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Record W4220952438 · doi:10.7554/elife.67911

Nuclear hormone receptor NHR-49 acts in parallel with HIF-1 to promote hypoxia adaptation in Caenorhabditis elegans

2022· article· en· W4220952438 on OpenAlexafffund
Kelsie R. S. Doering, Xuanjin Cheng, Luke Milburn, Ramesh Ratnappan, Arjumand Ghazi, Dana L. Miller, Stefan Taubert

Bibliographic record

VenueeLife · 2022
Typearticle
Languageen
FieldMedicine
TopicAdipose Tissue and Metabolism
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersNational Institute on AgingNatural Sciences and Engineering Research Council of CanadaNational Institutes of HealthCanada Research ChairsCancer Research SocietyCanadian Institutes of Health ResearchBC Children’s Hospital Foundation
KeywordsNuclear receptorBiologyCaenorhabditis elegansCell biologyHypoxia (environmental)Transcription factorRegulatorSignal transductionGeneGeneticsChemistry

Abstract

fetched live from OpenAlex

The response to insufficient oxygen (hypoxia) is orchestrated by the conserved hypoxia-inducible factor (HIF). However, HIF-independent hypoxia response pathways exist that act in parallel with HIF to mediate the physiological hypoxia response. Here, we describe a hypoxia response pathway controlled by Caenorhabditis elegans nuclear hormone receptor NHR-49, an orthologue of mammalian peroxisome proliferator-activated receptor alpha (PPARα). We show that nhr-49 is required for animal survival in hypoxia and is synthetic lethal with hif-1 in this context, demonstrating that these factors act in parallel. RNA-seq analysis shows that in hypoxia nhr-49 regulates a set of genes that are hif-1- independent, including autophagy genes that promote hypoxia survival. We further show that nuclear hormone receptor nhr-67 is a negative regulator and homeodomain-interacting protein kinase hpk-1 is a positive regulator of the NHR-49 pathway. Together, our experiments define a new, essential hypoxia response pathway that acts in parallel with the well-known HIF-mediated hypoxia 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.623
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.017
GPT teacher head0.243
Teacher spread0.226 · 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 designNot applicable
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

Citations36
Published2022
Admission routes2
Has abstractyes

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