Coniontins, lipopetaibiotics active against Candida auris identified from a microbial natural product fractionation library
Bibliographic record
Abstract
The rise of drug-resistant fungal pathogens, including Candida auris, highlights the urgent need for innovative antifungal therapies. We have developed a cost-effective platform combining microbial extract prefractionation with rapid mass spectrometry-bioinformatics-based dereplication to efficiently prioritize previously uncharacterized antifungal scaffolds. Screening C. auris and Candida albicans reveals coniotins, lipopeptaibiotics isolated from Coniochaeta hoffmannii, which are undetectable in crude extracts. Coniotins exhibits potent activity against critical priority fungal pathogens listed by the World Health Organization, including C. albicans, Cryptococcus neoformans, multidrug-resistant Candida auris, and Aspergillus fumigatus, with high selectivity and low resistance potential. Coniotin A targets beta-glucan, compromising fungal cell wall integrity, remodelling, and sensitizing C. auris to caspofungin. Identification of its hybrid polyketide synthase-nonribosomal peptide synthetase biosynthetic gene cluster facilitates discovering structurally diverse lipopeptaibiotics. Here, we show that natural product prefractionation enables the discovery of previously hidden bioactive scaffolds and introduces coniotins as candidates for combating multidrug-resistant fungal 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) | 0.001 | 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 | 0.001 |
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".