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Genetic or Pharmacological Induction of the Hypoxia Response Enhances Functional Regeneration Following Peripheral Nerve Injury

2019· article· en· W3174126062 on OpenAlexaff
Brittney D. Smaila, Erin Erskine, Seth D. Holland, Diana V. Hunter, Farshad Babaeijandaghi, Fábio Rossi, Matt S. Ramer

Bibliographic record

VenueThe FASEB Journal · 2019
Typearticle
Languageen
FieldNeuroscience
TopicNerve injury and regeneration
Canadian institutionsInternational Collaboration On Repair DiscoveriesUniversity of British Columbia
Fundersnot available
KeywordsHypoxia (environmental)AxonRegeneration (biology)Knockout mouseTranscriptomeSciatic nervePeripheral nerve injuryGenetically modified mouseNerve injuryBiologyTransgeneCell biologyMedicineNeuroscienceInternal medicineReceptorChemistryGene expressionGeneBiochemistry

Abstract

fetched live from OpenAlex

Peripheral nerve injuries (PNIs) occur in 1–3% of patients with traumatic injuries. While peripheral axons have the ability to regenerate at ~1mm/day, the distance over which they must do so means lengthy recovery times, which are associated with regenerative failure due to a decline in supporting cells' ability to maintain axon growth. Successful axonal regeneration depends heavily upon the neuronal response to injury, as well as that of myeloid cells recruited to the damaged nerve. In injured neurons, transcriptomic changes occur which mirror those observed in hypoxia. Hypoxia inducible factors (HIFs) effect these transcriptomic changes; these are governed in turn by prolyl hydroxylase domain (PHD) proteins. Under normoxic conditions, PHDs act as master regulators of the hypoxia response, targeting HIFs for proteasomal degradation. We hypothesize that deleting any or all of the PHDs (PHD1, 2, and 3), or inhibiting PHDs pharmacologically, will induce the hypoxia response and enhance axonal regeneration and functional recovery following PNI. We used global PHD1 knockout, global PHD3 knockout, and heterozygous PHD2 transgenic mice, as well as non‐transgenic mice treated with DMOG, a pan‐PHD inhibitor. We used a sciatic nerve crush model of injury and assessed functional recovery at various time points post‐injury using toe pinch and grasping tests, and electromyography. Using immunohistochemistry we characterized macrophage and axon densities in the nerves after injury. We also assessed DMOG‐induced changes in macrophage phenotype using FACS, RT‐PCR and immunohistochemistry. We found that deletion of PHD1 or PHD3, or inhibition of all three PHDs resulted in earlier functional recovery after injury, increased macrophage infiltration in the injured nerve, an M2‐skewed macrophage phenotype, and enhanced axonal regrowth. In addition, deletion of any of the PHDs, or their inhibition by DMOG, resulted in improved electromyographical responses of the nerve one month post injury. Our findings suggest that functional recovery after peripheral nerve injury can be aided by hypoxia‐independent induction of the hypoxia response. Support or Funding Information Wings for Life; ICORD Blusson Integrated Cures Partnership This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.042
GPT teacher head0.291
Teacher spread0.249 · 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
Published2019
Admission routes1
Has abstractyes

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