Endophytic Streptomyces from honeybee hives inhibit plant and honeybee pathogens
Bibliographic record
Abstract
Honey bees are the most common pollinator of crops worldwide. However, our reliance on honey bees to pollinate pesticide-treated monoculture crops, combined with their pest and disease susceptibility, have led honey bee populations to fluctuate in recent years. Current treatments for honey bee bacterial and fungal diseases are inadequate due to poor safety profiles and increased pathogen resistance to these treatments. There has been renewed interest in discovering natural products from actinobacteria associated with bees to use as new hive treatments; however, few studies have determined whether these microbes are truly unique to bees or part of their broader environment. We isolated actinobacteria from plant pollen and hive pollen stores and found that the isolated Streptomyces strains share many features with previously characterized endophytic Streptomyces strains. Selected Streptomyces strains were sequenced, and the genomes were used to search for phylogenetic relationships, identify genetic markers of endophytism, and compare biosynthetic gene clusters. LC-MS/MS was used to confirm the production and identities of the genetically predicted natural products. Finally, we tested the ability of the isolated actinobacteria to inhibit the growth of both plant and honey bee pathogens. Specific taxa, like Streptomyces albidoflavus and Streptomyces olivaceus, were regularly isolated from both plants and hives and produced many of the same natural products. These natural products and the Streptomyces strains that produce them may represent a starting point for antibiotics that could be used to help protect these critical pollinators.
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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 | 0.000 |
| 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".