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Record W2326853246 · doi:10.1242/jeb.036772

OF HIF, MUSCLES AND NORMOXIA

2010· article· en· W2326853246 on OpenAlexaff
C Moine

Bibliographic record

VenueJournal of Experimental Biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHigh Altitude and Hypoxia
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHypoxia (environmental)GlycolysisHypoxia-inducible factorsOxygenCell biologyMetabolismOxidative phosphorylationBiologyProtein subunitBiochemistryInternal medicineChemistryEndocrinologyMedicineGene

Abstract

fetched live from OpenAlex

Mammalian muscles present a wide range of contractile and metabolic properties resulting from the relative proportions of oxidative (type I) and glycolytic (type II) fibres making up a given muscle. Besides these innate properties, muscles are also highly plastic, responding to environmental change. In particular, oxygen availability has a strong influence on muscle metabolism. When oxygen concentration drops (hypoxia), proteins such as the hypoxia inducible factor-1 (HIF-1) are activated to orchestrate a switch to glycolytic (anaerobic) metabolism in an effort to spare oxygen. Over longer periods of hypoxia, HIF-1 also promotes increased vascularisation via angiogenic factors such as the vascular endothelial growth factor (VEGF) to improve oxygen delivery to the hypoxic tissue. The HIF-1 protein is composed of two components; HIF-1α is primarily regulated by oxygen availability, while the other subunit is constitutively expressed. When the oxygen concentration is normal, HIF-1α protein tends to be targeted for degradation, lowering HIF-1 activity. However, under hypoxic conditions HIF-1α is stabilized, allowing the HIF-1 dimer to act on several metabolic pathways to help the cell cope with low oxygen levels. Although the role of HIF-1 under hypoxia has been widely studied, relatively little is known about the function of this protein under normoxic conditions in resting muscle. Furthermore it is unclear whether this factor is differentially regulated in muscles with different metabolic phenotypes.Remi Mounier, Bente Pedersen and Peter Plomgaard from the University of Copenhagen, Denmark, investigated these two questions by looking at different muscle fibre types in humans. Specifically, the team looked at gene and protein expression profiles in resting muscles, and hypothesized that primarily glycolytic muscles would present higher expression levels of HIF-1 and associated factors than oxidative muscles.First, Mounier and colleagues selected three skeletal muscles, the oxidative soleus muscle (mostly type I fibres), the mixed vastus lateralis muscle (type I and type II fibres) and the glycolytic triceps brachii (mostly type II fibres) muscles. Using quantitative real-time PCR, the team established that HIF-1α mRNA expression was higher in glycolytic and mixed muscles than in the oxidative soleus muscle, as predicted by their hypothesis. In contrast, the mRNA expression of VEGF, a gene regulated by HIF-1, did not show any fibre type-specific pattern, confirming that HIF-1α mRNA levels do not necessarily reflect HIF-1 activity. So the team measured HIF-1α protein amounts in the muscles to see whether they correlated with the gene expression patterns. Contrary to their expectations, HIF-1α protein levels were threefold lower in glycolytic muscles than in mixed and oxidative muscles. In addition, VEGF protein levels were lowest in glycolytic muscles, intermediate in mixed muscles and highest in oxidative muscles.The results of this study strongly suggest that different muscle types regulate the HIF pathway at different levels, and further investigation will be necessary to unveil the mechanisms responsible for this tissue-specific regulation. Furthermore, these results also confirm the presence of HIF-1 under normal oxygen conditions, and thus suggest that this factor may also play an important role in regulating skeletal muscle homeostasis even under normoxia.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.277
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2010
Admission routes1
Has abstractyes

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