Application of a simple bioactivity profiling strategy to natural product discovery from endophytes of marine macroalgae
Bibliographic record
Abstract
The natural products chemistry of marine macroalgal endophytes is relatively unexplored despite these fungi being recognized as a promising source of new bioactive molecules [1]. As redundancy in natural products discovery increases, new techniques are needed to prioritise extracts for fractionation. The use of bioactivity profiling provides an excellent, albeit labour intensive screening approach that facilitates the discovery of antibiotics with novel modes of action or cellular targets [2]. Here we present a simplified method for bioactivity profiling that we have applied to a library of one hundred and forty-one extracts of endophytic fungi isolated from 20 species of marine macroalgae from the Bay of Fundy, Canada. Extracts were screened for antimicrobial activity against a suite of Gram positive and Gram negative bacteria, mycobacteria and fungi. These data were used to compile bioactivity profiles of each extract that were compared to each other and the profiles of known antibiotics representing a range of modes of action. Principle component analysis revealed that 34 extracts exhibited unique profiles within the extract library, and hierarchical cluster analysis indicated six of these extracts possessed profiles different from those of the antibiotics. We are currently subjecting these six extracts to bioassay-guided fractionation to isolate the biologically active constituents. We have therefore demonstrated that a simple, efficient and robust bioactivity profiling technique is effective for prioritising fungal extract libraries. We are confident that this technique will be a valuable tool for identifying natural products with unique antimicrobial modes of action.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".