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Record W2963133454 · doi:10.1038/s41467-019-11275-w

KLF-1 orchestrates a xenobiotic detoxification program essential for longevity of mitochondrial mutants

2019· article· en· W2963133454 on OpenAlexfundno aff
Marija Herholz, Estela Cepeda, Linda C. Baumann, Alexandra Kukat, Johannes CW Hermeling, Sarah Maciej, Karolina Szczepanowska, Victor Pavlenko, Peter Frommolt, Aleksandra Trifunović

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicKruppel-like factors research
Canadian institutionsnot available
FundersNational Institutes of HealthTokyo Women's Medical UniversityDeutsche ForschungsgemeinschaftAGE-WELL
KeywordsLongevityBiologyMitochondrial biogenesisCell biologyDetoxification (alternative medicine)XenobioticMitochondrionMutantBiogenesisGeneGeneticsBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Most manipulations that extend lifespan also increase resistance to various stress factors and environmental cues in a range of animals from yeast to mammals. However, the underlying molecular mechanisms regulating stress resistance during aging are still largely unknown. Here we identify Krüppel-like factor 1 (KLF-1) as a mediator of a cytoprotective response that dictates longevity induced by reduced mitochondrial function. A redox-regulated KLF-1 activation and transfer to the nucleus coincides with the peak of somatic mitochondrial biogenesis that occurs around a transition from larval stage L3 to D1. We further show that KLF-1 activates genes involved in the xenobiotic detoxification programme and identified cytochrome P450 oxidases, the KLF-1 main effectors, as longevity-assurance factors of mitochondrial mutants. Collectively, these findings underline the importance of the xenobiotic detoxification in the mitohormetic, longevity assurance pathway and identify KLF-1 as a central factor in orchestrating this response.

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 score0.077
Threshold uncertainty score0.469

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 science0.0010.000
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 head0.020
GPT teacher head0.362
Teacher spread0.343 · 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

Citations38
Published2019
Admission routes1
Has abstractyes

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