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Record W4393950309 · doi:10.1016/j.stemcr.2024.03.004

PINK1 deficiency alters muscle stem cell fate decision and muscle regenerative capacity

2024· article· en· W4393950309 on OpenAlexafffund
George Cairns, Madhavee Thumiah-Mootoo, Mah Rukh Abbasi, Melissa Gourlay, Jeremy J. Racine, Н. П. Ларионов, Alexandre Prola, Mireille Khacho, Yan Burelle

Bibliographic record

VenueStem Cell Reports · 2024
Typearticle
Languageen
FieldMedicine
TopicAutophagy in Disease and Therapy
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaOttawa Hospital Research InstituteCanadian Institutes of Health ResearchUniversity of Ottawa
KeywordsMitophagyPINK1BiologyCell biologyMitochondrionParkinStem cellRegeneration (biology)RegulatorCell fate determinationAutophagyGeneticsApoptosisGeneInternal medicine

Abstract

fetched live from OpenAlex

Maintenance of mitochondrial function plays a crucial role in the regulation of muscle stem cell (MuSC), but the underlying mechanisms remain ill defined. In this study, we monitored mitophagy in MuSCS under various myogenic states and examined the role of PINK1 in maintaining regenerative capacity. Results indicate that quiescent MuSCs actively express mitophagy genes and exhibit a measurable mitophagy flux and prominent mitochondrial localization to autophagolysosomes, which become rapidly decreased during activation. Genetic disruption of Pink1 in mice reduces PARKIN recruitment to mitochondria and mitophagy in quiescent MuSCs, which is accompanied by premature activation/commitment at the expense of self-renewal and progressive loss of muscle regeneration, but unhindered proliferation and differentiation capacity. Results also show that impaired fate decisions in PINK1-deficient MuSCs can be restored by scavenging excess mitochondrial ROS. These data shed light on the regulation of mitophagy in MuSCs and position PINK1 as an important regulator of their mitochondrial properties and fate decisions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.390
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.257
Teacher spread0.238 · 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.

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

Citations18
Published2024
Admission routes2
Has abstractyes

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