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Snf2 Proteins Are Required to Generate Gamete Pronuclei in Tetrahymena thermophila

2022· article· en· W4311856051 on OpenAlexfundno aff
Yasuhiro Fukuda, Takahiko Akematsu, Hironori Bando, Kentaro Kato

Bibliographic record

VenueMicroorganisms · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicProtist diversity and phylogeny
Canadian institutionsnot available
FundersJapan Society for the Promotion of ScienceYork UniversityStrongNihon University
KeywordsEuchromatinBiologyTetrahymenaChromatinPronucleusGeneticsCell biologyMacronucleusHistoneChromatin remodelingNucleosomeHeterochromatinDNAZygoteGene

Abstract

fetched live from OpenAlex

During sexual reproduction/conjugation of the ciliate Tetrahymena thermophila, the germinal micronucleus undergoes meiosis resulting in four haploid micronuclei (hMICs). All hMICs undergo post-meiotic DNA double-strand break (PM-DSB) formation, cleaving their genome. DNA lesions are subsequently repaired in only one ‘selected’ hMIC, which eventually produces gametic pronuclei. DNA repair in the selected hMIC involves chromatin remodeling by switching from the heterochromatic to the euchromatic state of its genome. Here, we demonstrate that, among the 15 Tetrahymena Snf2 family proteins, a core of the ATP-dependent chromatin remodeling complex in Tetrahymena, the germline nucleus specific Iswi in Tetrahymena IswiGTt and Rad5Tt is crucial for the generation of gametic pronuclei. In either gene knockout, the selected hMIC which shows euchromatin markers such as lysine-acetylated histone H3 does not appear, but all hMICs in which markers for DNA lesions persist are degraded, indicating that both IswiGTt and Rad5Tt have important roles in repairing PM-DSB DNA lesions and remodeling chromatin for the euchromatic state in the selected hMIC.

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)0.000
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 score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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