Genetic and biochemical diversity of terpene biosynthesis in cyanobacterial strains from tropical soda lakes
Bibliographic record
Abstract
Introduction: Terpenes and terpenoids are vital components in diverse metabolic pathways, forming the terpenome-the complete spectrum of terpene-related compounds biosynthesized by an organism. Integrating bioinformatic tools has significantly enhanced the ability to assess metabolic potential by combining these computational approaches with experimental biochemical data. Furthermore, gene annotation provides critical insights into specialized targets, facilitating the identification of shared or unique features across different strains. Aims and methods: This study investigates the presence of terpene compounds in cyanobacterial strains isolated from tropical soda lakes using a combination of gene mining, synteny analysis, phylogenetics, and metabolomics. Results and discussion: Key enzymes, including phytoene synthase and squalene hopene cyclase, were identified, showing significant similarities and evolutionary links to gene copies in Cyanobacteria from diverse ecological environments. Metabolomic analysis complemented genomic predictions, uncovering a rich diversity of tetraterpene compounds, particularly carotenoids. Notably, triterpene hopanoids were found exclusively in a unicellular strain. These compounds show significant potential for cellular protection, metabolic adaptation, and biotechnological uses. They might support microbial communities in extreme environments, such as the saline-alkaline lakes of the Pantanal Biome in Brazil, developing unique survival and resilience strategies in these harsh conditions. Conclusion: This study highlights the extensive range of insights that can be obtained by integrating genetics and biochemistry in exploring cyanobacterial diversity, especially from organisms thriving in extreme environments.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".