Metabolic cross-talk allows labeling of O-linked β- <i>N</i> -acetylglucosamine-modified proteins via the <i>N</i> -acetylgalactosamine salvage pathway
Bibliographic record
Abstract
Hundreds of mammalian nuclear and cytoplasmic proteins are reversibly glycosylated by O-linked β-N-acetylglucosamine (O-GlcNAc) to regulate their function, localization, and stability. Despite its broad functional significance, the dynamic and posttranslational nature of O-GlcNAc signaling makes it challenging to study using traditional molecular and cell biological techniques alone. Here, we report that metabolic cross-talk between the N-acetylgalactosamine salvage and O-GlcNAcylation pathways can be exploited for the tagging and identification of O-GlcNAcylated proteins. We found that N-azidoacetylgalactosamine (GalNAz) is converted by endogenous mammalian biosynthetic enzymes to UDP-GalNAz and then epimerized to UDP-N-azidoacetylglucosamine (GlcNAz). O-GlcNAc transferase accepts UDP-GlcNAz as a nucleotide-sugar donor, appending an azidosugar onto its native substrates, which can then be detected by covalent labeling using azide-reactive chemical probes. In a proof-of-principle proteomics experiment, we used metabolic GalNAz labeling of human cells and a bioorthogonal chemical probe to affinity-purify and identify numerous O-GlcNAcylated proteins. Our work provides a blueprint for a wide variety of future chemical approaches to identify, visualize, and characterize dynamic O-GlcNAc signaling.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".