Coupling Proximity Biotinylation with Genomic Targeting to Characterize Locus-Specific Changes in Chromatin Environments
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Regulating gene expression involves significant changes in the chromatin environment at the locus level, especially at regulatory sequences. However, their modulation following pharmacological treatments or pathological conditions remain mostly undetermined. Here, we report versatile locus-specific proteomics tools to address this knowledge gap, which combine the targeting ability of the CRISPR/Cas9 system and the protein-labeling capability of the highly reactive biotin ligases TurboID (in CasTurbo) and UltraID (in CasUltra). CasTurbo and CasUltra enabled rapid chromatin protein labeling at repetitive sequences like centromeres and telomeres, as well as nonamplified genes. We applied CasUltra to A375 melanoma cell lines to decipher the protein environment of the MYC promoter and characterize the molecular effects of the bromodomain inhibitor JQ1, which targets bromodomain and extra-terminal (BET) proteins that regulate MYC expression. We quantified the consequences of BET protein displacement from the MYC promoter and found that it was associated with a considerable reorganization of the chromatin composition. Additionally, BET protein retention at the MYC promoter was consistent with a model of increased JQ1 resistance. Thus, through the combination of proximity biotinylation and CRISPR/Cas9 genomic targeting, CasTurbo and CasUltra have successfully demonstrated their utility in profiling the proteome associated with a genomic locus in living cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".