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Sesamin Suppresses Aging-Associated Impairment of Protein Homeostasis in Adult <em>Drosophila </em>Muscles by Stimulating Autophagy

2025· preprint· en· W4410279612 on OpenAlexfundno aff
Yuuka Yonezu, Tadashi Nomura, Yoshihiro Inoué

Bibliographic record

VenuePreprints.org · 2025
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicSesame and Sesamin Research
Canadian institutionsnot available
FundersInstitute of Genetics
KeywordsAutophagyHomeostasisDrosophila (subgenus)Cell biologyBiologyEndocrinologyInternal medicineMedicineGeneticsApoptosisGene

Abstract

fetched live from OpenAlex

As Drosophila ages, damaged and no longer used proteins, which are ubiquitinated, accumulate in muscle. This is attributed to age-dependent impairment of protein homeostasis. Sesamin, which has antioxidant effects on cultured cells and Drosophila neurons, suppresses this muscle-aging phenotype. However, unlike in neurons, it failed to activate the transcription factor Nrf2 for antioxidant genes in muscle. The number and amount of ubiquitinated aggregates increased in the flies' muscles with aging. We investigated autophagy levels via anti-Ref(2)P immunostaining and demonstrated that autophagy in the muscle was stimulated in sesamin-fed flies. The effect on the muscle was eliminated via the administration of an autophagy inhibitor, chloroquine, and the muscle-specific depletion of Atg8, an autophagosome component. In contrast, the effect did not change via the administration of a proteasome inhibitor, bortezomib, and the depletion of proteasome components, suggesting that sesamin stimulates autophagy, but not the proteasome degradation system, to suppress the age-related impairment of protein homeostasis in muscle. Sesamin may be a useful anti-aging substance in delaying muscle aging. Another interesting finding from the study is that inhibition of the ubiquitin-proteasome system also influences autophagy in Drosophila muscles. Elucidating the mechanisms underlying sesamin's effects provides essential information related to the muscle aging process.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.558
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.004
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 head0.064
GPT teacher head0.318
Teacher spread0.253 · 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 teacher head, not a consensus.

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
Published2025
Admission routes1
Has abstractyes

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