Genetic and Environmental Factors Influencing the Production of Select Fungal Colorants: Challenges and Opportunities in Industrial Applications
Bibliographic record
Abstract
Natural colorants, mostly of plant and fungal origins, offer advantages over chemically synthetic colorants in terms of alleviating environmental pollution and promoting human health. The market value of natural colorants has been increasing significantly across the globe. Due to the ease of artificially culturing most fungi in the laboratory and in industrial settings, fungi have emerged as the organisms of choice for producing many natural colorants. Indeed, there is a wide variety of colorful fungi and a diversity in the structure and bioactivity of fungal colorants. Such broad diversities have spurred significant research efforts in fungi to search for natural alternatives to synthetic colorants. Here, we review recent research on the genetic and environmental factors influencing the production of three major types of natural fungal colorants: carotenoids, melanins, and polyketide-derived colorants. We highlight how molecular genetic studies and environmental condition manipulations are helping to overcome some of the challenges associated with value-added and large-scale productions of these colorants. We finish by discussing potential future trends, including synthetic biology approaches, in the commercial production of fungal colorants.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".