Cloning of Thalassiosira pseudonana’s Mitochondrial Genome in Saccharomyces cerevisiae and Escherichia coli
Bibliographic record
Abstract
Algae are attractive organisms for biotechnology applications such as the production of biofuels, medicines, and other high-value compounds due to their genetic diversity, varied physical characteristics, and metabolic processes. As new species are being domesticated, rapid nuclear and organelle genome engineering methods need to be developed or optimized. To that end, we have previously demonstrated that the mitochondrial genome of microalgae Phaeodactylum tricornutum can be cloned and engineered in Saccharomyces cerevisiae and Escherichia coli. Here, we show that the same approach can be used to clone mitochondrial genomes of another microalga, Thalassiosira pseudonana. We have demonstrated that these genomes can be cloned in S. cerevisiae as easily as those of P. tricornutum, but they are less stable when propagated in E. coli. Specifically, after approximately 60 generations of propagation in E. coli, 17% of cloned T. pseudonana mitochondrial genomes contained deletions compared to 0% of previously cloned P. tricornutum mitochondrial genomes. This genome instability is potentially due to the lower G+C DNA content of T. pseudonana (30%) compared to P. tricornutum (35%). Consequently, the previously established method can be applied to clone T. pseudonana’s mitochondrial genome, however, more frequent analyses of genome integrity will be required following propagation in E. coli prior to use in downstream applications.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".