Exploration of Predicted Nitrogen-Containing Natural Products from Northern Canadian Bark Beetle-Associated Bacteria
Bibliographic record
Abstract
Bark beetle-associated bacteria from the sub-boreal and boreal forests of northern Canada represent a largely unexplored source of bioactive natural products. This study aims to investigate the chemical potential of bacteria isolated from Dendroctonus ponderosae, Dendroctonus rufipennis, Dendroctonus pseudotsugae, and Ips perturbatus by focusing on nitrogen-containing secondary metabolites. Genomic analyses of the bacterial isolates identified diverse biosynthetic gene clusters (BGCs), including nonribosomal peptides (NRPs), NRPS-PKS hybrids, and ribosomally synthesized and post-translationally modified peptides (RiPPs), many of which exhibit low sequence homology, suggesting potential for novel bioactive compounds. Nitrogen-15 NMR spectroscopy was employed to detect nitrogen-containing functional groups in crude extracts, revealing distinct signals for amides, amines, and nitrogen heterocycles. The combination of BGC predictions and NMR data highlighted the genetic and chemical diversity of these bacteria and underscored the potential for discovering novel nitrogen-rich metabolites. These findings provide a foundation for further exploration of bioactive natural products with pharmaceutical and agrochemical applications and potential to contribute to the understanding of the chemical ecology of bark beetle–microbe interactions in northern ecosystems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".