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Record W2302104785 · doi:10.1099/jgv.0.000382

The human immunodeficiency virus (HIV) Rev-binding protein (HRB) is a co-factor for HIV-1 Nef-mediated CD4 downregulation

2015· article· en· W2302104785 on OpenAlexfundno aff
Alessia Landi, Cristina García‐Timermans, Evelien Naessens, Hanne Vanderstraeten, Veronique Stove, Bruno Verhasselt

Bibliographic record

VenueJournal of General Virology · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
FundersFP7 HealthNational Institutes of HealthNational Institute of Allergy and Infectious DiseasesVlaamse regeringEuropean CommissionUniversiteit GentFonds Wetenschappelijk OnderzoekYork University
KeywordsDownregulation and upregulationVirologyBiologyHuman immunodeficiency virus (HIV)Viral replicationIn vitroViral proteinVirusImmunologyGeneGenetics

Abstract

fetched live from OpenAlex

Human immunodeficiency virus type 1 (HIV-1)-mediated CD4 downregulation is an important determinant of viral replication in vivo. Research on cellular co-factors involved in this process could lead to the identification of potential therapeutic targets. We found that CD4 surface levels were significantly higher in HIV-1-infected cells knocked-down for the HIV Rev-binding protein (HRB) compared with control cells. HRB knock-down affected CD4 downregulation induced by Nef but not by HIV-1 Vpu. Interestingly, the knock-down of the related protein HRBL (HRB-like), but not of the HRB interaction partner EPS15 (epidermal growth factor receptor pathway substrate 15), increased CD4 levels in Vpu-expressing cells significantly. Both of these proteins are known to be involved in HIV-1-mediated CD4 downregulation as co-factors of HIV-1 Nef. These results identify HRB as a previously unknown co-factor for HIV-1 Nef-mediated CD4 downregulation and highlight differences with the related protein HRBL, which affects the CD4 downregulation in a dual role as co-factor of both HIV-1 Nef and Vpu.

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)0.001
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 score0.590
Threshold uncertainty score0.701

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.038
GPT teacher head0.312
Teacher spread0.274 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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