MétaCan
Menu
← Back to cohort

P53 Regulation of Cellular Homeostasis in Skeletal Muscle with Training: An Evaluation of Two Mouse Models

2017· article· en· W4389017363 on OpenAlexaffabout
Kaitlyn Beyfuss, Avigail T. Erlich, David A. Hood

Bibliographic record

VenueThe FASEB Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsYork University
Fundersnot available
KeywordsMitochondrial biogenesisSkeletal muscleOxidative stressMitochondrionHomeostasisAutophagyBiologyCytosolCell biologyConditional gene knockoutKnockout mouseEndocrinologyInternal medicineApoptosisPhenotypeMedicineBiochemistryGeneEnzyme

Abstract

fetched live from OpenAlex

The tumour suppressor protein p53, well‐known for its ability to mediate oxidative stress, plays an essential role in maintaining cellular homeostasis. This is accomplished through enhanced antioxidant enzyme transcription and cellular senescence with low to moderate levels of stress, or through increased apoptosis and the induction of cell death with greater levels of stress. However, recent literature has dictated a novel role for p53, dependent on its subcellular localization. With certain stressors such as exercise, phosphorylation of p53 at specific residues allows for enhanced mitochondrial localization whereby it functions to maintain mitochondrial DNA integrity, thus leading to enhanced oxidative metabolism. It is evident that understanding the effects of chronic exercise on p53 localization, and the regulation of p53‐dependent signaling pathways (autophagy, antioxidant capacity, and mitochondrial biogenesis) is essential for a full comprehension of how exercise affects muscle health. To study this further, two mouse models were compared to assess the role of p53 in mediating muscle phenotype, mitochondrial function and endurance performance. Whole body (WB) C57BL/6J p53 wild‐type (WT) and knockout (KO) mice, as well as MCK‐driven p53 muscle‐specific (MS) KO were used. All mice underwent a 6‐week progressive treadmill training protocol with concomitant pre‐ and post‐endurance stress tests to observe training adaptations. Phenotypic analyses confirmed matched body weight in KO and WT mice within both mouse models. Slight elevations in body mass were observed within MS mice relative to WB mice independent of genotype, with no observable effect of training. However, training did reduce epididymal fat mass in both groups. Tissue‐specific analysis revealed increased gastrocnemius mass corrected for body mass in WB KO relative to WT mice, however this phenotypic adaptation was not apparent in the MS mice. COX enzyme activity, a predictor of mitochondrial content, increased in WB mice, but this adaptation was not apparent MS mice. To delineate the effects of p53 on aerobic metabolism, respiration analyses confirmed increased state 3 respiration in WB WT mice with training, however this trend was not observed in the WB KO group, or in MS KO mice. Additionally, reduced state 3 respiration was observed in the WB mice relative to the MS mice. Mitochondrial reactive oxygen species (ROS) emission was elevated in both WB and MS mice, but training restored ROS levels to normal. Pre‐training exercise performance was similar between the WB WT and KO groups, as well as the MS WT mice, however MS KO distances were significantly decreased, concomitant with moderately elevated lactate levels. However, training effectively restored MS KO running distances to normal levels, although this was less pronounced than seen in the WB KO mice. Thus, whole body (WB) and muscle specific (MS) p53 knockout animals display largely similar phenotypic characteristics and adaptations to training, and provide equally useful experimental models for the investigation of the role of p53 in muscle function and metabolism. Support or Funding Information This work is supported by the Natural Science and Engineering Research Council (NSERC) of Canada.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.105
GPT teacher head0.331
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes2
Has abstractyes

Explore more

Same venueThe FASEB Journal→Same topicDiet and metabolism studies→French-language works237,207→