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Record W4282577837 · doi:10.1038/s41589-022-01047-x

MNK2 deficiency potentiates β-cell regeneration via translational regulation

2022· article· en· W4282577837 on OpenAlexaff
Christos Karampelias, Kathleen Watt, Charlotte L. Mattsson, Angel Fernandez Ruiz, Habib Rezanejad, Jiarui Mi, Xiaojing Liu, Lianhe Chu, Jason W. Locasale, Gregory S. Korbutt, Meritxell Rovira, Ola Larsson, Olov Andersson

Bibliographic record

VenueNature Chemical Biology · 2022
Typearticle
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of Alberta
FundersNovo Nordisk FondenVetenskapsrådetNovo NordiskKarolinska InstitutetCancerfondenMinisterio de Ciencia, Innovación y Universidades
KeywordsRegeneration (biology)BiologyCell biologyEIF4EKinaseTranslation (biology)KinomeIsletZebrafishCellGeneticsMessenger RNAGene

Abstract

fetched live from OpenAlex

Regenerating pancreatic β-cells is a potential curative approach for diabetes. We previously identified the small molecule CID661578 as a potent inducer of β-cell regeneration, but its target and mechanism of action have remained unknown. We now screened 257 million yeast clones and determined that CID661578 targets MAP kinase-interacting serine/threonine kinase 2 (MNK2), an interaction we genetically validated in vivo. CID661578 increased β-cell neogenesis from ductal cells in zebrafish, neonatal pig islet aggregates and human pancreatic ductal organoids. Mechanistically, we found that CID661578 boosts protein synthesis and regeneration by blocking MNK2 from binding eIF4G in the translation initiation complex at the mRNA cap. Unexpectedly, this blocking activity augmented eIF4E phosphorylation depending on MNK1 and bolstered the interaction between eIF4E and eIF4G, which is necessary for both hypertranslation and β-cell regeneration. Taken together, our findings demonstrate a targetable role of MNK2-controlled translation in β-cell regeneration, a role that warrants further investigation in diabetes.

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 categoriesInsufficient payload (model declined to judge)
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 score0.035
Threshold uncertainty score0.999

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)0.0010.000

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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.

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

Citations32
Published2022
Admission routes1
Has abstractyes

Explore more

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