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Record W2802410324 · doi:10.1038/s41467-018-03993-4

Viral proteins as a potential driver of histone depletion in dinoflagellates

2018· article· en· W2802410324 on OpenAlexafffund
Nicholas A. T. Irwin, Benjamín Martín, Barry P. Young, Martin J. G. Browne, Andrew Flaus, Christopher Loewen, Patrick J. Keeling, LeAnn J. Howe

Bibliographic record

VenueNature Communications · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicBacteriophages and microbial interactions
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHistoneChromatinNucleosomeBiologyCell biologyNucleoproteinHistone-modifying enzymesHistone codeHistone methylationHistone H2AGeneticsDNAGeneGene expressionDNA methylation

Abstract

fetched live from OpenAlex

Within canonical eukaryotic nuclei, DNA is packaged with highly conserved histone proteins into nucleosomes, which facilitate DNA condensation and contribute to genomic regulation. Yet the dinoflagellates, a group of unicellular algae, are a striking exception to this otherwise universal feature as they have largely abandoned histones and acquired apparently viral-derived substitutes termed DVNPs (dinoflagellate-viral-nucleoproteins). Despite the magnitude of this transition, its evolutionary drivers remain unknown. Here, using Saccharomyces cerevisiae as a model, we show that DVNP impairs growth and antagonizes chromatin by localizing to histone binding sites, displacing nucleosomes, and impairing transcription. Furthermore, DVNP toxicity can be relieved through histone depletion and cells diminish their histones in response to DVNP expression suggesting that histone reduction could have been an adaptive response to these viral proteins. These findings provide insights into eukaryotic chromatin evolution and highlight the potential for horizontal gene transfer to drive the divergence of cellular systems.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.881
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.295
Teacher spread0.288 · 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 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

Citations50
Published2018
Admission routes2
Has abstractyes

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