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Interactions between Rubella Virus Capsid and Host Protein p32 Are Important for Virus Replication

2005· article· en· W2016652506 on OpenAlexaff
Martin D. Beatch, Jason C. Everitt, LokMan J. Law, Tom C. Hobman

Bibliographic record

VenueJournal of Virology · 2005
Typearticle
Languageen
FieldMedicine
TopicVirology and Viral Diseases
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCapsidBiologySubgenomic mRNAViral replicationVirusRubella virusRNAGroup-specific antigenVirologyCell biologyMolecular biologyRubellaBiochemistryGene

Abstract

fetched live from OpenAlex

The distribution and morphology of mitochondria are dramatically affected during infection with rubella virus (RV). Expression of the capsid, in the absence of other viral proteins, was found to induce both perinuclear clustering of mitochondria and the formation of electron-dense intermitochondrial plaques, both hallmarks of RV-infected cells. We previously identified p32, a host cell mitochondrial matrix protein, as a capsid-binding protein. Here, we show that two clusters of arginine residues within capsid are required for stable binding to p32. Mutagenic ablation of the p32-binding site in capsid resulted in decreased mitochondrial clustering, indicating that interactions with this cellular protein are required for capsid-dependent reorganization of mitochondria. Recombinant viruses encoding arginine-to-alanine mutations in the p32-binding region of capsid exhibited altered plaque morphology and replicated to lower titers. Further analysis indicated that disruption of stable interactions between capsid and p32 was associated with decreased production of subgenomic RNA and, consequently, infected cells produced significantly lower amounts of viral structural proteins under these conditions. Together, these results suggest that capsid-p32 interactions are important for nonstructural functions of capsid that include regulation of virus RNA replication and reorganization of mitochondria during infection.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.376

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 head0.031
GPT teacher head0.345
Teacher spread0.314 · 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

Citations60
Published2005
Admission routes1
Has abstractyes

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