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The Aging Muscle Milieu and Transcription of PGC‐1α: a Role for Contractile Activity?

2016· article· en· W3184484759 on OpenAlexafffund
Heather N. Carter, Michael Shuen, Karli Gavendo, David A. Hood

Bibliographic record

VenueThe FASEB Journal · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsOccupational Cancer Research CentreYork University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsMitochondrial biogenesisBiologySkeletal muscleTranscription factorTFAMSarcopeniaEndocrinologyInternal medicineTranscription (linguistics)PhosphorylationMitochondrionCell biologyGeneMedicineGenetics

Abstract

fetched live from OpenAlex

PGC‐1α is an established regulator of mitochondrial content and function in skeletal muscle. With advancing age it is known that expression of this transcriptional coactivator is lower than in young, healthy muscle. This deficit in PGC‐1α is a contributing factor to the defects in mitochondrial homeostasis observed in aged muscle, but the molecular mechanisms influencing this decline have not been examined. Furthermore, it has not been investigated whether the transcription of the PGC‐1α gene is altered in the aging muscle environment. Thus, we electroporated a 1.5kb PGC‐1α‐luciferase construct bilaterally into the tibialis anterior muscles of young (5 mo) and aged (35 mo) FBN344 rats. Aged muscle exhibited an ~61% decrease in transcriptional activity of the PGC‐1α promoter. This coincided with ~30% decreases in the transcription factors NFE2L2 (Nrf2) and Upstream factor‐1 (USF‐1) which have putative and known binding sites on the PGC‐1α promoter. The basal phosphorylation state of the signaling kinases p38 and AMPK, which are known activators of PGC‐1α transcription, were decreased in aged muscle. Additionally, we observed a 1.3‐fold increase in global methylation of aged DNA, an epigenetic modification associated with transcriptional silencing. Exercise is a potent stimulus that induces mitochondrial biogenesis largely through the activation of PGC‐1α. Therefore we investigated whether an acute bout of in situ contractile activity may reverse the transcriptional deficiency seen in aged muscle. The left TA muscle of young and aged electroporated animals was subjected to fatiguing contractile activity (40 mins) and 2 hours of subsequent recovery, while the right TA served as the control. In young muscle, contractile activity‐induced PGC‐1α transcription increased by ~1.5‐fold and returned to baseline with recovery. This was accompanied by a 1.2‐fold increase in PGC‐1α mRNA during the recovery period. In aged muscle, transcription increased ~1.7‐fold with contractile activity and remained elevated with the recovery period. PGC‐1α mRNA responded with a 1.6‐fold increase during recovery in the aged group, despite having a lower magnitude of transcriptional induction (Δ=0.28 transcription units) compared to the young (Δ=0.99 transcription units). Examination of p38 and AMPK also revealed attenuated phosphorylation in aged muscle in response to contractile activity. To gain insight into epigenetic regulation of the PGC‐1α promoter with aging and contractile activity, we are presently performing bisulfite sequencing and examining multiple CpG sites adjacent to the transcription start site. Our data suggest that the aged muscle milieu exhibits numerous impairments which likely contribute to the attenuation of PGC‐1α transcription in resting muscle. Despite these basal differences, contractile activity is sufficient to elicit increases in PGC‐1α gene expression, underscoring the importance of exercise to potentially correct age‐related deficits in mitochondrial homeostasis. Support or Funding Information Supported by CIHR and NSERC.

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 score0.004

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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
Published2016
Admission routes2
Has abstractyes

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