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Record W4382919112 · doi:10.1093/nar/gkad550

Dimerization of an umbravirus RNA genome activates subgenomic mRNA transcription

2023· article· en· W4382919112 on OpenAlexafffund
Tamari Chkuaseli, K. Andrew White

Bibliographic record

VenueNucleic Acids Research · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant Virus Research Studies
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of CanadaIowa State University
KeywordsBiologySubgenomic mRNATranscription (linguistics)RNAMessenger RNAGeneticsGenomeComputational biologyMolecular biologyCell biologyGene

Abstract

fetched live from OpenAlex

Many eukaryotic RNA viruses transcribe subgenomic (sg) mRNAs during infections to control expression of a subset of viral genes. Such transcriptional events are commonly regulated by local or long-range intragenomic interactions that form higher-order RNA structures within these viral genomes. In contrast, here we report that an umbravirus activates sg mRNA transcription via base pair-mediated dimerization of its plus-strand RNA genome. Compelling in vivo and in vitro evidence demonstrate that this viral genome dimerizes via a kissing-loop interaction involving an RNA stem-loop structure located just upstream from its transcriptional initiation site. Both specific and non-specific features of the palindromic kissing-loop complex were found to contribute to transcriptional activation. Structural and mechanistic aspects of the process in umbraviruses are discussed and compared with genome dimerization events in other RNA viruses. Notably, probable dimer-promoting RNA stem-loop structures were also identified in a diverse group of umbra-like viruses, suggesting broader utilization of this unconventional transcriptional strategy.

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 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 score0.005

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 head0.103
GPT teacher head0.335
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2023
Admission routes2
Has abstractyes

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