A simple approach for multi-targeted shRNA-mediated inducible knockdowns using Sleeping Beauty vectors
Bibliographic record
Abstract
shRNA expression is an established technique to transiently or permanently deplete cells of a particular mRNA/protein. In functional analyses of oncogenic pathways it can thus be used as an alternative to pharmacologic inhibitors, or as a means to address otherwise undruggable targets. Here we describe and functionally validate a simple reiterative cloning system to generate concatenated multi-shRNA expression plasmids. The multi-shRNA expression cassette can eventually be subcloned into any suitably designed vector for the stable transfection of cells, here tested with derivatives of the Sleeping Beauty transposon vector for stable transfection of multiple myeloma cell lines at the lowest biosafety level. We finally test inducible versions of such multi-cassette knockdown vectors and show their efficacy for the induced concerted knockdown of all four components of the MEK/MAPK-module in the Ras/MAPK pathway. The described vector system(s) should be useful for functional knockdown analyses in a wide array of cell line models.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".