PEG Adjustment Enables Genome Transplantation Using <i>Mycoplasma Mycoides</i> Recipient
Bibliographic record
Abstract
Abstract Pioneering advances in synthetic biology, initiated at the J. Craig Venter Institute, have enabled the creation of the first cell driven by synthetic genome and opened a new era of creating designer microbes. This technology offers far greater potential for genetic modification than traditional genome editing techniques and holds promise for the routine creation of synthetic cells as DNA synthesis costs decrease and genetic knowledge expands. Two essential technologies underpin this achievement: the ability to clone entire genomes in host organisms, such as Saccharomyces cerevisiae , and the successful transplant of these genomes into recipient cells to generate living organisms. In all previous work the recipient cell in genome transplantation experiments has been Mycoplasma capricolum . In this study, we explored the potential of using Mycoplasma mycoides strains as recipient cells for genome transplantation. By increasing polyethylene glycol (PEG) concentration from 5% to 10%, we successfully transplanted various M. mycoides strains using several M. mycoides recipient cells. Additionally, we demonstrated the ability to transplant M. capricolum genomes into M. mycoides recipient cells; although with lower efficiency compared to M. mycoides strains. These findings provide a modified transplantation protocol and likely get us closer to expanding genome transplantation to other bacterial species.
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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 | 0.000 | 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".