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Record W4412775074 · doi:10.3389/fmolb.2025.1612531

Arfs on the Golgi: four conductors, one orchestra

2025· review· en· W4412775074 on OpenAlexaff
Selma Y. Dejgaard, John F. Presley

Bibliographic record

VenueFrontiers in Molecular Biosciences · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCellular transport and secretion
Canadian institutionsMcGill University
Fundersnot available
KeywordsGolgi apparatusBiologyCell biologyEffectorGene knockoutCOPIADP ribosylation factorSecretory pathwayContext (archaeology)Computational biologyGeneticsGeneEndoplasmic reticulum

Abstract

fetched live from OpenAlex

Arfs are small Ras-superfamily proteins important for regulating membrane trafficking including the recruitment of vesicular coats as well as a diverse range of other functions. There are five Arfs in humans: two Class I Arfs (Arf1 and Arf3), two Class II Arfs (Arf4 and Arf5) and one Class III Arf (Arf6), with Class I and Class II Arfs present on the Golgi apparatus among other locations. These Golgi Arfs (Arf1, Arf3, Arf4 and Arf5) are highly similar in sequence, and knockout studies have established a complex pattern of redundancy, with Arf4 alone able to support cell survival in tissue culture. Moreover, adding to the complexity, functions of Arfs on distinct membranes can involve non-overlapping sets of effectors (e.g., COPI on cis -Golgi membranes and clathrin adaptors on trans -Golgi network). The three classes of Arfs are found in most metazoans, suggesting biologically important specialization the details of which are beginning to emerge. This review examines recent studies using siRNA and CRISPR/Cas9 knockouts of mammalian Arfs combined with functional assays of the secretory pathway in the context of detailed localization of fluorescently-tagged Arfs by fluorescent and super-resolution microscopy and the existing literature using more conventional techniques. We suggest that specificity of effector recruitment involves additional membrane determinants which need to be considered in future studies.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.983
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.034
GPT teacher head0.273
Teacher spread0.239 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes1
Has abstractyes

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