Transcriptome Comparison of Secondary Metabolite Biosynthesis Genes Expressed in Cultured and Lichenized Conditions of Cladonia rangiferina
Bibliographic record
Abstract
Lichen secondary metabolites are natural products of high medicinal and industrial value, which are produced by the fungal symbiont (mycobiont) of lichens in response to environmental changes. It has been shown that the cultured mycobiont is capable of secondary metabolite production, specifically polyketides, and polyketide production is affected by the presence or absence of the algal or cyanobacterial symbiont (photobiont). Identification of polyketide synthases encoding genes is, in turn, key for understanding the regulation of secondary metabolite synthesis. Using a previously established method of resynthesis for Cladonia rangiferina as well as the sequenced and assembled genome of that species, we compared transcriptomes of C. rangiferina cultured alone and resynthesized with the photobiont (Asterochloris glomerata) to reveal transcriptionally active genes in secondary metabolic gene clusters, as well some of the neighbouring genes, induced by the presence of the photobiont and events of lichenization. The results identify potential candidates for PKS genes in C. rangiferina, identify potential neighbouring genes in the PKS cluster, and offer insights into further research. The study provides preliminary insights into the activity of several identified biosynthetic gene clusters (BGC) as well as interactions of genes within those clusters.
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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) | 0.001 | 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".