Introduction of Mutant APOBEC3G Into CD4+ T-Cells to Resist HIV-1
Bibliographic record
Abstract
Introduction: Human immunodeficiency virus 1 (HIV-1) is a potentially lethal retrovirus with high genetic diversity and replication ability. HIV-1 mainly targets human CD4+ T-cells, causing a rapid decline in CD4+ T-cell count after infection. Apolipoprotein B mRNA Editing Enzyme Catalytic Subunit 3G (APOBEC3G or A3G) is a human anti-viral enzyme that blocks early stages of HIV-1 replication by deaminating the cytosine residues to uracil in the viral minus-strand DNA during reverse transcription. Nevertheless, HIV-1 overcomes A3G anti-viral activity by employing Vif accessory protein to degrade A3G. Due to a proline/aspartate point mutation in residue 129 (P129D), an artificial A3G mutant, also known as A3G-P129D, was found to be resistant against diverse Vif variants, including HIV-1 Vif, thereby shedding light on a potential A3G-mediated gene therapy for HIV-1. Methods: We propose that, using CRISPR-Cas9 tools, the P129D mutation will be introduced into A3G expressed by human hematopoietic stem cells. Engineered stem cells will be transplanted into Non-Obese Diabetic Severe Combined Immunodeficient Gamma (NSG) mice, which will later be infected with HIV-1. Following infection, flow cytometry will be used to compare the CD4+ T-cell count in mice that express A3G-P129D versus control mice transplanted with unmodified human CD4+ T-cells. Results: We expect a notably higher CD4+ T-cell count in mice carrying the P129D mutation than in the control group, which would be attributed to the efficient inhibition of HIV-1 replication by the mutant A3G. Discussion: Transforming human A3G to A3G-P129D will prevent Vif-mediated degradation of A3G, allowing A3G to efficiently target HIV-1. Thus, this treatment would ultimately block HIV-1 replication and prevent CD4+ T-cell depletion. Conclusion: Overall, our novel gene therapy approach could open the door to a potential one-time treatment for HIV-1 infected patients.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".