Lost in Translation: Challenges with Heterologous Expression of Lichen Polyketide Synthases
Bibliographic record
Abstract
Abstract The ability to functionally express proteins in hosts is a precondition to an advanced understanding of the biosynthetic pathways that are responsible for producing life's complex molecules. The study of secondary metabolites in lichen‐forming fungi has long been hampered by slow growth. This study, reports on heterologous expression trials of four polyketide synthase (PKS) genes from C. uncialis in Aspergillus oryzae NSAR1. Isolation of mRNA and RT‐PCR demonstrated that A. oryzae can transcribe all lichen genes and remove introns to produce translationally‐coherent mRNA. Transformation of A. oryzae with a codon‐optimized PKS did not result in metabolite production, nor did co‐expression of a number of accessory genes restore function to any lichen PKS. Genes encoding an orsellinic acid synthase (OAS) from Fusarium sp. and a 6‐methylsalicylic acid synthase (6MSAS) from Penicillum sp. were transformed into A. oryzae . Readily detectable amounts of de novo orsellinic acid and 6‐methylsalicylic acid biosynthesis were observed in A. oryzae when transformed with these non‐lichen PKS genes. However, transformation with functionally homologous PKS genes from C. uncialis produced no detectable product. This work demonstrates that lichen PKS genes are correctly transcribed by A. oryzae but that polyketide biosynthesis failed for a reason that is presently unknown but may be attributable to a fault of translation
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".