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Record W2104300763 · doi:10.1242/jeb.106369

Regulation of hypometabolism: insights into epigenetic controls

2015· review· en· W2104300763 on OpenAlexaff
Kenneth B. Storey

Bibliographic record

VenueJournal of Experimental Biology · 2015
Typereview
Languageen
FieldEnvironmental Science
TopicPhysiological and biochemical adaptations
Canadian institutionsCarleton University
Fundersnot available
KeywordsBiologyEpigeneticsHistoneHibernation (computing)AestivationRegulation of gene expressionGene expressionGene silencingAcetylationTranslation (biology)Transcriptional regulationGeneProtein turnoverPhosphorylationCell biologyGeneticsMessenger RNAProtein biosynthesis

Abstract

fetched live from OpenAlex

For many animals, survival of severe environmental stress (e.g. to extremes of heat or cold, drought, oxygen limitation, food deprivation) is aided by entry into a hypometabolic state. Strong depression of metabolic rate, often to only 1-20% of normal resting rate, is a core survival strategy of multiple forms of hypometabolism across the animal kingdom, including hibernation, anaerobiosis, aestivation and freeze tolerance. Global biochemical controls are needed to suppress and reprioritize energy use; one such well-studied control is reversible protein phosphorylation. Recently, we turned our attention to the idea that mechanisms previously associated mainly with epigenetic regulation can also contribute to reversible suppression of gene expression in hypometabolic states. Indeed, situations as diverse as mammalian hibernation and turtle anoxia tolerance show coordinated changes in histone post-translational modifications (acetylation, phosphorylation) and activities of histone deacetylases, consistent with their use as mechanisms for suppressing gene expression during hypometabolism. Other potential mechanisms of gene silencing in hypometabolic states include altered expression of miRNAs that can provide post-transcriptional suppression of mRNA translation and the formation of ribonuclear protein bodies in the nucleus and cytoplasm to allow storage of mRNA transcripts until animals rouse themselves again. Furthermore, mechanisms first identified in epigenetic regulation (e.g. protein acetylation) are now proving to apply to many central metabolic enzymes (e.g. lactate dehydrogenase), suggesting a new layer of regulatory control that can contribute to coordinating the depression of metabolic rate.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.329
Teacher spread0.280 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations175
Published2015
Admission routes1
Has abstractyes

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