Expression of <scp>PTEN</scp>‐long mediated by <scp>CRISPR</scp>/Cas9 can repress U87 cell proliferation
Bibliographic record
Abstract
PTEN is a tumour suppressor that is frequently mutated in a variety of cancers. Hence, PTEN has significant potential as a therapeutic molecule. PTEN-long is an alternative translation variant, with an additional 173 amino acids added to the N-terminal of the canonical PTEN when CUG of the mRNA is utilized as the start codon. PTEN-long is secreted into serum and can re-enter cells throughout the body. One of the major barriers for gene therapy is to efficiently and specifically deliver DNA or RNA material to target cells. As an alternative approach, if a therapeutic protein can be directly delivered to target cell of interest, it should theoretically function well within the cells, particularly for genes that are deficiently expressed in vivo. Most therapeutic proteins are incapable of efficiently permeating the cell membrane. In this study, we have employed CRISPR/Cas9 gene editing tool combined with single-stranded template to edit CTG of PTEN-long to ATG in the genome. Two guide RNAs close to CTG site were found to have similar efficiency in driving PTEN-long expression. Furthermore, we detected PTEN-long expression in transfected whole-cell lysate and in concentrated culture media in Western blot. Interestingly, the culture media of PTEN-long expression can reduce Akt phosphorylation level and repress U87 cell proliferation compared to wild-type U87 or control media. Taken together, PTEN-long driven by CRISPR/Cas9 imports and exports cells and represses nearby cell proliferation, indicating the PTEN-long generated by CRISPR/Cas9 has potential to be an alternative strategy for PTEN gene therapy.
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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".