Diacylglycerol‐Activated Hmunc13 Serves as an Effector of the GTPase Rab34
Bibliographic record
Abstract
Hmunc13 is a cytosolic diacylglycerol (DAG)-binding protein, which is upregulated in renal cortical tubule and mesangial cells by hyperglycemia. In response to DAG activation, hmunc13 translocates to the Golgi. To investigate how this may relate to its function, we used a bacterial two-hybrid screen to look for hmunc13-interacting proteins. Full-length Rab34 was specifically isolated from a human kidney cDNA library. Co-expression of the two proteins confirmed Rab34 as a Golgi-associated protein, which was immunoprecipitated from cell lysates by hmunc13. Glutathione S-transferase fusion proteins of WT, Q111L (GTP bound), and T66N (GDP bound) mutants were created, and their GTP-binding activity verified by radioactive overlay assay. Binding of hmunc13 was observed with Q111L, barely detectable with T66N and enhanced with Rab34WT loaded with GTPgammaS compared with GDP loaded. Deletion of munc homolgy domain (MHD)-2, eliminated the hmunc13/Rab34 interaction. The Q111L mutant localized to the Golgi apparatus, but T66N was cytosolic. Localization of both mutants and Rab34WT was unchanged by DAG activation. The data suggest that DAG activation of hmunc13 causes it to be translocated to the Golgi, where it binds to GTP-bound Rab34 via MHD-2. Because Rab34 is known to regulate intracellular lysosome positioning, we propose that hmunc13 serves as an effector of Rab34, mediating lysosome-Golgi trafficking.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".