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Lysosomal Alterations in Skeletal Muscle Plasticity – An Investigation of Age, Exercise and Disuse

2020· article· en· W3017126457 on OpenAlexaff
Matthew Triolo, Mikhaela Slavin, Yuho Kim, Heather N. Carter, David A. Hood

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsYork University
Fundersnot available
KeywordsSkeletal muscleLysosomeAutophagyInternal medicineEndocrinologyIntracellularLAMP1DenervationCathepsinBiologyMitochondrionChemistryMedicineCell biologyBiochemistryEndosomeApoptosis

Abstract

fetched live from OpenAlex

Skeletal muscle displays a high degree of plasticity in response to a variety of contractile activity stimuli. This is exemplified in response to exercise training, in which muscle mitochondrial content is enhanced to drive an increase in metabolic capacity. The opposite is true with chronic muscle disuse and advancing age, in which the muscle loses its oxidative capacity. This plasticity is regulated by alterations in the synthesis and degradation of mitochondria. A major intracellular degradation system is the autophagy‐lysosome pathway. This includes the targeting of damaged intracellular components by autophagosomes, followed by delivery of these to the lysosomes for degradation. However, there is little understanding of the regulation of the lysosomes in response to physiological stimuli. Thus, our objective is to understand how muscle lysosomes are regulated in response to physiological stimuli, including exercise, disuse and age. Thus, we utilized unilateral chronic contractile activity (CCA;7 days) to elicit endurance exercise adaptations, and denervation of the peroneal nerve (7 days) to elicit disuse adaptations. These opposing activity paradigms resulted in parallel increases (50%) and decreases (50%) in mitochondrial content within muscle, respectively. To assess the effects of age, 36‐month‐old rats and 24‐month‐old mice were used. CCA elicited increases in lysosomal proteins Lamp1, Lamp2 and Cathepsin D by 2.9‐,1.8‐ and 1.3‐fold respectively, an effect that was blunted in aged rats. Both denervated and aged muscle exhibited elevated levels of these proteins, such as 3.0‐ and 1.6‐fold increases in Lamp1 and Lamp2 in denervated muscle, and 5‐fold increases in these proteins with age. To understand how these opposing processes (i.e. CCA vs age/disuse) elicit directionally similar changes in these proteins, we measured TFEB, a transcription factor that drives the formation of lysosomes. CCA elicited modest, 50% elevations in TFEB protein, whereas denervation and aging increased TFEB by 70% and 180%, respectively. However, TFEB localization was elevated differentially, by 10%, 25% and 100% in CCA, denervation and aging, respectively. Importantly, electron micrographs of denervated and aged muscle exhibited evidence of lipofuscin accumulation, suggesting impairments in lysosomal function with these wasting conditions, and this could account for an accumulation of dysfunctional lysosomal proteins. To evaluate the transcriptional control of lysosomal biogenesis, young and aged mice were injected with the TFEB‐promoter luciferase reporter. Acute exercise elevated TFEB promoter activity by 1.6‐fold in young muscle, and 2.4‐fold in aged muscle, whereas nuclear TFEB was enhanced to a greater extent in young muscle. This could explain the blunted CCA‐induced lysosomal adaptations in aged muscle. Our data suggest increases in lysosomal proteins are evident with both enhanced (i.e. CCA) and reduced (i.e denervation and age) contractile stimuli. Future work will evaluate whether the changes in lysosomal proteins represent functional lysosomes or dysfunctional accumulation of organelles. Support or Funding Information NSERC and CIHR

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: Observational · Consensus signal: none
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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.000
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 head0.028
GPT teacher head0.273
Teacher spread0.246 · 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
Published2020
Admission routes1
Has abstractyes

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