Unraveling the functional role of DNA methylation using targeted DNA demethylation by steric blockage of DNA methyltransferase with CRISPR/dCas9
Bibliographic record
Abstract
Abstract Although associations between DNA methylation and gene expression were established four decades ago, the causal role of DNA methylation in gene expression remains unresolved. Different strategies to address this question were developed; however, all are confounded and fail to disentangle cause and effect. We developed here a highly effective new method using only deltaCas9(dCas9):gRNA site-specific targeting to physically block DNA methylation at specific targets in the absence of a confounding flexibly-tethered enzymatic activity, enabling examination of the role of DNA methylation per se in living cells. We show that the extensive induction of gene expression achieved by TET/dCas9-based targeting vectors is confounded by DNA methylation-independent activities, inflating the role of DNA methylation in the promoter region. Using our new method, we show that in several inducible promoters, the main effect of DNA methylation is silencing basal promoter activity. Thus, the effect of demethylation of the promoter region in these genes is small, while induction of gene expression by different inducers is large and DNA methylation independent. In contrast, targeting demethylation to the pathologically silenced FMR1 gene targets robust induction of gene expression. We also found that standard CRISPR/Cas9 knockout generates a broad unmethylated region around the deletion, which might confound interpretation of CRISPR/Cas9 gene depletion studies. In summary, this new method could be used to reveal the true extent, nature, and diverse contribution to gene regulation of DNA methylation at different regions.
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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) | 0.001 | 0.000 |
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".