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Record W3008999977 · doi:10.1128/jvi.01597-19

Identification of Host Trafficking Genes Required for HIV-1 Virological Synapse Formation in Dendritic Cells

2020· article· en· W3008999977 on OpenAlexaff
Rebecca Bayliss, J. Wheeldon, Stephan M. Caucheteux, Carien M. Niessen, Vincent Piguet

Bibliographic record

VenueJournal of Virology · 2020
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsWomen's College HospitalUniversity of Toronto
FundersCardiff UniversityWellcome TrustWellcome
KeywordsBiologyHost (biology)Identification (biology)Human immunodeficiency virus (HIV)GeneVirologySynapseCell biologyComputational biologyGeneticsNeuroscience

Abstract

fetched live from OpenAlex

The lentivirus human immunodeficiency virus (HIV) targets and destroys CD4 + T cells, leaving the host vulnerable to life-threatening opportunistic infections associated with AIDS. Dendritic cells (DCs) form a virological synapse (VS) with CD4 + T cells, enabling the efficient transfer of virus between the two cells. We have identified cellular factors that are critical in the induction of the VS. We show that ADP-ribosylation factor 1 (ARF1), bridging integrator 1 (BIN1), and Rab GTPases RAB7L1 and RAB8A are important regulators of HIV-1 trafficking to the VS and therefore the infection of CD4 + T cells. We found these cellular factors were essential for endosomal protein trafficking and formation of the VS and that depletion of target proteins prevented virus trafficking to the plasma membrane by retaining virus in intracellular vesicles. Identification of key regulators in HIV-1 trans -infection between DC and CD4 + T cells has the potential for the development of targeted therapy to reduce trans -infection of HIV-1 in vivo .

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 score0.001
Threshold uncertainty score0.004

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)0.0010.000

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 head0.030
GPT teacher head0.281
Teacher spread0.252 · 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

Citations17
Published2020
Admission routes1
Has abstractyes

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