Global survey of secondary metabolism in <i>Aspergillus niger</i> via activation of specific transcription factors
Bibliographic record
Abstract
Abstract Genomics analysis confirmed the status of filamentous fungi as a rich source of novel secondary metabolites; however, the discovery of these compounds is hampered by the cryptic nature of their biosynthetic pathways under laboratory conditions. Consequently, despite substantial research effort over the past decades, much of the secondary metabolome remains uncharacterized in fungal organisms. Our manual curation of biosynthetic gene clusters (BGCs) in the Aspergillus niger NRRL3 genome revealed that only 13 of 86 BGCs have had their cognate secondary metabolite products confirmed or reliably inferred. We also identified 60 transcription factors (TFs) associated with cryptic BGCs. To further characterize A. niger secondary metabolism, we created a collection of strains each overexpressing a single BGC-associated TF. We analyzed the strain collection using a standardized pipeline where we monitored phenotypic changes and compound production using mass spectrometry. Strains showing evidence of secondary metabolism activation were selected for gene expression analysis. Our approach resulted in the production of multiple potentially novel secondary metabolites and linked a specific BGC to tensidol production in A. niger. More broadly, this study found evidence counter to the existing paradigm of BGC expression controlled by colocalized TFs, lending credence to the emerging picture of a complex regulatory network governing fungal secondary metabolism.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".