The involvement of mRNA processing factors TIA-1, TIAR, and PABP-1 during mammalian hibernation
Bibliographic record
Abstract
Mammalian hibernators survive low body temperatures, ischemia-reperfusion, and restricted nutritional resources via global reductions in energy-expensive cellular processes and selective increases in stress pathways. Consequently, studies that analyze hibernation uncover mechanisms which balance metabolism and support survival by enhancing stress tolerance. We hypothesized processing factors that influence messenger ribonucleic acid (mRNA) maturation and translation may play significant roles in hibernation. We characterized the amino acid sequences of three RNA processing proteins (T cell intracellular antigen 1 (TIA-1), TIA1-related (TIAR), and poly(A)-binding proteins (PABP-1)) from thirteen-lined ground squirrels (Ictidomys tridecemlineatus), which all displayed a high degree of sequence identity with other mammals. Alternate Tia-1 and TiaR gene variants were found in the liver with higher expression of isoform b versus a in both cases. The localization of RNA-binding proteins to subnuclear structures was assessed by immunohistochemistry and confirmed by subcellular fractionation; TIA-1 was identified as a major component of subnuclear structures with up to a sevenfold increase in relative protein levels in the nucleus during hibernation. By contrast, there was no significant difference in the relative protein levels of TIARa/TIARb in the nucleus, and a decrease was observed for TIAR isoforms in cytoplasmic fractions of torpid animals. Finally, we used solubility tests to analyze the formation of reversible aggregates that are associated with TIA-1/R function during stress; a shift towards the soluble fraction (TIA-1a, TIA-1b) was observed during hibernation suggesting enhanced protein aggregation was not present during torpor. The present study identifies novel posttranscriptional regulatory mechanisms that may play a role in reducing translational rates and/or mRNA processing under unfavorable environmental conditions.
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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".