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Record W4407913726 · doi:10.1128/mbio.03565-24

HIV-1 Gag-protease-driven replicative capacity influences T-cell metabolism, cytokine induction, and viral cell-to-cell spread

2025· article· en· W4407913726 on OpenAlexaff
Omolara O. Baiyegunhi, Kensane Mthembu, Ann‐Kathrin Reuschl, Doty Ojwach, Omotayo Farinre, Murunwa Maimela, Sheila N. Balinda, Matt A. Price, Madeleine J. Bunders, Marcus Altfeld, Clare Jolly, Jaclyn K. Mann, Thumbi Ndung’u

Bibliographic record

VenuemBio · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicHIV Research and Treatment
Canadian institutionsInstitute of Infection and Immunity
FundersMedical Research CouncilNational Institute of Mental HealthDeutsche ForschungsgemeinschaftWellcome TrustInternational AIDS Vaccine Initiative
KeywordsCytokineBiologyTumor necrosis factor alphaCell cultureGrowth factorFlow cytometryViral loadVirologyMolecular biologyVirusImmunologyBiochemistry

Abstract

fetched live from OpenAlex

ABSTRACT High replicative capacity (RC) HIV-1 strains are associated with elevated viral loads and faster disease progression in the absence of antiretroviral therapy. Understanding the mechanisms by which high RC strains adversely affect the host is essential for developing novel anti-HIV interventions. This study investigates cellular metabolism, cytokine induction, and cell-to-cell spread as potential mechanisms differentiating clinical outcomes between low and high RC strains of HIV-1. We constructed chimeric viruses containing patient-derived gag-proteases from HIV-1 subtypes B and C in the NL4-3 backbone. Viral RC was determined using a green fluorescent protein (GFP)-reporter T-cell line assay and cytokine production in T-cells was assessed using Luminex. Virus cell-to-cell spread efficiency was measured through flow cytometry-based detection of p24, while nutrient uptake assays and mitotracker dye detection served as surrogate markers for T-cell metabolism and mitochondrial function. Chimeric subtype C viruses exhibited significantly lower RC compared to subtype B viruses ( P = 0.0008). Cytokine profiling revealed distinct cytokine signatures associated with low RC subtype C viruses. Viral RC negatively correlated with tumor necrosis factor alpha (TNF-α), IL-8, and IL-13 induction, while it positively correlated with platelet-derived growth factor (PDGF-bb), IL-7, monocyte chemoattractant protein-1 (MCP-1), fibroblast growth factor (FGF)-basic levels, viral spread efficiency ( P = 0.008, r = 0.5), and cellular glucose uptake ( P = 0.02, r = 0.5). Conversely, RC was negatively correlated with glutamine levels ( P = 0.001, r = −0.7), indicating a link between RC and nutrient utilization. Furthermore, mitochondrial depolarization was elevated in subtype B infections when compared to subtype C infections ( P = 0.0008). These findings indicate that high RC strains exert distinct cellular effects that may influence HIV-1 pathogenesis, highlighting the need to develop novel therapeutic strategies. IMPORTANCE Virus replicative capacity (RC) influences disease progression following HIV-1 transmission; however, the mechanisms underlying the differential clinical outcomes remain poorly understood. Our study reveals variations in cytokine induction and cellular metabolism in T-cells infected with HIV-1 subtype B and C viruses exhibiting high or low RC. T-cells infected with high RC strains showed increased induction of IL-7 and platelet-derived growth factor (PDGF-bb), along with heightened glucose uptake and elevated glutamine consumption compared to those infected with low RC strains. By contrast, low RC strains induced higher levels of IL-8, IL-13, and tumor necrosis factor alpha (TNF-α) and demonstrated reduced efficiency in modulating cellular metabolism and virus cell-to-cell spreadability. These findings highlight distinct biological differences between high and low RC viruses, providing valuable insights into the mechanisms that may underpin varying clinical outcomes. This knowledge may inform the development of novel interventions aimed at limiting viral virulence or transmission.

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 score0.105
Threshold uncertainty score0.999

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)0.0000.002

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 head0.014
GPT teacher head0.254
Teacher spread0.240 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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