A Chemical Genetic Method for Monitoring Genome-Wide Dynamics of <i>O</i>-GlcNAc Turnover on Chromatin-Associated Proteins
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Advances in DNA sequencing are enabling new experimental modalities for studying chromatin. One emerging area is to use high-throughput DNA sequencing to monitor dynamic changes occurring to chromatin. O -Linked N -acetylglucosamine ( O -GlcNAc) is a reversible protein modification found on many chromatin-associated proteins. The mechanisms by which O -GlcNAc regulates gene transcription are of high interest. Here we use DNA precipitation methods to enable monitoring time-dependent turnover of O -GlcNAc modified proteins associated with chromatin. Using an antibody-free chemical reporter strategy to map O -GlcNAc to the genome, we performed time course metabolic feeding experiments with wild-type Drosophila larvae alongside larvae lacking O -GlcNAc hydrolase (OGA), which are accordingly unable to remove O -GlcNAc. Analysis of resulting next-generation DNA sequencing data revealed that O -GlcNAc on chromatin-associated proteins at most genomic loci is processed with a half-life in hours. Notably, loss of OGA only increases this half-life by ∼3-fold. Interestingly, a small set of genomic loci are particularly sensitive to loss of OGA. In addition to these observations and new strategies to permit monitoring turnover of O -GlcNAc on chromatin, we also detail methods for coded blinding of samples alongside new normalization strategies to enable time-resolved, genome-wide analyses using chemical genetic methods. We envision these general methods will be applicable to diverse protein and nucleic acid modifications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".