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Record W4220919015 · doi:10.1007/s13592-022-00914-9

Nuclear translocation of vitellogenin in the honey bee (Apis mellifera)

2022· article· en· W4220919015 on OpenAlexfundno aff
Heli Salmela, Gyan Harwood, Daniel Münch, Christine G. Elsik, Elías Herrero‐Galán, Maria K. Vartiainen, Gro V. Amdam

Bibliographic record

VenueApidologie · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect and Arachnid Ecology and Behavior
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaSchool of Life Sciences, Arizona State UniversityNorges Miljø- og Biovitenskapelige UniversitetNorges ForskningsrådArizona State UniversityAcademy of FinlandYork UniversityIowa State UniversityBrown University
KeywordsBiologyVitellogeninHoney beeProtein subunitChromatinOviparityVitellogeninsCell biologyNucleusDNA sequencingGeneticsDNAGeneMolecular biologyZoologyVitellogenesisOocyte

Abstract

fetched live from OpenAlex

Vitellogenin (Vg) is a conserved protein used by nearly all oviparous animals to produce eggs. It is also pleiotropic and performs functions in oxidative stress resistance, immunity, and, in honey bees, behavioral development of the worker caste. It has remained enigmatic how Vg affects multiple traits. Here, we asked whether Vg enters the nucleus and acts via DNA-binding. We used cell fractionation, immunohistology, and cell culture to show that a structural subunit of honey bee Vg translocates into cell nuclei. We then demonstrated Vg-DNA binding theoretically and empirically with prediction software and chromatin immunoprecipitation with sequencing (ChIP-seq), finding binding sites at genes influencing immunity and behavior. Finally, we investigated the immunological and enzymatic conditions affecting Vg cleavage and nuclear translocation and constructed a 3D structural model. Our data are the first to show Vg in the nucleus and suggest a new fundamental regulatory role for this ubiquitous protein. Supplementary information: The online version contains supplementary material available at 10.1007/s13592-022-00914-9.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Bench or experimentallow
models agreeAgreement compares identical category sets and study designs across arms.

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 score0.006

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)0.0020.001

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 head0.023
GPT teacher head0.257
Teacher spread0.234 · 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

Labeled directly by 2 models reading the full record.

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

Citations25
Published2022
Admission routes1
Has abstractyes

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