A Genetically Encoded Assay System to Quantify <i>O</i>‐GlcNAc Transferase (OGT) Activity in Live Cells
Bibliographic record
Abstract
Abstract O‐GlcNAc transferase (OGT) catalyzes O‐GlcNAcylation of many nucleocytoplasmic proteins and plays important roles in regulating diverse cellular functions. Dysregulation of OGT is implicated in various diseases, including cancers and neurodegeneration. Despite its vital roles, little is known about how this enzyme is regulated within cells in part because no current assays directly report on its activity within cells. Here we describe a genetically encoded reporter of cellular OGT glycosyltransferase activity by exploiting the transferase‐dependent proteolytic activity of OGT on host cell factor‐1 (HCF‐1). The reporter comprises sites at which OGT cleaves HCF‐1, which are flanked by two different fluorescent proteins that are linked to either nuclear export or import sequences. OGT‐catalyzed cleavage of this construct leads to separation and independent localization of these two fluorescent proteins. By quantifying their nuclear and cytoplasmic distributions, OGT activity can be measured. We validated this OGT cellular activity reporter (CAR) system using known modulators of the O‐GlcNAc pathway and assessed the effects of several metabolites on OGT activity. Analyses of the dose‐ and time‐dependent effects of these OGT modulators illustrate the sensitivity and precision of this OGT‐CAR strategy. We envision this OGT‐CAR system will aid in discovering and characterizing modifiers of OGT activity.
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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 | 0.000 | 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) | 0.001 | 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".