Mouse strain differences in metabolic fluxes and function of ex vivo working hearts
Bibliographic record
Abstract
In mice, genetic background is known to influence various parameters, including cardiac function. Its impact on cardiac energy substrate metabolism-a factor known to be closely related to function and contributes to disease development-is, however, unclear. This was examined in this study. In commonly used control mouse substrains SJL/JCrNTac, 129S6/SvEvTac, C57Bl/6J, and C57Bl/6NCrl, we assessed the functional and metabolic phenotypes of 3-mo-old working mouse hearts perfused ex vivo with physiological concentrations of (13)C-labeled carbohydrates (CHO) and a fatty acid (FA). Marked variations in various functional and metabolic flux parameters were observed among all mouse substrains, although the pattern observed differed for these parameters. For example, among all strains, C57Bl/6NCrl hearts had a greater cardiac output (+1.7-fold vs. SJL/JCrNTac and C57Bl/6J; P < 0.05), whereas at the metabolic level, 129S6/SvEvTac hearts stood out by displaying (vs. all 3 strains) a striking shift from exogenous FA (~-3.5-fold) to CHO oxidation as well as increased glycolysis (+1.7-fold) and FA incorporation into triglycerides (+2-fold). Correlation analyses revealed, however, specific linkages between 1) glycolysis, FA oxidation, and pyruvate metabolism and 2) cardiac work, oxygen consumption with heart rate, respectively. This implies that any genetically determined factors affecting a given metabolic flux parameter may impact on the associated functional parameters. Our results emphasize the importance of selecting the appropriate control strain for cardiac metabolic studies using transgenic mice, a factor that has often been neglected. Understanding the molecular mechanisms underlying the diversity of strain-specific cardiac metabolic and functional profiles, particularly the 129S6/SvEvTac, may ultimately disclose new specific metabolic targets for interventions in heart disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".