Enhanced snoMEN Vectors Facilitate Establishment of GFP–HIF-1α Protein Replacement Human Cell Lines
Bibliographic record
Abstract
The snoMEN (snoRNA Modulator of gene ExpressioN) vector technology was developed from a human box C/D snoRNA, HBII-180C, which contains an internal sequence that can be manipulated to make it complementary to RNA targets, allowing knock-down of targeted genes. Here we have screened additional human nucleolar snoRNAs and assessed their application for gene specific knock-downs to improve the efficiency of snoMEN vectors. We identify and characterise a new snoMEN vector, termed 47snoMEN, that is derived from box C/D snoRNA U47, demonstrating its use for knock-down of both endogenous cellular proteins and G/YFP-fusion proteins. Using multiplex 47snoMEM vectors that co-express multiple 47snoMEN in a single transcript, each of which can target different sites in the same mRNA, we document >3-fold increase in knock-down efficiency when compared with the original HBII-180C based snoMEN. The multiplex 47snoMEM vector allowed the construction of human protein replacement cell lines with improved efficiency, including the establishment of novel GFP-HIF-1α replacement cells. Quantitative mass spectrometry analysis confirmed the enhanced efficiency and specificity of protein replacement using the 47snoMEN-PR vectors. The 47snoMEN vectors expand the potential applications for snoMEN technology in gene expression studies, target validation and 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) | 0.002 | 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".