Roles of Inflammatory Signaling and MicroRNA in the Adipose Stress Response of Hibernating Ictidomys Tridecemlineatus
Bibliographic record
Abstract
Hibernating ground squirrels have an interesting ability to avoid organ dysfunction despite months of obesity, starvation, and low body temperature.However, pro-inflammatory signaling and conserved miRNA expression patterns have yet to be investigated in white and brown adipose tissues (WAT, BAT), organs with roles in fat storage and heat production, respectively.The inflammasome was activated in BAT during torpor and arousal relative to the control, as evidenced by increased inflammasome priming, elevated protein levels of NLRP3, AIM2, cleaved gasdermin D and IL-18, as well as increased caspase-1 activity.By contrast, caspase-1 activity, the ultimate indicator of inflammasome activation, was decreased during torpor and arousal in WAT relative to the euthermic control.Pro-inflammatory cytokines, matrix metalloproteinases (MMPs), and their inhibitors were also investigated to determine if cytokines and tissue remodeling proteins could be important in the stress response in hibernator adipose tissue.An increase in IL-1α during torpor in BAT furthered the idea that BAT may use proinflammatory pathways as part of the response to cell stress.By contrast, the only change in WAT was a decrease in the total protein levels of MMP2, suggesting tissue remodeling may not be important in the maintenance of WAT homeostasis.Finally, conserved BAT and WAT miRNAs were analyzed.There was an association between the BAT miRNA expression profile and condition (control or torpor), but no association between the two variables in WAT.Consistently, fewer miRNAs were differentially expressed in WAT than BAT, with more being downregulated than upregulated.As expected, microRNAs were predicted to inhibit energy expensive pathways during torpor in both tissues, suggesting an important role for non-coding RNAs in the regulation of metabolic rate suppression.Unexpectedly, KEGG pathway analysis suggested miRNAs were less likely to target pathways involved in damage sensing and wound repair in BAT, and DNA damage repair in WAT.Together, the data in this thesis suggest an iii upregulation of stress sensing and response in BAT in torpid and arousing ground squirrels through the regulation of inflammasomes, inflammatory signaling, and miRNA expression.By contrast, DNA repair may be increased in WAT but generally, pro-inflammatory pathways were suppressed.
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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".