Production of Cloned Pigs with Targeted Attenuation of Gene Expression
Bibliographic record
Abstract
The objective of this study was to demonstrate that RNA interference (RNAi) and somatic cell nuclear transfer (SCNT) technologies can be used to attenuate the expression of specific genes in tissues of swine, a large animal species. Apolipoprotein E (apoE), a secreted glycoprotein known for its major role in lipid and lipoprotein metabolism and transport, was selected as the target gene for this study. Three synthetic small interfering RNAs (siRNA) targeting the porcine apoE mRNA were tested in porcine granulosa cells in primary culture and reduced apoE mRNA abundance ranging from 45-82% compared to control cells. The most effective sequence was selected for cloning into a short hairpin RNA (shRNA) expression vector under the control of RNA polymerase III (U6) promoter. Stably transfected fetal porcine fibroblast cells were generated and used to produce embryos with in vitro matured porcine oocytes, which were then transferred into the uterus of surrogate gilts. Seven live and one stillborn piglet were born from three gilts that became pregnant. Integration of the shRNA expression vector into the genome of clone piglets was confirmed by PCR and expression of the GFP transgene linked to the expression vector. Analysis showed that apoE protein levels in the liver and plasma of the clone pigs bearing the shRNA expression vector targeting the apoE mRNA was significantly reduced compared to control pigs cloned from non-transfected fibroblasts of the same cell line. These results demonstrate the feasibility of applying RNAi and SCNT technologies for introducing stable genetic modifications in somatic cells for eventual attenuation of gene expression in vivo in large animal species.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".