Genome engineering of the major goat pathogen Mycoplasma capricolum subsp. capripneumoniae as a first step towards the rational design of improved vaccines
Bibliographic record
Abstract
Background- Mycoplasma capricolum subspecies capripneumoniae (Mccp) is the causative agent of contagious caprine pleuropneumonia (CCPP), a disease listed by the world organization for animal health (WOAH) threatening goat production in Africa and Asia. Although a few commercial inactivated vaccines are available, they do not comply with WOAH standards and their efficacy is questioned. One of the limiting factors to comprehend the molecular pathogenesis of CCPP and develop improved vaccines has been the lack of tools for Mccp genome engineering. Results- In this study, synthetic biology techniques, recently developed for closely related mycoplasmas, were adapted to Mccp. CReasPy-cloning was used to simultaneously clone and engineer the Mccp genome in yeast, prior to whole genome transplantation into M. capricolum subsp. capricolum recipient cells. This approach was used to knock-out an S41 serine protease gene identified as a potential virulence factor, leading to the generation of the first site-specific Mccp mutants. This approach was further extended to two other field strains of Mccp using CReasPy-Fusion, a method that allows to clone and edit bacterial genomes in yeast through cell-to-cell contact. Furthermore, the Cre-lox recombination system was applied to remove all DNA sequences added during genome engineering. Finally, the resulting unmarked S41 serine protease mutants were validated by genome sequencing and their noncaseinolytic phenotype was confirmed by casein digestion assay. Conclusion- Synthetic biology tools were successfully implemented in Mccp. This innovation allows constructing targeted Mccp mutants at ease, which will be of great help to decipher Mccp pathogenicity determinants and develop novel vaccines.
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 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".