Functional heterologous expression of the reversible Cu-decarboxylase from the lichen, Cladonia uncialis
Bibliographic record
Abstract
Despite the isolation of over 1,000 known bioactive lichen mycobiont-derived secondary metabolites (SMs), understanding the genetic basis of their biosynthesis remains elusive. Biosynthetic gene clusters (BGCs) have been tentatively linked to chemical structures, with core genes such as polyketide synthases (PKSs) surrounded by accessory genes like decarboxylases. In this study, we focused on a decarboxylase gene from the genome of the lichen cladonia uncialis (named as Cu -decarboxylase) to elucidate its role in SM biosynthesis. A 963 bp gene was cloned from C. uncialis and expressed in Escherichia coli (BL21(DE3) cells using the pQE80L expression vector. The resulting 35 kDa protein was purified by applying a Ni + -NTA column using an FPLC system. Functional activity assays revealed the decarboxylation and reversible carboxylation of resorcinol to 2,4-dihydroxybenzoic acid and orcinol to orsellinic acid. This suggests a potential role for this Cu -decarboxylase in SM biosynthesis. Furthermore, the lack of activity on substrates like anthranilic acid and aniline highlighted the importance of the phenolic OH group in facilitating these reactions. The 3D protein structure was predicted with AlphaFold3, based on sequence similarity with a known decarboxylases and revealed the importance of a zinc cofactor for the catalytic activity of the enzyme. The optimization of the reaction conditions, particularly for orsellinic acid production from orcinol, may enhance conversion rates and offer a viable route for industrial-scale production of bioactive compounds. This study marks the first known instance of functional heterologous expression of a non-codon-optimized gene isolated from lichen in E. coli .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".