Design and assembly of the 117-kb <i>Phaeodactylum tricornutum</i> chloroplast genome
Bibliographic record
Abstract
ABSTRACT There is a growing impetus to expand the repository of chassis available to synthetic biologists. The chloroplast genome presents a unique chassis for engineering photosynthetic eukaryotes by virtue of its compact size, lack of epigenetic regulation, and containment within the secluded lipid bilayers of the organelle. The development of the chloroplast as a synthetic biology chassis, however, has been limited by a lack of efficient techniques for whole genome cloning and engineering. Here, we demonstrate two approaches for cloning the 117 kb Phaeodactylum tricornutum chloroplast genome that have 90 to 100% efficiency when screening as few as ten Saccharomyces cerevisiae colonies following yeast assembly. The first method directly uses PCR-amplified fragments of the genome for yeast assembly, whereas the second method relies upon the pre-cloning of eight overlapping genomic regions into individual plasmids that they can later be released from. The cloned genome can be stably maintained and propagated within Escherichia coli , which provides an exciting opportunity for engineering a novel delivery mechanism for bringing DNA directly to the algal chloroplast. As well, one of the cloned genomes was designed to contain a single Sap I site within the yeast URA3 open-reading frame, which can be used to linearize the genome and integrate designer cassettes via golden-gate cloning or further iterations of yeast assembly.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".