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Record W2885675181 · doi:10.1101/gad.312173.118

Distinct patterns of histone acetyltransferase and Mediator deployment at yeast protein-coding genes

2018· article· en· W2885675181 on OpenAlexfundno aff
Maria Jessica Bruzzone, Sebastian Grünberg, Slawomir Kubik, Gabriel E. Zentner, David Shore

Bibliographic record

VenueGenes & Development · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsnot available
FundersInstitute of Genetics and Genomics of GenevaNational Institutes of HealthInstitute of GeneticsNational Institute of General Medical SciencesCanton de Genève
KeywordsBiologyRNA polymerase IIMediatorHistone acetyltransferaseCoactivatorAcetyltransferaseGeneticsGeneHistone AcetyltransferasesHistoneCell biologyTranscription factor II DTranscription (linguistics)Transcription factorGene expressionRNA polymeraseRNAPromoterAcetylation

Abstract

fetched live from OpenAlex

The transcriptional coactivators Mediator and two histone acetyltransferase (HAT) complexes, NuA4 and SAGA, play global roles in transcriptional activation. Here we explore the relative contributions of these factors to RNA polymerase II association at specific genes and gene classes by rapid nuclear depletion of key complex subunits. We show that the NuA4 HAT Esa1 differentially affects certain groups of genes, whereas the SAGA HAT Gcn5 has a weaker but more uniform effect. Relative dependence on Esa1 and Tra1, a shared component of NuA4 and SAGA, distinguishes two large groups of coregulated growth-promoting genes. In contrast, we show that the activity of Mediator is particularly important at a separate, small set of highly transcribed TATA-box-containing genes. Our analysis indicates that at least three distinct combinations of coactivator deployment are used to generate moderate or high transcription levels and suggests that each may be associated with distinct forms of regulation.

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 score0.023
Threshold uncertainty score0.678

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 head0.008
GPT teacher head0.234
Teacher spread0.226 · 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

Citations64
Published2018
Admission routes1
Has abstractyes

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