Regulation and Modification of Peripheral Circadian Molecular Clocks in 13-Lined Ground Squirrels during Hibernation.
Bibliographic record
Abstract
During winter, hibernators are able to conserve energy during times of limited resources through the virtual cessation of energetically expensive processes that are thought to be intrinsic to the cell in homeostasis.During prolonged hibernation, these mammals, such as the 13-lined ground squirrel (Ictidomys tridecemlineatus), shut down the bulk of transcription and translation in order to preserve resources yet still require the expression of subsets of genes to assist with the challenges encountered during hibernation.Hibernators provide a unique opportunity for examining the dynamics of circadian clock activation in a system that requires the selection of groups of transcripts against a backdrop of suppressed cellular activity.This research shows that peripheral circadian clocks are regulated and have adapted to function in a tissue-specific manner that is congruent with the tissues functions during hibernation.In addition, substantial transcriptional and post-transcriptional machineries are required to endure deep torpor and low body temperature, including increased regulation over genomic activity by epigenetic enzymes.Both RNA adenosine and protein arginine methylation act to regulate activity within the circadian clock via epigenetic mechanisms and provide novel opportunities to uncover information about the post-translational modifications used during hibernation.RNA N6-methyladenosine (m6A) dynamics were maintained during hibernation and levels of m6A were increased on mRNA transcripts during torpor in liver.Responses by protein arginine methyltransferase (PRMT) enzymes were tissue-specific and within liver and white adipose, revealed responses that characterized metabolic reprogramming, whereas skeletal muscle PRMT activity was centered around transcriptional regulation.This research suggests that dynamic epigenetic I first, foremost, and most-respectfully want to thank Dr. Kenneth B. Storey and Janet Storey for their thankless, amazing, and continuous support and guidance throughout my graduate career.I have no doubt that without their help, I would not be in the position I am in today.My family, biological or otherwise: you have supported me through thick and thin and through the hardest of times no matter what stood in the way.I am infinitely grateful to you.An unluckier and much less fortunate version of myself would not have had the luck to have landed in such a great lab, surrounded by some of the greatest graduate and undergraduate students Carleton has known.I want to thank all the current members of the lab, especially those who contributed to the thoughts and discussions required to make this work a reality; Rasha, Hanane, Sam, Liam and Stuart -you guys rock!There were even some people who I wasn't able to spend an entire six(!) years with but still greatly impacted my graduate life and made the Storey lab a great place to work: Bryan, Michael, Christie, Sanoji, Zephanie, and Jessica -THANKS.I must of course also thank those people who were not involved in my daily life in the lab but still gave me a reason to cheer.As I embark on a career in law I am reminded that people like Jeff Sessions and Bob Mueller should get a shout-out for making his life a misery; BoJo for being a "conservative I would think about," and oh sure, Nancy Pelosi and the squad, I guess, for being awesome.vi
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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".