Identification and characterization of the siderochelin biosynthetic gene cluster <i>via</i> coculture
Bibliographic record
Abstract
ABSTRACT Many microbial biosynthetic gene clusters (BGCs) are inactive under standard laboratory conditions, making characterization of their products difficult. Silent BGCs are likely activated by specific cues in their natural environment, such as the presence of competitors. Growth conditions such as coculture with other microbes, which more closely mimic natural environments, are practical strategies for inducing silent BGCs. Here, we utilize coculture to activate BGCs in nine actinobacteria strains. We observed increased production of the ferrous siderophores siderochelin A and B during coculture of Amycolatopsis strain WAC04611 and Tsukamurella strain WAC06889b. Furthermore, we identified the siderochelin BGC in WAC04611 and discovered that the GntR-family transcription factor sidR3 represses siderochelin production. Deletion of the predicted aminotransferase sidA abolished production of the carboxamides siderochelin A/B and led to the accumulation of the carboxylate siderochelin D. Finally, we deleted the predicted hydroxylase sidB and established that it is essential for siderochelin production. Our findings show that microbial coculture can successfully activate silent BGCs and lead to the discovery and characterization of unknown BGCs for molecules like siderochelin. IMPORTANCE Siderophores are vital iron-acquisition elements required by microbes for survival in a variety of environments. Furthermore, many siderophores are essential for the virulence of various human pathogens, making them a possible target for antibacterials. The significance of our work is in the identification and characterization of the previously unknown BGC for the siderophore siderochelin. Our work adds to the growing knowledge of siderophore biosynthesis, which may aid in the future development of siderophore-targeting pharmaceuticals and inform on the ecological roles of these compounds. Furthermore, our work demonstrates that combining microbial coculture with metabolomics is a valuable strategy for identifying upregulated compounds and their BGCs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".