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Record W4405442758 · doi:10.3390/cells13242074

MicroRNA, Myostatin, and Metabolic Rate Depression: Skeletal Muscle Atrophy Resistance in Hibernating Myotis lucifugus

2024· article· en· W4405442758 on OpenAlexafffund
W. Aline Ingelson‐Filpula, Sarah A. Breedon, Kenneth B. Storey

Bibliographic record

VenueCells · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBat Biology and Ecology Studies
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMyotis lucifugusBiologyDownregulation and upregulationMyostatinmicroRNASkeletal muscleCell biologyKEGGHibernation (computing)GeneEndocrinologyGene expressionGeneticsTranscriptomeEcology

Abstract

fetched live from OpenAlex

Little brown bats (Myotis lucifugus) cluster in hibernacula sites over winter, in which they use metabolic rate depression (MRD) to facilitate entrance and exit of hibernation. This study used small RNA sequencing and bioinformatic analyses to identify differentially regulated microRNAs (miRNAs) and to predict their downstream effects on Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms in the skeletal muscle of torpid M. lucifugus as compared to euthermic controls. We observed a subset of ten miRNAs whose expression changed during hibernation, with predicted functional roles linked to cell cycle processes, downregulation of protein degradation via ubiquitin-mediated proteolysis, downregulation of signaling pathways, including MAPK, p53, mTOR, and TGFβ, and downregulation of cytoskeletal and vesicle trafficking terms. Taken together, our results indicate miRNA regulation corresponding to both widely utilized MRD survival strategies, as well as more hibernation- and tissue-specific roles in M. lucifugus, including skeletal muscle atrophy resistance via myostatin inhibition and insulin signaling suppression.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.815
Threshold uncertainty score0.195

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations2
Published2024
Admission routes2
Has abstractyes

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