Dissecting OGT’s TPR domain to identify determinants of cellular function
Bibliographic record
Abstract
O-GlcNAc transferase (OGT) is an essential mammalian enzyme that glycosylates myriad intracellular proteins and cleaves the transcriptional coregulator Host Cell Factor 1 to regulate cell cycle processes. Via these catalytic activities as well as noncatalytic protein-protein interactions, OGT maintains cell homeostasis. OGT's tetratricopeptide repeat (TPR) domain is important in substrate recognition, but there is little information on how changing the TPR domain impacts its cellular functions. Here, we investigate how altering OGT's TPR domain impacts cell growth after the endogenous enzyme is deleted. We find that disrupting the TPR residues required for OGT dimerization leads to faster cell growth, whereas truncating the TPR domain slows cell growth. We also find that OGT requires eight of its 13 TPRs to sustain cell viability. OGT-8, like the nonviable shorter OGT variants, is mislocalized and has reduced Ser/Thr glycosylation activity; moreover, its interactions with most of wild-type OGT's binding partners are broadly attenuated. Therefore, although OGT's five N-terminal TPRs are not essential for cell viability, they are required for proper subcellular localization and for mediating many of OGT's protein-protein interactions. Because the viable OGT truncation variant we have identified preserves OGT's essential functions, it may facilitate their identification.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 | 0.001 |
| 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".