Genome Mining and Heterologous Expression-Guided Discovery of Mangicol Sesterterpenoids with Antimicrobial and Antineuroinflammatory Activities
Bibliographic record
Abstract
The accelerating increase in antimicrobial resistance presents serious dangers to global health, food safety, and agricultural productivity. In this study, we explored fungal-derived sesterterpenoids as potential antimicrobial agents, focusing on mangicols biosynthesized by a newly identified bifunctional terpene synthase from Fusarium oxysporum 14005. By combination of genome mining, heterologous pathway reconstitution, and feature-based molecular networking, eight new mangicols featuring epoxy and tetrahydrofuran moieties on their side chains were identified and characterized. Antimicrobial assays revealed that several of these derivatives exhibited potent activity against Ralstonia solanacearum, Staphylococcus aureus, and Streptococcus mutans, with minimum inhibitory concentrations ranging from 6.25 to 25 μM. Furthermore, compounds 3 – 5 demonstrated significant antineuroinflammatory effects in murine BV2 microglial cells with 50.0, 53.13, and 48.05%, respectively. The results offer mechanistic insights into the biosynthesis and bioactivity of mangicol-type sesterterpenoids and support their potential as novel antimicrobial and anti-inflammatory agents for addressing antimicrobial resistance.
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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".