Protein Kinase D Is a Key Regulator of Cardiomyocyte Lipoprotein Lipase Secretion After Diabetes
Bibliographic record
Abstract
The diabetic heart switches to exclusively using fatty acid (FA) for energy supply and does so by multiple mechanisms including hydrolysis of lipoproteins by lipoprotein lipase (LPL) positioned at the vascular lumen. We determined the mechanism that leads to an increase in LPL after diabetes. Diazoxide (DZ), an agent that decreases insulin secretion and causes hyperglycemia, induced a substantial increase in LPL activity at the vascular lumen. This increase in LPL paralleled a robust phosphorylation of Hsp25, decreasing its association with PKCdelta, allowing this protein kinase to phosphorylate and activate protein kinase D (PKD), an important kinase that regulates fission of vesicles from the golgi membrane. Rottlerin, a PKCdelta inhibitor, prevented PKD phosphorylation and the subsequent increase in LPL. Incubating control myocytes with high glucose and palmitic acid (Glu+PA) also increased the phosphorylation of Hsp25, PKCdelta, and PKD in a pattern similar to that seen with diabetes, in addition to augmenting LPL activity. In myocytes in which PKD was silenced or a mutant form of PKCdelta was expressed, high Glu+PA were incapable of increasing LPL. Moreover, silencing of cardiomyocyte Hsp25 allowed phorbol 12-myristate 13-acetate to elicit a significant phosphorylation of PKCdelta, an appreciable association between PKCdelta and PKD, and a vigorous activation of PKD. As these cells also demonstrated an additional increase in LPL, our data imply that after diabetes, PKD control of LPL requires dissociation of Hsp25 from PKCdelta, association between PKCdelta and PKD, and vesicle fission. Results from this study could help in restricting cardiac LPL translocation, leading to strategies that overcome contractile dysfunction after diabetes.
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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) | 0.001 | 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".