Modulation of Gene Expression in Key Survival Pathways During Daily Torpor in the Gray Mouse Lemur, <i>Microcebus Murinus</i>
Bibliographic record
Abstract
A variety of mammals employ torpor as an energy-saving strategy in environments of marginal or severe stress either on a daily basis during their inactive period or on a seasonal basis during prolonged multi-day hibernation. Recently, a few Madagascar lemur species have been identified as the only primates that exhibit torpor; one of these is the gray mouse lemur (Microcebus murinus). To explore the regulatory mechanisms that underlie daily torpor in a primate, we analyzed the expression of 28 selected genes that represent crucial survival pathways known to be involved in squirrel and bat hibernation. Array-based real-time PCR was used to compare gene expression in control (aroused) versus torpid lemurs in five tissues including the liver, kidney, skeletal muscle, heart, and brown adipose tissue. Significant differences in gene expression during torpor were revealed among genes involved in glycolysis, fatty acid metabolism, antioxidant defense, apoptosis, hypoxia signaling, and protein protection. The results showed upregulation of select genes primarily in liver and brown adipose tissue. For instance, both tissues showed elevated gene expression of peroxisome proliferator activated receptor gamma (ppargc), ferritin (fth1), and protein chaperones during torpor. Overall, the data show that the expression of only a few genes changed during lemur daily torpor, as compared with the broader expression changes reported for hibernation in ground squirrels. These results provide an indication that the alterations in gene expression required for torpor in lemurs are not as extensive as those needed for winter hibernation in squirrel models. However, identification of crucial genes with altered expression that support lemur torpor provides key targets to be explored and manipulated toward a goal of translational applications of inducible torpor as a treatment option in human biomedicine.
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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".