Development and characterization of synthetic human antibodies for sensing cellular metabolic states
Bibliographic record
Abstract
Metabolic reprogramming of cancer is reflected in aberrant activity of the hexosamine biosynthetic pathway that produces UDP‐GlcNAc (a donor substrate used to form the O‐GlcNAc modification on proteins). Rather than acting as a switch that turns signaling pathways on or off, O‐GlcNAc should be considered as a “rheostat” that controls the intensity of intracellular signals according to the nutrient and stress conditions. There is evidence that some cell surface proteins contain O‐GlcNAc modification in their extracellular regions. However, there is a lack of understanding for the role of extracellular O‐GlcNAcylation in biological processes. Tuning the level of O‐GlcNAcylation is one way that tumor cells can adapt to varying nutrient and environmental conditions. We hypothesize that cancer cells can “sense” stress through the O‐GlcNAc pathway and, consequently, reprogram the cell surface and adapt to tougher conditions. Neo‐epitopes on the cell surface that emerge from tuning O‐GlcNAc pathway activity may be prime candidates as readouts that reliably report the status of metabolic reprogramming. These nutrient‐dependent cell surface epitopes may have functional roles in defining cancer cell identity. In order to advance understanding of metabolic reprogramming from the perspective of cell surface, we combined genetics and advanced protein engineering using phage‐displayed synthetic human antibody libraries to generate binders that respond to changes in the O‐GlcNAc rheostat. Our method for discovery of s urface and n utrient‐ a ssociated p rotein s ensors (SNAPS) has allowed us to identify multiple antibodies that can detect different cellular metabolic states.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".