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Record W7134994339

Efficient HIV latency reversal with immune checkpoint blockade in a primary T-cell latency model.

2019· article· en· W7134994339 on OpenAlexaboutno aff
Renée Marije; id_orcid 0000-0002-7668-2517 van der Sluis

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsBlockadeLatency (audio)Cytotoxic T cellHuman immunodeficiency virus (HIV)Immune systemImmune checkpointAntibody
DOInot available

Abstract

fetched live from OpenAlex

Authors: Van der Sluis RM1,2, Rachel D. Pascoe1, Jennifer M. Zerbato1, Kumar NA1, Evans VA1, Dantanarayana AI1, Jenny L. Anderson1, Sékaly RP3, Fromentin R4, Chomont N4,5, Cameron PU1,6 and Lewin SR1,6 1The Peter Doherty Institute for Infection and Immunity, The University of Melbourne and Royal Melbourne Hospital, Melbourne, Australia; 2Aarhus Institute of Advanced Studies (AIAS), Aarhus University, Aarhus, Denmark; 3Case Western University, Cleveland, OH; 4Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada; 5Department of Microbiology, Infectiology and Immunology, Université de Montréal, Montreal, QC, Canada; 6Department of Infectious Diseases, Alfred Health and Monash University, Melbourne, Australia. Introduction: In HIV-infected individuals on antiretroviral therapy (ART), HIV latency is the major barrier to a cure. HIV persists preferentially in CD4+ T-cells expressing multiple immune checkpoint (IC) molecules. Aim: To determine whether IC molecules have a role in maintaining HIV latency and if blocking IC molecules with antibodies (IC blockers: ICB) alone or in combination, can reverse latency. Methods: Resting CD4+ T-cells, isolated from blood of HIV-uninfected individuals, were stained with the proliferation dye eFluor670, co-cultured with syngeneic monocytes and infected with full length CCR5-tropic EGFP-reporter HIV. On day 5 post-infection, non-proliferating (eFLuorhi) and proliferating (eFluorlo) CD4+ T-cells that were not productively infected (EGFP-) and thus potentially latently infected were sorted. Expression of the IC molecules: cytotoxic T lymphocyte-4 (CTLA-4), T-cell immunoglobulin and mucin-domain containing-3 (TIM-3), B- and T-lymphocyte attenuator (BTLA), T-cell immunoreceptor with Ig and ITIM domains (TIGIT), or programmed death-1 (PD-1), were determined. To assess the ability of ICB to reactivate latency, sorted cells were cultured: 1. alone (background EGFP); 2. with anti-CD3/anti-CD28+IL-7+IL-2 (max EGFP/latency reactivation); 3. with one or more ICB / corresponding isotype control(s) in the presence of monocytes (mono), with or without SEB; or 4.with classic latency reversing agents including vorinostat and bryostatin. Raltegravir and T20 (an HIV integrase and fusion inhibitor, respectively) were added to all reactivations to prevent subsequent rounds of infection. Results: Latent infection was enriched in proliferating cells expressing PD-1 and in non-proliferating cells expressing CTLA-4, TIM-3, BTLA or PD-1. ICB reversed HIV latency in both proliferating and non-proliferating CD4+ T-cells, but only when multiple ICB were used or when an ICB was combined with an additional T-cell activating stimulus (ICB+mono+SEB). Latency reversal was higher following IC blockade compared to classical latency reversing agents. Conclusion: Combination IC blockade should be further explored as a strategy to reverse HIV latency.

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 score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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