A <i>piggyBac</i> transposon‐ and gateway‐enhanced system for efficient BAC transgenesis
Bibliographic record
Abstract
Background: Bacterial artificial chromosomes (BACs) have become increasingly popular vectors for making transgenic mice, as they are able to carry large genomic DNA fragments that in many cases are needed to reproduce the endogenous gene expression pattern. However, the efficiency of BAC transgenesis is generally low, and gene transfer to BAC vectors by recombination‐mediated engineering (recombineering) is time‐consuming and technically demanding. Results and Conclusions: We present an enhanced system, comprising a BAC vector retrofitted with piggyBac DNA transposon elements and attL (Gateway) docking sites, that obviates these problems. Using this system, a gene‐of‐interest (such as a reporter gene) is transferred to the vector in a one‐step in vitro reaction, and piggyBac transposition mediates transgene integration at high efficiency when microinjected into mouse zygotes with piggyBac transposase mRNA. We establish proof‐of‐principle for this system using a Wilms tumour‐1 (Wt1) BAC to drive expression of an mCherry‐2A‐EGFP (RG) reporter gene, which yielded transgenic mice at a frequency of 33%, and recapitulated endogenous WT1 expression in developing gonads, kidneys and heart. The system we describe is applicable to any BAC transgenesis strategy. Developmental Dynamics 243:1086–1094, 2014. © 2014 Wiley Periodicals, Inc.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.013 |
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".