Rapid method for generating designer algal mitochondrial genomes
Bibliographic record
Abstract
ABSTRACT With synthetic biology, we can turn algae into bio-factories that produce high-value molecules (e.g. medicines or biofuels) or tackle global challenges (e.g. malnutrition and climate change). This realization has provoked rapid progress towards the creation of genetic tools for multiple algal species, notably Phaeodactylum tricornutum. The power of synthetic biology to generate more useful or productive organisms is contingent on the ability to produce diverse DNA molecules and rapidly screen them for beneficial variants. However, it is still relatively expensive to synthesize DNA, and delivering large DNA (>50 kbp) to eukaryotic cellular compartments remains challenging. In this study, we establish a robust system for building algal mitochondrial genomes as a practical alternative to DNA synthesis. Our approach permits an inexpensive and rapid generation of mitochondrial derivatives designed for testing targeted DNA delivery. First, we cloned the mitochondrial genome of P. tricornutum into the eukaryotic host organism S. cerevisiae using two different techniques: transformation-associated recombination; and PCR-based cloning. Next, we analyzed the cloned genomes by multiplex PCR, and correct genomes were transferred to prokaryotic host E. coli. Then these genomes were again analyzed by multiplex PCR, followed by diagnostic digest and complete-plasmid sequencing to evaluate the fidelity of each cloning method. Finally, we assessed the burden on eukaryotic and prokaryotic hosts to propagate the cloned genomes. We conclude that our system can reliably generate variants for genome-level engineering of algal mitochondria. HIGHLIGHTS TAR-cloned mitochondrial genome of P. tricornutum in yeast Developed PCR-based cloning method to create designer algal mitochondrial genomes Stably propagated algal mitochondrial genomes in S. cerevisiae and E. coli hosts
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".