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Record W2514841504 · doi:10.1139/cjas-2016-0038

14-3-3<i>γ</i> affects eIF5 to regulate <i>β</i>-casein synthesis in bovine mammary epithelial cells

2016· article· en· W2514841504 on OpenAlexvenueno aff
Cuiping Yu, Chaochao Luo, Xinyu Gu, Yanli Zang, Bo Qu, Nagam Khudhair, Qingzhang Li, Xuejun Gao

Bibliographic record

VenueCanadian Journal of Animal Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topic14-3-3 protein interactions
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaMajor State Basic Research Development Program of ChinaDalian Polytechnic University
KeywordsColocalizationCaseinMessenger RNABiologyImmunoprecipitationIn vitroMolecular biologyMammary glandTranslation (biology)Cell biologyGeneBiochemistryGenetics

Abstract

fetched live from OpenAlex

The 14-3-3γ protein participates in many biological processes; however, its regulatory mechanism in milk protein synthesis is not well studied. We hypothesized that 14-3-3γ might affect eIF5 (an initiation factor) to regulate β-casein synthesis in dairy cows. In this study, a possible interaction between 14-3-3γ and eIF5 was investigated using bovine mammary epithelial cells (BMECs). The expression levels of 14-3-3γ and eIF5 in the mammary gland tissues from cows producing higher quality milk were higher than those from cows producing low-quality milk. Moreover, the expression of 14-3-3γ, eIF5, and β-casein were increased at both mRNA and protein levels in BMECs cultured in vitro with methionine (Met) supplementation. Coimmunoprecipitation, colocalization, and FRET analysis further showed the evidences that 14-3-3γ physically bound to eIF5 in BMECs. Gene function studies revealed that 14-3-3γ positively regulated eIF5 through alteration of eIF2α/p-eIF2α ratio. Collectively, our data suggest that 14-3-3γ regulates β-casein translation in BMECs through interaction with eIF5.

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)0.001
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 score0.041
Threshold uncertainty score0.417

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.227
Teacher spread0.219 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

Explore more

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