The Diversity of Anti-Microbial Secondary Metabolites Produced by Fungal Endophytes: An Interdisciplinary Perspective
Bibliographic record
Abstract
Endophytes are microbes that inhabit host plants without causing disease and are reported to be reservoirs of metabolites that combat microbes and other pathogens. Here we review diverse classes of secondary metabolites, focusing on anti-microbial compounds, synthesized by fungal endophytes including terpenoids, alkaloids, phenylpropanoids, aliphatic compounds, polyketides, and peptides from the interdisciplinary perspectives of biochemistry, genetics, fungal biology, host plant biology, human and plant pathology. Several trends were apparent. First, host plants are often investigated for endophytes when there is prior indigenous knowledge concerning human medicinal uses (e.g., Chinese herbs). However, within their native ecosystems, and where investigated, endophytes were shown to produce compounds that target pathogens of the host plant. In a few examples, both fungal endophytes and their hosts were reported to produce the same compounds. Terpenoids and polyketides are the most purified anti-microbial secondary metabolites from endophytes, while flavonoids and lignans are rare. Examples are provided where fungal genes encoding anti-microbial compounds are clustered on chromosomes. As different genera of fungi can produce the same metabolite, genetic clustering may facilitate sharing of anti-microbial secondary metabolites between fungi. We discuss gaps in the literature and how more interdisciplinary research may lead to new opportunities to develop bio-based commercial products to combat global crop and human pathogens.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".