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Unbiased Proteomics Supports a Key Role for Mitochondria in Skeletal Muscle of Highly Functioning Octogenarians

2020· article· en· W3017315672 on OpenAlexaff
Ceereena Ubaida‐Mohien, Alexey E. Lyashkov, Sally Spendiff, Tanja Taivassalo, Russell T. Hepple

Bibliographic record

VenueThe FASEB Journal · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsSkeletal muscleProteomicsMitochondrionWestern blotElite athletesBiologyTandem mass spectrometryInternal medicineMedicineChemistryAthletesBioinformaticsCell biologyBiochemistryMass spectrometryPhysical therapyGene

Abstract

fetched live from OpenAlex

Introduction Skeletal muscle mass and function decline with aging, and more precipitously after the age 75 y. To help provide insights into biologically relevant mechanisms for preserving muscle mass and function in advanced age, in this study we performed proteomics on skeletal muscle of world class octogenarian track and field athletes in comparison to healthy octogenarian non‐athletes. Methods Muscle cross‐sectional area by MRI and a vastus lateralis muscle biopsy were performed in 15 octogenarian world class track and field athletes (8 of whom were world record holders in their discipline at the time of testing) and 14 non‐athlete age‐ and sex‐matched non‐athlete controls. From these subjects, a portion of muscle from a subset of 12 master athletes (MA mean age 81.19 ± 5.1 y) and 12 non‐athlete controls (NA mean age 80.94 ± 4.5 y) was used for liquid‐chromatography mass spectrometry to generate quantitative tandem mass tag proteomics data. In addition, we measured mtDNA copy number, COX/SDH histochemistry to identify respiratory compromised fibers, and Western blot of mitochondrial inner and outer membrane proteins. Results Muscle cross‐sectional area was higher in MA. Tandem mass spectrometry identified over 6000 proteins, and significant differences in abundance were found between NA controls and MA for more than 800 proteins. A pathway analysis revealed that pathways involved in mitochondria (e.g., TCA cycle, respiratory electron transport, cristae formation, sirtuins) were higher in MA, while proteins in pathways involved in the spliceosome and nuclear pore were downregulated in MA. Finally, 8 mtDNA‐encoded proteins that were included in the analysis were elevated in MA versus NA. These proteomics data are consistent with phenotypic data showing MA have higher mtDNA copy number, fewer respiratory chain compromised muscle fibers, and an increased ratio of ETC subunits (inner mitochondrial membrane proteins) relative to VDAC (outer mitochondrial membrane protein), suggesting an increase in cristae formation. Conclusion Our data underscore that mitochondrial pathways are key to maintaining a high level of physical function in advanced age. With the current study design we cannot determine the degree to which these differences are attributable to the physical activity habits of MA, but it is likely to play a role. Support or Funding Information CIHR (MOP 125986 to RTH) NIA Intramural Research program

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 score0.003
Threshold uncertainty score0.007

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.232
Teacher spread0.217 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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