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Record W2913473772 · doi:10.1073/pnas.1815361116

Magnesium-sensitive upstream ORF controls PRL phosphatase expression to mediate energy metabolism

2019· article· en· W2913473772 on OpenAlexafffund
Serge Hardy, Elie Kostantin, Shan Jin Wang, Tzvetena Hristova, Gabriela Galicia-Vázquez, Pavel V. Baranov, Jerry Pelletier, Michel L. Tremblay

Bibliographic record

VenueProceedings of the National Academy of Sciences · 2019
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersCanadian Institutes of Health ResearchNational Jewish HealthWellcome TrustScience Foundation IrelandMassachusetts Institute of Technology
KeywordsMagnesiumCell biologyPhosphataseIntracellularBiologyTranslation (biology)Mechanism (biology)Messenger RNAAMPKRepressorBiochemistryChemistryPhosphorylationGene expressionProtein kinase AGene

Abstract

fetched live from OpenAlex

Phosphatases of regenerating liver (PRL-1, PRL-2, and PRL-3, also known as PTP4A1, PTP4A2, and PTP4A3) control magnesium homeostasis through an association with the CNNM magnesium transport regulators. Although high PRL levels have been linked to cancer progression, regulation of their expression is poorly understood. Here we show that modulating intracellular magnesium levels correlates with a rapid change of PRL expression by a mechanism involving its 5'UTR mRNA region. Mutations or CRISPR-Cas9 targeting of the conserved upstream ORF present in the mRNA leader derepress PRL protein synthesis and attenuate the translational response to magnesium levels. Mechanistically, magnesium depletion reduces intracellular ATP but up-regulates PRL protein expression via activation of the AMPK/mTORC2 pathway, which controls cellular energy status. Hence, altered PRL-2 expression leads to metabolic reprogramming of the cells. These findings uncover a magnesium-sensitive mechanism controlling PRL expression, which plays a role in cellular bioenergetics.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.276

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 head0.019
GPT teacher head0.294
Teacher spread0.276 · 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 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

Citations65
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueProceedings of the National Academy of SciencesSame topicParathyroid Disorders and TreatmentsFrench-language works237,207