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Record W3024856951 · doi:10.1038/s41467-020-16129-4

Adaptation of the master antioxidant response connects metabolism, lifespan and feather development pathways in birds

2020· article· en· W3024856951 on OpenAlexfundno aff
Gianni M. Castiglione, Zhenhua Xu, Lingli Zhou, Elia J. Duh

Bibliographic record

VenueNature Communications · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Behavior and Reproduction
Canadian institutionsnot available
FundersUniversity of Toronto ScarboroughNational Eye InstituteUniversity of TorontoNational Institutes of HealthMaryland Ornithological Society
KeywordsAdaptation (eye)KEAP1BiologyFeatherReactive oxygen speciesOxidative stressAntioxidantCell biologyGeneMutationMechanism (biology)Most recent common ancestorPsychological repressionGeneticsEvolutionary biologyEcologyPhylogenetic treeBiochemistryTranscription factorNeuroscienceGene expression

Abstract

fetched live from OpenAlex

Birds (Aves) display high metabolic rates and oxygen consumption relative to mammals, increasing reactive oxygen species (ROS) formation. Although excess ROS reduces lifespan by causing extensive cellular dysfunction and damage, birds are remarkably long-lived. We address this paradox by identifying the constitutive activation of the NRF2 master antioxidant response in Neoaves (~95% of bird species), providing an adaptive mechanism capable of counterbalancing high ROS levels. We demonstrate that a KEAP1 mutation in the Neoavian ancestor disrupted the repression of NRF2 by KEAP1, leading to constitutive NRF2 activity and decreased oxidative stress in wild Neoaves tissues and cells. Our evidence suggests this ancient mutation induced a compensatory program in NRF2-target genes with functions beyond redox regulation-including feather development-while enabling significant metabolic rate increases that avoid trade-offs with lifespan. The strategy of NRF2 activation sought by intense clinical investigation therefore appears to have also unlocked a massively successful evolutionary trajectory.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.926
Threshold uncertainty score0.107

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 head0.073
GPT teacher head0.257
Teacher spread0.184 · 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 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

Citations55
Published2020
Admission routes1
Has abstractyes

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