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Record W4210363551 · doi:10.3390/v14020246

A Viral Long Non-Coding RNA Protects against Cell Death during Human Cytomegalovirus Infection of CD14+ Monocytes

2022· article· en· W4210363551 on OpenAlexfundno aff
Marianne Perera, Kathryn L. Roche, Eain A. Murphy, John Sinclair

Bibliographic record

VenueViruses · 2022
Typearticle
Languageen
FieldMedicine
TopicCytomegalovirus and herpesvirus research
Canadian institutionsnot available
FundersMedical Research CouncilMedical Research Council CanadaWellcome Trust
KeywordsHuman cytomegalovirusCD14BiologyVirologyReactive oxygen speciesVirusApoptosisDownregulation and upregulationSOD2Oxidative stressViral replicationMonocyteSuperoxide dismutaseGeneImmunologyImmune systemCell biologyGenetics

Abstract

fetched live from OpenAlex

Long non-coding RNA β2.7 is the most highly transcribed viral gene during latent human cytomegalovirus (HCMV) infection. However, as yet, no function has ever been ascribed to β2.7 during HCMV latency. Here we show that β2.7 protects against apoptosis induced by high levels of reactive oxygen species (ROS) in infected monocytes, which routinely support latent HCMV infection. Monocytes infected with a wild-type (WT) virus, but not virus deleted for the β2.7 gene (Δβ2.7), are protected against mitochondrial stress and subsequent apoptosis. Protected monocytes display lower levels of ROS and additionally, stress-induced death in the absence of β2.7 can be reversed by an antioxidant which reduces ROS levels. Furthermore, we show that infection with WT but not Δβ2.7 virus results in strong upregulation of a cellular antioxidant enzyme, superoxide dismutase 2 (SOD2) in CD14+ monocytes. These observations identify a role for the β2.7 viral transcript, the most abundantly expressed viral RNA during latency but for which no latency-associated function has ever been ascribed, and demonstrate a novel way in which HCMV protects infected monocytes from pro-death signals to optimise latent carriage.

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 categoriesMeta-epidemiology (narrow)
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 score0.035
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.043
GPT teacher head0.320
Teacher spread0.277 · 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

Citations13
Published2022
Admission routes1
Has abstractyes

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