A phosphoproteomic approach reveals that PKD3 controls PKA-mediated glucose and tyrosine metabolism
Bibliographic record
Abstract
Members of the protein kinase D (PKD) family (PKD1, 2, and 3) integrate hormonal and nutritional inputs to regulate complex cellular metabolism. Despite the fact that a number of functions have been annotated to particular PKDs, their molecular targets are relatively poorly explored. PKD3 promotes insulin sensitivity and suppresses lipogenesis in the liver of animals fed a high-fat diet. However, its substrates are largely unknown. Here we applied proteomic approaches to determine PKD3 targets. We identified more than 300 putative targets of PKD3. Furthermore, biochemical analysis revealed that PKD3 regulates cAMP-dependent PKA activity, a master regulator of the hepatic response to glucagon and fasting. PKA regulates glucose, lipid, and amino acid metabolism in the liver, by targeting key enzymes in the respective processes. Among them the PKA targets phenylalanine hydroxylase (PAH) catalyzes the conversion of phenylalanine to tyrosine. Consistently, we showed that PKD3 is activated by glucagon and promotes glucose and tyrosine levels in hepatocytes. Therefore, our data indicate that PKD3 might play a role in the hepatic response to glucagon.
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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 | 0.001 |
| 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".