Characterization of Rationally Designed CRISPR/Cas9-Based DNA Methyltransferases with Distinct Methyltransferase and Gene Silencing Activities in Human Cell Lines and Primary Human T Cells
Bibliographic record
Abstract
Nuclease-deactivated Cas (dCas) proteins can be used to recruit epigenetic effectors, and this class of epigenetic editing technologies has revolutionized the ability to synthetically control the mammalian epigenome and transcriptome. DNA methylation is one of the most important and well-characterized epigenetic modifications in mammals, and while many different forms of dCas-based DNA methyltransferases (dCas-DNMTs) have been developed for programmable DNA methylation, these tools are frequently poorly tolerated and/or lowly expressed in mammalian cell types. Further, the use of dCas-DNMTs has largely been restricted to cell lines, which limits mechanistic insights in karyotypically normal contexts and hampers translational utility in the longer term. Here, we extend previous insights into the rational design of the catalytic core of the mammalian DNMT3A methyltransferase and test three dCas9-DNMT3A/3L variants across different human cell lines and in primary donor-derived human T cells. We find that mutations within the catalytic core of DNMT3A stabilize the expression of dCas9-DNMT3A/3L fusion proteins in Jurkat T cells without sacrificing DNA methylation or gene-silencing performance. We also show that these rationally engineered mutations in DNMT3A alter DNA methylation profiles at loci targeted with dCas9-DNMT3A/3L in cell lines and donor-derived human T cells. Finally, we leverage the transcriptionally repressive effects of dCas9-DNMT3A/3L variants to functionally link the expression of a key immunomodulatory transcription factor to cytokine secretion in donor-derived T cells. Overall, our work expands the synthetic biology toolkit for epigenetic editing and provides a roadmap for the use of engineered dCas-based DNMTs in primary mammalian cell types.
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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 | 0.001 |
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".