Expression Profiling and Structural Characterization of MicroRNAs in Adipose Tissues of Hibernating Ground Squirrels
Bibliographic record
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs that are important in regulating metabolic stress. In this study, we determined the expression and structural characteristics of 20 miRNAs in brown (BAT) and white adipose tissue (WAT) during torpor in thirteen-lined ground squirrels. Using a modified stem-loop technique, we found that during torpor, expression of six miRNAs including let-7a, let-7b, miR-107, miR-150, miR-222 and miR-31 was significantly downregulated in WAT (P<0.05), which was 16%-54% of euthermic non-torpid control squirrels, whereas expression of three miRNAs including miR-143, miR-200a and miR-519d was found to be upregulated by 1.32-2.34-fold. Similarly, expression of more miRNAs was downregulated in BAT during torpor. We detected reduced expression of 6 miRNAs including miR-103a, miR-107, miR-125b, miR-21, miR-221 and miR-31 (48%-70% of control), while only expression of miR-138 was significantly upregulated (2.91±0.8-fold of the control, P<0.05). Interestingly, miRNAs found to be downregulated in WAT during torpor were similar to those dysregulated in obese humans for increased adipogenesis, whereas miRNAs with altered expression in BAT during torpor were linked to mitochondrial β-oxidation. miRPath target prediction analysis showed that miRNAs downregulated in both WAT and BAT were associated with the regulation of mitogen-activated protein kinase (MAPK) signaling, while the miRNAs upregulated in WAT were linked to transforming growth factor β (TGFβ) signaling. Compared to mouse sequences, no unique nucleotide substitutions within the stem-loop region were discovered for the associated pre-miRNAs for the miRNAs used in this study, suggesting no structure-influenced changes in pre-miRNA processing efficiency in the squirrel. As well, the expression of miRNA processing enzyme Dicer remained unchanged in both tissues during torpor. Overall, our findings suggest that changes of miRNA expression in adipose tissues may be linked to distinct biological roles in WAT and BAT during hibernation and may involve the regulation of signaling cascades.
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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) | 0.000 | 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".