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Myotube Morphology and Protein Metabolism are Negatively Regulated by Chemotherapy Drugs

2019· article· en· W3174581043 on OpenAlexafffundabout
Stephen Mora, Olasunkanmi John Adegoke

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldMedicine
TopicCancer Treatment and Pharmacology
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsmTORC1MyogenesisRibosomal protein s6CachexiaMyofibrilMyosinP70-S6 Kinase 1PharmacologyChemotherapyEndocrinologyInternal medicineBiologyChemistryCancer researchPhosphorylationProtein kinase BMedicineCancerMyocyteCell biology

Abstract

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Cachexia, a condition prevalent in many cancer patients, is characterized by weight loss and fatigue resulting from decreases in muscle mass and function. Severity of cachexia negatively correlates with treatment outcomes, drug toxicity and survival. Tumor burden and cancer related‐malnutrition have been implicated in the development of the condition. Other studies have suggested a causative link between chemotherapy treatment and cachexia. To understand the mechanisms of effects of these drugs on cachexia, we investigated the effects of a common chemotherapy drug cocktail on myotube morphology, myofibrillar protein abundance, and mTORC1 signalling. On day 4 of differentiation, myotubes were treated with vehicle or a chemotherapy drug cocktail (a mixture of cisplatin (20 μg/mL), leucovorin (10 μg/mL), and 5 fluorouracil (50 μg/mL)). Compared to myotubes treated with vehicle, those treated with the drug cocktail showed dysmorphic shape and shrinkage. Drug treatment also induced a 50% decrease in the abundance of myosin heavy chain (n = 5 independent experiments, P = 0.0001), 80% reduction in troponin and tropomyosin (n=3–4, P < 0.0003) by day 6 of differentiation. To explore the reasons for the low abundance of these myofibrillar proteins, we examined treatment effects on mTORC1 (mammalian/mechanistic target of rapamycin complex 1), a signaling complex whose activity is vital for muscle anabolism. Myotubes treated with the drug cocktail showed 75% reduction in the phosphorylation of mTORC1 activator AKT (n = 3, P = 0.0061), and ≥70% reduction in phosphorylation status of mTORC1 substrates ribosomal protein S6 and its kinase, S6K1 (n = 3, P < 0.03). Drug treatment also led to 50–75% decreases in markers of mitochondria content cytochrome C oxidase (COX IV) and pyruvate dehydrogenase (P < 0.05). Because of the link between AKT activation and regulation of both protein synthesis and proteolysis, these data suggest that chemotherapy drugs decrease the abundance of myofibrillar proteins likely through the modulation of pathways that regulate protein synthesis and degradation, and mitochondrial content. To improve treatment outcomes and quality of life of patients, it is critical to identify interventions that can limit the negative effects of these drugs on muscle protein status and mitochondrial content. Support or Funding Information Natural Science and Engineering Research Council of Canada This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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 studies0.0000.000
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 head0.010
GPT teacher head0.265
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".

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Citations0
Published2019
Admission routes3
Has abstractyes

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