Effect of environmental factors on polyketide synthase gene expression, usnic acid biosynthesis, and ground cover of a lichen-fungus, <i>Cladonia uncialis</i>
Bibliographic record
Abstract
Lichens are composed of symbiotic slow-growing organisms and are often exposed to extreme microenvironmental conditions, resulting in the production of unique secondary metabolites. One of the most commonly produced secondary metabolites is usnic acid, which is thought to be produced by two genes. The objectives of the present study were to compare polyketide synthase (PKS) gene expression and usnic acid concentration in Cladonia uncialis (L.) Weber ex F.H. Wigg., with two environmental factors. Seventy-five lichen samples were collected from three locations in Newfoundland, Canada, using a strip transect method (×5 transects, ×5 quadrats). Usnic acid concentration was measured using the liquid chromatography tandem mass-spectrometric method and gene expression of two PKS genes (methylphloracetophenone oxidase (MPAO) and methylphloracetophenone synthase (MPAS)) was examined using quantitative real-time PCR. The results showed that percent ground cover of C. uncialis was affected by soil pH level but not soil moisture, and usnic acid concentration was not affected by either soil pH or soil moisture. MPAO gene expression level was significantly affected by soil pH level but not soil moisture, and MPAS gene expression level was not affected by either soil pH level or soil moisture. There was no significant relationship between MPAS and MPAO gene expression levels and usnic acid concentration. These findings suggest that soil pH may be important for the production of usnic acid by C. uncialis but the genes involved require further study.
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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) | 0.000 | 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) | 0.000 | 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 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".