add chemical elicitors that disrupt bacterial metabolism. This one-strain-many-compounds (OSMAC) strategy has been used successfully to discover a broad range of natural products. However, traditional strategies for detecting changes in natural product production are not well suited to characterizing variations in the full secondary metabolome under elicitation conditions. One efficient tool to differentiate metabolites between experiments is IsoAnalyst, a parallel stable isotope labeling method that connects secondary metabolites to BGCs by determining the rates of incorporation for a set of isotopically labeled secondary metabolism building blocks. In this study three strains of Paraburkholderia were profiled under a range of OSMAC conditions and changes in secondary metabolism characterized using a combination of analytical tools including IsoAnalyst. Using these profiles, we assessed the degree of novel secondary metabolite production under different elicitation conditions. Prioritization of one compound class strongly induced in the presence of the antibiotic rifaximin led to the discovery of 2-hydroxyacyl putrescine compounds putrescinamides A ( 1 ) and B ( 2 ). The structures of these new metabolites were determined through a combination of multidimensional NMR experiments and total synthesis, which permitted the determination of their full absolute configurations. Together these stable isotope labeling experiments provide a unique perspective on system-wide variation in de novo secondary metabolite biosynthesis under elicitor conditions and highlight the impact of elicitor selection on metabolite induction in Burkholderiales strains.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".