Front Line Antioxidant Defenses in the Freeze Tolerant Wood Frog, Rana Sylvatica: An In-Depth Analysis of Mechanisms of Enzyme Regulation
Bibliographic record
Abstract
The wood frog, Rana sylvatica, is one of few species that can survive whole-body freezing during overwintering.Frogs endure freezing of up to 70% of their total body water, and demonstrate a complete lack of respiration, heart beat and brain activity.Freezing imposes multiple stresses including anoxia/ischemia, cellular dehydration when water is lost to extracellular ice masses, wide temperature changes, and potential physical damage by ice.One crucial adaptation for freezing survival is well-developed antioxidant defenses to protect tissues from abiotic stress while frozen and deal with rapid changes in the generation of reactive oxygen species associated with anoxia and reoxygenation over freeze/thaw cycles.This thesis explores the properties and regulation of key antioxidant enzymes, purified via novel schemes, from frog muscle -both Cu/Zn-and Mn-dependent isoforms of superoxide dismutase (SOD), glutathione reductase (GR), and catalase (CAT).The studies show that changes in activity, stability, and substrate affinity of antioxidant enzymes during the frozen state may be significant preparatory mechanisms employed by R. sylvatica to support the transition from frozen to thawed states and deal effectively with oxidative stress accompanying reperfusion.Moreover, reversible protein phosphorylation plays a central role in regulating the activity of these enzymes to suit physiological needs throughout freeze-thaw cycles.For example, CuZnSOD from muscle of frozen frogs showed a significantly higher V max compared to the control enzyme.Muscle MnSOD from frozen frogs showed a significantly lower K m for O 2 -, higher phosphorylation, and increased enzyme stability compared to control MnSOD.GR from had the pleasure of knowing and learning from.I would specifically like to thank Ken Storey for taking a chance on a student interested in "functional proteins."I thank you for the opportunity you afforded me, the enthusiasm you instilled in me, and for introducing me to the wonders of the comparative world of science.I cannot thank you enough for my time under your guidance.I offer a special thanks to Jan Storey for her fascinating discussions and endless editorial support, not only for myself, but the entire Storey lab.Unfortunately I have been in the Storey lab for far too long to list all those that have guided, assisted, and simply kept me sane throughout it all.However, I would like to give a special thanks to Kyle Bigger, Ryan Bell and Ben Lant.You have had an immeasurable impact on my scientific career thus far, and I hope to continue to learn, wonder, and explore with you for years to come.Lastly I would like to thank my family.To my mother and father, Joan and Donald Dawson, you provided a home life so incredibly loving, that I am only now starting to understand how truly lucky I was to have you as parents.Jennifer, Debbie, Dan and Olivia, I thank you for the love and laugher you
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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) | 0.002 | 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".