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Record W2109273557 · doi:10.1128/mcb.01100-09

Regulation of <i>Drosophila</i> Vasa <i>In Vivo</i> through Paralogous Cullin-RING E3 Ligase Specificity Receptors

2010· article· en· W2109273557 on OpenAlexaff
Jan-Michael Kugler, Jae‐Sung Woo, Byung‐Ha Oh, Paul Lasko

Bibliographic record

VenueMolecular and Cellular Biology · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA Research and Splicing
Canadian institutionsMcGill University Health CentreMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsBiologyCullinUbiquitin ligaseCell biologyUbiquitinGenetics

Abstract

fetched live from OpenAlex

In Drosophila species, molecular asymmetries guiding embryonic development are established maternally. Vasa, a DEAD-box RNA helicase, accumulates in the posterior pole plasm, where it is required for embryonic germ cell specification. Maintenance of Vasa at the posterior pole requires the deubiquitinating enzyme Fat facets, which protects Vasa from degradation. Here, we found that Gustavus (Gus) and Fsn, two ubiquitin Cullin-RING E3 ligase specificity receptors, bind to the same motif on Vasa through their paralogous B30.2/SPRY domains. Both Gus and Fsn accumulate in the pole plasm in a Vasa-dependent manner. Posterior Vasa accumulation is precocious in Fsn mutant oocytes; Fsn overexpression reduces ovarian Vasa levels, and embryos from Fsn-overexpressing females form fewer primordial germ cells (PGCs); thus, Fsn destabilizes Vasa. In contrast, endogenous Gus may promote Vasa activity in the pole plasm, as gus females produce embryos with fewer PGCs, and posterior accumulation of Vas is delayed in gus mutant oocytes that also lack one copy of cullin-5. We propose that Fsn- and Gus-containing E3 ligase complexes contribute to establishing a fine-tuned steady state of Vasa ubiquitination that influences the kinetics of posterior Vasa deployment.

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 score0.009
Threshold uncertainty score0.814

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 head0.009
GPT teacher head0.244
Teacher spread0.235 · 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

Citations40
Published2010
Admission routes1
Has abstractyes

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