Successful reversal of transgene silencing in <i>Chlamydomonas reinhardtii</i>
Bibliographic record
Abstract
Abstract Chlamydomonas reinhardtii has been successfully engineered to produce compounds of interest following transgene integration and heterologous protein expression. The advantages of this model include the availability of validated tools for bioengineering, its photosynthetic ability, and its potential use as biofuel. Despite this, breakthroughs have been hindered by its ability to silence transgene expression through epigenetic changes. Histone deacetylases (HDAC) are main players in gene expression. We hypothesized that transgene silencing can be reverted with chemical treatments using HDAC inhibitors. To analyze this, we transformed C. reinhardtii , integrating into its genome the mVenus reporter gene under the HSP70‐rbcs2 promoter. From 384 transformed clones, 88 (22.9%) displayed mVenus positive (mVenus + ) cells upon flow‐cytometry analysis. Five clones with different fluorescence intensities were selected. The number of integrated copies was measured by qPCR. Transgene expression levels were followed over the growth cycle and upon SAHA treatment, using a microplate reader, flow cytometry, RT‐qPCR, and western blot analysis. First, we observed that expression varies with the cell cycle, reaching a maximum level just before the stationary phase in all clones. Second, we uncovered that supplementation with HDAC inhibitors of the hydroxamate family, such as vorinostat (suberoylanilide‐hydroxamic‐acid, SAHA) at the initiation of culture increases the frequency (% of mVenus + cells) and the level of transgene expression per cell over the whole growth cycle, through histone deacetylase inhibition. Thus, we propose a new tool to successfully trigger the expression of heterologous proteins in the green algae C. reinhardtii , overcoming its main obstacle as an expression platform.
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) | 0.001 | 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 | 0.000 |
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".