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Record W2040542478 · doi:10.1155/2014/870695

The Role of Histone Ubiquitination during Spermatogenesis

2014· article· en· W2040542478 on OpenAlexaff
Kai Sheng, Xiaotong Liang, Sizhou Huang, Wenming Xu

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicUbiquitin and proteasome pathways
Canadian institutionsMcGill University Health Centre
FundersNational Key Research and Development Program of ChinaProgram for New Century Excellent Talents in UniversityNational Natural Science Foundation of China
KeywordsUbiquitinBiologyHistoneDeubiquitinating enzymeHistone H2ACell biologyEpigeneticsProteasomeSpermiogenesisGeneticsSpermDNAGene

Abstract

fetched live from OpenAlex

Protein ubiquitin-proteasome (ubiquitin-proteasome) system is the major mechanism responsible for protein degradation in eukaryotic cell. During spermatogenesis, the replacement of histone by protamine is vital for normal sperm formation, which is involved in ubiquitination enzymes expressed in testis. Recently, histone ubiquitin ligases have been shown to play critical roles in several aspects of spermatogenesis, such as meiotic sex chromosome inactivation (MSCI), DNA damage response, and spermiogenesis. In this review, we highlight recent progress in the discovery of several histone ubiquitin ligases and elaborate mechanisms of how these enzymes are involved in these processes through knockout mouse model. Using Huwe1, UBR2, and RNF8 as examples, we emphasized the diverse functions for each enzyme and the broad involvement of these enzymes in every stage, from spermatogonia differentiation and meiotic division to spermiogenesis; thus histone ubiquitin ligases represent a class of enzymes, which play important roles in spermatogenesis through targeting histone for ubiquitination and therefore are involved in transcription regulation, epigenetic modification, and other processes essential for normal gametes formation.

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)0.001
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 score0.251
Threshold uncertainty score0.189

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.022
GPT teacher head0.354
Teacher spread0.333 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

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