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Record W3136839827 · doi:10.26508/lsa.202101046

RNA polymerase backtracking results in the accumulation of fission yeast condensin at active genes

2021· article· en· W3136839827 on OpenAlexfundno aff
Julieta Rivosecchi, Daniel Jost, Laetitia Vachez, François DR Gautier, Pascal Bernard, Vincent Vanoosthuyse

Bibliographic record

VenueLife Science Alliance · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Chromatin Dynamics
Canadian institutionsnot available
FundersAgence Nationale de la RechercheUniversité de SherbrookeAssociation pour la Recherche sur le Cancer
KeywordsCondensinBiologyGeneTranscription (linguistics)GeneticsRNA polymeraseDNACell biologyRNAChromosome segregationChromosome

Abstract

fetched live from OpenAlex

The mechanisms leading to the accumulation of the SMC complexes condensins around specific transcription units remain unclear. Observations made in bacteria suggested that RNA polymerases (RNAPs) constitute an obstacle to SMC translocation, particularly when RNAP and SMC travel in opposite directions. Here we show in fission yeast that gene termini harbour intrinsic condensin-accumulating features whatever the orientation of transcription, which we attribute to the frequent backtracking of RNAP at gene ends. Consistent with this, to relocate backtracked RNAP2 from gene termini to gene bodies was sufficient to cancel the accumulation of condensin at gene ends and to redistribute it evenly within transcription units, indicating that RNAP backtracking may play a key role in positioning condensin. Formalization of this hypothesis in a mathematical model suggests that the inclusion of a sub-population of RNAP with longer dwell-times is essential to fully recapitulate the distribution profiles of condensin around active genes. Taken together, our data strengthen the idea that dense arrays of proteins tightly bound to DNA alter the distribution of condensin on chromosomes.

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 score0.078
Threshold uncertainty score0.228

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 head0.037
GPT teacher head0.315
Teacher spread0.278 · 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

Citations22
Published2021
Admission routes1
Has abstractyes

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