Arsenite Modulates Cardiac Substrate Preference by Translocation of GLUT4, But Not CD36, Independent of Mitogen-Activated Protein Kinase Signaling
Bibliographic record
Abstract
The protein thiol-modifying agent arsenite, a potent activator of stress signaling, was used to examine the involvement of MAPKs in the regulation of cardiac substrate uptake. Arsenite strongly induced p38 MAPK phosphorylation in isolated rat cardiac myocytes but also moderately enhanced phosphorylation of p42/44 ERK and p70 S6K. At the level of cardiomyocytic substrate use, arsenite enhanced glucose uptake dose dependently up to 5.1-fold but failed to stimulate long-chain fatty acid uptake. At the substrate transporter level, arsenite stimulated the translocation of GLUT4 to the sarcolemma but failed to recruit CD36 or FABPpm. Because arsenite did not influence the intrinsic activity of glucose transporters, GLUT4 translocation is entirely responsible for the selective increase in glucose uptake by arsenite. Moreover, the nonadditivity of arsenite-induced glucose uptake and insulin-induced glucose uptake indicates that arsenite recruits GLUT4 from insulin-responsive intracellular stores. Inhibitor studies with SB203580/SB202190, PD98059, and rapamycin indicate that activation of p38 MAPK, p42/44 ERK, and p70 S6K, respectively, are not involved in arsenite-induced glucose uptake. In addition, all these kinases do not play a role in regulation of cardiac glucose and long-chain fatty acid uptake by insulin. Hence, arsenite's selective stimulation of glucose uptake appears unrelated to its signaling actions, suggesting that arsenite acts via thiol modification of a putative intracellular protein target of arsenite within insulin-responsive GLUT4-containing stores. Because of arsenite's selective stimulation of cardiac glucose uptake, identification of this putative target of arsenite within the GLUT4-storage compartment may indicate whether it is a target for future strategies in prevention of diabetic cardiomyopathy.
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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".