Sweet Success: Metabolic Substrate Adaptations To Acute Hypoxia In The Naked Mole Rat (Heterocephalus Glaber)
Bibliographic record
Abstract
Naked mole rats are among the most hypoxia‐tolerant mammals identified. This tolerance is partially achieved through their ability to coordinately reduce metabolic rates by > 85% in acute hypoxia while staying warm and active in their hypoxic underground burrows. Our understanding of the physiological responses that enable their hypoxia‐tolerance is improving; however, the underlying molecular and biochemical mechanisms that support this remarkable energetic suppression are poorly understood. Of particular interest is a putative metabolic fuel substrate switch in hypoxia. Naked mole rats primarily metabolize lipids in normoxia, but carbohydrate metabolism may be favored in hypoxia because carbohydrate oxidation yields more energy per molecule of oxygen than does lipid oxidation. We hypothesized that naked mole rats switch to primarily glucose‐fueled metabolism in acute hypoxia and that the energetic needs of brain and heart are prioritized over those of other tissues. Furthermore, we predicted that heat production from brown adipose tissue, which is a glucose‐intensive process, would be turned off in hypoxia as an energy‐saving strategy. To test these hypotheses, we measured metabolic fuel usage and heat production in naked mole rats exposed to acute hypoxia (7% O 2 ) using non‐invasive in vivo imaging techniques, including infrared thermography, magnetic resonance imaging (MRI), and positron emission tomography (PET). We found that glucose metabolism is markedly upregulated in heart and brain during acute hypoxia, but decreased in liver. Furthermore, heat generation from brown adipose tissue is entirely turned off in hypoxia and the naked mole rat body temperature drops to near or below ambient temperature, indicating that all endogenous heat production ceases in hypoxia. We conclude that naked mole rats maximize the energy/oxygen ratio in their metabolic fuel substrate usage and prioritize feeding the energetic needs of brain and heart over all other metabolic processes in hypoxia. Support or Funding Information This study was supported by an NSERC discovery grant to MEP, a CIHR grant to AJS, and CRC grants to MEP and AJS. This abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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".