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Skeletal Muscle Health and Mitochondrial Function are Regulated By The Transcription Factor ATF5 During Aging

2025· article· en· W4411876154 on OpenAlexaffabout
Victoria C. Sanfrancesco, David A. Hood

Bibliographic record

VenuePhysiology · 2025
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsYork University
Fundersnot available
KeywordsSkeletal muscleActivating transcription factorTranscription factorMitochondrionBiologyCell biologyGeneticsAnatomyGene

Abstract

fetched live from OpenAlex

In skeletal muscle, the mitochondrial network is highly regulated by coordinated mitochondrial quality control (MQC) processes. The Mitochondrial Unfolded Protein Response (UPR mt ), a conserved regulatory process, is activated in response to a variety of organellar stressors. This quality control paradigm is primarily regulated by the transcription factor ATF5, in conjunction with ATF4 and CHOP. In order to sustain optimal functioning, skeletal muscle relies on a dynamic and efficient mitochondrial pool. However, with age, this mitochondrial capacity becomes impaired, demonstrated by reductions in respiration and elevations in ROS generation. These factors combined contribute to a decline in the quality of the mitochondrial pool and aberrations in muscle health. Within the context of the UPR mt , extensive literature using C.elegans has demonstrated the importance of UPR mt activation in maintaining mitochondrial health during aging and longevity, however, this relationship in mammals is unknown. In addition, whether ATF5 is required for this response has yet to be determined. To investigate this, we utilized young (4-6 months) and middle-aged (14-16 months) ATF5 KO and WT mice. Muscle characteristics and fatigability were examined via utilization of an in situ hindlimb protocol by acutely stimulating muscles at 0.25, 0.5, and 1 tetani/per second for 9 mins. Young ATF5 KO mice showed no change in fatiguability relative to young WT mice. With age, however, ATF5 KO mice experienced greater fatiguability compared to aged-matched WT mice, suggesting that ATF5 is required to maintain muscle endurance in aging muscle. Aged WT mice showed a decline in muscle mass relative to young counterparts. Interestingly, ATF5 KO mice maintained their muscle mass with age. To supplement these findings, the expression of the muscle atrophy markers, Atrogin-1, MuRF-1, GADD45α, p21, and p53, were blunted in aged ATF5 KO mice. Unexpectedly, a subunit of the 20S Proteosome, PSMB4, was also downregulated in ATF5 KO mice regardless of age, further fortifying the mechanism responsible for the maintenance of muscle mass in the absence of ATF5. Assessment of mitochondrial respiration and ROS emission via Clark Electrode revealed reductions in mitochondrial functioning and elevations in ROS production in ATF5 KO mice, a finding that was further amplified with age. In addition, UPR mt markers revealed an age-associated reduction in the expression of mitochondrial chaperones, mtHSP70 and HSP60 and elevations in CHOP and ATF4. The absence of ATF5 increased the expression of the protease ClpP and the antioxidant NQO1, suggesting plausible elevations in mitochondrial proteotoxicity and oxidative stress, elevated with age. These data thereby suggest that ATF5 is a critical factor required to promote mitochondrial hormesis and thus skeletal muscle health with age. Future work will reveal the precise underlying mechanisms that contribute to the activation of UPR mt signaling in aging muscle. Victoria C. Sanfrancesco is the recipient of NSERC Canada Graduate Scholarship-Doctoral (CGS-D). David A. Hood is the holder of a Canadian Research Chair in Cell Physiology. This abstract was presented at the American Physiology Summit 2025 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.013
GPT teacher head0.267
Teacher spread0.255 · 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 designBench or experimental
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
Published2025
Admission routes2
Has abstractyes

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