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Record W4389425762 · doi:10.1002/1873-3468.14783

Intrinsic disorder of a nucleoplasmin‐like histone chaperone specifies its discrete nuclear and nucleolar functions

2023· letter· en· W4389425762 on OpenAlexafffund
Courtney M. Gauthier, Josey LeGallais, Neda Savic, Sarah Moradi‐Fard, Arden Grew, Martin Loe, Baran Kirlikaya, Jennifer A. Cobb, Christopher J. Nelson

Bibliographic record

VenueFEBS Letters · 2023
Typeletter
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsUniversity of CalgaryUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsNucleolusChromatinChaperone (clinical)HistoneBiologyPsychological repressionCell biologyNuclear proteinComputational biologyGeneticsNucleusTranscription factorDNAGeneGene expression

Abstract

fetched live from OpenAlex

Nucleoplasmin (NPM) histone chaperones regulate distinct processes in the nucleus and nucleolus. While intrinsically disordered regions (IDRs) are hallmarks of NPMs, it is not clear whether all NPM functions require these unstructured features. We assessed the importance of IDRs in a yeast NPM-like protein and found that regulation of rDNA copy number and genetic interactions with the nucleolar RNA surveillance machinery require the highly conserved FKBP prolyl isomerase domain, but not the NPM domain or IDRs. By contrast, transcriptional repression in the nucleus requires IDRs. Furthermore, multiple lysines in polyacidic serine/lysine motifs of IDRs are required for both lysine polyphosphorylation and NPM-mediated transcriptional repression. These results demonstrate that this NPM-like protein relies on IDRs only for some of its chromatin-related functions.

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)0.001
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.007
GPT teacher head0.205
Teacher spread0.198 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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