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Record W2012219469 · doi:10.1097/qad.0000000000000637

Dendritic cell type-specific HIV-1 activation in effector T cells

2015· article· en· W2012219469 on OpenAlexaff
Renée M. van der Sluis, Toni M.M. van Capel, Dave Speijer, Rogier W. Sanders, Ben Berkhout, Esther C. de Jong, Rienk E. Jeeninga, Thijs van Montfort

Bibliographic record

VenueAIDS · 2015
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsInstitute of Infection and Immunity
FundersNederlandse Organisatie voor Wetenschappelijk OnderzoekAids Fonds
KeywordsProvirusBiologyEffectorDendritic cellCell biologyImmune systemFollicular dendritic cellsCytotoxic T cellCD40ImmunologyVirus latencyAntigen-presenting cellT cellVirusIn vitroViral replication

Abstract

fetched live from OpenAlex

BACKGROUND: Latent HIV type I (HIV-1) infections can frequently occur in short-lived proliferating effector T lymphocytes. These latently infected cells could revert into resting T lymphocytes and thereby contribute to the establishment of the long-lived viral reservoir. Monocyte-derived dendritic cells can revert latency in effector T cells in vitro. METHODS: Here we investigated the latency activation properties of tissue-specific immune cells, including a large panel of dendritic cell subsets, to explore in which body compartments effector T cells are most likely to maintain latent HIV-1 provirus and thus potentially contribute to the long-lived reservoir. RESULTS: Our results demonstrate that blood or genital tract dendritic cells do not activate latent provirus in effector T cells, whereas gut or lymphoid dendritic cells induce virus production from latently infected effector T cells in our in-vitro model for latency. Toll-like receptor 3-induced interferon production by myeloid dendritic cells abolished the dendritic cells' ability to induce viral gene expression. CONCLUSIONS: In this study, we show that HIV-1 provirus residing in effector T cells is activated from latency by tissue-specific dendritic cell subsets and other immune cells with remarkably different efficiencies.Our new assay system points to an important, neglected aspect of HIV-1 research: the ability of other immune cells, especially dendritic cells, to differentially affect latency establishment as well as virus reactivation.

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 categoriesInsufficient payload (model declined to judge)
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 score0.075
Threshold uncertainty score0.979

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)0.0000.022

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 head0.025
GPT teacher head0.257
Teacher spread0.232 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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