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Record W4402532842 · doi:10.1101/2024.09.09.612156

Identification of novel myokines and putative protein targets that mediate functional adaptations in response to chronic contractile activity induced skeletal muscle-extracellular vesicle treatment

2024· preprint· en· W4402532842 on OpenAlexaff
Tamiris F. G. Souza, Ying Lao, Kirk J. McManus, Joseph W. Gordon, Richard D. LeDuc, René P. Zahedi, Ayesha Saleem

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2024
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsUniversity of ManitobaChildren's Hospital Research Institute of ManitobaResearch ManitobaManitoba Health
Fundersnot available
KeywordsMyogenesisCell biologyBiologySkeletal muscleMyokineDownregulation and upregulationMyocyteExosomeMyosinMicrovesiclesBiochemistryGenemicroRNA

Abstract

fetched live from OpenAlex

Abstract We have previously shown that skeletal muscle-derived extracellular vesicles (EVs) released post-chronic contractile activity (CCA) increased mitochondrial biogenesis in murine myoblasts, and decreased cell viability and induced apoptosis and senescence in non-small cell lung cancer cells. While the underlying mechanisms are unknown, the effects perpetuated were dependent on membrane-bound proteins. Here, we performed an extensive LC-MS/MS proteomic analysis on EVs from control and CCA myotubes. A total of 2900 proteins were identified in CON-EVs and CCA-EVs, including EV-associated proteins such as TSG101, tetraspanins (CD9, CD81, and CD63), flotillin-1, and annexins. Of these, 856 proteins are novel and not listed in EV databases (ExoCarta and Vesiclepedia), indicating that myotube-EVs harbor proteins not yet identified in EVs of different origin. Additionally, we identified 2062 unique proteins that have not yet been previously reported in myotube-EVs to date. Remarkably, of the 2900 total proteins identified, we observed 46 upregulated, and 25 downregulated differentially expressed proteins (DEPs) in CCA-EVs vs . control-EVs. Most of upregulated DEPs include EV-associated proteins. Comparing the 71 DEPs with proteins expressed in skeletal muscle indicated 61 of these as potential myokines. We identified actin cytoskeleton signaling, integrin signaling and muscle contraction as the most enriched pathways among the DEPs using different databases/software including FunRich, KEGG, STRING and Ingenuity Pathway Analysis. Using a relevance score that prioritized membrane-bound proteins with known function in mitochondrial biogenesis and inhibition of cancer growth, we identified top-scoring highly enriched DEPs of interest: IGF1R, ATP7A, PFN1, GJA1, PRKCA and ITGA6. We confirmed upregulation of these targets in EVs using immunoblotting. Among these top-scoring DEPs, PFN1, and ITGA6 are associated with EVs, with expression upregulated following acute exercise. In summary, we report the first comprehensive analysis of skeletal muscle-EV proteome following CCA, with identification of putative protein targets and signaling pathways that may execute the pro-metabolic and anti-tumorigenic effects of CCA-EVs.

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 score0.002

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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