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Record W4376602781 · doi:10.26508/lsa.202201740

Functional characterization of C21ORF2 association with the NEK1 kinase mutated in human in diseases

2023· article· en· W4376602781 on OpenAlexafffund
Mateusz Gregorczyk, Graziana Pastore, Iván Muñoz, Thomas Carroll, Johanna Streubel, Meagan Munro, Paweł Lis, Sven M. Lange, Frédéric Lamoliatte, Thomas Macartney, Rachel Toth, Fiona Brown, C. James Hastie, Gislene Pereira, Daniel Durocher, John Rouse

Bibliographic record

VenueLife Science Alliance · 2023
Typearticle
Languageen
FieldMedicine
TopicAmyotrophic Lateral Sclerosis Research
Canadian institutionsLunenfeld-Tanenbaum Research InstituteUniversity of TorontoMount Sinai Hospital
FundersMedical Research CouncilCanadian Institutes of Health ResearchMotor Neurone Disease AssociationMerck KGaA
KeywordsBiologyGeneticsMutationHomologous recombinationCiliogenesisKinaseCell biologyComputational biologyDNAFunction (biology)Gene

Abstract

fetched live from OpenAlex

The NEK1 kinase controls ciliogenesis, mitosis, and DNA repair, and NEK1 mutations cause human diseases including axial spondylometaphyseal dysplasia and amyotrophic lateral sclerosis. C21ORF2 mutations cause a similar pattern of human diseases, suggesting close functional links with NEK1 . Here, we report that endogenous NEK1 and C21ORF2 form a tight complex in human cells. A C21ORF2 interaction domain “CID” at the C-terminus of NEK1 is necessary for its association with C21ORF2 in cells, and pathogenic mutations in this region disrupt the complex. AlphaFold modelling predicts an extended binding interface between a leucine-rich repeat domain in C21ORF2 and the NEK1–CID, and our model may explain why pathogenic mutations perturb the complex. We show that NEK1 mutations that inhibit kinase activity or weaken its association with C21ORF2 severely compromise ciliogenesis, and that C21ORF2, like NEK1 is required for homologous recombination. These data enhance our understanding of how the NEK1 kinase is regulated, and they shed light on NEK1–C21ORF2–associated diseases.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.438
Threshold uncertainty score0.160

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
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 head0.032
GPT teacher head0.304
Teacher spread0.272 · 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 designObservational
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

Citations24
Published2023
Admission routes2
Has abstractyes

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