Targeting ABL1 or ARG Tyrosine Kinases to Restrict HIV-1 Infection in Primary CD4+ T‐Cells or in Humanized NSG Mice
Bibliographic record
Abstract
BACKGROUND: Previous studies support dasatinib as a potent inhibitor of HIV-1 replication. However, a functional distinction between 2 kinase targets of the drug, ABL1 and ARG, has not been assessed. SETTING: We used primary CD4 T-cells, CD8-depleted peripheral blood mononuclear cells (PBMCs) from a treatment naïve HIV-1 patient, and a humanized mouse model of HIV-1 infection. We assessed the roles of ABL1 and ARG during HIV-1 infection and use of dasatinib as a potential antiviral against HIV-1 in humanized mice. METHODS: Primary CD4 T-cells were administered siRNA targeting ABL1 or ARG, then infected with HIV-1 containing luciferase reporter viruses. Quantitative polymerase chain reaction of viral integration of 4 HIV-1 strains was also assessed. CD8-depleted PBMCs were treated for 3 weeks with dasatinib. NSG mice were engrafted with CD34 pluripotent stem cells from human fetal cord blood, and infected with Ba-L virus after 19 weeks. Mice were treated daily with dasatinib starting 5 weeks after infection. RESULTS: siRNA knockdown of ABL1 or ARG had no effect on viral reverse transcripts, but increased 2-LTR circles 2- to 4-fold and reduced viral integration 2- to 12-fold. siRNA knockdown of ARG increased SAMHD1 activation, whereas knockdown of either kinase reduced RNA polymerase II activation. Treating CD8-depleted PBMCs from a treatment-naïve patient with 50 nM of dasatinib for 3 weeks reduced p24 levels by 99.8%. Ba-L (R5)-infected mice injected daily with dasatinib showed a 95.1% reduction in plasma viral load after 2 weeks of treatment. CONCLUSIONS: We demonstrate a novel nuclear role for ABL1 and ARG in ex vivo infection experiments, and proof-of-principle use of dasatinib in a humanized mouse model of HIV-1 infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".