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Record W3006668198 · doi:10.1242/jcs.237156

Passive diffusion through nuclear pore complexes regulates levels of the yeast SAGA and SLIK coactivator complexes

2020· article· en· W3006668198 on OpenAlexafffund
Tadashi Makio, Richard W. Wozniak

Bibliographic record

VenueJournal of Cell Science · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsNuclear poreBiologyNucleoplasmNucleoporinCell biologyCoactivatorActivator (genetics)MutantSaccharomyces cerevisiaeNuclear transportGeneCell nucleusTranscription factorGeneticsCytoplasmNucleolus

Abstract

fetched live from OpenAlex

ABSTRACT Nuclear pore complexes (NPCs) control gene expression by regulating the bi-directional exchange of proteins and RNAs between nuclear and cytoplasmic compartments, including access of transcriptional regulators to the nucleoplasm. Here, we show that the yeast (Saccharomyces cerevisiae) nucleoporin Nup170, in addition to binding and silencing subtelomeric genes, supports transcription of genes regulated by the SAGA transcriptional activator complex. Specifically, we show that a lower amount of SAGA complex is bound to target genes in the absence of Nup170. Consistent with this observation, levels of the SAGA complex are decreased in cells lacking Nup170, while those of the SAGA-related SLIK complexes are increased. This change in the ratio of SAGA to SLIK complexes is due to increased nuclear activity of Pep4, a protease responsible for production of the SLIK complex. Further analyses of various nucleoporin mutants revealed that the increased nuclear entry of Pep4 observed in the nup170Δ mutant likely occurs as the consequence of an increase in the sieving limits of the NPC diffusion channel. On the basis of these results, we propose that changes in passive diffusion rates represent a mechanism for regulating SAGA- and SLIK complex-mediated transcriptional events.

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 score0.024
Threshold uncertainty score0.201

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 studies0.0000.001
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 head0.026
GPT teacher head0.264
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 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

Citations11
Published2020
Admission routes2
Has abstractyes

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