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Record W2117312105 · doi:10.1093/molbev/msr005

HIV-1 Modulates the tRNA Pool to Improve Translation Efficiency

2011· article· en· W2117312105 on OpenAlexaff
Anna van Weringh, Manon Ragonnet‐Cronin, Erinija Pranckevičienė, Mariana Pavon-Eternod, Lawrence Kleiman, Xuhua Xia

Bibliographic record

VenueMolecular Biology and Evolution · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicRNA modifications and cancer
Canadian institutionsJewish General HospitalOntario GenomicsUniversity of Ottawa
FundersBiotechnology and Biological Sciences Research Council
KeywordsBiologyTranslation (biology)Transfer RNAHuman immunodeficiency virus (HIV)Computational biologyEvolutionary biologyGeneticsVirologyRNAGeneMessenger RNA

Abstract

fetched live from OpenAlex

Despite its poorly adapted codon usage, HIV-1 replicates and is expressed extremely well in human host cells. HIV-1 has recently been shown to package non-lysyl transfer RNAs (tRNAs) in addition to the tRNA(Lys) needed for priming reverse transcription and integration of the HIV-1 genome. By comparing the codon usage of HIV-1 genes with that of its human host, we found that tRNAs decoding codons that are highly used by HIV-1 but avoided by its host are overrepresented in HIV-1 virions. In particular, tRNAs decoding A-ending codons, required for the expression of HIV's A-rich genome, are highly enriched. Because the affinity of Gag-Pol for all tRNAs is nonspecific, HIV packaging is most likely passive and reflects the tRNA pool at the time of viral particle formation. Codon usage of HIV-1 early genes is similar to that of highly expressed host genes, but codon usage of HIV-1 late genes was better adapted to the selectively enriched tRNA pool, suggesting that alterations in the tRNA pool are induced late in viral infection. If HIV-1 genes are adapting to an altered tRNA pool, codon adaptation of HIV-1 may be better than previously thought.

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 score0.280
Threshold uncertainty score0.300

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 head0.010
GPT teacher head0.244
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

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

Citations130
Published2011
Admission routes1
Has abstractyes

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