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Record W4407111702 · doi:10.3389/fviro.2025.1547176

Effects of GS-CA1 on nuclear envelope-associated early HIV-1 infection steps

2025· article· en· W4407111702 on OpenAlexafffund
Amita Singh, Victor Fourcassié, Karen Cristine Gonçalves dos Santos, Hocine Chelbi, Natacha Mérindol, Arnaud Droit, Hugo Germain, Lionel Berthoux

Bibliographic record

VenueFrontiers in Virology · 2025
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicNuclear Structure and Function
Canadian institutionsCentre hospitalier de l'Université LavalCentre hospitalier universitaire de QuébecUniversité du Québec à Trois-Rivières
FundersMitacsFonds de recherche du Québec – Nature et technologiesCanadian Foundation for AIDS Research
KeywordsHuman immunodeficiency virus (HIV)Envelope (radar)VirologyNuclear engineeringMedicineComputer scienceEngineering

Abstract

fetched live from OpenAlex

The novel HIV-1 drugs GS-CA1 and the recently approved lenacapavir (GS-6207) target the viral structural protein capsid (CA). However, their multiple mechanisms of action have not been fully characterized. Here, we investigated the effects of GS-CA1 on the early stages of HIV-1 infection, specifically the steps involving the nuclear envelope, in comparison to the antiviral cytokine IFN-β. Mass spectrometry data indicated that nuclear envelope proteins were only modestly affected by either GS-CA1 treatment or HIV-1 infection, but combining the two had a more significant impact, altering the levels of many proteins including proteasomal components. GS-CA1 induced a small but clear accumulation of HIV-1 capsid cores at nuclear pores, as seen by microscopy, whereas IFN-β caused a strong accumulation of HIV-1 cores at the nuclear envelope but not specifically at nuclear pores. These observations are consistent with GS-CA1 inhibiting the nuclear translocation of HIV-1 capsid cores through nuclear pores.

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 score0.002
Threshold uncertainty score0.006

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.002
GPT teacher head0.185
Teacher spread0.183 · 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
Published2025
Admission routes2
Has abstractyes

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