Genome Editing by CRISPR-Cas: A Game Change in the Genetic Manipulation of Chlamydomonas
Bibliographic record
Abstract
Microalgae are promising photosynthetic unicellular eukaryotes among the most abundant on the planet and are considered as alternative sustainable resources for various industrial applications. Chlamydomonas is an emerging model for microalgae to be manipulated by multiple biotechnological tools in order to produce high-value bioproducts such as biofuels, bioactive peptides, pigments, nutraceuticals, and medicines. Specifically, Chlamydomonas reinhardtii has become a subject of different genetic-editing techniques adapted to modulate the production of microalgal metabolites. The main nuclear genome-editing tools available today include zinc finger nucleases (ZFNs), transcriptional activator-like effector nucleases (TALENs), and more recently discovered the clustered regularly interspaced short palindromic repeats (CRISPR)-CRISPR associated protein (Cas) nuclease system. The latter, shown to have an interesting editing capacity, has become an essential tool for genome editing. In this review, we highlight the available literature on the methods and the applications of CRISPR-Cas for C. reinhardtii genetic engineering, including recent transformation methods, most used bioinformatic tools, best strategies for the expression of Cas protein and sgRNA, the CRISPR-Cas mediated gene knock-in/knock-out strategies, and finally the literature related to CRISPR expression and modification approaches.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".