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Record W4293554017 · doi:10.7554/elife.80774

Hypoxia-inducible factor underlies von Hippel-Lindau disease stigmata

2022· article· en· W4293554017 on OpenAlexafffund
Michael Ohh, Cassandra C Taber, Fraser G. Ferens, Daniel Tarade

Bibliographic record

VenueeLife · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer, Hypoxia, and Metabolism
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsHypoxia-inducible factorsPhenotypeDiseaseCancer researchBiologyUbiquitin ligaseVon Hippel–Lindau diseaseProteasomeGeneticsUbiquitinMedicineInternal medicineGene

Abstract

fetched live from OpenAlex

von Hippel-Lindau (VHL) disease is a rare hereditary cancer syndrome that causes a predisposition to renal clear-cell carcinoma, hemangioblastoma, pheochromocytoma, and autosomal-recessive familial polycythemia. pVHL is the substrate conferring subunit of an E3 ubiquitin ligase complex that binds to the three hypoxia-inducible factor alpha subunits (HIF1-3α) for polyubiquitylation under conditions of normoxia, targeting them for immediate degradation by the proteasome. Certain mutations in pVHL have been determined to be causative of VHL disease through the disruption of HIFα degradation. However, it remains a focus of investigation and debate whether the disruption of HIFα degradation alone is sufficient to explain the complex genotype-phenotype relationship of VHL disease or whether the other lesser or yet characterized substrates and functions of pVHL impact the development of the VHL disease stigmata; the elucidation of which would have a significant ramification to the direction of research efforts and future management and care of VHL patients and for those manifesting sporadic counterparts of VHL disease. Here, we examine the current literature including the other emergent pseudohypoxic diseases and propose that the VHL disease-phenotypic spectrum could be explained solely by the varied disruption of HIFα signaling upon the loss or mutation in pVHL.

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 score0.680
Threshold uncertainty score0.802

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 head0.020
GPT teacher head0.259
Teacher spread0.238 · 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

Citations39
Published2022
Admission routes2
Has abstractyes

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