Gene Expression and Tracer-Based Metabolic Flux Analysis Reveals Tissue-Specific Metabolic Scaling <i>in vitro, ex vivo</i> , and <i>in vivo</i>
Bibliographic record
Abstract
Abstract Metabolic scaling, the inverse correlation of metabolic rates to body mass, has been appreciated for more than 80 years. Studies of metabolic scaling have almost exclusively been restricted to mathematical modeling of oxygen consumption. The possibility that other metabolic processes scale with body size has not been studied. To address this gap in knowledge, we employed a systems approach spanning from transcriptomics to in vitro and in vivo tracer-based flux. Gene expression in livers of five species spanning a 30,000-fold range in mass revealed differential expression of genes related to cytosolic and mitochondrial metabolic processes, in addition to detoxication of oxidative damage. This suggests that transcriptional scaling of damage control mechanisms accommodates increased oxidative metabolism in smaller species. To determine whether flux through key implicated metabolic pathways scaled, we applied stable isotope tracer methodology to study multiple cellular compartments, tissues, and species. Comparing mice and rats, we demonstrate that while scaling of metabolic fluxes is not observed in the cell-autonomous setting, it is present in liver slices and in vivo . Together, these data reveal that metabolic scaling extends beyond oxygen consumption to numerous other metabolic pathways, and is likely regulated at the level of gene expression and substrate supply.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".