Genome and epigenome engineering CRISPR toolkit for <i>in vivo</i> modulation of <i>cis</i> -regulatory interactions and gene expression in the chicken embryo
Bibliographic record
Abstract
ABSTRACT CRISPR/Cas9 genome engineering has revolutionised all aspects of biological research, with epigenome engineering transforming gene regulation studies. Here, we present an optimised, adaptable toolkit enabling genome and epigenome engineering in the chicken embryo, and demonstrate its utility by probing gene regulatory interactions mediated by neural crest enhancers. First, we optimise novel efficient guide-RNA mini expression vectors utilising chick U6 promoters, provide a strategy for rapid somatic gene knockout and establish a protocol for evaluation of mutational penetrance by targeted next-generation sequencing. We show that CRISPR/Cas9-mediated disruption of transcription factors causes a reduction in their cognate enhancer-driven reporter activity. Next, we assess endogenous enhancer function using both enhancer deletion and nuclease-deficient Cas9 (dCas9) effector fusions to modulate enhancer chromatin landscape, thus providing the first report of epigenome engineering in a developing embryo. Finally, we use the synergistic activation mediator (SAM) system to activate an endogenous target promoter. The novel genome and epigenome engineering toolkit developed here enables manipulation of endogenous gene expression and enhancer activity in chicken embryos, facilitating high-resolution analysis of gene regulatory interactions in vivo.
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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 | 0.001 |
| 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".