Primate Torpor: Regulation of Stress-Activated Protein Kinases during Daily Torpor in the Gray Mouse Lemur, <i>Microcebus Murinus</i>
Bibliographic record
Abstract
Very few selected species of primates are known to be capable of entering torpor. This exciting discovery means that the ability to enter a natural state of dormancy is an ancestral trait among primates and, in phylogenetic terms, is very close to the human lineage. To explore the regulatory mechanisms that underlie primate torpor, we analyzed signal transduction cascades to discover those involved in coordinating tissue responses during torpor. The responses of mitogen-activated protein kinase (MAPK) family members to primate torpor were compared in six organs of control (aroused) versus torpid gray mouse lemurs, Microcebus murinus. The proteins examined include extracellular signal-regulated kinases (ERKs), c-jun NH2-terminal kinases (JNKs), MAPK kinase (MEK), and p38, in addition to stress-related proteins p53 and heat shock protein 27 (HSP27). The activation of specific MAPK signal transduction pathways may provide a mechanism to regulate the expression of torpor-responsive genes or the regulation of selected downstream cellular processes. In response to torpor, each MAPK subfamily responded differently during torpor and each showed organ-specific patterns of response. For example, skeletal muscle displayed elevated relative phosphorylation of ERK1/2 during torpor. Interestingly, adipose tissues showed the highest degree of MAPK activation. Brown adipose tissue displayed an activation of ERK1/2 and p38, whereas white adipose tissue showed activation of ERK1/2, p38, MEK, and JNK during torpor. Importantly, both adipose tissues possess specialized functions that are critical for torpor, with brown adipose required for non-shivering thermogenesis and white adipose utilized as the primary source of lipid fuel for torpor. Overall, these data indicate crucial roles of MAPKs in the regulation of primate organs during torpor.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".