Hibernation is super complex: distribution, dynamics, and stability of electron transport system supercomplexes in <i>Ictidomys tridecemlineatus</i>
Bibliographic record
Abstract
Complexes of the electron transport system can associate with each other to form supercomplexes (SCs) within mitochondrial membranes, perhaps increasing respiratory capacity or reducing reactive oxygen species production. In this study, we determined the abundance, composition, and stability of SCs in a mammalian hibernator, in which both whole animal and mitochondrial metabolism change greatly throughout winter. We isolated mitochondria from thirteen-lined ground squirrels ( Ictidomys tridecemlineatus) in different hibernation states, as well as from rats ( Rattus norvegicus). We extracted mitochondrial proteins using two nonionic detergents of different strengths and quantified SC abundance using two-dimensional gel electrophoresis and immunoblotting. Rat heart and liver had fewer SCs than ground squirrels. Within ground squirrels, SCs are dynamic, changing among hibernation states within a matter of hours. In brown adipose tissue, Complex III composition in different SCs differed between the torpid and interbout euthermic phase of a hibernation bout. In heart and liver, complex III composition changed between winter and summer. We also evaluated the stability of liver SCs using a stronger detergent and found that the stability of SCs differed; torpor SCs were more stable than the SCs of ground squirrels in other states and rats. This study is the first report of SC changes during hibernation and the first to demonstrate their dynamics on a short timescale.
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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".