Feature-Based Molecular Networking Guided Characterization of Clonocoprogen Siderophores with Anti-<i>Pseudomonas aeruginosa</i> Activity in Nematodes through Immune Up-Regulating Effects
Bibliographic record
Abstract
Antibiotic resistance poses a severe threat to human health, necessitating research into antibiotics with unique mechanisms to combat drug resistance. Natural siderophores and their synthetic derivatives have become a promising resource for the development of anti-infectious agents. In this study, we introduce a new anti-infective agent with a mode of action that enhances host immunity without exerting direct antibacterial activity. We identified immune-activating clonocoprogen siderophores from the fungus Clonostachys rosea isolate CR15020 using an integrated approach, including genome mining, feature-based molecular networking (FBMN) and a nematode screening model. Although these siderophores displayed no inherent antibacterial properties, they significantly improved survival of Caenorhabditis elegans exposed to Pseudomonas aeruginosa, with EC 50 values ranging from 1.85 to 10.86 μM. This protection was achieved through up-regulation of the nematode’s p38-MAPK and DAF/IGF immune pathways, as well as reducing the excretion of pyoverdine by P. aeruginosa . By leveraging immune modulation rather than direct bacterial inhibition, this approach offers a promising alternative to conventional antibiotics, addressing the urgent challenge of antibiotic resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".