Cleavage of Protein Kinase D After Acute Hypoinsulinemia Prevents Excessive Lipoprotein Lipase–Mediated Cardiac Triglyceride Accumulation
Bibliographic record
Abstract
OBJECTIVE: During hypoinsulinemia, when cardiac glucose utilization is impaired, the heart rapidly adapts to using more fatty acids. One means by which this is achieved is through lipoprotein lipase (LPL). We determined the mechanisms by which the heart regulates LPL after acute hypoinsulinemia. RESEARCH DESIGN AND METHODS: We used two different doses of streptozocin (55 [D-55] and 100 [D-100] mg/kg) to induce moderate and severe hypoinsulinemia, respectively, in rats. Isolated cardiomyocytes were also used for transfection or silencing of protein kinase D (PKD) and caspase-3. RESULTS: There was substantial increase in LPL in D-55 hearts, an effect that was absent in severely hypoinsulinemic D-100 animals. Measurement of PKD, a key element involved in increasing LPL, revealed that only D-100 hearts showed an increase in proteolysis of PKD, an effect that required activation of caspase-3 together with loss of 14-3-3zeta, a binding protein that protects enzymes against degradation. In vitro, phosphomimetic PKD colocalized with LPL in the trans-golgi. PKD, when mutated to prevent its cleavage by caspase-3 and silencing of caspase-3, was able to increase LPL activity. Using a caspase inhibitor (Z-DEVD) in D-100 animals, we effectively lowered caspase-3 activity, prevented PKD cleavage, and increased LPL vesicle formation and translocation to the vascular lumen. This increase in cardiac luminal LPL was associated with a striking accumulation of cardiac triglyceride in Z-DEVD-treated D-100 rats. CONCLUSIONS After severe hypoinsulinemia, activation of caspase-3 can restrict LPL translocation to the vascular lumen. When caspase-3 is inhibited, this compensatory response is lost, leading to lipid accumulation in the heart.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".