Design and assembly of the 117-kb <i>Phaeodactylum tricornutum</i> chloroplast genome
Bibliographic record
Abstract
There is growing impetus to expand the repertoire of chassis available to synthetic biologists. Chloroplast genomes present an interesting alternative for engineering photosynthetic eukaryotes; however, development of the chloroplast as a synthetic biology chassis 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 10 yeast (Saccharomyces cerevisiae) colonies following yeast assembly. The first method reconstitutes the genome from PCR-amplified fragments, whereas the second method involves precloning these fragments into individual plasmids from which they can later be released. In both cases, overlapping fragments of the chloroplast genome and a cloning vector are homologously recombined into a singular contig through yeast assembly. The cloned chloroplast genome can be stably maintained and propagated within Escherichia coli, which provides an exciting opportunity for engineering a delivery mechanism for bringing DNA directly to the algal chloroplast. Also, one of the cloned genomes was designed to contain a single SapI site within the yeast URA3 (coding for orotidine-5'-phosphate decarboxylase) 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. The methods presented here could be extrapolated to other species-particularly those with a similar chloroplast genome size and architecture (e.g. Thalassiosira pseudonana).
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".